[
  {
    "path": ".gitignore",
    "content": "# Other\n.pytorch-lightning_ipynb_new/\n\n# Log files\npytorch-lightning_ipynb/mlp/logs/\npytorch-lightning_ipynb/cnn/logs/\n\n# Datasets\npng-files\n*-ubyte*\npytorch-lightning_ipynb/*/data\npytorch_ipynb/viz/cnns/cats-and-dogs/dogs-vs-cats\npytorch_ipynb/gan/dogs-vs-cats\npytorch_ipynb/viz/cnns/cats-and-dogs/dogs-vs-cats\npytorch_ipynb/rnn/yelp_review_polarity_csv/\npytorch_ipynb/rnn/ag_news_csv/\npytorch_ipynb/rnn/amazon_review_polarity_csv/\nHistoricalColor-ECCV2012*\nAFAD-Lite\ntarball*\npytorch_ipynb/rnn/.data/\npytorch_ipynb/rnn/.vector_cache/\ncifar-10-batches-py\nceleba_gender_attr_test.txt\nceleba_gender_attr_train.txt\niris.h5\ntest_32x32.mat\ntrain_32x32.mat\ncode/model_zoo/pytorch_ipynb/svhn_cropped/\nlist_attr_celeba.txt \nlist_eval_partition.txt\nimg_align_celeba\nquickdraw-*\n*.csv\n*.zip\n*.npz\n*.npy\n*.tar.gz\n*ubyte.gz\n*archive.ics.uci.edu*\ncode/model_zoo/cifar-10\ncode/model_zoo/pytorch_ipynb/data\n\n# Binary PyTorch models\n*.pt\n*.state_dict\n\n# Temporary OS files\n.DS_Store\n\n# TensorFlow Checkpoint files\ncheckpoint\ncode/*/*.data-?????-of-?????\ncode/*/*.index\ncode/*/*.meta\ncode/model_zoo/tensorflow_ipynb/*.data-?????-of-?????\ncode/model_zoo/tensorflow_ipynb/*.index\ncode/model_zoo/tensorflow_ipynb/*.meta\ncode/model_zoo/tensorflow_ipynb/cifar-10/*\n\n# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nenv/\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\n*.egg-info/\n.installed.cfg\n*.egg\n\n# PyInstaller\n#  Usually these files are written by a python script from a template\n#  before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*,cover\n.hypothesis/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\ntarget/\n\n# IPython Notebook\n.ipynb_checkpoints\n\n# pyenv\n.python-version\n\n# celery beat schedule file\ncelerybeat-schedule\n\n# dotenv\n.env\n\n# virtualenv\nvenv/\nENV/\n\n# Spyder project settings\n.spyderproject\n\n# Rope project settings\n.ropeproject\n\n# Datasets\nMNIST*\n"
  },
  {
    "path": ".pep8speaks.yml",
    "content": "# File : .pep8speaks.yml\n\nscanner:\n    diff_only: True  # If False, the entire file touched by the Pull Request is scanned for errors. If True, only the diff is scanned.\n    linter: flake8\n\nflake8:\n    max-line-length: 89  # Default is 79 in PEP 8\n    ignore:  # Errors and warnings to ignore\n        - W504  # line break after binary operator\n        - E402  # module level import not at top of file\n        - E731  # do not assign a lambda expression, use a def\n        - C406  # Unnecessary list literal - rewrite as a dict literal.\n        - E741  # ambiguous variable name\n\nno_blank_comment: False  # If True, no comment is made on PR without any errors.\ndescending_issues_order: False  # If True, PEP 8 issues in message will be displayed in descending order of line numbers in the file\n\nmessage:  # Customize the comment made by the bot\n    opened:  # Messages when a new PR is submitted\n        header: \"Hello @{name}! Thanks for opening this PR. \"\n                # The keyword {name} is converted into the author's username\n        footer: \"\"\n                # The messages can be written as they would over GitHub\n    updated:  # Messages when new commits are added to the PR\n        header: \"Hello @{name}! Thanks for updating this PR. \"\n        footer: \"\"  # Why to comment the link to the style guide everytime? :)\n    no_errors: \"There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers: \""
  },
  {
    "path": "LICENSE",
    "content": "MIT License\n\nCopyright (c) 2019-2022 Sebastian Raschka\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n"
  },
  {
    "path": "README.md",
    "content": "\n# Deep Learning Models\n\nA collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n\n\n\n## Traditional Machine Learning\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- | \n| Perceptron | 2D toy data | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/basic-ml/perceptron.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/basic-ml/perceptron.ipynb) |\n| Logistic Regression | 2D toy data | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/basic-ml/logistic-regression.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/basic-ml/logistic-regression.ipynb)|\n| Softmax Regression (Multinomial Logistic Regression) | MNIST | TBD |  [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/basic-ml/softmax-regression.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/basic-ml/softmax-regression.ipynb) |\n| Softmax Regression with MLxtend's plot_decision_regions on Iris | Iris | TBD |  [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/basic-ml/softmax-regression-mlxtend-1.ipynb) |\n\n\n## Multilayer Perceptrons\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- | \n| Multilayer Perceptron | MNIST | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/mlp/mlp-basic.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mlp/mlp-basic.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mlp/mlp-basic.ipynb) |\n| Multilayer Perceptron with Dropout | MNIST | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/mlp/mlp-dropout.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mlp/mlp-dropout.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mlp/mlp-dropout.ipynb) |\n|Multilayer Perceptron with Batch Normalization | MNIST | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/mlp/mlp-batchnorm.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mlp/mlp-batchnorm.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mlp/mlp-batchtnorm.ipynb) |\n|Multilayer Perceptron with Backpropagation from Scratch | MNIST | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mlp/mlp-fromscratch__sigmoid-mse.ipynb)|\n\n\n\n## Convolutional Neural Networks\n\n\n#### Basic\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- | \n| Convolutional Neural Network | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-basic.ipynb)  [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-basic.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/cnn/cnn-basic.ipynb) |\n| CNN with He Initialization | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-he-init.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-he-init.ipynb)  |\n\n\n\n#### Concepts\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Replacing Fully-Connected by Equivalent Convolutional Layers | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/fc-to-conv.ipynb)  |\n\n\n\n---\n\n\n#### AlexNet\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| AlexNet Trained on CIFAR-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-alexnet-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb) |\n| AlexNet with Grouped Convolutions Trained on CIFAR-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-alexnet-grouped-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-alexnet-grouped-cifar10.ipynb) |\n\n\n\n#### DenseNet\n\n|Title | Description | Daset | Notebooks |\n| --- | --- | --- | --- | \n| DenseNet-121 Digit Classifier Trained on MNIST | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-densenet121-mnist.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-densenet121-mnist.ipynb)  |\n| DenseNet-121 Image Classifier Trained on CIFAR-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-densenet121-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-densenet121-cifar10.ipynb)  |\n\n\n\n#### Fully Convolutional\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| \"All Convolutionl Net\" -- A Fully Convolutional Neural Network | TBD |  TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-allconv.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-allconv.ipynb) |\n\n#### LeNet\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- | \n| LeNet-5 on MNIST | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-lenet5-mnist.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-lenet5-mnist.ipynb)  |\n| LeNet-5 on CIFAR-10  | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-lenet5-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-lenet5-cifar10.ipynb)  |\n| LeNet-5 on QuickDraw | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-lenet5-quickdraw.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-lenet5-quickdraw.ipynb) |\n\n\n\n\n#### MobileNet\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- | \n| MobileNet-v2 on Cifar-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb)  |\n| MobileNet-v3 small on Cifar-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-small-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-mobilenet-v3-small-cifar10.ipynb)  |\n| MobileNet-v3 large on Cifar-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-large-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-mobilenet-v3-large-cifar10.ipynb)   |\n| MobileNet-v3 large on MNIST via Embetter | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-embetter-mobilenet.ipynb)   |\n\n\n\n\n#### Network in Network\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Network in Network Trained on CIFAR-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-nin-cifar10.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/nin-cifar10.ipynb)  |\n\n\n#### VGG\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Convolutional Neural Network VGG-16 Trained on CIFAR-10 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-vgg16.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-vgg16.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/cnn/cnn-vgg16.ipynb) |\n| VGG-16 Smile Classifier | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-vgg16-celeba.ipynb) [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-vgg16-celeba.ipynb)  |\n| VGG-16 Dogs vs Cats Classifier | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-vgg16-cats-dogs.ipynb)  |\n| Convolutional Neural Network VGG-19 | TBD | TBD | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/cnn/cnn-vgg19.ipynb)  [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-vgg19.ipynb)  |\n\n\n\n\n#### ResNet\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- | \n| ResNet and Residual Blocks | [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/resnet-ex-1.ipynb)  |\n| ResNet-18 Digit Classifier| [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet18-mnist.ipynb)  |\n| ResNet-18 Gender Classifier | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet18-celeba-dataparallel.ipynb)  |\n| ResNet-34 Digit Classifier | [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet34-mnist.ipynb)  |\n| ResNet-34 Object Classifier | [QuickDraw](https://quickdraw.withgoogle.com) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet34-quickdraw.ipynb)  |\n| ResNet-34 Gender Classifier| [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet34-celeba-dataparallel.ipynb)  |\n| ResNet-50 Digit Classifier| [MNIST](http://yann.lecun.com/exdb/mnist/) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet50-mnist.ipynb)  |\n| ResNet-50 Gender Classifier | [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet50-celeba-dataparallel.ipynb)  |\n| ResNet-101 Gender Classifier| [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet101-celeba.ipynb)  |\n| ResNet-101| [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet101-cifar10.ipynb)  |\n| ResNet-152 Gender Classifier| [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet152-celeba.ipynb)  |\n\n\n---\n\n## Transformers\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Multilabel DistilBERT | [Jigsaw Toxic Comment Challenge](https://www.kaggle.com/competitions/jigsaw-toxic-comment-classification-challenge) | DistilBERT classifier fine-tuning | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/transformer/distilbert-multilabel.ipynb)  |\n| DistilBERT as feature extractor | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | DistilBERT classifier with sklearn random forest and logistic regression | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/transformer/1_distilbert-as-feature-extractor.ipynb)  |\n| DistilBERT as feature extractor using `embetter` | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | DistilBERT classifier with sklearn random forest and logistic regression using the scikit-learn `embetter` library | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/transformer/distilbert-embetter-feature-extractor.ipynb)  |\n| Fine-tune DistilBERT I | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | Fine-tune only the last 2 layers of DistilBERT classifier |  [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/transformer/distilbert-finetune-last-layers.ipynb) |\n| Fine-tune DistilBERT II | [IMDB movie review](https://ai.stanford.edu/~amaas/data/sentiment/) | Fine-tune the whole DistilBERT classifier | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/transformer/distilbert-hf-finetuning.ipynb) [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii.ipynb)  |\n\n---\n\n## Ordinal Regression and Deep Learning\n\nPlease note that the following notebooks below provide reference implementations to use the respective methods. They are not performance benchmarks.\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Baseline multilayer perceptron | Cement | A baseline multilayer perceptron for classification trained with the standard cross entropy loss | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/ordinal/baseline_cement.ipynb) [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/ordinal/baseline-light_cement.ipynb) |\n| CORAL multilayer perceptron | Cement | Implementation of [Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation](https://www.sciencedirect.com/science/article/pii/S016786552030413X) 2020 | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/ordinal/CORAL_cement.ipynb) [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/ordinal/CORAL-light_cement.ipynb) |\n| CORN multilayer perceptron | Cement | Implementation of [Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities](https://arxiv.org/abs/2111.08851) 2022 | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/ordinal/CORN_cement.ipynb) [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/ordinal/CORN-light_cement.ipynb) |\n| Binary extension multilayer perceptron | Cement | Implementation of [Ordinal Regression with Multiple Output CNN for Age Estimation](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Niu_Ordinal_Regression_With_CVPR_2016_paper.pdf) 2016 | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/ordinal/niu2016_cement.ipynb) [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/ordinal/niu2016-light_cement.ipynb) |\n| Reformulated squared-error multilayer perceptron | Cement | Implementation of [A simple squared-error reformulation for ordinal classification](https://arxiv.org/abs/1612.00775) 2016 | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/ordinal/beckham2016_cement.ipynb) [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/ordinal/beckham2016-light_cement.ipynb) |\n| Class distance weighted cross-entropy loss | Cement | Implementation of [Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation](https://arxiv.org/abs/2202.05167) 2022 | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/ordinal/polat2022_cement.ipynb)  [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/ordinal/polat2022-light_cement.ipynb) |\n\n---\n\n\n\n## Normalization Layers\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| BatchNorm before and after Activation for Network-in-Network CIFAR-10 Classifier | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/nin-cifar10_batchnorm.ipynb)  |\n| Filter Response Normalization for Network-in-Network CIFAR-10 Classifier | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/nin-cifar10_filter-response-norm.ipynb)  |\n\n\n\n## Metric Learning\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Siamese Network with Multilayer Perceptrons | TBD | TBD | [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/metric/siamese-1.ipynb) |\n\n## Autoencoders\n\n#### Fully-connected Autoencoders\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Autoencoder (MNIST) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-basic.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/autoencoder/ae-basic.ipynb) |\n| Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/autoencoder/ae-basic-with-rf.ipynb) |\n\n\n\n\n\n\n#### Convolutional Autoencoders\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- | \n| Convolutional Autoencoder with Deconvolutions / Transposed Convolutions | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-deconv.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/autoencoder/ae-deconv.ipynb) |\n| Convolutional Autoencoder with Deconvolutions and Continuous Jaccard Distance | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-deconv-jaccard.ipynb)  |\n| Convolutional Autoencoder with Deconvolutions (without pooling operations) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-deconv-nopool.ipynb) |\n| Convolutional Autoencoder with Nearest-neighbor Interpolation | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-conv-nneighbor.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/autoencoder/ae-conv-nneighbor.ipynb) |\n| Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on CelebA | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb)  |\n| Convolutional Autoencoder with Nearest-neighbor Interpolation -- Trained on Quickdraw | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb)  |\n\n\n\n#### Variational Autoencoders\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Variational Autoencoder | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-var.ipynb)  |\n| Convolutional Variational Autoencoder | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-conv-var.ipynb)  |\n\n\n\n#### Conditional Variational Autoencoders\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Conditional Variational Autoencoder (with labels in reconstruction loss) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-cvae.ipynb)  |\n| Conditional Variational Autoencoder (without labels in reconstruction loss) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb)  |\n| Convolutional Conditional Variational Autoencoder (with labels in reconstruction loss) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-cnn-cvae.ipynb) |\n| Convolutional Conditional Variational Autoencoder (without labels in reconstruction loss) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb)  |\n\n\n\n\n## Generative Adversarial Networks (GANs)\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Fully Connected GAN on MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gan/gan.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/gan/gan.ipynb) |\n| Fully Connected Wasserstein GAN on MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gan/wgan-1.ipynb)  |\n| Convolutional GAN on MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gan/gan-conv.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/gan/gan-conv.ipynb) |\n| Convolutional GAN on MNIST with Label Smoothing | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gan/gan-conv-smoothing.ipynb) [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/gan/gan-conv-smoothing.ipynb) |\n| Convolutional Wasserstein GAN on MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gan/dc-wgan-1.ipynb)  |\n| Deep Convolutional GAN (DCGAN) on Cats and Dogs Images | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gan/dcgan-cats-and-dogs.ipynb) |\n| Deep Convolutional GAN (DCGAN) on CelebA Face Images | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gan/dcgan-celeba.ipynb)  |\n\n\n## Graph Neural Networks (GNNs)\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Most Basic Graph Neural Network with Gaussian Filter on MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gnn/gnn-basic-1.ipynb)  |\n| Basic Graph Neural Network with Edge Prediction on MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gnn/gnn-basic-edge-1.ipynb)  |\n| Basic Graph Neural Network with Spectral Graph Convolution on MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/gnn/gnn-basic-graph-spectral-1.ipynb)  |\n\n\n\n## Recurrent Neural Networks (RNNs)\n\n\n\n\n#### Many-to-one: Sentiment Analysis / Classification\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| A simple single-layer RNN (IMDB) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_simple_imdb.ipynb)  |\n| A simple single-layer RNN with packed sequences to ignore padding characters (IMDB) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_simple_packed_imdb.ipynb)  |\n| RNN with LSTM cells (IMDB) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_lstm_packed_imdb.ipynb) |\n| RNN with LSTM cells (IMDB) and pre-trained GloVe word vectors | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_lstm_packed_imdb-glove.ipynb)  |\n| RNN with LSTM cells and Own Dataset in CSV Format (IMDB) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb) |\n| RNN with GRU cells (IMDB) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_gru_packed_imdb.ipynb) |\n| Multilayer bi-directional RNN (IMDB) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_lstm_bi_imdb.ipynb) |\n| Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (AG News) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_bi_multilayer_lstm_own_csv_agnews.ipynb)  |\n\n\n\n#### Many-to-Many / Sequence-to-Sequence\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| A simple character RNN to generate new text (Charles Dickens) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb) |\n\n\n\n## Model Evaluation\n\n### K-Fold Cross-Validation\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Baseline CNN  | MNIST | A simple baseline with traditional train/validation/test splits | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/kfold/baseline-cnn-mnist.ipynb) [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/kfold/baseline-light-cnn-mnist.ipynb) |\n| K-fold with `pl_cross` | MNIST | A 5-fold cross-validation run using the `pl_cross` library | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](pytorch-lightning_ipynb/kfold/kfold-light-cnn-mnist.ipynb)  |\n\n\n\n## Data Augmentation\n\n| Title                      | Dataset | Description | Notebooks                                                    |\n| -------------------------- | ------- | ----------- | ------------------------------------------------------------ |\n| AutoAugment & TrivialAugment for Image Data | CIFAR-10     | Trains a ResNet-18 using AutoAugment and TrivialAugment         | [![PyTorch Lightning](https://img.shields.io/badge/PyTorch-Lightning-blueviolet)](./pytorch-lightning_ipynb/data-augmentation/autoaugment) |\n\n\n\n\n## Tips and Tricks\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Cyclical Learning Rate  | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/tricks/cyclical-learning-rate.ipynb)  |\n| Annealing with Increasing the Batch Size (w. CIFAR-10 & AlexNet) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/tricks/cnn-alexnet-cifar10-batchincrease.ipynb)  |\n| Gradient Clipping (w. MLP on MNIST)  | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/tricks/gradclipping_mlp.ipynb)  |\n\n\n\n\n## Transfer Learning\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Transfer Learning Example (VGG16 pre-trained on ImageNet for Cifar-10)  | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/transfer/transferlearning-vgg16-cifar10-1.ipynb)  |\n\n\n## Visualization and Interpretation\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Vanilla Loss Gradient (wrt Inputs) Visualization (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/viz/cnns/cats-and-dogs/cnn-viz-grad__vgg16-cats-dogs.ipynb)  |\n| Guided Backpropagation (Based on a VGG16 Convolutional Neural Network for Kaggle's Cats and Dogs Images) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/viz/cnns/cats-and-dogs/cnn-viz-guided-backprop__vgg16-cats-dogs.ipynb)  |\n\n\n\n## PyTorch Workflows and Mechanics\n\n\n#### PyTorch Lightning Examples\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| MLP in Lightning with TensorBoard  -- continue training the last model | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/lightning/lightning-mlp.ipynb)  |\n| MLP in Lightning with TensorBoard  -- checkpointing best model | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/lightning/lightning-mlp-best-model)  |\n\n\n\n\n#### Custom Datasets\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Custom Data Loader Example for PNG Files | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-dataloader-png/custom-dataloader-example.ipynb)  |\n| Using PyTorch Dataset Loading Utilities for Custom Datasets -- CSV files converted to HDF5 | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-data-loader-csv.ipynb)  |\n| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Face Images from CelebA | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-data-loader-celeba.ipynb) |\n| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from Quickdraw | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb) |\n| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Drawings from the Street View House Number (SVHN) Dataset | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-data-loader-svhn.ipynb) |\n| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Asian Face Dataset (AFAD) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-data-loader-afad.ipynb)  |\n| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Dating Historical Color Images | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-data-loader_dating-historical-color-images.ipynb) |\n| Using PyTorch Dataset Loading Utilities for Custom Datasets -- Fashion MNIST | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb)  |\n\n\n\n#### Training and Preprocessing\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| PyTorch DataLoader State and Nested Iterations | Toy | Explains DataLoader behavior when in nested functions | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/dataloader-nesting.ipynb)|\n| Generating Validation Set Splits | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/validation-splits.ipynb)  |\n| Dataloading with Pinned Memory | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-resnet34-cifar10-pinmem.ipynb) |\n| Standardizing Images | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-standardized.ipynb) |\n| Image Transformation Examples | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/torchvision-transform-examples.ipynb) |\n| Char-RNN with Own Text File | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/char_rnn-charlesdickens.ipynb) |\n| Sentiment Classification RNN with Own CSV File | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/rnn/rnn_lstm_packed_own_csv_imdb.ipynb)  |\n\n\n\n#### Improving Memory Efficiency\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Gradient Checkpointing Demo (Network-in-Network trained on CIFAR-10) | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/gradient-checkpointing-nin.ipynb)  |\n\n#### Parallel Computing\n\n|Title | Description | Notebooks |\n| --- | --- | --- | \n| Using Multiple GPUs with DataParallel -- VGG-16 Gender Classifier on CelebA | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/cnn/cnn-vgg16-celeba-data-parallel.ipynb)  |\n| Distribute a Model Across Multiple GPUs with Pipeline Parallelism (VGG-16 Example) | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/model-pipeline-vgg16.ipynb)  |\n\n\n#### Other \n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| PyTorch with and without Deterministic Behavior -- Runtime Benchmark | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/deterministic_benchmark.ipynb)  |\n| Sequential API and hooks | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/mlp-sequential.ipynb)  |\n| Weight Sharing Within a Layer | TBD | TBD |  [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/cnn-weight-sharing.ipynb)  |\n| Plotting Live Training Performance in Jupyter Notebooks with just Matplotlib | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/plot-jupyter-matplotlib.ipynb)  |\n\n\n\n#### Autograd\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Getting Gradients of an Intermediate Variable in PyTorch | TBD | TBD | [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/mechanics/manual-gradients.ipynb)  |\n\n\n## TensorFlow Workflows and Mechanics\n\n#### Custom Datasets\n\n|Title | Description | Notebooks |\n| --- | --- | --- | \n| Chunking an Image Dataset for Minibatch Training using NumPy NPZ Archives | TBD | [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mechanics/image-data-chunking-npz.ipynb) |\n| Storing an Image Dataset for Minibatch Training using HDF5 | TBD | [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mechanics/image-data-chunking-hdf5.ipynb) |\n| Using Input Pipelines to Read Data from TFRecords Files | TBD | [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mechanics/tfrecords.ipynb) |\n| Using Queue Runners to Feed Images Directly from Disk  | TBD | [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mechanics/file-queues.ipynb) |\n| Using TensorFlow's Dataset API | TBD | [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mechanics/dataset-api.ipynb) |\n\n\n\n\n#### Training and Preprocessing\n\n|Title | Dataset | Description | Notebooks |\n| --- | --- | --- | --- |\n| Saving and Loading Trained Models -- from TensorFlow Checkpoint Files and NumPy NPZ Archives | TBD | TBD | [![TensorFlow](https://img.shields.io/badge/Tensor-Flow1.0-orange)](tensorflow1_ipynb/mechanics/saving-and-reloading-models.ipynb) |\n\n## Related Libraries\n\n|Title | Description |  Notebooks |\n| --- | --- | --- | \n| TorchMetrics | How do we use it, and what's the difference between .update() and .forward()? |  [![PyTorch](https://img.shields.io/badge/Py-Torch-red)](pytorch_ipynb/related-libraries/torchmetrics-update-forward.ipynb)  |\n\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-alexnet-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"9c9ec640\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f85dcc1e\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"c28bc5ac\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"43d420d1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"517d2bf4\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# AlexNet Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d37a7a82\",\n   \"metadata\": {},\n   \"source\": [\n    \"AlexNet [1][2] trained on CIFAR-10 [3].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] L13.7 CNN Architectures & AlexNet (20:17), https://www.youtube.com/watch?v=-IHxe4-09e4\\n\",\n    \"- [2] Imagenet classification with deep convolutional neural networks, https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0d9eeb2a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"37bb3dc1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"8350f2d0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 40\\\\nLEARNING_RATE = 0.0001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 40\\\\nLEARNING_RATE = 0.0001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 40\\n\",\n    \"LEARNING_RATE = 0.0001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6517b50d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7938d4cf\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ddb917fd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"061ba942\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch.nn as nn\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(nn.Module):\\\\n    def __init__(self, num_classes):\\\\n        super().__init__()\\\\n        self.features = nn.Sequential(\\\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            nn.Conv2d(64, 192, kernel_size=5, padding=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            nn.Conv2d(192, 384, kernel_size=3, padding=1),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Conv2d(384, 256, kernel_size=3, padding=1),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n        )\\\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\\\n        self.classifier = nn.Sequential(\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(256 * 6 * 6, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(4096, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Linear(4096, num_classes)\\\\n        )\\\\n\\\\n    def forward(self, x):\\\\n        x = self.features(x)\\\\n        x = self.avgpool(x)\\\\n        x = torch.flatten(x, start_dim=1)\\\\n        logits = self.classifier(x)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch.nn as nn\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(nn.Module):\\\\n    def __init__(self, num_classes):\\\\n        super().__init__()\\\\n        self.features = nn.Sequential(\\\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            nn.Conv2d(64, 192, kernel_size=5, padding=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            nn.Conv2d(192, 384, kernel_size=3, padding=1),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Conv2d(384, 256, kernel_size=3, padding=1),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Conv2d(256, 256, kernel_size=3, padding=1),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n        )\\\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\\\n        self.classifier = nn.Sequential(\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(256 * 6 * 6, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(4096, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Linear(4096, num_classes),\\\\n        )\\\\n\\\\n    def forward(self, x):\\\\n        x = self.features(x)\\\\n        x = self.avgpool(x)\\\\n        x = torch.flatten(x, start_dim=1)\\\\n        logits = self.classifier(x)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class PyTorchModel(nn.Module):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            nn.Conv2d(64, 192, kernel_size=5, padding=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            nn.Conv2d(192, 384, kernel_size=3, padding=1),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Conv2d(384, 256, kernel_size=3, padding=1),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Conv2d(256, 256, kernel_size=3, padding=1),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"        )\\n\",\n    \"        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(256 * 6 * 6, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(4096, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Linear(4096, num_classes)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = self.avgpool(x)\\n\",\n    \"        x = torch.flatten(x, start_dim=1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"449af085\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"007135a4\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ebdfebbb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"db9e40b9\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"ed84f607\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\",\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_cifar10_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3b2403d4\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8c800bfc\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"5d6f9749\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 3\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"350d3627\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"9261f0a1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0141679a\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a91eb5d9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"15dd7261\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path, download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.RandomCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.CenterCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=self.train_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=self.test_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8b10de24\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"31f5d214\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e188e446\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0b671155\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"c233056a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"c5d908c1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"930e5070\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"87a0d60b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 57.0 M\\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"57.0 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"57.0 M    Total params\\n\",\n      \"228.179   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b78391602428499884263ff7f9ba101e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7f20f98b6fd84ce4ada7a87e867278f0\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 10.91 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"261901c0\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"75d7903f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"29ddfdbd\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c5dd507e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"cd2de3ee\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_18/checkpoints/epoch=35-step=6299.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_18/checkpoints/epoch=35-step=6299.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"9ded09b2c8c14186894a4e3773d23207\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.7247999906539917}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.7247999906539917}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8b2bc82\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"35dddec4\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"2ba699c5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_18/checkpoints/epoch=35-step=6299.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"2e43e04a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"71e8e78f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"68c432d3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([5, 5, 8, 0, 8])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e95cf2d6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"d942de45\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.7248 (72.48%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"82d33651\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"67e59adf\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"0039aede\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"class_dict = {0: 'airplane',\\\\n              1: 'automobile',\\\\n              2: 'bird',\\\\n              3: 'cat',\\\\n              4: 'deer',\\\\n              5: 'dog',\\\\n              6: 'frog',\\\\n              7: 'horse',\\\\n              8: 'ship',\\\\n              9: 'truck'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"class_dict = {\\\\n    0: \\\\\\\"airplane\\\\\\\",\\\\n    1: \\\\\\\"automobile\\\\\\\",\\\\n    2: \\\\\\\"bird\\\\\\\",\\\\n    3: \\\\\\\"cat\\\\\\\",\\\\n    4: \\\\\\\"deer\\\\\\\",\\\\n    5: \\\\\\\"dog\\\\\\\",\\\\n    6: \\\\\\\"frog\\\\\\\",\\\\n    7: \\\\\\\"horse\\\\\\\",\\\\n    8: \\\\\\\"ship\\\\\\\",\\\\n    9: \\\\\\\"truck\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"cc4cefe2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5ae6820c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"66f56407\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"f54b6f28\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-alexnet-grouped-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"a6fe1f2c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"df2b50ba\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"8bbd8b4f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"a921ddea\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b7975ec2\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# AlexNet with Grouped Convolutions Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4d929930\",\n   \"metadata\": {},\n   \"source\": [\n    \"AlexNet [1][2] trained on CIFAR-10 [3].\\n\",\n    \"\\n\",\n    \"This implementation uses grouped convolutions like in the original AlexNet paper [2]:\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/alexnet/alexnet-paper.png)\\n\",\n    \"\\n\",\n    \"Here, the network is essentially split into two parts to train it on two GPUs with 1.5 Gb RAM each. This was purely done for computational performance reasons (and the video RAM limitation back then). However, there are certain benefits to using grouped convolutions ...\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Taking a step back, how do grouped convolutions work?**\\n\",\n    \"\\n\",\n    \"In a nutshell, you can think of grouped convolutions as convolutional layers that process part of the input independently and merge the results. So, for example, if you consider grouped convolutions with two filter groups, each filter group would process half of the channels. \\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/alexnet/grouped-convolutions.png)\\n\",\n    \"\\n\",\n    \"**One of the benefits of grouped convolutions is**, as noted by Yani Ioannou [4], that AlexNet has a slightly improved accuracy when using two filter groups:\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/alexnet/alexnet-groups.png)\\n\",\n    \"\\n\",\n    \"**Another benefit is the reduced parameter size**. \\n\",\n    \"\\n\",\n    \"Say we have kernels with height 3 and width 3. The inputs have 6 channels, and the output channels are set to 12. Then, we have kernels with 3x3x6 weight parameters with a regular convolution. Since we have 12 output channels, that's 3x3x6x12=648 parameters in total.\\n\",\n    \"\\n\",\n    \"Now, let's assume we use a grouped convolution with group size 2. We still have a 3x3 kernel height and width. But now, the number of input channels is split by a factor of 2, so each kernel is 3x3x3. The first group produces the first 6 output channels, so we have 3x3x3x6 parameters for the first group. The second group has the same size, so we have (3x3x3x6)x2 = 3x3x3x12 = 324, which is a 2x reduction in parameters compared to the regular convolution.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**And how do we do this in PyTorch?**\\n\",\n    \"\\n\",\n    \"Implementing grouped convolutions in PyTorch is now really straightforward. We just used the `groups` parameter. For example, to implement a grouped convolution with two filter groups we use\\n\",\n    \"\\n\",\n    \"    torch.nn.Conv2d(..., groups=2)\\n\",\n    \"\\n\",\n    \"Note that a requirement for this is that the number of input and output channels is divisible by groups (here: 2).\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] L13.7 CNN Architectures & AlexNet (20:17), https://www.youtube.com/watch?v=-IHxe4-09e4\\n\",\n    \"- [2] Imagenet classification with deep convolutional neural networks, https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/CIFAR-10\\n\",\n    \"- [4] https://blog.yani.ai/filter-group-tutorial/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a9c61f88\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0588c1b3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"83ce1a2d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 40\\\\nLEARNING_RATE = 0.0001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 40\\\\nLEARNING_RATE = 0.0001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 40\\n\",\n    \"LEARNING_RATE = 0.0001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"883ece2d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c56da1dd\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0d65d8d2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"a7641c1f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch.nn as nn\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(nn.Module):\\\\n    def __init__(self, num_classes):\\\\n        super().__init__()\\\\n        self.features = nn.Sequential(\\\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            \\\\n            nn.Conv2d(64, 192, kernel_size=5, padding=2, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            \\\\n            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            \\\\n            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            \\\\n            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n        )\\\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\\\n        self.classifier = nn.Sequential(\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(256 * 6 * 6, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(4096, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Linear(4096, num_classes)\\\\n        )\\\\n\\\\n    def forward(self, x):\\\\n        x = self.features(x)\\\\n        x = self.avgpool(x)\\\\n        x = torch.flatten(x, start_dim=1)\\\\n        logits = self.classifier(x)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch.nn as nn\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(nn.Module):\\\\n    def __init__(self, num_classes):\\\\n        super().__init__()\\\\n        self.features = nn.Sequential(\\\\n            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            nn.Conv2d(64, 192, kernel_size=5, padding=2, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.MaxPool2d(kernel_size=3, stride=2),\\\\n        )\\\\n        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\\\n        self.classifier = nn.Sequential(\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(256 * 6 * 6, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Dropout(0.5),\\\\n            nn.Linear(4096, 4096),\\\\n            nn.ReLU(inplace=True),\\\\n            nn.Linear(4096, num_classes),\\\\n        )\\\\n\\\\n    def forward(self, x):\\\\n        x = self.features(x)\\\\n        x = self.avgpool(x)\\\\n        x = torch.flatten(x, start_dim=1)\\\\n        logits = self.classifier(x)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class PyTorchModel(nn.Module):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(64, 192, kernel_size=5, padding=2, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"        )\\n\",\n    \"        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(256 * 6 * 6, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(4096, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Linear(4096, num_classes)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = self.avgpool(x)\\n\",\n    \"        x = torch.flatten(x, start_dim=1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"b410e766\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c06de7ac\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e9cffd20\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8dac1f73\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"be3c17e8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\",\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_cifar10_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7e239588\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d0f6de7c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"041c2950\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 3\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2b95e4c1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"546e593f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e5f4cf99\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"af8ed10b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"9abaa47d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path, download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.RandomCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.CenterCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=self.train_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=self.test_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d957b00d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"cf180925\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8592d0e2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c905e372\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"7660c860\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"01797000\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3e0ac279\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"3ba6ef60\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 55.8 M\\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"55.8 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"55.8 M    Total params\\n\",\n      \"223.289   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3b34070fe52847ed8492ec8f9bbc440c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"43f7bcc9f3a54feb8a0d1292127eb40e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 10.80 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"601b1ca9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d43d6e92\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"9776f752\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"00611a4e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"86a2c9e6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_19/checkpoints/epoch=36-step=6474.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_19/checkpoints/epoch=36-step=6474.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4490fa909a154f87b540d061b971a5ae\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.7178000211715698}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.7178000211715698}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7b49bb38\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ffac9f60\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"20818ae0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_19/checkpoints/epoch=36-step=6474.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"094f161f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"475eacb7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"610b9515\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([7, 3, 9, 0, 8])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cda9dad7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"ab1373cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.7178 (71.78%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8f783531\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cd991baa\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"e6fea8a5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"class_dict = {0: 'airplane',\\\\n              1: 'automobile',\\\\n              2: 'bird',\\\\n              3: 'cat',\\\\n              4: 'deer',\\\\n              5: 'dog',\\\\n              6: 'frog',\\\\n              7: 'horse',\\\\n              8: 'ship',\\\\n              9: 'truck'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"class_dict = {\\\\n    0: \\\\\\\"airplane\\\\\\\",\\\\n    1: \\\\\\\"automobile\\\\\\\",\\\\n    2: \\\\\\\"bird\\\\\\\",\\\\n    3: \\\\\\\"cat\\\\\\\",\\\\n    4: \\\\\\\"deer\\\\\\\",\\\\n    5: \\\\\\\"dog\\\\\\\",\\\\n    6: \\\\\\\"frog\\\\\\\",\\\\n    7: \\\\\\\"horse\\\\\\\",\\\\n    8: \\\\\\\"ship\\\\\\\",\\\\n    9: \\\\\\\"truck\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"c96bfe90\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6d0aadb9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"efd81a69\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"242fb162\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torchmetrics     : 0.6.2\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-allconv.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"d6b34308\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"26637448\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"0349303a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"4a01b5f5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5258ee70\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"#  All-Convolutional Neural Network Trained on MNIST.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1100821b\",\n   \"metadata\": {},\n   \"source\": [\n    \"All-Convolutional neural network [1][2] Trained on MNIST [3]\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] L14.4.1 Replacing Max-Pooling with Convolutional Layers (08:19), https://www.youtube.com/watch?v=Lq83NFkkJCk\\n\",\n    \"- [2] Striving for Simplicity: The All Convolutional Net, https://arxiv.org/abs/1412.6806\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/MNIST_database\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c7e27d9d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d137a5ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"05ce9388\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"BATCH_SIZE = 128\\\\nNUM_EPOCHS = 20\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"BATCH_SIZE = 128\\\\nNUM_EPOCHS = 20\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"874744ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"75d875b4\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"50635763\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"4f14a375\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\nclass PyTorchModel(torch.nn.Module):\\\\n\\\\n    def __init__(self, num_classes):\\\\n        super().__init__()\\\\n        \\\\n        self.num_classes = num_classes\\\\n        # calculate same padding:\\\\n        # (w - k + 2*p)/s + 1 = o\\\\n        # => p = (s(o-1) - w + k)/2\\\\n        \\\\n        self.features = torch.nn.Sequential(\\\\n            # 28x28x1 => 28x28x4\\\\n            torch.nn.Conv2d(in_channels=1,\\\\n                            out_channels=4,\\\\n                            kernel_size=(3, 3),\\\\n                            stride=(1, 1),\\\\n                            padding=1),  # (1(28-1) - 28 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n        \\\\n            # 28x28x4 => 14x14x4\\\\n            torch.nn.Conv2d(in_channels=4,\\\\n                            out_channels=4,\\\\n                            kernel_size=(3, 3),\\\\n                            stride=(2, 2),\\\\n                            padding=1),\\\\n            torch.nn.ReLU(),\\\\n            \\\\n            # 14x14x4 => 14x14x8\\\\n            torch.nn.Conv2d(in_channels=4,\\\\n                            out_channels=8,\\\\n                            kernel_size=(3, 3),\\\\n                            stride=(1, 1),\\\\n                            padding=1),  # (1(14-1) - 14 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n        \\\\n            # 14x14x8 => 7x7x8                             \\\\n            torch.nn.Conv2d(in_channels=8,\\\\n                            out_channels=8,\\\\n                            kernel_size=(3, 3),\\\\n                            stride=(2, 2),\\\\n                            padding=1),\\\\n            torch.nn.ReLU(),\\\\n        \\\\n            # 7x7x8 => 7x7x16                             \\\\n            torch.nn.Conv2d(in_channels=8,\\\\n                            out_channels=16,\\\\n                            kernel_size=(3, 3),\\\\n                            stride=(1, 1),\\\\n                            padding=1),  # (1(7-1) - 7 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n        \\\\n            # 7x7x16 => 4x4x16                             \\\\n            torch.nn.Conv2d(in_channels=16,\\\\n                            out_channels=16,\\\\n                            kernel_size=(3, 3),\\\\n                            stride=(2, 2),\\\\n                            padding=1),\\\\n            torch.nn.ReLU(),\\\\n        \\\\n            # 4x4x16 => 4x4xnum_classes                             \\\\n            torch.nn.Conv2d(in_channels=16,\\\\n                            out_channels=self.num_classes,\\\\n                            kernel_size=(3, 3),\\\\n                            stride=(1, 1),\\\\n                            padding=1),  # (1(7-1) - 7 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n            torch.nn.AdaptiveAvgPool2d(output_size=1),\\\\n            torch.nn.Flatten()\\\\n        )\\\\n        \\\\n    def forward(self, x):\\\\n        logits = self.features(x)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\nclass PyTorchModel(torch.nn.Module):\\\\n    def __init__(self, num_classes):\\\\n        super().__init__()\\\\n\\\\n        self.num_classes = num_classes\\\\n        # calculate same padding:\\\\n        # (w - k + 2*p)/s + 1 = o\\\\n        # => p = (s(o-1) - w + k)/2\\\\n\\\\n        self.features = torch.nn.Sequential(\\\\n            # 28x28x1 => 28x28x4\\\\n            torch.nn.Conv2d(\\\\n                in_channels=1,\\\\n                out_channels=4,\\\\n                kernel_size=(3, 3),\\\\n                stride=(1, 1),\\\\n                padding=1,\\\\n            ),  # (1(28-1) - 28 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n            # 28x28x4 => 14x14x4\\\\n            torch.nn.Conv2d(\\\\n                in_channels=4,\\\\n                out_channels=4,\\\\n                kernel_size=(3, 3),\\\\n                stride=(2, 2),\\\\n                padding=1,\\\\n            ),\\\\n            torch.nn.ReLU(),\\\\n            # 14x14x4 => 14x14x8\\\\n            torch.nn.Conv2d(\\\\n                in_channels=4,\\\\n                out_channels=8,\\\\n                kernel_size=(3, 3),\\\\n                stride=(1, 1),\\\\n                padding=1,\\\\n            ),  # (1(14-1) - 14 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n            # 14x14x8 => 7x7x8\\\\n            torch.nn.Conv2d(\\\\n                in_channels=8,\\\\n                out_channels=8,\\\\n                kernel_size=(3, 3),\\\\n                stride=(2, 2),\\\\n                padding=1,\\\\n            ),\\\\n            torch.nn.ReLU(),\\\\n            # 7x7x8 => 7x7x16\\\\n            torch.nn.Conv2d(\\\\n                in_channels=8,\\\\n                out_channels=16,\\\\n                kernel_size=(3, 3),\\\\n                stride=(1, 1),\\\\n                padding=1,\\\\n            ),  # (1(7-1) - 7 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n            # 7x7x16 => 4x4x16\\\\n            torch.nn.Conv2d(\\\\n                in_channels=16,\\\\n                out_channels=16,\\\\n                kernel_size=(3, 3),\\\\n                stride=(2, 2),\\\\n                padding=1,\\\\n            ),\\\\n            torch.nn.ReLU(),\\\\n            # 4x4x16 => 4x4xnum_classes\\\\n            torch.nn.Conv2d(\\\\n                in_channels=16,\\\\n                out_channels=self.num_classes,\\\\n                kernel_size=(3, 3),\\\\n                stride=(1, 1),\\\\n                padding=1,\\\\n            ),  # (1(7-1) - 7 + 3) / 2 = 1\\\\n            torch.nn.ReLU(),\\\\n            torch.nn.AdaptiveAvgPool2d(output_size=1),\\\\n            torch.nn.Flatten(),\\\\n        )\\\\n\\\\n    def forward(self, x):\\\\n        logits = self.features(x)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchModel(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            # 28x28x1 => 28x28x4\\n\",\n    \"            torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                            out_channels=4,\\n\",\n    \"                            kernel_size=(3, 3),\\n\",\n    \"                            stride=(1, 1),\\n\",\n    \"                            padding=1),  # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"        \\n\",\n    \"            # 28x28x4 => 14x14x4\\n\",\n    \"            torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                            out_channels=4,\\n\",\n    \"                            kernel_size=(3, 3),\\n\",\n    \"                            stride=(2, 2),\\n\",\n    \"                            padding=1),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            \\n\",\n    \"            # 14x14x4 => 14x14x8\\n\",\n    \"            torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                            out_channels=8,\\n\",\n    \"                            kernel_size=(3, 3),\\n\",\n    \"                            stride=(1, 1),\\n\",\n    \"                            padding=1),  # (1(14-1) - 14 + 3) / 2 = 1\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"        \\n\",\n    \"            # 14x14x8 => 7x7x8                             \\n\",\n    \"            torch.nn.Conv2d(in_channels=8,\\n\",\n    \"                            out_channels=8,\\n\",\n    \"                            kernel_size=(3, 3),\\n\",\n    \"                            stride=(2, 2),\\n\",\n    \"                            padding=1),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"        \\n\",\n    \"            # 7x7x8 => 7x7x16                             \\n\",\n    \"            torch.nn.Conv2d(in_channels=8,\\n\",\n    \"                            out_channels=16,\\n\",\n    \"                            kernel_size=(3, 3),\\n\",\n    \"                            stride=(1, 1),\\n\",\n    \"                            padding=1),  # (1(7-1) - 7 + 3) / 2 = 1\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"        \\n\",\n    \"            # 7x7x16 => 4x4x16                             \\n\",\n    \"            torch.nn.Conv2d(in_channels=16,\\n\",\n    \"                            out_channels=16,\\n\",\n    \"                            kernel_size=(3, 3),\\n\",\n    \"                            stride=(2, 2),\\n\",\n    \"                            padding=1),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"        \\n\",\n    \"            # 4x4x16 => 4x4xnum_classes                             \\n\",\n    \"            torch.nn.Conv2d(in_channels=16,\\n\",\n    \"                            out_channels=self.num_classes,\\n\",\n    \"                            kernel_size=(3, 3),\\n\",\n    \"                            stride=(1, 1),\\n\",\n    \"                            padding=1),  # (1(7-1) - 7 + 3) / 2 = 1\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.AdaptiveAvgPool2d(output_size=1),\\n\",\n    \"            torch.nn.Flatten()\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        logits = self.features(x)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"063db1bd\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"689f43c8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7832b63a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6514660d\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"a2cda77f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 980),\\n\",\n       \" (1, 1135),\\n\",\n       \" (2, 1032),\\n\",\n       \" (3, 1010),\\n\",\n       \" (4, 982),\\n\",\n       \" (5, 892),\\n\",\n       \" (6, 958),\\n\",\n       \" (7, 1028),\\n\",\n       \" (8, 974),\\n\",\n       \" (9, 1009)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_mnist_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73b0868f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"37c9f167\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"a3d2484d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 1\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.11 (11.35%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"074b4cbc\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"6f2c157f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7870b0fc\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"44006335\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"22f66688\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path, download=True)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"588ac808\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"f6ced83f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7b14b35d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f5e945cd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"f5904d2b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"7f4918ad\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e7c6020b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"33118695\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 6.0 K \\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"6.0 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"6.0 K     Total params\\n\",\n      \"0.024     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e3bebc4e40d34236a1bc5ace2f19305c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"767a4f516311404fa871ab8d10cb0ca6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 1.93 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"704a3a83\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"accd76cb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"d0a6d2ae\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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BSyrZCmQgygTy/5XzfuhYikglcBywJYRztyvBqjUApZV+uEL63BFhnWi0/ATxkjGkUCVTc90YiC4GFAOnp6eTm5nYpoIqKioD71pfVcqS4gbVr19JWHKEWLL5IEukxanzdo/F1T6THF5QxJiQTMAVY7bf8I+BHrcp8CRz0TRVYzUPfbOt9J0yYYLpq7dq1Adcvyd1nBj30Z1NaXdfl9z4XgsUXSSI9Ro2vezS+7onk+IBNJsh5NZQ1go3AcBHJBo4A84CbWyWh7OZ5EXkR+LMx5s0QxhRQS1+C0hoSPe6e/nillAqrkF0jMMY0APdi3Q20C3jVGLNDRBaJyKJQfW5XtPQu1usESikbCmWNAGPMKmBVq3UBLwwbY+aHMpa2tAxZqXcOKaVsyPY9i+F072K9c0gpZUeaCACP20lSrFubhpRStqSJwMcaoEYfM6GUsh9NBD46ZKVSyq40EfhkJOog9kope9JE4JPuG7KyXoesVErZjCYCn4xED8bAqXK9TqCUshdNBD4Z3mhAO5UppexHE4FPS18C7VSmlLIZTQQ++pgJpZRdaSLwSYmLIsrp0ESglLIdTQQ+IkLfxGhtGlJK2Y4mAj/al0ApZUeaCPykez2cKNPbR5VS9qKJwI/1vKGa5tHTlFLKFjQR+MlI9FBd30hZTUO4Q1FKqR6jicBPulfHJVBK2Y8mAj8tfQn0ziGllI1oIvCjncqUUnakicBP30TreUPal0ApZSeaCPx43E6SdchKpZTNaCJoRUcqU0rZTYcSgYjEiYjDNz9CRK4REXdoQwuPDK/2LlZK2UtHawTrAI+IZAJrgDuBF0MVVDjpIPZKKbvpaCIQY0wVcD3wlDHmOuCC0IUVPumJHgordchKpZR9dDgRiMgU4BbgL751rtCEFF4ZXmvIypM6ZKVSyiY6mgjuB34ErDDG7BCRIcDakEUVRtqpTCllNx36VW+MeR94H8B30bjAGPP9UAYWLi1DVuoFY6WUTXT0rqGXRSRRROKAncAXIvJgB/abIyJfiMg+EVkcYPu1IrJNRLaIyCYRmd75r3BuZXi1RqCUspeONg1dYIwpA74JrAIGAre1tYOIOIGngcuxLizfJCKtLzCvAcYYY8YC3wGe73DkIZIc6ybK5dAagVLKNjqaCNy+fgPfBN4yxtQD7T20fxKwzxhzwBhTBywDrvUvYIypMKcf/h/XgfcMOREhPTFa+xIopWxDOjIIi4h8H3gI2ApciVUj+B9jzIw29pkLzDHGLPAt3wZMNsbc26rcdcB/AH2BK40xGwK810JgIUB6evqEZcuWdezbtVJRUUF8fHy75X7+STUC/GhyTJc+p6s6Gl84RXqMGl/3aHzdE8nxzZ49e7MxJifgRmNMlybA1c72bwHP+y3fhtUHIVj5mcB77X3uhAkTTFetXbu2Q+W+99Jmc8l//q3Ln9NVHY0vnCI9Ro2vezS+7onk+IBNJsh5taMXi70i8rjvgu4mEfk1VlNOW/KBAX7LWcDRYIWNMeuAoSKS1pGYQql5EHujQ1YqpWygo9cIlgLlwI2+qQx4oZ19NgLDRSRbRKKAecBK/wIiMkxExDc/HogCCjsefmhkeD3U1DdRVq1DViqler+O9g4eaoy5wW/5YRHZ0tYOxpgGEbkXWA04gaXG6oy2yLd9CXADcLuI1APVwLdNBPwMT/cboMYb2yufraeUUi06mgiqRWS6MWY9gIhMwzpxt8kYswrrdlP/dUv85n8J/LLj4faMlr4EZTWcl5EQ5miUUiq0OpoIFgF/EBGvb7kYuCM0IYVf82MmdKQypZQddPQRE1uBMSKS6FsuE5H7gW0hjC1smoes1L4ESik76NQIZcaYMmP1MAb4YQjiiQjRLicpcVGaCJRSttCdoSrlnEURgdITPdo0pJSyhe4kgrDf3RNKGfqYCaWUTbR5jUBEygl8whegZ5+/0MMyvB4+P1Ia7jCUUirk2kwExhjb3juZnuihoKKOuoYmolzdqTgppVRk0zNcEM23kJ4s1+YhpVTvpokgiHSvjlSmlLIHTQRBnB67WAexV0r1bpoIgsjwe96QUkr1ZpoIgkjSISuVUjahiSAIEbHGJdBOZUqpXk4TQRuaB6hRSqneTBNBG9K9Hm0aUkr1epoI2pCRGM3xUh2yUinVu2kiaEN6oofahiZKq+vDHYpSSoWMJoI2+I9UppRSvZUmgjac7lSmiUAp1XtpImhD8yD2esFYKdWbaSJoQ7o+ZkIpZQOaCNoQ5XKQqkNWKqV6OU0E7UhP1L4ESqneTRNBOzK8+pgJpVTvpomgHVojUEr1dpoI2pGR6KGwso7ahsZwh6KUUiGhiaAdGd5oAE6W6Z1DSqneSRNBO7QvgVKqtwtpIhCROSLyhYjsE5HFAbbfIiLbfNNHIjImlPF0RfNjJo7pBWOlVC8VskQgIk7gaeBy4ALgJhG5oFWxL4FLjDGjgf8DPBuqeLoqQ2sESqleLpQ1gknAPmPMAWNMHbAMuNa/gDHmI2NMsW/xYyArhPF0iTfGTbTLobeQKqV6LQnVs/ZFZC4wxxizwLd8GzDZGHNvkPIPACOby7fathBYCJCenj5h2bJlXYqpoqKC+Pj4Tu/3L+uqGJzo4J6xni59bkd1Nb6eFOkxanzdo/F1TyTHN3v27M3GmJxA21wh/FwJsC5g1hGR2cBdwPRA240xz+JrNsrJyTGzZs3qUkC5ubl0Zd/BX2zAGMOsWVO79Lkd1dX4elKkx6jxdY/G1z2RHl8woWwaygcG+C1nAUdbFxKR0cDzwLXGmMIQxtNlOnaxUqo3C2Ui2AgMF5FsEYkC5gEr/QuIyEDgDeA2Y8yeEMYCgKu+okv7ZXg9nCir1SErlVK9UsgSgTGmAbgXWA3sAl41xuwQkUUisshX7KdAKvDfIrJFRDaFKh52vMnFH38XTu7u9K7piR7qGpoortIhK5VSvU8orxFgjFkFrGq1bonf/ALgrIvDITFwCk0ON7x+FyxYA+6OX/j1H6ksJS4qVBEqpVRY2KdncUI6u0d+H05sh/d+1qldmx8zoX0JlFK9kX0SAVCUmgOTF8Enz8Cev3Z4v5aRyjQRKKV6IVslAgC+/jD0HQVv3QMVJzu0S98EHcReKdV72S8RuD0w93dQWw5v3g1NTe3uEuVykBYfpU1DSqleyX6JAKDv+XDZ/4V971nNRB2Qrn0JlFK9lD0TAUDOXXDelfDuv8Oxre0Wz0jUISuVUr2TfROBCFzzFMSlwesLoK6yzeLpXh2yUinVO9k3EQDEpcJ1S6BgL6z+cZtFMxI9FFfVU1OvQ1YqpXoXeycCgCGzYNp9sPlF2LkyaLHmTmV5RVU9E5dSSvUQTQQAX/s36D8eVv4TlOYHLJIzOJkYt5N7XvqMU+U6frFSqvfQRADgdMMNz0NjPbzxj9B0dvPPkD7xLJ0/kbziKm5+7mNNBkqpXkMTQbPUoXDlY3BoPaz/TcAiU4am8sL8SS3JoKBCk4FS6qtPE4G/MTfBhTfA2p9D3saARaYMTW2pGdz0rCYDpdRXnyYCfyJw5eOQmGk9pbSmLGCxqUPTzmgm0mSglPoq00TQWkySdb2gNA9WPRC02NShaSy9YyKHi6q45blPOpYM6rUfglIq8mgiCGTgZLhkMWx7Bba+ErTY1GFWMjhUVMktz31CYaBkUHQAPvg1LJkOP+8PW5eFMHCllOo8TQTBzPhnGDgF/vLPUPRl0GJTh6XxO18yuLk5GRQfgvVPwP+7BJ4cB2seAVcM9B9nPehu22s99z2UUqodmgiCcbrg+mdBHNYjKBqDD1M5bVgaf5ybyeyiVzj1m+nwX6PhvX8HhxO+8Sjc/zkseBfuWAkDp8KKhbD99R78MkopFVxIh6r8yksaCFc/AcvvhNxfwKU/OXN76RHY+RbsWMHE/E+Z6ITtDUN4PmY+N9z6PZKzRpxZPioObn4FXvoWvP5dECeM+mZPfRullApIE0F7Lrwe9q+x2vmHzoaUobBrJexYAYc3WGUyLoJLfwqjrqO0KJFfvbiR5cuP89KCQaTGR5/5ftHxcMur8D9zrTuTHE44/+qe/15KKeWjTUMdMeeXkDLE+iX/+Pnw9r9Yt5Z+7d/g3s2waL11TSFlCNN81wy+LKjkluc/oaiy7uz3i06AW16zrhm8Nh92/6XHv5JSSjXTRNAR0fHwrRchcwLMWgzf+xTu+QhmPghpw84qPn346WRw83MfB04GnkS49XXoNwZevQO+eCf030MppQLQRNBR/UbD/D9biaDPee0Wnz48jefvyGknGXjh1jcg40J49TZSCjeFIHCllGqbJoIQmjG8T0syCNpMFJMEt62APiO5cPsvrOEzlVKqB2kiCLEZw/vw3O05HDhVwc3PfczeE+VnF4pJhtvfoio2C5bdAvvX9nygSinb0kTQA2aOsGoGR0qqueyJdfx4xeecLG/1uInYFLaOecS6K+l/58GB98MTrFLKdjQR9JAZw/uw7sHZ3DF1MK9uzGPWr3J5cs1equoaWsrURyVanc6Ss+Hlb8PB9WGMWCllFyFNBCIyR0S+EJF9IrI4wPaRIrJBRGpFJPgT3nqJ5Lgo/v3qUbz7w0u4ZEQfHn93D7Mfy+XVTXk0NhmrUFyalQySBsJLN8Khj8IbtFKq1wtZIhARJ/A0cDlwAXCTiFzQqlgR8H3gsVDFEYmy0+J45tYJLF80hX7eGP5l+TaufPIDthf4agfxfeGOP0Fif6vvwuGPwxuwUqpXC2WNYBKwzxhzwBhTBywDrvUvYIw5aYzZCAR/kE8vljM4hRX3TOXpm8dTWdfAY5tquX3pp+w+XgYJ6VYyiE+3eiEHGShHKaW6S4wxoXljkbnAHGPMAt/ybcBkY8y9Acr+DKgwxgSsGYjIQmAhQHp6+oRly7r2KOeKigri4+O7tG+o1TcZ3t5byep8oaoeZmS5uG6Ym3QpZtzff4y7voytYx6mPHFE+28WhDEGEelWnJF8DEHj6y6Nr3siOb7Zs2dvNsbkBNoWymcNBTrjdCnrGGOeBZ4FyMnJMbNmzepSQLm5uXR1357gduTy09um8tu/7eP3Gw6y8YThuzMvYuxdf8X18jVM2P4wZF8Cfc+3pvRRkDoMnO6W96iua+RwURVfFlRyqLCSg4VVHCqs5FBhFUWVdVw/PpN7vzaMft6YjgVVXQwF+6BwL5QfZyOpTJx1VYiOQPdF+r+xxtc9Gl9ohDIR5AMD/JazgKMh/LxeISk2in+76gJunzKYX67ezZNr9vLyJ9H8ZMazXH3qORzHt2H2vIOYRgAaxcXJqIHsl4F8Xt+fzdX9+MJkkW/6YHCQEhfFoNRYJmWnIAKvbsrjtc353DxpIPfMHkrfBA801EHxQetkX7DXei3cb81XFZwR30SAk8tg4l1w/jXgiurxY6SUOrdCmQg2AsNFJBs4AswDbg7h5/UqA1Njefrm8dw1vZif/2UX971dwH8m3Ux94zxKqisYIsc4Tw5zniOfCznCSNnJ9KZc8J2XG12xmD4jcWVcAH1HWTWI5MEsHlnABx9/TPHGP7Jj8zGcnlOk1B1tSSwAxPWB1OEw8gqrxpE6HNKGQ3QC+1f8nKHF71tPTo3rA+Nvhwl3QtKAgN9DKRX5QpYIjDENInIvsBpwAkuNMTtEZJFv+xIRyQA2AYlAk4jcD1xgjAk8arwNjR+YzGuLprB6x3Fe3ZRPWnwUg1IHMzh1MoNSYxmUGkuCx9c0VFMGp76AkztwntwFJ3daD7P7+/+0vF9f4AagKcrDcVd/Pq3qx2FHDlnDRjNzyhQSMs+3HnsRRN7A6xl66xOw/2+w8XlY/xtrGjEHcu6CoV8DRy/qnlJ+3OrPcXA9HN8Gg6bBhPmQOjTckSl1zoR0PAJjzCpgVat1S/zmj2M1Gak2iAhzLuzHnAv7tV3QkwgDJlqTv4pTVlIoPgjeTEgdjsM7gP4OB1UnK1i1Zi+/2HaU+H1lfGf6Se6aEUeixx3wIwDrRD/869ZUchg2vwif/QG+WGV1hsv5Doy7FWJTuvvVe57/if/gequZDCAqwapVbXgaPnoSsmdaCWHk1do8pr7ydGAaO4jvA/GXAJectWlY33ieumkc984exm/e3cN/rdnLCx9+ycKZQ5g/LZv46Hb+RJIGWoPyXLLYGrBn01J49yfwt0etQX1y7oKsHOjm3UohU34CDvmd+Av2WOujEmDQVKvpa/B0yBhtDV9afhz+/kfY/AdY/h2ITYOxN2stQX2laSJQAJyXkcCS2yaw/UgpT7y3h8f+uoelHx7kH2cO4fYpg4mJcrb9Bq4ouGiuNZ3YCZt+B1tfga3/a51EJy6wtkXFQUMt1JRCdYn1WlMKNcHm/abaCmv/mCTwJFkP62uZt5bTTuXDQZe1rXm9O/Z0ImrzxD/FqskMng4ZY6wTf2sJGdY4FNP/GQ78zaoNnVVLuApc0Wfvq1SE0kSgznBhppfn75jIlrwSHn93D//x9m6e++BL7p41lFsmD+zYm6RfAFf+Gr7+M9j2Kmz8Hfzp+9bIbgANNW3ujjPKOol7vL4pCZIGWQME1VVat7RWF0Pxl75kUgKmyYofYEeQ93NFQ2meta6jJ/5gHA4Y9nVrKj9uXYf57Pe+WkIqjL0lsmoJ5cet6zrigJgUq9kuJtl6jfb2rus6qtM0EaiAxg5I4g/fmcSmg0U8/u4e/s+fd/JM7j6SXA28cOBTEjwu3+QmPvrM+USPi/jm5ZG3kjDmDjzHN8HOt8Dhsk7uzb/kW072fid9t6dzwTY1QV05VJewaf175IwaejpBVBefnq+rhPQFMHiGNTJcZ078bUnIgJkPwPQfnl1LGDwDcu4MTy2h5DDs+hPsXAl5nxC0G484fLWrFMbVO+Fo9pmJojlxxKZatTtPYo9+ja8U0wRlx6DkkHX8iw9ByUHrtTQfEvpB1gTIzLGaTBMzI6LZVBOBalPO4BRe/u7FbNhfyMufHmZ//nFKqurIK6qivLaB8pp6auqb2n2fKKeDeM/X8Ma4SYxxW68eF17fvDfGhTemDm9M8RllvLFu4qNcOBxt/GdxOFoSSUXCUBgy69wdgM5or5Yw5iaSK1Oh8iKISw1NDIX7rYS7ayUc/bu1Lv1CmPUj63ZgdyxUFUF1UcDXpqMHrBPW8W3W+obqM99fnJA10bo7bOhs6D/+3CXUrwJjrONScsiaiv1fDzOz6CC83+qJOfHpVo22/1goyYNP/h80PuXblmElhKwcKzn0H2fVfHuYjf4FVXdMGZrKlKGpvp6T08/YVt/YREVNA+U1DZTX1lNe02At19ZTUdNAWU0DFbUNlFXXU1bTQGl1PaXV9eQVVbXMtzx9NQCHQGKMm0SPG7dTcDsdOB2CyyG4fPNup+B0OCgtruHlw5tw+ZbdDrHKOh1EuxyMzvIybVga6YmdrHV0VqBawsfPMMY0wraHwTsQ+o+BfmOt//z9x3XtLitj4OQu68S/cyWc9LWL9R9vNc2df83ZzVNtNFdtbd0ztr7aqlVVFUHFcetpuPvXQu5/QO7PrWal7BlWUhj6NUgZ0vnvEKmaGuHUbjiy2Td9BkUHoK7izHIxydaJPv0C8mMvZOBF0yB5sLUuaQC4W/Xib6iF459D/iY4ssl63f1na5s4oM/5Z9Ya+owERzvX6LpJE4HqNrfTQXJcFMlxXbuN0hhDZV2jlRSq6luSQ1n16fnS6nrKauppaDQ0NDX5Xg2NTYb6xiZq65toaGqkpNZQW1RFY/O2piYafWUraxt48aODAAztE8e0YWlMHZrGlCGpeGPbuF22O/xrCTWlbHn794zti/Vr/dgWq+mmmXeg9aux/9jTCSJQcjDG2nfnSisBFO4DBAZeDJf9B5x/9bnr4OeOsabE/sCF1ve49KdWYvjyfSsp7F97+kSWNOh0bSF7pnWS7Ky6SqtW0noqP8oF5fXQ+IGVzFKGWq+xqd1vXjHG+oyWk/5mOLoF6iut7Z4kyBxv9SNJHmTdLZfke/VrKjuQm8vASbPa/ixX9OlaQLPKQt/n+hLDzpXWLdkAUfHW30JWDoy4HAZO7t53DRTSOX9HpTpJRIiPdhEf7SIzqYPPQArCqrHMDLitqcmw63gZH+0r5MP9BSzfnM8fNhzCIdZF8qlD05g2LJWcQSnt3yXVFR4vJcmjYdqs0+uqS+DYVuvEfnSLlSB2rTy9PWng6aSQNtx6JPmulVb7szitC90X3231Z0hIP/cxBxObAqOusyZjrF/K+/9mJYXPl8PmF6xft/3Hn04MWROtdRUnfCf3vFYne99ydfGZnyUOSOgPCRkklOfD+g0tNwcAVq0kdcjpxJDiNx+sllVdbP3CP/LZ6RN/5UlrmzMa+o2G8bdB5gRrShkS2rb8uFQY8Q1rAuuYFu6H/I2nk8NHT1k3PmgiUKrrHA5hVH8vo/p7+e7MIdQ1NLE1v4QP9xXw0b5Cfrf+AEve30+U08H4QUlMG5rG1GFpjMny4nKG6K6amCQYcok1NWtODs21hqNbTicHh9s6qc78Fxh5ZWR02hOxTrqpQ2HSd6Gx3jqxNieGDx6Ddf8JrhhoqoemhjP3j/aCN8uasib55gecXpfQr+U6xCe5ucyaPtVKhEX7rZNl82v+p7D9dc64KO5JOl17SB5k7Xdks68W5ZM2wqrpZI63TvrpF4a/k6AIpA2zprE3Wevqq61mpRDQRKBsK8rlYOLgFCYOTuH+r0NlbQOfHizio30FfLivkF+/u4dfv7uH+GgXk7NTmDrMqjGcl57Q7cd5tylgciiGU3ug70jrwngkc7qtZqqBF8PsH1uJ7eAHcPBDq5npjBN9Zue/jyvq9EmytYZaqwd90YEzk8ThDfD5a9agT5k5VifAzAlWTSvSj2ez5ma6ENBEoJRPXLSL2ef1ZfZ5fQEoqqxjw36rGemjfQWs2W01HaTFRzFlaBrThqYybVgaA1JiQx9cTHJImgR6REySdd3i/KtD/1muaOhznjW11lhv3b4cAbdrRhpNBEoFkRIXxZWj+3HlaOsZT0dKqn3NSAV8uL+QP221nqo+ICWmpRlp6tBU0uK/Or2KaxsaKamqp7iqjpKqejYdb+D4p4cprqqnpLqOkkrfNt/FexHB47buwPK4nWe9tl4X7bcc43Zyfr8EspJ7IHEG4gzRDQG9gCYCpTooMymGG3MGcGPOAIwx7DtZwYe+pPCXz4+xbKPVa3lkRkLLhedJ2Smnnw7bAdV1jZwqr+VURQ0ny2o5VVFrLZfXUlBRR2OTdZG0uWlK8P+B61sn/kvWsiAYDOU1DRRX1VNaVUdxVT3V9X6PH2+25XPAajpLjnWTHBuFN8bNwJRYmoyVPGrrmyiurKOmvoka37L/a1sDHw5IiWHqkLSWW5JDfiuvapcmAqW6QEQYnp7A8PQE5k/LpqGxie1Hy6waw/4CXvrkEEs//BKnQxjj67swKTuFbacaOLkpr+Xk7n+iP1VeS0Vtw1mf5RBIjY8mNS6KKJej5SRrfBdFjcFvne/V70zsf1JO8LjITPIwqn8iSTFukuOiSIp1kxQTRXKsm707t/IPM6eSHBuFx+3o0rUQYwz1jeZ0YqhvpLahkfKaBrbklbBhfyHv7DjOK5usxDkkLY6Lh6YyZUgqFw9JpU9CaGpU9Y1NuEN10f8rThOBUueAy+lg7IAkxg5I4nuzh1FT38hnh4r5cL914fnptft46m++wpu3AZAQ7aJPQjRpCdGM6p9In4Roa4q3XvsmeOiTEE1KXBTOtnpWn0N1+U76d/MWXhEhyiVEuRzQ6sf+uIHJ3Dktm8Ymw65jZWzYX8iGA4Ws3HKUlz85DMCI9HimDLFqC5OzUzvUP6W+sYkTZTUcK63haEk1x0prOFZSzVG/5aLKOrKSm5vxUpk6NC1kSeerRhOBUiHgcTutawbD0njwMiirqWdbXim7tm/lsplT6JMQHZq+Cl8RTodwYaaXCzOtW3mba1Qf7S9gw/5CXt2Uz+83HEIERmYkMmVIKpOHpLDteAP713/JMd/J/WhpNcdKajhZXkPrzukJHhf9vTH0S/IwOiuJPgnR7D5Wxqrtx1pqI83NeNOHpzIpO7X9x673Uvb81kr1sESPm+nD02g44mRgapgulkYw/xrVPbOGUdfQxLb8kpYaw//4mtoA2LITj9vRcpKfPjyN/l4P/ZJi6J8U0zIf7KTe2GTYfqSU9b5mvOb3djmEMQOSmDbMuiNs3MBkq1bTCU3GkF9cxeGiKvKLqjlcVEWeb/lIcTUinPGgxkS/+eYHNSa0Xva4fOvcXW6ua48mAqVUxIlyOcgZnELO4BT+6dLh1NQ3suNoGdu3fsY1l84gKdbd5ROi03fCH+PXjLf5UHHLhf/f/m0vT67ZS4zbyaTsFKb5mpEu6JeICJRU1Z9xgs8rqibPt5xfVEXj6rVnfFb/JA8DkmOZdV4fBGl5Hld5TQPHSmsor7GeyVVZF+DCfSsLZw7hx1ec36Xv3RZNBEqpiOdxO5kwKJnyL51dfqZVW+89bVga04alAVBaXc/HBwpbbhP++ardAHhj3DQ1GcpbXdBPiYtiQEosF2V6uchbz7SxIxmYEsuA5Fj6JXk6fIG6scm0PKyx3PegxvKa00mjoraB0Vmh6fymiUAppfx4Y9xcNiqDy0ZlAHC8tIaP9hfw6ZdFRLscDEiJtU70vsm/CSo3N5dZkzo4gFMrTofgjXWH7gGIbdBEoJRSbcjwerh+fBbXj88KdyghozfVKqWUzWkiUEopm9NEoJRSNqeJQCmlbE4TgVJK2ZwmAqWUsjlNBEopZXOaCJRSyubEtDWCRAQSkVPAoS7ungYUnMNwzrVIjw8iP0aNr3s0vu6J5PgGGWP6BNrwlUsE3SEim4wxOeGOI5hIjw8iP0aNr3s0vu6J9PiC0aYhpZSyOU0ESillc3ZLBM+GO4B2RHp8EPkxanzdo/F1T6THF5CtrhEopZQ6m91qBEoppVrRRKCUUjbXKxOBiMwRkS9EZJ+ILA6wXUTkSd/2bSIyvgdjGyAia0Vkl4jsEJH7ApSZJSKlIrLFN/20p+Lzff5BEfnc99mbAmwP5/E7z++4bBGRMhG5v1WZHj9+IrJURE6KyHa/dSki8q6I7PW9JgfZt82/1xDG9ysR2e37N1whIklB9m3z7yGE8f1MRI74/TteEWTfcB2/V/xiOygiW4LsG/Lj123GmF41AU5gPzAEiAK2Ahe0KnMF8DYgwMXAJz0YXz9gvG8+AdgTIL5ZwJ/DeAwPAmltbA/b8Qvwb30cq6NMWI8fMBMYD2z3W/efwGLf/GLgl0G+Q5t/ryGM7xuAyzf/y0DxdeTvIYTx/Qx4oAN/A2E5fq22/xr4abiOX3en3lgjmATsM8YcMMbUAcuAa1uVuRb4g7F8DCSJSL+eCM4Yc8wY85lvvhzYBWT2xGefQ2E7fq1cCuw3xnS1p/k5Y4xZBxS1Wn0t8Hvf/O+BbwbYtSN/ryGJzxjzV2NM80jsHwNhG4sxyPHriLAdv2YiIsCNwP+e68/tKb0xEWQCeX7L+Zx9ou1ImZATkcHAOOCTAJuniMhWEXlbREb1bGQY4K8isllEFgbYHhHHD5hH8P984Tx+zdKNMcfA+gEA9A1QJlKO5XewanmBtPf3EEr3+pqulgZpWouE4zcDOGGM2RtkeziPX4f0xkQgAda1vke2I2VCSkTigdeB+40xZa02f4bV3DEGeAp4sydjA6YZY8YDlwPfE5GZrbZHwvGLAq4BXguwOdzHrzMi4Vj+K9AAvBSkSHt/D6HyDDAUGAscw2p+aS3sxw+4ibZrA+E6fh3WGxNBPjDAbzkLONqFMiEjIm6sJPCSMeaN1tuNMWXGmArf/CrALSJpPRWfMeao7/UksAKr+u0vrMfP53LgM2PMidYbwn38/JxobjLzvZ4MUCbcf4t3AFcBtxhfg3ZrHfh7CAljzAljTKMxpgl4Lsjnhvv4uYDrgVeClQnX8euM3pgINgLDRSTb96txHrCyVZmVwO2+u18uBkqbq/Ch5mtP/B2wyxjzeJAyGb5yiMgkrH+nwh6KL05EEprnsS4obm9VLGzHz0/QX2HhPH6trATu8M3fAbwVoExH/l5DQkTmAA8B1xhjqoKU6cjfQ6ji87/udF2Qzw3b8fP5OrDbGJMfaGM4j1+nhPtqdSgmrLta9mDdTfCvvnWLgEW+eQGe9m3/HMjpwdimY1VdtwFbfNMVreK7F9iBdQfEx8DUHoxviO9zt/piiKjj5/v8WKwTu9dvXViPH1ZSOgbUY/1KvQtIBdYAe32vKb6y/YFVbf299lB8+7Da15v/Dpe0ji/Y30MPxfdH39/XNqyTe79IOn6+9S82/935le3x49fdSR8xoZRSNtcbm4aUUkp1giYCpZSyOU0ESillc5oIlFLK5jQRKKWUzWkiUKoVEWmUM59wes6eaCkig/2fYKlUJHCFOwClIlC1MWZsuINQqqdojUCpDvI9V/6XIvKpbxrmWz9IRNb4Ho62RkQG+tan+57zv9U3TfW9lVNEnhNrPIq/ikhM2L6UUmgiUCqQmFZNQ9/221ZmjJkE/BZ4wrfut1iP5R6N9eC2J33rnwTeN9bD78Zj9SwFGA48bYwZBZQAN4T02yjVDu1ZrFQrIlJhjIkPsP4g8DVjzAHfgwOPG2NSRaQA6/EH9b71x4wxaSJyCsgyxtT6vcdg4F1jzHDf8kOA2xjzaA98NaUC0hqBUp1jgswHKxNIrd98I3qtToWZJgKlOufbfq8bfPMfYT31EuAWYL1vfg1wN4CIOEUksaeCVKoz9JeIUmeLaTUQ+TvGmOZbSKNF5BOsH1E3+dZ9H1gqIg8Cp4A7fevvA54VkbuwfvnfjfUES6Uiil4jUKqDfNcIcowxBeGORalzSZuGlFLK5rRGoJRSNqc1AqWUsjlNBEopZXOaCJRSyuY0ESillM1pIlBKKZv7/0aFk83U2pACAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6d5dede8\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"cacdd1c6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_12/checkpoints/epoch=11-step=5147.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_12/checkpoints/epoch=11-step=5147.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"07eed61e781f480dac305dd6e4f2a512\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.9821000099182129}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9821000099182129}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3d02a1a1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"486b490d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"8eaed4eb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_12/checkpoints/epoch=11-step=5147.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"80f6d64d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a0fc9346\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"db15eda6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([1, 2, 3, 4, 5])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4cf16956\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"d6e53410\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9821 (98.21%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"62bc77a8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"482f724b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"fa06b964\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"class_dict = {0: 'airplane',\\\\n              1: 'automobile',\\\\n              2: 'bird',\\\\n              3: 'cat',\\\\n              4: 'deer',\\\\n              5: 'dog',\\\\n              6: 'frog',\\\\n              7: 'horse',\\\\n              8: 'ship',\\\\n              9: 'truck'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"class_dict = {\\\\n    0: \\\\\\\"airplane\\\\\\\",\\\\n    1: \\\\\\\"automobile\\\\\\\",\\\\n    2: \\\\\\\"bird\\\\\\\",\\\\n    3: \\\\\\\"cat\\\\\\\",\\\\n    4: \\\\\\\"deer\\\\\\\",\\\\n    5: \\\\\\\"dog\\\\\\\",\\\\n    6: \\\\\\\"frog\\\\\\\",\\\\n    7: \\\\\\\"horse\\\\\\\",\\\\n    8: \\\\\\\"ship\\\\\\\",\\\\n    9: \\\\\\\"truck\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"b343358f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAbIAAAEbCAYAAAC/YAE6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABx9UlEQVR4nO2dd3hsVdW435WZTDKZlEm7aTftplykgyCgolcFQeECFkRpIoifovL5fYIF0R8q2PWzIIJdigpiQXoTEASRIh3vTW56vellkplkZs7vj3P2yWRuepuS/T5Pnsycfc6ZfdbZ56y99l57LTEMA41Go9FoEpWUWFdAo9FoNJrVoBWZRqPRaBIarcg0Go1Gk9BoRabRaDSahEYrMo1Go9EkNFqRaTQajSahiXtFJiItInJcrOuR7CwkZxE5VkR2rdX5NKtHRB4WkQ/PU1YhIuMi4lhs33hDRM4SkftWcfx5IvLYWtYpmRGRX4vIlbGux2rZEEVmvdQmrYerV0R+JSKZG/Hbm4n1krNhGI8ahrF9Leq4HsSqfcWrgjAMo80wjEzDMELr9Rvr2NZuMgzj7WtRx2RFv0/3ZSMtsp2GYWQChwNHApdv4G8jIs6N/L0YsqFyjiO5xrR9bVI2a1uLB+K6vW/0vdrwoUXDMDqBu4EDl3HYoSLygoiMiMjNIpKuCkTkQhFpFJFBEfmriJRGlBki8nERaQAaxOT/RGSvda4XRORAa980EfmOiLRZvZxrRcS9Vte90axQzkeKyCsiMmT18tIBRGSHiHSonawe4WdF5AXAJyJOETlHRFpFZEBEvrC2V7N0lnvdIvI5EdkjImPWtb8rouwKEbkx4nuV1aacInIVcCxwtdUzvtra5/Ui8pTVvp4SkddHHP+wiFwpIo9bx9wuIvkicpOIjFr7V0XsP++5LGpE5F9W+W0ikhddz3mu+XwRedW6z/eKSOVSZDUfayzzWUOD0c9wxLaLRaRJRPpF5NsiMue7TER+ICLtlnyfEZFjI8quEJFbROR6qy4vi8gREeWlIvJHEekTkWYRuXjZwllnViD7w0TkWet6bwbSo8pPFpHnRGTYaqcHR5TNKw9LlreKyI0iMgqctyYXuEQ2XJGJSDnwTuDf1vfPicgdixz2PuBEoBo4GEtIIvJW4OtWeQnQCvw+6tjTgKOA/YG3A28C6gEvcAYwYO33TWv7oUAtUAZ8aQWXGBesUM5nAScANZiyWKiX9wHgJEw51gM/Ac4BSoF8YOsqqr9iVnDdezAVUg7wZeBGESlZ7HcMw/gC8CjwCWsY7xOWIrkT+CGmDL4H3Cki+RGHvh9TTmWYcn4C+BWQB7wK/D+r3ks517nA+ZgyD1r7LoiInAZcBrwbKLSu4XeLHbfIOddb5qcx8wwr3gUcgWmRnIoph7l4CvOZzgN+C/xBIjrCwCmY7wwv8FdAdUhSgNuB5zHv1duAT4nICQvUc8NZjuxFxAX8BbgBUx5/AN4TUX448EvgvzDb3HXAX8Xs5C9FHqcCt2LK8qY1u8ilYBjGuv8BLcA4MIypbK4B3Ms49uyI798CrrU+/wL4VkRZJjANVFnfDeCtEeVvBXYDRwMpEdsF8AE1EduOAZo3Qj5xJOePRnx/J7DH+rwD6Ija9/yI718Cfh/x3QNMAcfF+3XPca7ngFOtz1cAN0aUVVltyml9fxj4cET5OcC/os73BHBexP5fiCj7LnB3xPedwHPLONc3Isr2t2TuWKiemL33CyKOSwEmgMo4kfl5wGMRZbOe4YhtJ0Z8vwh4cK7j5/itIeCQiPv7QJQMJ63PRwFtUcd+HvjVRrTp9ZA9Zie+C5CIbY8DV1qffwJ8NeqYXcCbF5OHJcu/x0omGzmOeZphGA+s8NieiM8TmD1QrP/PqgLDMMZFZACzx9BibW6PKP+bNQT0Y6BCRP4MXIJpXmcAz4iI2l0wXwqJxmrk3B7xuZUZOS+2bymz5eyz7sNGsqLrFpFzgf/FfPmD2RkqWGEdSjHlFkkrZntU9EZ8npzju5q0X8q5ou9XKovXvRL4gYh8N2KbWOeN/r3F2CiZty+ybd62KiKfBj5slRtAdtRvRb9b0q0h2UqgVESGI8odmBZsPLAS2ZcCnYaleSwi73kl8EER+WTENpd1XIjF5THXfdoQ4t79fhG6MIUPgIh4ME3izoh9ZoX3Nwzjh4ZhvBY4AHNI7FKgH/MlcoBhGF7rL8cwJ1M3E+URnysw5TsfkXLtjjxWRDIw70NcY80N/Qz4BJBvGIYXeAnzxQ6mlZ4RcUhx1CmiU0fMao8WFcxuj0tlKeeKvl/TmG15IdqB/4po517DMNyGYTy+gjoumyXIfC7mStGxaFu15sM+izn1kGv91sgiv6VoxxyRiZRTlmEY71zCsfFKN1AmEb11TNkp2oGroq45wzCM37E0ecQslUqiK7LfAh8SkUNFJA34GvCkYRgtc+0sIkeKyFEikor5kvIDIcMwwpgP1/+JyBZr37J4Gw/fAD4uIlut+ZnLgJuXeNytwMki8kZrHP4rJEbb8mA+fH0AIvIhZk+aPwe8Scx1WTmYQymR9ALbIr7fBdSLyJliOoScgTlctdjc5Fws5Vxni8j+VsfhK8CtxuIu99cCnxeRAwBEJEdETl9B/VbKYjJfKpeKSK41R/TfzN1WszDnDvsAp4h8CdMiWwr/AkbFdGpyi4hDRA4UkSNXUNd44QlMeVxstal3A6+LKP8Z8FHrHSki4hGRk0QkiziXR8xfNiJymYjcvZJjDcN4EPgi8EfM3kYN5mT6fGRj3qwhTJN6APiOVfZZoBH4p+V18wAQt2unlssS5fxb4D6gyfpb0kJJwzBeBj5uHd+NKd+OBQ/aIBa6bsMwXsGcp3oCUykdBPwjovx+zBfkC8Az7KuQfgC8V0zvvx8ahjEAnAx8GrNtfQY42TCMxaykueq2lHPdAPwac3gsHVjUq84wjD9jOjb93mrnLwHvWG79FmI1Ml8Gt2Hek+cwnWJ+Mcc+92LOCe7GfN79LHH4y+oQ7MR0FGnGtHR/jumgErcsIvspTCef8zCf0TOAP0WUPw1ciOnwMoT5PjzPKotrecjs4VKNRqOJb0TEAOoMw2iMdV008UHMLTKNRqPRaFaDVmQajUajSWj00KJGo9FoEhptkWk0Go0moYl7RSZmXLXaWNcjmVlIxrKCtBr6nq0vsoyUOwvtG49YXnc/X8Xxs+JjahZG4jSDw3LZqDQu4xF/YZlJQTAuImdtRB2SnfWSsRHnaTVi1bbiVUEYG5RyZx3b29cMw0j4F+t6ot+n+7IhIaoiI2SISAtm3LeVhlFaESLiNAwjuJG/uZHEQsbxINN4aFubkc3a3uKBRGjzIuJYwuL8NSPuhxYtjhORBmvh6Y9FzBArIpIiIpeLmT5kr5jpGHKsMpXK4gIRaQP+JiLpYqYZGBAzTcFTIlJk7Z8jIr8QkW4R6RQz3UYixlpcKe+UOdJiyNLSalxqya1LROaLQh53yMIpPn4tEZlzJSKVjYjcgBna53arF/wZa/spYqYCGbaGbF4TcXyLJacXRMRntbUiEblbzJQaD4hIbsT+857LYkkpd6KuN0VmUqgMiJnCJG8NRLksFpG7PTQ4zzOstn3Eam/dYsZTnO+3/iAiPWKmuvm7WBFNrLJfW++TO6178KSI1ESU7yci94uZImqXiLxvnUSyYYjI8SLyH0seVxMVrksWSPGzkDwsWf5ERO4SER/wlo27qjhQZCJyjYhcs8huJ2MmjzsEM26aCh11nvX3FsxQQZlYaRgieDPwGuuYD2KuRC/HjAX4UcwYiwC/wQzfUgschpnyJSmGOJYo46WmxYCItBoiciJm4OXjgTogbobblnDdi6X4mBPDMM4B2rCSGxqG8S0RqcdMh/IpzPQod2EqOlfEoe/BlFM9ZpSEuzFDgRVgPosXW/VeyrmWk3JHcTHmvXszZiDYIcwA2mvKOsg98hlWvAWzvb0d+JzMP8x7t7XfFswA49HpRT6AmUomFzOSxVXWNXiA+636bbH2uyZSEcYjC8leRAowoyBdjtnm9gBviCg/jXlS/CxRHmdiyi8LeIyNZKPD7WNGpV9yeg/MuGxvjPh+C/A56/ODwEURZdsxA6c6mUllsS2i/HzMtAUHR/1GERAgIhUC5o16aKPlE0MZLyktBvumxvkls9OJ1Fv71Mb7dc9xfGSKj19jpbewvu9g31Q2x0V8/yJwS8T3FMwAvzsi9j8rovyPwE8ivn8S+MsyzrWclDvHWZ9fBd4WUVainpc4kvsVWKlz5nmG1bb9IrZ9C/hF9PFz/I7XOjYn4h7/PEqO/7E+nwE8GnX8dcD/2+h2vVayx8xf98+I74IZSm7RFD+LycOS5fWxkkOipA6PTrUwX6qLVkwlVhSxLTK22g2Y1tjvRcQL3Ah8AfNGpQLdMhMYOoUYpiWIAatJ4fJM1LEJgSye4mM5zGqLhmGERaSdNUjhMs+5lnO/FJXAn0UkHLEthPm8rCRC/4pYgdyXksbloDl+x4FpIZyOaWGo6y7AjIIP879bKoGjZHbaEifmOyRRiU63ZFjtSrFQip+lyCNm78tEUWTzEZ3qogJzeLCXmQzF9opvwzCmMYcRvixmSvm7MBPH3YVpkRUYm3cyuRx42fq84hQuzE4LEbfITIqPtwEvW8piiNWlcLFfpmL2iMpZeQqXxc61nJQ7inbMhKgrCdK7JixB7nMxXxqX/1if57v+MzGHyY/DtFxyMK2/paZxecQwjOOXsG+iEJ1uSZjdjlQal32yO1tzZYvJQ6dxWSG/A/5HRKpFJBMzjcvN8ykjEXmLiBxk9dRGMYdVQoZhdGNGff+uiGRbk+I1IvLmjbqQOGApaTHm4hbgPJlJJ/L/1q2Ga8tiKT6ew3SAyRORYsz5qkiiU7jcApwkIm8TM03QpzE7RyvJ87WUc60k5c61wFVqAl9ECkXk1BXUbzWsJrVKJF8UkQxrjuZDzJ/GJYCZPSAD8/2wVO7ATKNzjoikWn9Hyr5ON4nEncABIvJuMZOHXszsDtpCKX7iWh4xV2Qicq2IXLvCw3+Jadr+HTO1gB9zrmE+ijFzZ41izhc8gjm8COb4sQt4BbPXdivmHELCs0QZLyUtxj4YhnE38H3gb5iT5X9bcUXXmEWue7EUHzcAz2P25O9j3xfl14HLxfQqvMQwjF3A2cCPMFNc7MR0Bplabr2XeK6VpNz5AfBX4D4RGQP+iem0s6asUu5L5RHM9vYg8B3DMOZatH+99RudmM/1P5d6csMwxjAdSd6Pae31YKa/SVtBXTeMhWRvmCmATge+ganc65idtmjeFD/xLg8da1Gj0SQM1pRAM5C6iacBNFHE3CLTaDQajWY1aEWm0Wg0moRGDy1qNBqNJqHRFplGo9FoEpr1WkeW7GbeUtahrCVanmuLlufaouW59miZLgNtkWk0Go0modGKTKPRaDQJjVZkGo1Go0lotCLTaDQaTUKT6EGDNcskHA4zNjZGOBy2UyBMT08TCoWYnJwkHDYDhKekpJCZmYnD4cDlcuF0OnG73aSk6L6PRqOJL7Qi22SMjY1x3333MTw8zPT0NIFAgPb2doaHh3nmmWeYmJgAICsrix07dlBYWMi2bdvIz8/nmGOOwePxxPgKNBqNZjZakW0SQqEQg4OD9PX10djYyNDQkK3IOjo6GB4epqGhgclJM2F2VlYWW7duZXh4mJSUFCYnJ/H7/aSnp+NwOGJ8NYlNMBhkamqK3t5ewuEwXq+X1NRUMjMztcU7B4ZhEAqFGBkZYWpqCp/Ph2EYuFwuUlNTycvLIzU1VbfLTYxWZJuEwcFBrrvuOpqamnj44YcZHx/HMAzC4TDBYJBQKEQgELD39/l8PPLII6Snp/Pvf/+b6upqDj74YMLhMHl5efqlsUKCwSA9PT20tLRw+eWXEwgEeP/7309VVRVvfetbycrKinUV4wrDMAgEAgwMDPCHP/yBtrY2Hn74Yaanp6msrKS8vJyPfvSjlJSUkJ+fr9vlJkUrsiRHWWIdHR00NjbS3NxMX18fPp/P3sflciEiZGdnIyKEQiFCoRDj4+P4/X5SU1PxeDwMDAyQlZVFTk6OfmGskHA4jM/nY2hoiJaWFvx+PyMjI0xMTKDDxe2LYRgEg0EmJiZoaWlhz5497Nmzh6mpKaanp5menmZgYACPx4PX69Xtcg7UPLiSVUpKCsXFxTidy3v9G4aB3+8HwOl0kpKSEjfy1oosyVGWWGNjIw888ID90lS4XC6Ki4vJzs7mgAMOIDU1lb6+PkZGRnjhhReYmJigr68PEeGxxx6jt7eXLVu24HK5YnhVicvU1BTt7e20t7cTDAZxOp3k5ubi9Xr1sOIchEIhhoeH6ezs5OGHH6a1tZXJyUkMw6C1tRW/388zzzzD8PAwRUVFul3OwfT0NG1tbbS3t3PNNdeQnp7ON77xDcrKypZ1Hr/fzyuvvEIoFKKkpIT09PS4GZ3RiixJUb2wsbExmpqaaGlpYWRkBJ/PZ88t5Ofn43a7KS0tJScnh/r6epxOJ16vl/7+fvbs2UMoFGJqaopAIMDo6Cijo6O2Z6Nm+Siv0fHxccLhMGlpaWRnZ5OVlRUXL4R4IhwOMzU1RXd3N11dXQwPDzM+Pm6XBQIBAoEAPp+PiYkJ3S7nQXUG+vv7aW1txePxMD09vaLzDAwM2FMQmZmZZGdnx0W71YosSZmenqalpYXGxkaeeuop2tra8Pv9uFwuSkpKqKio4BOf+AQlJSV4vV77hZqSkmJ7Mvb19dHS0kJ7ezuhUAifz4fP59MvjFUwPT1Ne3s7nZ2dOBwOcnJyeO1rX0tNTQ3p6emxrl7coBR+V1cXv/jFL2hqamJoaCjW1UpIAoEAL7/8Mk1NTYyPj6/YavX7/Tz//PPs3bsXv99PYWEhH/vYxygsLFzjGi+fmCsywzCYnJxkenra7lU5HA5EzJiSDocDt9uNw+Gwty93bHczEg6HmZiYwOfzMT4+zuTkpL0WrLKykurqampraykpKSEzMxOn00laWhrhcJihoSFSU1PtewDmurL09HTS09NnbdcsDTXXMzk5SV9fH4ODg2RkZJCTk0NmZiZutzvWVYwLwuEw4+PjBAIB+vr66OjooLW1la6urjmtiHA4jN/vZ3JykpGREQDbg1HN/W52gsEgg4ODDA4OEgqFVnwe5T2qPG7VqI9hGDGXc8w1wuTkJE8//TS9vb08+uijTE5Okp2dbS/CzcrK4uijjyY7O5ucnBzS0tLYsmWLVmaLoNyVR0ZGCIVCOBwO2xK75JJLqKqqorq6mrS0NFJSUhARRITx8XHuu+8+GhsbaWhooK+vj+npadLT0znooIOoq6vTlsMKCAaDdHR00NTUxL333svQ0BAHH3wwNTU1WolFMD4+zoMPPkhPTw9PP/00fX19PPvss4yNjREMBvfZf2pqit27d9tD3l6vl7KyMnuoPC0tLQZXET+oDu0LL7xAc3Oz7ayxElwuF5WVlYgIL774ItPT0/h8PgKBAGlpaTFVZjHXBtPT03R0dNDR0WGvY8rJycHpdJKamkpWVhZ5eXlkZ2fj9Xpxu91MTk6ualJXWXdZWVn278S6R7HWpKSkkJaWRmZmJhUVFWRmZlJWVkZ5eTlVVVWUlpbOuSZsenqarq4uurq6mJiYYGpqCsMwSElJwe1243a7k05WG4HyVhwbG7PnegoLC3WnLAo1JN7e3k5zczNDQ0P4fL5553SUtQEzczbT09Pk5eVRVlaGiCTl870UwuEwk5OTjI+PMzg4yMjICBkZGSter6hGwxwOBxMTEzidToLBYFxMNcT8CRoZGeHWW29lz549tLW1MT09bb9clZVw5513kpqaSkZGBunp6VRWVq5YkSlnhvz8fE444QS2bNlCVVVV0nk7paenc8ghh1BfX091dTXT09NkZWWRnp5ORUUFLpdrzknayclJnnrqKRoaGubtBWuWTzAYpLe3l+7ubiYmJkhNTeWII46gtrZWW2QRDA8Pc/PNN9su9mooaz78fj/PPvssDoeDxx9/HJfLRVFREVu3bsXtdrN169ZVvS8SmUAgwH/+8x8aGxt59dVXGRsb4/jjj6eurm5FbU5Zd8qLdHx83O7sxnrKIeaKDEwLyel04vF4bJdkwNb2ao3NyMgILpfL3idyv4VwOp2Ew2HC4bDt7jw8PMzevXtJTU2lvLx83a9xoxER3G43LpeL6upqwuGwbYGp4cRI1OS6GksfHh4mHA4jIrhcLtLT03G73TEfQkhUgsEg/f39DAwM2N6Kubm55ObmxoXXV6wJBoMMDAzQ1dVFb28vAwMDi7YzNWeuFN34+DgOh8N+bzQ0NBAMBiktLd1UikzNx/p8PlpbW2lrayMQCOB0Otm6dStlZWWkpqYu+7yhUIixsTHGxsYIBAJ2RyMe1j/GXJHl5ubywQ9+kP7+flpaWgAoKSkBoKuri/Hxcdrb2xkdHWXXrl1MTk7S3NyMiJCXlweYa6XmUmZKQWZmZgKm9aeUYF5eHlu2bKG2tnbFPZREwOFwkJ+fbw8PAnMOK4yNjXHvvffS2NhIY2OjPTemFGFtbS3bt2+nvLx8U70U1oqxsTHuuecempubSUtLo6SkhJqaGqqqqlb0Ukk2BgYG+MEPfkBDQwODg4OLvhwdDoc9ROZ2u+2F/1NTUwwMDDA8PMxXvvIV6urq+NWvfrWpYoSq+diWlhZ+/OMf09XVhcPhoKqqilNOOYXKykr7nbgclNdiQ0PDquba1oOYK7LU1FS2bt1qz1cBlJaWAuDxePD5fKSlpTE2Nmb3CHp7exERioqKAPPFHO2NIyKkpKSQmppKbm4uU1NTDA8PMzU1Zbuh+/1+ew4omVmox69W64+MjNDc3GwvOFVDvOnp6ZSUlFBaWkpmZmbMhxASkWAwSCAQoLe3l/7+ftvKdbvdm16eKgTV2NgY7e3tdHR0LDiU6HA48Hg8pKWlkZeXZ8txenoaEcHv9zM2NmbP9Xo8HoaGhsjPz8fj8WyKRefBYJC+vj66u7vp7u5meHiYqqoqysrKKCgoWNEoQDgctiODDA0Nxd2UQ8wVWVpaGvvttx/hcJhDDz0UYJ+hxenpaXtSN9Ii27ZtGwB79uyZFScQsCd5XS4XpaWl9PT08IUvfIH29nbGx8dxOp0UFhZSUFCwqSfbVS+rqamJ3//+93R3dzM0NIRhGGRmZlJaWsrpp5/Otm3bKCgo0C7Ny0QNmXV3d9PU1ERnZycFBQWkpqaSlpa26eUZCATYvXs3LS0ttLW10dPTY7tzzyWXzMxMduzYwZYtWzjooIPweDxkZGTYa6W6u7u54447bFf8iYkJHn74YTo7O9mxY8eKLJFEY2xsjDvvvNO2bj0eDxdccAE1NTUUFxcve3pAOSoNDg7y6quv0tzcvKIF1etJzN/gIrIkd+5wOEx2djZ+v98eG6+srLTPMZ8iczqdZGdnMzU1ZR+nenFer5ecnJxN0UubD7Xqf2BggMHBQTsqvsvlIicnh/z8fMrKyigpKdm03l+rQa2LGhsbw+/3Mz09bXt+qWUPmxW1rq6trY3W1lZGR0fx+/2zvOBSUlJwOp32nG9+fj7V1dWUlJSwbds2PB4Pbrcbv9+Pz+ezRxHUwv2pqSlaW1txOp0ceOCBhMNhPB5PUs5LqjW5IyMjdHR02J0Ct9tNWVkZZWVlK+o4qWHbgYEBJicnbeeO9PR0O+ZirIm5IlsqKSkpZGRk4Ha77V6VmlvYf//95xweFBEmJiZ4/vnn7dQlgUCAkpISqquref3rX09lZWXSzo8tBfWgt7S02EOKAG63myOPPJLa2loOOeQQHcduhQQCAXbt2kVDQwOBQAARsd3E4+EFECuUpdra2so111xDS0uLPawYOWylnLNycnI44ogj2Lp1K+9///spLCy0w3qJCNPT0xQVFdHU1MTdd99NIBBgfHyc0dFRbr31VrZs2cLU1JSdZSBeQiutJWpNblNTE08//TR79+4lOzubsrIyamtrqaysXNF8bKSF5/P5SE9PZ/v27dTX19tLomLdIUsYRQYzTgrRDXC+Bqk8Hjs7O+ns7CQQCOBwOCgqKqKkpITc3FyysrI25QtFDdXu3buXrq4u27nD4XCQkZGB1+ulvLyc8vJysrKyNv3C0pWivEHVgl3laKS9Fc01Y5OTk3R3d9PT00MgELCVmLLAMjIyqKqqIj8/n9raWsrKyiguLsbr9c6yLlwuF7m5ueTn51NYWGjHX1QKE8wpCDCHG1W281i/gNcSNTfW29vL6OgogUCA8vJy8vPzycjIWFFHVFnNHR0ddHZ2EgwGSU1NpaSkxB6mjIzEFCsSSpEth3A4zOjoKG1tbdx4443s2bOH4eFhcnNzOemkk6ivr6e4uHjTTABHMzg4yNVXX01zczNPPvmkHVA4IyODY445hurqas4880xKSkp0jqxVoBb8d3Z2Mj09TWZmJieeeCL19fWbYr5mPlS4IxUdIjqGZ1paGvX19VRWVnLRRRdRUlJCYWGhvcg/+uWpvHMBdu7cSWNjI7/73e8YGBhgamqKvr4+/vCHP7Bt2zaOOuooRCTpFqMri6yxsZHh4WHS09N585vfTG1tLRkZGcs+n+oEtLe38/jjj9tphwoLC3nnO99JXV1d3KR0Sp67GEVkxOeenh76+/sJh8O4XC62bNlCYWEhqampm1KJhUIhJicnaW9vp7W1lb6+PjszdFpaGmVlZVRUVFBUVBQ3aRoSDcMwmJqaYmJiwvb0cjqdZGRkUFJSQlFRUVK9RFeKWvOklJiaE8vKyqKiooLq6moqKyspLi5eNCKFmh8rLS1lYmICj8djx22cnp5mcHAQr9fL9PT0qmIOxiMq7qGay1Jh6UpLS1e1bmxoaIj+/n6GhoYYHR2d1YaLi4vjZulI0j5J4+PjPPTQQ+zevZuenh58Ph9ut5vs7GxKS0s37YtExWDs6+tj165dNDc32wvOPR4PJSUlnHrqqVRXV1NQUKAXQK+Qqakp9uzZQ1NTEw899BA9PT3k5+dTVVXFIYccQkVFhR6unYPU1FQqKyupqKjgIx/5iD28Pdci/rlQEW28Xi/bt28nPT2dlpaWfZzBkgnDMOyIGyp57vT0NB6Ph7e85S0rtsh8Ph9///vfaWhosCPeFxUVUVFRwUEHHRRXEVOSzhxR61LGx8dpa2ujo6ODQCBgZ0UtKysjOzt70w4pBoNBurq66OzsZGxsjMnJScLhMCkpKeTk5NgLxdWyBK3EVobqMAwNDdnxAt1uN1lZWbbL+GZsf/OhIu+oBfg1NTVs3bqV4uJiXC7XkmWlnMI8Hg/p6en7vGiDwaB9T5LFKjMMg3A4bGfS9vv9pKenk52dTXZ29opjKyqLTC00B3P5g8rWEE+d3KQzSaampmhoaKCpqYm//OUvtLW12QFaP/7xj1NTU8NBBx1EZmZm3JjFG8nAwADXXHMNe/bsob29HZ/PRygUIjMzk6OPPpra2loqKiooLCzclBbrWhEIBHj11VdpaGiwvWWVk5Hb7d6UbW8+DMOwvY4LCwu57LLLqKmpIS8vz16qsJLzhcNh+7M6/8jICLfddht1dXV89KMfTQqrWA0rBgIBhoaG8Pv9HHHEEauOWBQKhexF/KFQiNTU1FUNVa4nSfemil4XNT4+TmpqKtnZ2VRWVlJZWUlGRkbc3Yj1JnqNSXt7O4FAwI7BmJ2dbQ/juN1urcRWiXI2GhkZYWpqChHB6/Xi9Xq1bJmRT3TGcafTSXFxsR2mbiXnVXn4/H4/gUBgn6U58WJFrCVqAbmKZqTW0CqFvlyLTK3BGxkZYXh4mFAoZIf7i0dP76R7ovx+Py+++CINDQ1MTEzgcDjYb7/9qKur4zWveQ1bt26Nm3HdjWRyctJeT7dr1y66urqYmpoiLS2N7du3U11dzemnn26722tWx/T0tD20HQqF8Hg8vPa1r9X53Cx8Ph+PPPIIjY2NTE5OzlIuq1E0KlJNY2Mju3fvpre3l2AwaL/oc3JyOPXUU6mtrU2adq6S3mZlZVFZWYnT6aS3t5e0tDSGh4ft4eylKh/Vydi7dy9PPfWUPY9eUFBAfX09tbW1cfcOTSpFpjyglDWm1jyUlpZu6liB4XCYQCAwK8+Y6qk6HA5ycnLIzc1dcRw2zWxU6hEVKVxlOVfestoiM+eq9u7daysa9ZJdybOp5teCwSBjY2N0dnbuk08PsLOg5+bm4vV6k6qdOxyOWamuxsfHGRkZobOzk5SUFIqKiuYdhVJDk0qGwWDQTtUyOjqKz+cDZnIcxmNYtaR5otTNGB0d5aWXXmLPnj2EQiHy8/PtHliyNd6loEIk9fT0cMcdd9DU1MTw8LC98NTpdFJeXm5HxI6nCdxEJBQKMTo6Sl9fH//5z3/o6OggNzeXqqoqjjzySKqqqrRFxswIwWojqasF536/n66uLtrb2+258bGxMXu/yGGxlcy7xTMqHJ/b7aaoqIjx8XH27NlDX18f3/3udyktLeWEE04gOzt7zuOnp6fp7OxkYmLCXorT19dnO8VE/1a8DStCEikyNTem8mmNjo6SlpZGVlaWveZhM/aEleeRioa9d+9ee6hFRfAoLi62e2xaia2OcDjM8PAwQ0ND9gu2oKDA9h7bzOHQIlFztpOTk6vKPhEKhRgYGGBsbMx2YOrp6WFoaMieG1KWRGlp6Sxnm2Rq60qZbdmyhbGxMTIyMvD7/XR2duL3+2loaFhQkXV1dTE5OUl/f7+tyHw+nz2/C6bVl5WVpefI1pPx8XHuueceGhsbeeWVV/D5fGzfvp3a2lr2339/O2DmZmN8fJz77ruPxsZGXnrpJTvSQUZGBocddhjV1dWcccYZlJWVJc2cQSzx+Xz84x//oLGxkd7eXvx+P1VVVWzbtm1Ttr/1ZnR0lLvuuoumpibuv/9+2108FAoRDodxOBzk5uZSUlLChRdeSFVVFbW1tXZ0kGQiOzubU045hc7OTgzDoKuri1dffZXOzk527do1r+JWC8lFhGAwaC8diY57mZGRwRve8IYVr0tbTxJekUXm02ppaaG1tZWJiQkA2/tJ5S/ajExPT9Pe3k5bWxs+n89eGKqyxVZWVrJlyxY9N7ZGKItM5b5T0cc349zseqLmwnt7e+08Zh0dHYyPj8/aL3LkQXnlqkzpyYbD4aCwsBDDMKipqcHtdjM0NGSvLYv0Do1EBWZWI1bKlR+Ypci0RbaO+P1+XnjhBfbs2cOtt95Kd3c3oVCI4uJiTjnlFGpqaja1pTE2Nsb9999PY2OjPWkL4PV6OfPMM6mrq2PLli16bmyNUMGYBwYGCIfD9otUL4BeW1RG6ba2Nnbt2mWngIlGOXtVVlZywAEHUFpamrSWcWpqKsXFxRQWFlJRUYHP56OhoYHx8XH27t07bw6xzMxMjjjiCDweDyLC3r17+fnPf05LSwtPPvnkrM6Bcu2Pt3dFQiuycDiM3++no6ODtrY2+vr6GB0dtdM+FBcXJ11g0OUSDocZGRlhdHR0n/QYkfnY5uutKbfl6IYbvdBU/VYoFGJ8fHze8ykvyXiImL0eRMpbRbuP17U3sSa67ahtah0YzMzxqniM6pje3l4aGhro6Oigv78fv99vr5mKRI08lJeX285MyYxSNOnp6eTk5BAKhfD5fGRnZ8+b1dnj8bBt2zZ7uNDtdlNQUMDQ0NA+bVZ5NcbbOtyEfcMrb7zOzk5uuukmO7p9WloaBxxwAPX19ey///6UlpbGndDjgVAoxMTEBGNjYwt6caWkpOB2u3E4HPY+oVDIDjysXhxqIerQ0BD3338/o6Ojs86jvJ3y8vJ4z3veY6epTzZlFrmOUUUKP/jgg/X6sQWI7BBNTk7y0ksv2d5yfX19XH/99ezdu5fh4WG7vU1NTdmhkyJDNEWTn5/PJz/5Serq6sjLy9u4i4oDUlNTqaioIBwOU1NTM+9+KmWOwul0UlhYyPDw8CwjYGpqio6ODjtRZzy9VxNWkSlPm7a2Njo7O9m7dy9gBg3dunUrZWVlZGZmJu0wwmoJBoO0tbXZmbfnU2Qqf1Zqaqoty6mpKTvStnp5RCqyhoaGfRQZmNZYQUEBfX19pKSkUFBQkDRzFZHR7kdHR22rVHUE4iH5YDyRkpJiz7f09PTY2wOBAM3NzbZF1t/fT0NDA319fYyMjMxSVkr5zSVXh8OBx+OxHT0KCwvX+YriD+XJCCzbEnU6nbYSi7Sc47UNJ6QiC4fD9PX18f3vf5+GhgZ2796N3+8nPz+f8vJy3vOe91BdXb2p58YWo7e3lyuuuMIe756vgarx85ycHLxeL4A9dPavf/3LHj+PjG83NjY2r0u1WnS9bds2du7cSU5Ozrpc30YzNTXF7t277Zdu9FCuZjYej4cdO3ZQVlZGT0+PHZR2aGiIH/3oRzgcDlJSUuzFuaFQCMMw5mync23LzMxkx44d1NXVxZ2HXSIQ+TwrUlNTKSsriztrDBJQkamFzxMTE7S1tdHe3m6HuCksLLTXiiRTb381pKSk4PF4cLvd9hwWzFi0i+HxeMjLyyMrK8tWOipGXlNT0ywHkkhUNIuUlJRZQ4hOp9MeZ1/N+qF4Q0VVHxwctKMkpKWlkZ6ebkdbiNfebCxQMRXVek+n00k4HGZ6epre3l5g7sW36nt0fEaVx0zN/ebm5toBsOPtpZuoKAsvHuWZcIpMpfPu7Oxkz549tLW1MTU1RVFREe9///upra2lpqaG7OzsTe3koUhPT+eggw4iPT2df//73/bShKUyMTHB008/bS8shZmQQOpcynEj0rJzu90ceuih5ObmUllZaQ9tZGRkcPjhh9u5zpIFv9/Pv//9bxoaGvD5fDgcDioqKqitreWAAw7Q+ceicLvdHH744eTk5PDHP/6RiYmJBZ2E5kM507jdbkpKSigpKeHcc8+luLiY0tJSOwehZnlEBiGO3h6PJNSbPhgM4vf7aW9vn5WCJDc31074tnXrVh29PQKVqDAUCtHZ2YnP55vTElJxKpWXk9rHMAz7xaxcyNULWTmBZGRk4HQ6Z1leGRkZ1NfX4/V6ZymytLQ0CgsLF5yXSzTUKEF/f7+d8iIlJYXc3Fzy8vLs3Fjx+hKIBQ6Hg+zsbPLy8igrKyMcDtvJG8fHx5ecK8zhcJCfn09WVhY1NTWUlpZSW1vLli1byMnJSbpwVBtFpAOOUmjx3H4T5m2v1ue0tbXx7W9/m+bmZgYGBsjOzub000+ntraWN77xjeTn52vvsAjy8/O56KKL6O/vZ7/99rNdmaNR8daGh4d56qmn9hkyVKv6vV4vhYWF9stBRXXPycmhpKTEHnZQEblVMFP1EIgILpfL3p7oKCeP0dFRez1jIBAgIyPDjnav15Dti1JALpeLiy66iN7eXp599lm6urp48MEH53QWmousrCxOOukkqqurefvb324vvYlUYPH8Ao5HVMdMDZGrYdt49jJOGEWm1pPs3buX9vZ2urq67PUMJSUllJaWkp2drSd2o1Cr/V0uFzU1NYyMjMzZ252amsLj8dhRKSYnJ2eVZ2Zm2haWyh4NpoKrra21FdlmtYRVhoHp6Wk7v5uar00Ghb0eqHnU8vJyPB4Po6OjeDwedu/ebWfVDoVC9kJnNQKQmZmJiGAYhu3gVV5ersOsrRHKYUt1JpxOJ/n5+eTl5cWtdZswb52JiQk7f1FHR4ft+q3yadXV1ek5iAXIysrixBNPnLWoNJLIocXI9WEK9dJR68lUzyzS8tqMSkxEZqXQUENlZWVlnHzyyZSVleHxeGJdzbjF5XJRX19PKBTioIMOYnR0lMMOO4yenh4eeeQR+vr6eP755xERDjnkEIqKinjTm95EZmYmYM4B77fffuTk5OiRmDUiMl4okBBRkuL+zaOGbsbGxux4apOTk/bwmIiQlpamQywtQkpKip70XifUcKmK45ednU1xcbEd9V4PK86Pen7BtO7dbrf9wuzp6cHr9TIxMUFKSgp1dXUUFRVRV1dHZmYmhmHgcrnIz8+3PWQ1a4MKZ1VUVERhYSHbtm2Law/QuFdkan1Oc3Mz99xzj+3kodHECw6Hg5KSEj7/+c/bjh4qEGuyhuJaL9LT0znwwAMJBoMcccQRtoOXKnM6nbPmHEXEdr+P12GvRCM1NZWqqiry8/M5/vjjKSkp4cgjj7QzTccjca/IVEqBwcFBhoaG7MWRMBNXTC2e1GhihVoXpVkdImIPEarhQ83GokJb+f1+ampq2LJlC/n5+XE9dRP3iiwQCNDY2Mju3bsZHR0lEAhgGAapqakUFBRQWFhop2nRykyj0WhWR0FBAZ/85CcJh8NkZmbaHovxTNwrMpVvzO/32/NiaWlpuN1uqqqqqKqqshWZHsLRaDSa1aE8nROJuFdk0bhcLsrKyqisrORzn/scFRUVlJeX22FuNBqNRrO5iPs3v8PhwOv1smXLFurq6piYmGDr1q1UVFRQVVVFSUmJ9ljSaDSaTYysU+DWNTupyjumXPDD4bDt5JGfn297LG0wGz2GmTzRdedGy3Nt0fJcW2IxZ6FlupyTxbsii1P0i2Jt0fJcW7Q81xatyNaehFBkGo1Go9FsCHpiSaPRaDQJjVZkGo1Go0lotCLTaDQaTUKjFZlGo9FoEhqtyDQajUaT0GhFNgci8rCIfHiesgoRGRcRx2L7JiIi8msRuTLW9dDMRkQMEamNdT3iCd1W44tYttFFFZmItIjIpPXy7hWRX4nIuoeljlcFYRhGm2EYmYZh7JtmOUbE6h4lG6uRo3Xscetdx0RHt9XVodvo3CzVIttpGEYmcDhwJHD5+lVJs0Li+h6JSNyHQ7NYFzkm0PVvBHHdVhMA3UajWNbQomEYncDdwIFL2V9EPicie0RkTEReEZF3RZRdISI3RnyvskxTp4hcBRwLXG31PK629nm9iDwlIiPW/9dHHP+wiFwpIo9bx9wuIvkicpOIjFr7V0XsP++5LGpE5F9W+W0ikhddz3mu+XwReVVEhkTkXhGpXIqs1ooV3KPDRORZ6x7dDKRHlZ8sIs+JyLAl24MjykpF5I8i0icizSJycUTZFSJyq4jcKCKjwHlrcoEbxHLkKCI3ABXA7Vbb+0xEO7lARNqAv4nIDhHpiDrW7iWLiENELot4Zp4RkfI5fu+NItIuIm9Zk4uNEevQVi8UkUYRGRSRv4pIaUTZ20Vkl/U8XyMij0gcjvgsB91GIzAMY8E/oAU4zvpcDrwMfNX6/jngjgWOPR0oxVSYZwA+oMQquwK4MWLfKsywLE7r+8PAhyPK84Ah4BzMYMcfsL7nR+zfCNQAOcArwG7gOGv/64FfLeNcnZgNxAP8UdV1oXoCp1l1eI113suBxxeT8Wr/VnqPABfQCvwPkAq8F5gGrrTKDwf2AkcBDuCD1m+lWff0GeBL1nm2AU3ACRH3d9qSSQrgXm85xEqO0cdGtZPrrTbkBnYAHQv85qXAi8B2zBA+h0S0SQOoBU4A2oHXxVpecdZW3wr0W202DfgR8HerrAAYBd5tPZf/bR374fW81niSX7K30aUKbhwYthrSNSt9KQHPAadan69geYrsHOBfUed7AjgvYv8vRJR9F7g74vtO4LllnOsbEWX7A1OYL/N564nZO7og4rgUYAKo3IDGvex7BLwJ6MIKVWZte5yZl8NP1EMSUb4LeDOmcmuLKvs8M52FK7BeJInyt5q2zvwviW0R23aw8Etil3o+5ji/Ycm3FTgo1rLaaBkvoa3+AvhWRFkmprKqAs4FnogoE8wXbaIqMt1Go/6WOiZ6mmEYDyxxXxsRORf4X0tgYDauguWex6IUU0CRtAJlEd97Iz5PzvFdTYou5VztUWWpLF73SuAHIvLdiG1inTf699aaldyjUqDTsFqhRWQ9K4EPisgnI7a5rONCQKmIDEeUOYBHI75HyjBRWFFbX4DlyKAc2LNA+aeA6w3DeHFVNYo969FWS4Fn1RfDMMZFZADz2Ssl4j4YhmFED58lGLqNRrFu7vfW3NDPgE9gmp5e4CVmoh77gIyIQ4qjThEdzbgL88UaSQXmEOByWcq5yqPKpjGHLhaiHfgvwzC8EX9uwzAeX0EdN4JuoExkVmrtiojP7cBVUdeTYRjG76yy5qiyLMMw3hlx/GaKSD3ftUZun9XmxVzCEZmKtx1zaHw+TgdOE5FPrbCOicxibXXWMy0iHiAf85nuBrZGlEnk901E0rbR9VxH5sEUUB+AiHyI2ZOSzwFvEnNdVg6mSRpJL+a8i+IuoF5EzhTTIeQMzCG/O1ZQt6Wc62wR2V9EMoCvALcai7vcXwt8XkQOABCRHBE5fQX12yieAILAxZYc3g28LqL8Z8BHReQoMfGIyEkikgX8CxgVkc+KiNuaBD5QRI6MwXXEA9HtdS52A+mWDFMx51DTIsp/DnxVROoseR8sIvkR5V3A2zDv10VrWfkEYLG2+lvgQyJyqIikAV8DnjQMowW4EzhIRE4T00nr4+zbcd4MJG0bXZUis7xX7p6rzDCMVzDnqZ7AFOBBwD8iyu8HbgZewHQaiFZIPwDeK6b33w8NwxgATgY+DQwAnwFONgxjMStprrot5Vw3AL8GejC9oy5mEQzD+DPwTeD3YnrqvQS8Y7n1W0sWuUdTmBPg52E6u5wB/Cmi/GngQuBqq7zR2hdLqe8EDgWaMa3Vn2M62iQdC8nR4uvA5WJ6d14y1w6GYYwAF2HKqROz9xs5xPU94BbgPkznhF9gTsBHnqMN80Xx2UT3uotmlW31QeCLmI5Z3ZhWw/utsn5MS+FbmM/7/sDTQGCdLiUmbOY2qvORaTSaTYWIpGC+nM8yDOOhWNdHs3p0iCqNRpP0iMgJIuK1hh0vw5yr/2eMq6VZI7Qi02g0m4FjML3t+jGHxE8zDGMytlXSrBV6aFGj0Wg0CY22yDQajUaT0GhFNgeyQJRoETlWRHYtZd9EQ+I048BmRxaJ77kZ0W01foiH9rmUNC7jEX9hmUkhMC4iZ61XxeJVQRiG8ahhGNtjXQ9FrO5PMrIaWYrOF7Youq2uDt0+52dRDWqY6QIAU7lgxidby/AomlWQCPdHRBxLWEwec9ZTliLiNAwjuBbnSlQSoa3GM7p9zs+6Di2KyA/EDOU/Kma4/2MjymZld5WI9AEyR8oBa/spIvKytaDvYRF5TcTxLSJyqYi8ICI+EfmFiBSJyN1ipht4QERyI/af91wWR4qZemZIzOR16dH1nON6U2Qmdc2AiNwiVvqXeEREjheR/4iZ2uJqZsKHqfJ5U9KIyH4icr+YKTN2icj7Isp+LSI/EZG7RMQHJHS6kcUQkb9bH5+32usZqp2IGfmkB/iViJwnIo9FHWv3lMWMkPJdEWm17sljIuKe4/feY7X3JaU/SQYWaqvWc3e5Jbe9InK9mNGCVPm5VtmAiHxR4nS0Z73YDO1ztZE9rhGRaxbY5SnMyA95mCFk/qAUwkIYhnEO0IaVQM4wjG+JSD3wO8yglIWYYaZuFxFXxKHvAY4H6jFdbO/GXDNSgHmtF1v1Xsq5zsJMR1BjnW8pyesuxkxb8mbMQKVDwI+XcNy6sND9EZECzCgIl2PKZw/whojy0zBl925MGT2KKTMVx+5+zHu6BTMNzjViheayOBO4CsgCZj0cichCsjQM403Wx0Os9nqz9b0Ys+1XAh9Zws98B3gt8HrruM8A4ah6fAgzesxxhmG8tOwLiVNW01Yxo32ch9lh2oYZHFzlMNwfM0L8WUAJZuSZyODgScGmb5/LCZVPVBqA5f5hvtgPsT7/GisFgzFH+oDo38IMP3NLxPcUzBAqOyL2Pyui/I/ATyK+fxL4yzLO9dGI8ncCexarJ/Aq8LaIshLMYMPOlcpsve4PZmqLf0Z8F8xoB4umpMEMD/Ro1PmuA/5fxL29fiOuOR5kae1vALVR7XkKSI/Ydh7w2FzHWfKdVM9H1D5V1n6XYObZ2xpr+WykfJfQVh8ELooo366eO8x8eb+LKMuw7suK32Px8Kfb5+y/9R5a/LQ1NDUiZrqPHNYojYthGGHMSMyrTuMyz7mi07iUsjiVwJ+t4cphTMUWAoqWcOxGs09qC2Zfs0pJo65lkJmUNJXAUarMKj+L2YFYEzGFy1rTZxiGf4n7FmDG9FwoRcalwI8Nw0jkFCQrYbG2Gp2WqRVTiRXNcewEZrxFTRK1z3VzlxRzPuyzmMEjXzYMIywiQ6wujctBEecXzFQrK03jsti5otO4dC3hvO3A+YZh/GPRPWNPNxHXGCEDhUrhclP0gdZc2SOGYRy/wPn1Svt9ZRCdIiOyzfcDfsyh7OfnOd/bgXtEpMcwjD+uZUXjnMXaanRapgrMSPm91rHbI451Y6Z30SRR+1xPiywLszH1AU4R+RKQHVH+HPBOEcmzBPapqOOjUw7cApwkIm8TM73ApzGjV68k19dSzvVxEdkqprPGZZiR+hfjWuAq60WPiBSKyKkrqN9GcCdwgIi8W8z1HxczuzOxUEqaOzDT4JwjIqnW35Gyr8PMZmIpKTKex5T5odZc8RWqwBoV+CXwPREpFTMtzjFixgZUvAycCPxYRE5Z2+rHNYu11d8B/yMi1SKSiZnC5WbD9MK7FdgpIq+35sC/TJRT0yYhqdvnap09rhWRa+cpvhdznmU3pqnvZ/ZwwA2YgmvBTAkQrShmpRwwDGMXcDbwI2bipe00zPQOy2KJ5/qtVa8m6+/K6PPMwQ+AvwL3icgYZlDSo5Zbv7VioftjzKS2+AbmUEsds9PszJuSxjCMMcze1/sxe8M91r6RjTqpWKStg/nQ/8Zqr++bawfDMHZj5rZ7AGhgXyeYS4AXMZ2kBjFlmhJ1jucxUxD9TERimiJoLVlNW8V8wd4A/B0zpZAfc04cwzBetj7/HtM6GwP2knwpXDZ1+9SxFjUazabBstiGgTrDMJpjXB3NGqFDVGk0mqRGRHaKSIa1bOQ7mFZFS2xrpVlLtCLTaDTJzqmYQ+BdmMOS7zf0UFRSoYcWNRqNRpPQaItMo9FoNAnNeq0jS3Yzb6Pdd7U81xYtz7VFy3Pt0TJdBtoi02g0Gk1CoxWZRqPRaBIarcg0Go1Gk9BoRabRaDSahEYrMo1Go9EkNFqRaTQajSahWbc0LhqNRrORDA0NAdDW1jZneWWlmenl//7v/wA48MADAaivrwfgkEMOWe8qatYJrcg2EeFwmOnpaQYHBwmHw7hcLpxOJxkZGTgcDpzO1TUHwzAIh8MEAmZg8fT0dFJStNGv0WjWl6RUZLfffjsAp5xipsT50Y9+BMDHPvYxABwOR2wqFkPC4TCjo6N0d3dz3XXXMTo6Sm1tLQUFBbzpTW8iNzeXgoKCFcvGMAwmJiaYnJxk9+7dpKSkcMABB9hKUqNZa+644w5g5nl/+OGHAWhoaJhz/+3bzfyaLS0tAHaHSxEOh9ehlpqNICkVmWY24XAYv99PW1sbbW1tNDY2Mj4+TkaGmQw2GAyu6vyGYRAIBOjo6GBkZITGxkZcLhfbtm0jNTWVlJQUzKS+Go1Gs/YklSIbGBgAZiwvxSc/+UkALrjgAgDcbvfGViyGhMNhJiYmaGlp4fLLL6e5uZnOzk7S0tLIzc21/6/UGlOWWEdHB1/+8pdpb2+nv7+fkpIStm7dSnV1NUVFRasetkwERkdHAfjc5z4HwMsvvwzAAw88AEBqampsKpbg7NmzB4Af//jHAPz0pz8FYHJyEjDb4FLYtWvXOtROEw8k/9tlE6Pmq7q7u+no6KClpYWOjg6cTidut5vc3Fzy8/NJS0tb8fBfMBhkcHCQ3t5empub6erqwuFwEA6HtSWm0cQ5wWAQwzAIhUKEw2EmJycJhUIEAgG7g+BwOPB4PDgcDtLS0khJSYm7jml81WaV/P3vfwegs7Nz1vYPfOADgOl8sFkIh8OMj4/T3d3NT37yE5qammhvb0dEOPbYY6mqquL888+nuLiY3NzcFf/O2NgYd955J7t376alpQUR4YwzzqCmpoa6ujpyc3PjrtGvNTfeeCMAl19+ObCv15yy1PLz8ze2YklCR0cHAN///vdXdPx+++0HzHgpakyCwSB79+4lEAgwPj7O2NgYzz77LIODg/znP/8hEAiQkpJCVlYWO3bsoKCggNraWjweT9yNssRPTTRriupdjY6O0traSnt7O36/n8zMTHvIr7S0lLy8vBX/xvT0ND6fj9bWVjo7OxERMjMzKS8vp7y8HLfbHVeNXaPZzBiGQTAYJBgM4vP5mJycpLW11X5PjI2N0dDQwODgII2NjQQCAUSE7OxsSktLGRoaIhwOk5WVxcTEBGlpabhcLlJTU8nJyYnps54UbxnlfXTllVfOWX7OOecAbKphrunpaVpbW2lsbOSll16io6ODqakpsrKy2LlzJ7W1tWRlZa3q/F1dXTQ2NnLXXXexd+9etm/fTmVlJcceeyxlZWVJPxepLIX/+Z//AaC/vx/Yt52pOdqrr74aYFWdh2REyU1ZXG984xsBOPHEEwFwuVwA5OTkAJCZmQnA+Pg4ACeccAIwY3EdddRRABx22GHAzJy4x+NZv4tIAILBIB0dHQwMDPDQQw/R3d3NU089xejoKKOjo0xPTzM5OWk7hykcDgf/+c9/cDgcpKenk56eztatW8nKyqKuro7S0lI+8IEPkJeXFzNllhSKTDObcDjM1NQUQ0NDDA4O4vP5CIVCFBQUUFRURGFhIXl5eSueF4u0xFpaWhgYGGBycpK8vDyKiorwer1kZmbqNWQaTQyJtsAmJiZoamqir6+PhoYGenp6aG5utssAew7M7XZjGAajo6P2HBpAKBTC4XAQDAbJzMzE6XQyPT3NwMCAbZnF4rlPCkX2wgsvAPDss8/O2q56B+94xzs2vE6xQq0X6+vr46mnnqKpqYnJyUlyc3P54Ac/SF1dHeXl5WRlZa2owSlLrLW1lW984xu0tbUxMDBATk4Ohx12GHV1dXi9XtLT05PeAv7Od74DzHjLzsfvf/97AO6++25gZi5NWWrK4ths+Hw+AI4//ngAnn/+eQD+8pe/zNrvmGOOAeDf//43AFVVVcDMXOTWrVsBdMcpCmWB9ff389BDD9HV1cUTTzzB8PAw/f39TE9P4/f7ERFycnLweDzU1dWRmZlJQUEB4+Pj3HPPPfj9fjsqSktLC6FQiPHxcaampnj++efp6upi27ZtVFVVsWPHDtti3kiSQpFpZggGg/T19dHd3U1nZyd79+7F6XTi8XioqqqisrJyVRE3wuEwY2NjDA4O0tnZSXd3N+np6Xi9XoqLi9myZQupqalJr8Q0mnglFAoxPDyMz+ebZYF1d3fT2tqKz+fDMAxEBI/HQ3p6OpWVlWRnZ9tTDgUFBYyMjJCdnY3L5aKiooJwOGw7h4RCIaampmyrrbm5GQC/309GRsaGdyqSQpH96U9/mnO76ultJkZGRrj55ptpamri/vvvZ2JigsLCQqqrqznmmGOoqKhYlfdmIBBg9+7dNDQ00NfXRzgc5uijj6ampoa3vvWtFBcX2wutk5XW1lYAfvWrX83armL1FRUVAXD//ffPKh8ZGQFmLLmzzjoLgOLi4vWrbBwyNTUFwJlnngnMWGKXXXYZAMcdd9ycxylLTFFRUbFONUxshoeHuf7662lra+PJJ59kdHSU3t5eAoEAk5OTpKWlUV9fT05ODmVlZRQVFXH66adTUFBgu9k7HA46Ozt55ZVXmJ6e5kMf+hDhcJif/vSndHV10dnZSSAQIDs7m5GREX77299SWVnJG9/4RlJTU1c84rNSkkKRaWYIhUIMDQ0xMDDAyMgI09PTpKenk5GRQWZmJh6PZ9kNTI2RR1tiyj23qqqKbdu24fV6N/2EukYTC1Sc07GxMfbu3UtTUxMtLS20tbUxPj5uLx7PzMwkJyeHmpoavF4v5eXlFBUVUV1dTV5e3qxF+xMTE6Snp+NwOCgoKEBEKCsrIxgM0tnZyfT0tL0ObXBwkKysLKampmIS6ispFNkjjzwy67uac/ja174Wi+rEHLUQWURwOp14vV7y8vJIS0vD6XQua9jPMAymp6cZGhrirrvuorW1lTvvvJO+vj7Gx8epqKjgvPPOo7a2Fq/Xu34XFUc899xzwMz6sDe96U3ATDtUHl+//e1vAfj6178OQGNjIwA9PT0AnHrqqcDM3FmyezMqL0P1XKoYiYWFhQBceumlAElv0a81yqljaGiIe+65h6amJu68807bCUtEyMjIwOv1smPHDioqKjjllFPIy8vD4/GQmppKdnb2Ps5fai2qUli5ubns3LmT1tZWXn31VbujDDMLq2NFUigyzfyoOIg+n4/u7m7C4TCpqak4HI4FPQuV52MwGGRsbIyBgQEaGhpob2+np6eHsbEx0tPTyc7OprCwUC/21WhihAoTNzo6SktLCy0tLQwNDTE2NobD4cDlclFSUsKWLVuoqamhoqKCrVu34vV6cblc+3RsIyN9uN1uRIS9e/fi9/uZmJiwo34oBarWjyovRu21uEwef/xxAJ544olZ21WP7tBDD93oKsUdk5OTvPjii7S0tDAxMUFWVhYej4ecnBx27Ngx71oyv9/P7t27GRwctP8///zzjI+P20MOhx9+OHV1dUm/XiwatW5RvQDUOjKFmoM8//zzAbj11luBmZiBqueq2ulm8VpU3ojf+MY3gJn8YI8++igws05MszwCgQC7du2iubmZ22+/nY6ODnw+Hw6HA6/XS2lpKZ/4xCeorKzkgAMOsJXOXCHkwuGwHXZucnKS173udXR1dfHTn/7Ujv7h9/vt3G9grs87+uijqaurIz8/31Z+G0lCKzLNvjgcDrKzs8nJycHlcuFwOJiYmCAUCtHS0oLb7cbtduP1eikrK5tXkal0LMPDwzQ0NDAyMkJ/fz+BQIDU1FR7UeTWrVt19A6NJoYYhoHf78fn8zE8PMzIyAjBYBCn04nT6cTlcpGTk0N2djYiwtTUFL29vbPOISL26M3U1BR9fX0MDw8zOTlJIBCgq6uL4eFhJiYmmJ6etocUU1JScLlc9rtAxW3VimwZPPXUU3Nuj45+v5nweDwcd9xxVFdX295gIyMjTE1NsWvXLruRpaSk8MQTT8yrhFTgUJWMU7nbpqamUlhYSHl5OaeffjpVVVVkZ2dv5CXGnN/97nezvt95550AnHbaaXPu//TTT8+5/eijjwaIybqbWKBGUBQq8oZaB6ZZGcrRwzAMUlJSZs11qcXLzc3NDA4O8thjj+H3++np6bGVkYjgcrkIBoN0dXXh8/no6OggGAzicDiYnp6mu7ub6elp+3fUb2VnZ1NeXs4pp5xiO3zFIv9gQisyzb44nU4KCgqYnJykurqatLQ0+vr6mJ6eJhAIMD09zejoqJ3exeFw4Ha79xnXVnNoau1IZA/M4/Hg9XrtKCE6caZGEztSUlJIT0/H7XaTkZGB2+224ySqDqmyqIaGhpicnJxXkXV3d+Pz+ejp6SEcDuPxeGxLTXkjpqSkkJGRQVpaGhUVFVRWVlJSUkJBQUHMRmdknTxNNsR95eyzzwbgpptuArC95l588UVgXXt6G73ad8nyVF6GKtHl+Pg4LS0tDA8P89JLL9HT08O9997L2NgY2dnZeL1ejjnmmH2sgszMTI455hiGh4f58pe/TFdXFwBZWVm84Q1voK6ujksvvZTi4uK1aLxxK8+5uOWWWwB4//vfD8DBBx8MzETwUO3vz3/+MwB/+MMfAOxhXDW/oLwU1RzR/vvvv5pqRRKX8tyyZQswE1sxLS0NmMnfpjK6K0stjojF6v4lt1Hldt/e3s63v/1tmpubeemll+y4iSrklMPhIBQK2U4aMBMNJdJ5Q43CKKtL/QZgO3YcdthhlJeXc/bZZ1NSUkJNTQ3p6enLeResqUy1RZZkqN6V0+mkvLwcv9+P0+lkZGTEXsDY3NzM+Pi4PZdWX1+/jyJTkUD6+vrsxpmWlkZGRgZlZWWUlpbq6PYaTRwgIrjdbnJycqiqqsLhcBAIBBgdHWVoaIipqSn8fj+GYdjLb9RzGzk3pjyVI42byDVhKqWL1+ulurqayspKqqurKSwstBVlrEhIi+yxxx4DZtbvqGuIjAe2zsRljzcaNZ6txrb9fr8dQFT11NQwQfTQolpDsmfPHi688EK6u7spLy+nqqqKyy67jMrKSsrLy9cq63FCyFMxODgIQE1NDTATsUO1w+iJbhVhRmU4PvnkkwHYvXs3AB/5yEcAuPbaa1dTrUjiUp5KLvM5AqgX4Uc/+lFgJop9e3s7ALW1tQAccMABs45TmbhVTMZ1GImJa4sMZlzmh4aGCAQCdlAElZli9+7dhEIhPB6PnRXe6XQSDAbx+/10dHQwNjZGc3Oz7ZUbTXZ2Njt27KCqqooPfOADFBcXU1xcbC/nWSbaItMsDaWcVCNbzkJTNSE8MjJCKBTC6XRSXFxMWVkZxcXF5Ofn67kxjSZOUFZWYWEh4XCY3NxcvF4vdXV1trdiMBjE4/Hgcrns7BfBYJDx8XE7Q0Z0J0NE7Kj2+fn5dnDgsrIyO8hCPMRVTUhFpqKNR1uTmzG24noxNjbG7bffTkNDgz2f9p73vIfa2lpKS0s3PJZaPKHmttTc13vf+15gX8vs4osvBuCb3/wmMLO+7N3vfjcwE/Hj3nvvBWbWmSlLL9m45JJLAPjud787Z7lKFaIsV/V/qag5uB07dgAzc5abjUhnjNNPP51gMGgPGaq1Y8p72TAMent7ue6663A4HDQ0NNiRaUSE9PR0CgsLOeOMM6iqquL4448nNzfXTqQZD0oMElSRadYPFSVgeHiY9vZ2Ojs7CYfDuFwuCgoKKCgowOVybVolptEkApHh6eZDOYk4HA6mpqZsBw9FamoqW7ZssZ05KisrKSoqistcgwmpyFRPWKFulppr0KyciYkJnnnmGRobG/nb3/5mu+G63e5ZEQE0M1HaVeQOFVtRtcevfOUrAPtkG/jiF78IwKuvvgrAbbfdNmv/3/zmN+tY69ihInq8733vA2ai/ys3cJVxW1lmy2Xv3r3AzPtBZYxW+d80sxkbG+Oee+5hz549PPTQQ+zdu9dOsAlmOz777LOpqanh+OOPx+v1xiRFy1KIvxppYkooFKK/v5+9e/cyMjLCxMQEGRkZdmirzZAwU6NJZpQj1+Dg4D6xGZWXosvlIiMjw14nlpubG9ed2ISyyFSPTfV8FcpL6cgjj9zwOiUbfr+fF198kYaGBgKBAOnp6Rx88MHU1dVRX19PSUnJWnkqJg3KMpsvj1Y0KjblGWecAcxYZA899BAw4xWZbNHwlXOQek6V16biwQcfBGYstCuuuAKAf/3rX8v6HTU89swzz6y4rsnMxMQEjz76KHv27OHmm2+2nboilVhpaSm1tbUcddRRVFZWxn081YRSZJr1JxQKMTg4yODgoB0pv7i4mJKSEjIyMjZNgFuNJtkwDIOpqSnGxsZoaWmhubmZvr4+hoaG7AXSah1qSUkJpaWldo7BeLXEFAmlyFSstmhvRZXXSbN6lEW2Z88eDMMgPz+fE044gdra2k0TE3CjUHNFf/3rX4EZL7urr74agC996UuxqViMeNvb3jbru8r7piwyNRLwoQ99CIALL7wQgP/7v/8D9h2p0cxmamrKVmA33XQTbW1t9PX12R6NIkJaWhrFxcWce+651NbWkp+fnxAjMAmlyDTrRzgcxufzMTIywsjIiJ0EMS0tjcLCQgoKCvS6MY0mgQmHwwwNDdlz4P39/fsosZKSErZu3UplZSWlpaUJocQgwRSZWj+mKCgoAOBTn/pUDGqTXPh8Ph555BF2795Nb28v4+PjeDweMjMzqaqqoqKiImEadaKghms+85nPADP5utTckIrlWF9fv+F1iwfe/va3A3DZZZcBM3NnP/3pTwFoaGgA4OGHH57z+LKysnWuYWIxOTnJ888/b6dnUmGrIi2x//7v/2bbtm287nWvIzMzM2GmEhJKkWnWj+npaTo7O+ns7LRD1GRmZtqeii6Xy04XEQ6H7UWVGo0m/lHzY93d3XZKFjVF43A4yMrKIi8vj6qqKiorK8nMzLSDOicCCaXIVAQERXl5OaAzy64FyiJraGjA5/PZ3or19fWkp6fbyfuCwSATExOkpKTYYW40q0NlMv/qV78KzETA+PznPw/AjTfeCBD3nmNrzWte8xpgxrvz5ptvnlWuvDwVKhDuSSedBMxEVNnsqGe3v7+fu+++m8bGRnvqAMzQdUcffTS1tbUceuihFBcXJ4wlpkgoRaZZP9Qq//HxcTuUjQpz4/P57CCkgUCAYDCIy+UiOztbKzKNJo5Rke3b2tpoaWmhr6+P0dFRgsGgHUcxIyODyspKKisrycrKSihLTJEQikyNjTc2Ns7ariIm6Lmb9UFE8Pv9PPnkkwSDQe677z4mJiYoLS2ltLSUCy+8MCEbfbxy7rnnAnDdddcB8Kc//QmYmQtSec82C8oC/f73vw+YkShgZn1Yb28vAFVVVcCM/NQc42YnUoldfvnlNDY20t3dbbvap6amUlZWRk1NDWeffTaVlZUJm+09IRSZZuNRFtrg4CB79uwhGAyyd+9egsEgRUVFOrqHRhPnqCzwIyMjtLa20tbWZisxMBVZaWkpZWVlFBUVkZ+fn7D5BROi1sq7S0UEUPmH6urqYlanZGdiYoLHHnsMh8NBWloaHo+Hww8/nLKyMk477TSKi4sTtvcWrxQWFgLwwAMPADP59VSMws26TqqoqAiAO+64A4AbbrgBgCeeeAKYscBU9HuNSSAQ4JVXXqGxsZGenh6Gh4dnJcrMzs7mfe97H3V1dQmzXmw+4nu5tmbDcDgc5OfnU1BQQEZGhp22XMVcy87OpqysjPLycoqLi/W6Mo0mzgkGg/T09NDd3W1ngAbTMPB6vRQWFlJeXk5paWnCOXdEkxAWmXphXnXVVcBMhtnDDz88ZnVKNrxeL2eeeSbt7e3ceuut+P1+6urq2LJlCzt27CA3N5eCggLS09Pxer04nU6tyNaJiooKYCa/nor88corrwCw//77x6ZiccI555wz679mbkZHR7nttttoaGhgdHTU3p6dnW1H63nta19LUVGRVmSa5ECNl6ekpFBXV4ff76e+vp7CwkJqa2vJyckhIyMDh8ORsOPoGs1mIhgMMjAwYMdNBdMaS0tLo7y8nIqKioT1Uowmod5IpaWlAPzyl7+McU2SD5fLRV1dHdXV1Rx44IEYhoHL5cLhcODxeOyMstrJY+NQec4OOeQQYMZrd7NbZJqlYRgGPp+P8fFxwuEwKSkpZGdnU1JSwgknnMC2bduSJn5qQikyzfqhwtQoxw6NRpPYpKSkkJWVRVZWFgMDA3YQgy1btlBQUIDX6437qPZLRSsyjSZOUV6hzc3NMa6JJhHJzMzk+OOPp6qqiptuuomUlBR27txJbW0tZWVl5OTkJM08t1ZkGo1Gk4SkpqZSXl5OKBRi+/btiAg1NTVUVFSQnp6eNEoMQKJze60R63LSOGKjJ4q0PNcWLc+1Rctz7Vm1TFVqpqmpKdtrMScnh9TU1HhIlrmmMtWKbGXoF8XaouW5tmh5ri0JqcjinIRQZBqNRqPRbAjJ4bKi0Wg0mk2LVmQajUajSWi0ItNoNBpNQqMVmUaj0WgSGq3INBqNRpPQxLUiE5GzROS+VRx/nog8tpZ1SkZEZLuI/FtExkTk4ljXR7MvImKISG2s67He6Gc+ORCRh0Xkw/OUVYjIuIg4Ftt3qayJIhORFhGZtCrXKyK/EpFVR6M0DOMmwzDevhZ1THZWeQ8+AzxsGEaWYRg/XM96xjOrkaF17HHrXcd4QT/zS2e9ZLWE3121glgPDMNoMwwj0zCM0Fqdcy0tsp2GYWQChwNHApev4bn3QUR0eK19Wek9qARenq9Q9Zw2CevSjpO0vepnfulsqKw2G2s+tGgYRidwN3DgUvYXkc+JyB5rWOsVEXlXRNmsYQJreOXjItIANERsu1hEmkSkX0S+LSJzXpeI/EBE2kVkVESeEZFjI8quEJFbROR6qy4vi8gREeWlIvJHEekTkeZ4HoJbzj0Qkb8BbwGutnqM9SLyaxH5iYjcJSI+4C0i8hqrhzdsyeaUiHPki8jtllyfEpErE314Z5kyvAGoAG63ZPgZEamy2uYFItIG/E1EdohIR9SxtiUnIg4RuSzieXhGRMrn+L03Wu34LWtysatEP/NLZ41ldYWI3BjxXbU5p4hcBRzLzHN9tbXP661ndMT6//qI4x+2nt3HrWNut57tmyKe7aqI/ec9l0WNiPzLKr9NRPKi6znPNZ8vIq+KyJCI3CsilYsKyjCMVf8BLcBx1udyzN79V63vnwPuWODY04FSTKV6BuADSqyy84DHIvY1gPuBPMAdse0ha1sFsBv48DzHnw3kYwZL/jTQA6RbZVcAfuCdgAP4OvBPqywFeAb4EuACtgFNwAlrIb84uAcPK5lZ338NjABvsK49C2gELrOu/63AGLDd2v/31l8GsD/QHin3RPlbpQztY63vVVbbvB7wAG5gB9CxwG9eCrwIbMcM4XMIkB/RzmuBEyz5vi6BZbWpnvl1lNUVwI1ztDmn9f1hZj/XecAQcI4ljw9Y3/Mj9m8EaoAc4BVLtsdZ+18P/GoZ5+rEVNge4I+qrgvVEzjNqsNrrPNeDjy+qIzXsFGPA8NAK3CNanQrONdzwKkLNOq3Ru1vACdGfL8IeHCu4+f4rSHgkIhG8UBE2f7ApPX5KKAt6tjPq5saD3+ruQdzNPhfA9dHfD8W8wWQErHtd5bMHMA0llKzyq5cSO7x+rdKGbYwtyLbFrFtBwsrsl2q7c9xfsNqc63AQYksqznOldTP/DrK6gqWp8jOAf4Vdb4ngPMi9v9CRNl3gbsjvu8EnlvGub4RJdspzPfFvPXEtFYviDguBZgAKheSy1qOOZ9mGMYDyz1IRM4F/hfz4gAygYIFDmlfZFsrZg9mrt/6NPBhq9wAsqN+qyfi8wSQbpm/lUCpiAxHlDuARxeoZyxY0T2Yh0iZlgLthmGEI7a1AmVAIWbPqX2eYxONtZQhLE8W5cCeBco/hdnBeHFVNVo79DO/dDZKVgtRiimrSNRzrOiN+Dw5x3flpLKUc0Xfo1QWr3sl8AMR+W7ENrHOG/17NrGN42+Off4M+ASmSeoFXmLhyMjGHNsi5xEqgK45futY4LPA+4Bc67dGFvktRTvQbBiGN+IvyzCMdy7h2EQlUs5dQHnUPEQF5tBBHxAEtkaU7TOvswmYq11Gb/dhDr8CthNNYUR5O+awznycDpwmIp9aYR1jjn7ml84SZDWrPQHFUaeIllsXpqKIRD3Hy2Up54q+R9NA/yLnbQf+K0rubsMwHl/ooFivI/NgCrsPQEQ+xBInQaO4VERyrYnx/wZunmOfLMwXbh/gFJEvYfbOlsK/gFER+ayIuK1J+QNF5MgV1DUReRLzofmMiKSKyA7MYYbfG6YL7Z+AK0QkQ0T2A86NWU1jRy/mPMpC7Mbs8Z8kIqmY4/9pEeU/B74qInVicrCI5EeUdwFvAy4WkYvWsvIbiH7ml85isnoOeJOY67JyMIc+I4luk3cB9SJypuUQcgbmkN8dK6jbUs51tojsLyIZwFeAW43FXe6vBT4vIgcAiEiOiJy+WGXWXZGJ6YV191xlhmG8gjkO+wSm0A8C/rGCn7kNc2L2OeBO4Bdz7HMv5vjrbkwT1c8Sh30s4e8EDgWaMXsVP8ecEI17FroHS8EwjCngFOAdmNd+DXCuYRj/sXb5BKYseoAbMOfPAquqdJyxBBl+HbhcTK/OS+bawTCMEcz5nJ9j9lx9QKQX4/eAW4D7gFHMduyOOkcbpjL7rMThGiHQz/xyWI2sDMO4H1OBv4Api2iF9APgvZb33w8NwxgATsZ0ehnAXD96smEYi1lJc9VtKee6AXO+vQdIBxb1+jQM48/AN4Hfi8gopgX6jsWOS/h8ZCJiAHWGYTTGui4aExH5JlBsGMYHY10XTfKhn3lNNLEeWtQkASKynzUMJiLyOuAC4M+xrpdGo9kcJPJKeU38kIU5nFgK7MUcDrktpjXSaDSbhoQfWtRoNBrN5kYPLWo0Go0moYlrRWZ59Px8FcfPikWmmRsReYOINFjx1U6LdX00s1ksNl0yoZ/55EAWyAYhIseKyK6l7LtU1iqNy3jEX1hmUhaMi8hZKz2vYRhfMwwjLl2M441V3oOvAFcbZmqFv2xAdeOS1chQNkm+MIV+5pfOeslqCb8bl6mFDMN41DCM7Wt5zjXp4RlmegLAFB5m3Ky1DPOzDyLiNAwjuJ6/kUis8h5UMk8aFxERzLnU8FzlycR6tuNka6/6mV86sZDVZiPmQ4uyeJqFG63Pc6XFUNs+IiJdItItZmy1+X7rDyLSI2Zagb+r1eNW2a9F5McicqeYKROeFJGaiPL9ROR+ERkUkV0i8r51EsmGIiJ7MFf/qxQkaWKmc7hKRP6BGX9umyyc/qHakueYiDxgyXHTDO+IyN+tj89bMjxDrJQtYkaG6AF+JXNkL4605MSMIPFdEWm15PyYiLjn+L33WL3tlUTEiDn6mV86i8jq1yJyZcR3O02QzJFayNp+ipjpaoat5/w1Ece3iMilIvKCiPhE5BciUiQid0c827kR+897LosjxUw9MyRmMtH06HrOcb0pMpO6ZkDMNDt5i8lpIyJ7XCMi1yywy1OYq+fzgN8Cf1AXPA9vxgzxf0LEtrcAdcDbgc/J/Ob03dZ+W4BngZuiyj8AfBnIxUwlcJV1DR7MVBK/tY79AHBN5EMRzyx0DwzDqAHasBL/GYahInKcA3wE07V+DDN6wg8xU2J8D7hTZsIn/RYzpE8+ZkTuc9bpUmLGIjJ8k/XxEEuGKlxSMWa7rsSU5WJ8B3gt8HrruM8AsyxhMcMUfRMzYv5Ly76QDUA/80tnHWQFgGEY5zD7uf6WiNRjLpP5FGaMz7swFZ0r4tD3AMcD9ZiRTe7GTN9UgKkvLrbqvZRznYV5z2qs8y0lmejFmKlc3oy5nGcI+PGiRy01fcBS/4hKZ7GC46PTLETnsIlMi6G27Rex7VvAL6KPn+N3vNaxOdb3XwM/jyh/J/Af6/MZwKNRx18H/L+1ll8s7kH0/phpFb4S8X3elA2Yvb4gkBFRduN8ck+UvxXI0ABqI77vwExbkR6x7TyiUoyo4zBfEpOq7Ufto9r5JZg5orbGWj6rkdUcx2+aZ36NZfVr4MqoNtcx328BXwRuifieghkqbUfE/mdFlP8R+EnE908Cf1nGuT4aJds9i9UTeBV4W0RZCWawYedCcomHocVPi5kNdETMlAk5rENKBzGDfn7DMllHMYUHC6d0UGPblcBRlgk9bNXzLPaNNp1MRKdxmS9lQykwaBjGxDzHbmb6DMPwL3HfAsx4dAulcbkU+LFhGHMOyyQK+plfOiuQ1ULMeo4Nc967nTVI4zLPuZaUaieKSuDPETJ/FQgBRQsdFFN3XplJs/A24GXDMMIiMsTKUjqoALZzpnQAzgROxcx22oLZIBb7LUU78IhhGMcvYd9kITqNy1wpG+4BuoE8EcmIUGabMY3LXES31eg0LpEvxX7MoLY1wPPznO/twD0i0mMYxh/XsqIbhX7ml84SZLWSNC4HRZxfMOW40jQui51r0VQ7c9AOnG8YxrICScfaIltNmoVIvihmCpEDgA8xf0qHAGak5gzga8s4/x2YKQvOETONSaqIHDnH5GayMm/KBsMwWoGnMdO4uETkGMyx9c3GUtK4PA8cICKHWvMcV6gCq0f7S+B7IlJqWRPHiEhkmpeXgROBH4vIKWtb/Q1DP/NLZzFZPQe8U0TyrE7Rp6KOj26TtwAnicjbxEwj9GlM+SyY62selnKuj4vIVstZ4zLmvkfRXAtcJWYuNkSkUEROXeygjXD2uFZErp2neMVpFqJ4BHOi9kHgO4Zh3DfHPtdbv9GJOc/wz6We3DCMMcze8PsxexU9mBPuaQsdFy8scg8WxVg8ZcNZwDFW2ZWYDTbZ0rgsJsMrgN9YQyJzercZhrEbc83eA0AD8FjULpcAL2JO8A9itrGUqHM8j3kvfiYii6a3iAX6mV86q5TVDZidoxbM1D/RimJWaiHDMHYBZwM/whwB2InpDDK13Hov8Vy/terVZP1dGX2eOfgB8FfgPhEZw7xnRy12UELHWhSRKsxcQalGAq4vSVZE5GbMSfP/F+u6aJIL/cxr5iLWQ4uaJMAacqkRcw3IiZjzEn+JcbU0Gs0mIeljt2k2hGLgT5jryDqAjxmG8e/YVkmj0WwWEnpoUaPRaDQaPbSo0Wg0moRmvYYWk93MW8o6lLVEy3Nt0fJcW7Q81x4t02WgLTKNRqPRJDRakWk0Go0modGKTKPRaDQJjVZkGo1Go0lotCLTaDQaTUKjF0RvQgIBMwzi619vJnn+97/NtcunnGLGof3LX/4Sk3ppNBrNStCKTKPRaDYh4XCY0dFRpqamGBsbIxwOk5aWhsvloqCgAKczcdRD4tQUePTRR4EZS2LXrl0A3HHHHQDceeedAJx00kmzjjvmmGMAOPbYYzeknvGKssT+53/+B4DnnnsOADOVELz2ta+NSb00Gs3GMzo6yl//+ldaWlq47bbbmJqaor6+nm3btnHJJZdQVLRgLsu4IqEUmUaj0WjWhmAwSE9PD+3t7TQ1NeH3+0lJMd0mxsbGyMnJIS0tze7oxjNxrchGR0cBOOusswB48MEHAXC73QBMT08DptAj+fvf/z7ru9rf4/EA8JOf/ASA9773vetR7bjlhz/8IQDXXXcdAG9729sA+MpXvgLA0UcfHZuKaTQLMDQ0BMzM5d5zzz0AfPvb3wZmRhROP/10ACorzWTmn/70pwESyrLYSAKBALt27aKxsZFAIIDf72fXrl34/X5eeOEFJiYm2L59O2lp8Z92UXstajQazSYkFAoxPDzM8PAw4XAYMJWbz+djaGiI4eFhQqFQjGu5NOLaIvvsZz8LzMyBKSYnJwF4zWvMrONbtmwBIDt7dsZ0dXPU3Jk67oILLgCgvr4egIMPPnjN6x6PdHd3z/p+3HHHAdoS08QXaqTlu9/9LgBXX301sG/7VZaY+n/rrbfOKu/vNxOY//KXv1y/yiYwgUCA3bt309DQYM+fq+27du0iHA5z8MEHk5GREcNaLg1tkWk0Gs0mxDAM/H7/LCUGkJKSgtvtxu12J8T8GMSpRfbSSy8B+/awysvLAbj++usBqK2tBcDr9QKQmZk5a39lkak5oK9+9avAzNzbFVdcAcAvfvELAHJzc9fsGuKR8fFxAFwuFzBjkWnWFjWX88UvfhGAu+66CzBfHLDvnM5VV10FQElJCQAPPfQQMDOHqeZ4NwtqDvcLX/jCgvvt2LEDgEceeWTO8t/85jeAtsiWS2ZmJjt37qSuro6srKxYV2dJxKUi02g0Gk1scDqdeL1ecnJyYl2VJROXikxZDmqMW/VgP/OZzwAzPbHFUK6kyvKampoC4Dvf+Q4Af/7znwE4//zzATj55JNXWfP4pKurC4Cf//znwMw6vMMPPzxmdUom1JyOsgzOO+88YGZOJ3p4JnpOR1lcbW1tADz88MPAzMjD2WefvU41jy/USIwaOZmPb37zmwD893//NwBf+tKXAPjWt761jrXTxDN6jkyj0Wg2IQ6HA6/Xi9frndXZCgaDtteimp6Jd+LSIouefFQ93E984hOrOu/XvvY1AH7/+98D0NzcDMCf/vQnIHktsiuvvHJNz/fEE08A0NHRMWv7IYccAsx4g24Wnn32WQBOOOGEWdtLS0uBGa+7aO+v1tbWWds/+clPAtjrdtScWbKjLLHLLrsMgL6+PmDGclXrwv76178CsP/++wMzIy5qDvxd73oXMBMzVJ1HeSW/8MIL63gViUdWVhannHIKu3fv5vbbb2dkZAQw1+Xefvvt1NXVccoppyTEEKO2yDQajWaTYhiG7YSkiFxfpteRrQLl7aU46qij1vT8J554IjAT4eOf//znmp4/3lDr6BQf/vCHl3X8xz72sVnnUZEWJiYmZu2n1vH97//+L7DvfUw2lCWhLACF8gb9+te/Dsw/F6nmLk899VQAhoeHgZm5YOW1mOwoL0+1XlS9WFNTUwH4+Mc/DsCBBx445/Fqv9e97nXAzAiOWof24osvAvCRj3wEgJ/+9KdrWv9EZWxsjNtuu409e/bYntyJSlwqMo1Go9GsL6FQiNHRUXtIMZGJK0XW1NQEQGdnJzCzPuyggw5a099561vfCsxYZMmKspiUV93WrVuBmR5rNMFgEJiZ8znttNMA6OnpAWZ6yoWFhcCM5aH2V153ah3QueeeC8zMcSQbau5RzcWoOVZlCdTV1S14vLLolPwUasRgs3D33XcD+3p3Ku9kFTNxqXzjG9+YdV5lkT311FOrqaYmjtFzZBqNRqNJaOLKIrvxxhuBGctMRadX6540y0OtG+vt7QXgv/7rv+bcT83VqLmD6HU8ZWVlAJxzzjkAXHTRRcCMhadQc0VqLk2to0o2i+zCCy8E4JZbbgFmIsooS2AxS0xZyGoOTVm6ygJ585vfvLYVjlMGBgYAePLJJ+csV+1tpajj1ZyjJnnRFplGo9FoEpq4ssh+97vfATNzY2rlvmZlKG8wxXyWgprrufbaa4GZuQrlNfe9730PmN9rTKFiXyY7Tz/9NDAjJ5XnTq1vmg9liSlvTpU3T51HRajYLDzzzDMAtLS0zNr+pje9Cdg30/tqUV6haqRgs6zT2wxoi0yj0Wg0Nk6nk9zcXHJzc3E4HLGuzpKIK4tMsd9++wHwxje+McY1SWzU3Nd87N69G5iJdKJQ621+8IMfADPR8pfKa1/7WkDHclQoi+Oaa64BZrwaFSoCyKGHHrqR1Yo5yrKN5stf/jKw9tkolFet8hbVFtnc6Oj3Go1Go0lodPT7FeLz+YCZdUyatUGt1ldecdGhaH70ox8BM3MHZ511FrDy9XUqa4HTaTar5VpyiYLKTK5i9w0ODgJw2GGHzbm/WmemLOTo9VJqLlLNDW8W1DrH6Ha51l6b0efXJB96jkyj0Wg0CU1cWGQ333wzAI2NjQAUFBSs6++pKNoKFast2VA9/+j/imgLYbE5tfmIznf2nve8Z0XnSRRURvGxsTFgZt3cYtHVVbu74YYbgJl8ZB/96EfXpZ7xTrT353oxX/vXJA/aItNoNBpNQhMXFtlGodat3H777bO2X3XVVbGoTsxRkTwef/zxWf9V3jYVCSQ/P3/B87z73e8GZvJqLTc2XqKhMjqrdqQyOkd74al1Ze985zuBmYgof/jDHwDYvn07ADU1NetbYQ2A7YG3WHvWJB7aItNoNJpNiIiQnp5Oenr6rO06Q3ScoiwxtX5HeempdWrJFm1czVmpCAbzoXqmKvq6ipWoIk/ce++9wEyeKNWjVd9VRBAVQeTyyy8H4Oijj16Dq0gcVIxE9X8+oiOnHHnkkcBMNgHN2nL99dfP+n7FFVcAen2jIi0tjfr6elJSUti1axeBQADQGaI1Go1GkyCkpKTgdrtJT0+f5QijM0SvkKqqKmAmw/BaoW7Cd77zHWAmgoWK2q62q3VPyYKKFFFfXw9Aa2srAH/729+AmbkvNaelIhyofE3K4lLrpZQFq+a+lHeiOl5ZYsmeEXqlRMcSVJbtpz71qY2vTByhsgU899xzwMx6u/PPPx+AX/7yl6s6vzrfli1bgM3rHboZSK43uEaj0WgWxDAMAoEAExMTTExM4Pf7E37ReFwoMpWxWVkSKvV2f38/sPR1ZWodj4ppp+Z+ojPDqrxnRx111GqqHfeo9U4qirha7/T2t78dgP/93/8F9o05p/JDKe9F9V01duVtp8rf9a53rc8FJAlf+cpXZn1XmaQ3+1yNii357W9/G4APfvCDwEyet0984hPA8uWk8sWpPHzve9/7APZxatisBAIBdu3aRUNDAy+++CLt7e32/FiioufINBqNZhMRDAbp6emhu7ub8fHxhFdiECcWWTSvvvoqACeccAKw9CjVynJQlpxCeYXt3LkTmPEWS3bUXOA999wDwFve8hYAnnjiCQBOP/30Wfsri2u+CAgf+tCHAPjWt74F6PU4i6GirP/pT3+atT3ZvGRXyxve8AYAzjzzTAB++9vfAvDII48AS7fI1BywkndRURGw+fK8Lcbo6Ci33347u3fvtqPTJDpxqcg0Go1Gs7aoubHx8XG6urro7e2dFajd6XRSUFDAli1bKCws1PnIVoqac/nqV78KzMxxLZeUFHPEVFkMai7oc5/73GqrmJAoi/af//wnsG9sy5/97GcAXHDBBcCM/BRqu8oTp1kaan2dykKgLF09VzObbdu2ATPrEv/xj38AM3nJlPehej8oVD69f/3rX8DMc668bC+55BJg8czdm4XF5sYKCgq49NJL2bZtG0cccQRZWVk6H5lGo9Fo4gfDMPD7/UxOTjI5OcnU1BRerxeXy0VmZibl5eXU1dVRWVlJYWEhaWlpsa7ykpF1crtc1UlVZAo1l/Diiy8u6TiV2VjlhVrHdSMbHUY7sX1jFycp5fm9730PgEsvvRSAAw88EIDnn39+vX86oeWpItKo51fNlVVXV8/arua+oufE1Vy4iuSzBrEsYxE2f83b6OTkJC+88AK7d+/m85//PD6fj507d1JdXc3OnTspKCggPz8fl8uFy+Va72wBa3pybZFpNBrNJkBEcLvdZGdns23bNgKBAPX19VRVVVFXV5cQoajmIy4tsgQgoXu8cUhSylONDKj1jd/85jeBmbmbdSQp5KnWk+7atQuYmTu/6667gH3lqPLgKS/HNYzYkxQWmWEYTE1NMTU1xcDAAIZhkJWVRWpqKllZWfvMja8z2iLTaDQazfIQEdLS0khLS0sYJ46lohWZRrNOqFiVi2WO1syNGup63eteB+ybR1CjUejIHhqNRqNJaLRFptGsE+94xzsAaGpqAjZPRBmNZqPRFplGo9FoEhrttbgyksIrLI7Q8lxbtDzXlqTwWowz1lSm2iLTaDQaTUKzXhaZRqPRaDQbgrbINBqNRpPQaEWm0Wg0moRGKzKNRqPRJDRakWk0Go0modGKTKPRaDQJjVZkGo1Go0lo/j99LjW8kpFn2QAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6bde3a44\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"2554411c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"a083942d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-basic.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"37a4ca10\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d69e4366\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"dab647f8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"df774680\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d1c4c922\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"#  A Basic Convolutional Neural Network Trained on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f6ca7538\",\n   \"metadata\": {},\n   \"source\": [\n    \"A basic convolutional neural network [1][2] trained on MNIST [3].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] L13.3 Convolutional Neural Network Basics (18:39), https://www.youtube.com/watch?v=7fWOE-z8YgY\\n\",\n    \"- [2] https://en.wikipedia.org/wiki/Convolutional_neural_network\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/MNIST_database\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"12715ae0\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a17e6079\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"5102ee38\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ae17116b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"47c3db2b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4d9ccfc9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"cf256434\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class PyTorchModel(torch.nn.Module):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        # Calculate \\\"same\\\" padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            # 28x28x1 => 28x28x8\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=1,\\n\",\n    \"                out_channels=8,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1),  # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            \\n\",\n    \"            # 28x28x8 => 14x14x8\\n\",\n    \"            torch.nn.MaxPool2d(\\n\",\n    \"                kernel_size=(2, 2),\\n\",\n    \"                stride=(2, 2),\\n\",\n    \"                padding=0),  # (2(14-1) - 28 + 2) = 0\\n\",\n    \"            \\n\",\n    \"            # 14x14x8 => 14x14x16\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=8,\\n\",\n    \"                out_channels=16,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1),  # (1(14-1) - 14 + 3) / 2 = 1 \\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            \\n\",\n    \"            # 14x14x16 => 7x7x16                             \\n\",\n    \"            torch.nn.MaxPool2d(\\n\",\n    \"                kernel_size=(2, 2),\\n\",\n    \"                stride=(2, 2),\\n\",\n    \"                padding=0)  # (2(7-1) - 14 + 2) = 0\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.output_layer = torch.nn.Linear(7*7*16, num_classes)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = torch.flatten(x, start_dim=1)\\n\",\n    \"        x = self.output_layer(x)\\n\",\n    \"        return x   # x are the model's logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"62a5f90a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d00cf846\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6570816f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fc3d054b\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"b366b602\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 980),\\n\",\n       \" (1, 1135),\\n\",\n       \" (2, 1032),\\n\",\n       \" (3, 1010),\\n\",\n       \" (4, 982),\\n\",\n       \" (5, 892),\\n\",\n       \" (6, 958),\\n\",\n       \" (7, 1028),\\n\",\n       \" (8, 974),\\n\",\n       \" (9, 1009)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_mnist_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"885089c6\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"43804979\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"11bc7c47\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 1\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.11 (11.35%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"722aba24\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"d386a471\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"740666d5\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c25de5e3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"c31b543e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path, download=True)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4f33b7f3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"84090b2f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"227f013d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6f11c42b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"bd1db7d7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"672d2f61\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7a93e646\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"60710815\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 9.1 K \\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"9.1 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"9.1 K     Total params\\n\",\n      \"0.036     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4470d9cfe0924ebb98e606f98502ffa9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 2.65 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f72da03f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9d2779e2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"1a142920\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d8f13c32\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"e3b3241a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_2/checkpoints/epoch=15-step=6863.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_2/checkpoints/epoch=15-step=6863.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"059ea2ee2d4444869afa2801e8857642\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9879000186920166     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9879000186920166    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9879000186920166}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"433906c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0087d833\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"6d19af28\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_2/checkpoints/epoch=15-step=6863.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"0fc7c925\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1bf6cd82\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"63c2db97\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([1, 2, 3, 4, 5])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"77491b8a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"d49d1cc4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9879 (98.79%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"80e7461b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7dd9e65a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"11eb66f6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"5de41c23\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8d7b767d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"cdae5045\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAATQAAAErCAYAAABQAX/RAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABMoUlEQVR4nO2dd5gU1dKH32LJOYOASM5RlhwEJBiIXgkqSlBBJYgBRQwXvah4wayfqNeAgoCiKGIAyYikJUmQIEoQkCA5L0t9f5zeZVg2TOje3RnO+zzzzHSqrjndU3NS109UFYvFYokEMqW3AxaLxeIWNqBZLJaIwQY0i8USMdiAZrFYIgYb0CwWS8RgA5rFYokYMqe3A5GGZM6hki2f63avrVrKdZsAdtaOd5w9f8ETu9mzhE89xKv7a9WqlQdVtUji9TaguYxky0e26ne4bjdm+cuu2wQ4c94TsxZg+4GTntitclUuT+x6gVf3V44ssiOp9eET6i0WiyUVbECzWCwRgw1oFoslYrABzWKxRAw2oHnIwB7NiZn0KCsnD2NQz+YAfPr8nSyd8DBLJzzMpq+fZOmEhwFo3aASi8cPZcVnj7J4/FCui64Q8Pl+/PFHKleuTIUKFRg9erRr32PAPf0oXaIo9erUyNA2w83u2TNn6H7TdXRp04gOLaN5c8wo12x7dS94YdfNsg2rgCYi34tI/gCP+VhEbvXIpWSpVq44fbs0pHmf12lwx8vc2Kwa5a8uzJ1PfkqjXq/QqNcrfD3vV76Ztw6Af46c5NZHPqT+7WO599nJfDjy9oDOFxcXx8CBA/nhhx/YuHEjkyZNYuPGja58lzt79+GbGT+6YstLm+FmN2u2bHz0xXd8PXsp035aws/zZ7Nm5fKQ7Xp1L3hl182yDauApqo3qeoR33ViyHDfo0rZoixfv5PTZ2OJi7vAolXb6Nyy5iX7/KtNHT6ftRqAtVt2s/fgMQA2/vE32bJlJmuWKL/Pt3z5cipUqEC5cuXImjUrPXv25JtvvnHluzRr3oKCBQu6YstLm+FmV0TIlSs3AOdjY4mNjUVEQrbr1b3glV03yzbDBYJ4RORrEVkpIhtEpL+zbruIFBaRMiLym4j8H7AKuFpETojIyyKySkTmiMhlk+5E5BkRWSEi60XkPXHuHhGZLyIvichyEdkiIs2d9VEiMsY55lcRGeCv/xu2/U2zuuUomC8nObJl4YamVSlVLH/C9qZ1y7Hv0HG27Tp42bFdW9di7ebdnIuN87u8du/ezdVXX52wXKpUKXbv3u338Zb0IS4ujq5tGtOsVlmatGhN7Wvrh2zTq3shHO6xDBvQgH6qWg+IBoaISKFE2ysDn6hqXVXdAeQCVqnqtcAC4N9J2HxLVeurag0gB9DBZ1tmVW0ADPU59m7gqKrWB+oD94pI2cRGRaS/iMSISIyePwXA5u37efmTucx4cwDT37iXX7fu4XzcxQDVvV1dvpi5+jIHq5YrxqhBNzPoxamplc8lJJWo041/e4u3REVFMW32Euat3My6NTFs2bQhZJte3QvhcI9l5IA2RETWAkuBq4GKibbvUNWlPssXgCnO5wlAsyRsthKRZSKyDmgNVPfZ9pXzvhIo43xuB9wlImuAZUChJPxAVd9T1WhVjZbMORPWj5++nCZ3vUrbAf/H4aOn+H2nqY1FRWWic8uaTJ295hI7JYvmY8p/+3LPyEn8ufufJNxPnlKlSrFr166E5b/++osSJUoEZMOSfuTNl58GjZvz87zZIdvy6l4Ih3ssQwY0EWkJtAEaq2ptYDWQPdFuqT1XcsnfiYhkB/4PuFVVawLvJ7J51nmP4+IjYQIMVtU6zqusqs7y93sUKWD6R64ulp/OrWol9Je1rl+RLTv2s3v/0YR98+XOzlev3sMzb3/Hkl+3+3uKBOrXr8/WrVv5888/OXfuHJMnT6ZTp04B27GkHYf+OcCxo0cAOHP6NEsWzaNshUoh2/XqXgiHeyxDBjQgH3BYVU+JSBWgkR/HZALiRzNvB35OtD0+eB0Ukdw++6bETOB+EckCICKVRMTvB+kmvdSbVZOHMfWVuxk65iuOHD8NQLd2dROCWzz3dW9G+VKFGH5324RpHfEB0R8yZ87MW2+9Rfv27alatSrdu3enevXqqR/oB3f1uo2WzRuzZfNmypcpxccffpAhbYab3QP79tHn1pvofH1Dut3UgsYtWtOq7Y0h2/XqXvDKrptlKxlRJEVEsgFfAyWBzUARYCTwMaZPLTcww+kLiz/mBPAqcBNwFOihqgdE5GNn36kiMgroCWwHdmGarSNFZD7wqKrGiEhhIEZVyzijp6OAjpja2gGgi6perFolIlOu4urFw+mn7cPpYYd9ON3Th9NXqmp04vUZMqAFg4icUFX/qzQeYQOaJR4b0NI+oGXUJqfFYrEETMQEtIxQO7NYLOlLxAQ0i8VisQHNYrFEDDagWSyWiMFqCrjMtVVLeZL/P0fdQa7bBFj9/Uuu2yxTxJtRuOwe3a1ejcTl9shhL/zdvOe4+0aB2qXzeGI3OWwNzWKxRAw2oFkslojBBjSLxRIx2IBmsVgiBhvQLBZLxGADmsViiRhsQEsnAlXP2THnRWK+GJGwfEubuqyc+iQnV77BtdVKJ6xv3bAKiyc+xorPR7B44mNcV/9ifq1v3nqAZVOGs3Lqk7zxZE8yZUo926hXykReqTNBxlcmimfPX7vo3qkdrRrW5vrGdflg3Fuu2PWybOPi4uhxYzMG9+3mmk03r1eGCWgi0kVEqnl8jjIisj6Zbf+LP3+8doFXfgSjntN54NuXLG/Ytoeej7zPz6u2XbL+nyMnuHXou9Tv/gL3PvMpH466K2Fbr8c/pGGP0dS79XmKFMjNv9pem6qvXikTeaXOFA7KRPFEZc7M0/95iXnL1vLNrIWM/2AcWzb9FrJdr8oW4LMP33ElCWU8bl+vDBPQgC6ApwEtJVT1HlV1R/ctFYJRzzl09NQly5v/3MfWHfsv22/t5r/Ye8Cka9u4bS/ZsmYhaxYzwfP4yTMAZM6ciSyZo5LMEZ8Yr5SJvFJnCgdloniKFb+KmrXrApA7Tx4qVKrC33tDFx3xqmz37d3NorkzuaVnb9dsun29PA1oySg3nfDZfqujm9kE6ASMEZE1IlJeROqIyFJHbWmaiBRwjpkvIq+KyEJH+am+iHwlIludBI7xth921J3Wi8hQH7cyi8h4x+5UEcnpY/fyhHEivRw1qDUi8q6I+K8tlwxppZ7TtU0d1m7exbnYi1PLp789kJ1zRnPi1Fm+mn25SEtSeKFM5BXhoEyUFLt2bmfDr2uoW69BeruSLGOeHc7QEc8hmdwLG25fL69raKkpNwGgqr8A04FhTu7+bcAnwOOqWgtYx6UqTudUtQUwDvgGGAjUAPqISCERqQf0BRpi0nffKyJ1nWMrA+85do8BDyTnvIhUBXoATVW1DkZv4LLsjb6qTwcOHEi1UNJCPadqueKMGtKZQaMmX7K+08C3Kdt2BNmyZqZl/cp+2fJCmcgrwkGZKDEnT5xgQO/bGPnCWPLkzZve7iTJwjk/UKBQYarVrJv6zgHg9vXyOqClptyUJCKSD8ivqgucVeOBFj67THfe1wEbVHWvqp4F/nDO0wyYpqonVfUERtGpuXPMLlVd7HxOTh0qnuuBesAKR/npeqBc4p18VZ+KFLlMDvQyvFbPKVk0P1Ne6c89T3/Kn39drvt59tx5ZixYR8dEwsep4aYykVeEgzKRL7GxsfTv3ZMut/bkxo5d0tudZFkTs4wFs3/gxqY1GD64Lyt+WciIB+8J2a7b18uzgJaCcpNvSE6s5OQv8QpNF3w+xy9nxuT/T47EfwkpdSQJMN5H9amyqo4M1NnEeKmeky93Dr568z6eeXM6S9b+kbA+V46sFC9s/v2jojJxQ9NqbN6+L1V7XikTeUU4KBPFo6oMGzKAipWq0H/gg+ntTooMeXwks5Zt4ofF6xn95kfUb9KCF17/X8h23b5eXtbQklNu2iciVR0Bkq4++x8H8gA4IiSH4xXMgTsx4sH+shDoIiI5HZWmrsAiZ1tpEWnsfL6Ny9WhfJkD3CoiRQFEpKCIXBOAH0kSjHrO/PGPUOmaYvz+43/o3aUxnVrV4vcf/0PDWmX46o37mP72QADu69mC8lcXYfi9N7B08nCWTh5OkQK5yZUjG1NfG8DyKU+wfMoTHDh8gvenpvTVDV4pE3mlzhQOykTxrFj2C19O+YzFi+bTvkUD2rdowNyfQh+d9KpsvcDt6+WZSEoKyk2FgZcwqkvrgdyq2kdEmmK0Ms9iJObyYPrIcmKakn1V9XAihaaWzucOzjl9tz0M9HPc+Z+qviYiZYDvMQGvCbAVuNMJur7HbgeiVfWgiPQAnsAE/1hgYCKB40uIjo7WmJiYEEouaWz6oPBLH3Tw+NnUdwqCwnmyuW4z3NIHiUS46lNGwQY0G9DisQEt7QNaRpqHZrFYLCFhA5rFYokYbECzWCwRgw1oFoslYrABzWKxRAxW9cllzsUpfx12f3Tr9Gp3UsskJkf0UNdtno55zXWbXuLV6KkXo5EA2w+cdN1m5RJpq87kFbaGZrFYIgYb0CwWS8RgA5rFYokYbECzWCwRgw1oFoslYrABzWKxRAw2oKUDXqn9BKOes2PWf4iZ8njCcoG8OZnx9v2s++pJZrx9P/nz5ACgdcNKLP70EVZMfozFnz7CddEXc3XOfHcQa78cwdKJw1g6cZin/qaXXa989UKhKdyUuiJS9SktSE71yVfxKZXj+4hIyNHHC7WfYNVzOg9+95LlR/tcz/zlW6h5y/PMX76FR/u0AeCfIye59aH3qd/zv9w7ciIfPndpJvK+T31KozvG0OiOMZ76mx52vfIVvFFoCielrkhWfUo3klN8ckMQJSm8UPsJVj3n0LFL1aQ6XFeTCTNWADBhxoqENN1rN+9m78FjAGzc9rejJhV88XilzuSFXa98BW8UmsJJqSusVJ8yKJepPvkqPonICRF5TkSWAY1FpK+IbBGRBUBTt51xS+3HLfWcogXz8Pc/JnD9/c8xihTIfdk+Xa+vzdrNf3EuNi5h3bv/vo2lE4cx/O52aepvWtgNRyWpcFHqCjfVp4xIaqpPuYD1qtoQ2AY8iwlkbUlGN9RX9enQwdRVn+JxU+0nrdSOqpYrzqjBHRn0wucJ6/o+9Sn1e/6XNve+QdO6l2nIJIlX/nphNxyVpMJFqSvcVJ8yIqmpPsUBXzqfGwLzVfWAqp4DpiRl0Ff1qWDh1FWfwH21H7fUc/YfOk7xQia4Fi+UlwOHE2RUKVk0H1PG9OOef0/kz93/JKzf4wgbnzh1lik/rkpTf9PCbrgpSfmS0ZW6wkb1KQOTmurTGVWNS2F76A54oPbjlnrOdwvW06uDaZ706lCfGQvWAY6a1Gv9eebtGSxZ+2fC/lFRmSiUz6TczhyViZuapzq24qq/aWE3nJSkILyUusJJ9SmjEojq0zKgpSNenAXo5oYDXqj9BKueM/+jB6l0TVF+/24kvTs3ZOz42bRuWJl1Xz1J64aVGfvxHADu69GM8lcXZvjd7ROmZxQpkJtsWTIz/a37WD7pMZZ9Now9+4966m962PXKV/BGoSmclLrCRvUpI5Kc6pOzLl7x6YSq5vY5pi9G9WkvsAaIUtVkFUtq1a2n38/9xXXfSxXwJhWNTR/kHV6Jr3iRPijchG2SE0m5ovKhqep2ku7Yb+mzzyXDeqr6EfCRp45ZLBZXuBKbnBaLJUKxAc1isUQMNqBZLJaIwQY0i8USMVxRgwJpQdYo8WxE0gu8GJHMUf9h120CnF7xiid2vRC1Ae9Gpqtc5f6IpFdl4JVQTHLYGprFYokYbECzWCwRgw1oFoslYrABzWKxRAw2oFkslojBBjSLxRIx2ICWToSTkEegdsc93YMdM58lZvJFwZQCeXMy460BrPvyCWa8NSBBfAWMjsH6r0awdupw2jSqnLB+5rgHWDt1OEsnPsLSiY945m9qeCVqAxnnmvmDV+XgqviKqtpXKi/Mw+tN/Nm3Xr16mhrnz5/XcuXK6bZt2/Ts2bNaq1Yt3bBhQ6rHpbXNYO1ef++b2uiOsbr+9z2aPfohzR79kL48fo4+9ea3mj36IX3qzW917MdzNHv0Q1qn22hdu3m35m38qFbu9B/dtuuA5mzwsGaPfkgXxGzVJne+nGDDK393HTqT4itm45/6/bwluuvQGf1txwEtW76CzvlldarHeeFrepVBsOVwOlZTff00d4H+smylVqte3a/9T8eqAjGaxO/P1tD8oyUm3ZArhJOQRzB2F6/+IwnxlRqJxFdqJKz/4qfVnIuNY8eeQ2zbdZD61Uunqb+p4YWojVe+emnXq3JwU3zlig5oInKXI5ayVkQ+FZGOIrJMRFaLyGwRKebkULsPeEhE1ohI81DPG05CHu6KrxwH4O9/jieIr5Qsko+/9h25eL79RylRJF/C8rvP3MbSiY8w/O62aepvcrglagMZ/5qlhJvl4CZX7KNPIlIdeBJoqqoHRaQgJt12I1VVEbkHeExVHxGRccAJVR3rxrk1jIQ8vLJ70VhS5zTvfZ+eyJ4DR8mdMxuTXurjlzkv/XVT1AbC95q5XQ5uciXX0FoDU1X1IICqHgJKATNFZB0wDPArF7Cv6tOBA6mrPoWTkIe74it5ACheKE+C+Mru/UcpVSx/wn4li+Zj70GTxvsS8ZWZ6Su+4raoDWT8a5YUXpSDm1zJAU24XADlTeAtVa0JDACy+2NIfVSfihRJXfUpnIQ8XBNfWbghkfjKemf9erq1rUvWLFFcU6IgFUoXYcWGnZeLrzRLP/EV9UDUBjL+NUuMV+XgJldskxOYA0wTkVdV9R+nyZkPiO9s6O2z73HAtbq1rzBEXFwc/fr1c1XIwy2bwdodP6oXzetVoHD+XPw+4xn+895Mxo6fw4QX76J3p4bs2neYO4Z/AsBvf+zjy9lrWP3545yPu8DQ/37JhQtKzuxZmP5mf7JkjiIqKhPzlm9Jt3KIF7WpUq0G7VuYPqPHn36O1m1vCMluRrpm/uBVOdzV6zYWLZjPwYMHKV+mFE8/8yx9+t0dlK0rSiQlMSLSG9O0jANWA9OAVzFBbSlQX1VbikglYCpwARisqouSsxkdHa0xMTGe+56RsemDDOGURirc0gflyGJFUi5DVccD4xOtvmx8W1W3ALXSxCmLxRI0V3IfmsViiTBsQLNYLBGDDWgWiyVisAHNYrFEDDagWSyWiOGKHuUMJ86cT28P/Mer6RU5GvifQigQTi9/2RO7a3ce98Ru7dJ5PLHrBUt+/ydNz5dsQBOR41ycSR//IJg6n1VVM9ZDXBaL5Yon2YCmquHzN2CxWCz42YcmIs1EpK/zubCIlPXWLYvFYgmcVAOaiPwbeBx4wlmVFZjgpVMWi8USDP7U0LoCnYCTAKq6B7DNUYvFkuHwJ6CdU/MEuwKISC5vXbJYLJbg8CegfS4i7wL5ReReYDbwvrduRT5eqPK4qp6TBnZDKYOBPZoTM+lRVk4exqCeF7Oi39+9GWu/eJyVk4fx/OAOAPRsfy1LJzyc8Dq5dAy1KgaW8NArdSaAuLg4etzYjMF9u7lmM5xUn3b+uZV7u7ZMeHWILsPU8eOCsuVX+iARaQu0cxZnqepPQZ0tnRGRkbiYSjsp/EkfFBcXR6VKlfjpp58oVaoU9evXZ9KkSVSrlnwSQ3/mof28aCG5cuXmnn53sXLN+kBdd81udj9mNwZTBvHz0KqVK84nz/eieZ/XOXc+jumv38uQl76kZNF8PN63DV0f+h/nYuMoUiB3QmbceKqXL84XY/tRresLCetSm4cWjK/g/zy0T99/iw2/ruLkieO8+dEXqe6f2jy0YPz1J33Qvr/3sn/f39SsXZcTx49zU+vG/O/TL6hUpWqyx2zddyLZbcn53r1lTd6ePJPiJa9Odr/WVQsnmT7I3ycF1gGLgIXO5ysWEQl5MrJXqjxuqud4bTeUMqhStijL1+/k9NlY4uIusGjVNjq3rEn/fzVh7Pi5nIuNA7gsmAF0b1eXz2etTjNfU2Pf3t0smjuTW3r2Tn1nPwk31SdfVi1dSImry6QYzFLCn1HOe4DlwC3ArcBSEekX1NnSARF5UkQ2i8hsoLKzrryI/CgiK0VkkYhUcdYXEZEvRWSF82rqrB8pIu+JyCzgk1B9SgtVnoxOKGWwYdvfNKtbjoL5cpIjWxZuaFqVUsXyU6F0EZrWKcfCD4cwa9wD1Kt6+Y/i1rZ1+HxmYAHNy+s15tnhDB3xHJLJvacQw1n1ad7302h98y1BH+9PbWMYUFdV/wEQkULAL8CHQZ81jRCRekBPoC7mu64CVgLvAfep6lYRaQj8H0Y05XXgVVX9WURKAzOB+Pp0PaCZqp5O4jz9gf4ApUunrinpuZJSGBBKGWzevp+XP5nLjDcHcPL0WX7duofzcXFkjspEgbw5aNHvDaKrXc2EF++kapeLTcv61Utz6kwsG//4O818TYmFc36gQKHCVKtZlxVLkk2CHDDhqvoUe+4cv8z9kXseeipoG/4EtL8wOfXjOQ7sSmbfjEZzYJqqngIQkekY4ZMmwBc+Fzk+T3AboJrP+rwiEt9hMT2pYAZGJAUTJImOjk61U9JLVZ5wIdQyGD99OeOnLwfg2ftvZPf+o1QuU4yv55kekZiNu7hwQSmcPxcHj5wEoFu7OgE3N93wNTnWxCxjwewf+Hn+T5w7e4aTx48z4sF7eOH1/4VkN1xVn5Yvmk3FarUoWLho0DaSreeKyMMi8jAmv/4yp9n1b0yu/d+DPmPakzjAZAKOqGodn1dVn22NfdaXVNX4YH7SLYe8UuUJJ0Itg3ih4quL5adzq1p8Pms13y5YT8voCgBUKF2YrFkyJwQzEeGW1rX5IoiA5tX1GvL4SGYt28QPi9cz+s2PqN+kRcjBzEt/vVZ9mvvdVyE1NyHlPrQ8zmsb8DUXA8M3wN6Qzpp2LAS6ikgOp6bVETgF/Cki3QDEUNvZfxYwKP5gEanjhVO+qjxVq1ale/furqjy3NXrNlo2b8yWzZspX6YUH3/4gQveemM31DKY9FJvVk0extRX7mbomK84cvw046cvp2zJQsRMepRPRt3JPc9OSti/Wd1y7N5/lO17DqW5r2mNV/7Gqz4tXjSf9i0a0L5FA+b+9GPoDgNnTp9i5S8LaN62Q0h2Il71SUSeBO4CdmCazxuBL4F3gKuALMBkVX1ORAoDb2P6zTIDC1X1vkCme3il+hRO6YP8mbYRDDZ9kMGL9EFeqT4FOm3DX5KbtpHqrSciRYDHMCriCcK7qtraVQ89QlWfB55PYtNlYoKOinqPJNaPdN8zi8XiNv6MFU8ENgFlgWeB7cAKD32yWCyWoPAnoBVS1Q+AWFVdoKr9gEYe+2WxWCwB409vR6zzvldEbgb2AKW8c8lisViCw5+ANkpE8gGPAG8CeYGHPPXKYrFYgiDVgKaqM5yPR4FW3rpjsVgswZOSSMqbXD4pNQFVHeKJR5Yk8WoqhBcs3nrYE7teTa/I0Xi4J3ZXf/W0J3bDiVZVCqXp+VL6mbg/mcpisVg8JCXVp/Fp6YjFYrGEilVOt1gsEYMNaBaLJWKwAc1isUQM/mSsrSQic0RkvbNcS0SCz8BmsVgsHuFPDe19jMhwLICq/orJAmsJAS9UebxSJvLK7vFjR3lqcG9ub9+QO25oyPrVy12xG4q/g3s2Y+XEh4iZMJTxz/YkW9bM1Kp4FQvef4Cl44fw84eDiK5mHpQpmDcnP751LwfmPMurjwSeb+zsmTN0v+k6urRpRIeW0bw5ZlTANpIjnFSf3PTVn9lNOVV1eaIUvmGUzCZ50kIFKini4uIYOHDgJao8nTp1SlVFKK1temkX4PVRT9Cw+fWMenM8sefOceZMkgmB08zfEkXy8kC3JtS9/RXOnD3PhFG3061NbXq0q83zH8xm1tIttG9cmecH3kT7ge9x5lwsz703i2rli1O9XLGAfc2aLRsfffEduXLlJjY2ll5d2tK8dTvqhJin36trFpU5M0//56VLVJ+at7w+RdWntPbVnxraQREpz0Wh4VsJnwSPGRIvVHm8Uvrxyu7JE8dYG/MLHbrdCUCWrFnJkzdfyHZD9TdzVCZyZMtCVFQmcmTPwt6Dx1CFvLlM5qx8ubOz9+AxAE6dieWXX3dw5mxw/+8iQq5cJvPu+dhYYmNjXcn9H06qT2776k8NbSAmX34VEdkN/An0CvqM6YxPwsddwAFgpZOZdhyQE5Oht5+qHhaR+sAHmPTbPwM3qmrIartJqfIsW7Ysw9n00u6enTvIX6AwLwwfxO+b1lO5em0efOpFcuTMFZLdUPzdc+AYr322iC3ThnP6bCxzlm9lzvKt/LXvCN++djcvDr6JTJmEVv3fCclHX+Li4ri1fTN2bv+D2/r0p/a19UO26dU188Ut1Se3fU21hqaqf6hqG6AIUEVVm6nq9qDPmI4kUoG6BYi/ez4BHlfVWhjd0X876z/CqEM1BuJSsNtfRGJEJObAgQOp+uGFKo9XSj9e2Y2LO8+WjWvpcntfPvpmAdlz5mTCe6+FbDcUf/PnyUGH5tWo+q//Uq7jC+TKnpWe7evQ/5ZGPPb6DCp2Gc1jr8/gnRH/CtnPeKKiopg2ewnzVm5m3ZoYtmzaELLNcFJ9cttXf0Y5nxGRZzDZNh7yWQ5HElSgVPUYMB3IBeRX1QXOPuOBFiKSH8ijqr846z9Lzqiqvqeq0aoaXaRIkVSd8EKVxyulH6/sFilegiLFS1C9tsmi3Kp9Z7Zs+DVku6H427p+BbbvPcTBIyc5H3eBrxdsoFHNa7jjpnp8Pd8oxn85Zx3R1YITwU2JvPny06Bxc36eNztkW+Gk+uS2r/70oZ30ecUBNwJlgj5j+uOviIJnQpleqPJ4pfTjld1CRYpRtHhJdv6xFYCYJQsoU6FyyHZD8XfX30doUL00ObJlAaBVdHk2bz/A3oPHaF63HAAto8vz+66DIfsJcOifAxw7egSAM6dPs2TRPMpWqBSy3XBSfXLbV3/SB12S4kBExmJqNuHIQuBjERmN+e4dgXeBwyLSXFUXAXcCC5w+tOMi0khVl+LiVBVfVZ64uDj69esXsiqPFza9tAvw0NMv8eyjAzgfe44SpcrwxOjQpwGE4u+KjbuYNm8dS8YP5vz5C6zdsocPvlnG2i27GfNQRzJHRXH2XCyDRk9LOGbTV4+TJ1c2smaOomOL6nR48AM2bd/v1/kO7NvHEw/2J+5CHBcuXOCGjrfQqu2NQX1vX7y6ZvGqT1Wq1aB9C9N39vjTz9G67WXyHOnma8CqTyJSAFiuqhWDPms6kowK1GwuDgr8AfR1AlpDzDy8k8B8oIWqNk3JvleqT+GEV+mDmlYs4IndcEsfVOWq0AZOksIr1adSBbKlvlMQiEjQqk/ruNhMi8IMDjznrntpRwoqUEnpJGxwBgoQkeHYlEoWS4bGn2kbvsqf54F9qhoRE2v94GYReQJTTjuAPunrjsViSYkUA5qIZAK+c2PuVTiiqlOAKenth8Vi8Y8URzlV9QKwVkRKp5E/FovFEjT+NDmvAjaIyHJM5zgAqhr6OLDFYrG4iD8B7VnPvbBYLBYX8Ceg3aSqj/uuEJGXgAXJ7H9Fcy5OPRkCP3HGm3EYL6YA1CvrzfSKTXtPpr5TECyd8qQndut2GemJ3dPLxrhu85/j51y3Cd5N20gOf54UaJvEutBn/1ksFovLpKTLeT/wAFBORHwfsssDLPbaMYvFYgmUlJqcnwE/AC8CvlOpj6vqIU+9slgsliBISZfzKHAUuC3t3LFYLJbgsapPFoslYrABzWKxRAw2oKUDXqjnhJuC0IB7+lG6RFHq1XH3qTovyyEuLo4eNzZjcN9uAR87sEczYj57hJWTHmFQz2aXbBt6x3WcXjaGQvlyAo6a1P8N4MC8Ubz6aJegfPVKqSuUMkgON329ogKaiAwRkd9EZGJ6+hGvnjNv2Vq+mbWQ8R+MY8um30KyGa8g9PXspUz7aQk/z5/NmpWhy8LFq/L88MMPbNy4kUmTJrFx48aQ7d7Zuw/fzPgxZDuJ8aocAD778J2gEjBWK1eMvp0b0rzvGzTo9So3Nq1G+asLA1CqaD5aN6jIzr0XUy6dORfLc+/O5Ik3ZgTlp1fXDIIvg+Rw29crKqBhpqHcpKp3xK8QEX8mF7uKF+o54aYg1Kx5CwoWLBiyncR4VQ779u5m0dyZ3NKzd8DHVilTjOXrd3D6bCxxcRdYtPoPOl9naqb/fagTT7713SW59U+dieWXtds5cy64ydReXbNQyiA53Pb1igloIjIOKAdMF5GjIvKeiMwCPhGRaxx1+F+d99LOMeVFZKmIrBCR50TkhNt+uaWeA+bfrmubxjSrVZYmLVp7piC0e3dowddrvCiHMc8OZ+iI55BMgf9kNvzxN83qlqNg3pzkyJaFG5pUoVSxfNzcvBp7Dhxl3VZ3VSG9umahlEFyuO3rFRPQVPU+YA/QCngVqAd0VtXbgbeAT5xkjhOBN5zDXgdeV9X6zrFJ4qv6dOhg6qpP8bipngPhqSDkBW6Xw8I5P1CgUGGq1awb1PGbt+/n5U/mMePNe5n++j38unUP5+Mu8Hif63nu3Vkh+ZYUXlyzUMsgOdJc9SmCma6q8VLdjbmo6vQp0Mxn/RfOZ79UnwoWTl31CdxXz/ElXBSEvMatclgTs4wFs3/gxqY1GD64Lyt+WciIB+8JyMb4b1fQpPfrtL3vHQ4fO8WOvYe5pkRBlk94iE3TnqBk0Xws+WQoxQrmCclX8OaauVEGSZEeqk+RSkpPOgcmtBAgXqjnhJuCkFd4UQ5DHh/JrGWb+GHxeka/+RH1m7Tghdf/F5CNIgVMEoCri+Wnc8uaTPx+Jdfc+CxVur5Ila4vsnv/URrf9Rr7Dh0PyVfw5pq5UQZp4Wuad4hnUH7BqDp9CtyBUUkHWAr8C5O11jXVJy/Uc8JNQeiuXrexaMF8Dh48SPkypXj6mWfp0+/ukO16VQ6hMmn0XRTMl4vY83EMHTONI8dPp7j/pmlPkCdXdrJmiaLjddXpMOR9Nv3pn5qUl0pdbpPuqk/hjIhsB6KBQcAJVR3rrC8DfAgUBg5gVJ92ikhFYAJGo/M7oL+qlkzpHLXq1tPv5/6S0i5BEU7pgzxyle0HvEkfdDb2gid2G3XzRkvIi/RBa3eGXjNMitqlQ29CJ0XQqk+RhKqWcT6OTLR+O9A6iUN2A41UVUWkJ1b1yWLJ0FxRAS0I6gFviRl2OQL0S193LBZLStiAlgKOknrt9PbDYrH4x5U8ymmxWCIMG9AsFkvEYAOaxWKJGGwfmstkjRJPlG7OnPdGPWfFH0ddt1mzdD7XbQKUKeL+FBMv8WJ6BUCO6KGu2zy89DXXbQJMX/e3J3aTw9bQLBZLxGADmsViiRhsQLNYLBGDDWgWiyVisAHNYrFEDDagpRPhJDzSuUVNbruxCXd0aMZdnVu6YtMrX8PNbjD3wY5Z/yFmyuMJywXy5mTG2/ez7qsnmfH2/eTPkyNh26N92rB+2pOs/XIEbRpVSVh/a9u6LJ/0GCunPM7zQzr6dV6vyuDbCe8zuGtLBnW9jumfvheSrYgJaCJSRkTWp7cf/hBuwiMA70z8lokzfuaTb+a7Ys8rX8PJbrD3QefB716y/Gif65m/fAs1b3me+cu38GifNgBUKVuMbu3qcm330XQaPI7Xh99KpkxCwXw5eeHBTtx0/9vU6/ESRQvmIZMfSWK9KIMdWzcx68uJjP3se17/Yg4rFs5mz44/grYXMQEtFNJaKCXchEe8wCtfw8lusPfBoWOnLlnucF1NJsxYAcCEGSvo2LJmwvovZq3mXGwcO/YcYtuug9Svfg1lSxZi6479HDxi0jHNXb7Fr4DmRRn89edWKtWqR7YcOYnKnJka0Y1YOueHoO1FWkCLEpH3RWSDiMwSkRwiUscROvlVRKaJSAEAEZkvIi+IyALgQRHpJiLrRWStiCx09okSkTGOSMqvIjLADSfDTnhEhMF9unJXp+uYNunj9PYmYnDrPihaMA9//3MMgL//OUaRAkb1qmTRfPy176I83u79RyhRNB/bdh2kcplilL6qIFFRmejUsibppRJRukJlNq5ayrEjhzh7+hQrF83l4L5k5TtSJdKeFKgI3Kaq94rI55hss48Bg1V1gYg8B/wbGOrsn19VrwMQkXVAe1XdLSL5ne13A0dVtb6IZAMWi8gsVf0zFCfDTXjkf5/PpEixqzh08ACDenfhmvIVubZB0/R2K+xJj/tAVTly/DRDRn/BhBd7c+GCsvTXkG7nkLi6XCVu6TuQf/fvQfacuShTuRqZoqKCthdpNbQ/VXWN83klUB4TtBY468YDLXz2n+LzeTHwsYjcC8SXaDvgLhFZAywDCmGC5iX4qj4dOJC66lO4CY8UKXYVAAULF6Fluw5sXLsqnT2KDNy6D/YfOk7xQkY1rHihvBw4bNQWd+8/SqliBRL2K1k0P3sPmJrc94s20KLPq7Ts9xpbduz3VkQjFdrecjuvfv4TL378NXny5qdE6XJB24q0gHbW53MckD+V/RNyOjsyd08BVwNrRKQQJvX2YFWt47zKquplumO+qk9FiqSu+hROwiOnT53k5InjCZ+XLZpH+UpV09mryMCt++C7Bevp1cFoj/bqUJ8ZC9aZ9QvX061dXbJmieKaEgWpcHVhVmzYAZDQLM2fJwf9b21GnDdZyP3iyD8HATiw9y+WzPmeFjd1CdpWpAW0xBwFDotIc2f5TmBBUjuKSHlVXaaqzwAHMYFtJnC/iGRx9qkkIiE/Ie0rDFG1alW6d+/umvBIy+aN2bJ5M+XLlOLjDz8I2eahgwe4t8cN3H5zU/rccj1NW7Wj8XVtMqSv4WY32Ptg/kcPUumaovz+3Uh6d27I2PGzad2wMuu+epLWDSsz9uM5APz2x998OXsNq794gulv3sfQ/37JhQumLjb20VtY9flw5n7wIC+Pn+1XDc2rsn3p4bsZ2KUFowb3ZsCIF8mdN3/QtiJGJMUROpmhqjWc5UeB3MDXwDggJ/AHRgDlsIjMBx5V1Rhn/68wzUkB5mD62QQYBXR0Ph8AuqhqsikqoqOjNSbGfekBr4RH1u0Mn2wb4UZ2j3qowynbxqzfvMm20bnWVZEtkuIIndTwWR7rs7lREvu3TLR8S1JmgRHOy2KxZHAivclpsViuIGxAs1gsEYMNaBaLJWKwAc1isUQMNqBZLJaIIWJGOTMKZ2IvsGnvydR3DJAqV3kjEOLFFAuvpit4hVdTYryyezrmNddt5mjwiOs2AVZ/85wndpPD1tAsFkvEYAOaxWKJGGxAs1gsEYMNaBaLJWKwAc1isUQMNqBZLJaIwQa0dODsmTN0v+k6urRpRIeW0bw5ZpQrdr1QkoKMpXiUXnavBDWpeAb2aE7MpEdZOXkYg3o2T1h/f/dmrP3icVZOHsbzgzsAUPqqAhxaOJqlEx5m6YSHeWP4vwI6l9u/hSs2oInIdhEpnMT6TiIy3MtzZ82WjY+++I6vZy9l2k9L+Hn+bNasXB6STa+UpCBjKR6ll90rQU0KoFq54vTt0pDmfV6nwR0vc2OzapS/ujAt6pWnQ4vq1L99LPV6juG1CfMTjvlj90Ea9XqFRr1eYcjoLwPy1e3fwhUb0JJDVaerqnvVhSQQEXLlMhlDz8fGEhsbG3Iuea+UpCBjKR6ll90rQU0KoErZoixfv5PTZ2OJi7vAolXb6NyyJv3/1YSx4+dyLjYOICHNd6i4/Vu4IgKaiOQSke8cRaf1ItLD2TRYRFaJyDoRqeLs20dE3nI+fywi40RkkYhsEZEObvkUFxdH1zaNaVarLE1atKb2tfVDshduSlJe+Rtu5eAFoZTBhm1/06xuOQrmy0mObFm4oWlVShXLT4XSRWhapxwLPxzCrHEPUK/qRftlShRkyacPM2vcAzStUzZgf938LVwRAQ24AdijqrWdjLbxdfyDqnot8A7waDLHlgGuA24GxolI9sQ7+IqkHHbyo6dGVFQU02YvYd7KzaxbE8OWTRsC/EqXEm5KUl75G27l4AWhlMHm7ft5+ZO5zHhzANPfuJdft+7hfFwcmaMyUSBvDlr0e4MRb3zLhBfvBODvg8eo1GkUje98hcdfm87H/+lFnlzZAvLXzd/ClRLQ1gFtROQlEWnuk0L7K+d9JSZwJcXnqnpBVbdiUnhXSbyDr0hKgUKXdculSN58+WnQuDk/z5sd0HGJCTclKa/8Dbdy8IJQy2D89OU0uetV2g74Pw4fPcXvOw+ye/9Rvp5nxFdiNu7iwgWlcP5cnIuN49BRI3y8etNf/PHXQSqWTl0oKCnc+C1cEQFNVbcA9TCB7UURecbZFK8SFUfyD+on/rsLWYTh0D8HOHb0CABnTp9myaJ5lK1QKSSb4aQkBd75G27l4AWhlkG8ItTVxfLTuVUtPp+1mm8XrKdldAUAKpQuTNYsmTl45CSF8+cikyO7XqZEQSpcXYQ/d//j97nc/i2EWV6E4BCREsAhVZ0gIieAPgEc3k1ExgNlgXLA5lD9ObBvH0882J+4C3FcuHCBGzreQqu2N4Zk01dBKC4ujn79+rmiJAVG7WfRgvkcPHiQ8mVK8fQzz9Kn390Z0l+v7HpRBl7ZDbUMJr3Um4J5cxIbd4GhY77iyPHTjJ++nHef7kHMpEc5FxvHPc9OAqBZ3XI8PeAGzsddIC7uAoNHT+XwsdN+n8vt30LEqD6lhIi0B8YAF4BY4H5gKhCtqgdFJBoYq6otRaSPs36QiHwMHAaigWLAw6o6I6Vz1ah9rU79cZHr38Gr9EFepLix6YO8xYvyDbf0QVVL5I5s1aeUUNWZGI1NX8r4bI8BWjqfPwY+9tlvsao+5KmDFovFFa6IPjSLxXJlcEXU0IJFVfuktw8Wi8V/bA3NYrFEDDagWSyWiMEGNIvFEjHYPjSXyZ4lkydTLMJtaoEXLN562BO71xTO6YndUgUCewTIX7woh60zX3DdJkDFdk94Yjc5bA3NYrFEDDagWSyWiMEGNIvFEjHYgGaxWCIGG9AsFkvEYAOaxWKJGGxASyesMpF3qk/Hjx3lqcG9ub19Q+64oSHrV4cmQLPnr11079SOVg1rc33junww7i2XPA2fMoDQy2Fgz+bETB7GyimPMei2FgDUrFiC+R8MYcWkYUx95e6EbLcF8+Xkx3ce4MCCF3l12C1+nyPiApqI5BeRB1yy1VJEUkwXFAxWmchblarXRz1Bw+bX89nMZXw8fRHXlK8ckr2ozJl5+j8vMW/ZWr6ZtZDxH4xjy6bfQvYznMoAQiuHauWL07dLI5r3fo0Gt49NUJN656nuPPX2d9S/bQzT563joTtbAXDm7HmeG/cDT7w+PSAfIy6gAfmBywKaiESlvStJY5WJvCuDkyeOsTbmFzp0Mznvs2TNSp68+UKyWaz4VdSsXReA3HnyUKFSFf7eG7rwSjiVAYRWDlXKFGP5uh2XqUlVLF2Un1dtA2Du8i10aVULgFNnzvHL2j85cy6wGeWRGNBGA+VFZI2IrBCReSLyGbBORMqIyPr4HUXkUREZ6XyuICKzHWWoVSJS3teoiNQXkdUiUi5UB60ykXdlsGfnDvIXKMwLwwfRt/N1jB4xhNOnToZsN55dO7ez4dc11K3XIGRb4VoGEHg5bNi291I1qSZGTWrjH3vp0MJk073l+tqUKpY/JL8iMaANB7apah1gGNAAeFJVq6Vy3ETgbVWtDTQB9sZvEJEmwDigs6r+kfhAX9WnAwcOpOqgVSbyrgzi4s6zZeNautzel4++WUD2nDmZ8N5rIdsFOHniBAN638bIF8aSJ2/ekO2FYxlAcOVg1KTmMeOt+5j+Rn9HTeoCA56bwoBuzVj8yUPkzpktQfczWCIxoCVmuar+mdIOIpIHKKmq0wBU9YyqnnI2VwXeAzqq6s6kjvdVfSpSJHXFG6tM5F0ZFClegiLFS1C9tsnO3Kp9Z7Zs+DVku7GxsfTv3ZMut/bkxo5dQrYH4VcGEFo5jJ++jCZ3vkLbAW9z+Ngpft91gC079tNx8Ls0vetVPp+1OiCBlaS4EgKab137PJd+53iNzZT+FvcCZ4C6bjlklYm8K4NCRYpRtHhJdv6xFYCYJQsoUyG0DnFVZdiQAVSsVIX+Ax8M2cd4wqkMIPRyuFRNqiafz1ydsE5EGN6vDe9/+UtIPkZito3jQJ5ktu0DiopIIeAE0AH4UVWPichfItJFVb8WkWxA/CDCEeBuYJaInFTV+aE6aJWJvFWpeujpl3j20QGcjz1HiVJleGJ0aNMsViz7hS+nfEaVajVo38L0GT3+9HO0bntDSHbDqQwg9HKY9FIfCubLSez5Cwz9r1GTGtizOQNubQrAN/PX8cm3F6eXbPrmKfLkyk7WLFF0vK4GHQa/y6Y/96V4johUfXIGAWoBp4F9qtrBZ9sQYAjwJ7Ab2K6qI0WkIvAuUBijDNUNKA08qqodRKQ08APQT1WXJXfu6OhojYmJcf07hVP6IK9Un2z6IIMX5eBVGXiVPuhMzKtXjuqTqt6ewrY3gDeSWL8VaJ1o9R/AfGf7TsCdv0+LxeIJV0IfmsViuUKwAc1isUQMNqBZLJaIwQY0i8USMdiAZrFYIoaInLaRnojIAWCHn7sXBg564EY42Q0nX8PNbjj5Gqjda1T1ssdybEBLR0QkJqm5NFeS3XDyNdzshpOvbtm1TU6LxRIx2IBmsVgiBhvQ0pf3rN2w8jXc7IaTr67YtX1oFoslYrA1NIvFEjHYgGaxWCIGG9AsqSIiYXOfyJWWy9xyCWFzo1oCw60ftohUAf4tIrndsOdjN8rnc3IJOQO1Kep0CouIaxmGkzpPRrYXLiT+3m78cdqAFiEkvjnUhdEe5wYrBRQBHhIRV7IAOsGsjaN7OgToLSIh5+bzCWZ3As+KSOjabT6ISNn487gRhHxsZHaWQ5Za9PXLzZp1vF0RySciBVwym/AnKSJ9gMdCNWgDWhrjc2NUcqTxQk5rmqhmcoeIPC0i/0osxRegzUyqekFVZwNrgBrAIJeCmgB5gTGY7MHfq+p5N36AItII6AIMVdWjoQYJn+tVEfheRJ6E0INa/DUTkQbAbyJSTFXjQvE30X1wFzBcRG4TkZBFVR1fOwGzgJnOPRa0XRG5BpgkIvWdVVkwCVVDwga0NMbnxvgCuA9zc9QL1SYkpBe/F5Ne/CmgTQg2Lzg2h2IChAItgMdEJFeI/p4HlgPngF+AKiKSI/6cgZCoRpINaAlUBG5xfuBxoQQe53p1AEY6PncXR8s1lKDmHHs9cCOmbOeLSHHH36B+lz73wb3AAMff/wEdg7Hni4hUxtxbA4A+QH2SEPQOgNPAImCEiNTClEHoXQ+qal9p+ALKAj8CBYCuwDqgYJC2yvl8zge8ian99AdmYoResgI5A7ApPp+LAXOAXM5yO+D/MHqnfttM4hzFnPdswG2OzducddWA4kH4WhrI73y+D3gNo6N62b4B+pofWAE0w1QAagGLgSdCvA8qA9uBxpgm/Rhgp0/ZZArCZiagIGaCaingTuAnICqYa4QJXOLY+hpTO8vpbC8DbAV6BOqjz+fCwFBgGjDFKYNoTLC8Kb4sAnnZGlracxTzg7gf02fQWVUPiUjrQPqRRCQHpgk00ll1DHPzLXZstlfVOMxNXcdPm5k0/tcv0h5zI1+F+TEDzHX874lpfgZcOxGRQcAEERkDdFfVSUAM0EREJgOTMP/WqeLj61DgA2CyiLwGfAbsApqLSHfffYMgDpMBYruaGuQGx/7dIhKKpt1JYK6qLgEOquowYC0wT0SKqOoFf8rXdx81XQSHgN+B94E7gHZqan2PiUirAPyrgKk9F1TVv4CvnPXtRaSgqm7H1P5y+GvQqTHH1/xvA0oCnwPzgHoYYaL2wOOY30bWAPw1hPIvY19+/SPFP42RzblAWYGPgNVANWdbC8wPpaqfNks575UwP4KnnOUuGFGXfznLvRy75QL0uTEwGxMg+2JqUE18bL4EFA2iLPpgAm4Z50ZeCzzmbGsCjACqB2izC6bmkAkYC8xz1ud17L0I5Pb3WvlcrxJANufzv4GlQA5n+UanDKbHX8MA7oOsznse5/v389mnB6ZG/AOQPcByGAyMdj4/gGluNnKWb3XOVSlAm3mAcTi1UYyc43innDsD24Drg7gP7gPWx9+XmBrqAOCbQO/Vy2yHcrB9+X0BO2Gq7O9ilKNqOTftC86PZQPQwQ87gqmmvw8UcNaVAzZimoF5nBt7rnNzrPEnQGACYz7nc0dgPxeDYg1Mx/1m57y/B/rDcOxEA//CNLUHYZrd1zmBIujmG6bPrD0w3AlsWXz8zkIQzXngBmAJ8AkwEbgGeAb4zSnn34GmTnnUCMBue0zzahymqX0NRiv2SUw3wSKgOfAWgXUTPIz5o6jhc5+MBD52yvkXoKaf95dvMz4zpun3f5hBFjC1vsWYJn0bZ51fzWPHfklgYfw9xKV/ICMwNfQc/tq87BzB3kj25ffNVhlTa+rhExiqYvrS+mKq1s19L64fNrM7N/4DznJ558f2oLNc0DlHET9usGzA6zgB0lm/DFjisxyFUY6/CSgbRBnc7/yQK2D+jb8GCjvbvgK+jV9Ozd8k/G8J/AVM91l/D/AlTt+fH3aLArcDOZ0f3BanfCs512cx5s+iJ6YJXx1Ti10FlPbzHI0wfzxdMX2R251yKYmpRf4fUBMzkLOcFAJxoqBTyDm2KObPrR8mEFfHdBdUw4/aNE5t1PncyvmerXyW3wWGOMt9MMH8Zn/L2DkuE5AL+B6n1szFGmsJ57sE1Z+ccI5QDravVC9gLefHOtJn3d3AJqBpgLYyJVrugKlF9HOWywO/Ai8EYDPKec8CNMDUDOJrODHANy6UQSfHr2uc5aswNcgmzo9mgj/BLJHNQcDLzo+qEKbWtBFTA3rCOZ/fTVdMreMD4C4nqLzjW+bA28AdPvvXx9Qsa/tZvgWcgPaSz7YimGZXbZ91zTAd7cnW+hIFs7swzbdvMJ3/0zH9T1OADwP4/gUwtbiqTiDcgdGu/ZqLzc2WmObmI87yCMwglL/N+WZc/AP+DJjqs60P5o8toGZ2kucJ1YB9pXgRc2Oq0DMwHezx1ev7MCNaBQlwBMr5MV2NqZ1cCywA7nG2VXKCXKABogQmIC5wfhDxQW0hpuM6lDK4DxjhfI63+4hTLr+kFhR8/IsfXRuI6d8rh+kXetXH5ihMbaVKEH4+DLyDGXXbA/T12fZc/A/ZWb6GFEZiMX2EZZzP1zuB5l5gWaL9XuNi32SUcz396kPCNOG/8FnuzMUR0naYGrFfwcY55knMH8F/gdbOumsxwXK4z3ep5XNMgRTsxd/rmZzv1h9Ty+uBqQl/4dxfY4CVvnZDut/cMGJfl13EGkBt50Jmw9RC3gRK+uxbyk+b9bhYY+iD+Vf/GvgUUwOsg+lIHujsk8UPm02Ans7ngU5geNkJMsswzaz44DPLX1+TOdeNmP7Cyj7rOjjfJYcfx5d0ym4Apib5KKZ28wjwHab5HeXjbzDTHdphprkswtQeXsOMko4AbsH0Rbb001YZTA28BlAF+DD+x+pTvtUxTcv1QINA7zHn3lqL6d/LnWj7g862VPvMnP0zO+/5fb53fK0/s3N//QQ87XOM33/COE1yTBDrjan53e6s64oZ1Kng2m/QLUNX+ouLwawT5h/nI8w/0rVcHNn8INDggKnZ/YwZFfzQufHKYvp8pmPmC7XDNG3z+WnzZszk22cxzYhyjo3BmNrPXODVYIJDEufKi6k5veAEsl6YeV1+3cTOD7gPJuD2w/w5LAAm+/wYB2H6ozLhZz+kj/2iTvlWdZYHYmpkz2KC2yj8GLDx8fVWTJ9kbeAIZgDBt7b3unMfzAFuDuTeSrTuTkzfXnMuNo2jgP/g52i5j60umP7AEpgR0o04TXYu1hzrBeqrY28HcKOznIuLcyTvir9+br5cNXYlvpyLFN/BWRdTjS6MGQDYipnk2ABTU/uMAKclOHZLYqr+m3zWFcX07XSN9yNAm20xNYSJznI2TB/Ky5hayQSCmJqRzLmuwjQ9v8fUUvxqXnDxT+IuTLP9RyfQHORif0wf5wdYMUjfCmCa6S2c5SyYvrlZmGai+Prih738wN+YP4yumBrlWOA6n32yAHkCsevsewdm9PIBzB/FbZjaUzP8qJknY7MOpgZaxWfdREyQD6gZmCiYPYwZ1b4DM0Wpnc+2uc59lt+N+8v3ZSfWhoDz8PNooIMz0fUEpg+mDqZ63QsTKF4AGmI6ljf4YTeTz+esqrobcxMfE5H3AVR1PxebHwCnAvFdVX/C9JvcJCI9VPWsqv6GGYk8oqq9nHOEjKruVdVxmJpAb1X91c/jVETuwNQcR2BqO3GYUbyhIvIOJujcqqpbg/TtMKY/p6WI1FDVWEyn+j/AQnV+gfHvfnAM8wPOjKkxTsI85tNeRFo7tmJV9XggdkVkIKYcDmNGzmc6r/h5YcGqJZ3FBLTrROQZEZkLXMAE5s8lgEwo8d9FRNpiBkFWqOpETD/ZKyJys/MY2TFgrKoeCdLnFJ2wrxBemD6LDzD/xtmddU9wsZ/gfkx/V0DNAOfYfpiRx+cwo29XY2oOCzC1lgUE0QGe6BwdMA8FP4tpiq4Gyqd3ufr49xwwzPmcFfOH8TmmD60wfjazUzlHKUxTbSbwPGZeYMsQbV6D09wEimM628eQQkd6ouPjm5HxNcRx+PS3YQL8/5zPA/Fz+kgS58mNabIvxtTMa2D+PFv7axPTWohvovbB1Ji/TbTPrZia8FxcGgBI0pf0vmHD9cWlz6QNcILWLZjmRF/MI0L9MP9+TYKwfwemSdgW0//2X0wHe0nnBzebEDrrE52rC6bmMxlndC6jvBzfvsanqY4ZHX0ByOviefJg+hEfxqd5GKLNupj5gf0xze5gJiRXcu6pGThPVTjraxHA1Aw/zhM/HyzaCUitAzi2IuaPdjzmMaY7Mf3IQxLtl48Au0YC/h5eGo/Ul8+/Zimfdd0wfQ/xfVr3YAYFbgrUNqYj9iWcuU+Yfp6HuTjaWQIo4fJ3ug5nrlhGemGaPs9jOudbY2qR0/HzAfb0fmFGqf/098+HS0egB2NqeWOcMviLiyOQd2BqO/kJcCAkmfNGOb4uw+eh/gCOH4tpSt7vLN/o/BENTsvyDjmp3pWIqqqI3ACMFZFVQCzm5jsPdBWRLJgRyY/V5PlKyFOVFL7bnfc4EdkJdBaRhaq6y+k7+05ESqvqTg++0wK3bbqBqh4Rkbcxtd+nMGX8iKr+nb6e+YeqrhSRWur0mflBAeBFMZmCS2EmC7fDDALMBkaJycbbCpPp4ohLfsaJyCZMMP0ztXs2CcZhpos8LCKHVHWKiOwH/k9EDqpJQuA5NqAFgZO/7EZM38V+TL/B15j+qEKYSY4LVHUfpN7xG79dRP6FaWJMxFThS2KyuU7FdNYL4O8PI2JQ1T3AWyLyEaY2ciK9fQoQv/1V1e9E5BzwCrBUVbeJyIeYEUMwI6jvYZ4++cdNJ1X1JKY2meo9m8SxvwO/i8gR4HnnPTsm591SN/1MCavL6Sc+GUYLYLIMbFDV5k6GUcUM9f+gqlP9rUUlyjDaCzOh9RdM1X8opsO2EaapdRbTh7LG/W9nyWiISGfMPTVEVSc7I999MH9s/3WrZuYFTutlDCZF0t3qx8i+a+e2AS11fILZjZgRnW2YOVEPquoHzj7/Bfap6suB2HQ+F8c0IVao6u9Ofq+2mGf/FjpBNE5Vj7n+5SwZFhG5GfPg+gs+QS1XAM3XdENEimIqegfS8ry2yekHTjBrgBlxm6yqPzv/QnNEpAZmln5bzHOQqZIomA0CHgLOYDpk+6nqayISB4wRkWGqutD9b2XJ6DjNzwvAeyJyXlWnEiZdDurSHMZAsTW0ZBCRcpih6/85ueo/xyTzq+izTxPMTO25QH9V3SsiUWoyxfpzjmaYCbgvYuaY9Qd+V9V/O9sHYJqxrg8CWMIHZ6LqNlUNWUQk0rEBLRlEpCQmyPyhqvtFpDTm8aOlqnq/z37RmIekh6vqR/6MDjlNh3KYjAi/YSbJXsD0nQ0E9qpJyWyxWALAPvqUBE4tazfmIer5IjLaqSV1BMqLyOvx+6pqDNAdI8abPwWbiXO//46Z7X4VZlg+FtPkfA8oICKF3f9mFktkY2toifAZACiPme2fEzMl40tVfV5ESmGe9Vurqg+ISGZnrll2VT3jh/3emBnk+7mY3vk/mOwW32CmZmT1x5bFYrkUOyiQCCeYdcTMTN+OGdEcAbwqInGqOlpEemJqVqjRmAQzrSJFROQ+zONQ72Cas99injB4FpN9IFZVv8MMEFgslgCxAS0RYpS3n8GMWrbFNAFPY0Yi33NqZKMwifASSKrfLIkBgmuAx1V1nrN9FzBKVbuJyPOYZzctFkuQ2D60y/kLk22gDiaTRm3M5NY7neVF/hhx5o5Vcz7fJCIlMLW6O3x2mwecdZqrU1V1h1tfwmK5ErEBLRGq+peqrsA8rD3R6bz/GJODaqWqLvDt4E+BUkAvEfkEeM15fGcoUEdExjr7NMbU2nK5/DUslisS2+RMnnXAADFq5h0xWQN2QsrPucUPKqjqOud5tiEYzcj4B607AV+LyKeY3PJ3uv1MnsVypWJHOZNBRPJikjZ2Aj5Q1e8DPL4Z5hnPYhg9y4XAj86ctvyY/GOZ1WRMtVgsLmADWir4TMvwO52KiBTBZK3Njul3a4NJx/01RgmoMCYFzvnkbFgslsCxAS0VgsgLFX9cfUwOr5yY4HYdJrA1wgh8rHXVUYvFYgOam4hIX4w825PO8rWYUc3zwPOqekxEcodhPi+LJSywo5whkMRo53zMQMITAKq6CpP/vy3wpDMvzQYzi8Uj7ChnkCSRAqgGZmT0ZmCGiFxQ1Zcw6V7mAa/6m4XDYrEEh21yhoiIPAD0wDQtf8UIoyzHyM/NB5oC7VV1c3r5aLFcKdgaWgg4UzuuBXpinslcgZkoWxSTEugU5lGnv9LNSYvlCsLW0ELESf5YBfM0QCsn19k/mOwZo1X1XLo6aLFcQdgaWoio6lkROQVkFpF4dfPvMBJ2NphZLGmIraG5gFNLG4qZZ1YM6K6qm9LVKYvlCsQGNJcQIy5cHLjgZLu1WCxpjA1oFoslYrATay0WS8RgA5rFYokYbECzWCwRgw1oFoslYrABzRLWiEhLEZnhfO4kIsNT2De/86haoOcYKSKP+rs+0T4fi8itAZyrjIhYsZwgsQHNkiERkahAj1HV6ao6OoVd8mMEcCwRig1oljTFqYFsEpHxIvKriEwVkZzOtu0i8oyI/Ax0E5F2IrJERFaJyBciktvZ7wbHxs+YJJrxtvuIyFvO52IiMk1E1jqvJsBojPL9GhEZ4+w3TERWOL4862PrSRHZLCKzMQI5qX2vex07a0Xky/jv5NBGRBaJyBYR6eDsHyUiY3zOPSDUsrXYgGZJHyoD76lqLeAYl9aazqhqM2A28BTQRlWvBWKAh0UkO/A+RrimOWYyc1K8ASxQ1dqYBAIbMGI121S1jqoOE5F2QEWgAUa2sJ6ItBCRepiEA3UxAbO+H9/pK1Wt75zvN+Bun21lMBmLbwbGOd/hbuCoqtZ37N8rImX9OI8lBeyznJb0YJeqLnY+T8AoY8VL+01x3hthdE0XO3k0swJLMIkA/lTVrQAiMgHon8Q5WmMynuDkoTvqaKX60s55rXaWc2MCXB5gmqqecs4x3Y/vVENERmGatbmBmT7bPlfVC8BWEfnD+Q7tgFo+/Wv5nHNv8eNclmSwAc2SHiR+PMV3+aTzLsBPqnqb744iUieJ44NFgBdV9d1E5xgaxDk+Brqo6loR6QO09NmW1PcVjDSib+BDRMoEeF6LD7bJaUkPSotIY+fzbcDPSeyzFGgqIhUARCSniFQCNgFlRaS8z/FJMQe43zk2yslddxxT+4pnJtDPp2+upIgUxUgOdhWRHCKSB9O8TY08wF7nmd47Em3rJiKZHJ/LAZudc9/v7I+IVBIRKzgdIjagWdKD34DeIvIrUBB4J/EOqnoA6ANMcvZbClRR1TOYJuZ3zqDAjmTO8SDQSkTWASuB6o6g82IRWS8iY1R1FvAZsMTZbyqQx9GCmAKsAb4EFvnxnZ4GlgE/YYKuL5uBBcAPwH3Od/gfsBFY5UzTeBfbYgoZ+3C6JU1xmlQzVLVGevtiiTxsDc1isUQMtoZmsVgiBltDs1gsEYMNaBaLJWKwAc1isUQMNqBZLJaIwQY0i8USMdiAZrFYIob/BzGL0/vjFyEsAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"6d55360a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"numpy            : 1.22.2\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"pandas           : 1.2.5\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-densenet121-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"27500e8e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ee948974\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"7aadc729\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"2a488475\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"916685d3\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# DenseNet121 Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aeb94f20\",\n   \"metadata\": {},\n   \"source\": [\n    \"The network in this notebook is an implementation of the DenseNet-121 [1] architecture on the CIFAR-10[2] dataset.  \\n\",\n    \"\\n\",\n    \"The following figure illustrates the main concept of DenseNet: within each \\\"dense\\\" block, each layer is connected with each previous layer -- the feature maps are concatenated.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/densenet/densenet-fig-2.jpg)\\n\",\n    \"\\n\",\n    \"Note that this is somewhat related yet very different to ResNets. ResNets have skip connections approx. between every other layer (but don't connect all layers with each other). Also, ResNets skip connections work via addition\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{x}_{\\\\ell}=H_{\\\\ell}\\\\left(\\\\mathbf{X}_{\\\\ell-1}\\\\right)+\\\\mathbf{X}_{\\\\ell-1},$$\\n\",\n    \"\\n\",\n    \"whereas $H_{\\\\ell}(\\\\cdot)$ can be a composite function  of operations such as Batch Normalization (BN), rectified linear units (ReLU), Pooling, or Convolution (Conv).\\n\",\n    \"\\n\",\n    \"In DenseNets, all the previous feature maps $\\\\mathbf{X}_{0}, \\\\dots, \\\\mathbf{X}_{\\\\ell}-1$ of a feature map $\\\\mathbf{X}_{\\\\ell}$ are concatenated:\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{x}_{\\\\ell}=H_{\\\\ell}\\\\left(\\\\left[\\\\mathbf{x}_{0}, \\\\mathbf{x}_{1}, \\\\ldots, \\\\mathbf{x}_{\\\\ell-1}\\\\right]\\\\right).$$\\n\",\n    \"\\n\",\n    \"Furthermore, in this particular notebook, we are considering the DenseNet-121, which is depicted below:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/densenet/densenet-tab-1-dnet121.jpg)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Densely Connected Convolutional Networks, https://arxiv.org/abs/1608.06993\\n\",\n    \"- [2] https://en.wikipedia.org/wiki/CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7cdf49f9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7b24bb89\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"ed17d19c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 20\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 20\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"579ff844\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"814d3fac\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"93962a9b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"3c514273\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"# The following code cell that implements the DenseNet-121 architecture \\\\n# is a derivative of the code provided at \\\\n# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\\\\n\\\\nimport re\\\\nimport torch\\\\nimport torch.nn as nn\\\\nimport torch.nn.functional as F\\\\nimport torch.utils.checkpoint as cp\\\\nfrom collections import OrderedDict\\\\n\\\\n\\\\ndef _bn_function_factory(norm, relu, conv):\\\\n    def bn_function(*inputs):\\\\n        concated_features = torch.cat(inputs, 1)\\\\n        bottleneck_output = conv(relu(norm(concated_features)))\\\\n        return bottleneck_output\\\\n\\\\n    return bn_function\\\\n\\\\n\\\\nclass _DenseLayer(nn.Sequential):\\\\n    def __init__(self, num_input_features, growth_rate,\\\\n                 bn_size, drop_rate, memory_efficient=False):\\\\n        super(_DenseLayer, self).__init__()\\\\n        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\\\\n        self.add_module('relu1', nn.ReLU(inplace=True)),\\\\n        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *\\\\n                                           growth_rate, kernel_size=1, stride=1,\\\\n                                           bias=False)),\\\\n        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),\\\\n        self.add_module('relu2', nn.ReLU(inplace=True)),\\\\n        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,\\\\n                                           kernel_size=3, stride=1, padding=1,\\\\n                                           bias=False)),\\\\n        self.drop_rate = drop_rate\\\\n        self.memory_efficient = memory_efficient\\\\n\\\\n    def forward(self, *prev_features):\\\\n        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\\\\n        if self.memory_efficient and any(\\\\n                prev_feature.requires_grad for \\\\n                prev_feature in prev_features):\\\\n            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\\\\n        else:\\\\n            bottleneck_output = bn_function(*prev_features)\\\\n        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\\\\n        if self.drop_rate > 0:\\\\n            new_features = F.dropout(new_features, p=self.drop_rate,\\\\n                                     training=self.training)\\\\n        return new_features\\\\n\\\\n\\\\nclass _DenseBlock(nn.Module):\\\\n    def __init__(self, num_layers, num_input_features, \\\\n                 bn_size, growth_rate, drop_rate, memory_efficient=False):\\\\n        super(_DenseBlock, self).__init__()\\\\n        for i in range(num_layers):\\\\n            layer = _DenseLayer(\\\\n                num_input_features + i * growth_rate,\\\\n                growth_rate=growth_rate,\\\\n                bn_size=bn_size,\\\\n                drop_rate=drop_rate,\\\\n                memory_efficient=memory_efficient,\\\\n            )\\\\n            self.add_module('denselayer%d' % (i + 1), layer)\\\\n\\\\n    def forward(self, init_features):\\\\n        features = [init_features]\\\\n        for name, layer in self.named_children():\\\\n            new_features = layer(*features)\\\\n            features.append(new_features)\\\\n        return torch.cat(features, 1)\\\\n\\\\n\\\\nclass _Transition(nn.Sequential):\\\\n    def __init__(self, num_input_features, num_output_features):\\\\n        super(_Transition, self).__init__()\\\\n        self.add_module('norm', nn.BatchNorm2d(num_input_features))\\\\n        self.add_module('relu', nn.ReLU(inplace=True))\\\\n        self.add_module('conv', nn.Conv2d(\\\\n            num_input_features, num_output_features,\\\\n            kernel_size=1, stride=1, bias=False))\\\\n        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))\\\\n\\\\n\\\\nclass PyTorchModel(nn.Module):\\\\n    r\\\\\\\"\\\\\\\"\\\\\\\"Densenet-BC model class, based on\\\\n    `\\\\\\\"Densely Connected Convolutional Networks\\\\\\\"\\\\n    <https://arxiv.org/pdf/1608.06993.pdf>`_\\\\n\\\\n    Args:\\\\n        growth_rate (int) - how many filters to add each layer (`k` in paper)\\\\n        block_config (list of 4 ints) - how many layers in each pooling block\\\\n        num_init_featuremaps (int) - the number of filters to learn in the \\\\n        first convolution layer bn_size (int) - multiplicative factor for \\\\n        number of bottle neck layers (i.e. bn_size * k features \\\\n        in the bottleneck layer) drop_rate (float) - dropout rate after \\\\n        each dense layer num_classes (int) - number of classification classes\\\\n        memory_efficient (bool) - If True, uses checkpointing. \\\\n        Much more memory efficient, but slower. Default: *False*. \\\\n        See `\\\\\\\"paper\\\\\\\" <https://arxiv.org/pdf/1707.06990.pdf>`_\\\\n    \\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\n    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),\\\\n                 num_init_featuremaps=64, bn_size=4, drop_rate=0, \\\\n                 num_classes=1000, memory_efficient=False,\\\\n                 grayscale=False):\\\\n\\\\n        super().__init__()\\\\n\\\\n        # First convolution\\\\n        if grayscale:\\\\n            in_channels = 1\\\\n        else:\\\\n            in_channels = 3\\\\n        \\\\n        self.features = nn.Sequential(OrderedDict([\\\\n            ('conv0', nn.Conv2d(\\\\n                in_channels=in_channels,\\\\n                out_channels=num_init_featuremaps,\\\\n                kernel_size=7,\\\\n                stride=2,\\\\n                padding=3,\\\\n                bias=False)),  # bias is redundant when using batchnorm\\\\n            ('norm0', nn.BatchNorm2d(num_features=num_init_featuremaps)),\\\\n            ('relu0', nn.ReLU(inplace=True)),\\\\n            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\\\\n        ]))\\\\n\\\\n        # Each denseblock\\\\n        num_features = num_init_featuremaps\\\\n        for i, num_layers in enumerate(block_config):\\\\n            block = _DenseBlock(\\\\n                num_layers=num_layers,\\\\n                num_input_features=num_features,\\\\n                bn_size=bn_size,\\\\n                growth_rate=growth_rate,\\\\n                drop_rate=drop_rate,\\\\n                memory_efficient=memory_efficient\\\\n            )\\\\n            self.features.add_module('denseblock%d' % (i + 1), block)\\\\n            num_features = num_features + num_layers * growth_rate\\\\n            if i != len(block_config) - 1:\\\\n                trans = _Transition(num_input_features=num_features,\\\\n                                    num_output_features=num_features // 2)\\\\n                self.features.add_module('transition%d' % (i + 1), trans)\\\\n                num_features = num_features // 2\\\\n\\\\n        # Final batch norm\\\\n        self.features.add_module('norm5', nn.BatchNorm2d(num_features))\\\\n\\\\n        # Linear layer\\\\n        self.classifier = nn.Linear(num_features, num_classes)\\\\n\\\\n        # Official init from torch repo.\\\\n        for m in self.modules():\\\\n            if isinstance(m, nn.Conv2d):\\\\n                nn.init.kaiming_normal_(m.weight)\\\\n            elif isinstance(m, nn.BatchNorm2d):\\\\n                nn.init.constant_(m.weight, 1)\\\\n                nn.init.constant_(m.bias, 0)\\\\n            elif isinstance(m, nn.Linear):\\\\n                nn.init.constant_(m.bias, 0)\\\\n\\\\n    def forward(self, x):\\\\n        features = self.features(x)\\\\n        out = F.relu(features, inplace=True)\\\\n        out = F.adaptive_avg_pool2d(out, (1, 1))\\\\n        out = torch.flatten(out, 1)\\\\n        logits = self.classifier(out)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# The following code cell that implements the DenseNet-121 architecture\\\\n# is a derivative of the code provided at\\\\n# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\\\\n\\\\nimport re\\\\nimport torch\\\\nimport torch.nn as nn\\\\nimport torch.nn.functional as F\\\\nimport torch.utils.checkpoint as cp\\\\nfrom collections import OrderedDict\\\\n\\\\n\\\\ndef _bn_function_factory(norm, relu, conv):\\\\n    def bn_function(*inputs):\\\\n        concated_features = torch.cat(inputs, 1)\\\\n        bottleneck_output = conv(relu(norm(concated_features)))\\\\n        return bottleneck_output\\\\n\\\\n    return bn_function\\\\n\\\\n\\\\nclass _DenseLayer(nn.Sequential):\\\\n    def __init__(\\\\n        self,\\\\n        num_input_features,\\\\n        growth_rate,\\\\n        bn_size,\\\\n        drop_rate,\\\\n        memory_efficient=False,\\\\n    ):\\\\n        super(_DenseLayer, self).__init__()\\\\n        self.add_module(\\\\\\\"norm1\\\\\\\", nn.BatchNorm2d(num_input_features)),\\\\n        self.add_module(\\\\\\\"relu1\\\\\\\", nn.ReLU(inplace=True)),\\\\n        self.add_module(\\\\n            \\\\\\\"conv1\\\\\\\",\\\\n            nn.Conv2d(\\\\n                num_input_features,\\\\n                bn_size * growth_rate,\\\\n                kernel_size=1,\\\\n                stride=1,\\\\n                bias=False,\\\\n            ),\\\\n        ),\\\\n        self.add_module(\\\\\\\"norm2\\\\\\\", nn.BatchNorm2d(bn_size * growth_rate)),\\\\n        self.add_module(\\\\\\\"relu2\\\\\\\", nn.ReLU(inplace=True)),\\\\n        self.add_module(\\\\n            \\\\\\\"conv2\\\\\\\",\\\\n            nn.Conv2d(\\\\n                bn_size * growth_rate,\\\\n                growth_rate,\\\\n                kernel_size=3,\\\\n                stride=1,\\\\n                padding=1,\\\\n                bias=False,\\\\n            ),\\\\n        ),\\\\n        self.drop_rate = drop_rate\\\\n        self.memory_efficient = memory_efficient\\\\n\\\\n    def forward(self, *prev_features):\\\\n        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\\\\n        if self.memory_efficient and any(\\\\n            prev_feature.requires_grad for prev_feature in prev_features\\\\n        ):\\\\n            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\\\\n        else:\\\\n            bottleneck_output = bn_function(*prev_features)\\\\n        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\\\\n        if self.drop_rate > 0:\\\\n            new_features = F.dropout(\\\\n                new_features, p=self.drop_rate, training=self.training\\\\n            )\\\\n        return new_features\\\\n\\\\n\\\\nclass _DenseBlock(nn.Module):\\\\n    def __init__(\\\\n        self,\\\\n        num_layers,\\\\n        num_input_features,\\\\n        bn_size,\\\\n        growth_rate,\\\\n        drop_rate,\\\\n        memory_efficient=False,\\\\n    ):\\\\n        super(_DenseBlock, self).__init__()\\\\n        for i in range(num_layers):\\\\n            layer = _DenseLayer(\\\\n                num_input_features + i * growth_rate,\\\\n                growth_rate=growth_rate,\\\\n                bn_size=bn_size,\\\\n                drop_rate=drop_rate,\\\\n                memory_efficient=memory_efficient,\\\\n            )\\\\n            self.add_module(\\\\\\\"denselayer%d\\\\\\\" % (i + 1), layer)\\\\n\\\\n    def forward(self, init_features):\\\\n        features = [init_features]\\\\n        for name, layer in self.named_children():\\\\n            new_features = layer(*features)\\\\n            features.append(new_features)\\\\n        return torch.cat(features, 1)\\\\n\\\\n\\\\nclass _Transition(nn.Sequential):\\\\n    def __init__(self, num_input_features, num_output_features):\\\\n        super(_Transition, self).__init__()\\\\n        self.add_module(\\\\\\\"norm\\\\\\\", nn.BatchNorm2d(num_input_features))\\\\n        self.add_module(\\\\\\\"relu\\\\\\\", nn.ReLU(inplace=True))\\\\n        self.add_module(\\\\n            \\\\\\\"conv\\\\\\\",\\\\n            nn.Conv2d(\\\\n                num_input_features,\\\\n                num_output_features,\\\\n                kernel_size=1,\\\\n                stride=1,\\\\n                bias=False,\\\\n            ),\\\\n        )\\\\n        self.add_module(\\\\\\\"pool\\\\\\\", nn.AvgPool2d(kernel_size=2, stride=2))\\\\n\\\\n\\\\nclass PyTorchModel(nn.Module):\\\\n    r\\\\\\\"\\\\\\\"\\\\\\\"Densenet-BC model class, based on\\\\n    `\\\\\\\"Densely Connected Convolutional Networks\\\\\\\"\\\\n    <https://arxiv.org/pdf/1608.06993.pdf>`_\\\\n\\\\n    Args:\\\\n        growth_rate (int) - how many filters to add each layer (`k` in paper)\\\\n        block_config (list of 4 ints) - how many layers in each pooling block\\\\n        num_init_featuremaps (int) - the number of filters to learn in the\\\\n        first convolution layer bn_size (int) - multiplicative factor for\\\\n        number of bottle neck layers (i.e. bn_size * k features\\\\n        in the bottleneck layer) drop_rate (float) - dropout rate after\\\\n        each dense layer num_classes (int) - number of classification classes\\\\n        memory_efficient (bool) - If True, uses checkpointing.\\\\n        Much more memory efficient, but slower. Default: *False*.\\\\n        See `\\\\\\\"paper\\\\\\\" <https://arxiv.org/pdf/1707.06990.pdf>`_\\\\n    \\\\\\\"\\\\\\\"\\\\\\\"\\\\n\\\\n    def __init__(\\\\n        self,\\\\n        growth_rate=32,\\\\n        block_config=(6, 12, 24, 16),\\\\n        num_init_featuremaps=64,\\\\n        bn_size=4,\\\\n        drop_rate=0,\\\\n        num_classes=1000,\\\\n        memory_efficient=False,\\\\n        grayscale=False,\\\\n    ):\\\\n\\\\n        super().__init__()\\\\n\\\\n        # First convolution\\\\n        if grayscale:\\\\n            in_channels = 1\\\\n        else:\\\\n            in_channels = 3\\\\n\\\\n        self.features = nn.Sequential(\\\\n            OrderedDict(\\\\n                [\\\\n                    (\\\\n                        \\\\\\\"conv0\\\\\\\",\\\\n                        nn.Conv2d(\\\\n                            in_channels=in_channels,\\\\n                            out_channels=num_init_featuremaps,\\\\n                            kernel_size=7,\\\\n                            stride=2,\\\\n                            padding=3,\\\\n                            bias=False,\\\\n                        ),\\\\n                    ),  # bias is redundant when using batchnorm\\\\n                    (\\\\\\\"norm0\\\\\\\", nn.BatchNorm2d(num_features=num_init_featuremaps)),\\\\n                    (\\\\\\\"relu0\\\\\\\", nn.ReLU(inplace=True)),\\\\n                    (\\\\\\\"pool0\\\\\\\", nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\\\\n                ]\\\\n            )\\\\n        )\\\\n\\\\n        # Each denseblock\\\\n        num_features = num_init_featuremaps\\\\n        for i, num_layers in enumerate(block_config):\\\\n            block = _DenseBlock(\\\\n                num_layers=num_layers,\\\\n                num_input_features=num_features,\\\\n                bn_size=bn_size,\\\\n                growth_rate=growth_rate,\\\\n                drop_rate=drop_rate,\\\\n                memory_efficient=memory_efficient,\\\\n            )\\\\n            self.features.add_module(\\\\\\\"denseblock%d\\\\\\\" % (i + 1), block)\\\\n            num_features = num_features + num_layers * growth_rate\\\\n            if i != len(block_config) - 1:\\\\n                trans = _Transition(\\\\n                    num_input_features=num_features,\\\\n                    num_output_features=num_features // 2,\\\\n                )\\\\n                self.features.add_module(\\\\\\\"transition%d\\\\\\\" % (i + 1), trans)\\\\n                num_features = num_features // 2\\\\n\\\\n        # Final batch norm\\\\n        self.features.add_module(\\\\\\\"norm5\\\\\\\", nn.BatchNorm2d(num_features))\\\\n\\\\n        # Linear layer\\\\n        self.classifier = nn.Linear(num_features, num_classes)\\\\n\\\\n        # Official init from torch repo.\\\\n        for m in self.modules():\\\\n            if isinstance(m, nn.Conv2d):\\\\n                nn.init.kaiming_normal_(m.weight)\\\\n            elif isinstance(m, nn.BatchNorm2d):\\\\n                nn.init.constant_(m.weight, 1)\\\\n                nn.init.constant_(m.bias, 0)\\\\n            elif isinstance(m, nn.Linear):\\\\n                nn.init.constant_(m.bias, 0)\\\\n\\\\n    def forward(self, x):\\\\n        features = self.features(x)\\\\n        out = F.relu(features, inplace=True)\\\\n        out = F.adaptive_avg_pool2d(out, (1, 1))\\\\n        out = torch.flatten(out, 1)\\\\n        logits = self.classifier(out)\\\\n        return logits\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# The following code cell that implements the DenseNet-121 architecture \\n\",\n    \"# is a derivative of the code provided at \\n\",\n    \"# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\\n\",\n    \"\\n\",\n    \"import re\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.utils.checkpoint as cp\\n\",\n    \"from collections import OrderedDict\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def _bn_function_factory(norm, relu, conv):\\n\",\n    \"    def bn_function(*inputs):\\n\",\n    \"        concated_features = torch.cat(inputs, 1)\\n\",\n    \"        bottleneck_output = conv(relu(norm(concated_features)))\\n\",\n    \"        return bottleneck_output\\n\",\n    \"\\n\",\n    \"    return bn_function\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _DenseLayer(nn.Sequential):\\n\",\n    \"    def __init__(self, num_input_features, growth_rate,\\n\",\n    \"                 bn_size, drop_rate, memory_efficient=False):\\n\",\n    \"        super(_DenseLayer, self).__init__()\\n\",\n    \"        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\\n\",\n    \"        self.add_module('relu1', nn.ReLU(inplace=True)),\\n\",\n    \"        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *\\n\",\n    \"                                           growth_rate, kernel_size=1, stride=1,\\n\",\n    \"                                           bias=False)),\\n\",\n    \"        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),\\n\",\n    \"        self.add_module('relu2', nn.ReLU(inplace=True)),\\n\",\n    \"        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,\\n\",\n    \"                                           kernel_size=3, stride=1, padding=1,\\n\",\n    \"                                           bias=False)),\\n\",\n    \"        self.drop_rate = drop_rate\\n\",\n    \"        self.memory_efficient = memory_efficient\\n\",\n    \"\\n\",\n    \"    def forward(self, *prev_features):\\n\",\n    \"        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\\n\",\n    \"        if self.memory_efficient and any(\\n\",\n    \"                prev_feature.requires_grad for \\n\",\n    \"                prev_feature in prev_features):\\n\",\n    \"            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\\n\",\n    \"        else:\\n\",\n    \"            bottleneck_output = bn_function(*prev_features)\\n\",\n    \"        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\\n\",\n    \"        if self.drop_rate > 0:\\n\",\n    \"            new_features = F.dropout(new_features, p=self.drop_rate,\\n\",\n    \"                                     training=self.training)\\n\",\n    \"        return new_features\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _DenseBlock(nn.Module):\\n\",\n    \"    def __init__(self, num_layers, num_input_features, \\n\",\n    \"                 bn_size, growth_rate, drop_rate, memory_efficient=False):\\n\",\n    \"        super(_DenseBlock, self).__init__()\\n\",\n    \"        for i in range(num_layers):\\n\",\n    \"            layer = _DenseLayer(\\n\",\n    \"                num_input_features + i * growth_rate,\\n\",\n    \"                growth_rate=growth_rate,\\n\",\n    \"                bn_size=bn_size,\\n\",\n    \"                drop_rate=drop_rate,\\n\",\n    \"                memory_efficient=memory_efficient,\\n\",\n    \"            )\\n\",\n    \"            self.add_module('denselayer%d' % (i + 1), layer)\\n\",\n    \"\\n\",\n    \"    def forward(self, init_features):\\n\",\n    \"        features = [init_features]\\n\",\n    \"        for name, layer in self.named_children():\\n\",\n    \"            new_features = layer(*features)\\n\",\n    \"            features.append(new_features)\\n\",\n    \"        return torch.cat(features, 1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _Transition(nn.Sequential):\\n\",\n    \"    def __init__(self, num_input_features, num_output_features):\\n\",\n    \"        super(_Transition, self).__init__()\\n\",\n    \"        self.add_module('norm', nn.BatchNorm2d(num_input_features))\\n\",\n    \"        self.add_module('relu', nn.ReLU(inplace=True))\\n\",\n    \"        self.add_module('conv', nn.Conv2d(\\n\",\n    \"            num_input_features, num_output_features,\\n\",\n    \"            kernel_size=1, stride=1, bias=False))\\n\",\n    \"        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchModel(nn.Module):\\n\",\n    \"    r\\\"\\\"\\\"Densenet-BC model class, based on\\n\",\n    \"    `\\\"Densely Connected Convolutional Networks\\\"\\n\",\n    \"    <https://arxiv.org/pdf/1608.06993.pdf>`_\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        growth_rate (int) - how many filters to add each layer (`k` in paper)\\n\",\n    \"        block_config (list of 4 ints) - how many layers in each pooling block\\n\",\n    \"        num_init_featuremaps (int) - the number of filters to learn in the \\n\",\n    \"        first convolution layer bn_size (int) - multiplicative factor for \\n\",\n    \"        number of bottle neck layers (i.e. bn_size * k features \\n\",\n    \"        in the bottleneck layer) drop_rate (float) - dropout rate after \\n\",\n    \"        each dense layer num_classes (int) - number of classification classes\\n\",\n    \"        memory_efficient (bool) - If True, uses checkpointing. \\n\",\n    \"        Much more memory efficient, but slower. Default: *False*. \\n\",\n    \"        See `\\\"paper\\\" <https://arxiv.org/pdf/1707.06990.pdf>`_\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),\\n\",\n    \"                 num_init_featuremaps=64, bn_size=4, drop_rate=0, \\n\",\n    \"                 num_classes=1000, memory_efficient=False,\\n\",\n    \"                 grayscale=False):\\n\",\n    \"\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # First convolution\\n\",\n    \"        if grayscale:\\n\",\n    \"            in_channels = 1\\n\",\n    \"        else:\\n\",\n    \"            in_channels = 3\\n\",\n    \"        \\n\",\n    \"        self.features = nn.Sequential(OrderedDict([\\n\",\n    \"            ('conv0', nn.Conv2d(\\n\",\n    \"                in_channels=in_channels,\\n\",\n    \"                out_channels=num_init_featuremaps,\\n\",\n    \"                kernel_size=7,\\n\",\n    \"                stride=2,\\n\",\n    \"                padding=3,\\n\",\n    \"                bias=False)),  # bias is redundant when using batchnorm\\n\",\n    \"            ('norm0', nn.BatchNorm2d(num_features=num_init_featuremaps)),\\n\",\n    \"            ('relu0', nn.ReLU(inplace=True)),\\n\",\n    \"            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\\n\",\n    \"        ]))\\n\",\n    \"\\n\",\n    \"        # Each denseblock\\n\",\n    \"        num_features = num_init_featuremaps\\n\",\n    \"        for i, num_layers in enumerate(block_config):\\n\",\n    \"            block = _DenseBlock(\\n\",\n    \"                num_layers=num_layers,\\n\",\n    \"                num_input_features=num_features,\\n\",\n    \"                bn_size=bn_size,\\n\",\n    \"                growth_rate=growth_rate,\\n\",\n    \"                drop_rate=drop_rate,\\n\",\n    \"                memory_efficient=memory_efficient\\n\",\n    \"            )\\n\",\n    \"            self.features.add_module('denseblock%d' % (i + 1), block)\\n\",\n    \"            num_features = num_features + num_layers * growth_rate\\n\",\n    \"            if i != len(block_config) - 1:\\n\",\n    \"                trans = _Transition(num_input_features=num_features,\\n\",\n    \"                                    num_output_features=num_features // 2)\\n\",\n    \"                self.features.add_module('transition%d' % (i + 1), trans)\\n\",\n    \"                num_features = num_features // 2\\n\",\n    \"\\n\",\n    \"        # Final batch norm\\n\",\n    \"        self.features.add_module('norm5', nn.BatchNorm2d(num_features))\\n\",\n    \"\\n\",\n    \"        # Linear layer\\n\",\n    \"        self.classifier = nn.Linear(num_features, num_classes)\\n\",\n    \"\\n\",\n    \"        # Official init from torch repo.\\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, nn.Conv2d):\\n\",\n    \"                nn.init.kaiming_normal_(m.weight)\\n\",\n    \"            elif isinstance(m, nn.BatchNorm2d):\\n\",\n    \"                nn.init.constant_(m.weight, 1)\\n\",\n    \"                nn.init.constant_(m.bias, 0)\\n\",\n    \"            elif isinstance(m, nn.Linear):\\n\",\n    \"                nn.init.constant_(m.bias, 0)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        features = self.features(x)\\n\",\n    \"        out = F.relu(features, inplace=True)\\n\",\n    \"        out = F.adaptive_avg_pool2d(out, (1, 1))\\n\",\n    \"        out = torch.flatten(out, 1)\\n\",\n    \"        logits = self.classifier(out)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"fdea77b7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"898ff217\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"737dc1c3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fc714f7d\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"4b81c954\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\",\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_cifar10_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bce7e382\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"14d85549\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"8ad1007e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 3\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8b19c08\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"f2e25203\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"872ac6a1\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7e108c5a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"e5dbdca8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path, download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.RandomCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.CenterCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=self.train_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=self.test_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"df6d2769\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"d9751726\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\n\\\\n\\\\ntorch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6592489d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"30cbbca2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"544db16f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model = PyTorchModel(num_classes=10)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"36da6429\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cab2f480\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"9a7804d8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 7.0 M \\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"7.0 M     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"7.0 M     Total params\\n\",\n      \"27.856    Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d69c6e26567342e7b7da0116be341b0a\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"8f9310ddb974472a9ad03af154d99c70\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 21.67 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"420f571a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"63c761bf\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"a17f048a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3bc12c76\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"6f14b73d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_14/checkpoints/epoch=16-step=2974.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_14/checkpoints/epoch=16-step=2974.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"afba40e70fa049698b41045251bd2ff6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.8289999961853027}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.8289999961853027}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"81d43277\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2e55397c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"56af2723\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_14/checkpoints/epoch=16-step=2974.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"c219951c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"56664cff\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"839be5f4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([7, 5, 8, 0, 8])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40953be4\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"2f5a368c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.8290 (82.90%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"39d97bc8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c239efb2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"eac623cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"class_dict = {0: 'airplane',\\\\n              1: 'automobile',\\\\n              2: 'bird',\\\\n              3: 'cat',\\\\n              4: 'deer',\\\\n              5: 'dog',\\\\n              6: 'frog',\\\\n              7: 'horse',\\\\n              8: 'ship',\\\\n              9: 'truck'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"class_dict = {\\\\n    0: \\\\\\\"airplane\\\\\\\",\\\\n    1: \\\\\\\"automobile\\\\\\\",\\\\n    2: \\\\\\\"bird\\\\\\\",\\\\n    3: \\\\\\\"cat\\\\\\\",\\\\n    4: \\\\\\\"deer\\\\\\\",\\\\n    5: \\\\\\\"dog\\\\\\\",\\\\n    6: \\\\\\\"frog\\\\\\\",\\\\n    7: \\\\\\\"horse\\\\\\\",\\\\n    8: \\\\\\\"ship\\\\\\\",\\\\n    9: \\\\\\\"truck\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"9dc59be0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c734323b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"e4931576\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"e7426e8f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"re               : 2.2.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-densenet121-mnist.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"d4312f94\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.10\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"fd249fbd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4fed5208\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- DenseNet121 Trained on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5445100c-532b-4bc6-b8ce-4dbcb014e70f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Network Architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bf7a7f81-98a8-418f-8983-8f99abfd9c42\",\n   \"metadata\": {},\n   \"source\": [\n    \"The network in this notebook is an implementation of the DenseNet-121 [1] architecture on the MNIST digits dataset (http://yann.lecun.com/exdb/mnist/) to train a handwritten digit classifier.  \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b9d81a55-d071-4a6e-ae9e-6e22823a8ecf\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following figure illustrates the main concept of DenseNet: within each \\\"dense\\\" block, each layer is connected with each previous layer -- the feature maps are concatenated.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/densenet/densenet-fig-2.jpg)\\n\",\n    \"\\n\",\n    \"Note that this is somewhat related yet very different to ResNets. ResNets have skip connections approx. between every other layer (but don't connect all layers with each other). Also, ResNets skip connections work via addition\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{x}_{\\\\ell}=H_{\\\\ell}\\\\left(\\\\mathbf{X}_{\\\\ell-1}\\\\right)+\\\\mathbf{X}_{\\\\ell-1}$$,\\n\",\n    \"\\n\",\n    \"whereas $H_{\\\\ell}(\\\\cdot)$ can be a composite function  of operations such as Batch Normalization (BN), rectified linear units (ReLU), Pooling, or Convolution (Conv).\\n\",\n    \"\\n\",\n    \"In DenseNets, all the previous feature maps $\\\\mathbf{X}_{0}, \\\\dots, \\\\mathbf{X}_{\\\\ell}-1$ of a feature map $\\\\mathbf{X}_{\\\\ell}$ are concatenated:\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{x}_{\\\\ell}=H_{\\\\ell}\\\\left(\\\\left[\\\\mathbf{x}_{0}, \\\\mathbf{x}_{1}, \\\\ldots, \\\\mathbf{x}_{\\\\ell-1}\\\\right]\\\\right).$$\\n\",\n    \"\\n\",\n    \"Furthermore, in this particular notebook, we are considering the DenseNet-121, which is depicted below:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/densenet/densenet-tab-1-dnet121.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9d036f2f-4246-495e-b874-449b4e811722\",\n   \"metadata\": {},\n   \"source\": [\n    \"**References**\\n\",\n    \"    \\n\",\n    \"- [1] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708), http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html\\n\",\n    \"\\n\",\n    \"- [2] http://yann.lecun.com/exdb/mnist/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ab5f0e1e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ca269eeb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"54fa873b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eec6da61\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d1dca95d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- We start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"c073af3b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2:1: E266 too many leading '#' for block comment\\n\",\n      \"18:1: E303 too many blank lines (3)\\n\",\n      \"34:80: E501 line too long (80 > 79 characters)\\n\",\n      \"119:24: E225 missing whitespace around operator\\n\",\n      \"121:24: E225 missing whitespace around operator\\n\",\n      \"130:30: E261 at least two spaces before inline comment\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# The following code cell that implements the DenseNet-121 architecture \\n\",\n    \"# is a derivative of the code provided at \\n\",\n    \"# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\\n\",\n    \"\\n\",\n    \"import re\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.utils.checkpoint as cp\\n\",\n    \"from collections import OrderedDict\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def _bn_function_factory(norm, relu, conv):\\n\",\n    \"    def bn_function(*inputs):\\n\",\n    \"        concated_features = torch.cat(inputs, 1)\\n\",\n    \"        bottleneck_output = conv(relu(norm(concated_features)))\\n\",\n    \"        return bottleneck_output\\n\",\n    \"\\n\",\n    \"    return bn_function\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _DenseLayer(nn.Sequential):\\n\",\n    \"    def __init__(self, num_input_features, growth_rate,\\n\",\n    \"                 bn_size, drop_rate, memory_efficient=False):\\n\",\n    \"        super(_DenseLayer, self).__init__()\\n\",\n    \"        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\\n\",\n    \"        self.add_module('relu1', nn.ReLU(inplace=True)),\\n\",\n    \"        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *\\n\",\n    \"                                           growth_rate, kernel_size=1, stride=1,\\n\",\n    \"                                           bias=False)),\\n\",\n    \"        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),\\n\",\n    \"        self.add_module('relu2', nn.ReLU(inplace=True)),\\n\",\n    \"        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,\\n\",\n    \"                                           kernel_size=3, stride=1, padding=1,\\n\",\n    \"                                           bias=False)),\\n\",\n    \"        self.drop_rate = drop_rate\\n\",\n    \"        self.memory_efficient = memory_efficient\\n\",\n    \"\\n\",\n    \"    def forward(self, *prev_features):\\n\",\n    \"        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\\n\",\n    \"        if self.memory_efficient and any(\\n\",\n    \"                prev_feature.requires_grad for \\n\",\n    \"                prev_feature in prev_features):\\n\",\n    \"            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\\n\",\n    \"        else:\\n\",\n    \"            bottleneck_output = bn_function(*prev_features)\\n\",\n    \"        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\\n\",\n    \"        if self.drop_rate > 0:\\n\",\n    \"            new_features = F.dropout(new_features, p=self.drop_rate,\\n\",\n    \"                                     training=self.training)\\n\",\n    \"        return new_features\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _DenseBlock(nn.Module):\\n\",\n    \"    def __init__(self, num_layers, num_input_features, \\n\",\n    \"                 bn_size, growth_rate, drop_rate, memory_efficient=False):\\n\",\n    \"        super(_DenseBlock, self).__init__()\\n\",\n    \"        for i in range(num_layers):\\n\",\n    \"            layer = _DenseLayer(\\n\",\n    \"                num_input_features + i * growth_rate,\\n\",\n    \"                growth_rate=growth_rate,\\n\",\n    \"                bn_size=bn_size,\\n\",\n    \"                drop_rate=drop_rate,\\n\",\n    \"                memory_efficient=memory_efficient,\\n\",\n    \"            )\\n\",\n    \"            self.add_module('denselayer%d' % (i + 1), layer)\\n\",\n    \"\\n\",\n    \"    def forward(self, init_features):\\n\",\n    \"        features = [init_features]\\n\",\n    \"        for name, layer in self.named_children():\\n\",\n    \"            new_features = layer(*features)\\n\",\n    \"            features.append(new_features)\\n\",\n    \"        return torch.cat(features, 1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _Transition(nn.Sequential):\\n\",\n    \"    def __init__(self, num_input_features, num_output_features):\\n\",\n    \"        super(_Transition, self).__init__()\\n\",\n    \"        self.add_module('norm', nn.BatchNorm2d(num_input_features))\\n\",\n    \"        self.add_module('relu', nn.ReLU(inplace=True))\\n\",\n    \"        self.add_module('conv', nn.Conv2d(\\n\",\n    \"            num_input_features, num_output_features,\\n\",\n    \"            kernel_size=1, stride=1, bias=False))\\n\",\n    \"        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchDenseNet121(nn.Module):\\n\",\n    \"    r\\\"\\\"\\\"Densenet-BC model class, based on\\n\",\n    \"    `\\\"Densely Connected Convolutional Networks\\\"\\n\",\n    \"    <https://arxiv.org/pdf/1608.06993.pdf>`_\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        growth_rate (int) - how many filters to add each layer (`k` in paper)\\n\",\n    \"        block_config (list of 4 ints) - how many layers in each pooling block\\n\",\n    \"        num_init_featuremaps (int) - the number of filters to learn in the \\n\",\n    \"        first convolution layer bn_size (int) - multiplicative factor for \\n\",\n    \"        number of bottle neck layers (i.e. bn_size * k features \\n\",\n    \"        in the bottleneck layer) drop_rate (float) - dropout rate after \\n\",\n    \"        each dense layer num_classes (int) - number of classification classes\\n\",\n    \"        memory_efficient (bool) - If True, uses checkpointing. \\n\",\n    \"        Much more memory efficient, but slower. Default: *False*. \\n\",\n    \"        See `\\\"paper\\\" <https://arxiv.org/pdf/1707.06990.pdf>`_\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),\\n\",\n    \"                 num_init_featuremaps=64, bn_size=4, drop_rate=0, \\n\",\n    \"                 num_classes=1000, memory_efficient=False,\\n\",\n    \"                 grayscale=False):\\n\",\n    \"\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # First convolution\\n\",\n    \"        if grayscale:\\n\",\n    \"            in_channels=1\\n\",\n    \"        else:\\n\",\n    \"            in_channels=3\\n\",\n    \"        \\n\",\n    \"        self.features = nn.Sequential(OrderedDict([\\n\",\n    \"            ('conv0', nn.Conv2d(\\n\",\n    \"                in_channels=in_channels,\\n\",\n    \"                out_channels=num_init_featuremaps,\\n\",\n    \"                kernel_size=7,\\n\",\n    \"                stride=2,\\n\",\n    \"                padding=3,\\n\",\n    \"                bias=False)), # bias is redundant when using batchnorm\\n\",\n    \"            ('norm0', nn.BatchNorm2d(num_features=num_init_featuremaps)),\\n\",\n    \"            ('relu0', nn.ReLU(inplace=True)),\\n\",\n    \"            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\\n\",\n    \"        ]))\\n\",\n    \"\\n\",\n    \"        # Each denseblock\\n\",\n    \"        num_features = num_init_featuremaps\\n\",\n    \"        for i, num_layers in enumerate(block_config):\\n\",\n    \"            block = _DenseBlock(\\n\",\n    \"                num_layers=num_layers,\\n\",\n    \"                num_input_features=num_features,\\n\",\n    \"                bn_size=bn_size,\\n\",\n    \"                growth_rate=growth_rate,\\n\",\n    \"                drop_rate=drop_rate,\\n\",\n    \"                memory_efficient=memory_efficient\\n\",\n    \"            )\\n\",\n    \"            self.features.add_module('denseblock%d' % (i + 1), block)\\n\",\n    \"            num_features = num_features + num_layers * growth_rate\\n\",\n    \"            if i != len(block_config) - 1:\\n\",\n    \"                trans = _Transition(num_input_features=num_features,\\n\",\n    \"                                    num_output_features=num_features // 2)\\n\",\n    \"                self.features.add_module('transition%d' % (i + 1), trans)\\n\",\n    \"                num_features = num_features // 2\\n\",\n    \"\\n\",\n    \"        # Final batch norm\\n\",\n    \"        self.features.add_module('norm5', nn.BatchNorm2d(num_features))\\n\",\n    \"\\n\",\n    \"        # Linear layer\\n\",\n    \"        self.classifier = nn.Linear(num_features, num_classes)\\n\",\n    \"\\n\",\n    \"        # Official init from torch repo.\\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, nn.Conv2d):\\n\",\n    \"                nn.init.kaiming_normal_(m.weight)\\n\",\n    \"            elif isinstance(m, nn.BatchNorm2d):\\n\",\n    \"                nn.init.constant_(m.weight, 1)\\n\",\n    \"                nn.init.constant_(m.bias, 0)\\n\",\n    \"            elif isinstance(m, nn.Linear):\\n\",\n    \"                nn.init.constant_(m.bias, 0)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        features = self.features(x)\\n\",\n    \"        out = F.relu(features, inplace=True)\\n\",\n    \"        out = F.adaptive_avg_pool2d(out, (1, 1))\\n\",\n    \"        out = torch.flatten(out, 1)\\n\",\n    \"        logits = self.classifier(out)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"040cf1e2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7c0e755b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a7d20b72\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c55dbaed\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"dce74462\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(root='./data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='./data', \\n\",\n    \"                              train=False,\\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"all_train_labels = []\\n\",\n    \"all_test_labels = []\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    all_train_labels.append(labels)\\n\",\n    \"all_train_labels = torch.cat(all_train_labels)\\n\",\n    \"    \\n\",\n    \"for images, labels in test_loader:  \\n\",\n    \"    all_test_labels.append(labels)\\n\",\n    \"all_test_labels = torch.cat(all_test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"552008d6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training labels: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\",\n      \"Training label distribution: tensor([5914, 6729, 5948, 6123, 5827, 5413, 5909, 6256, 5845, 5940])\\n\",\n      \"\\n\",\n      \"Test labels: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\",\n      \"Test label distribution: tensor([ 980, 1135, 1032, 1010,  982,  892,  958, 1028,  974, 1009])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Training labels:', torch.unique(all_train_labels))\\n\",\n    \"print('Training label distribution:', torch.bincount(all_train_labels))\\n\",\n    \"\\n\",\n    \"print('\\\\nTest labels:', torch.unique(all_test_labels))\\n\",\n    \"print('Test label distribution:', torch.bincount(all_test_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4ebda321\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cd70fdad\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"c9a9b881\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline ACC: 11.35%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"majority_prediction = torch.argmax(torch.bincount(all_test_labels))\\n\",\n    \"baseline_acc = torch.mean((all_test_labels == majority_prediction).float())\\n\",\n    \"print(f'Baseline ACC: {baseline_acc*100:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f760a26d\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"35e59891\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"4584dd13\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path,\\n\",\n    \"                       download=True)\\n\",\n    \"        self.resize_transform = transforms.Compose(\\n\",\n    \"            [transforms.Resize((32, 32)),\\n\",\n    \"             transforms.ToTensor()])\\n\",\n    \"        \\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(root=self.data_path, \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=self.resize_transform,\\n\",\n    \"                               download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(root=self.data_path, \\n\",\n    \"                                   train=False, \\n\",\n    \"                                   transform=self.resize_transform,\\n\",\n    \"                                   download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"259e866c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"c94ae789\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dfeec84f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"430baf56\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"b487c4ee\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchDenseNet121(grayscale=True)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"219ad684\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"b52cc6f1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type               | Params\\n\",\n      \"-------------------------------------------------\\n\",\n      \"0 | model     | PyTorchDenseNet121 | 8.0 M \\n\",\n      \"1 | train_acc | Accuracy           | 0     \\n\",\n      \"2 | valid_acc | Accuracy           | 0     \\n\",\n      \"3 | test_acc  | Accuracy           | 0     \\n\",\n      \"-------------------------------------------------\\n\",\n      \"8.0 M     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"8.0 M     Total params\\n\",\n      \"31.890    Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1957d688968f4f6db4cc9d9bef6cf8c6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 9.59 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"17f10432\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ecab8c55\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"7a5c1cbf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"128705f2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"4ebf782c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_13/checkpoints/epoch=19-step=4279.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_13/checkpoints/epoch=19-step=4279.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b6d15bbfa3ab4c86a91488ebfdcb5a94\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.9911999702453613}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9911999702453613}]\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"354b53ad\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"77512166\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"24c0a90c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_13/checkpoints/epoch=19-step=4279.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"07d994cf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b822fe2c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"ec894236\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([7, 2, 1, 0, 4])\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"         logits = lightning_model(features)\\n\",\n    \"    \\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b7301365\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"e08e8836\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9912 (99.12%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-he-init.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"14749f84\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"41a16fc7\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"3434d08b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"3b3ef378\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"11172ece\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"#  A Basic Convolutional Neural Network Trained on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e1786096\",\n   \"metadata\": {},\n   \"source\": [\n    \"A basic convolutional neural network [1][2] trained on MNIST [3]. The weights are manually re-initialized using Kaiming He initialization scheme [4].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] L13.3 Convolutional Neural Network Basics (18:39), https://www.youtube.com/watch?v=7fWOE-z8YgY\\n\",\n    \"- [2] https://en.wikipedia.org/wiki/Convolutional_neural_network\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/MNIST_database\\n\",\n    \"- [4] Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, https://openaccess.thecvf.com/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"323d276f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8b6230f0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"74a29375\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8fb4f14e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"760684c1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b327d9ab\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"e0e79521\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class PyTorchModel(torch.nn.Module):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        # Calculate \\\"same\\\" padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            # 28x28x1 => 28x28x8\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=1,\\n\",\n    \"                out_channels=8,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1),  # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            \\n\",\n    \"            # 28x28x8 => 14x14x8\\n\",\n    \"            torch.nn.MaxPool2d(\\n\",\n    \"                kernel_size=(2, 2),\\n\",\n    \"                stride=(2, 2),\\n\",\n    \"                padding=0),  # (2(14-1) - 28 + 2) = 0\\n\",\n    \"            \\n\",\n    \"            # 14x14x8 => 14x14x16\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=8,\\n\",\n    \"                out_channels=16,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1),  # (1(14-1) - 14 + 3) / 2 = 1 \\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            \\n\",\n    \"            # 14x14x16 => 7x7x16                             \\n\",\n    \"            torch.nn.MaxPool2d(\\n\",\n    \"                kernel_size=(2, 2),\\n\",\n    \"                stride=(2, 2),\\n\",\n    \"                padding=0)  # (2(7-1) - 14 + 2) = 0\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.output_layer = torch.nn.Linear(7*7*16, num_classes)\\n\",\n    \"\\n\",\n    \"        ###############################################\\n\",\n    \"        # Reinitialize weights using He initialization\\n\",\n    \"        ###############################################\\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, torch.nn.Conv2d):\\n\",\n    \"                torch.nn.init.kaiming_normal_(m.weight.detach())\\n\",\n    \"                m.bias.detach().zero_()\\n\",\n    \"            elif isinstance(m, torch.nn.Linear):\\n\",\n    \"                torch.nn.init.kaiming_normal_(m.weight.detach())\\n\",\n    \"                m.bias.detach().zero_()\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = torch.flatten(x, start_dim=1)\\n\",\n    \"        x = self.output_layer(x)\\n\",\n    \"        return x   # x are the model's logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"7644dab9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cae6c7f9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2c5400f5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3b930598\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"6ba6bf9c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 980),\\n\",\n       \" (1, 1135),\\n\",\n       \" (2, 1032),\\n\",\n       \" (3, 1010),\\n\",\n       \" (4, 982),\\n\",\n       \" (5, 892),\\n\",\n       \" (6, 958),\\n\",\n       \" (7, 1028),\\n\",\n       \" (8, 974),\\n\",\n       \" (9, 1009)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_mnist_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c28a806e\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7ff6d9b9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"3ed1ef8b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 1\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.11 (11.35%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7dc3a1a1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"ff2d1efb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2c5b7124\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d107bd10\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"bae7e3af\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path, download=True)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6f9c4f68\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"53e05ebe\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cadb198e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6f3fa662\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"29e94d50\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(num_classes=10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"60d52673\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73e23b91\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"98e8453e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 9.1 K \\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"9.1 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"9.1 K     Total params\\n\",\n      \"0.036     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2849fb6cf586489999ff5ee0a66654df\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 2.66 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"49555c2e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"94c99e88\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"5cb6a64e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2dcb96f0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"4ee52559\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_1/checkpoints/epoch=15-step=6863.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_1/checkpoints/epoch=15-step=6863.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ac4f3c84e20a4fc38e24dfcb7e521c60\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9865000247955322     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9865000247955322    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9865000247955322}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"225cce7c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7334a256\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"d29621f0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_1/checkpoints/epoch=15-step=6863.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"b06512b7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0c5b0803\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"c0d62c53\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([1, 2, 3, 4, 5])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e50fa7b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"018e99a8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9865 (98.65%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e764010a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1ba517ab\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"e93be369\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"e72aa090\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"363e4ef2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"5d3b37b8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"d066f028\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torchmetrics     : 0.6.2\\n\",\n      \"numpy            : 1.22.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"pandas           : 1.2.5\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-lenet5-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"1212b481\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"4775e637\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"074f80cd\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- LeNet-5 Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"14838d26\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements the classic LeNet-5 convolutional network [1] and applies it to MNIST digit classification. The basic architecture is shown in the figure below:\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/lenet/lenet-5_1.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"daea9947\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"LeNet-5 is commonly regarded as the pioneer of convolutional neural networks, consisting of a very simple architecture (by modern standards). In total, LeNet-5 consists of only 7 layers. 3 out of these 7 layers are convolutional layers (C1, C3, C5), which are connected by two average pooling layers (S2 & S4). The penultimate layer is a fully connexted layer (F6), which is followed by the final output layer. The additional details are summarized below:\\n\",\n    \"\\n\",\n    \"- All convolutional layers use 5x5 kernels with stride 1.\\n\",\n    \"- The two average pooling (subsampling) layers are 2x2 pixels wide with stride 1.\\n\",\n    \"- Throughrout the network, tanh sigmoid activation functions are used. (**In this notebook, we replace these with ReLU activations**)\\n\",\n    \"- The output layer uses 10 custom Euclidean Radial Basis Function neurons for the output layer. (**In this notebook, we replace these with softmax activations**)\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. [Gradient-based learning applied to document recognition](https://ieeexplore.ieee.org/document/726791). Proceedings of the IEEE, november 1998.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ab60f759\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9dd68213\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"1484a931\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d84a1eb\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"18717c08\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- We start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"9407f94a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchLeNet5(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes, grayscale=False):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        self.grayscale = grayscale\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"\\n\",\n    \"        if self.grayscale:\\n\",\n    \"            in_channels = 1\\n\",\n    \"        else:\\n\",\n    \"            in_channels = 3\\n\",\n    \"\\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            \\n\",\n    \"            torch.nn.Conv2d(in_channels, 6*in_channels, kernel_size=5),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=2),\\n\",\n    \"            torch.nn.Conv2d(6*in_channels, 16*in_channels, kernel_size=5),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=2)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.classifier = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Linear(16*5*5*in_channels, 120*in_channels),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.Linear(120*in_channels, 84*in_channels),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.Linear(84*in_channels, num_classes),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = torch.flatten(x, start_dim=1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"ea5f6167\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"19d96f93\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c86ee414\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"53623b3b\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"85c2233b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=transforms.ToTensor(),\\n\",\n    \"                                 download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                train=False,\\n\",\n    \"                                transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"all_train_labels = []\\n\",\n    \"all_test_labels = []\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    all_train_labels.append(labels)\\n\",\n    \"all_train_labels = torch.cat(all_train_labels)\\n\",\n    \"    \\n\",\n    \"for images, labels in test_loader:  \\n\",\n    \"    all_test_labels.append(labels)\\n\",\n    \"all_test_labels = torch.cat(all_test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"48007518\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training labels: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\",\n      \"Training label distribution: tensor([4990, 4991, 4994, 4994, 4991, 4991, 4995, 4993, 4991, 4990])\\n\",\n      \"\\n\",\n      \"Test labels: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\",\n      \"Test label distribution: tensor([1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Training labels:', torch.unique(all_train_labels))\\n\",\n    \"print('Training label distribution:', torch.bincount(all_train_labels))\\n\",\n    \"\\n\",\n    \"print('\\\\nTest labels:', torch.unique(all_test_labels))\\n\",\n    \"print('Test label distribution:', torch.bincount(all_test_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6cc21f44\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cf7a9b53\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"0c1fed8d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline ACC: 10.00%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"majority_prediction = torch.argmax(torch.bincount(all_test_labels))\\n\",\n    \"baseline_acc = torch.mean((all_test_labels == majority_prediction).float())\\n\",\n    \"print(f'Baseline ACC: {baseline_acc*100:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0437c28f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"67b34a24\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"e953aac8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path,\\n\",\n    \"                         download=True)\\n\",\n    \"        \\n\",\n    \"        self.train_transform = transforms.Compose(\\n\",\n    \"            [transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"        self.test_transform = transforms.Compose(\\n\",\n    \"            [transforms.ToTensor()])\\n\",\n    \"        \\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=self.train_transform,\\n\",\n    \"                                 download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                     train=False, \\n\",\n    \"                                     transform=self.test_transform,\\n\",\n    \"                                     download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"337c34dc\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"1381fbdf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a750225f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"27a1e845\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"432879ad\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchLeNet5(\\n\",\n    \"    num_classes=10, grayscale=False)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4f7231e9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"470fdfeb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type          | Params\\n\",\n      \"--------------------------------------------\\n\",\n      \"0 | model     | PyTorchLeNet5 | 548 K \\n\",\n      \"1 | train_acc | Accuracy      | 0     \\n\",\n      \"2 | valid_acc | Accuracy      | 0     \\n\",\n      \"3 | test_acc  | Accuracy      | 0     \\n\",\n      \"--------------------------------------------\\n\",\n      \"548 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"548 K     Total params\\n\",\n      \"2.196     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6c1f9947c53a47c4b52f17e74322d422\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 3.88 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e5b470c8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"36ded203\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"4d43da0f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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kjzVc5mq9yXJ0vMOMwMYc/oP7J78nzCuRo01s41ngoBV7l22Oo69uvMvr3759kjIl3tsy2PQhjzLkizxeLyKsiEom1x9C0SNMmWHsYJf2cmcBMgPj4eJOQkFChPImJiVR03eqg+SpH81XOZeUzBvLOQ9aZckxn4fxpOLEDfEOg3xN4976P5v71aO6qfDZw93wlsa1AiEhD4LgxxohIT6x7Mk4BZ4FWIhILHANGAr+xK6dSqgzrX4ft8379xV+QW3J7Tx/r8NGFKawZtLsJet2rg/i4GVde5voBkABEikgy8FfAG8AYMwMYDtwnIvlAFjDSWMe78kVkCrAE6zLXWY5zE0opd5M02xrCM6oTRLb69Rd/SZN3gHagV0O48iqmUWUsfwXrMlhnyxYDi12Ry5mtyWfJLdA7rZW6LLsXw+cPQ8vrYNQH4OltdyJVxey+isl2Z8/nMvr1ddT3K6Rrz2waBLvv3dRKuY0f18End0F0VxjxthaHWqrW98VUlnoBPvx7eGeS0wu5ddoadv10ruyVlKrLUvfAnBFWVxejPwafQLsTKRep8wUCYHCnaP7Uy4/8wkKGT1/DtzuP2x1JKfd0LgXevc3q4mLspxAYaXci5UJaIBxiQj1ZNOVqmtcP4rfvbuT1FQe1B1iliso6C+/dDtlp1p5DWIzdiZSLaYEoIirEj4/uvZLBHRvyj8W7eGLeNnLzC+2OpZTtPApyYe5v4OQ+GPkeRHexO5KqBlogivH38eSVUd353YCWfLjxKGPfXMeZzFKu6VaqtissoN2u5+HIarh1BjRPsDuRqiZaIJzw8BAevb4NL9zZhR9+PMstr65m/4kMu2MpVf2MgS8fp/7JtXDDM9BpuN2JVDXSAlGKW7s14YNJvcnMyefWV1ezat9JuyMpVb1W/hc2vM6PTW+FK++3O42qZlogyhDXLIwFD/ShUag/499az7vfH7E7klLV44f34Lv/B53v5GDzcXanUTbQAlEOTcICmHf/VfRrXZ8nF2znqUU7yC/Qk9eqFtu7BBb9HloMgJtfAdGvirpI/9UBdi7CJ+dUqU2CfL14fVw891wdy+w1h7n77Y2cy86rpoBKVaPkjfDReGjYCUa8A14+didSNtECcf40LHyALlv+CpmlFwlPD+HJoe155rZOrNl/kttfXcOPp85XU1ClqsHJffD+HRDc0LrXwTfY7kTKRlogAsJh5Bz8so/De7dZNwGVYVTPK3jnnp6cSM/hlldXs/7Q6WoIqpSLnfvJukvaw9O6Szqogd2JlM20QADEXsOODo/D8e0w507ILXuv4KoWkSx4oA/1/L0Z/cb3fLntp2oIqpSLZKdZew7nT1l7DuGXM1yPqq20QDicjoiH29+Ao+vgw9GQn1PmOrGRgcy/vw8dG4fy8EebtaM/VTPl58Dc0ZC6C+58Fxp1szuRchNaIIrqcCvc/D848B18cjcU5Je5SmiAN6+NjSPEz5t7300i7byeuFY1SOYpqzgcXgnDXoWWA+1OpNyIFojiuo2Bwf+G3Z/DwgegsOzLWRsE+zF9TBw/pWXx4Ic/UFionfypGuBgIky/Cg4th6EvQJc77U6k3IwWCGd63QsDnoStc63hFMvRq2tcszD+clMHEvek8uK3e6shpFIVlJ8L3/wV3rkF/EJg4rcQf7fdqZQbqvMjypXomkchJx1Wvwi+QXDt38ocR3dMryvYcvQsL3+3n05N6nFd+6jqyapUeZ06APPugZQfIG4C3PBPHfBHlUj3IEoiAtc+BT0mwuqXYOV/yrGK8PdbOtKpcSiPfLiZg6nawZ9yE8bA5jkw4xo4fci6Ae6ml7Q4qFK5rECIyCwROSEi20tYPlpEtjqmNSLSpciywyKyTUQ2i8hGV2UskwgMfg66jILv/g7fTy9zFT9vT6aP6Y6Xp3Dvu0lk5pR9olspl8o6a+01LLjPukLpvtXQfpjdqVQN4Mo9iNnAoFKWHwL6GWM6A/8PmFlseX9jTFdjTLyL8pWPh4fVF027m+CrJ2DTu2Wu0iQsgP+N6s6B1Az+8MlWHZlO2efHddZew44FMODPMH4RhDaxO5WqIVxWIIwxK4ASbzE2xqwxxpxxvPwecN9PracX3P4mtLwWPvs9bP+0zFWubhXJHwa15YttP/H6yoPVEFKpIgoLIPFf8NZga0/47iXQ9zHrLmmlyklc+detiMQAnxtjOpbRbirQ1hgz0fH6EHAGMMBrxpjiexdF150ETAKIioqKmzt3boWyZmRkEBQUVGobj4IcOm99ipBze9je8U/WzXWlMMYwbXMOSccLeKyHH+0jKv6fszz57KT5Kqcq8/lmp9Ju1/PUS9vJ8Qb92Nt6MgVeAW6TzxU0X8X1798/qcQjNcYYl01ADLC9jDb9gV1ARJF5jRyPDYAtQN/yvF9cXJypqGXLlpWvYVaaMa/1M+bp+sYcXF5m8/TsPHPtfxNNt6e/Nslnzrs+n000X+VUWb7t8415pqkx/2hszOa5VfMzTR3afi7izvmAjaaE71Rbr2ISkc7AG8AwY8zFrlSNMSmOxxPAfKCnPQmd8AuBMZ9afdXMGQlHN5TaPMjXixlj48jLL+S+95LIziuopqCqTsnNhIVT4OPxENESJq/QG99UpdlWIETkCuBTYKwxZm+R+YEiEnzhOXA94PRKKNsEhMO4BVZvl+/fDj9vK7V5i/pB/HdEF7Ymp/Hkgu160lpVrWNJ8FpfawS4ax61zjdoZ3uqCrjyMtcPgLVAGxFJFpF7RGSyiEx2NPkLEAG8Wuxy1ihglYhsAdYDXxhjvnJVzgoLbmhdEeITZN2Renxnqc2v79CQ3w1oycdJycxZ/2P1ZFS1W9oxmD8ZXh9o9UA8/jMY+Bfw9LY7maolXHYntTFmVBnLJwITncw/CHS5dA03VO8KGLfIulJkZj+4Zipc/RB4+Tpt/tC1rdmanMZTi3bQLjqE7leEVW9eVTvkpFs3b655BUwB9Pm9tefgF2p3MlXL6J3UlRXZ0rrxqN3NkPhP65rzI2udNvX0EF4a2ZXoUH/uey+J1PSyuxRX6qLCAkiaDS93hxXPQdshMGUjXPe0FgflElogqkJQAxj+Joz+BPKy4K1B8NmD1h2sxdQL8GHGmDjSsvJ4YM4m8grK7i1WKfZ/CzOutj5X4bEwcSkMnwVhzexOpmoxLRBVqdV18MD3cOUU2PQOTOsJO+Zf0hts+0YhPHtbZ9YfOs0zi3fbFFbVCMd3WMOAvnc75J2HO962TkI3sbeDAVU3aIGoaj6BcMM/4LfLrBPZH0+AD0bC2aO/anZLt8bc1SeGWasPsXDzMXuyKveVfhwW/c7aazi20ep19YH10OGWMnsVVqqqaIFwlUZdYeJ31n/sQytgWi9Y+6p1HNnhT0Pa0TM2nMfnbWVnig5XqrCuRlr+b3i5G2z+AHpNht9vhisfKPHiB6VcRQuEK3l6Wf+x7/8eml0FS/4IbwyEn7YA4O3pwbTfdKeevw93zV5P8pnzNgdWtjGFVnfc/4uDZf+AlgPggXUw6BnrvhulbKAFojqENYPRH1snFdOSYWZ/+PpJyM2kfrAvs+/uQVZuAeNmred0Zq7daVV1Mgb2LyUu6VGrO+7ghnDXl3DnexDRwu50qo7TAlFdRKDj7dZx5G6jYc3L8Gpv2P8tbRuG8Mb4Hhw7k8VdszfoGBJ1QX4ubJlrnWN47za889Lhtjesq5OaXWV3OqUALRDVLyAcbv4fTPgCPH2tq1PmTaRnRA6v/KY725LPct/7evlrrZV1Fla9CC91hvn3Wuekhk1jXa/p0PkOa/wRpdyEfhrtEnM1TF4F/R63BnN5sSPX7fo/XutvWLE3lcc+3kJhofbZVGucOQJf/RFe6ADf/hUiW8PoeXD/Wug2BuOh3WMo9+OyrjZUOXj7Qf8/WUOarp8Jm97lutyP+b5+R/65tR/PBHjwp5s6I3pZY811bBOs+R/sXPjLYcYrp0B0Z7uTKVUmLRDuIDzWulql/59g8xyi1r3Gyz7TOJ70PhtOj6Ln8EftTqguR2Eh7Fti9ZV0ZBX4hlhXs/WaDKGN7U6nVLlpgXAnvsHQ616kx28p3PcNpxc+R8/D0yl4/k3aNLga2oRBdM3ox7BOysuGrXOtwnBqH4Q2te6D6TbWGkdEqRpGC4Q78vDAo80NtHjkOh5/Yx4dj33IncdXWn3+X3EV9LoX2g617rNQ9kv/GZLetg4Tnj9pFfHb34T2w7TrbVWj6TeMG/Px8uDJu27lN69H80LKCBb2OUzTfe9ao4aFNIGeE6H7eL2RqrqlHYMja6zDR0fWwEnHeFetboCrfmddgKDnjVQtoAXCzQX5evHWhB4MeX4pN27owif3TqJ12mpYNwO+fQoS/wWdR1hT43jrxLeqOsbA2SNwePUvReHMYWuZbwhc0Ru6joY2g6F+G1ujKlXVtEDUABFBvkyN9+O5HwoZ91YS8+4fQOPxN1o9fa57DbZ+CJvetu6raNIDYvpAsz7Wc58Au+PXLMbAqQNWIbhQFM4lW8v8w6xDfD0nWdu3YSfw8LQ3r1IupAWihqgf4MHbd8cz4rW1jH1zHZ9MvorwqA5w88tw/f+zvsgOr7KmFc+B+Rd4eEPjOKtgxFwNTXtZvc2qXxgDp/bDwUQ44igIGcetZYH1rULQ7EFrG9ZvpzeyqTpFC0QN0i46hDfH92DMm+u4a/YG5kzsRaCvlzWaWJvB1gSQnQY/rvvlr+BVL8LK/4KHFzTqZn3pXSgYdfHqmoJ8OPo97PnSmk4fsOYHN4LYvo6i0AciW+m5BFWnuaxAiMgsYChwwhjT0clyAV4ChgDngQnGmE2OZYMcyzyBN4wxz7oqZ03TMzacV0Z1Y/J7Sdz3/ibeHB+Pt2exv2r9QqH19dYE1hjGR9c5DpmshrXTYPWLIB4Q3dX667hBe2tkvKCGVodx/uG16q9lz/zz1uBNe76EvUsg+6y1hxXbF3rfBy0HQlisFgSlinDlHsRs4BXgnRKWDwZaOaZewHSgl4h4AtOA64BkYIOILDLG7HRh1hrl+g4N+eetnXji02089vEWnh/RFQ+PUr7YfIOh5bXWBJCbCUfXW8Xi8GrrPEZBsV5kPbwgKKpI0YiyHoMaWAXkwrzABuDl47pftjLO/gh7voI9i+lzaCWYfOs8QpvB0HoQtBhQN/eglConlxUIY8wKEYkppckw4B1jjAG+F5F6IhINxAD7jTEHAURkrqOtFogiRva8glOZuTy3ZA8RQb78+cZ25e+SwycQWvS3JrBu8EpPsUYxy/jZ8eiY0n+GtKPWqGaZJwEn/UP5h9MTX9gdCd6B1olx7wDwCSryPNCaLjy/ZHkQ+AY5lgVWbO+lsBB++uGXQ0fHt1vzI1qR3GQoV1x7LzTpqfePKFVOYkzZHcKJSCCQZYwpFJHWQFvgS2NMXhnrxQCfl3CI6XPgWWPMKsfrpcDjWAVikDFmomP+WKCXMWZKCe8xCZgEEBUVFTd37twyfx9nMjIyCAoKqtC61cFZPmMM7+/K5dsf82kR6kGEvxDqK4T6OB59hRCfXx49S9vLKAcpzMc7Lw3fnNP45J7FJ/cMPrmn8ck9A9lp+Eo+ngU5eBZk41FoPV6cCnMu670KPPzI9/KnwNOPAs9LH61lFyZfgjIOE3FqA765ZzB4kBballMRPTkZ2YOsgCY18t/XnWi+ynHnfP37908yxjgd5Ly8f0qtAK4RkTBgKbARuBMYXYlczr6tTCnznTLGzARmAsTHx5uEhIQKhUlMTKSi61aHkvL162d4/pu9rD90mtSMHHadySE959K6LQLhAT7UD/alfrAvkUHWY33HY5+WkdQPrviQlmVuv8JCyM+yDm/lZkLeeWt4zbzMX+blZkBOBuRm4JmbiWdO+iXzyT0O2Zm/vL7w0fAJss4jtBmCtLyOeoER1AMuDLlTU/993YXmqxx3z1eS8hYIMcacF5F7gP8ZY/4tIj9U8r2TgaZFXjcBUgCfEuYrJzw8hKk3/PoGrazcAk5m5HAiPYfU9BxOZliPqRce03M4dDKTE+k55OZb405EBPowbXR3ejePcFXQXw4zVZULRScnA/zr6ZjNSlWxchcIEbkSa4/hnstctySLgCmOcwy9gDRjzE8ikgq0EpFY4BgwEvhNJd+rTvH38aRpeABNw0u/Sc4YQ3pOPvtPZDD14y2MfmMdf76xHROuiqkZXYy7ougopS4q75nAh4A/AvONMTtEpDmwrLQVROQDYC3QRkSSReQeEZksIpMdTRYDB4H9wOvA/QDGmHxgCrAE2AV8ZIzZcXm/lioPESHEz5vuV4Sx8IE+9G/TgL99tpNHP9pCdl6B3fGUUjYr116AMWY5sBxARDyAk8aY35exzqgylhvggRKWLcYqIKqaBPt5M3NsHK8s288L3+5l74l0ZoyJo0mYdtWhVF1Vrj0IEZkjIiGOq5l2AntE5DHXRlPVzcND+P3AVrwxLp4jJ89z8yurWbP/pN2xlFI2Ke8hpvbGmHPALVh/2V8BjHVVKGWvge2iWDilDxGBPox5cx1vrDxIeS6HVkrVLuUtEN4i4o1VIBY67n/Qb4xarHn9IOY/0Ifr2zfk71/s4sG5m8nK1fMSStUl5S0QrwGHgUBghYg0A865KpRyD0G+Xkwf053HbmjDZ1tTuG36Go6ePm93LKVUNSlXgTDGvGyMaWyMGWIsR4D+Ls6m3ICI8ED/lsya0INjZ85z0yurWLkv1e5YSqlqUN6T1KEi8ryIbHRM/8Xam1B1RP82DVg05Wqigv0YP2s9M5Yf0PMSStVy5T3ENAtIB0Y4pnPAW64KpdxTTGQgn95/FYM7RfPsl7uZ8sEPnM/NtzuWUspFyns3dAtjzO1FXv9NRDa7II9yc4G+XrwyqhudGofy7692c+BEBq+NjbM7llLKBcq7B5ElIldfeCEifYAs10RS7k5EmNyvBbPv6slPadnc9L9VLDmcx46UNAoK9bCTUrVFefcgJgPviEio4/UZYLxrIqmaom/r+nw25Wp+98EmPtidxge7VxHs50V8szB6xIbTMyacTk1C8fXytDuqUqoCytvVxhagi4iEOF6fE5GHgK0uzKZqgCsiAlg45Wrmffkdng3bsO7QaTYcPs2yPXsA8PXyoGvTevSMDadHTDjdm4UR5KsD9ihVE1zW/1TH3dQXPAK8WKVpVI0V4e9BQrfG3NKtMQCnMnLYcPgMGw5bBePVxAMUFO7H00Po0CiEHjHhjimMiCDtplspd1SZP+VqQH/Qyi4RQb4M6tiQQR0bApCRk8+mI1bBWH/oNO99f4Q3Vx0CoEX9QH57TXNG9rzCzshKqWIqUyD0bKQqtyBfL/q2rk/f1vUByMkvYFtyGusPn+abncd54tNtZOcVMKFPrM1JlVIXlFogRCQd54VAAH+XJFJ1gq+XJ/Ex4cTHhPPba5ozZc4mnvpsJx4ewrgrY+yOp5SijMtcjTHBxpgQJ1OwMUbPNKoq4e3pwf9Gdee69lH8ZeEO3vv+iN2RlFKU/z4IpVzKx8uDab/pzrXtGvDnBduZs+5HuyMpVedpgVBuw8fLg2mjuzOgbQP+NH8bc9drkVDKTloglFvx9fLk1dHdSWhTnz/O38ZHG47aHUmpOsulBUJEBonIHhHZLyJPOFn+mIhsdkzbRaRARMIdyw6LyDbHso2uzKnci5+3JzPGxHFNq/o8/ulWPklKtjuSUnWSywqEiHgC04DBQHtglIi0L9rGGPOcMaarMaYr8EdguTHmdJEm/R3L412VU7knP29PZo6N4+qWkTz2yRY+3aRFQqnq5so9iJ7AfmPMQWNMLjAXGFZK+1HABy7Mo2oYq0jEc1WLCKZ+vIUFPxyzO5JSdYq4atAXERkODDLGTHS8Hgv0MsZMcdI2AEgGWl7YgxCRQ1idAhrgNWPMzBLeZxIwCSAqKipu7ty5FcqbkZFBUFBQhdatDnU5X06B4cWkbHafLuTezr70bnT5V1jX5e1XFTRf5bhzvv79+yeVeJTGGOOSCbgDeKPI67HA/0poeyfwWbF5jRyPDYAtQN+y3jMuLs5U1LJlyyq8bnWo6/kyc/LMiBlrTOwTn5tFm49d9vp1fftVluarHHfOB2w0JXynuvIQUzLQtMjrJkBKCW1HUuzwkjEmxfF4ApiPdchK1VEBPl7MmtCD+GbhPPThZr7Y+pPdkZSq9VxZIDYArUQkVkR8sIrAouKNHGNM9AMWFpkXKCLBF54D1wPbXZhV1QCBvl68dVcPujWtx+/n/sBX27VIKOVKLisQxph8YAqwBNgFfGSM2SEik0VkcpGmtwJfG2Myi8yLAlaJyBZgPfCFMeYrV2VVNUegrxez7+5J16b1mDLnB5bs+NnuSErVWi7tT8kYsxhYXGzejGKvZwOzi807CHRxZTZVcwX5ejH7rh6Mm7WeB97fxPQxcVzXPsruWErVOnontaqRgv28efvunnRoHMr97yexbM8JuyMpVetogVA1VoifN+/c3ZNWDYJ55MPNnMrIsTuSUrWKFghVo4X6e/PSyK5k5hTw1Gc77Y6jVK2iBULVeK2igpkyoCWfbUnhm53H7Y6jVK2hBULVCvcltKBtw2D+vGAbaVl5dsdRqlbQAqFqBW9PD54b3oXU9ByeWbzL7jhK1QpaIFSt0alJKL/t25y5G46yev9Ju+MoVeNpgVC1ysPXtiY2MpAnPt3K+dx8u+MoVaNpgVC1ip+3J8/e1omjp7P479d77Y6jVI2mBULVOr2aRzC2dzNmrT7Eph/P2B1HqRpLC4Sqlf4wqA3RIX48/slWcvIL7I6jVI2kBULVSsF+3vzjtk7sO5HBtO/22x1HqRpJC4Sqtfq3acBt3RrzauIBfjynexFKXS4tEKpWe3Joe+oFeDNrey75BYV2x1GqRtECoWq1sEAf/nZzRw6fK+SNVYfsjqNUjaIFQtV6Qzo1JC7Kkxe+2cvB1Ay74yhVY2iBULWeiDC2nQ++Xh48MW8bhYXG7khK1QhaIFSdUM/Pgz8Pbc/6w6d5f/2PdsdRqkbQAqHqjDvimnBNq0ieXbyLY2ez7I6jlNtzaYEQkUEiskdE9ovIE06WJ4hImohsdkx/Ke+6Sl0uEeGft3bCAH/6dBvG6KEmpUrjsgIhIp7ANGAw0B4YJSLtnTRdaYzp6pievsx1lbosTcMD+MMNbVi+N5X5PxyzO45Sbs2VexA9gf3GmIPGmFxgLjCsGtZVqlTjrowhrlkYT3++k9R0HcdaqZKIq3azRWQ4MMgYM9HxeizQyxgzpUibBGAekAykAFONMTvKs26RnzEJmAQQFRUVN3fu3ArlzcjIICgoqELrVgfNVznF86VkFPKX1Vl0i/Lkga5+Niaz1LTt5240X8X1798/yRgT72yZlwvfV5zMK16NNgHNjDEZIjIEWAC0Kue61kxjZgIzAeLj401CQkKFwiYmJlLRdauD5qscZ/nOBO7nuSV7yI5sy6CODe0J5lATt5870Xyu4cpDTMlA0yKvm2DtJVxkjDlnjMlwPF8MeItIZHnWVaqyJvVtTvvoEJ5cuJ208zqOtVLFuXIPYgPQSkRigWPASOA3RRuISEPguDHGiEhPrIJ1Cjhb1rpKVZa3pwf/Ht6ZYdNWc/2Ly2kWEUhUiB8NQ3yJCvGjQYgfDUP8iHK89vP2tDuyUtXKZQXCGJMvIlOAJYAnMMtxfmGyY/kMYDhwn4jkA1nASGOdFHG6rquyqrqrY+NQXh7Zja92/MzxtGy2Jp/l67RscvIv7dgv1N/7YrGIchSOho5CEtcsjMggXxt+A6Vcx5V7EBcOGy0uNm9GkeevAK+Ud12lXOHGztHc2Dn64mtjDOey8jmens3xc9n8nJbNifQcfk6zXh9Pz2Hf8ZOkZuRQ4Oi2IyLQh5nj4olrFmbXr6FUlXNpgVCqJhIRQgO8CQ3wpnVUcIntCgoNpzJzOJSayR/mbWXU69/z3zu6cFOXRtWYVinX0a42lKogTw+hQbAfvZpHMP/+PnRuHMrvPviBV77bp3dpq1pBC4RSVSA80If3f9uLW7o24j9f72XqxzoWtqr59BCTUlXE18uTF+7sSkxkIC9+u4+jZ87z2pg4wgJ97I6mVIXoHoRSVUhEeOja1rx4Z1c2/3iW26av4dDJTLtjKVUhWiCUcoFbujVmzm97kZaVx62vrmbdwVN2R1LqsmmBUMpF4mPCmX//VYQH+jDmzXXMS0q2O5JSl0ULhFIu1CwikPn39SG+WTiPfryF57/eo1c4qRpDC4RSLhYa4M3bd/dkRHwTXv5uP7+fu5nsPL3CSbk/vYpJqWrg4+XBv27vTGxkEP/6ajfHzpxn5rh47Z5DuTXdg1CqmogI9yW04NXR3dmRco5bX13NvuPpdsdSqkRaIJSqZkM6RfPhvVeSlVvIbdPXsGrfSbsjKeWUFgilbNC1aT0WPHAV0aF+jH9rPZ8dyCUtS8ekUO5FC4RSNmkSFsAn911F/zYNmLcvj97/XMr/zd+mh52U29CT1ErZKMTPmzfGx/P2oqVsz4nk46Rk3l/3I31aRjD+yhgGtovC08PZCLxKuZ4WCKXcQLMQT8YndOGPQ9oxd8OPvLv2CJPeTaJJmD/jrmzGnfFXEBrgbXdMVcfoISal3Eh4oA/3J7Rk5R/6M310dxrX8+efi3fT65lv+eOnW9n98zm7I6o6RPcglHJDXp4eDO4UzeBO0ez66RxvrznMp5uO8cH6o/RuHs6Eq2K4tl0UXp76N55yHf10KeXm2kWH8Oztnfn+jwN5YnBbjp7OYvJ7m+j3XCLTEw9wJjPX7oiqlnJpgRCRQSKyR0T2i8gTTpaPFpGtjmmNiHQpsuywiGwTkc0istGVOZWqCcICfZjcrwUr/tCf18bG0SwigH99tZvezyzlwbk/sGhLil4qq6qUyw4xiYgnMA24DkgGNojIImPMziLNDgH9jDFnRGQwMBPoVWR5f2OM3kWkVBGeHsINHRpyQ4eG7Pk5nXfWHubL7T+zcHMKXh5Cj5hwBrZrwMB2UcRGBtodV9VgrjwH0RPYb4w5CCAic4FhwMUCYYxZU6T990ATF+ZRqtZp0zCYf9zaiaeHdWTz0TMs3XWCpbtO8PcvdvH3L3bRPDKQge0aMKBtFPExYXjrOQt1GcRVXQ+LyHBgkDFmouP1WKCXMWZKCe2nAm2LtD8EnAEM8JoxZmYJ600CJgFERUXFzZ07t0J5MzIyCAoKqtC61UHzVU5dy5d6vpAtqQVsTi1g96kC8g0EeEGnSE+6NvCiU6QnQT7lv7+irm2/qubO+fr3759kjIl3tsyVexDOPn1Oq5GI9AfuAa4uMruPMSZFRBoA34jIbmPMikt+oFU4ZgLEx8ebhISECoVNTEykoutWB81XOXUx3x2Ox4ycfFbtO8nSXcdZtucE637OwdNDiGsWxsC21qGoFvUDESm5YNTF7VeV3D1fSVxZIJKBpkVeNwFSijcSkc7AG8BgY8zFcRmNMSmOxxMiMh/rkNUlBUIpVbogXy8GdWzIoI4NKSw0bEk+y3e7T/DtrhM88+VunvlyNzERAVzbLorr2kcR1yxML59VgGsLxAaglYjEAseAkcBvijYQkSuAT4Gxxpi9ReYHAh7GmHTH8+uBp12YVak6wcND6HZFGN2uCOPR69uQcjaLpbtPsHTXcd5Ze4Q3Vh2iXoA3A9o04Nr2UfRtXZ8gX71dqq5y2b+8MSZfRKYASwBPYJYxZoeITHYsnwH8BYgAXnXs3uY7joVFAfMd87yAOcaYr1yVVam6qlE9f8b2bsbY3s3IyMln5d5Uvtl1nO92n+DTH47h4+nBlS0iaOaVR5u0LKJD/e2OrKqRS/80MMYsBhYXmzejyPOJwEQn6x0EuhSfr5RynSBfr4t3b+cXFJJ05Azf7jrONzuPs/xULu/s/I5OjUO5tl0U17ZvQPvokFLPW6iaT/cdlVKX8PL0oFfzCHo1j+BPQ9rxwRfLSAtqxre7jvPi0r288O1eGtfz59p21qGoXrER+HjpeYvaRguEUqpUIkKjIA9+k9CC+xJakJqew7LdJ/hm13E+3HiUt9ceoXE9f/56U3uu79DQ7riqCmmBUEpdlvrBvozo0ZQRPZqSnVfA8r2pPP/1Xia9m8S17Rrw15s60DQ8wO6YqgroPqFSqsL8vD25oUNDPv/91fxpSFvWHDjFdS8s59XE/eTmF9odT1WSFgilVKV5e3owqW8Lvn2kHwmtG/Dvr/Yw5OWVrD1wquyVldvSAqGUqjKN6vkzY2wcb03oQU5+AaNe/56HP9xManqO3dFUBWiBUEpVuf5tG/D1Q/343YCWfL41hQH/TeTdtYcpKHRN32/KNbRAKKVcwt/Hk0evb8NXD/WlU+NQnly4g9teXc225DS7o6ly0gKhlHKpFvWDeH9iL14a2ZWUtGxunraKvyzcroMb1QBaIJRSLiciDOvamKWP9mP8lTG89/0RBv53OQt+OIarhhxQlaf3QSilqk2InzdP3dyB27s34c8LtvHQh5v5cMNRHr6uNW2iggkN8LY7YpUzxpCTbzh6+jxnzudyKjOXM5m5nHZMZ87n4uvlSZ+WkfRuHk6wn/tsAy0QSqlq16lJKJ/e34cP1v/Iv7/azYjX1gIQHuhDbGTgxal5ZCCx9QOJiQjEz9vT5tSXyi8oZPPRs+z6Of2SL/3TRV7n5BfCt8suWd/LQwgL9CEjO5/Zaw7j5SF0u6Ie17SqzzWtIuncpB6eHvb1d6UFQillC08PYUzvZtzYKZqkI2c4dDKTgyczOZiawYq9qXySlPyr9o3r+f+qeMTWtwpI43rV28Ps8XPZLN+byvI9qazcl8q57PyLy4J9vQgP8iEswIeGIX60iw4hPNCHsz8fJb5TO8IDfQgL9CHC8Rji54WIkJNfwKYjZ1m5L5VV+0/ywrd7ef6bvYT4edGnZeTFglHdd6hrgVBK2Sos0Idr20ddMj8jJ5/DJzM5VGQ6eDKTBZuPkV7kS9nbU2jgDz1+/oEOjULp0CiEDo1Cq+xwVZ6jZ9vEPaks35vKrp/OAdAg2JcbOjQkoU0DujerR0Sgb4kdFiYmHiehR1OnywB8vTy5skUEV7aI4A/A6cxcVu8/ycp9qazcd5Ivt/8MQGxkIFe3jOSaVpFc2SLC5YejtEAopdxSkK8XHRuH0rFx6K/mG2M4nZlbZI8jk7U7D7P24CkWbP5l0MrG9fwvFov2jULo0CiE6FC/cnVRfuxsFsv3pLJ87wlW7z9FRk4+Xh5CfEwYjw9qS0Kb+rRtGOyy7s7DA324qUsjburSCGMMB1IzLxaLT5KSeff7I3h6CN2vqMfVLetzTetIujaph0cVH47SAqGUqlFEhIggXyKCfImPCQcg0f9nEhISOJmRw86Uc+xIOceOlDR2ppzjm13HuXChVFiA98W9jPaO4hEbGUh+YSEbDp1h+d4TJO5JZd+JDAAahfpxU5dGJLSpz1XV8Bd7Sb9vywZBtGwQxF19Yi8ejlq13yoYLy7dy9trD7Px/66t8vfWAqGUqjUig3zp27o+fVvXvzgvMyefXT9ZRWNnyjl2/JTGW6sPk1tgdSbo7zj5nZVXgI+nBz1jw7mzR1P6ta5PywZBbjcoUtHDUY/dgGNvKqPK9x5AC4RSqpYL9PUiPib84t4GQG5+IftPZLAjJY0dKdY5hQvH9QN8atbXYnigD+GB4WU3rICatSWUUqoK+Hh50N5xmOkOu8O4MZfeSS0ig0Rkj4jsF5EnnCwXEXnZsXyriHQv77pKKaVcy2UFQkQ8gWnAYKA9MEpE2hdrNhho5ZgmAdMvY12llFIu5Mo9iJ7AfmPMQWNMLjAXGFaszTDgHWP5HqgnItHlXFcppZQLufIcRGPgaJHXyUCvcrRpXM51ARCRSVh7H0RFRZGYmFihsBkZGRVetzpovsrRfJWj+SrH3fOVxJUFwtk1V8W7bSypTXnWtWYaMxOYCRAfH28SEhIuI+IvEhMTqei61UHzVY7mqxzNVznunq8kriwQyUDRe8ubACnlbONTjnWVUkq5kCvPQWwAWolIrIj4ACOBRcXaLALGOa5m6g2kGWN+Kue6SimlXMhlexDGmHwRmQIsATyBWcaYHSIy2bF8BrAYGALsB84Dd5W2rquyKqWUupTUptGcRCQVOFLB1SOBk1UYp6ppvsrRfJWj+SrHnfM1M8bUd7agVhWIyhCRjcaYeLtzlETzVY7mqxzNVznunq8kOia1Ukopp7RAKKWUckoLxC9m2h2gDJqvcjRf5Wi+ynH3fE7pOQillFJO6R6EUkopp7RAKKWUcqpOFYjKjE9RTfmaisgyEdklIjtE5EEnbRJEJE1ENjumv1RzxsMiss3x3hudLLdtG4pImyLbZbOInBORh4q1qdbtJyKzROSEiGwvMi9cRL4RkX2Ox7AS1nX5mCgl5HtORHY7/v3mi0i9EtYt9bPgwnxPicixIv+GQ0pY167t92GRbIdFZHMJ67p8+1WaMaZOTFh3ZB8AmmP19bQFaF+szRDgS6zOAnsD66o5YzTQ3fE8GNjrJGMC8LmN2/EwEFnKclu3YbF/75+xbgKybfsBfYHuwPYi8/4NPOF4/gTwrxLyl/p5dWG+6wEvx/N/OctXns+CC/M9BUwtx7+/Lduv2PL/An+xa/tVdqpLexCVGZ+iWhhjfjLGbHI8Twd2YXV9XpPYug2LGAgcMMZU9M76KmGMWQGcLjZ7GPC24/nbwC1OVq2WMVGc5TPGfG2MyXe8/B6rs0xblLD9ysO27XeBiAgwAvigqt+3utSlAlHS2BOX26ZaiEgM0A1Y52TxlSKyRUS+FJEO1ZsMA3wtIklijcVRnLtsw5GU/B/Tzu0HEGWsTilxPDZw0sZdtuPdWHuEzpT1WXClKY5DYLNKOETnDtvvGuC4MWZfCcvt3H7lUpcKRGXGp6hWIhIEzAMeMsacK7Z4E9Zhky7A/4AF1RyvjzGmO9ZwsA+ISN9iy23fhmL1AHwz8LGTxXZvv/Jyh+34f0A+8H4JTcr6LLjKdKAF0BX4CeswTnG2bz9gFKXvPdi1/cqtLhWIyoxPUW1ExBurOLxvjPm0+HJjzDljTIbj+WLAW0QiqyufMSbF8XgCmI+1K1+U7dsQ6z/cJmPM8eIL7N5+DscvHHZzPJ5w0sbW7Sgi44GhwGjjOGBeXDk+Cy5hjDlujCkwxhQCr5fwvnZvPy/gNuDDktrYtf0uR10qEJUZn6JaOI5ZvgnsMsY8X0Kbho52iEhPrH/DU9WUL1BEgi88xzqZub1YM1u3oUOJf7nZuf2KWASMdzwfDyx00sa2MVFEZBDwOHCzMeZ8CW3K81lwVb6i57RuLeF97R5T5lpgtzEm2dlCO7ffZbH7LHl1TlhX2OzFurrh/xzzJgOTHc8FmOZYvg2Ir+Z8V2PtBm8FNjumIcUyTgF2YF2V8T1wVTXma+543y2ODO64DQOwvvBDi8yzbfthFaqfgDysv2rvASKApcA+x2O4o20jYHFpn9dqyrcf6/j9hc/gjOL5SvosVFO+dx2fra1YX/rR7rT9HPNnX/jMFWlb7duvspN2taGUUsqpunSISSml1GXQAqGUUsopLRBKKaWc0gKhlFLKKS0QSimlnNICodRlEJEC+XWPsVXWS6iIxBTtFVQpu3nZHUCpGibLGNPV7hBKVQfdg1CqCjj69v+XiKx3TC0d85uJyFJHx3JLReQKx/wox1gLWxzTVY4f5Skir4s1HsjXIuJv2y+l6jwtEEpdHv9ih5juLLLsnDGmJ/AK8KJj3itY3Z93xur07mXH/JeB5cbqNLA71t20AK2AacaYDsBZ4HaX/jZKlULvpFbqMohIhjEmyMn8w8AAY8xBR4eLPxtjIkTkJFZXEHmO+T8ZYyJFJBVoYozJKfIzYoBvjDGtHK8fB7yNMX+vhl9NqUvoHoRSVceU8LykNs7kFHlegJ4nVDbSAqFU1bmzyONax/M1WD2JAowGVjmeLwXuAxARTxEJqa6QSpWX/nWi1OXxLzYI/VfGmAuXuvqKyDqsP7xGOeb9HpglIo8BqcBdjvkPAjNF5B6sPYX7sHoFVcpt6DkIpaqA4xxEvDHmpN1ZlKoqeohJKaWUU7oHoZRSyindg1BKKeWUFgillFJOaYFQSinllBYIpZRSTmmBUEop5dT/BxHYv0DyNziDAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"00811a53\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"2f972950\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_29/checkpoints/epoch=7-step=2807.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_29/checkpoints/epoch=7-step=2807.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4070de9968ea4d72b73fbcaec6a3a1c8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.6643999814987183     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.6643999814987183    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.6643999814987183}]\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"77f0f212\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9f11137f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"473d9139\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_29/checkpoints/epoch=7-step=2807.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"7ca9ba4c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7c1db4af\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"e1c35e59\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([3, 1, 0, 0, 4])\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"    \\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"715bf727\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"03d5a220\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.6644 (66.44%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"97a69f77\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"f15ef928\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"e1029db9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5a293e6a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"36032a1a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/cifar10_pngs/90_airplane.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"df4829b7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we have to use the same image transformation that we used earlier in the `DataModule`. \\n\",\n    \"- While we didn't apply any image augmentation, we could use the `to_tensor` function from the torchvision library; however, as a general template that provides flexibility for more complex transformation chains, let's use the `Compose` class for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"d990431b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = transforms.Compose([transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"image_chw = resize_transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"811de2fd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"14785180\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d17591e9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"b4422205\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cdd09c4c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"6e76be24\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"id\": \"88b1576d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_to_str = {\\n\",\n    \"    0: 'airplane',\\n\",\n    \"    1: 'automobile',\\n\",\n    \"    2: 'bird',\\n\",\n    \"    3: 'cat',\\n\",\n    \"    4: 'deer',\\n\",\n    \"    5: 'dog',\\n\",\n    \"    6: 'frog',\\n\",\n    \"    7: 'horse',\\n\",\n    \"    8: 'ship',\\n\",\n    \"    9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"id\": \"ceabf906\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: airplane\\n\",\n      \"Class-membership probability 98.94%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {int_to_str[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-lenet5-mnist.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"c60c3300\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"0e5f9ee4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b74fe944\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- LeNet-5 Trained on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"864369a2\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements the classic LeNet-5 convolutional network [1] and applies it to MNIST digit classification. The basic architecture is shown in the figure below:\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/lenet/lenet-5_1.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a919782c\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"LeNet-5 is commonly regarded as the pioneer of convolutional neural networks, consisting of a very simple architecture (by modern standards). In total, LeNet-5 consists of only 7 layers. 3 out of these 7 layers are convolutional layers (C1, C3, C5), which are connected by two average pooling layers (S2 & S4). The penultimate layer is a fully connexted layer (F6), which is followed by the final output layer. The additional details are summarized below:\\n\",\n    \"\\n\",\n    \"- All convolutional layers use 5x5 kernels with stride 1.\\n\",\n    \"- The two average pooling (subsampling) layers are 2x2 pixels wide with stride 1.\\n\",\n    \"- Throughrout the network, tanh sigmoid activation functions are used. (**In this notebook, we replace these with ReLU activations**)\\n\",\n    \"- The output layer uses 10 custom Euclidean Radial Basis Function neurons for the output layer. (**In this notebook, we replace these with softmax activations**)\\n\",\n    \"- The input size is 32x32; here, we rescale the MNIST images from 28x28 to 32x32 to match this input dimension. Alternatively, we would have to change the \\n\",\n    \"achieve error rate below 1% on the MNIST data set, which was very close to the state of the art at the time (produced by a boosted ensemble of three LeNet-4 networks).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. [Gradient-based learning applied to document recognition](https://ieeexplore.ieee.org/document/726791). Proceedings of the IEEE, november 1998.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6f250573\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"542f0b67\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"c9c9f45f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bf4c7a28\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"017ca5a5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- We start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"5bf908bd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchLeNet5(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes, grayscale=False):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        self.grayscale = grayscale\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"\\n\",\n    \"        if self.grayscale:\\n\",\n    \"            in_channels = 1\\n\",\n    \"        else:\\n\",\n    \"            in_channels = 3\\n\",\n    \"\\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Conv2d(in_channels, 6, kernel_size=5),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=2),\\n\",\n    \"            torch.nn.Conv2d(6, 16, kernel_size=5),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=2)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.classifier = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Linear(16*5*5, 120),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.Linear(120, 84),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.Linear(84, num_classes),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = torch.flatten(x, start_dim=1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"b5a989d0\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9a4f8ac0\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f99658ec\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c0193d9e\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"a85aeca4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(root='./data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='./data', \\n\",\n    \"                              train=False,\\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"all_train_labels = []\\n\",\n    \"all_test_labels = []\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    all_train_labels.append(labels)\\n\",\n    \"all_train_labels = torch.cat(all_train_labels)\\n\",\n    \"    \\n\",\n    \"for images, labels in test_loader:  \\n\",\n    \"    all_test_labels.append(labels)\\n\",\n    \"all_test_labels = torch.cat(all_test_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"7e7d853a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training labels: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\",\n      \"Training label distribution: tensor([5915, 6732, 5945, 6121, 5832, 5412, 5909, 6256, 5839, 5943])\\n\",\n      \"\\n\",\n      \"Test labels: tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\",\n      \"Test label distribution: tensor([ 980, 1135, 1032, 1010,  982,  892,  958, 1028,  974, 1009])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Training labels:', torch.unique(all_train_labels))\\n\",\n    \"print('Training label distribution:', torch.bincount(all_train_labels))\\n\",\n    \"\\n\",\n    \"print('\\\\nTest labels:', torch.unique(all_test_labels))\\n\",\n    \"print('Test label distribution:', torch.bincount(all_test_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0fd094dc\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"26126a41\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"8a23adbe\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline ACC: 11.35%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"majority_prediction = torch.argmax(torch.bincount(all_test_labels))\\n\",\n    \"baseline_acc = torch.mean((all_test_labels == majority_prediction).float())\\n\",\n    \"print(f'Baseline ACC: {baseline_acc*100:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0db99229\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac1c0141\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"9bcf3be3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path,\\n\",\n    \"                       download=True)\\n\",\n    \"        self.resize_transform = transforms.Compose(\\n\",\n    \"            [transforms.Resize((32, 32)),\\n\",\n    \"             transforms.ToTensor()])\\n\",\n    \"        \\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(root=self.data_path, \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=self.resize_transform,\\n\",\n    \"                               download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(root=self.data_path, \\n\",\n    \"                                   train=False, \\n\",\n    \"                                   transform=self.resize_transform,\\n\",\n    \"                                   download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d68d27e2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"92f2d713\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e70c23c9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5dec03e7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"cf93512a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchLeNet5(\\n\",\n    \"    num_classes=10, grayscale=True)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    model=pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"58fb64a9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"0f5fef0d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type          | Params\\n\",\n      \"--------------------------------------------\\n\",\n      \"0 | model     | PyTorchLeNet5 | 61.7 K\\n\",\n      \"1 | train_acc | Accuracy      | 0     \\n\",\n      \"2 | valid_acc | Accuracy      | 0     \\n\",\n      \"3 | test_acc  | Accuracy      | 0     \\n\",\n      \"--------------------------------------------\\n\",\n      \"61.7 K    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"61.7 K    Total params\\n\",\n      \"0.247     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"dfb9f7e0835941c0a4d7db2203a79f0e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 1.67 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9fccc4e9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"914f982f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"0ed54af7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2cbe0151\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"b726c1a0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_22/checkpoints/epoch=10-step=2353.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_22/checkpoints/epoch=10-step=2353.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"02006ec6d039406f8c71dcda15169e82\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9873999953269958     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9873999953269958    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9873999953269958}]\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"461b364a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f32de2de\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"ba4570eb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_22/checkpoints/epoch=10-step=2353.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"8c1f7796\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4389e871\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"3ff7cd66\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([7, 2, 1, 0, 4])\"\n      ]\n     },\n     \"execution_count\": 17,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ff0a65db\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"6c06ef45\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9874 (98.74%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4e80fe10\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"335380c9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"45998e5f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ab0bdec5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"9fd24838\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/mnist_pngs/613.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"039753a3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we used a resize-transformation in the `DataModule` that rescaled the 28x28 MNIST images to size 32x32. We also have to apply the same transformation to any new image that we feed to the model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"a8944f63\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"resize_transform = transforms.Compose(\\n\",\n    \"            [transforms.Resize((32, 32)),\\n\",\n    \"             transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"image_chw = resize_transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0937a26c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"e4b2b30e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6a36612a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"740d681f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 1, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"774b7498\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"2be68b34\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"10c37394\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: 7\\n\",\n      \"Class-membership probability 99.99%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {predicted_label}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-lenet5-quickdraw.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"8382b5bf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"50d23702\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9672fdf6\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- LeNet-5 Trained on QuickDraw\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3ecbe454\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements the classic LeNet-5 convolutional network [1] and applies it to MNIST digit classification. The basic architecture is shown in the figure below:\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/images/lenet/lenet-5_1.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8855551c\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"LeNet-5 is commonly regarded as the pioneer of convolutional neural networks, consisting of a very simple architecture (by modern standards). In total, LeNet-5 consists of only 7 layers. 3 out of these 7 layers are convolutional layers (C1, C3, C5), which are connected by two average pooling layers (S2 & S4). The penultimate layer is a fully connexted layer (F6), which is followed by the final output layer. The additional details are summarized below:\\n\",\n    \"\\n\",\n    \"- All convolutional layers use 5x5 kernels with stride 1.\\n\",\n    \"- The two average pooling (subsampling) layers are 2x2 pixels wide with stride 1.\\n\",\n    \"- Throughrout the network, tanh sigmoid activation functions are used. (**In this notebook, we replace these with ReLU activations**)\\n\",\n    \"- The output layer uses 10 custom Euclidean Radial Basis Function neurons for the output layer. (**In this notebook, we replace these with softmax activations**)\\n\",\n    \"- The expected input size is 32x32; so, here, we rescale the Quickdraw images from 28x28 to 32x32 to match this input dimension. Alternatively, we would have to change the \\n\",\n    \"achieve error rate below 1% on the MNIST data set, which was very close to the state of the art at the time (produced by a boosted ensemble of three LeNet-4 networks).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. [Gradient-based learning applied to document recognition](https://ieeexplore.ieee.org/document/726791). Proceedings of the IEEE, november 1998.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"244b1b58\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2b0d68bc\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"96da32cc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 10\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"20a830a6\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1155c0e2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- We start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"01d25119\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchLeNet5(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes, grayscale=False):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        self.grayscale = grayscale\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"\\n\",\n    \"        if self.grayscale:\\n\",\n    \"            in_channels = 1\\n\",\n    \"        else:\\n\",\n    \"            in_channels = 3\\n\",\n    \"\\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Conv2d(in_channels, 6, kernel_size=5),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=2),\\n\",\n    \"            torch.nn.Conv2d(6, 16, kernel_size=5),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=2)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.classifier = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Linear(16*5*5, 120),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.Linear(120, 84),\\n\",\n    \"            torch.nn.Tanh(),\\n\",\n    \"            torch.nn.Linear(84, num_classes),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = torch.flatten(x, start_dim=1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"ee923af2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"edc328e9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d6bd485d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40a1f6e6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we are going to use Google's Quickdraw dataset (https://quickdraw.withgoogle.com). \\n\",\n    \"- In particular we will be working with an arbitrary subset of 10 categories in PNG format:\\n\",\n    \"\\n\",\n    \"    label_dict = {\\n\",\n    \"             \\\"lollipop\\\": 0,\\n\",\n    \"             \\\"binoculars\\\": 1,\\n\",\n    \"             \\\"mouse\\\": 2,\\n\",\n    \"             \\\"basket\\\": 3,\\n\",\n    \"             \\\"penguin\\\": 4,\\n\",\n    \"             \\\"washing machine\\\": 5,\\n\",\n    \"             \\\"canoe\\\": 6,\\n\",\n    \"             \\\"eyeglasses\\\": 7,\\n\",\n    \"             \\\"beach\\\": 8,\\n\",\n    \"             \\\"screwdriver\\\": 9,\\n\",\n    \"    }\\n\",\n    \"    \\n\",\n    \"For more details on obtaining and preparing the dataset, please see the\\n\",\n    \"\\n\",\n    \"- [custom-data-loader-quickdraw.ipynb](../../pytorch_ipynb/mechanics/custom-data-loader-quickdraw.ipynb)\\n\",\n    \"\\n\",\n    \"notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0cfe6405\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"0ce1ffa6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"fddd2d42\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(28, 28)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"df = pd.read_csv('quickdraw_png_set1_train.csv', index_col=0)\\n\",\n    \"df.head()\\n\",\n    \"\\n\",\n    \"main_dir = 'quickdraw-png_set1/'\\n\",\n    \"\\n\",\n    \"img = Image.open(os.path.join(main_dir, df.index[2]))\\n\",\n    \"img = np.asarray(img, dtype=np.uint8)\\n\",\n    \"print(img.shape)\\n\",\n    \"plt.imshow(np.array(img), cmap='binary')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0acc2af0\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating a custom Dataset and DataLoader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"433bb7ac\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import Dataset\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class QuickdrawDataset(Dataset):\\n\",\n    \"    \\\"\\\"\\\"Custom Dataset for loading Quickdraw images\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, txt_path, img_dir, transform=None):\\n\",\n    \"    \\n\",\n    \"        df = pd.read_csv(txt_path, sep=\\\",\\\", index_col=0)\\n\",\n    \"        self.img_dir = img_dir\\n\",\n    \"        self.txt_path = txt_path\\n\",\n    \"        self.img_names = df.index.values\\n\",\n    \"        self.y = df['Label'].values\\n\",\n    \"        self.transform = transform\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        img = Image.open(os.path.join(self.img_dir,\\n\",\n    \"                                      self.img_names[index]))\\n\",\n    \"        \\n\",\n    \"        if self.transform is not None:\\n\",\n    \"            img = self.transform(img)\\n\",\n    \"        \\n\",\n    \"        label = self.y[index]\\n\",\n    \"        return img, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.y.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"993c35e1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"custom_transform = transforms.Compose([transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"train_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_train.csv',\\n\",\n    \"                                 img_dir='quickdraw-png_set1/',\\n\",\n    \"                                 transform=custom_transform)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset,\\n\",\n    \"                          batch_size=BATCH_SIZE,\\n\",\n    \"                          shuffle=True,\\n\",\n    \"                          num_workers=NUM_WORKERS) \\n\",\n    \"\\n\",\n    \"valid_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_valid.csv',\\n\",\n    \"                                 img_dir='quickdraw-png_set1/',\\n\",\n    \"                                 transform=custom_transform)\\n\",\n    \"\\n\",\n    \"valid_loader = DataLoader(dataset=valid_dataset,\\n\",\n    \"                          batch_size=BATCH_SIZE,\\n\",\n    \"                          shuffle=False,\\n\",\n    \"                          num_workers=NUM_WORKERS) \\n\",\n    \"\\n\",\n    \"test_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_test.csv',\\n\",\n    \"                                img_dir='quickdraw-png_set1/',\\n\",\n    \"                                transform=custom_transform)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset,\\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         shuffle=False,\\n\",\n    \"                         num_workers=NUM_WORKERS) \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"7a921ec4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 90149),\\n\",\n       \" (1, 87122),\\n\",\n       \" (2, 125242),\\n\",\n       \" (3, 82893),\\n\",\n       \" (4, 178028),\\n\",\n       \" (5, 84541),\\n\",\n       \" (6, 86707),\\n\",\n       \" (7, 158087),\\n\",\n       \" (8, 87143),\\n\",\n       \" (9, 81109)]\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTraining label distribution:')\\n\",\n    \"sorted(train_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"d9bc4966\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Validation label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 12944),\\n\",\n       \" (1, 12260),\\n\",\n       \" (2, 17938),\\n\",\n       \" (3, 11773),\\n\",\n       \" (4, 25347),\\n\",\n       \" (5, 12089),\\n\",\n       \" (6, 12220),\\n\",\n       \" (7, 22679),\\n\",\n       \" (8, 12657),\\n\",\n       \" (9, 11668)]\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"valid_counter = Counter()\\n\",\n    \"for images, labels in valid_loader:\\n\",\n    \"    valid_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nValidation label distribution:')\\n\",\n    \"sorted(valid_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"6500b4c7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 25756),\\n\",\n       \" (1, 24808),\\n\",\n       \" (2, 35646),\\n\",\n       \" (3, 23792),\\n\",\n       \" (4, 50416),\\n\",\n       \" (5, 24221),\\n\",\n       \" (6, 24840),\\n\",\n       \" (7, 44996),\\n\",\n       \" (8, 25138),\\n\",\n       \" (9, 23536)]\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTest label distribution:')\\n\",\n    \"sorted(test_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3b2b8c34\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f525eb46\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"a7f564f5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 50416)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"majority_class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"88a73add\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.17 (16.63%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print('Accuracy when always predicting the majority class:')\\n\",\n    \"print(f'{baseline_acc:.2f} ({baseline_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"45b342bb\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3568e334\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"d9dc13c9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        # We assume the dataset is already downloaded, otherwise\\n\",\n    \"        # put the code for downloading it here\\n\",\n    \"        self.resize_transform = transforms.Compose(\\n\",\n    \"            [transforms.Resize((32, 32)),\\n\",\n    \"             transforms.ToTensor()])\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        self.train = QuickdrawDataset( \\n\",\n    \"            txt_path=os.path.join(\\n\",\n    \"                self.data_path, 'quickdraw_png_set1_train.csv'),\\n\",\n    \"            img_dir=os.path.join(self.data_path, 'quickdraw-png_set1/'),\\n\",\n    \"            transform=self.resize_transform)\\n\",\n    \"\\n\",\n    \"        self.valid = QuickdrawDataset( \\n\",\n    \"            txt_path=os.path.join(\\n\",\n    \"                self.data_path, 'quickdraw_png_set1_valid.csv'),\\n\",\n    \"            img_dir=os.path.join(self.data_path, 'quickdraw-png_set1/'),\\n\",\n    \"            transform=self.resize_transform)        \\n\",\n    \"\\n\",\n    \"        self.test = QuickdrawDataset( \\n\",\n    \"            txt_path=os.path.join(\\n\",\n    \"                self.data_path, 'quickdraw_png_set1_test.csv'),\\n\",\n    \"            img_dir=os.path.join(self.data_path, 'quickdraw-png_set1/'),\\n\",\n    \"            transform=self.resize_transform)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ebb73cb1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"4bcb535f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2474ebe2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bcf5df82\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"5efbb4d1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchLeNet5(\\n\",\n    \"    num_classes=10, grayscale=True)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    model=pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e56fece3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"114513ec\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type          | Params\\n\",\n      \"--------------------------------------------\\n\",\n      \"0 | model     | PyTorchLeNet5 | 61.7 K\\n\",\n      \"1 | train_acc | Accuracy      | 0     \\n\",\n      \"2 | valid_acc | Accuracy      | 0     \\n\",\n      \"3 | test_acc  | Accuracy      | 0     \\n\",\n      \"--------------------------------------------\\n\",\n      \"61.7 K    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"61.7 K    Total params\\n\",\n      \"0.247     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4e5225d94d144c9b87b02e0ccab245d7\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 21.72 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=1000)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a19c0dbe\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ceaf63b5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"7d870b0f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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sRMcAbxphZtvVhxpiDAMaYgyISWtGHi8hkYDJAWFgYycnJ1TqJvLy8KpVt5V7Ej1mFfLxgCaG+9e8WUlXroz7TuihL66Os+lwfzkwcFU3Ndd4VhTFmHjBPRIYAzwFX2jYNNMYcsCWG70Uk1RizrLIfbks0swDi4+NNQkJCVeMHrKGRq1I2KvskH6Qmc6pxNAkDa/ewz9VR1fqoz7QuytL6KKs+14cz/yTOAFravY8EDlxoZ1tSiBGRYNv7A7afmcA8rKYvgMMiEg5g+1mr2oWigv1oE+zHEh0tVylVTzkzcawB2olItIh4AeOBr+x3EJG2Yps0WER6Al5Ajoj4iUiAbb0fcBWQYiv2FXCnbflO4EsnnkO1JHQI5eedOZwqLHZ1KEop5XBOSxzGmGJgKrAI2Ap8bIzZLCJTRGSKbbcbgRQR2YDVA+sW283yMGC5iPwKrAbmG2O+tZV5ARguItuB4bb3tcqw2FAKi0tZkZ7j6lCUUsrhnDp1rDFmAbCg3LqZdst/B/5eQbmdQPcLHDMHuMKxkTpW7+im+Hm5k5SWyZWdwlwdjlJKOVT96/ZTC3h7uDOwbTBJqZmc622slFL1gyYOJxkWG8qB4/mkHc51dShKKeVQmjicJME2Wm5SqvauUkrVL5o4nKR5Yx86hQfq8CNKqXpHE4cTJcaGsG7vUY6fKnJ1KEop5TCaOJxoWGwoJaWGZdu1uUopVX9o4nCiuJZNaeLrSZIOeqiUqkc0cTiRu5swtH0IS9OyKC3VbrlKqfpBE4eTDYsNJedkIb9mHHN1KEop5RCaOJxsSLsQ3ASSdNBDpVQ9oYnDyZr6edGjVVPtlquUqjc0cdSAYbGhbNp/nMzcfFeHopRSl00TRw1I6BACQLI2Vyml6gFNHDWgU3ggYYHeJGu3XKVUPaCJowaICIkdQvlxWzZFJaWuDkcppS6LJo4akhgbSm5BMWt2H3F1KEopdVk0cdSQQW2D8XQXvc+hlKrzNHHUED9vD/pGB7FEu+Uqpeo4pyYOERkhImkiki4iT1awfYyIbBSRDSKyVkQG2da3FJEkEdkqIptF5CG7Ms+IyH5bmQ0iMsqZ5+BIibGhpGfmse/IKVeHopRS1ea0xCEi7sB0YCTQCbhVRDqV220x0N0YEwf8BphtW18M/N4Y0xHoBzxQruzLxpg426vMnOa1WaKtW64OeqiUqsucecXRB0g3xuw0xhQCc4Ex9jsYY/LMuUm5/QBjW3/QGLPetpwLbAUinBhrjWgT4k9UkK82Vyml6jQPJx47Athn9z4D6Ft+JxEZC/w/IBS4poLtUUAPYJXd6qkiMhFYi3VlcrSCcpOByQBhYWEkJydX6yTy8vKqXbYi7fwLSd5+ikWLk/B2F4cdt6Y4uj7qMq2LsrQ+yqrX9WGMccoLuBmYbfd+AvCfi+w/BPih3Dp/YB1wg926MMAd62rpr8CcS8XSq1cvU11JSUnVLluRpWmZpvUT35jFWw859Lg1xdH1UZdpXZSl9VFWfagPYK2p4DvVmU1VGUBLu/eRwIEL7WyMWQbEiEgwgIh4Ap8BHxhjPrfb77AxpsQYUwq8idUkVmf0bdOMRp7uJKVqt1ylVN3kzMSxBmgnItEi4gWMB76y30FE2oqI2JZ7Al5Ajm3df4Gtxph/lSsTbvd2LJDixHNwOG8Pdwa2DWZJauaZKyillKpTnJY4jDHFwFRgEdbN7Y+NMZtFZIqITLHtdiOQIiIbsHpg3WK7PBqI1bQ1rIJut/8QkU0ishFIBB5x1jlQUoR3vuNvZA+LDWX/sdOkZ+Y5/NhKKeVszrw5jrG6yi4ot26m3fLfgb9XUG45UOGdY2PMBAeHeWHzH6VnyjfQpzc0i3bYYc+MlrskNZN2YQEOO65SStUEfXL8YvpOwa20EN4dAycueHumylo0aURs8wDtlquUqpM0cVxMWGc2dnsGTh2xkkee425oD4sNZe2eo5zIL3LYMZVSqiZo4riE3MB2cNtHcGwfvDcWTp/3yEi1JMaGUlJq+HFbtkOOp5RSNUUTR2VEDYTx70N2GnxwMxTkXvYhe7RsQuNGnjr8iFKqztHEUVltr4Sb3oL96+HDW6Ho9GUdzsPdjSHtQ0hOy6S0VLvlKqXqDk0cVdHxWhg7E3Yvh48nQnHhZR1uWGwI2XmFpBw47qAAlVLK+TRxVFW3cXDty7D9O/h8EpQUV/tQQ9qFIIL2rlJK1SmaOKoj/m64+m+w5Uv46ndQWr15xIP8vYlr2YQkTRxKqTpEE0d19X8AEp6GX/8HCx+Hag4fMqxDKL9mHCcrt8DBASqllHNo4rgcQ/8AAx6ENbPhh79UK3kkxoYCsHSbDnqolKobNHFcDhEYPg3i74Gf/g3LXqzyITq3CCQ0wFu75Sql6gynjlXVIIjAqBeh6BQkPQ9eftD/t1UoLiR0CGFhyiGKSkrxdNdcrpSq3fRbyhHc3GD0a9BxNCx6Cta9XaXiw2JDyc0vZt0exzyVrpRSzlSpxCEifiLiZltuLyKjbRMtqTPcPeDG/0Lb4fD1w7Dxk0oXHdg2GE930eYqpVSdUNkrjmWAj4hEAIuBu4G3nRVUneXhBbe8B1GDYN59kDq/UsUCfDzpHdVMu+UqpeqEyiYOMcacAm7Amjd8LNDJeWHVYZ6N4NYPoUUP+OQuSF9cqWLDYkPZdjiPjKOnnBufUkpdpkonDhHpD9wOnPkzWm+sX4h3ANzxKQR3gLm3w54VlyyS0MHqlpuUpt1ylVK1W2UTx8PAU8A82/SvbYAkp0VVHzRqChPmQeNI+GCcNTjiRcSE+NGqmS/J2lyllKrlKpU4jDFLjTGjjTF/t90kzzbGPHipciIyQkTSRCRdRJ6sYPsYEdlom1N8rYgMulRZEWkmIt+LyHbbz6aVPNea5x8CE78E36bw/g1weMsFdxUREjuE8NOObPKLSmowSKWUqprK9qr6n4gEiogfsAVIE5HHL1HGHZgOjMS6H3KriJS/L7IY6G6MiQN+A8yuRNkngcXGmHa28uclpFqlcQRM/Ao8fKxZBHN2XHDXxNhQ8otKWbkzpwYDVEqpqqlsU1UnY8wJ4HpgAdAKmHCJMn2AdGPMTmNMITAXGGO/gzEmz5iz43T4AaYSZccA79iW37HFVLs1i7auPEwJvDMaju2tcLd+bYLw8XTT5iqlVK1W2cThaXtu43rgS2NMEee+5C8kAthn9z7Dtq4MERkrIqlYN91/U4myYcaYgwC2n6GVPAfXCukAE76AwlzryiP30Hm7+Hi6MzAmmCVpmZhqDpqolFLOVtmeUW8Au4FfgWUi0ho4cYkyUsG6874NjTHzgHkiMgR4DriysmUv+uEik4HJAGFhYSQnJ1el+Fl5eXnVLluRwI5/pPuvfyZ/xnA2xP2VIq/AMtsj3IpYfKSQD+cn0cK/9j3Y7+j6qMu0LsrS+iirXteHMaZaL8DjEtv7A4vs3j8FPHWJMruA4IuVBdKAcNtyOJB2qVh79eplqispKanaZS9o51Jjngs1ZuZgY04fK7Mp4+gp0/qJb8yspTsc/7kO4JT6qKO0LsrS+iirPtQHsNZU8J1a2ZvjjUXkX7aeT2tF5CWsexIXswZoJyLRIuIFjAe+KnfctiIituWegBeQc4myXwF32pbvBL6szDnUKtFDYNy7cHiz1VW38OTZTRFNGtEhLEBnBVRK1VqVbQuZA+QC42yvE8BbFytgjCkGpgKLgK3Ax8Z6BmSKiEyx7XYjkCIiG7B6Ud1iS3QVlrWVeQEYLiLbgeG293VP+6vhxtmQsRrm3gZF+Wc3JcaGsmb3EXLzi1wYoFJKVayy9zhijDE32r1/1vZlf1HGmAVYvbDs1820W/478PfKlrWtzwGuqFzYtVznsVB0Gr64Hz6927oKcfcksUMIM5fuYPn2bEZ2DXd1lEopVUZlrzhOl3s4byBw2jkhNTBxt1nzeaQtsAZGLC2hV+umBPh46Gi5SqlaqbJXHFOAd0Wkse39Uc7dZ1CXq8+91n2OH/4Cnr54XPcqQ9qHkJSWRWmpwc2tok5mSinlGpVKHMaYX4HuIhJoe39CRB4GNjoxtoZl0MNQmAfL/glefgxrP5X5Gw+y5eAJukQ0vmRxpZSqKVV6UMAYc8JYT5ADPOqEeBq2xD9Cv9/CqpmMzP4vImjvKqVUrXM5T5hp+4mjicDVf4OeE/H9+WWebfadJg6lVK1zOXNq6JgYziAC174ChaeYmPI2O44ZcvLiCfL3dnVkSikFXCJxiEguFScIARo5JSIFbu4wdibHc0/w7J53WPtDO4Ku/52ro1JKKeASTVXGmABjTGAFrwBjjM4A6EzungTc/i4/S3d6bvgzrJwOp464OiqllLqsexzKydy8GvFV7D9YS0dY9DS82B4+uBk2fAj5x10dnlKqgdKrhlpuUKfWjPvlaebf5E/nI4th8zzYPgXcvaHtldDlBmg/Arz9XR2qUqqB0MRRyw1qF4yHmxvv7G7Kn6/7C/7Dp0HGWtj8OWz+AtLmg0cjaH+VNYRJu6vBy9fVYSul6jFNHLVcoI8nV3duzsdrM5j3y376RDcjsUMoCT3/SMxVzyP7VkHK57DlS+vl6QcdRkDnG6wrEk8fV5+CUqqe0cRRB7wyPo4J/VuTlJpJUlomz8/fyvPzt9KqmS+JHUJIiP0D/a/8Gz4HfraSyNavIOUz8A6EDqOs5qw2ieDh5epTUUrVA5o46gBPdzf6tQmiX5sgnhrVkYyjp0hKyyI5NZOP1u7jnZV78PF0Y0BMMIkdHibhN9NoeWyN1Zy19RvYOBd8GkPsddBlLEQPBXdPV5+WUqqO0sRRB0U29WVCv9ZM6Nea/KISft6ZQ3JaFktSM88+ad4u1J/E2Kkk3vBHepduxGPrF9aVyIb3oVEz6DTaas6KGmQ9N6KUUpWkiaOO8/F0J6FDKAkdQvnLdZ3YmX2SpNRMktOyeOunXcxaZvD39mBwu3u5IvExrvTaRJOd38DGT2Dd2+AXAp3GWEmkVX9w0x7aSqmL08RRj4gIMSH+xIT4M2lwG/IKivkpPZvktEySUrNYmHII8KJLxF0M7/kg1/ltJurQItx++QDWzIaAcOh0vXVPJLK3NfyJUkqVo4mjHvP39uDqzs25unNzjDFsPZhLUlomyWmZ/HtZBi+bxjT1vZ3hbSczLnAL3Y4twWvtHFg1Axq3tK5EutwALXpqElFKnaWJo4EQETq1CKRTi0AeSGzLsVOFLNueTXJqJj9sy+Ljky1wkzsYEPkbJjbbQv/TS/Ff9Qay8jVoGmU9I9L5BjClrj4VpZSLOTVxiMgI4N+AOzDbGPNCue23A0/Y3uYB9xtjfhWRDsBHdru2Af5sjHlFRJ4B7gWybNuets1Prqqgia8Xo7u3YHT3FpSUGjZmHLN6aqVlMvnXtkBb2gZM5L7wrSQULyf4p1eR5S8zFIFVgeDTxOqp1ajJBZabVrxeuwQrVec5LXGIiDswHRgOZABrROQrY8wWu912AUONMUdFZCQwC+hrjEkD4uyOsx+YZ1fuZWPMi86KvaFxdxN6tGpKj1ZNeXR4ezJz81malkVyWhbTtjXi8YJuhLpP4J6QNOK9M+jVuinkH7PGyzp9DLK3nVsuvsRU9J6+F0k6ja33F1r28tMms1rq25SDrNtfRIKrA1E1wplXHH2AdGPMTgARmQuMAc4mDmPMCrv9fwYiKzjOFcAOY8weJ8aq7IQG+HBzfEtujm9JUUkp6/YcJSktk7c3hPBqTj7f3JxAdLBfxYWLC6wEkn/cSi7nLR8ru/5EBhzebC0XXGLgRjePc1cz/s0hsIX1ahx5bjkwAnyDtXdYDfpg1R7+OC8Fd4G7j52mRROdcaG+E2OcMx+TiNwEjDDGTLK9n4B1NTH1Avs/BsSe2d9u/RxgvTHmNdv7Z4C7gBPAWuD3xpijFRxvMjAZICwsrNfcuXOrdR55eXn4++sAggBZp0p5ZsUpGnu78X/9GuHr6eC//k0JHsWn8Cg+iUdxHp5FebZl6731OolnUS5ehUfxLsjGu+AIbqa4zGFKxYMC72YUeAdT4B1ke5VdLvRqAnJ5z6/o7wYs2VvEu1sK6djMjbSjJVzRypPbO+qkY1A/fj8SExPXGWPiy6935hVHRd8qFWYpEUkE7gEGlVvvBYwGnrJbPQN4znas54CXgN+c90HGzMJq+iI+Pt4kJCRU+QQAkpOTqW7Z+ij79GJeWlfAp/v9eXNiPO5uLm46Ki2FU9lwYj+cOADH9+N2Yj+NThyg0YkD1hVNzmooKShbTtyt7sf2VyrllwOaX/QJ+4b+u/Huyt28u2UzV3YMZfrtPfnN6z+w/IDh7xMH0MxP72XV598PZyaODKCl3ftI4ED5nUSkGzAbGGmMySm3eSTW1cbhMyvsl0XkTeAbRwatLq5jkDvPjO7M/32Rwj8XpfHkyFjXBuTmBv6h1qtFj4r3McaaBOtMcinzcz8cToHt30HRqXIFBfzDKmgSsxKLd35WhR/XELz10y6e/XoLwzuFMf22nnh5uDGqjSc/HTjN2yt28+jw9q4OUTmRMxPHGqCdiERj3dweD9xmv4OItAI+ByYYY7ZVcIxbgQ/LlQk3xhy0vR0LpDg6cHVxd/RrTeqhE8xcuoPY5gFc3yPC1SFdnAj4BVmv8G4V72OMde/lxIEKkssByEmHnUuhMPdskf4Au16xdVUeC0ExNXAyrjf7x508P38rV3cO4z+3WkkDIMLfjas6hfHOit3cN6QNft7a27++ctq/rDGmWESmAouwuuPOMcZsFpEptu0zgT8DQcDrYvWWKT7TniYivlg9su4rd+h/iEgcVlPV7gq2qxrwl+s6k56Zxx8+20hUsB9xLZu4OqTLI2LddG/UFMI6X3i//BNnE0r6ym9oW7gZljxnvcK7Wwmk0/XQLLrGQq9Js5bt4G8LUhnZpTmv3toDT/eynRDuT4jhuy2H+XD1XiYNbuOiKJWzOfVPAtvzFQvKrZtptzwJmFS+nG3bKaykUn79BAeHqarB092N12/vxejXljP53bV8/btBhAU2gLk/fAKtV2gsGRnutE14GY7ts+ZC2TwPfnjGerXoabsSuR6atHJx0I4xc+kOXliYyjVdw3llfNx5SQOgR6um9G8TxJs/7mRC/9Z4e+gAmvWR9llU1dbMz4vZd8ZzsqCYye+uJb+oxNUhuUaTljBgKty7GB7aCMOnWU/Yf/8neKUrzL4SVk6H4xmujrTapiel88LCVK7r3oJ/l08aRadhzwqCs1bCzqU83j0fz9wM5q9OtTovqHpHGyHVZYltHsjLt8Qx+b11PPX5Jv41rjvSkB/Sa9oaBj5kvY7stKb33fw5LHraerXsZ2vOGgOB4a6OtlL+s3g7L32/jTFxLXjp5u545B2Efatg32rIWA0Hf4XSYroAbIaewHJv4Dsw37sh3oHnHvY87+HOC61rak1E5q5fUbWR/quoy3ZV5+Y8dlV7XvxuG7HNA7hvaMO4SXxJzdrA4EetV3Y6bJkHKfPg2yfg2yeh9YBzScQ/1NXRVujV77bwQ9IPvNw6kzFuGbj9e43VxRmsue4jesKA30HLvqzddpD4Lu0g/xgb0/fw5c9bGN81kHYBxWUfAj1x4NzDoCWFFw/AK+DiCcZ+BIIzPxtHgpevM6qj9is8aSXy/evhwHrr543/hcheDv0YTRzKIR5IbEvqoVxe+DaV9mEBJMbWzi9ClwluC0Met15ZaeeuRBY8Bgv/AK0HnksifsGui/NkNuxbjdm3iv2blnLv8c086F0Ih4H8ltCyD7T8nfWzedcyz7nkHUyG6MEAdO5geDhtKauy3Pn6lkEXvgotOn0uiVQ4ysDxstuP7Dy3ruhkxccUN2gWY8Vn//IPq19D1hQXWl3JD6yH/b9YP7NSzw1EGhgJET2cMoqCJg7lECLCP2/qzq7skzz44S/Me2AAbUMDXB1W7RTSARKesF6ZW6154jd/DvMfhQWPW1++ncdCx9Hg28x5cZSWWJ+fsdpqdtq3yvpixnr6Pqskil0hYxiYeA1uLftA48p3u3Z3E+4b2oYnPtvE8vRsBrcLqXhHz0bWqzrNdsWF5xLNmWRy+igc2QGHNsH+dVa9nuEbfH4yCWpXN5rDSkusMeHsryQOp5y7YvMNsjpkdLzO+hnR06lXsXWgxlRd0cjLnTcnxjP6teVMemctXz4wiMa+Orf5RYV2hGF/hMSnrTG7Nn9u9c76+iH45lFok2DNiRJ7jdXufznyj0PG2nNJImPtuedS/EKgZV9Mz7v434Ewpq33ZmzvGP42titu1Rwd4PoeEbz8/XZeT9px4cRxOTy8wD/Eel1I/nGrXg9tgkMb4VAKrHrj3EgC7t7Wv4F9MgnrbDWBuYoxcGxP2SRx8FcozLO2e/lbD7v2nWIliBY9rZ57NXg1pYlDOVSLJo14Y0Ivxs/6makfruetu3rjUUG3TVWOCDTvYr2G/cn6kkuxJZEvH4CvH4aYYdaVSOyoS3+xGQM5O2xXE7Yb2ZlbAWM15YR2hm7joGVfaNkbmkZjgBe+TeWN9Tu5rW8rnh/TpdpJA8Dbw51Jg6N5fv5Wftl7lB6tLjPxVYdPY+teUusB59aVFEH2dusv9jPJJG0h/PLeuX2atC6XTLo478s59/C5BHHm5+kj1jZ3L+vzu996LkkEtwM313Zz1sShHK5X62b89fqu/OGzjfx1wVb+ct1FHqhT5xOxHiYM7w5XPgMHfrFdiXwB2xdZXyZtr7SSSPsR1nMlhaesL519q8/1djplG8HHu7GVHDqPte5NRPQC77LNiMYY/rZgK2/+uIs7+rVi2ujLSxpn3NqnFf9Zks6M5B3MmnjeWHmu4e4JYZ2sV7dx1jpjIPdQ2WRyaBOkzufsEHvejW2JpMu5ZBLaETyqMKjj6WPWv+fZRPGLNToBWAk9pKP1h8GZ5qbQzrVyDhtNHMopxvVuSeqhXOb8tIvY5gHc0rt+PARX40SsL5CInjD8Oat5afM865W2wGpqCWoL2WlQahslOLg9tB9pu5Hd13p/kRukxhie+2Yrc37axZ39W/PM6M4O61Lt5+3BnQOieHXxdtIzc2vvfS8R6z5LYDi0G35ufeFJ60rNPpmsf+/cjXk3DwjuUDaZNO8GfkG4lRTA3lVlryZy0s8du2k0tOpnJfIWPa3hcLwuMF1BLaOJQznN06Ni2Z6Zy/99kUJMiD/xUU680dsQiFhXDi17w1XPW1cVm+dZN007jLCSRGTvKt1QN8bw7NdbeHvFbu4eGMWfr+3k8Odw7hoQxZvLdjIjeScvjevu0GM7nZcfRMZbrzNKS+HorrLJZNePsNFu0lK/EAafzIEfbT2cAsKt5NB9vPWzRQ/ndnxwMk0cymk83N147daeXP/6T0x5fx1fTh1EhE7y4xhubtZfq636VfsQxhie+Woz76zcwz2Dovm/azo65eHNZn5ejO/TkvdW7uHRq9rX/d8BNzdrQMugGKv574yTOXB4k5VMMrey90gBrQeMtRJFHXnYs7L0rqVyqsa+nrw5MZ6ColLufWctpwqLL11IOV1pqeFPX6bwzso9TB7SxmlJ44x7bQMevrlsp9M+w+X8gqxecAOmwvXT2dXmDqs3XD1LGqCJQ9WAtqH+vHpbD7YeOsHjn2zEWbNOqsopLTX88YsU3v95L1OGxvDUyFinDxPTokkjru8Rwdw1e8nJK7h0AVWraeJQNSKxQyhPjYxl/qaDvLYk/dIFlFOUlhqenreJD1fv5YHEGJ4Y0aHGxhabMjSGguJS3lmxu0Y+TzmPJg5VY+4d3IYbekTw0vfb+DblkKvDaXBKSw1PfLaRuWv28bthbXnsqppLGmBdeV7dqTlvr9hNXoE2WdZlmjhUjRER/nZDV7q3bMKjH28g9dAJV4fUYJSUGh7/dCOfrMvgoSva8ejw9i4Zxfj+hBhO5Bfz4aq9Nf7ZynE0caga5ePpzqwJvQjw8WDSO2u1vbsGlJQaHv/kVz5bn8EjV7bnERclDYDuLZswsG0Qs5fvpKC4gc7fUg9o4lA1LizQh1kT4snMLeD+D9ZTWFy3JvspKC7hg1V7+NfafF5YmMrSbVm1trdYcUkpj368gc9/2c9jV7XnoSvbuTok7h/alsMnCvh8/X5Xh6KqyamJQ0RGiEiaiKSLyJMVbL9dRDbaXitEpLvdtt0isklENojIWrv1zUTkexHZbvvpggFw1OXq3rIJ/7ixG6t3HeHZrze7OpxKOV1Ywpzluxj6j2T+OC+FAydLmf3jTu6cs5puz3zHjTNW8OKiNH5Kz64VsyEWl5TyyMe/8uWGA/xhRAemDnN90gAY2DaIbpGNeWPpDkpKtYddXeS0BwBFxB2YDgwHMoA1IvKVMWaL3W67gKHGmKMiMhKYBfS1255ojMkud+gngcXGmBdsyehJ4AlnnYdynut7RJB6KJeZS3cQGx7IhH6tXR1ShXLzi3j/573M/nEnOScL6RvdjBdv7k5Rxib6DBjMuj1HWbkzh5U7cpixdAevJaXj5e5GXKsm9G8TRP+YIHq0alKj828XlZTy8NwNzN90kKdGxtaqybVEhPuHxnD/B+tZmHKQa7u1cHVIqoqc+eR4HyDdGLMTQETmAmOAs4nDGLPCbv+fgchKHHcMkGBbfgdIRhNHnfX41R3YfjiXZ7/aTEyIHwNiXDiJUTnHThXy1k+7eeunXZzIL2Zo+xCmDmtLb9vQKcn7BT9vD4a0D2FIe2to79z8ItbuPpdI/rNkO/9evB1vDzd6tW5KP1si6R7ZBC8P51zwF5WU8uCHv7Aw5RB/HNWRe4e0ccrnXI6rOzenTYgfM5J3cE3X8IY93XAdJM56GEtEbgJGGGMm2d5PAPoaY6ZeYP/HgFi7/XcBR7GGpnzDGDPLtv6YMaaJXbmjxpjzmqtEZDIwGSAsLKzX3Llzq3UeeXl5+Pv7V6tsfeSM+jhdbHhu5WlOFBr+3L8Rob6uvfV2osDw7e4iluwtIr8Eeoa6c12MJ9GNy14xVKYuThYZth0tYWtOCVuPlLIv17qf4+UO7Zq40bGZO7FB7kQHuuHugNFoi0sNM34tYN3hEm6N9eLqqJqbD6Wqvxs/ZhTx35RCft/Lm64h9W/0o/rw3ZGYmLjOGHPesMbO/Neq6H9BhVlKRBKBe4BBdqsHGmMOiEgo8L2IpBpjllX2w22JZhZAfHy8SUhIqHTg9pKTk6lu2frIWfXRMe4kY6b/xH/TPPjstwPw9675L5KDx08za9lOPly9l4LiUq7t1oIHEmOIbR5Y4f6VrYtr7JaPnixk1a4j/Gy7Ivl0ey5sL8LPy53e0c3ONm11btG4yomksLiUB/63nnWHT/GX6zpx98DoKpW/XFX93RhQXMqCfybx01Fffndzf+cF5iL1+bvDmf87M4CWdu8jgQPldxKRbsBsYKQxJufMemPMAdvPTBGZh9X0tQw4LCLhxpiDIhIOZDrxHFQNiQr24/XbezJxzmoe+WgDb9zRyyHzQVTG3pxTzFi6g8/WZVBiDGN7RHB/QgwxIY7/a7GpnxcjujRnRJfmAGTnFbBq5xFW7sxm5Y4cktOyAAjw8aBvdLOzTVsdmwdetD4Kikv47fvrWZyaybQxnZnYP8rhsTual4cbkwa34blvtrB+71F6umKiJ1Utzkwca4B2IhIN7AfGA7fZ7yAirYDPgQnGmG126/0AN2NMrm35KmCabfNXwJ3AC7afXzrxHFQNGtg2mD9d05Fnvt7Cyz9s4/dXdXDq56Vn5vF6cjpfbjiAuwg3x0cyZWgMLZv5OvVz7QX7e3NNt3Cu6WYNhJd5Ip+VO3POXpH8sNX6u6iJryd9z16RBNM+zP/sfYH8ohLuf38dSWlZPH99F+6opZ0MKjK+d0v+s2Q7M5J38GZtmehJXZLTEocxplhEpgKLAHdgjjFms4hMsW2fCfwZCAJet/0nKLa1p4UB82zrPID/GWO+tR36BeBjEbkH2Avc7KxzUDXvzgFRpB7K5T9L0mkfFsB13R3f42brwRO8lpTOgk0H8fZw464BUdw7uA3NG/s4/LOqKjTQhzFxEYyJiwCs5rMzSWTlzhwWbT4MQJCfF/3aBNEvJojFWw+TnJbF38Z25ba+dWvCLD9vD+4aEMUrP2xn2+Fc2ofV0omeVBlObUg2xiwAFpRbN9NueRIwqYJyO4EKZ3yxNWdd4dhIVW0hIkwb04UdWXk8/umvRAX50TXyEvNrV9KGfcd4bUk6P2w9jL+3B/cPjeGeQdEE+Vdh6s8aFt64EWN7RDK2h9XhMOPoqbNJ5OcdOczfdBAReOGGrozvU7eSxhl39o9i1rKdzEzewb9uiXN1OKoS6l9XBlXneXm4MeOOXoz+z3Imv7eWL6cOJDSg+lcDq3bm8FpSOj9uz6aJryePDm/Pnf2jaOxbcz2OHCWyqS83x/tyc3xLjDHsPXKKohJD29C623unqZ8Xt/ZpxdsrdvPoVe2JbFpzTYWqenTIEVUrBft78+ad8Rw7VcSU99ZVeVwjYwzLtmUxbuZKbpn1M1sPnuCpkbEsf2IYD17Rrk4mjfJEhNZBfnU6aZwxaXA0blLPJ3qqRzRxqFqrc4vGvDSuO+v3HuOP81IqNQGUMYbvtxzm+uk/MXHOavYdPcUz13Vi+RPDuG9ojEu6+apLs5rkIpi7Zh/ZOvDlZSsuKWX59myenreJQ8fzHX58/V+karVRXcN56Ip2/HvxdjqGB3LPoIqfTSgpNSxMsSaJSj2US6tmvvy/G7pyQ8+IGh3qQ1XffUNj+GRdBm//tJvHrnZuj7r6qLC4lBU7slm46RDfbTnE0VNF+Hq5M7xTmMM7fmjiULXeQ1e0I+1QLn+dv4V2of5nh/cAa3iNrzYcYHpyOjuzThIT4sfLt3Tnum4t8HDXC+q6JCbEnxGdm/Puyt3cN7QNAT51vznR2QqKS1i+PZsFmw7x/ZZDnMgvxt/bgys7hjKyazhD24fg4+n4P5w0cahaz81NeGlcd26ccZKp/1vPFw8MJKJpIz5dl8GM5B1kHD1Nx/BAXr+9JyM6N6+xBweV492fEMPClEP8b9XeWjUwY22SX1TC0m1ZLNx0kMVbM8ktKCbQx4PhnZozqmtzBrYNdkqysKeJQ9UJft4evDkxnjG2exfFJYZDJ/KJa9mEZ0d3ZlhsqA6UVw90i2zCoLbBzF6+izsHRDn9C7CuOFVYTHJaFgs2HWRJaianCkto4uvJqK7hjOzanAExwU4bNLMimjhUndGymS8zbMOS9GjVhJfGdWdATJAmjHrmtwkx3DZ7FZ+v31/nHmh0pLyCYpakZrJw00GS0jLJLyolyM+L63tEMKpLOH3bNMPTRc2xmjhUndK3TRAbn7lKb3jXY9aw8415Y9kOxsVHNqh7VSfyi1i89TALNh1i6bYsCotLCQnwZlx8S0Z2CadPdDOHjKJ8uTRxqDpHk0b9JiLcn9CWKe+vY2HKIacMO1ObHDtVyPdbDrMw5RA/bs+iqMTQPNCH2/u2YlTXcHq1alrr7ttp4lBK1TpXdQojJsSP15N3cG23+jfR05GThXy3+RALUg6xIj2b4lJDRJNG3DUgipFdw4mLbFLrkoU9TRxKqVrHzU2YMjSGxz/dyNJtWSR0CHV1SJctK7eARZsPsTDlID/vPEJJqaF1kC+TBrdhVNfmdI1oXGcSpCYOpVStNCYugpe/38bryTvqbOI4fCKfb1MOsWDTQVbvPoIx0CbYj/uHxjCya3M6hQfWmWRhTxOHUqpWOjPR07RvtrBuzxF6tW7m6pAqpbiklA/X7OPdn0+z/dvFALQP8+fBYe0Y1TW8zFwqdZUmDqVUrTW+z7mJnmbfWfsTx56ckzzy0QbW7z1GywA3fj+8PSO7NqdtaP2aZ0QTh1Kq1vL18uCuAdG8/MM20g7l0qF57fwCNsbw0Zp9TPtmCx5uwqu39iDw6DYSEtq5OjSnaDgdpJVSddKdA1rj6+XOzKU7XB1KhbLzCrj33XU8+fkm4lo24duHhzC6nnch1sShlKrVmvh6cVufVnz16wH2HTnl6nDKWJJ6mBGvLGPZ9iz+75qOvH9PX1o0aeTqsJzOqYlDREaISJqIpIvIkxVsv11ENtpeK0Sku219SxFJEpGtIrJZRB6yK/OMiOwXkQ221yhnnoNSyvUmDW5jTfT0Y+2Y6OlUYTFPz9vEb95eS7C/N19PHWTFWIufvXAkp93jEBF3YDowHMgA1ojIV8aYLXa77QKGGmOOishIYBbQFygGfm+MWS8iAcA6EfneruzLxpgXnRW7Uqp2ad7Yhxt7RvLRmn38blg7QgJcN0/8L3uP8shHG9hz5BT3DWnDo1e1b3CjGTjziqMPkG6M2WmMKQTmAmPsdzDGrDDGHLW9/RmItK0/aIxZb1vOBbYCEU6MVSlVy00e0obCklLeXrHLJZ9fXFLKy99v46aZKykqMXx4bz+eGtWxwSUNcG7iiAD22b3P4OJf/vcAC8uvFJEooAewym71VFvz1hwRaeqAWJVStVybEH9GdQnn3ZV7yM0vqtHP3pV9khtnruTfi7czpnsLFj48mH5tgmo0htpEKjOPc7UOLHIzcLUxZpLt/QSgjzHmdxXsmwi8DgwyxuTYrfcHlgJ/NcZ8blsXBmQDBngOCDfG/KaCY04GJgOEhYX1mjt3brXOIy8vD39//2qVrY+0Ps7RuiirJupj9/ESnlmZz83tPbmmjZdTPwusbrbJ+4r5MK0QTze4s7M3fZpXroW/Pvx+JCYmrjPGxJdf78znODKAlnbvI4ED5XcSkW7AbGBkuaThCXwGfHAmaQAYYw7b7fMm8E1FH26MmYV1z4T4+HiTkJBQrZNITk6mumXrI62Pc7Quyqqp+licvYqkA7k8N2GwUyd6ysot4MnPNrI4NZPB7YL5503dqzR3d33+/XBmU9UaoJ2IRIuIFzAe+Mp+BxFpBXwOTDDGbLNbL8B/ga3GmH+VKxNu93YskOKk+JVStdD9CTFk5xXw6boMp33G91usbrbL07P5y3WdeOfuPlVKGvWd0644jDHFIjIVWAS4A3OMMZtFZIpt+0zgz0AQ8Lpt7JZi22XRQGACsElENtgO+bQxZgHwDxGJw2qq2g3c56xzUErVPv3bBBHXsgmzlu1kfO+WDp3o6WRBMdO+3sJHa/fRKTyQuePjaBdWO59WdyWnDjli+6JfUG7dTLvlScCkCsotByrsEG2MmeDgMJVSdYiI8NuEGCa/t475mw4yJs4xHS7X7TnKox9vYO+RU/w2IYaHr2xfo/N41yVaK0qpOufKjmG0C/VnRvIOLreDT1FJKS99l8bNM1dQUmr4+L7+/GFErCaNi9CaUUrVOWcmeko9lEtyWla1j7MjK48bZ6zgP0vSuaFnJAsfGkzvqNo/Cq+raeJQStVJo+NaENGkEa8np1e5rDGG91bu5ppXf2TvkVPMuL0nL97cnQAfTydEWv9o4lBK1Ume7m7cOziaNbuPsmb3kUqXy8zN5+631/CnLzfTNzqI7x4ewsiu4ZcuqM7SxKGUqrNu6d2KZn5ezEiu3JDr36Yc4uqXl7FyRw7TxnTm7bt7Exqo3WyrShOHUqrOauTlzt0DoliSmsnWgycuuF9eQTGPf/IrU95fR2RTX+Y/OJiJ/aPq/BSurqKJQylVp03sH4XfRSZ6Wrv7CCP/vYzP1mcwNbEtn90/gLahdXsoEFfTxKGUqtMa+3pye7/WfP3rAfbmnJvoqbC4lH8uSmXcGysRhE+m9OexqztoN1sH0BpUStV59wyKxsPNjVk/Wlcd6Zm53DDjJ6Yn7eCmXpEseGgwvVprN1tHceqT40opVRPCAn24sVcEH6/NILxxI15dvB0/bw/emNCLqzs3d3V49Y5ecSil6oX7hsRQXFLKPxelMSAmiG8fHqxJw0n0ikMpVS9EBfvx7JgueLu7cXN8pPaYciJNHEqpemNCv9auDqFB0KYqpZRSVaKJQymlVJVo4lBKKVUlmjiUUkpViSYOpZRSVaKJQymlVJVo4lBKKVUlmjiUUkpViVzuRO91gYhkAXuqWTwYyHZgOHWd1sc5WhdlaX2UVR/qo7UxJqT8ygaROC6HiKw1xsS7Oo7aQuvjHK2LsrQ+yqrP9aFNVUoppapEE4dSSqkq0cRxabNcHUAto/VxjtZFWVofZdXb+tB7HEoppapErziUUkpViSYOpZRSVaKJ4yJEZISIpIlIuog86ep4XEVEWopIkohsFZHNIvKQq2OqDUTEXUR+EZFvXB2Lq4lIExH5VERSbb8n/V0dk6uIyCO2/ycpIvKhiPi4OiZH08RxASLiDkwHRgKdgFtFpJNro3KZYuD3xpiOQD/ggQZcF/YeAra6Ooha4t/At8aYWKA7DbReRCQCeBCIN8Z0AdyB8a6NyvE0cVxYHyDdGLPTGFMIzAXGuDgmlzDGHDTGrLct52J9KUS4NirXEpFI4BpgtqtjcTURCQSGAP8FMMYUGmOOuTQo1/IAGomIB+ALHHBxPA6niePCIoB9du8zaOBflgAiEgX0AFa5OBRXewX4A1Dq4jhqgzZAFvCWrelutoj4uTooVzDG7AdeBPYCB4HjxpjvXBuV42niuDCpYF2D7rssIv7AZ8DDxpgTro7HVUTkWiDTGLPO1bHUEh5AT2CGMaYHcBJokPcERaQpVstENNAC8BORO1wbleNp4riwDKCl3ftI6uElZ2WJiCdW0vjAGPO5q+NxsYHAaBHZjdWEOUxE3ndtSC6VAWQYY85chX6KlUgaoiuBXcaYLGNMEfA5MMDFMTmcJo4LWwO0E5FoEfHCusH1lYtjcgkREaz2663GmH+5Oh5XM8Y8ZYyJNMZEYf1eLDHG1Lu/KivLGHMI2CciHWyrrgC2uDAkV9oL9BMRX9v/myuohx0FPFwdQG1ljCkWkanAIqyeEXOMMZtdHJarDAQmAJtEZINt3dPGmAWuC0nVMr8DPrD9kbUTuNvF8biEMWaViHwKrMfqjfgL9XDoER1yRCmlVJVoU5VSSqkq0cShlFKqSjRxKKWUqhJNHEoppapEE4dSSqkq0cShlAOISImIbLB7OezJaRGJEpEURx1Pqculz3Eo5RinjTFxrg5CqZqgVxxKOZGI7BaRv4vIaturrW19axFZLCIbbT9b2daHicg8EfnV9jozXIW7iLxpm+fhOxFp5LKTUg2eJg6lHKNRuaaqW+y2nTDG9AFewxpVF9vyu8aYbsAHwKu29a8CS40x3bHGezozWkE7YLoxpjNwDLjRqWej1EXok+NKOYCI5Blj/CtYvxsYZozZaRso8pAxJkhEsoFwY0yRbf1BY0ywiGQBkcaYArtjRAHfG2Pa2d4/AXgaY56vgVNT6jx6xaGU85kLLF9on4oU2C2XoPcnlQtp4lDK+W6x+7nStryCc1OK3g4sty0vBu6Hs3OaB9ZUkEpVlv7VopRjNLIbORis+bfPdMn1FpFVWH+o3Wpb9yAwR0Qex5o978xosg8Bs0TkHqwri/uxZpJTqtbQexxKOZHtHke8MSbb1bEo5SjaVKWUUqpK9IpDKaVUlegVh1JKqSrRxKGUUqpKNHEopZSqEk0cSimlqkQTh1JKqSr5/9N9jL7+ByiqAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"18304f53\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"8bff53a0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_36/checkpoints/epoch=8-step=74600.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_36/checkpoints/epoch=8-step=74600.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"80900a8ff1054ce0abdc8a2cbcc7b646\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9217909574508667     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9217909574508667    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9217909574508667}]\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ebe513ab\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f0674611\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"99fb98a9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_36/checkpoints/epoch=8-step=74600.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"ab60d544\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eb52e61c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model, requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"b544b139\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([7, 1, 3, 4, 9])\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a9afe2e3\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"89cd4554\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9218 (92.18%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"74a2323e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"331fa78f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"694f1e4c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"62f39922\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"0882c122\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('./quickdraw-png_set1/binoculars/binoculars_093136.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2625ac92\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we used a resize-transformation in the `DataModule` that rescaled the 28x28 images to size 32x32. We also have to apply the same transformation to any new image that we feed to the model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"64dafca9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"resize_transform = transforms.Compose(\\n\",\n    \"            [transforms.Resize((32, 32)),\\n\",\n    \"             transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"image_chw = resize_transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"003c8d20\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"02845a79\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0ecfcca3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"b442de8b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 1, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"25690363\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"db887578\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"c8fdd29f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"label_dict = {\\n\",\n    \"         0: \\\"lollipop\\\",\\n\",\n    \"         1: \\\"binoculars\\\",\\n\",\n    \"         2: \\\"mouse\\\",\\n\",\n    \"         3: \\\"basket\\\",\\n\",\n    \"         4: \\\"penguin\\\",\\n\",\n    \"         5: \\\"washing machine\\\",\\n\",\n    \"         6: \\\"canoe\\\",\\n\",\n    \"         7: \\\"eyeglasses\\\",\\n\",\n    \"         8: \\\"beach\\\",\\n\",\n    \"         9: \\\"screwdriver\\\",\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"be912947\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: binoculars\\n\",\n      \"Class-membership probability 99.95%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {label_dict[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-mobilenet-v2-cifar10-2.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"4de1313e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchvision,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"4ac15d6c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0c130c38\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- MobileNet-v2 Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e224d072\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements MobileNet-v2 [1] and applies it to the CIFRAR-10 dataset. MobileNet offers a great trade-off when it comes to predictive and computational performance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c0914c6d\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). [Mobilenetv2: Inverted residuals and linear bottlenecks](https://arxiv.org/abs/1801.04381). In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1b37c5a0\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"59b24dea\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"d407207c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 50\\n\",\n    \"LEARNING_RATE = 0.01\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8120234c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73eeee37\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- When using PyTorch Lightning, we can start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"e174fad6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading: \\\"https://github.com/pytorch/vision/archive/v0.11.0.zip\\\" to /home/jovyan/.cache/torch/hub/v0.11.0.zip\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = torch.hub.load(\\n\",\n    \"    'pytorch/vision:v0.11.0',\\n\",\n    \"    'mobilenet_v2',\\n\",\n    \"    pretrained=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"955c5356\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Since the Torchvision model above was implemented for ImageNet, which has a different number of classes than CIFAR-10, we define our own output layer below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"e5f3bd84\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pytorch_model.classifier[-1] = torch.nn.Linear(\\n\",\n    \"        in_features=1280,  # as in the original output layer\\n\",\n    \"        out_features=10)  # number of class labels in CIFAR-10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"883ccd78\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our `LightningModule` as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"d843f37f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"093af3a1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1e2d9d92\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7cd05d82\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"1c9a86d9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=transforms.ToTensor(),\\n\",\n    \"                                 download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                train=False,\\n\",\n    \"                                transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"00688b13\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 4992),\\n\",\n       \" (1, 4993),\\n\",\n       \" (2, 4986),\\n\",\n       \" (3, 4994),\\n\",\n       \" (4, 4997),\\n\",\n       \" (5, 4991),\\n\",\n       \" (6, 4996),\\n\",\n       \" (7, 4988),\\n\",\n       \" (8, 4994),\\n\",\n       \" (9, 4989)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTraining label distribution:')\\n\",\n    \"sorted(train_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"8e17d149\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTest label distribution:')\\n\",\n    \"sorted(test_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a3f02538\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"c5826f13\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"f8568daa\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"969b73db\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training Images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a797e408\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"433a0831\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"31ad9d29\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 1000)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"majority_class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d72bbc7f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- (To be fair, the classes in the test set are perfectly evenly distributed, so the majority class is an arbitrary choice in this case)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"98988746\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print('Accuracy when always predicting the majority class:')\\n\",\n    \"print(f'{baseline_acc:.2f} ({baseline_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fe03db22\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f21a8e5f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"e553a5bb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path,\\n\",\n    \"                         download=True)\\n\",\n    \"\\n\",\n    \"        # Note: Network trains about 2-3x faster if we don't\\n\",\n    \"        # resize (keeping the orig. 32x32 res.)\\n\",\n    \"        # However, the test set accuracy is approx 10% lower\\n\",\n    \"        # on the original 32x32 resolution\\n\",\n    \"        self.train_transform = torchvision.transforms.Compose([\\n\",\n    \"            torchvision.transforms.Resize((70, 70)),\\n\",\n    \"            torchvision.transforms.RandomCrop((64, 64)),\\n\",\n    \"            torchvision.transforms.ToTensor(),\\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"        self.test_transform = torchvision.transforms.Compose([\\n\",\n    \"            torchvision.transforms.Resize((70, 70)),        \\n\",\n    \"            torchvision.transforms.CenterCrop((64, 64)),            \\n\",\n    \"            torchvision.transforms.ToTensor(),                \\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=self.train_transform,\\n\",\n    \"                                 download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                     train=False, \\n\",\n    \"                                     transform=self.test_transform,\\n\",\n    \"                                     download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5a05fe33\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"0efad232\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8b1d079d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e3d7fed7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"74489a01\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4de1430a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"ecd65465\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type        | Params\\n\",\n      \"------------------------------------------\\n\",\n      \"0 | model     | MobileNetV2 | 2.2 M \\n\",\n      \"1 | train_acc | Accuracy    | 0     \\n\",\n      \"2 | valid_acc | Accuracy    | 0     \\n\",\n      \"3 | test_acc  | Accuracy    | 0     \\n\",\n      \"------------------------------------------\\n\",\n      \"2.2 M     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"2.2 M     Total params\\n\",\n      \"8.947     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c38da9c273094d46a4c96798fa877237\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 21.51 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8fd4778b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"79dea1e1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"7d49d0c9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4bc78344\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"3575a7e5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_40/checkpoints/epoch=45-step=16145.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_40/checkpoints/epoch=45-step=16145.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1631b7ce9ca84028b9a09caca8993b0f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.8456000089645386     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.8456000089645386    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.8456000089645386}]\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1fb91480\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b4bb2aca\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"f6c2c008\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_40/checkpoints/epoch=45-step=16145.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"83c8bdd1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"499f84e6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"ea226e7c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([3, 8, 8, 0, 6])\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9318a1c1\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"3a1aaa4d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.8456 (84.56%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5b84af45\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f634dfd0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"af8ec268\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Append the folder that contains the \\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append('../pytorch_ipynb')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"8ae4f695\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from helper_data import UnNormalize\\n\",\n    \"from helper_plotting import show_examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 41,\n   \"id\": \"e7a68d6f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\\n\",\n    \"\\n\",\n    \"# We normalized each channel during training; here \\n\",\n    \"# we are reverting the normalization so that we \\n\",\n    \"# can plot them as images\\n\",\n    \"unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    data_loader=test_dataloader,\\n\",\n    \"    unnormalizer=unnormalizer,\\n\",\n    \"    class_dict=class_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 42,\n   \"id\": \"a0fa1a65\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from helper_evaluate import compute_confusion_matrix\\n\",\n    \"from helper_plotting import plot_confusion_matrix\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 43,\n   \"id\": \"efc0e442\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mat = compute_confusion_matrix(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    data_loader=test_dataloader,\\n\",\n    \"    device=torch.device('cpu'))\\n\",\n    \"\\n\",\n    \"plot_confusion_matrix(\\n\",\n    \"    mat, class_names=class_dict.values())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ebd5b5ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 44,\n   \"id\": \"857aadf8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 45,\n   \"id\": \"e9b3eb27\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"05076a4f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 46,\n   \"id\": \"9436deac\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/cifar10_pngs/90_airplane.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"262f373f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we have to use the same image transformation that we used earlier in the `DataModule`. \\n\",\n    \"- While we didn't apply any image augmentation, we could use the `to_tensor` function from the torchvision library; however, as a general template that provides flexibility for more complex transformation chains, let's use the `Compose` class for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 47,\n   \"id\": \"d3010f85\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = data_module.train_transform\\n\",\n    \"\\n\",\n    \"image_chw = transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8fd165b3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 48,\n   \"id\": \"2bcb9382\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([3, 64, 64])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"37d9054f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 49,\n   \"id\": \"7ada9fa4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 3, 64, 64])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"280d3d76\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 50,\n   \"id\": \"d447fe7d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 51,\n   \"id\": \"c4b52544\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_to_str = {\\n\",\n    \"    0: 'airplane',\\n\",\n    \"    1: 'automobile',\\n\",\n    \"    2: 'bird',\\n\",\n    \"    3: 'cat',\\n\",\n    \"    4: 'deer',\\n\",\n    \"    5: 'dog',\\n\",\n    \"    6: 'frog',\\n\",\n    \"    7: 'horse',\\n\",\n    \"    8: 'ship',\\n\",\n    \"    9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 52,\n   \"id\": \"6872006d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: airplane\\n\",\n      \"Class-membership probability 98.26%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {int_to_str[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"0af894ce\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchvision,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"7cff0a69\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c9db2e3\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- MobileNet-v2 Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac033500\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements MobileNet-v2 [1] and applies it to the CIFRAR-10 dataset. MobileNet offers a great trade-off when it comes to predictive and computational performance.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aaca1a0a\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). [Mobilenetv2: Inverted residuals and linear bottlenecks](https://arxiv.org/abs/1801.04381). In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dcd2de05\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cacb509a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"8328a8ef\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 50\\n\",\n    \"LEARNING_RATE = 0.01\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0f6a60a8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1dd4ce9f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- When using PyTorch Lightning, we can start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"075e2f69\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using cache found in /home/jovyan/.cache/torch/hub/pytorch_vision_v0.11.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = torch.hub.load(\\n\",\n    \"    'pytorch/vision:v0.11.0',\\n\",\n    \"    'mobilenet_v2',\\n\",\n    \"    pretrained=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"be17ee74\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Since the Torchvision model above was implemented for ImageNet, which has a different number of classes than CIFAR-10, we define our own output layer below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"d8f269ca\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pytorch_model.classifier[-1] = torch.nn.Linear(\\n\",\n    \"        in_features=1280,  # as in the original output layer\\n\",\n    \"        out_features=10)  # number of class labels in CIFAR-10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2c00b162\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our `LightningModule` as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"fd6f09e1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bca6a008\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e0b56a7b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1ed6cae1\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"4a15e32b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=transforms.ToTensor(),\\n\",\n    \"                                 download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                train=False,\\n\",\n    \"                                transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"bb0c4311\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 4996),\\n\",\n       \" (1, 4992),\\n\",\n       \" (2, 4994),\\n\",\n       \" (3, 4996),\\n\",\n       \" (4, 4993),\\n\",\n       \" (5, 4989),\\n\",\n       \" (6, 4988),\\n\",\n       \" (7, 4990),\\n\",\n       \" (8, 4990),\\n\",\n       \" (9, 4992)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTraining label distribution:')\\n\",\n    \"sorted(train_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"efb2b428\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTest label distribution:')\\n\",\n    \"sorted(test_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1fe7c5eb\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"68bc9876\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"7bbd4493\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"a205b0a4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training Images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1827d9a7\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6b8fd662\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"09c867cc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 1000)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"majority_class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e8c0fdfb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- (To be fair, the classes in the test set are perfectly evenly distributed, so the majority class is an arbitrary choice in this case)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"0f522edf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print('Accuracy when always predicting the majority class:')\\n\",\n    \"print(f'{baseline_acc:.2f} ({baseline_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9c4bf687\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"45fd155d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"05f521ed\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path,\\n\",\n    \"                         download=True)\\n\",\n    \"\\n\",\n    \"        # Note: Network trains about 2-3x faster if we don't\\n\",\n    \"        # resize (keeping the orig. 32x32 res.)\\n\",\n    \"        # However, the test set accuracy is approx 10% lower\\n\",\n    \"        # on the original 32x32 resolution\\n\",\n    \"        self.train_transform = torchvision.transforms.Compose([\\n\",\n    \"            torchvision.transforms.Resize((70, 70)),\\n\",\n    \"            torchvision.transforms.RandomCrop((64, 64)),\\n\",\n    \"            torchvision.transforms.ToTensor(),\\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"        self.test_transform = torchvision.transforms.Compose([\\n\",\n    \"            torchvision.transforms.Resize((70, 70)),        \\n\",\n    \"            torchvision.transforms.CenterCrop((64, 64)),            \\n\",\n    \"            torchvision.transforms.ToTensor(),                \\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=self.train_transform,\\n\",\n    \"                                 download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                     train=False, \\n\",\n    \"                                     transform=self.test_transform,\\n\",\n    \"                                     download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7b9c9d8b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"4683263c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9fe670fc\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a0e4f3ce\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"22c7cd19\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"da05ca78\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"278a75f6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type        | Params\\n\",\n      \"------------------------------------------\\n\",\n      \"0 | model     | MobileNetV2 | 2.2 M \\n\",\n      \"1 | train_acc | Accuracy    | 0     \\n\",\n      \"2 | valid_acc | Accuracy    | 0     \\n\",\n      \"3 | test_acc  | Accuracy    | 0     \\n\",\n      \"------------------------------------------\\n\",\n      \"2.2 M     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"2.2 M     Total params\\n\",\n      \"8.947     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"278e160223064be58a178444153a0de5\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 37.24 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ba276e94\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4d8f53f8\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"9c20526b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7502c0e3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"240d2dac\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_50/checkpoints/epoch=46-step=16496.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_50/checkpoints/epoch=46-step=16496.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a4d7c534c423452b8e708c299b8bf066\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.8450000286102295     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.8450000286102295    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.8450000286102295}]\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eeb4c934\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ea82acfa\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"eccf8e52\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_50/checkpoints/epoch=46-step=16496.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"b5d6cd26\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"111c3664\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"a3193484\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([3, 8, 8, 0, 6])\"\n      ]\n     },\n     \"execution_count\": 23,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"385b0c41\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"c4e2a894\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.8450 (84.50%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2838fbfc\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3a0c0419\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"972a0bba\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Append the folder that contains the \\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append('../pytorch_ipynb')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"32bfb7a5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from helper_data import UnNormalize\\n\",\n    \"from helper_plotting import show_examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"101cc3ed\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\\n\",\n    \"\\n\",\n    \"# We normalized each channel during training; here \\n\",\n    \"# we are reverting the normalization so that we \\n\",\n    \"# can plot them as images\\n\",\n    \"unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    data_loader=test_dataloader,\\n\",\n    \"    unnormalizer=unnormalizer,\\n\",\n    \"    class_dict=class_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"e98c7232\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"4ddf6faa\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from helper_plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_confusion_matrix(\\n\",\n    \"    cmat_tensor.numpy(),\\n\",\n    \"    class_names=class_dict.values())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"87597418\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"e5a56b91\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"19ddc131\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"01a6534c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"222562df\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/cifar10_pngs/90_airplane.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"82c76d9a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we have to use the same image transformation that we used earlier in the `DataModule`. \\n\",\n    \"- While we didn't apply any image augmentation, we could use the `to_tensor` function from the torchvision library; however, as a general template that provides flexibility for more complex transformation chains, let's use the `Compose` class for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"a8443741\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = data_module.train_transform\\n\",\n    \"\\n\",\n    \"image_chw = transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d8ca63d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"73af9d4f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([3, 64, 64])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3f118223\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"2c55a701\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 3, 64, 64])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7a0b819a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"id\": \"01860ec4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"id\": \"155bcdb2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_to_str = {\\n\",\n    \"    0: 'airplane',\\n\",\n    \"    1: 'automobile',\\n\",\n    \"    2: 'bird',\\n\",\n    \"    3: 'cat',\\n\",\n    \"    4: 'deer',\\n\",\n    \"    5: 'dog',\\n\",\n    \"    6: 'frog',\\n\",\n    \"    7: 'horse',\\n\",\n    \"    8: 'ship',\\n\",\n    \"    9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"id\": \"35e8349d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: airplane\\n\",\n      \"Class-membership probability 100.00%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {int_to_str[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-large-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"769229ce\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"24cee7ef\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"6aae6f76\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"6604cf12\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b5a2daee\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# MobileNetV3 (Large) Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d9ad59f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"MobileNetV3 [1] trained on CIFAR-10 [2].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Searching for MobileNetV3, https://arxiv.org/abs/1905.02244.pdf\\n\",\n    \"- [2] https://en.wikipedia.org/wiki/CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"791cf670\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"62b088bb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"d3a7f40f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 150\\\\nLEARNING_RATE = 0.001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 150\\\\nLEARNING_RATE = 0.001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 150\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6bb03d6c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d7a2d6b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6145ca8b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- Here, for the PyTorch model, we are using an implementation from the Torchvision hub:\\n\",\n    \"\\n\",\n    \"In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"af36f155\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using cache found in /home/jovyan/.cache/torch/hub/pytorch_vision_v0.11.0\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\n\\\\n\\\\npytorch_model = torch.hub.load(\\\\n    'pytorch/vision:v0.11.0',\\\\n    'mobilenet_v3_large',\\\\n    pretrained=False)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\n\\\\n\\\\npytorch_model = torch.hub.load(\\\\n    \\\\\\\"pytorch/vision:v0.11.0\\\\\\\", \\\\\\\"mobilenet_v3_large\\\\\\\", pretrained=False\\\\n)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = torch.hub.load(\\n\",\n    \"    'pytorch/vision:v0.11.0',\\n\",\n    \"    'mobilenet_v3_large',\\n\",\n    \"    pretrained=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b1404890\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Since the Torchvision model above was implemented for ImageNet, which has a different number of classes than CIFAR-10, we define our own output layer below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"7669c141\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model.classifier[-1] = torch.nn.Linear(\\\\n        in_features=1280,  # as in the original output layer\\\\n        out_features=10)  # number of class labels in CIFAR-10)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model.classifier[-1] = torch.nn.Linear(\\\\n    in_features=1280, out_features=10  # as in the original output layer\\\\n)  # number of class labels in CIFAR-10)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model.classifier[-1] = torch.nn.Linear(\\n\",\n    \"        in_features=1280,  # as in the original output layer\\n\",\n    \"        out_features=10)  # number of class labels in CIFAR-10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"056a50a5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our LightningModule as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"230660b1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"51424191\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c7aac5a4\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"63292b0e\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"b204d78e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\",\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_cifar10_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"94612edb\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d9122146\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"73c408db\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 3\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c9e20607\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"a3984318\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"80b11784\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"41995793\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"409ec35d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path, download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.RandomCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.CenterCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=self.train_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=self.test_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"480b16c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"639adb2f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"torch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"torch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e0072aee\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6d87af38\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"43c816f2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8bfbfb6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"c544d03f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type        | Params\\n\",\n      \"------------------------------------------\\n\",\n      \"0 | model     | MobileNetV3 | 4.2 M \\n\",\n      \"1 | train_acc | Accuracy    | 0     \\n\",\n      \"2 | valid_acc | Accuracy    | 0     \\n\",\n      \"3 | test_acc  | Accuracy    | 0     \\n\",\n      \"------------------------------------------\\n\",\n      \"4.2 M     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"4.2 M     Total params\\n\",\n      \"16.859    Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a99321ea1c29471d99f43362c2b3b6f3\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1222d6354a5046c8a6f90d9176bfc793\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      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\"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 50.26 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b5e3365f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3daab48c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"00bc9400\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aeace471\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"1b007a6e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_10/checkpoints/epoch=110-step=19424.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_10/checkpoints/epoch=110-step=19424.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2e7c8b561f21492e99794b3c9eaa7cc7\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.807200014591217}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.807200014591217}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ee72af7b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dd4ed5db\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"bcc7b22a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_10/checkpoints/epoch=110-step=19424.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"ef01b578\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fd4ecccd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"19fc6c11\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([7, 3, 8, 0, 8])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aad979b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"28e3c503\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.8072 (80.72%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"06ec5a85\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac9773b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"483028cc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"class_dict = {0: 'airplane',\\\\n              1: 'automobile',\\\\n              2: 'bird',\\\\n              3: 'cat',\\\\n              4: 'deer',\\\\n              5: 'dog',\\\\n              6: 'frog',\\\\n              7: 'horse',\\\\n              8: 'ship',\\\\n              9: 'truck'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"class_dict = {\\\\n    0: \\\\\\\"airplane\\\\\\\",\\\\n    1: \\\\\\\"automobile\\\\\\\",\\\\n    2: \\\\\\\"bird\\\\\\\",\\\\n    3: \\\\\\\"cat\\\\\\\",\\\\n    4: \\\\\\\"deer\\\\\\\",\\\\n    5: \\\\\\\"dog\\\\\\\",\\\\n    6: \\\\\\\"frog\\\\\\\",\\\\n    7: \\\\\\\"horse\\\\\\\",\\\\n    8: \\\\\\\"ship\\\\\\\",\\\\n    9: \\\\\\\"truck\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"06a81b4b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"08ca97ed\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"f0b7e651\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"93b88252\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torchvision      : 0.11.2\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-mobilenet-v3-small-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"c4a2457e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"55c28ce9\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"6a80a3d5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"b7e13545\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e18ceb5a\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# MobileNetV3 (Small) Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4b0d4018\",\n   \"metadata\": {},\n   \"source\": [\n    \"MobileNetV3 [1] trained on CIFAR-10 [2].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Searching for MobileNetV3, https://arxiv.org/abs/1905.02244.pdf\\n\",\n    \"- [2] https://en.wikipedia.org/wiki/CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0254dd9a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"84304d17\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"aa33e94f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 150\\\\nLEARNING_RATE = 0.001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"BATCH_SIZE = 256\\\\nNUM_EPOCHS = 150\\\\nLEARNING_RATE = 0.001\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 150\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"95a361d7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ebc5b39d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"225f3881\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- Here, for the PyTorch model, we are using an implementation from the Torchvision hub:\\n\",\n    \"\\n\",\n    \"In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"cccb0db9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using cache found in /home/jovyan/.cache/torch/hub/pytorch_vision_v0.11.0\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\n\\\\n\\\\npytorch_model = torch.hub.load(\\\\n    'pytorch/vision:v0.11.0',\\\\n    'mobilenet_v3_small',\\\\n    pretrained=False)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\n\\\\n\\\\npytorch_model = torch.hub.load(\\\\n    \\\\\\\"pytorch/vision:v0.11.0\\\\\\\", \\\\\\\"mobilenet_v3_small\\\\\\\", pretrained=False\\\\n)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = torch.hub.load(\\n\",\n    \"    'pytorch/vision:v0.11.0',\\n\",\n    \"    'mobilenet_v3_small',\\n\",\n    \"    pretrained=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"31b672eb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Since the Torchvision model above was implemented for ImageNet, which has a different number of classes than CIFAR-10, we define our own output layer below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"b018ec1d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model.classifier[-1] = torch.nn.Linear(\\\\n        in_features=1024,  # as in the original output layer\\\\n        out_features=10)  # number of class labels in CIFAR-10)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model.classifier[-1] = torch.nn.Linear(\\\\n    in_features=1024, out_features=10  # as in the original output layer\\\\n)  # number of class labels in CIFAR-10)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model.classifier[-1] = torch.nn.Linear(\\n\",\n    \"        in_features=1024,  # as in the original output layer\\n\",\n    \"        out_features=10)  # number of class labels in CIFAR-10)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"78d64895\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our LightningModule as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"a392671b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7d82de0f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"63ff1a40\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"416dfb19\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"4b0d68f5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\",\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_cifar10_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.CIFAR10(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_cifar10_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5496d9c6\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"028122aa\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"043b6e24\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 3\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"75bfc480\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"e152605d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6f190981\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1a305d2f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"7ec3fd18\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import os\\\\n\\\\nfrom torch.utils.data.dataset import random_split\\\\nfrom torch.utils.data import DataLoader\\\\nfrom torchvision import transforms\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.CIFAR10(root=self.data_path, download=True)\\\\n\\\\n        self.train_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.RandomCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n\\\\n        self.test_transform = transforms.Compose(\\\\n            [\\\\n                transforms.Resize((70, 70)),\\\\n                transforms.CenterCrop((64, 64)),\\\\n                transforms.ToTensor(),\\\\n            ]\\\\n        )\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        train = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=self.train_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.CIFAR10(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=self.test_transform,\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path, download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.RandomCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test_transform = transforms.Compose(\\n\",\n    \"            [\\n\",\n    \"                transforms.Resize((70, 70)),\\n\",\n    \"                transforms.CenterCrop((64, 64)),\\n\",\n    \"                transforms.ToTensor(),\\n\",\n    \"            ]\\n\",\n    \"        )\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=self.train_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=self.test_transform,\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c1c0895b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"543677b9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"torch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"torch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a7ed6931\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"61a73a12\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"256d8185\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"53dc9e43\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"cc14f439\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type        | Params\\n\",\n      \"------------------------------------------\\n\",\n      \"0 | model     | MobileNetV3 | 1.5 M \\n\",\n      \"1 | train_acc | Accuracy    | 0     \\n\",\n      \"2 | valid_acc | Accuracy    | 0     \\n\",\n      \"3 | test_acc  | Accuracy    | 0     \\n\",\n      \"------------------------------------------\\n\",\n      \"1.5 M     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"1.5 M     Total params\\n\",\n      \"6.112     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"67bdac66b52544a0a5ec6eb5e62d6fa4\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2468eb677c0447cca4310809b994d9d9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n  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\"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 27.66 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=False,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=False,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40326150\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"819f9169\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"88d7cc4e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d4b55af\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"c4707995\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_9/checkpoints/epoch=148-step=26074.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_9/checkpoints/epoch=148-step=26074.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"36b288408b8e41ab8641ab59e37972b0\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.7702000141143799}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.7702000141143799}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"93a80de6\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"06be60cf\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"4493c4b4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_9/checkpoints/epoch=148-step=26074.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"0523a8f5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0531cb79\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"f199891f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([7, 3, 8, 0, 8])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2e869d20\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"0f7e604e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.7702 (77.02%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"27c05abe\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f8fe7945\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"dbb00eb0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"class_dict = {0: 'airplane',\\\\n              1: 'automobile',\\\\n              2: 'bird',\\\\n              3: 'cat',\\\\n              4: 'deer',\\\\n              5: 'dog',\\\\n              6: 'frog',\\\\n              7: 'horse',\\\\n              8: 'ship',\\\\n              9: 'truck'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"class_dict = {\\\\n    0: \\\\\\\"airplane\\\\\\\",\\\\n    1: \\\\\\\"automobile\\\\\\\",\\\\n    2: \\\\\\\"bird\\\\\\\",\\\\n    3: \\\\\\\"cat\\\\\\\",\\\\n    4: \\\\\\\"deer\\\\\\\",\\\\n    5: \\\\\\\"dog\\\\\\\",\\\\n    6: \\\\\\\"frog\\\\\\\",\\\\n    7: \\\\\\\"horse\\\\\\\",\\\\n    8: \\\\\\\"ship\\\\\\\",\\\\n    9: \\\\\\\"truck\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"cce50985\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5aab81c6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"f66cc607\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"fb26fdd2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-nin-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"b127a562\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchvision,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"643d931d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0b41ca19\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- *Network in Network* Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9ffa141f\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements the *Network in Network* convolutional network [1] and applies it to CIFAR-10 digit classification.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c0c5c28a\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Lin, Min, Qiang Chen, and Shuicheng Yan. \\\"[Network in network](https://arxiv.org/abs/1312.4400).\\\" arXiv preprint arXiv:1312.4400 (2013).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"be64a288\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7fc4cd58\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"c28c0592\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 100\\n\",\n    \"LEARNING_RATE = 0.0005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"09bc4c66\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"52fcd29f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- When using PyTorch Lightning, we can start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"a44c7269\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.nn as nn\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchNiN(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"\\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"                nn.Conv2d(3, 192, kernel_size=5, stride=1, padding=2),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.Conv2d(192, 160, kernel_size=1, stride=1, padding=0),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.Conv2d(160,  96, kernel_size=1, stride=1, padding=0),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\\n\",\n    \"                nn.Dropout(0.5),\\n\",\n    \"\\n\",\n    \"                nn.Conv2d(96, 192, kernel_size=5, stride=1, padding=2),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.AvgPool2d(kernel_size=3, stride=2, padding=1),\\n\",\n    \"                nn.Dropout(0.5),\\n\",\n    \"\\n\",\n    \"                nn.Conv2d(192, 192, kernel_size=3, stride=1, padding=1),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.Conv2d(192, 192, kernel_size=1, stride=1, padding=0),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.Conv2d(192,  10, kernel_size=1, stride=1, padding=0),\\n\",\n    \"                nn.ReLU(inplace=True),\\n\",\n    \"                nn.AvgPool2d(kernel_size=8, stride=1, padding=0),\\n\",\n    \"                )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.classifier(x)\\n\",\n    \"        logits = x.view(x.size(0), self.num_classes)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"09168cea\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our `LightningModule` as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"8e5cebdf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"98aa5b94\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e94bc996\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"50473f25\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"d4193362\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=transforms.ToTensor(),\\n\",\n    \"                                 download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                train=False,\\n\",\n    \"                                transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"e48180c5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 4993),\\n\",\n       \" (1, 4991),\\n\",\n       \" (2, 4991),\\n\",\n       \" (3, 4997),\\n\",\n       \" (4, 4990),\\n\",\n       \" (5, 4997),\\n\",\n       \" (6, 4990),\\n\",\n       \" (7, 4990),\\n\",\n       \" (8, 4997),\\n\",\n       \" (9, 4984)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTraining label distribution:')\\n\",\n    \"sorted(train_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"9cbfd2bd\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTest label distribution:')\\n\",\n    \"sorted(test_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2db7f40a\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"9b18de51\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"bc893a39\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"3fe97814\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training Images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9ba2f6fd\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f32009f2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"3751122d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 1000)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"majority_class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fbb75f37\",\n   \"metadata\": {},\n   \"source\": [\n    \"- (To be fair, the classes in the test set are perfectly evenly distributed, so the majority class is an arbitrary choice in this case)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"28b7cf89\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print('Accuracy when always predicting the majority class:')\\n\",\n    \"print(f'{baseline_acc:.2f} ({baseline_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0a18fce4\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"aa471a0b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"d259d0f2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path,\\n\",\n    \"                         download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = torchvision.transforms.Compose([\\n\",\n    \"            # torchvision.transforms.Resize((70, 70)),\\n\",\n    \"            # torchvision.transforms.RandomCrop((64, 64)),\\n\",\n    \"            torchvision.transforms.ToTensor(),\\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"        self.test_transform = torchvision.transforms.Compose([\\n\",\n    \"            # torchvision.transforms.Resize((70, 70)),        \\n\",\n    \"            # torchvision.transforms.CenterCrop((64, 64)),            \\n\",\n    \"            torchvision.transforms.ToTensor(),                \\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=self.train_transform,\\n\",\n    \"                                 download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                     train=False, \\n\",\n    \"                                     transform=self.test_transform,\\n\",\n    \"                                     download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"64f01775\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"f97a5ce2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e03d8650\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0587690f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"5804bedf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchNiN(num_classes=10)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"22b87a2a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"d4b23628\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type       | Params\\n\",\n      \"-----------------------------------------\\n\",\n      \"0 | model     | PyTorchNiN | 966 K \\n\",\n      \"1 | train_acc | Accuracy   | 0     \\n\",\n      \"2 | valid_acc | Accuracy   | 0     \\n\",\n      \"3 | test_acc  | Accuracy   | 0     \\n\",\n      \"-----------------------------------------\\n\",\n      \"966 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"966 K     Total params\\n\",\n      \"3.868     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"88115eea3f3d45af931d7fea46c2d639\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": 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[00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 44.84 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"45e25f1f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b6e02f73\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"8deccdb8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6c6e49c2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"296f5525\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_56/checkpoints/epoch=89-step=15749.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_56/checkpoints/epoch=89-step=15749.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3f52fb8ac7204cf5889d728c136e8de9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.6541000008583069     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.6541000008583069    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.6541000008583069}]\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60cca6ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8de228ef\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"87669285\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_56/checkpoints/epoch=89-step=15749.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"521c99b6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b7b9e53b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"6fc1adbb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([3, 8, 0, 0, 6])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7f17f712\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"9f3196da\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.6541 (65.41%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4e35148b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7dff75c6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"6a547ecf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Append the folder that contains the \\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append('../pytorch_ipynb')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"031f39ed\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from helper_data import UnNormalize\\n\",\n    \"from helper_plotting import show_examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"22d2f0ad\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\\n\",\n    \"\\n\",\n    \"# We normalized each channel during training; here \\n\",\n    \"# we are reverting the normalization so that we \\n\",\n    \"# can plot them as images\\n\",\n    \"unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    data_loader=test_dataloader,\\n\",\n    \"    unnormalizer=unnormalizer,\\n\",\n    \"    class_dict=class_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"001f48b3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"    pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"a880946c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from helper_plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_confusion_matrix(\\n\",\n    \"    cmat_tensor.numpy(),\\n\",\n    \"    class_names=class_dict.values())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c59d3078\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"a7736b12\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"cebdf862\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0725e801\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"0d36891a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/cifar10_pngs/90_airplane.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9ce5cb17\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we have to use the same image transformation that we used earlier in the `DataModule`. \\n\",\n    \"- While we didn't apply any image augmentation, we could use the `to_tensor` function from the torchvision library; however, as a general template that provides flexibility for more complex transformation chains, let's use the `Compose` class for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"a6d62b7b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = data_module.train_transform\\n\",\n    \"\\n\",\n    \"image_chw = transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c6df4bde\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"f544b9f9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6e3256eb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"0fbd273a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"35de85d3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"73e8af5c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"id\": \"3703d4d5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_to_str = {\\n\",\n    \"    0: 'airplane',\\n\",\n    \"    1: 'automobile',\\n\",\n    \"    2: 'bird',\\n\",\n    \"    3: 'cat',\\n\",\n    \"    4: 'deer',\\n\",\n    \"    5: 'dog',\\n\",\n    \"    6: 'frog',\\n\",\n    \"    7: 'horse',\\n\",\n    \"    8: 'ship',\\n\",\n    \"    9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"id\": \"e49a1c72\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: airplane\\n\",\n      \"Class-membership probability 100.00%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {int_to_str[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-vgg16-celeba.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"4af38d0c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchvision,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"7257302b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b0f4c4f1\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- VGG16 Trained on CelebA\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"24a6f24c\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements the VGG16 convolutional network [1] and applies it to CelebA smile classification.\\n\",\n    \"\\n\",\n    \"![](../pytorch_ipynb/images/vgg16/vgg16-arch-table.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cc7ece6b\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Simonyan, K., & Zisserman, A. (2014). [Very deep convolutional networks for large-scale image recognition](https://arxiv.org/abs/1409.1556). arXiv preprint arXiv:1409.1556.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"47e4d2c1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2679bfd1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"d07306da\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 4\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9ff35eb9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3a86627d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- When using PyTorch Lightning, we can start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"1d2844a8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch.nn as nn\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchVGG16(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        self.block_1 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=3,\\n\",\n    \"                          out_channels=64,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          # (1(32-1)- 32 + 3)/2 = 1\\n\",\n    \"                          padding=1), \\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=64,\\n\",\n    \"                          out_channels=64,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_2 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=64,\\n\",\n    \"                          out_channels=128,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=128,\\n\",\n    \"                          out_channels=128,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_3 = nn.Sequential(        \\n\",\n    \"                nn.Conv2d(in_channels=128,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"          \\n\",\n    \"        self.block_4 = nn.Sequential(   \\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_5 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),    \\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))             \\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"            self.block_1, self.block_2, \\n\",\n    \"            self.block_3, self.block_4, \\n\",\n    \"            self.block_5\\n\",\n    \"        )\\n\",\n    \"            \\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"            nn.Linear(512*4*4, 4096),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Dropout(p=0.5),\\n\",\n    \"            nn.Linear(4096, 4096),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Dropout(p=0.5),\\n\",\n    \"            nn.Linear(4096, num_classes),\\n\",\n    \"        )\\n\",\n    \"             \\n\",\n    \"        # self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\\n\",\n    \"        \\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, torch.nn.Conv2d):\\n\",\n    \"                # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\\n\",\n    \"                # m.weight.data.normal_(0, np.sqrt(2. / n))\\n\",\n    \"                m.weight.detach().normal_(0, 0.05)\\n\",\n    \"                if m.bias is not None:\\n\",\n    \"                    m.bias.detach().zero_()\\n\",\n    \"            elif isinstance(m, torch.nn.Linear):\\n\",\n    \"                m.weight.detach().normal_(0, 0.05)\\n\",\n    \"                m.bias.detach().detach().zero_()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        # x = self.avgpool(x)\\n\",\n    \"        x = x.view(x.size(0), -1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5c2295a5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our `LightningModule` as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"23b3848b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"80811535\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fbd5a50d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"Note that the approx. 200,000 CelebA face image dataset is relatively large (approx. 1.3 Gb). If the following automatic download below does not work (e.g., returning a `BadZipFile: File is not a zip file` error), this is usually to rate limit restrictions by the provider hosting the dataset. You can try to download the dataset manually via the download link provided by the author on the official CelebA website at http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html. \\n\",\n    \"\\n\",\n    \"Alternatively, you can download the dataset from here: https://drive.google.com/file/d/1m8-EBPgi5MRubrm6iQjafK2QMHDBMSfJ/view?. \\n\",\n    \"1. Delete the existing `celeba` folder with the partially downloaded files.\\n\",\n    \"2. Place the .zip file in the same directory as this notebook, then unzip it.\\n\",\n    \"3. The new `celeba` folder should contain the following files:\\n\",\n    \"\\n\",\n    \"![](../../pytorch_ipynb/cnn/images/celeba-files.png)\\n\",\n    \"\\n\",\n    \"4. Unzip the `celeba/img_align.celeba.zip` archive inside the `celeba` folder\\n\",\n    \"5. Call the `get_dataloaders_celeba` below with `download=False`\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3e1c8aea\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"f16b9505\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"# Dataset\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"custom_transforms = transforms.Compose([\\n\",\n    \"    transforms.CenterCrop((160, 160)),\\n\",\n    \"    transforms.Resize([128, 128]),\\n\",\n    \"    transforms.ToTensor(),\\n\",\n    \"    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def get_dataloaders_celeba(batch_size, num_workers=0,\\n\",\n    \"                           train_transforms=None,\\n\",\n    \"                           test_transforms=None,\\n\",\n    \"                           download=True):\\n\",\n    \"\\n\",\n    \"    if train_transforms is None:\\n\",\n    \"        train_transforms = transforms.ToTensor()\\n\",\n    \"\\n\",\n    \"    if test_transforms is None:\\n\",\n    \"        test_transforms = transforms.ToTensor()\\n\",\n    \"        \\n\",\n    \"    def get_smile(attr):\\n\",\n    \"        return attr[31]\\n\",\n    \"\\n\",\n    \"    train_dataset = datasets.CelebA(root='.',\\n\",\n    \"                                    split='train',\\n\",\n    \"                                    transform=train_transforms,\\n\",\n    \"                                    target_type='attr',\\n\",\n    \"                                    target_transform=get_smile,\\n\",\n    \"                                    download=download)\\n\",\n    \"\\n\",\n    \"    valid_dataset = datasets.CelebA(root='.',\\n\",\n    \"                                    split='valid',\\n\",\n    \"                                    target_type='attr',\\n\",\n    \"                                    target_transform=get_smile,\\n\",\n    \"                                    transform=test_transforms)\\n\",\n    \"\\n\",\n    \"    test_dataset = datasets.CelebA(root='.',\\n\",\n    \"                                   split='test',\\n\",\n    \"                                   target_type='attr',\\n\",\n    \"                                   target_transform=get_smile,\\n\",\n    \"                                   transform=test_transforms)\\n\",\n    \"\\n\",\n    \"    train_loader = DataLoader(dataset=train_dataset,\\n\",\n    \"                              batch_size=batch_size,\\n\",\n    \"                              num_workers=num_workers,\\n\",\n    \"                              shuffle=True)\\n\",\n    \"\\n\",\n    \"    valid_loader = DataLoader(dataset=valid_dataset,\\n\",\n    \"                              batch_size=batch_size,\\n\",\n    \"                              num_workers=num_workers,\\n\",\n    \"                              shuffle=False)\\n\",\n    \"    \\n\",\n    \"    test_loader = DataLoader(dataset=test_dataset,\\n\",\n    \"                             batch_size=batch_size,\\n\",\n    \"                             num_workers=num_workers,\\n\",\n    \"                             shuffle=False)\\n\",\n    \"\\n\",\n    \"    return train_loader, valid_loader, test_loader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader, valid_loader, test_loader = get_dataloaders_celeba(\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    train_transforms=custom_transforms,\\n\",\n    \"    test_transforms=custom_transforms,\\n\",\n    \"    download=False,\\n\",\n    \"    num_workers=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"be59a2fe\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note that the target vectors of the CelebA datasets are vectors containing 40 attributes: \\n\",\n    \"    \\n\",\n    \"```\\n\",\n    \"    00 - 5_o_Clock_Shadow\\n\",\n    \"    01 - Arched_Eyebrows\\n\",\n    \"    02 - Attractive \\n\",\n    \"    03 - Bags_Under_Eyes\\n\",\n    \"    04 - Bald\\n\",\n    \"    05 - Bangs\\n\",\n    \"    06 - Big_Lips\\n\",\n    \"    07 - Big_Nose\\n\",\n    \"    08 - Black_Hair\\n\",\n    \"    09 - Blond_Hair\\n\",\n    \"    10 - Blurry \\n\",\n    \"    11 - Brown_Hair \\n\",\n    \"    12 - Bushy_Eyebrows \\n\",\n    \"    13 - Chubby \\n\",\n    \"    14 - Double_Chin \\n\",\n    \"    15 - Eyeglasses \\n\",\n    \"    16 - Goatee \\n\",\n    \"    17 - Gray_Hair \\n\",\n    \"    18 - Heavy_Makeup \\n\",\n    \"    19 - High_Cheekbones \\n\",\n    \"    20 - Male \\n\",\n    \"    21 - Mouth_Slightly_Open \\n\",\n    \"    22 - Mustache \\n\",\n    \"    23 - Narrow_Eyes \\n\",\n    \"    24 - No_Beard \\n\",\n    \"    25 - Oval_Face \\n\",\n    \"    26 - Pale_Skin \\n\",\n    \"    27 - Pointy_Nose \\n\",\n    \"    28 - Receding_Hairline \\n\",\n    \"    29 - Rosy_Cheeks \\n\",\n    \"    30 - Sideburns \\n\",\n    \"    31 - Smiling \\n\",\n    \"    32 - Straight_Hair \\n\",\n    \"    33 - Wavy_Hair \\n\",\n    \"    34 - Wearing_Earrings \\n\",\n    \"    35 - Wearing_Hat \\n\",\n    \"    36 - Wearing_Lipstick \\n\",\n    \"    37 - Wearing_Necklace \\n\",\n    \"    38 - Wearing_Necktie \\n\",\n    \"    39 - Young      \\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Via the custom `get_smile` function above [31], we fetched the Smile label.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"56a47fdf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 84690), (1, 78080)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTraining label distribution:')\\n\",\n    \"sorted(train_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"950573c9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 9975), (1, 9987)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTest label distribution:')\\n\",\n    \"sorted(test_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"65002a39\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"d8fba173\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"a644b8f5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"e2f29282\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training Images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eb815f5e\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"42f8492e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"fa508e4b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(1, 9987)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"majority_class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"313d97e7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.50 (50.03%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print('Accuracy when always predicting the majority class:')\\n\",\n    \"print(f'{baseline_acc:.2f} ({baseline_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f85db0c8\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0f503194\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"1daceecc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CelebA(root='.', download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = transforms.Compose([\\n\",\n    \"            transforms.RandomCrop((160, 160)),\\n\",\n    \"            transforms.Resize([128, 128]),\\n\",\n    \"            transforms.ToTensor(),\\n\",\n    \"            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"        ])\\n\",\n    \"\\n\",\n    \"        self.test_transform = transforms.Compose([\\n\",\n    \"            transforms.CenterCrop((160, 160)),\\n\",\n    \"            transforms.Resize([128, 128]),\\n\",\n    \"            transforms.ToTensor(),\\n\",\n    \"            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"        ])\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        \\n\",\n    \"        def get_smile(attr):\\n\",\n    \"            return attr[31]\\n\",\n    \"        \\n\",\n    \"        self.train = datasets.CelebA(root='.',\\n\",\n    \"                                     split='train',\\n\",\n    \"                                     target_type='attr',\\n\",\n    \"                                     target_transform=get_smile,\\n\",\n    \"                                     transform=self.train_transform,\\n\",\n    \"                                     download=False)\\n\",\n    \"        \\n\",\n    \"        self.valid = datasets.CelebA(root='.',\\n\",\n    \"                                     split='valid',\\n\",\n    \"                                     target_type='attr',\\n\",\n    \"                                     target_transform=get_smile,\\n\",\n    \"                                     transform=self.test_transform)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CelebA(root='.',\\n\",\n    \"                                    split='test',\\n\",\n    \"                                    target_type='attr',\\n\",\n    \"                                    target_transform=get_smile,\\n\",\n    \"                                    transform=self.test_transform)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fe8c5947\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"39f99136\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8af695e4\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a38aa2d7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"6fcf76a8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchVGG16(num_classes=2)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a404697f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"5604a8ad\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchVGG16 | 65.1 M\\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"65.1 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"65.1 M    Total params\\n\",\n      \"260.251   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"71053dc52c174ebeb7966f7d58c31435\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"bb3ffb994e0c4ba198335609a1ab1440\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 63.86 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cda89bc3\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5dd5b3e6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"59385b7c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7ece5571\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"a73fee67\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_9/checkpoints/epoch=3-step=2539.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_9/checkpoints/epoch=3-step=2539.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"02e936a726ad4103b9787f7b6928172b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.9210500121116638}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9210500121116638}]\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"889664c1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6b94e54a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"bfc58621\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_9/checkpoints/epoch=3-step=2539.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"8ed341dd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e87a5375\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"a4d822f3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([1, 0, 0, 1, 0])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eb49d33c\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"fb92c235\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9211 (92.11%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d4237f8a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fe29da66\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"bb42413b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Append the folder that contains the \\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append('../pytorch_ipynb')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"c61877df\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from helper_data import UnNormalize\\n\",\n    \"from helper_plotting import show_examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"79d6f447\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'no smile',\\n\",\n    \"              1: 'smile'}\\n\",\n    \"\\n\",\n    \"# We normalized each channel during training; here \\n\",\n    \"# we are reverting the normalization so that we \\n\",\n    \"# can plot them as images\\n\",\n    \"unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    data_loader=test_dataloader,\\n\",\n    \"    unnormalizer=unnormalizer,\\n\",\n    \"    class_dict=class_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"0cc14b5a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"aa79136c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from helper_plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_confusion_matrix(\\n\",\n    \"    cmat_tensor.numpy(),\\n\",\n    \"    class_names=class_dict.values())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"238c8edd\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"3d3c264e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"ef00fa8f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bbf56293\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"5df29e57\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/celeba_jpgs/000183.jpg')\\n\",\n    \"plt.imshow(image)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8579da87\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we have to use the same image transformation that we used earlier in the `DataModule`. \\n\",\n    \"- While we didn't apply any image augmentation, we could use the `to_tensor` function from the torchvision library; however, as a general template that provides flexibility for more complex transformation chains, let's use the `Compose` class for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"3d4c2600\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = data_module.train_transform\\n\",\n    \"\\n\",\n    \"image_chw = transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b11c651b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"53e6faeb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([3, 128, 128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e529417a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"2a0ef528\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 3, 128, 128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"be96c132\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"ecdc8f5a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"id\": \"a5c56913\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_to_str = {\\n\",\n    \"    0: 'no smile',\\n\",\n    \"    1: 'smile'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"id\": \"f3b98691\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: smile\\n\",\n      \"Class-membership probability 99.89%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {int_to_str[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-vgg16.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"f451fcc1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchvision,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"f5cc865d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d473e2b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- VGG16 Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c3a9bc7\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements the VGG16 convolutional network [1] and applies it to CIFAR-10 digit classification.\\n\",\n    \"\\n\",\n    \"![](../pytorch_ipynb/images/vgg16/vgg16-arch-table.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cda873cf\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Simonyan, K., & Zisserman, A. (2014). [Very deep convolutional networks for large-scale image recognition](https://arxiv.org/abs/1409.1556). arXiv preprint arXiv:1409.1556.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"98fad1b3\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8f59a6fd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"07178557\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 25\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"69a6e176\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b593a8c1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- When using PyTorch Lightning, we can start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"47f051b5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"137:17: E265 block comment should start with '# '\\n\",\n      \"138:17: E265 block comment should start with '# '\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch.nn as nn\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchVGG16(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        self.block_1 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=3,\\n\",\n    \"                          out_channels=64,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          # (1(32-1)- 32 + 3)/2 = 1\\n\",\n    \"                          padding=1), \\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=64,\\n\",\n    \"                          out_channels=64,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_2 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=64,\\n\",\n    \"                          out_channels=128,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=128,\\n\",\n    \"                          out_channels=128,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_3 = nn.Sequential(        \\n\",\n    \"                nn.Conv2d(in_channels=128,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"          \\n\",\n    \"        self.block_4 = nn.Sequential(   \\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_5 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),    \\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))             \\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"            self.block_1, self.block_2, \\n\",\n    \"            self.block_3, self.block_4, \\n\",\n    \"            self.block_5\\n\",\n    \"        )\\n\",\n    \"            \\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"            nn.Linear(512, 4096),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Dropout(p=0.5),\\n\",\n    \"            nn.Linear(4096, 4096),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Dropout(p=0.5),\\n\",\n    \"            nn.Linear(4096, num_classes),\\n\",\n    \"        )\\n\",\n    \"             \\n\",\n    \"        # self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\\n\",\n    \"        \\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, torch.nn.Conv2d):\\n\",\n    \"                #n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\\n\",\n    \"                #m.weight.data.normal_(0, np.sqrt(2. / n))\\n\",\n    \"                m.weight.detach().normal_(0, 0.05)\\n\",\n    \"                if m.bias is not None:\\n\",\n    \"                    m.bias.detach().zero_()\\n\",\n    \"            elif isinstance(m, torch.nn.Linear):\\n\",\n    \"                m.weight.detach().normal_(0, 0.05)\\n\",\n    \"                m.bias.detach().detach().zero_()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        # x = self.avgpool(x)\\n\",\n    \"        x = x.view(x.size(0), -1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bc0e9709\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our `LightningModule` as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"5b08824d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"48f1ff04\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7bad9dfc\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c6be32a5\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"6d1caa3f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=transforms.ToTensor(),\\n\",\n    \"                                 download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                train=False,\\n\",\n    \"                                transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"5c95b9e6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 4994),\\n\",\n       \" (1, 4987),\\n\",\n       \" (2, 4990),\\n\",\n       \" (3, 4989),\\n\",\n       \" (4, 4991),\\n\",\n       \" (5, 4993),\\n\",\n       \" (6, 4999),\\n\",\n       \" (7, 4992),\\n\",\n       \" (8, 4993),\\n\",\n       \" (9, 4992)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTraining label distribution:')\\n\",\n    \"sorted(train_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"149506b1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTest label distribution:')\\n\",\n    \"sorted(test_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"502b07f0\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"f990672c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"a1aaf3e9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"41970545\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training Images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1c724380\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0add60c9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"cf958e62\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 1000)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"majority_class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"54ff15a3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- (To be fair, the classes in the test set are perfectly evenly distributed, so the majority class is an arbitrary choice in this case)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"cd6abb36\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print('Accuracy when always predicting the majority class:')\\n\",\n    \"print(f'{baseline_acc:.2f} ({baseline_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9a49e122\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0a8a4c47\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"01db5c1b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path,\\n\",\n    \"                         download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = torchvision.transforms.Compose([\\n\",\n    \"            # torchvision.transforms.Resize((70, 70)),\\n\",\n    \"            # torchvision.transforms.RandomCrop((64, 64)),\\n\",\n    \"            torchvision.transforms.ToTensor(),\\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"        self.test_transform = torchvision.transforms.Compose([\\n\",\n    \"            # torchvision.transforms.Resize((70, 70)),        \\n\",\n    \"            # torchvision.transforms.CenterCrop((64, 64)),            \\n\",\n    \"            torchvision.transforms.ToTensor(),                \\n\",\n    \"            torchvision.transforms.Normalize(\\n\",\n    \"                (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=self.train_transform,\\n\",\n    \"                                 download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                     train=False, \\n\",\n    \"                                     transform=self.test_transform,\\n\",\n    \"                                     download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d9770789\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"0a1283da\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"58f947ba\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4748ec89\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"68bdf048\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchVGG16(num_classes=10)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fd0a3630\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"1d937b42\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchVGG16 | 33.6 M\\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"33.6 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"33.6 M    Total params\\n\",\n      \"134.553   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"778ba70e3c8c4271b4cc2bf3f245fc57\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 13.36 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a27f4514\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"54967a2f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"7fcf851d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c30e1fbe\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"5fce019d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_66/checkpoints/epoch=19-step=3499.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_66/checkpoints/epoch=19-step=3499.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c0f993950a8949c3ab73fa931368e15d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.7236999869346619     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.7236999869346619    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.7236999869346619}]\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0328722e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1f393936\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"ac3cda9a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_66/checkpoints/epoch=19-step=3499.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"f262b172\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1fb6be44\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"ef0dd717\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([3, 8, 8, 8, 6])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3660caa3\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"37ecd40d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.7237 (72.37%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9e941553\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dfeeb9b0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"3b5bd329\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Append the folder that contains the \\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append('../pytorch_ipynb')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"17f24da8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from helper_data import UnNormalize\\n\",\n    \"from helper_plotting import show_examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"ad0c897e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\\n\",\n    \"\\n\",\n    \"# We normalized each channel during training; here \\n\",\n    \"# we are reverting the normalization so that we \\n\",\n    \"# can plot them as images\\n\",\n    \"unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    data_loader=test_dataloader,\\n\",\n    \"    unnormalizer=unnormalizer,\\n\",\n    \"    class_dict=class_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"6313daf9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"    pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"e5e75157\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from helper_plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_confusion_matrix(\\n\",\n    \"    cmat_tensor.numpy(),\\n\",\n    \"    class_names=class_dict.values())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"98ffa685\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"e2cc71a3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"dc77205d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"33b00bfa\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"1e4b4ee7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/cifar10_pngs/90_airplane.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"28df1155\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we have to use the same image transformation that we used earlier in the `DataModule`. \\n\",\n    \"- While we didn't apply any image augmentation, we could use the `to_tensor` function from the torchvision library; however, as a general template that provides flexibility for more complex transformation chains, let's use the `Compose` class for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"f30a06f5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = data_module.train_transform\\n\",\n    \"\\n\",\n    \"image_chw = transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ebb7a5c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"1b30aa16\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dec27e3a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"2f90f50b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"671f3d2c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"8a75a440\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"id\": \"a3dbeda1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_to_str = {\\n\",\n    \"    0: 'airplane',\\n\",\n    \"    1: 'automobile',\\n\",\n    \"    2: 'bird',\\n\",\n    \"    3: 'cat',\\n\",\n    \"    4: 'deer',\\n\",\n    \"    5: 'dog',\\n\",\n    \"    6: 'frog',\\n\",\n    \"    7: 'horse',\\n\",\n    \"    8: 'ship',\\n\",\n    \"    9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"id\": \"f0918be3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: airplane\\n\",\n      \"Class-membership probability 99.54%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {int_to_str[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/cnn/cnn-vgg19.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"f21eca24\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchvision,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"bf0cecc5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"94923ca2\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Model Zoo -- VGG19 Trained on CIFAR-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d6e9eed7\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook implements the VGG19 convolutional network [1] and applies it to CIFAR-10 digit classification.\\n\",\n    \"\\n\",\n    \"![](../pytorch_ipynb/images/vgg19/vgg19-arch-table.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5b91b384\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] Simonyan, K., & Zisserman, A. (2014). [Very deep convolutional networks for large-scale image recognition](https://arxiv.org/abs/1409.1556). arXiv preprint arXiv:1409.1556.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"90b1382a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"495262bf\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch. So, if you have problems with the data loader later, try setting `NUM_WORKERS = 0` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"8ae04eae\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 25\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"77a09f38\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"19e8aac3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- When using PyTorch Lightning, we can start with defining our neural network  model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\\n\",\n    \"- In this case, since Torchvision already offers a nice and efficient PyTorch implementation of MobileNet-v2, let's load it from the Torchvision hub:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"13ce93c3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"159:17: E265 block comment should start with '# '\\n\",\n      \"160:17: E265 block comment should start with '# '\\n\",\n      \"169:5: E303 too many blank lines (2)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import torch.nn as nn\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchVGG19(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        self.block_1 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=3,\\n\",\n    \"                          out_channels=64,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          # (1(32-1)- 32 + 3)/2 = 1\\n\",\n    \"                          padding=1), \\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=64,\\n\",\n    \"                          out_channels=64,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_2 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=64,\\n\",\n    \"                          out_channels=128,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=128,\\n\",\n    \"                          out_channels=128,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_3 = nn.Sequential(        \\n\",\n    \"                nn.Conv2d(in_channels=128,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=256,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"          \\n\",\n    \"        self.block_4 = nn.Sequential(   \\n\",\n    \"                nn.Conv2d(in_channels=256,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),        \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),   \\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))\\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.block_5 = nn.Sequential(\\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),            \\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),\\n\",\n    \"                nn.Conv2d(in_channels=512,\\n\",\n    \"                          out_channels=512,\\n\",\n    \"                          kernel_size=(3, 3),\\n\",\n    \"                          stride=(1, 1),\\n\",\n    \"                          padding=1),\\n\",\n    \"                nn.ReLU(),   \\n\",\n    \"                nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                             stride=(2, 2))                   \\n\",\n    \"        )\\n\",\n    \"        \\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"            self.block_1, self.block_2, \\n\",\n    \"            self.block_3, self.block_4, \\n\",\n    \"            self.block_5\\n\",\n    \"        )\\n\",\n    \"            \\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"            nn.Linear(512, 4096),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Dropout(p=0.5),\\n\",\n    \"            nn.Linear(4096, 4096),\\n\",\n    \"            nn.ReLU(True),\\n\",\n    \"            nn.Dropout(p=0.5),\\n\",\n    \"            nn.Linear(4096, num_classes),\\n\",\n    \"        )\\n\",\n    \"             \\n\",\n    \"        # self.avgpool = nn.AdaptiveAvgPool2d((7, 7))\\n\",\n    \"        \\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, torch.nn.Conv2d):\\n\",\n    \"                #n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\\n\",\n    \"                #m.weight.data.normal_(0, np.sqrt(2. / n))\\n\",\n    \"                m.weight.detach().normal_(0, 0.05)\\n\",\n    \"                if m.bias is not None:\\n\",\n    \"                    m.bias.detach().zero_()\\n\",\n    \"            elif isinstance(m, torch.nn.Linear):\\n\",\n    \"                m.weight.detach().normal_(0, 0.05)\\n\",\n    \"                m.bias.detach().detach().zero_()\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        # x = self.avgpool(x)\\n\",\n    \"        x = x.view(x.size(0), -1)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"41cf5dfe\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we can define our `LightningModule` as a wrapper around our PyTorch model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"03eb1002\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        \\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_acc\\\", self.valid_acc,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c95b45db\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"69727afa\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"410ff226\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"b917ca2c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=transforms.ToTensor(),\\n\",\n    \"                                 download=True)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE, \\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.CIFAR10(root='./data', \\n\",\n    \"                                train=False,\\n\",\n    \"                                transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=NUM_WORKERS,\\n\",\n    \"                         drop_last=False,\\n\",\n    \"                         shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"8f69bb9b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 4994),\\n\",\n       \" (1, 4993),\\n\",\n       \" (2, 4994),\\n\",\n       \" (3, 4992),\\n\",\n       \" (4, 4994),\\n\",\n       \" (5, 4992),\\n\",\n       \" (6, 4986),\\n\",\n       \" (7, 4993),\\n\",\n       \" (8, 4988),\\n\",\n       \" (9, 4994)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from collections import Counter\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTraining label distribution:')\\n\",\n    \"sorted(train_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"3152c5bc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 1000),\\n\",\n       \" (1, 1000),\\n\",\n       \" (2, 1000),\\n\",\n       \" (3, 1000),\\n\",\n       \" (4, 1000),\\n\",\n       \" (5, 1000),\\n\",\n       \" (6, 1000),\\n\",\n       \" (7, 1000),\\n\",\n       \" (8, 1000),\\n\",\n       \" (9, 1000)]\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print('\\\\nTest label distribution:')\\n\",\n    \"sorted(test_counter.items(), key=lambda pair: pair[0])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ba4d4163\",\n   \"metadata\": {},\n   \"source\": [\n    \"### A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"e1bd7d00\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"a630be5f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"b1702562\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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e+LxntMt7Dnqg43pvCaI1WCq317vq6vkNrTWYynr7zhMPDBe88fpSMPKXIspzFYs7BYkFVZOm7QqAoCvI85+j4mPVmw3K94hf+1t8iyzKOj4/51re+xcnxMU8eP+Zv/9Iv8fFHHzMvM9brNZvNmtdXN2zbjpv1Fucc/q2xGf8nA1ry9nhGwmDU3X9f3FM2b79jf1w/J/eUlNz9+hGKMQ5I4xd929t3ct+ZGHRLlC/lAY7ypT1BQaFEQ5TkpUXwAiYAaJQuyIops8MjilITgyPLM5rrK5rbW+ztkuAc0TveTtAXuVN4yYCSAZ5U6KxEFSWHT9/l4NFjDh8/wlQltu+R5ZJc6aSYY8TjIcSdZ+VjTPsjjpCUIOH+d2uVfhL8owbYSIPkRCkpZ4+pZkccnL3D7PicvJxSTGZobdA6WdzeNdhuycWrE7bLC25efkzse3Ae8IgiHS57Ezq68QnOjETC4EWkIVCjxzwaCdzFwxJMendAxBhRezMZibx4/gyJnjzP2aw3eO/YbjcYkyFKcXl1QyAiRqOUEEOgW28otEaLsFre4rzDeU9WXJPlBS+evxiUoOHw4ICu7WibhqOjQ7RWRCJt2xKip8hzvA8453j18hW97fHOE2JAa8PBYgGS5sp7u1OQo2U6wjSX1zeEcHeoCsnTQ+5GM8Q4ePADpD0YNSIjRKWSvxQhiAdl0GrG4ew9qBXd9XOoPiKbvmbhjwntLZe3Hc9ffZ+otpw9AmvOCGqG4jAtUgkMQPsge7GvyG7zp7kaLNZxbkUQ9ZYxphOcG8KwHgLJc0oxBLx4YgwJWrMuhQpshxONF00vmijJ7dMBlMS07IfDXUlSlvhI30e0igQd0WpUmJoQ1IBgJOQCEfrtlvr2gmff/Q5Xr19Rr1cUEjDiqaqCG12A8Uy0ob36hG27va9cgicO85cgUdnzBEc/9oslxjs4dGcO7KEg6f1hhyiMj9yfCJKSfEsRxnh3zI7+X9wdvfvH9miI3r9ONUDrWhS5NjtEpipLyrLg8OiI05NTnjx5wk//1Nc5PJhzdDDbQfVZllPkOXmeoUfHYFC4CU5VWO+xzvHVD94jywtmsxlHR0fkWYZWws988xu8++iMb33jq6xWS25vbvilX/kury4u6T5+Ru1r4gCf7w5YtTcKe2jKKJkOGJXCO6OlKQhf1G3zLuYmA9y753mOxs49B37vu4TPfffdU2lvhx/C24wjtsw91Pnu/eqLjKUfLl+eGDMoJhBCiDvMOOkshVIGbXKyvKKYFigJeNcRvcNZS79J3kjwsje43E364MEhEHZWtkYXBflkwuzoiNnxMdOjY8JwHValGGCKOw4Q1AB1hBhxISRrOjLAeGMc6k4U46GkBrhIgVKgckRV5NNzJgdnHJy+z+zonLyakJcTzKAElVbE0BF8jWjHsprSbNb06yU+NgkKUIPzfDeYu/hfWiNhUNzp6V1sEIhy90SCjEc74Q4ijBGUuvMuY4Sbm1uqsmAyqViv1ljnaPtuUJpwu9oQYiAMtxtDILQd2eBxtnWNDwEfA0rXiNKY7Db9NobV4hBne/quY7tZDWMhNM0W590Qv1SEELm+utrFLEKMKKVYr5YDLJyg31GlWWeHdRJwzrNab+5tlp1Sk72FHsLusdG6TTHBMZaaDnZkgMWUQknOtDrGuw39MtJvbsjiiipEcFva3nJx84bpNCB2QpaZBA+F+eDURQJhgEQTADau612YazgYkrMyfHc62UF9Hg1I743s7fEdPB4YQgQxEvqe0FtsbQmmIJickE2IWhNVgp8iEUJERZAohLh/BqXPiiEQFckIGhVNlF3sThFxfUezWXP95hXLyzf09RYzK5EY0FoIWoGJ5EbTNzf06+W9PRZjGFzhSFRp7Y3bW4b1/cNQq4Tuq0FJ/bADM3227Kmy+wO7Pxm7AR197R/i1XDvtW8/PhqkWmkyrclNhkgKB0yqivl8zle/+iHvvfseH374Id/+qa+zmE2YFmZ35iXYM3nhyUi4i5mH4GnbnjjA1+8+fUJe5FRVRZZlhBBpm5pH56ccHcw5OTlktVxyfXXN7WqDKMXF9RJCoKPDezecJYP/lHyGzylBETAKMpW86zgoKu98Ut6kfRVi2p/ajHC87EZz3+/bHeP3BvmHzfbbIsgX7pAv8U75TVCCEQha4ZUkiz1tyQQzaciMUGYGFSPb1Rrne5RRBJXjsxJXTOjKKU4yQrSYMW5j9G6JKWNAkiJyfToIffBURUVWTSjmM1SeYWOgrhtcU+O6lmg7JPRUOg5WlmARfADld5jZMOyBMHhbabQGK1gE0RqUgcGi1uUEUx1z8sE3mC5OmR6fkU1m6CzHFAVGK4wWtMkQmSByzPvTA5r1NUU24+Unv8zy8jnRboavkl1sa/jqnQkqBCJ+d52jNySjJxjHs3uEW95eSILK7g5hiCxXG7I8J1ttWK1WWNvv4g1aGXpr8cFjoyMEiwImucGJoBGs94OxE+lsmw561eN9euzy8npnn19cXqC1YDJNiG4gNoVEBhFN23SJRBMGTwZomgY3KMUQkmJM3khSjCGk2GFn3f3FOChBYKfolLqL1+yPkVJ7G2mwrpOnZFAhozp6Qm3g2fKrfO/Nb1DIJWfnjnnuOJgGfv2z13R2xYena95554Zyek6twccZQSpkbyElL32I7YgQZDxq00ETVYLkkzJMa3F/Dvve0lsLYpIiiiQkY4g3Ou+SRxIi9cVLmqsrrj/5AYsnHzA5eUT15EMsQh/SJwtpDIMVVBCyaIaDW5JikeFa9IDUK/ageHZx7L5taOot2/Ua1zb4rmPjW1yw9L7HFzVkac2ZLqL9W4fPHvFFBlLU7q5HWEw+/9r03+S5yA4murd7dh7gnSc4eHJR7r/8niIbDMq943r0/u7vzvteyz1HSIQ8M0zKCfPpjCov0UqhtHD26JxHTx7xj/7Jf5RHZ2ecHB/jt1twDrE90VtC8Gy6ejC+SR8uad0oVLKVeo+XBEr6TMBn6FhCVhERrE0IkykyCpcz9RNidPzcz/0MH3z4Hu+995RPPvqEi4tLvv/xx7TO0jm/P0pJme3BhgIclIpZDk3nEa2o6y2ffP/7FMaQm4zjo0Nulyuurm/4xs/8LHk1TaTBkM4qiXeqK0pSkHffkJTr6DmO3vP9eWK3Z2U3u+MMDVc/ch/e1qXDw4pBEfL5l3yRfGk4FCXIaLmo0buCGD3OdXhnwRmUNwSnicPA+KjwYuiixqucWOZEbTDakFXVzhvAZMMtRrxOn+dsR5nlqCxZWtE7QtsS+57Q99iuw/Ud0VoKrQa8XWNQ+BCxEpPCDsnbSKr7LatOaZTWKCMDVKOAAp3NyMsDdFEmEo5OC1Y0aB3RZoi1qEBSGwpjJpRV4OT8fZrNNd5ZNtcdEvwAzd25g3tLZe/fERJRu0kO3CntHXNVqUFh3i2Ee2zICNu2o9g2GGNpepeUFwETI1FHvB+YhCHBkEoiXicLdYSjA+BDYgzGCFEFfEiHunN2pwS1Bm0UaEmsVkI6YENahp11u3kOg/UZQ/qsECPOeZSKKB931mkY4Kre+bdW8ujhCZCgzwSNjp7xXZzjzmBIv2R4nSKtY4xCTSbkx09xqxUSM2ZnB3ygFXEW+U//xgvWjee7n91wuJgyKaGaXNKriEXhXcloVid0OpKi5uP1DEpFAn54DTIQDt7a//cZ0+n5sNv2g66IEQmBfnVDffGMy9/4Rfp6zWx9y6P5MbqckeXVLu6evk92HkZiLA+e1Yi+DAZWHJQncaR0JbhXFGSZ4eDgAPoOnKdp1ljf0/uOvmsT72S41/h2SsQ+xPG5Iyne1zdvKcFxbNPbx3mUHXNz38vb9xTT4fr514zfMa6/XTxZKWT/Ou5dq+xd190LjNJURcHh4oAnjx4zm06Yzyecnp9wcnbC2ckhi3lJlQvOJi+xtw2u7wjeEmyLdQ4fHFFrVGbIqxJrHdEFQmuJKiZjRTtaJdRGM6kWKJMTpEBnOVrpZIxrQWtFWWTMpxWPz4/JiBwfzGm7mtfX17S3S0DfP4PemhIjgUyDN0JmNC566ssXLDdLsD3N0QFXyxUX10senR0xPz4lm8wTn0KrFI/bQxQGDjC7uitRdns8xp2FA9xBqeNchuG9d3HyyGjifX6m7hCFcS9+WfmJ4FDRCqUVMi6gmHaP6zt83yHGoHxG8IlYkg45IQwxi6A15BmYHMlz1GKRDs0QkSwxFEMIBLZ42+NihLxAsjzdpnP4toXeErse27a4riXYntIoMp08HYvC+YgiYEnUex/cMJD3LXC1U4KJlJCslxKdz8iqQ1Seg1HEgdinJCZ6uYpoLcOkjJ5JQV4Ip2fvsV1dYvuWevka6NGEwYuR3cSPsdC0cEaoM6Z44PAjou7igruYl/o8lPHW4bNtOrK8SaSXMc6WcBwkCt4nzyT4lIcXJeJNIGoQna4nRI8LDLT5mDJBSNMevE+HMmAyIRONDoaRTTemeIQQ6ZzdO4PSdXt/FzPrfQTvUTIu9ztvuLPh3nJOx526MwgGo+Cen7AHh+xgkcEyTMSZFCOMRpCqojp7l01cEnzJ4vGc4rTg5Knhb/76hs12ya99csu3Pyg5O4JJdYWonBhLYj0lBoHoB3s1fUM6psfvD8PGHMwvUXvpAvsSeVsJjJzTkVEtISDeY1dXbN98xuX3fpGu3tCul5y+/3U0kTwz9EIi8ai7Ay8OyzSOeJgSVJSB9bfnsTKiFBFwKInkmebw8Ai3rXFtz83NFZ3rsKGjb9rEGJeIiYYgX6QE7+Z1Z7jtoJD93+P3DtcoKTaaHIbRO9gzbHav3xs3GZTW3uF6N7Z37wkDNyFGMMYM++eLlPUXKFnA6MTWPDw84Gtf/ZCz02OePDrh6PSQxeGco4MpVaEx4tA6YMXRdFtsU+NcT3QdXdfSdh3R5ORVhck07bbG9z2h7kAFoop4GmL0hOCZz4/Jiylmckw5m2N0OZD7QGshM4pJVXB+csjhZML5yTE3yxsa23F5ewPoPT863t83DEpQRYLRlJmmxdNdveLqs+/RLq+oD2dc3q55fbPhm9/8KTIcuQZTlChtiJKl82xY5+mDh9NtmJow8ja42wj3qRppzFWMCXEcrjWFMzxEzZi+EeOdwhun/a0V8mPlSytBNQyfHrwmJB2EPkDfdmxWtxTBMy9zCBlRJYgxLyomMzg9UzgXsD6x2LTRZGakDCuKIlHqre2x3qFKw/RgSplnmDwHKQhOcN6yub2hb2rq9YrYO3QUlM4SzDockpEUcwkh4LzHWpus4nukIUFnFSqbEEXh0Hhyghyg4gzlJqzWHaiSg4UmU4JRghaNkuT9RZVo2hJDOvqUJq8OOTp5nxiE61efErsV4rZDwFYRJcFn40QF1D1PYB+vDyEQVTI6EuFjdNTSVO/SEt7CwOvOYjqHyUYPMsWFog0E54kuDrFXlaytGHEuEDOIKGwM9D7S2jTHI1swSspzcy7slKCXNA4ZDMoyJIJMMISgcdaz894Gc82PNlQUrE9srhADavBcxrhg09m3VqKwSzUYPJv92MEdGeatd43jHQNBRYIIWnsmpeb8/JTJt/4Iuekoyo71uqWVLSfnB7x47fjVN2s+WWkWnfAzh1vivCeUntVtpKkj25XH9YYYhtg0fgfgjASacWOOENg9qBZQpLUbCMSYLGpiCqf5AZoM/YZw84ar7/0tVp98j2lzS/zBr7D57BN+Y7Vm9v7XWXztmyy++k2kmuNUxngkaPwwUrL7LiUR0R4GcthohmmJYGtCu2R58X2a22uCS4e3tY6mtliX0lpil3RcHx2176jr9i0b3LPTZPsK8Id6h3vTzP6Blt44Epzu3run4MbPvQez3VeScdhftu+JMSYFuOdF/DB5+1DNs5zZpOL4cM7pyYJHpwe8/+iEyaykKDRhe0vbCBbolzdsl0teffYJN1dXNNstXb3dpX89fv8bTBcLglP07RbXdbTLFSH0RCxi3KAEHWtzjTYlxeyIcjrDFCWIorc+hQ5CyiclpHSgaVHxrW/8FB6h95EXFze4ARZNyNTdXUci1gf8sDmj91SZ8NNfOeVN+zGb0BH7DX23omnXfPJX/0MuDo6YHJ6wODlnMjvk6NF7TA5OmM6P0Mbc7UNtiAg2KnzQhAh2iFWP8eqk2ORuPoOkFJToCeITP4L0YhkMuJ0zu/f3yHb+svITVIwZAqMj+QAGdnPy3mzXgdGo7YaciMozojZ0vaXvXTqsBoslhrjLsdEqJYzmRuNcJAw5XiGCzrI0eQHa8SBVYG2f6Ptd2nDaaJTXO7hrR4tXiTCi1AAxKvmcmywqQ1QJpsSYHKUKMMfk1QJdlEMqRDoenE0eTVmUd++X+7apDIqwqKZUswOKcooNLcHXd6zG8Wd4hxot8XGjy50ShJFiH77QwxmV4duB4BSDcwQPjN5jEAIOh0fcsKgkabgUdwp01hOjwrqQoNIhZhcGhcWA8495l5CMAB8l0bEHKNOHmOj8ISVxp8Uru7PQe5+Ub2AXF4yD5QfsDID99AiGcd5HOZPRsG8cvL1y490cDXDkGBsUHIqeTNVoOoLreflyxdX1mjcXK27WNevGsukVFzeO1xct715dU+kpeZ4zm80wWY7onO064m0kWptSY4ZjNd1RGLy5kamadtO9+4pDHuBwgMdBAUaVxk7jcc2G/uol/e0FfnuLIeCbDb7uWD//iCAQjKBnM4rDM7Kjx3hJ8HYYPK+RNRp3v+/gIyEdRio4+mbD9uY1q4uX1Mtr2tsbNssbmu02XVhIVPowxKzVsCb2887Gedyn19/NowxrJw2VDJM7knJ2SnA4ID+nhe594H0VFsdTUT7/bPJARlh+9PFGd3nv43YfIeOb7r5NhNl0yvHRIe88ecTJ8QGL+TTld45wbRjGJgTq7YbNesnt7RU315dsN1ts0yKiUTrH2ogPChk4CYgQ4sCW9o5oW8bUha72iDTobUs1nZGVJTqv8FGwIWKj4HwgOId3yXieTiacHp/w9EnDctNRNy19fxfO2JcwjI+QvGVioMoNx4czJuEQ1/e0veX6dkV9e4ltt9SrG+x2RT07IFpLv11hD5bkZfIOtTFkkzmYHFSR9v2+4ToYjknx7q2VeBdYGNdGYFB+xM+deeMkKYmfu68fJV9aCWqRpLC4A3tSpZH03X3b0DnLqm2YHB1hyhJMwbbpaJseYvIkjchQ+SIirkdLhtFCaRQ2pBiYUUKIGlEZbW9prSXbtizKHCkzemtpu5bVdkPUgi6ydIiEvUUticKcRVKycQwEwLoRXxmGWhWobI4UZ5j5AaqYYiYnKFOhdMl0NqUoCmKMbLc1Wmtms9ngho+KaByVce4U1WRGCMdMF8dsQ0fTbdL4jcpvp9BkD4ra38gjHDrYwaOrv6dEgV2axv0FESH6FIQnJNLR4B27gXCkh4o5cUiPiDHSeo/3PUp5PAzkpDF+ks492cst2h3wIaWBWtuj1Z0S8yGmKhZ+j403jL3fEW/YpQWMpBlg58WPRJn7MsTXdq9Oim1E4j4XH4LhdI8gKT6r0ejYIn5Nt33FtllTb2p+6Tuv+OTFDZ+8uuXj647eR0LQfP/jmri1zKTj6U+1nH+w4egrU7ycMnfHvHm1otlauhtL9HmCbIRhfDw+JuNCSY7CINHcv59oESwqmmSsxGQFp6RfTxY7+ts3LH/wK/QXLwjrWzKtiXaL67Zsn3+fullxvXxNHzxH736Vp7MDOpngZajuNBqFOsUlRZJSRAVSKuuglGxHffmGF9//FV59/1fZ3l6xvLxgu+6xvcfofOfh9iHhrMZolPV7+ZPjnXkCHgbDQJBd/mxKog7DOtzTM3LnMA4mHuNxvUvajumFo+ETd97f5/25XUxxeEkIMfnqAkHpvYN1XNdpjY420552TtegFI/Pz/n6hx/yh3/uZzk/P6IwhjgQ+pLjqyF4go+sV2tubq+5ur7k6vqKeluDhTyvKIuSIBliSorJHCTFYU29Gfawo60tSERrWK9q+s7S9y3T2ZSyqigXx0SdEVSWSCpRCFHh+pRKU+UF777zlOnikIvLFZfxmr7p0hn01nCNnhkRnO/xtkdJ5PzJY8zZDOciVn+fl1cbtps1cbXE2571mxcU1ZTVmxdU80Oq2YLJwSF5NaWcHXD4+H3y6QFMT7BRdlgJo0oMNinufiDhiaAkcTxEaaIp8JJCVhJGo23fMmGHjCd86O9xnuAw86SqKntpBkO1Ca2E1nZ0fUe92TJHk1cWKSx1XdO0LSF4MmOoyoqqLDC5ppgXTKZTirIkyzO0NdBp2mtL3/f0XUPfJdii3mqiy+n7HF3NqLQhas3m4hV9hNjb0acexyQpag2KVMaLEHYljkbJigMm83dYnP802xBxSjM9Oma+OGIxP2RWTbDWslqtuLi4QCnF6elpyouLOsVVRpgPdq6ILkrysGB+/AjnGprtDaLS4a3i3mGv1a7yyH6C544hKqNnOOZvvXXIfE5BpLt/+uQJx8dHKKWS1xVjgrJJWLtrm2QNi9C75HF1zuGRBFVKYv9mChxuF5cEuZt+xlSZVCloZHsypM/ogY3aMaY9ePyg2GzfpzVFgqh3OXDjwTZUfXg76XUoWMO+ZznCxOO8j4tgZJGJ3AXmRaWUGhM9lXEEt2V5/Qmvnr9heVuzulHUG0vbKqI3aAJlHlhvGl7T8uoTC76nX11i8inlieXg5ACRFc26YWlX9M0hvp/RhxkumiFftUkwDn4wmPbmLUa67TO6jUZlU0x5iqiSqAStIgpHf/GS5aff5+V3foF+eY3rWtrW0vUdvu8pioy8X1LWmqu//VdpL56jlXDw9T9AfnRGHTUyIDBa0o8KQoagvCKokRgTiE3L6uaWl5895+rVBe3mlm5bo3VBNi3Ji4qmqbFbR1QKZz1tvUb5gOv7t+YrotSY+6kGRDTsDOnP+4jjI8PvXdUdEgoxGoJ7r99b9j8c09wdWSPzOH2OHgoJpAP0jswByWaKg9LdVxZKhPOzU85OjljMJ+QqorCE0GN0QZ6l0n0+DqXoRCP5hMXxI2IsqKqGzaphcXTMwckZX/nmNyjKHHBc31ywXd2yvblBxYCEFKYQrdBiUg6D7+g2a1TX4PF0SKpqFTVBG0QZlMmJThGC4CWiRTEpS85PDpBg8e2G2WyKC4H183o3gCHuoZHW0nUty82G83lFPit58/wNl1vH5aZjs2mJIWKUwsaMSR+I8Tnb69dkmSYvCsRkkFWcf+VbLM7e4Z1v/iGUqRBliLYj9C2h29LcXNA3a+rbix36FtGEKETRTB6/Tz4/pDx5QiQjvoWk3K2YeOc9fkn5ckpwxJLS6QSE3UiNrqoLgc4FNr2HbUMRhEJpfPDp9eKIIgTxib2mhawwFFVOWRUoo4kqoqND6URv1xqUikQf8M7RAt4FZrMpKi+ZzKBbrwnWYZsmKTkGRTiu2iHenfJb4ufwMtElJp8zmZ+zWa+wwaNMTjWZcnB4yKQoaOomlf7aS+R+O6SxD9FFhhhlVlJOF+TlBDEZCn8PCEjG56Dc7kZz+Lz9v8eY4d3hvu8pfi6BWISz0zPOz88QSfT7MNQl1AP01W03CYYkKUHnw8AiTbE5GSx0hWC7fiDQJOgxRBKRKBpCdJhMo7Uk0pQEiApRASUGkSwVVvCBIJLiEQO1WLgzIHYkwgH7+kLdvrcMkbuE7vtQaLx3QN5TgCSvQqtIrjy56un7hma1ZHV9y2rZomVBpoUyN0wyj9LCfGaYZo4si9jO0yzXrLOO+uYVppygj8+psjUq39KZK0RFrHgCJRGDj2bIVRygee57qxDp6kv6eoIpe0wxAzFEMYgEVPBsl9fU1xesr97gmxrbWzY+YAeGrQkWcQ26W9O0PULg5tMnlKepuISqZimOHUdm8TAiKZdjgEshRkGFcc953FhKLUBRleTllNn8ANYrOu+RtgOSAYN3CULbu68QLSG6pMziWEn4Dq2J+N1meBuNHC6IXQ3LAX25t1vuQa33dtYeBnqnzGIMBO8QQqrdyVA6juR9R4Y8yRiHWNUYQrmPLkynE6bTCVWZpz0VEopibZdyh40MSEpATI4pKiazA/o6EJymKxWTg2OOHj/h4PiIEBzL29dcX12yurmiXq3ItSHTGq0VWilEcmSo9hNQWB/AWWzbJs8vCJgMpTPyMkLIgTtinNHC0eECiR4VLSdHh7S95XsvXu/Ol/FOhbgzTK0PRF0QDSy3Lau6Y91a1nViBmcmI8stIBgVCQa8FkKmiKIJKiPPS4LtOT57jCqmoDNCW+O7Glev6W9e0dcruuvXKdymFD5qnAcXwNqWyfEjsixHTw5RpmSfTJjmJV29GhGOLylf2hOMShO1TqwydPrqAcuKPmCjovWRbQ/rixV5aflwccjhYUGRQ5Z5rI/UXcR2Dm17qjynMoZCK1QmOOvp+5o+dKhCeHJ+TLPeYNse7yJNU3PTepoe5rMJ5yfvQO+pdcHtpgYseJcCqWO+WUyHrgz5UPcDpkKgIqgpUky5+PQzlps11XTKyUGgMDlKFGVZ8ujRIw4ODhARiiKVGgOGHLd4550k/xN0js7h4OQxbbNkdfsa1a1RwaKCHwnowJg/GIdc6sEKvcdWGxXv5138pAi5R4xRSvFzf+jnee+994YCvC4Vqd6ZeYGua4bUkeQBhBjprd9VbXF9s/PctpsGaz22tzifEq1T5QlPiG7HrIt4rOtwziHbeiBgaKZTvbuOMWm+71ONyRjBWpuIS9bu7gkSpKT1XQ5gguD3vD4ZFv4PW/B3eNpADgItgXnlOZpZ+uUruu41m8sVEhWz2ZQn7x1xtnI8vu15/tkVhwvDT3/9gIPDgkJF1OUNuJpu2XL98S/ht69QzXPy7IDcO2b+UzKZ0psZmr+f3p+g5AhlyoTjaJt+6f25Ddxe/G1MdkU5e4SuMvLsFJhCdERX8+yj3+Dm5TO2TcPtekvTO9YxFS7IlJDjybstrCyLakZ/Zfnef7rEhZ6Tr36Lpz/388S8JOicXidikURNgCGpI6JU+nsym3Hy6DFf+ca3wLWsbq64vrxi9ugpi5Mzvv6tn+bNy5c8+/j7dN/5WxhaDirD6upyqO5zB4nXm1uKIsGJ2hQ7iGtYtATxu3mFIYSRZTuP3jLmzA6PhHhPJd2FZ2SX86aU2kNShoMyMhSkdkTfk6lEqTLeARqiwochPDAcrrtVFyMx7il3EabzKbPFlPl0Quw32L5lu77i+uoVIcLR2SPysqKoJiwOj5lM5lTZlM11R/Ad5fyIxx9+la//zLfJjebixTN++Rd+ge/87V/k+uqKtus5Xiw4XMx55/33KcsSUx1iMkU0LardYJWljwFfd/S9o2stZvDA5oeHZNkBojTBg/OW4Ht+5qc+pCh+isW04r3Hp7x4fcn/52/9ciLDMCqSQIgenWWYssRUC/oQsOuaX//VX+OTz15xvW7oep+MJufA9PQ+YghUi4JFmTPN0jh6H2ie/Tr9m0/g6jMm0yl5kdM2G7zt8X1HoVJd3LxvqLue3jkCmm3dsNk0tL/015gdP+Ldb/0hvvKH/ziLx+8Tsgk+pspgKb4/7vNIrn8TlOA92UP/Uqwo0HWetvM0TU9QAevh8uKa2TRnWmUcn8wo85xqUnB9cYXvHFev3uAXcyZliY+Wumupm4aqLNDaDAW0I84n5aKNoSw0QsDantV6RQRMliFaE7xNymLwXJUCHRPcOAZLjb+7doC297S9xbqWsjBEKmZlSa5MMkGMRmtNVVXkeUrVMPe6JuxZpWNYYqfcNMVkRllNycsJwdUIbijzJjtDfKxNGIPsitbeKz01mLIxDF6EDHG84Tdyv2sCgNY5xpRoHTBm9NxlN3dF1e8IKT4mJej8WLotIq7bWbJtn+BS50dCRIrx+YG2jWII5Dust3jvaAfq/FB8Aj8wdMfPtNamlBgfE9PQOqxzu5SZsVjCZrP5XLzz8+SXcQLuu+dj6bSRyKUEyixQFg1VuWbz5gV9e43gyXMhV4qDeWRxNOX9D474yhlMC8X7T2Yszo/QElmXNeurmmZlaW7WGAKltPRqATHiu+tUVUcqMhZEaQlkBFUQRA3eXTq6724p4tpbXFPh8xLXr1F5hdYZ4AGHk0gsK7KzR5yfPyYqg6+m+M0av1mz+cGvYyOp0k6zxekeyTzLj7+D71YUmSc/OSc7OiE/foSYPFVFGhSgdpbY1diu4fL6DdvbC+LyhuPpjInJmM6OmT9+h+nxKYfvvE/QOS5EXn7316B3FCanLEr6vmd022KIvHr5GZvtLTEEynKCMRmZyYec3IiNbkjiVxiVkZuMxWy+Ywlv+maYy/SascKQH+LHuc4wpPJlbqgLrEQSAhVJkPugdNu2geAgerJcoRX0AloXiDL0MdFko7kry6cjZAjB36X5pAvSWBtYrWtM6GnrhucvXiNKYfKc08cZRV4wKVMutO971qsldVvjouPk9IzDgwMmkwm3V7e8eHnJL3/nN/js2QXrzYbeBULMibrika6I2QTySdrropkdntF2G9qu5s3VJevVhvV6w3RS7ghbelZhMpWIb74neIuOGVWR8fj8lMePzlIe7j0EisGo1Uj06KxgdnBMLg3eW5ptPYSoxllOBKC+T2lgfQFQprMy2iHUkYo8xL6mvnqO32ZkRuODS+EUn85ZozV5nkHf4YKncz0ueJTR9OuGm4vXdP4XKI4fEQicvv8NvNL4OBQZgF3esNFfHhD9iZXgXibR8ECCY/rO0Xce2/uUVyeOm5sVzpZ4W3B4cEBZVExmC9bXK9qm5+biCuktrirp+4beWzrvOZ7P0Vm2a+3iQkSLkGWazKSgevCW7dZTEFFG7yrkB0YQnx0MuuNEaDDmPgzVdpa27ej6hiJTaF0yzQsypcAnX01pvfP8gFTLMd4RNnZI5c4yGP5QmqJMZdbyoqKvFYQE9YaR+STsPLmoFPsfFRkW0KAEd9XYh4UqMabSSsjncuVAIyqVaRovfVcwVxQmjlXm4y4B3o9tegDtu8FTjDj0AIPGIaEi4n0qzhtCKq8UYsA6i/UuVaLp+2RE+IC3LinBIV8wxpgShZ3feYjOpd/eu+SJumS153n5Q1lg4zyP8E0agLe9ZdkdaEpFiixQmpbc3ODsJd6u0DpSlILKhNkUDo5L5gcHrI46CiWcLCrmT06JKvDafYZrhXrlaFcOEztqlug4HVJkOqQM6Dwj00ck2/gYJ6cgCi937Y/278d3a3w7JZRznK1RrkEXU4g+/RiNns8oH73DwfEx+WRKdnBEc33J9vKC288+woVkWFjb41WHCp7Ni+/Rb6+Z5JHpu19h4t4jn+RIOUXlFRIltZ9ot7jlNf3qlptPP8I2a1yzZppnVLOS6qBg9uQp1fEJ0+MzfADb9RSmIEpLpg1FkZP3+d02iYGL1y/ZbG7x3jGdzsnzpCxTkCTSB5uKUCtNaQqqvKIgke4ikVW9HHSZkCtDjBHnLd0Au1ZZSS4ajcb5BMmJpJZuaX+mFmyIYrNeAgEtkbI0KJVgzKKYokyORaXNYoacEQb0SBuCvx/rDAE669lsawrx1HXHq9dXVJMJ05mglSE3OUWWY21HcI71ekXXtwQCB0eHzOYzijxnvdry+tUlv/H9T7i5uaHrelxU5IWjmEaimSD5FCkmCWTKcqYGwlrRusDNcsvV5Q2XV1cczqccHVmODo+oylQlihiIwRJ8YosbpTiYzzlcLLhZru+txDFEEYfguzYZ08UhWQdNvaZpWqx1wziDxFSEw7oeLQHnUs5llmcp4T8m4pzEQHSOZt3i6qFmbaYGqFsRs4KY55TlQUIICHS2I6CRzGDtmq5uuby+5eSDr5NXJY/e+wBRA7owZB+Mxdr1T5Aj8eXh0DgkVYeYvBcG70pSAndvLVpnPHlyzON33yUrCy5vrmg2Gy4vboheODg64OwxzGYLcpPz5rPntO0NCiHLcw6ODnj0+ARTZkmxxhadl+Q6R4tmUuRMy4LpdEbb9lxeXdOs17i6TvRwNcIZ7ALcwSVmo1JCbgzVrstACkJcX7xgVgrz6ZQQFcbk+K7HW5uqOVDsnVc7Vw8V1V1T1CGni7BXEWZQvMaU5OWccnJIv7wEHCI+da4YXqxkgHuGKrURSUnHokCZIb8QcBZ8IHpP9D1jrHVXDmuPSbqpW1brevh82XmLY+h4nwAQwkBPjpFMpTjJ+uaSru9prWN6eI42eWJmRUv0lrau0cakNjF5nqzt3GCG+w9jn8WBrh0H5ReG4N/YYy2GgVAzpEPEEAYPNbEqry4v7iAtUrFt7x27+qFDn7xREcpd6wVEp7JtoDBZIDeew8kt4l6yevMJVVGiTzWmqvF2jRbPk4ViOnFU5ZbJOxqlM8pyASYi0TFflNQHJW5bUm9bajyagPhbNEIpislck1cOk30XJZYo0GZ/P6g5gRIjAbXnCcYIdt3TVg3OL5k9Dhil0OIhOkQHvvazfwDXfQPbdRTTCSrPkCLHLpds31zwyS/+IvHmiu12iWQJGnTNkiz2uO0lz25eQLVATQ756h/6oxycPeb0ybts11vazYaLZ9/j9uVz1ldvaC8vUASMgkZlqOmCxYff5N08pypz2uUVm5s3rK7fMC0KdFESvSfPMsqi2BlwxMh2tcTbJlVMqTdorciKfOfNeSVkKiM3OTiYlBWZD2RZTiDy4uLZAKE7ChmAW4m4VBuLKi/JEt+WPMt3Bk/d1Gk9RVBZ6gCyWq1IMSPI87HAemQ+mZNnebLZVPoJcge7f/DoMYQ7JRh84Dd+8ClaNKeHh1QqsN103K47TLlAZRO8j2zWG7arNddXF6xWt7x88RnlZMri+IhyWgCOdrPkB9/9ZZ598l3a7RJFIMs0kYzF4Snnj9/n5Pwp86ND5keH5CobEIeW5eqa6e0Nz17dstxaLDdcLzc4FzmYvKEqq9STkpRrJ95xfXGN72pKHTCh5eriiruo6lDmLIz51IosKzg/f8T1szXrbcPttsWJZnZwRL28xjk7IDiOGIXppGQ6Kamqgk2zpHcpRc5UE0QbIoG6b/CuJyt06p6iNU3foVtD5xxahCrX3N62eEnVcbwukoHsHR//+t9iu73h6YdfJ5+foasDUuOSmHKAlfB2qs6Pkp/AE0y4lg8hWSMjVV8pjKjkxmKGdARHxDCZFORGEdyUyWRKlhdEYDZfMJsvmBRTri6v2W5rpsfnHD864/G7T2jaJb3tCKLJiyGoHVISb4yRtmlo+z7VU1QKZXSqVqB0Kts0kCYSW/8OSny7uWYk0mxWrK5Lrl49p5wfUU4X+GAJ8a4fV2rNcd+yGJsDj3lECR6IO5hzJM+IMmhTkJVzRJcEnXD0sZxQRO0KgjPQgY3OUFmZyDV5mSraaBmUnyc4S99scK6l7zbE6InqflL5CDsS47ChZajGMySjpzWfDqyhwLHEOPRDi9SrJdumZdt0lLNjTFZglKJrtnTNltcvXzCZzJjPF5RTEhytEoyUPMNEKhhjpDKESlPaWyolFiMDc94k4o4Ou3SNlD7hyYvyPvw55lQIiB5GMO6iq4MhMBBmxmLapN55RR7ITIfQE5xnOpmgdce2TbBmpqDMNEZFiBZiP1jGkeA8wQesMyhlyHNDvVHYPrJZC7mBTJGqg3gQC9HXBHUD6jk6/zAVZFAZdybr3m3ZgO88kllCXxNtjQ4VMuSHmapEZQZV5qm33VDwPatKisWc2fEZtmnolzdU0xKRiNiU2+qcTXT5dQNmxausZHP4KfWzT2i3DbZr2N68Zru8olsv6W5vKDNDOa1S1aAGVs8+5rVE6stXHL/3PqvlDcvLN/iuJvgObx3O9rsGzrsDRikyrQlKEiNVGNKZPCpGJsWEqpwwnczxnSczeSpOrzQSI0WWo2IqrlCQCCI6M3QyFEF0LjEaRaHzDEiMZbxDQtgVqo6iiM7uWgv5qHcoTJm5VGrRWvrg6fAEkV0Ywh8dIXudTCKRrrOs1jVv3lyzqDR92xCiousdm23D5eU1Rqe119VbgnNMqpJyUpGXJSF4bN/R1Vu67S3iW44WFb2PRDFks0O+/rWv8uFXPuTp06dM5jPK2YRMp1J3rrfoLENnJe88/YAoCk9gc32D0Zq+7XG2Sx6sUsTgkgHpLH3Xsl2vqddrmrq+N1+jQR9japSgSF55CAHrApKXHM/PmB6e8/qzH7BZ3XJ1+Sb1O1VCURYYk8Y2FX5I85TyczTORzofsNYRJKZwzRD2Egl4v2U2rVJbKp1yx1tnhyIrYJQkItvVBevlknl+QFWpPb7EHcHny8pPkCyf8F/vLcThgDEGM/Rxm00n1CGyDZbl+obClRwdHzKpTijzalAIgjKKw5MTFvMFxwen/M2/+Yt8+uwFj7/2bd599ylf+cp7fPqDX6au12RFR1YUiebf99TrZaIPb7Y4H+gDTIxBlxU6z4nBDQHspPBiBCUeT4p3IWlid0MUYX1zQx49wQaefvhT5EWZFktwiVQTZEhW309P2CtlNZzksutkwA7ySzFDg8mmFJMjyBYEr/AxG1ILUrUYGIrn6hyTFxTVnGp2RF5MqKYHZLlJlS1i8qqc7VndvKKul1xfPSe4lvC2Eox7OXZD3DE9PtbdT7Oqhs4GY96NQmGIrK+vuF2tuVnVPHr3Q8wsxTo2ty3Lm0u++yu/zNnZI955+h6QIBoxKW0lkhLkZVDCO6Ty7h/GGpZxeEhUGkNU2F2PRg9xsTtJ3epDWksx7DqIjCUHRviToTqPUoLGURaBSRnIsxYlDlDk5Ryz1by5aCmUp9JCmWeIkKqjdE1K3s0s1hqsC6wbTcBQFBkKTd9F6q2wWGhiHhDTo30k2EB0Aa8u8NqTlV9DlOB1BTpPRMn9qEIbCa0n5D22ucU2FbqqUuxQBMkzxAjikueMdygPWivyScXRk3dZLm9ZPfuERTUBLfStpl6v8bYnF0vwW4IXPnr5Gp1lFFVJ8Kk02nRiQCWDgmZFlc04nS5otWbdNnzy67/E1fd+DVNWvPcz36YLnk3f0myucb3FO0/bNXRNc3cAiVAVJZOyZCwYLsnW27FRH5+ccHR4wtnpI9o6FWiPPsXxQowcL45Svc2+J/dClhmq6ZQ6Wjpnub25RgOalNQdY8TZgFUQlWIyKcmGqirXlwnNcjHiBsOQoYiERIhNQ287atslbzBGvHP4d54gez0SAayP3C7XfP+jT3l8PENJQLRhU7e0nSM6T24UuRHmVUZmhPOzk1QCUhn6vqWtN+QK3PaGUiwfPD3FRkEXFefvfY2f+emf5asf/hQnjx+hM40YhR4QLOeEyWzB7OAYGwKHpyccHB/y0a/+Gq5psL3F9h3OtkiW4b1NVXK8x3U9m/U6xSg363uxzpGlneL4KYcvErHOY30gmx7y3ld/mm/+fX+EX/kbf5Xnn/2A65urxL/INNNphcnMbp+GHRchNSawwdH0ga5LdVON1hgddvUXvG8wxjCZJHZ93/Xcbhpsn06Mygj16palKK4vrjCzR5SHmhhdcnx+mAL7EfKllWCeC2WhsErvSj+lNiwpd3Ba5bg+sGqSF6hMzrQqKcucLDds2oBWhkznbLYtTWt58fKKy5tbeud49vwz2r5h02wg9ISQkudzJZgs5cjYPkfnJZ2vsT4ZLaqYYAqYn0a2Nxc0AQg1CsgMWCN4wDtP9Arr79sIJgr0ju3tLZvba7Ii47A+oa0nNPWUzEzQxqCMuoMlhxjYGPANQRGV2nUwSGkL6fmIoZwecfJIoaPCdTXRNjhn8SHQ+7BTqHk5oaxmLA5PmUwOMVmJyScpv2/XIysp14PHNzT1LeXL73Fz8RnLZi8oKaT+dCbR4ZUeSiIHP1C+R882MVLHyhBaQ64yMgFsgwmWSaYoM02ZGfIioy0zukwnmn69xdY1fVGSFYF8kgL3EcH71NtRYiqjNgbcR2g2DN75UHhkCJON1eiHuqZxIOPsSUATRCOih/qEKlmZ440DY27Z4GeT645C95S6RcUVWlqUimyvX7FcblheLGkUlHnG7BCqoqQsClTr8G1kVd/y7NUbbm5bPvnkFSeV46j0FEVB8J5tY7myDp0Jk4Uh7yJGg7SgVIM2b6jMdzGTjnx2iFWGsN+XIEb6bkXXKrwJvH7+XTbNkmk5JyumaFMSfJ+Yu9YP7ZG464CSZTz92tcw61v6V58Ro2A7y2bTIEESSSoKQcaWTD4x+pqOSVmQ54bDWUUfA9anVKTgUtrR4uSYKsuhC2w7R+d7Lr//XdSkwkwrjg8XOGvZbDYpT87cHUMCFFlJkVXE6FKqBCGF25Qiz3J+9ps/w6PzJzx68pS+Dym29uqS29Watus4Oz4jV4pCFHa1RStNNZsSCkPjOr7bNJRGUeUZpyfHWGdZrTcp5qSE2WyOzhKUX5UFJgpRZ5jJDB9h2/YUkwnTTFPYnizTFCHDS7wrt+gttmu40xZCWVaIyaht6uKeSYShTu429FxcX/P4/JivvPuYk/PjhIh5y+1qTdc0aF3QbjaIdVxf3FJvevLigG99+9ucPnnC1779Myzmh0wn06H8WGKRd02dOvlEzfRgxuTwEFXmLI4OWRzMKaOwvb1he3MNouh6SyaSwju9pa87SmWY5CXtpmazXN3bX2PnqzCgXL11rC9f88lnz3nz4jWLs/d456vf5us/+/PkZcXi1055/tn3CU29S5Py3tI0fYpt+ojH0HQeFz3LzZa26XDW4XIwOmB0REkq+mGd53bVUHeRVe9Zbjuulxsqk1NowcSICoFoA9tNS2/DgADuoUCRe4r9x8mXrx2qZEgQVzuGohrah4hSlHlGFz26Hdp1xMH7iJEYPC5EggS0D0OPOBLtPvgUI/COzWbNxRvFpDKIpJQA730qtTaclEoblMkSJOAdPqaqKcV0RtfUqLbBt3XyCiTeKaVBebydXK4iSAi4rqdrtrTbNfV2SbWZUU1mVMWCGHN0TJDkEMC7g0MHNz4GhTapndPY4y8xcwSTFVTTA+LxY1zfEvoG61KqQOdSNREfI0U5oZrMmB6cUpYLjClQukhezy7+leBGUySDoGk3NNsVJru651moAZ4Y5yFd0/0yz7vXkuDjTEBcTwgO22zxfY8ERfCpcgXRkGuhyjSFFlRw2LbBtk2CqWRKVImwroK/19R1jP+JGpT1fjV57hZtGMpNpZJrfiivtidD4Jtx0Q8/cXQpAfY7SZCahBo6dGyIrsa7hmA76s2GzWrLatWjlaIsDCcdKJ+RSYVzObbpaLdrLt80XF63vHq5QR8L5aFiMjNkJim8to9EC31UZHmq7qFayI2jyD1Fe4Uyc0xo8Gr+ObKPcz3etihf4LstvtsSgxtYV2mNSoipa3wYvPahN6KIYnF8zObwkGI6w7kGZx0SochztAjGRXy0eBUGmMujiZRZ6jxQ5RnBWqwbYrdDnmBKOVLkRHoSI3i1WlIQyIqM+fE8GTwERCI2hLuYIIJIhlJDUWUPSEC0kOmMSVVxcvKI45Nzjo7P6GzAlA23mw6pW3zXk5ucPM+ZZDlNP3AQTE5WVeAzTJYnG0gUYnSq4atUMlwHA13rVDS6KEu0aMgKpJynOpkWvJhUWVVpNJFMkgekSFV0fN9ju/beEszyHG2y1DYrJs9WKYXrezqbaqsiQjWpmE4nGC20bSKU9V1HWWZ0bYfrksIIYpgfHPD43Q948t67PHn3PbLB+A0D61VIrex666i7niJm6EyTlxMm0wUHB8ecnZ1TGYMOfiieH1FuKOFG8mxjCIlNa5N3+HaIaDwrBcFHoe0dde9oHMwOTpkdnDBbHHF4esbi6Ji8KOn7FmLAeUffgwnpOv1gfEXvsQHq1iYv0CYz0GvB65AM9QjORbaNo/MdWxdpektvHbOiINOS4unDeeacu4v9jfbv50hnP16+tBIcsX09Hi4iuwWmlOE0M2Sdpw7QBkfbtnRdi7MWEUXjNQHFWilur28IITCdTjk/PefR40dED3Vd89mnHzOZFJRlzuHBhG2wtIrUeDIKWV5wdHjEZrvl4vKSddhS5jmPjo5wzhJi4LpeEn2ymOPetYpIgtz2REmCQsQ7tjdXBN+hDKl3Wt+SqyylOFTl0HNQDa1wZBf8Hw8q482970rJ6SlRtCgqZpPZLqbnXWJM9s5hg8MGj8lyjCko8glaF4NnmYrQJkq5Hmj2Cp0bcp1z9tjTbVdMLq53ik0AbTK0yYYDiqFQgBpSHNixpyIRnWkyhJkKdLcXbG6vuHn+CX0QYjFlvbwmKpjGCZUK5NOSD8+PaG1gdfEKFYf8srNHUGREEXQvqXuHjzjGyvEp1nf//B/IOUNneefdjjEaQqDv+ntWnVYxWY4mJeyjUkx1LKeVHMqUnjKStwrt0KzA3dLVb+i3K7r1kpvlhsvbno+edXjJyctA9kh4nBXEYs7NasX6uuH185dcXPZsNo523dMUmqYwmEPPbBbIjfDJs8BqE7n+zKAT4kVGz7wQjqaKyfwlpRiyySVBLcj2WKyRiO1aXF+QB8eiyFiUJcWw3yRGshAwQ0mu6EaYO6UBiFKcv/uU/s1zVo/PefGdX4LgOT48YDGfkxmD3TS09Za23uJ9QGtFUWQcH8ypqpKqqthc37DZNvS9x5lA9JHbi2us9yzXHWQpb9Y3DSHPUL3lyZNzVGbYbjfU25o3V7fIL313tyeCTIiSet1F6RNDuypYLA45PDji4NFXKY6OcOUCZxwhGtR8QXd5w8YFVBvwukRVFWvdpu4lnWM200RdoiZzttsVy2aNmk4hRhwKi0YC1L1H4xCtWJyeEk2BFFPWTtE2PdtVz1UHvQsUMTFSPRFvO5QIU5NRr1bcXi3HW0JEqGZTqskUo2PiIahIVZXcrlZsN1uevvcOTx8/4r2nj1nMK5zrWdUbNnXLdlMzKeasrpesVmtWvePo5JRv/+E/wjd/9g9xdHZGPqkgdMTQJTJaUISg6a1htbF89OlLxFyhs5yDw2N6qzDZnPe/8g268xU3x4dcXb+mbbZ4bwk+YrTGe4vzibVpe0/f2d322lWAGvsMKk3QGaGYwuQEfRB5Z/GEw6NHiA9oozCFoSgK+nXEuZ7laok30OlI3/apApVOhTOsj6y2LX1r8dYTo8boiDEBkcT2jTHSrlui6ll3jt5ZREWODipKDbpv6BF0riF0qNCjgx3QNHY8g/tdKX6MbvuyL4wDaCt7noWWFKQl+GTZBp/god4TbWDT+VT2SYQuhJT5HyLPXr2i6zqqsuLNxWVi6Ukq7+WsZTafMJ9NyLNH5LlKbnYc2v64wMXrS1brDVfX1ywWB/jZDCsKXU0pg0ddT3Fd6t2VFu0PH5EwTD7B0TdbYrBc4GnrDaubFO+YHxxxdHJKVkwQYxA9sCGVTp89MiKDH9ocmZTwKyODduBBDPlKgkrVVHREtEPHQBZDKkQgesC2U6siJWGXCx4lQ5Ktmu4LRaYTs64w+R07lAT/eueHxPakHu4a9I5191KAJjiHCx5rOza3tywvLqjbHq9ydG64XTf4vCWfTwl1i12tqJcrtp2j8YAqMVlFqsaiiFqRiyEMDQScNqlcmh8ILSRoZz9FYqwQH/xo3YVUDX6fQBIj7c1nNGqDyUtiUaGznKysEEmkIqVSc+Y9ZBiFI/RbbFzi1kvsdku37tg2jlXjebOBVecRY8k/XXNTZ5zcCq8/vaVZr9kuO0qlmFYaWRiqAkQSM7fIYFoKzmVMVsK2i2z6SFenldVVCZE467ao/oqi+x5iFqjwlifYge8FrKZf9dSsuZm8QmclymTYdj1U1FcUeTkYoBC0GbxvkwpXQGopZgyL+XyoLRvRRqONxhiNVjHF1qqCMk85W855Opvq9PohjUZIhpx1gaZtE6QmqTZl2zT4mxtul7eYMqPrG27XK5br5T1aQsQAOUqZ1C0F8GTo6pD84IytmeCCYdV6rlcr1psNLy+uuOksNZroYdsFbmNLFwTQSFDcbtNBvqJgYxXN1rF9vRzqG6d+f5kxRDPF6NTrrs8NQTJ8zNgGaCWD6oBOCyIh1c61Hb4PaKcwEsEIvvU07f1k+SzPExEnJgWrNWSSc3x4wHQy4fH5GfPZhOAdm+UtXdexul7RNxbXB26Wa6x1eBF++mf/Pk7Ozvjw619jfnBAlmd3cfQoNNstfefpukDtCtZ1x3Lb8vzFR9wsV4hKbY8muebx8SR1DCkKyskCxNA1G5R4kMBsVjKZFGijKLKMoig/5zftlGKMKK2ZzA9472s/xfnTD8izBYvFAhdBdKpOo40BSUnx601NLAw+N3RdKssYtQCe3kfq3g28DOi80IeIOJ/203AWCwn96QIDqUbTD4U+pHdse9BZT9029K4nkHgfY67aT9JLEH4SdmhMmPcOCmVo3zNGUcUTQ+rdV3ee4KEPqWC2ZiifIxEXHau6pt5u2WwblrdLMq3IjUmQmlI43xGj57Q7BFJJLk3EWY/tHdfXV6xWG25vl2RZQV6UOATJC7I4wxQTvAv4ONQT5a7g9NvDE4fJFiKuS818re/p2sSgyouSrtlgFBTTBSYr0PkU0QalTSIrDDHS9BWRuzqfSQHLnYsCQYNWKHSqZCMp+V8PZJu7uoh+YJuGPbLNLqliuPqkNI0yuwT+8aaCH5ROTN+R3MlBoY7eq0qHXSRBOs56msFS7YOAztC6ZNt5pLXMfYSmp980tHVL01lqGzHllsmiwTuPjklxGUlx0hiS0tch4AZrb/SSxxnxnt1YjYD+WDP1nhIk0i1f0qotppgQqhlZUaJYgCnQJgNVDa+9Y57EGPCuA7+lb7bYpqNvHU0X2LawbOHNBhye+euaus9ZroVXn66w3ZbYe947VpSFxkwUeRYQlYhXWguTUjg6MqCgvHCs2kDTpg4cSqDIoetb8n5F1r9Aig9Rvtx5S0TwTghWwAl23dH4Dav8DTrLUVpjm2UyMrSB2SFZliO5wimTWKemILgU202ojaLMC/oh9jxCSEoplDJkJhlqWifouLd2ID/4AXVIxCKtNKKSRd47hxOF5CXWWvr1mu12QxZyet+xrTds6+39/TXEmLQoiGmMUDmSV1DMWAdFbQMRy6vVltVqzcvlmt563KBwaxvRwRFC2kgKhe7SedOogk00rK2iXrcYnTqgV4Wm0DmZFFhJyEyvFA6Di5o2QC8KVWisSqpZnMXbxEo23pIRduGStrtPjNGZQRtN9CHFurVQmoIwC1TOM5+mrhKua2m7mq5t2SzXibXpPNumTUhRUfDBVz/k+PSU00fnFEXKswzOJZMhplZ19bZltWrYxhnrxnK93PCDjz/h2YvnNK3lcD7h/GhBlb3PpMxQxmCKijwItu0Iwzk0qUqqKkdrhTEZxozttj6vOMZ+pnk55bSYJKgxZumMjhHRGTrLUycVUfgYaLouFf5AYW0kSkRFwRPovaezfjiTUnegSCrQf6/+8sgQF0G0RjKNDUN4rHdpPfQ9Td9hvd2RDMc70J+7kx8tX54dGrjbtMNF2uGQjTFiTUHbW1Z1y7IX1KRk/ug9Dudz5tMJxWSBC56m78gnE26urri5vCA6h8RIlWcpzyjPKcqSPDM0TUOMGVoLfduw3dSsVxueffqCruuwznMyFFjddH0KnBYVB+dP2d5csewt2GZ3D2Fs9bI33yGmsjuRtPDEJxivb3vWt7cE57g9f4zzjsnhOXk5Yzr3ZHmZFHCVYTKDMTl5nqG1Hg4XvUtOv2dpjf8ZO9VHjaCHdIwBIovJE4pDa53RtfcuEHEEcSgNhARzOA/e3+dFhSGnJnWhT/FU4Y5dqU2qte5QRDPBSc66F+r8hG4mTD44ICvnVPMz1mKoG+Hyo2vK7RbZOjq9wOkO7y3b1nF1u+bjz55x9vSMyWyCypMSuuuCMcBjw4Ifx0YplZivI7oweC5jXGqbFzv4NMbA9cd/G3mTIZJhisQKni4OqBZzqtmM+fEjTHFAVpzi1RynFa1X+M6h+ga/bXA24iXntoarreOmhttO6KLwnU/XTF40lOo1YXvDogp8cK6oykBlUnw7zwMmjzR9gieDS7my85niK+8pTB4olsLtJoMYqGvPamVRakVRfQ8x5+huxj7RQoIm9tBvO7bXrxAuuX2+pMgVmRGC36QarGiq2TF5XlBVZeoagKbMKlaffYrbbsiGBOmb5S112+C9J1MaNTRCLocKSz7CelsTSU2Y17bHaQW5RvIMlWcUkxKnNU4p2ghOhOlkRt91NE3H7WpF7jKC9GzbDW1f724rxsj68gW+rhARiumEfFqxmJ9Q9x2bi9e8iEK5WDA9PORitWK9rXmz7RLKpDMkDMaMF8a+hEoU4iSlj2SHuIXBZIe0XYdC6JUm5BN6o2k6jWt9gvdMlnBqneNNymmpqmSExhignGFsD33L7fNP8U1NqDeUyrNd9nd8AoG8KDBFju9TDM1kGY/Pjnjx7Dk32w0vPvuY9bRgNStZX15iO4vtPBYhGsPhkxmP33uPp++9y2JWkmcZ+JqrVzdYHwhRMZsdUJUV3dZx9eaajz/9jO98esWbmzWfvHjJ6zevWa2WdNYxKTIOJgWnpyUnRwcUWao9q7KKspzTd2ts6Dk5PWJSVZRVQd906Yx9SwGOR9Sd46Duwj8IDpAQqeaHHJ4+4elXvs765oqm3tK7gN10LMWioqCNUBBZrrd0ztH5hG4pSVUSRx9qLHKREMZ0DS54zKSgKOZsQ4FtW24utrQ+UPaGr9Q1Xd+nalXDNf+mskPvD9RdpZFRMfa2p7eW3lkWB6dMjk45OTvnYDZjOqkoJovkCYbUIHJ1e82b5zNc0xKcpcxyMqPJtElkkqFcWQwp2L7eNKzXW9abmnXdQIgUeUFuUjNd2/fo3CRWZFEgRUHMMnzXpHjTLib19n2ln5QInpiTagjip5QEC0OQWWcZOs+QId6mB2vaZFn6bZJlnWJ5IzljAB5H0lIKUsJIURlWnBpIK4yU4vHffSUaB9JCdDgiElKOlGiDyu6nEiTDKh3aqYrOrvpiuqIQCAIOsAMduu8iS2/YUFIrCC7jdmVZug4bPZ6eIzoqD2Z2Qln0KGdpekdrHZum5pHR5GVO7y3BBYIbCDnxfq+5XR7loAjH59RQGmtUgm/XS82UJROfkpfbDm8Nja+JdkloJpjQUE6OMPMeyjPAEIKnaz00gdg6QgAXoe4iTRvp+4CgUQJdb2GAqeYqMMnhaGaY5p5SByjBZILJ0hz6AF0fMSbFOzMtFBpKDZNMD8H+wLaOZJlnWtcUxRX0LfvWmNrNl0tNj4Oj3yxRpUEygdgMNV4VvSh8lxPajCAGxBBNg603KSQxFBrwnaXrEwELHYfuGaQqTNGlxOQ+jfe66djEQENA+UjvUwNs6ZP1bgN4EaLWmKIgWkfvPM5ZdFDoHLIs7Yc7xyKiYyrVJaLwrie4DGN0ih2XGctmxTb0LG3Ddd0kdCG4lFMIGEyCz4cuNru0hoFd7FzEeUnFlsl2nOAuCM4nJKp3ARc8uZhUWGK3z8KOyT2mFOE90TpuNzX9Zo1brzldlPh4n/WqRJFpQ1ZEDg4qDqclRyfHvH71ir5voLHQZ8SuoF6vcdYTfYS8IDM5hwcHLOYzplWF7xrarqGvhW3TEaJQzI5Q2mDyCaqcI/mWqHKurq559fqCZ8+fsa23dH2Hcx4jgT5T6SwwOWI0ru+Tga+Gohs6pygq8iKVpbSxxfr7Hu7d+bEXT9g7j6KkYuNEUMownR3w3le/yavPPqara7ytd4nzx4t5WhPGcLtt8TbFsKPsdYxPAOPO4Je9b+uCcHB4yjsffMjp8Sm319fcrhrsZo24iO3TXpGQ9u/e1X/hPf0w+QlaKaWffZq+c3Y3WJ139F0qkHx6esrJu+/z+MkTppMJZVGQVXOUUahMcXZ2RLNZ8fxoSrve4NqOPEsLWBC6vks1JGOqH2mt52a5YbPZst5s2TQtuck5KqcUeUritm2LkQItWcKfygKKArdOccRd77r4lt2zQ6RGJSQQNCIDrT+mxr95XlBWE7LJlKwoMHlOZvIUn8lMagU1eH+78mTstx3aG0pJ0GYyW/Yn/s5jGkWNWGpM5KCxvZAPfiALgTI5Ji/Zn/zd24ZKLGPtxdGeS4nz4GKkdoHWeraNZ2U121BwE6HZBupmxbKz9N7S+5p3ZwUnleHJ4pwcTxUcm5fPab1j09WY3FBOSpplQ98HXB/JTJ6qwMQwdFMYW0Ulha+1fqt1lAztn8b2SndLsCoCkzwQbaTvW0IPXQuhMdgiR/e3sDimCC0oDzIhGINtHG7tkdqBhqAjdeep21TqTxudcrZ6y8i8nCwih1PN2YFhnjlyiaBBG5XCu0NT2d6lcQ5B0FGRC5Q6EgpN30PXB9Zbj9aBxabFlJfQbe+tikT8TSy6GCzRg28hYIhBoVSfxiYqbJ3a83g0DHUvg85xzRZigjTT+PV0fSpjp2JakwHoYqrWYr0duFKBVdvRaUWvhSwKrfO01mNDz7azdCHiMw3GYMqSWLdJOTpLFjS51uRlQVHud5EAgyMXjyihdT3OJtZmMSmRacXLq2u6Zkm3VKx8wEUhYhITViKlytCi0CIonWD9KLLrdlIPTMPgUjqSGvZxdBEdUvS86TzWO6ZZSrlSkKoZCUj0Q4/NQNf1hLbFNw2XN0va9ZJ+uWJWPkGJYR82VAiZNphcc3pywsnBlJOzY7RRtG1NbDf4RmPrnLbrUnxNKcoyIysMp6cnHC7mlHnG7c0rnO2I3rHtHKJzytkxOivJJnP0tEdPWlS5YHlzy+uXL3n+4jPQQ9pWSE15Q4yYosKUE0QLrrG41ASUqAzKFBRFRZHnGJOlfMlwf77eln0D/C7edrdvZ/NDvvbTf5CPvvurbJZL6jdbepvifrP5hDwzu04dieU6FgpJa2K/T2MaoyGPOUIfNQdn7/DNP/jzfPCVr/H800/57vefERqHdYLrbUISvU9l9nZhox95S5+Tn6BsWkqaTIWaB8Zalu0sqLbviUpzdHrC0/fe5fidd4YWO2qXs5aC8wqMochzDhcLfJ4RbJ88wGF8+77DOkvdNNzcLllvNtzcLFmtN2y2W0xmmFQVi8WMZrOmb2uKaUGeHWOmJU0fCEqjqwnlwSGh6+jqOvW+ErdT5En2q8iMOXTDPYc41LVM1rQ2qT5iURbkpiAzWYoN6Dsixl090QE8EPkiuP0thuT+43LvtxqINCDkolLF9BAS6YSIIhX3nk6ne++FTGtyY4ghkhf5rvhwah+T6CKNc7Rtx0effMTV7Yrr25reBjobuFxuUpU2K0STIUZhJoZeK7YRbl1InbRNzvzsBDFgcXzy/FNeX79mvV5TZlPKbEqeO5RKcz9Cn2O3itQ5Pu4OibHI9tue4zA9nJ4ans4VGk3XOpz11I1Lh1rokNtL+m7FsntN3n9GVs3JZo9Sv7JNw2JSoTKP5J6jA0cfAu9tNXXI6IOm6bYsSsXhRPN0kXE8hQpHSSBXAVOYlCMrEFzYdf7o2pQw3ncGOyj/4NPn1wFeriJbH8mKgGRLXN/udqsIVOWUsqiIKHzsQQmT4zlZnnJUY+hSRZO2JfQtBkVRVENheIPQEVWPLxXbkGr5BpcqoIgIR5OKYB1tb1l2DX302OiH9CHoIxycHnN4dJBSZdqel+sNugfrU4zfTHJ0VaIyRZBAby2b9RYfHb3VQzH6u/JiMUaub2+ouxalM+oQIc+5jZHz995hfnpETj9UG1EEFE4UPqbODhI1hfeokGLo4u7WgRqKuRe0RO0gkzumd4TUtzHlQxbiMNpDu0oEJElISAwB7yzbtqPtLdu6JnQtoesI3YbSwMHxnKzQuKbfwTkxQlM35EaT50JmNGWRkxXZrnLKo0dnKTYWwfmkaMUoVDacgTHQty3r5S2Xl1e09ZZms4asopod8MgUeBG64LAihCwnm8w4f/SIumt5c3NB5yzOpybRh7MZT588YVJNU4HymKpe+ehQCmz0Q0s1MCbn4OAQI8LNth0DJWlg47C4fyjB5O4xHwIqKzg8f8rJ43e5vbrg+tlHKQVHFCEGepeS9lVRUWWpkUAMLSH2WO8YC5wfHRympgku8OrlK9abmtnJY87e+YAPvvZtnrz7AdX8hP/qpuVv/JW/zPLqgvbmgmZ5Q7fZkC2OEO4g27crfP0o+Qng0GH97RNMRs07DqE2TIop1XRCWZWD9ziyI+PA9htYZ4DRieqPIhEphoPPWYje09Rb2iYFlZN1nqj9VTVlWpZonbofh6iopNjlTI/NYlWWUcwWxKJH6yzl8NDev7GhQ3YcbMi4MyUGyNcnss9dPsoIzcjnFNl+f79dn7+3B3Bv3L5I7vURfOstohQqRrQAMcX0JKb8zXsFvoFsiK9GIM/zAWZMnpgWRUCwXSA2juXyisvLK65ua1xQuJBiRXiFBI3EIsGFPqNzngbFJghFpshJMaQonrpreHP1JlHyneNgKmjJkwLUEaWy3V4DBmV3p+hGL3DXL5H7nrEA80XBwWGqVNTXFts7tAp4G4k2oLCI87jGImshuiYlitsGpQLFpEJnDsl6FnNH7x1H84huoLEBk8FhpTiZG2ZFpDIBHVMh8EhEdCSOxXMDQzuxlN/kXMC5kCBqGfphBkBBbUF3UHfgbZ+gsbfmS4mi7Xv6tkNpnWLNeZYIGCFtbKUsIn5oGwNEP4R8JaUflDmSaWKXyC59TMUSAomJ1/nA1nssES+SSpRpTVkVLM5PODg7otBCe7Nm3Vn6ukklrkKkyDLyqkypKTCgEbKrqqRUYp/uS+sCWI8ERRsCMQphs2Gy2ZBVGbpM9xWiIouSQhJBE4NCohp6cA7FHvbWQhhi2/QW8SGR9kaDc8DYBsATcKmAMwE/eCWM+cu2x3eWaB1iWyRYND7F+skotSIrMoLtdhs3xkhd1xgtOKuwfb9LRRKVkILDw3kqs9fbu7i8sEsTijEMHTdgu23Yrjesb2+pFgpd+ASzdz1BbambmrZr6W2PMZqqyJlXFVIHuuDJs5z5ZMLRwQFaVLovl+qthuiB5An3th/qfCaUy2SGLHtbBQyEvnjfUL+3WncGf1rnWVExPzhifng8MIAjokwqkxYCdWcpZqfMygVnT97BuS3O1jRtnapMKs3J8SkKYbvacnF1Q6DBZAm+LaspWVExWxzy3le+yqe/fk7oaprtku36lu1mxeHiMKEE4fO54D9O/s5aKZFuPgxlcWKMiMnJiwnzwxOK6RRd5NwlrHu0RLSSFL8behBKTEV61VDk1XuH85a2SczPTz7+OPWVCql/oNGKMjecn5+jYqSva5zvMWJYHMx2rY68SxRbnZccn5xiRLBNw/r6Gndzc0/RKJXc8hhSJ+Nd6sAO8k0Qr3MJVkr3HNhVhBliaxIUUcWdFzNCovvtlnbyY+borlGuDN7bWBBsYOSKRkwcAsrJmnobB5/PphwcLO6S7Hf3m8rcJT5eT/BbLl9/xPNnL7hZt0TJiErjACOGQhVEZwhR0aJZWZ1gM6UockNZZCwWOd53rNfXPH/dIiKcHJ6iYkZuqqEaxAAjSUIGIkO7JO/vcjhFErowPO6sTRBKvBuXR09Peec0Q3uPbzps23N1GbBtIh7gPDam9k/N1TViVnSTJdODBfOjCadPzlC6A9mCVswmPX3d8NnLHt1H8hmcH2ecH5fodkspHuV8SvyVSMgC4ykhBGSoeNP3EWtTay5EkeeKXAt0ij5GLrfJxNq0Cmc7JNi9hSCURYnEwOrqhs16Q14UFO9/hTwrMEWGUOFUg3ERxCQj0mh6m8ZK5UJWZBTlIav5Dd46lssNvUql47Y+srWOddexIRmspsgpJ1Om04onX3nM+QfnHJ0fMikNV8/e8IkEXi0/pbMtIUSmsymz4yOCEqKK9M6RZRV5VqFUYD7LEHN3pESgJSPEDMJAwkLhOst6s8XkmiNVDfWGwYzKy8WUWB+FyJBoH2La1/Gu7GEc0qZGZD3Z3JGRZDx2XUmqLxm7ycMZdktI1XOKKGRGmM5KFGWqNiVgREhOW8TvwYYxRt5cvGG7rSgy4frpMdMqw4fDoX5mxjvvPqFrWm5vbtHXkmpfhphahg1Ks24aNtua65s1y9sll6/f8ETPyKaRpnXU9oYoN2zWa25vbri+eE10PWVmeOfkhDfes3Wew4MDnp6d8v6Tx+jo6euattti2zqVuSTQ9DWbesVmO6XINW0728XhducOMaVX4Hehmv1DK8b7j41nlNIZj995j2674hdMKvYsOnWU77qWy23P177xNd798Bv8l/6Bf5Dt5pp6e8Py6mIoeBI4Ojimbzs+/f5HPHv5mrhco41B6/QTolBNF3z9p/8Ar77/HZRv+PgHv8Hrl59w/PI95k/eRYse5tmza9b8JeQnUoJju50wxByctUMhX43KDFlZkS0WVNMZ5WRKURXkRUaWm5TrRyS6kDrB9z1t0xD7GnyPIhVV7fuepk6FXw8W86EIskJleWoc2fd0TeptpXXg9OycsixRSvDe0vWDNyWDR9P05JlhNl9wkGWEqkLUrwAJRnj8zimLylBve+q6x/aJlICAqFSVwPY9TdvghoawYaiAr0O8q2gifqfwxt5+Y1zw7TzFfUJM8qRHWEwxKt+3PcoUlBbeWocAhCHX7t5jwaeEfO4UaJYZXPDECHVf07RbjHYczg31YY6iR2epIk9rG3AO6RsaG7FWsF6oe52o5jonzw2FzeijIUaH7bb43qGHuo8xpJ+ua+n7FCsNVUAPra+8T3mMIxlmNCDGbhMyVPzY3a8Is+NHzI4Mod1iZUMUwRQG6zzeejoUNio6r3G9J8s9ixPN+dNjjs6PmVSCxJroI653uDZwkAeaqWdiYHFckRvAdvjeYVWgHlJLtIpMYqLD36UHJSXYOaF3QucUjlQwelZEykI4mBv6lwEk0nYydF+/f8DU2w2ZBKLrUC7Bx3QdaqTiB4/2kVIZVDa0sTHpWkIEnSm0EbSKzOcFvstZ5praOoKPtCFQHR+wmJ6jqpwsz5lMJszKCXlhKBYGUyms29B0Apnl8HyKvT6k25T4znN+fsz07IiL2xWiFSpXHJ8dMj+YorLU5y+RjAY4TRTVyRPKapJahWkBo8nmE8ppCZLR1TE9rhX41HWE3hLs0FdSzD1PUGBXQjDC0Ch6H/4awwhp/ylhV1U25aft0cNUKuChSDFHLXdsblFA6nyW4tl5cW++7GAcS0z9Rauy5OjokMePz7DtiqapU0WizRo3VGEKQRBtsC6VmcuLEq0zVuuGm9s1ry+vmZ48wWwaPvn0OVql61/f3rC8veXi9WvWF5d06zWxbjDOU5Cqs/i2ZbO85abKUEqwtiUmdh/aCNZbOttRNzVVnrHd1kRvaZo7ZCzGQLt+RZsF0Bme1IJKD41qFfvuYWTMC4uiOX/yBBV73vvaN7i+vGK72ZAXVVqb2nBwdMzx+SMOz99BlyWqmBCjoeta2q7BRSEozfzwgMXRAavVknq7YrO+ZbO65fDsMZKVaDFMjp9QHb7mZv3X+ez5D8gOD/jKt36OajIbeDyBMe/wy8hP5gnGOJSycikPLYShk68CbZAhcTkr8pQmYNIG1loPhIiBieXTAW37Ht82BNuiST3krE195kSE6aRKuXhDYrrWFqUUbb0BieRFxnw+pShK6rYf2F0Bow1eB7TJ6P1QlNkY8umUat8zE+HoeM7hNCfL68HDG3oiDhMcvMc6R9eljunB73etD4Sg0qCH/Vhewsr2y5WJ7OGA98Y0/RPv/vMFL4m733Ektu1Zw34XW7sT21v6vtu1LEr5dkVKCQmBbbOltw0xOKal4WBWDO1NSnSWpWT51uJCj+3twK4VnFIEpRCd42OGi/kADwei+/+z9yextu15fif0+Xer2d1pbv+6iMiMTGdWutI2MgNUEhJMmZaEB6iYYKnGTEBCJSEGTBjBBAkxxEaoRkxghoRqgKpwIdtplx3pzIh47W1Pt5vV/TsGv//ae5/7IiJvoHJJSF5P591un332Wuu//r/u20ibT6GZqYg5ZybvZdqqFNZabLZF0i0fXyOIRjmHY3fheD2Pt4tqsaFaOgKJMI3kaSJpRUDhs2JImilqxqghBKyDxbJmc73m4tklOk/kEMneUVtDYxXLKnO5hLZSbC6cZOujh5AIKjPmIuitBRVqUaJ/mebOuWKKkiTMv8as0CrRVApXab6/E9L5NAmp+OORRQgj1iicgWg1TmuSn8ihQgUDOWFyFuuZeYkZefZyVmhnsFYc650zWKfRVpFDqX+sZnmx4vLZFdW6oa4qlk3Loq6lXWonvJkIcSQo0CaxXDf0Fy2V1oQ+sd4sWawXfNjt0E5jK8tiVbNY1aAzfT/KTOl0x6iWF9TLlRQWFrQ1VOsFzkrAiSFLq10brMBfiSFLEIyp8NvKHlMWgS5EfhB0sySI8//kNYpZW7WgEJWSjRYBpc3m1UqLUPs8Jiiwao62j7pUlmdtXoH156P4RFVVNE1D2zZcXmw4XF4S+i3TODJN4xHgFVLG2EgIkWmaZG9T4p6+23fsDj1dN1Lte+Kbd7jCj97d3rB7eODhww3Dw46p62D0uAxJaVSM+GHgsNuyaxzGalLyWCtYDG0Msxza7Os5jiOkIlRxtpcMhzu6yoh9kRLQVWWtdOxU2YtK1a2dK2Al6RLEJ0959vIzumFiuz9gjMFa+f5F29AuFtiqwdULKu+p6gUxgQpe5s4pUbc1i+WCdtFy/7CjO+zYb+/E1JgG7Srq5YZqsWGYRu4fbnj/7num/kDlHNqasqf8bsDP+fHpM8GYyCGiStdXK3BtSyxGoZ1u0K6halusVVhdPNOUIWtLLkimDIQU8H5k2B+Y9jviOFAZg3GW2rU0rVQSo490w8Q0BQ67AzcfPnB7c8Oz6ysuLzZ8/tkLHh623N3dMQyey6snLBdCaJ/dyx8ebvHTxNsPN2xWS6I+LWYFfPHlc148WXHYD3z77Rvubh54+/aenDUpW4Zxojt0PNzd83zoaYPYxZgzIXFdcs05QzU6yuKUYShn/TxgRnyeHtZ5s8+FGK9mpGppZeZyvcmp/L1UHzPyM4YozgJnoJzXr1+XDbK0LJSiaZoyJ4FxGvFhYJoOXCzWuOeGpxdX1IsFzjnut7f0+47uYYdWW7qCxMo5kgNE71HRQrAcvBJ9Sq2xyqG1RSVDSoIgniZfELZiMDobnmqtj3ZHQDHTDcdqcJZQO+UGClVdoOsKHTPdzZbtIfDurme3T3QddN4Qs1AALjeK9tmCP/j3f87qxSvq9QbffSAlRQwCTNBh4skanl2vyNqxj5b3HwK3+xHnFT5nvMrYSmOtQvsKRxYidVLkmEkR+hH6CR46OPSeEDI1iUVteHbh+Oqp4WEPd/eB/SExmceAqbqGy3XNi2cvGA+RGDPd4Z6sJmJsWC9XGKul5ROkUg5J9FgoprS6tGiHcRSgGpmqcuiq4tWrZ3z1B5/x+VfPCWkS6b7BE6dOrrtKmIXB1oYUMs4YFuslYbNi0o7UZpbrlrp1YDPLi4YXXzzBtp7AgW7fs7sP3N4ejpsqSuGWF7jVWla0BmUUxlZYK1V+UzlcZXC1PY4eOjp6hqI9yQydReq3QmMqAD2XMwmZeWZljy3RioAlUSGjGI1isBafMmPKzPokWoFTIlYxzxATugRQRBhbJSYVTvARpaiMY1G3bBYVr56/5MWTZ5iYePHkijqN/PJf3ZCTF41WDSonxm6ArEXwoMySY4h899333N89MIyZ12/ec3PfEdV31Fpjc+b+3QemfmDsB8ZuJIWIDYmlqnHOcrffM/qJ/e6B7v4DbVuxWDqW6xVVVROCpTaWJxdXNM0ClGF36GiLxOF8pBh5890veffmO+52HdotqaqGy4sLlm2NNYbD4SAYD2148uwZddtSL5boFKgqy9/69/6M3W7H6++/xXcdjszLzYa1ydhhz83XfyWFhPf0+3sO/Z5dtyWECQO01nF5fcnQd7x9+5a3P3zNX/7L/5LPv/gc8yRiF5fUqwXtxQZrLcNuy4fvv+b29V+Rp+csVxsiMGwfPjW0/R4C2kpQT7IAjcyZjCOXDNxTymYtWWuOoVR8Ca8TyqRCsk8Mw4Fuv+P+5oZhe08Yeqw2YtBaV2AUIUW6fiIkcWHXWrOoW+yTp1xfrKlrJ4tiGJgmsXfKSUx0TW0xGlG72FwyDB23Nx/ouo5+PCO9lgVtrKJpDetNS4ie27sdQg/M4n81eA77PePQ4ceBpvWkaIhai/yVmoE0pU2WRLuS8nvU45+XzhCgEgDlS5eMVc2p6NF9Pp1NMhSkAiopzu8o4SaeT7JDqWBzFoFxtMKXWWnKFECDIQWN1Qsqq0hRE8bENPQMh4CfIFNTuQUJT1LhpO0ZI0aLAnxNJc7iVcOikcr8YnXJolkU/qSYZ7qqwpb2Zoon4MsMKph/P9MiZmTuoyPPzV3LOMGhTzx0iW7MDAGhKyiF0pnL5xdcf/6U1cvPaNaXmKoi9jBOgcODqHCEMNEuxBpnDJkPNyPbbWSYFER1rEq1gWxE0EkX5SSbFSkq/JQZvaabFB/2iFxUzDzUmmYJ6xC4WGpUUmzvFIc+03FG/VCw2dRsNjXWKKoKYpB7aB1o7cl5Ipe2oNFg8ly5WEAfnTq0EvSf6NA61lWNqWsx/g2eNI0YLeLFzugSKjJOS7syA0OcUAiSd6YOHB4OLLpr1KpiuV6A0SxWK6HmaBG2b1rDoo1wDBdni3v+PeoI8hFURKnaUsYWMJTYs0lQlwaLevyeqJk3X3SBitjz2YyhsopaKVZagw9ltmzKM6bx0aNVpnVQG0FM91OUajElrNFUKtMqQdkOcfxoR5S7UWmL01pMsmdkasEKyNzWHrtgKSaCn/DjRAyB7rDHh8Q4jsSUsbbi4WFH3h7wCUzO6JyxAUxWLOsFeoCkxGi5rg3BZg4HEcEeh46pt1g8yTUQaqlgoxKxhFrmaxlVujOSMJ32JrHmMhYqk1i0UFWwqAKV0SgdSHEvZ68NzggQJ46J3X5HfzhgLDincVYz+ZHKamytialjGO64vflWcBQxMI23hNCTYid2WYDLNVVlWCxbjFFEP9BtbzjcvcbqSJ0mUuxABQyGNAam7Z6bH35F9jvi9SWjj9zffPvJAJlPV4wpbQijpaWljCYbS4iQI/hcjCu1FmPUGIghELRFqYQ2Ecik7Bn6ju6w4/72lv7hDt/1Iv/lZFiftVi67PY9yjZYV3OxuWC9WFJfXrBsK1IM9F3HMAyEEDGmkaAVBE2mtUg+NRtLbQ13Hz7QH6TlcH5xckFDVY1hvRGzy6q2BdkFIWWmsQTBvsOPPSlOpGhJxhCzFs+5nKQ9pkogM4Wfo04/SR0D3FkALPZMau6xzw/8rIyfpB+PKhVk2Txy0eKMMYESU+HzI5W5Wkr5yK+a/5wSKCX+hDlZrGmprCL4xP6wp+t7ut5LtUlF5TJaR6wRYXSfPEPqcQmqrGh0ReMalosNF5tLmmbBZr2hbRuqapZoEiWdmeQc1Ql1OwdB4BgEUwElhSjr5ngVs6i0qGwYhsyhS2y7xOhhiooUpW3prOLq5TVPvnzJ8vlnGFMhsnEJPwZ2DwN9J+LEzQLGbabvI2/fjAw+MgUwuSTsMeMajYVCUZFer8qaEDKTh34yHAa42UeCFwupVW9oh8TaRzYLhwoKh+HQJe5Lm7o8XazWNetNjdIJXxtSVIQpHAf9KU3Sccji+ChVtEUpSUR7Pxzjja1ESrCqKtxaeK0mRZIfCUNPVVmZgRmhMCUA4/BaaBN+imA0zsmstR8H7u5u2XTPcX7BYr2gWS5JUaGcJqWIMZHFQjON6RGqcJ4OnEAVqnQxlHROcxZN4oT4Q+Zi5Dq3PfP8JvL987xYF/6BSjP3NIuoTMnynFG0RrExSsYscSJSkZUlaU0sBPOlNTROHrcQhG+oksJmS0VkyYSPAy58hCpnXmcGM9OOcoYoCdAsBWaLm4U0cgQtauwosnNTz6ET77+cM9ZV3D1sGcZJLNZCQMXMi8unrJsF63aN7sQNhAx64YiV4jbs8H7ATyN+6Ag6koImx4BKEaUM1lU4V0kViowoUvQSBM9ihXUG5wxtBZcrQ1Vp6jqijYhUp7gvExyD1RMmwzh03N++47A/gIrYEggnP6KVpWotU+zohltub0uRlCJJDYLOTQMhjMKBzRHnxAfSWk0KA8P+jv3ta4wOxOzxfktmEunJKeJ3HTevf0mO95CvOQwT9zff/Y5o9vj49CCohePirBaDRy2+buRQACMe5QM5BPw44q0jVg1RGbQyJCMPS/A9vu/I3rNeNtR5ja9Fv85VNU3bUNUW0aJTmKpB24pF3RL9RJgmcvIoLe29YfSAVAsxBsZx4PburnCAAutliyJztbni4eFesojzpXzkainWmzXONby867l5vyf6AzElwjSxu71je3NHUy9YLNaoJCRVRZYBu9ZFeLhUb1qCoDhpzxWfKmPBk5wY+jSE13oOlFqcsZUWAW09F5UCG5cgGEgpkmLAWkNV14/Oa7O54PLy6thehBkZKvMXrR0hBuE3Ocsw9IBi6KeiXq8LTL9iubo4vm/f90zTxDD2aCP0jNVizWK54umTZ6yWaxEWaNrCCyzDsxmQkCWgu8oRB2ktd93++Pnm1wgoCIx+jHx1RpHixO3NDR8+PHBze2DoNV6AoeTgWWxqnn+25g//5E94/uVXLC9+Spi2hOGe7v0D/qHHBkXTLPApcOcz/9XXnh/ejHz33UBlRW9hsAanM5WFRmeUA+c80Wv6oLjpoZ8U+15xc8jsh8zbe7E/MgowCW/FLuaPXhlcq3n2LPOwH7nb57PNJ7PbPaBzJ1LAqkGEopVkmDnRhR4dFSZAqxsqW7FoN2hX5PlicVpPsFlvyBG29wc2mw11U0N3EL1f7fAJpkL7UKb4MSbN2w+33G4fuOsfqFzNsl0x9IExwDh6uv2A23YMWpzZM5am3ZCzEvscnZgbjfMRC4gOOAqAhxxQJHJS9ASCBm8yRAGcTT4Sgmivppny8NGRCr9UpSQeiWiS0fNDzTiMwkXze0I/EqZAT02uGnKzOKI1+2kSDAKKwWd81oSsGSaPTxODfyBMEw/b/RmATXF9dcnzZ0/57OklzcKhjfyrdg5bN4QEMatCUndoK3tUCJ6+63n37h3TFOiH8TgGOGz37LpBNFqjjBJySiynJW3TiuycqiCK0MXARJ9GdIpUGnRjcU5h7PmzhPCZi52UD16SDBIpR3FpOdsL7/s3rFSDdoEUwCfN2Ce2Q0c3juyHUxGx9+9wxqGy4WF/IHhP2xiUG6kbzTiJMH6uFbeHDxxMz97sBPBD5jA+EEJg8gGNw2pLZkHUAVNH6oXGVJnAxJs3v+Rh+wPaWd7v9tze3JMrhU2ZOk8c3v0Km2/Q+gqlDHn68KM189uOT58JKoqFEBz7Lur0FWME75nGkTB5QuUJ3qO1RStPLGaL49ALtWEaaaoKu2hJTnQMq7qibltc0Z1MWWGqFmOdCETnSIoaba0AUmLJZmMiIzMo7z0hiaJE8F4uuJY+f4hJWoIfP1MlXbXWUNeKi4s1Yx/oDwPeR+EVpSyD+jB/BZIJ5GBIRob7mRPR/ohMmxGiZ4FvBsyAVIJ5DoLGlMA3t0ezBNP5M6rCgcmqKItIm2cGkZyd0Ec0kJNOp9H2aP4LmlyU5AGGYaCqGqpqYgoZYy1VJX5u55/dWiuqKUpMNJumoW0amqahrmsRd1YAuXCScqGUyKZ4PP/8Y7DQ+XzwEYq2HN5PdF3m5uaO7banO3j8JLMchWK5NlxeL3j28oL11QXtak3GEvzI2G8ZDg+E8YBCgE7DmLjbZvpepPU2a3eceY8hEjQko/FoQlYF1ADjBDf7zH6Ehx7uu8QwwVDQ5UbB3ieqXlNvNa+uoNKZ5Qr2fZnpzs9WBj9GRquIOaK0R6ks+pnlfs5IVIV0OIwW0QHx6owcur04cRThxdkxom4q6qZi6A+iPZoyU3HviCFQNyJpNowT+11Ptx9EQSdDrgzG1DgXqFxD9InhMLKNXjogGLyXNmXwA85p/OQfVRbnHp5H/mcufoUoQpSqWei6gZgEuDXzvWbg1PnamN8XxO+vDCOYPUdVlo4QOcIUiDETsxKd1VzGCVnap1M8rTWfCsU+K3wqfL4IMQmo5tE61QJAWixanJNqLyuw1uGqhrppiQl8Gou7iSmozUQOge3DAyFmJh9K279QsbwnhOK/WgB43diz7Q/cHXa4ZI7yjkOYGMIgqlZGUTlDVVkqZx+1YY/leHnmMsUqqQgGnK5pYnu4JdFiUkbFUZJQpTj0e7pp5DD0gjHQmu2u8EwR7ESI4iEamDBOQTiBkKY4kb1BTRpbul+DH8u5R6ySB2FSo6Dd4yR7jNUooxinDpRHj5k4BVSeaNqKOiQRd4hjsWIZpSMZJz71+D2DIMwdfzXnRUajrcHvPX4Y2G13rJZrrHE4N0jbI8om7aeew+6e3f0dse9Ytg26lrbclBKurqgXLZlITBkfErYSt4YYIlElssm0i4X0+Pse52wxjBTpLelxx+NMzMdYyOSa/ThxGMfT4B4KulUCgtEWXVuev3hC8JGhG+gOWxQRawy6kNNzaVVEPRGNkHtVssRcBLljxBQrIHWWKJwHwqML/RwEFSgrljMyz5RqQgpKRdaiiZjLTJAYSSkQgxf5tEfI0sf8xqZpjm0ZY+ThiDGhNVirWSxarDWM48hyOcnGowuJugS2Wd9Ta800TdShllmO1tR1TdM0VJXDOjEfDtFDPLlBnM/+Zo5pRojcYjuljoT/o58gj4mvOWcO2y16P/DLX37D+3cHukNg6BXGaara8tkXlzz//IKf/K0XbJ4/wa2WTD5w2N/Q3X3Pw90bVOgxTBx2nvv7zLffKmLOXCwVP/tqw2Gf2G0D37zZyZp3FV206ACq9xy6zL7P/Oomcd9nPnQweHlAaqOxOmNUIk6ZsINxNDy/yFytMpeXmYcHcdlQZ1XT1Au9I8WM1h6txedRGSNi6Uq0OZuqZmkXR5L7GDz9NPH67RtBUlrN1foCrRRVbWmXNVVT0z2IZug0JR7GUYj048jVZglk3ry55dCPjN5jtKVRLav6kriwVMmRLnrSlHm42fN29yBrXSkWqwcBbyVYtC39MJ09X/nRs3Yuoh6VzB+9h6zLMC2nEqiR90+FFP87gmCe9yak3Ti/whd/yimIog5Wk3RDNPKcoowQ6YNURZBJmGMQjDljkiaqGqwl2vOZoHjnKZVZLFqq2mKdjByqpmW53nB1/RT0A2N4KFy3ooU8yXP59t07xN1FF+/ViXGamCZx84jIc4/W3O13dOPEQ9+zXqypjMNhGPo93vcYErXTLBaG1aqW57GuH9GLVAHHgVTAKcaj7N98xJx4/f5bVl1DpSwL43BGEuHOj/R+Yt/vsc5RVRXb3ShjLGXFM5TMfjgwpg7XWlQn2BGtZQabPMQhiN2Vkjm8uN1kshISf0yROAgaXxtBINvaMcUBphFDwmJoHVxcLrHDRB0iloTNEZuT+CWGj2e4v/34PbRDxUw2lUdX5ZJxakXtLEsTmPxAd/ue3aIRFKGGahyx1nJ/Ezjst9x9eMtwf4slU12ssVphlKVtRbG+XS1JWRQODv3IFMQGR6FxdUWzaDEK/DCw70f6KeBDRGldVBUCKYu8WEwJ+sNxA769veH2/v7RjS8nx7xatIblquHps0uMUhy6EUzD5mLNcrWiaRfFWJdC9wiImk2ZhaZIHnv8dIA4SdY1tzKV1IRzpaZKJTUb4Jp6jbI10bU4Y8Sc1kg2pZIEwfmpTyFIEPTT0aT3eDZKsWgXLJdL4TKpk0ODCJNbWSxBkcYo5OSC1pQvw2q1EmsrK1nlfMwB0XhzdO6u64q6bo4gGOFszi3QLLSXKLDwtm2xRXicIhpujTuqaMzk+cViUQyZH298r797zVZNfHh3oOsiPgBkrq5bnr7c8Of/7T/m4tmai5cXmGVF1BFFhzEdVdXTbib6+4n7u5H+IH6Mf/yHC0ydyDrxYRvZ7gP3vWeImYQi9In4PuAM6BToR3GQ+OGQ2XnFwyiVhdVKMleVQEtVhxeQy7s74RperjJNo1guDMK0l88/jBlLQnlPrSLWZtarC9w8y1lYeeaSwmSRw+uHkW1/YN91vLu/JSmFtobaNuSYiuu4pWoMichuvydk2HmPrSvW6w3LzRqjBay13e849B2KzGJhWbSw30qyNfQjyhpIsKgW9NNIP4707KXKMY5m8wSjW85luGYqw7ka0El1Sp3o63PXHBFXLuDJs+fzHFw2/935cfLIlJa7AVOha3lmBFUtlAQlMNXy0yMJuQ+xkPmPab6mMD4SVCfFGIVi0TSs2pbVUgSpbVVhm1o+p1Zcv/qM7CrGnLnfH8RLVBusdeQQmcbx2DnKUbxAnAFtpDK22rBeb2iXCxF8L4INH7oHNIpGO1Z1xapdoTK4OlM3imbRyiy4qgR5XboIIhIuDg0yNk3kkiQ/uoqFrhSVJ1ggR/IkbvROw8VygTbFm1IJHsJ7oTiEFImMaKuolzWEhKst7aIhFIGMEAOqjMicrkhkeU6KEAkpHZWXYphNuCVhN0ZTa3MEI33x2VPi9kDuB5GuTJk4ijbw7+Mm8XsJaGfmC6jQ5Q8asAoWRqFSZOgP9LutfE8xtjTGEIaew+6B2/fv8PsdldFsnKOtK5Sz1K7COWmlhejxMeF9pBsDISasE+kvnGYKooHYjVNpJ0TQitEX1fxUKrKUiUVsepwmHnZ7dt1j65CZ9zKfI4B1msWyJoYldVuBqWgXLVXTYKuqDPHOWjBJk7VwDHP0xPGA398Shz0pe1JBhJ429NOvxmiqusXVLfVGoasi4ZYcaEk6VJKKMSHzQJVVmQcKcT+FQIwfCRcXRf/zLPok8K0EYKEft03neaTWhtqWgFgyWOAYoEAeFslu7fG+HUEASs7rN7U6PxYYJ3Nsk4bgj59Xfs6sUnG8WewediQ8fRcJQf7dOsVqU3P9dMnzz56xuF7TXKzEFVtldBohTag0obXMU3w0KCP2V8t1hTaeKcL4wdONkf0ozgkpS4YconQ/ok+MQZCkDyN0AboodO65U5JU+TPimaayVI7rRtw6rNVU9XnPUJGShezQSom/pnFUpsKZBmcrrHMF/SyKKikmpknIzoe+Z/BektNkGEaBm2utsU7jKoOxipQDkx/JiLRXs6ypG7EqW7MgmyDrLyWaxlLVCm2EeJxywiJ800XTkHJiHAcp15Q+IjrPq9vjmlKP19mjZ+A4YuEMAMNZ3PvNHNs5EJYc4vjrvEsJQM2Qi0izTBGK1OA8olDz98uoYf4gEjjKM27K++nHW2Vl3dEhQRuDsgZlLaaucCqz2FzQjSPNfle4zsUFQxu0zmQ/FUF/UCR0UdTSOqM1aGdoFjXr9QqlDOPk5T4PAmRRTlO3FW1lIUxYl6kqcJU9PYdnz/cRUMQ8mkgFKPc41TBKnyhcSOt05iBrwLnTHmKVKvZeGakDZT6rrMLWhmAS2ohIB9aQdOEApyT7ISesQs4FHDe3yoOMT2RZlGCTJQHRSpDN63WLz4mo5+6SSBdqXTpmn3j8HgLaRUszK0FA5SyixTljU+ZVYzj4xIfhwPuvf8lkHO3lNUYhhrFjxzRO9Iee3d09BsXhbsfnn73i6ZNr1m6BsRVgOez3POy2fP3daw6DBLX1xQXLtqFtxJpkHAb2uz2hoAl9lPZpTImsFN4HxmHEe2mJduPI7nBg30+PNtZ55nCu0KJUpmkdWi+5vF6DW3L57Jp2tcI1DRhxT0+mqFjkJOl+CqTxwP7dt9x9/2843L0jDjvJ+NRHC/KojgKrqyesnzzn5c//HLe8RLeRaMXHTXwHZa4pOH1RuMjxBIwZx14Ews9OzGjzyNXCGBHanm12RMUiobU62j9JYLRYI2LA2s6VnoBu5qpSNA+VvNZalssFdV2zWCxISZR41puVbNopHyvJ880wxsQ0Svvn4WFL3w/0fU/btjjnaNuWGALTODJnKTlnbt7d460iBYfKCWdhc2V4+cWSz392yfrFZ7jFJbg1UQ0CMIoD466nvx04bA0hbKgWLRcvlmQdGfwD33w38f7DyD//VxMPh8SuS1gldUlKiSlDyIrOi/VUSIohK3yatwqpXkLOuCMZW4k8XQ5sd4qFVuy34FxitTzb85WiaZ6x2ixpa8tqIbZiCkvWikmJt5qKGeUT02HAe08/dtzvtuz6Dl1ZeQ5y5G57T20MVWWoF5bFynH1tMVWlqqGxZMrqqahWVUYJSao7WXLajJMfoWfhCKhUXRNZGwi1cKwum5pNmvayw03d3fwLqC0o3I16/UF4zCx3Z3xBD+6548P0eDVRhdQdEaZAjYzs6eL2IXJc/m7W6ISvPKx8swzdxB9HOUUqfb5kpfXzcOd47vKOxSpwpBLAni2VSoFjaupjJNYrS3JOHJTCTgl1qyml3ilmELg7et39IcOpURDN2ewRkjzOQYqU4TytaH3CrTCtY7VasHFZs3F5uK4P/W9+KMu2gUqBAgT+7seVynqhdDMjs+9EQCjMeL8nlNiShNkoW/IvTm3iFIs3YrWWnLymDxr9UTBIJQKO5sCYii0uLaxqGyYcuTgB2xrcJNj+jAAicYamrYhOQHzxDAxTZGkNdZYnK0x1swVFp2fGLoJo0VsxTqNDyMqFZs6PZEVtBvHolrBumbcHRhjRA0BpytC+rcQBOfBfIkaEs3LrzlnTASXM23OvL2/Zxcy/WGkrhyVsxA90zSx7/rSk4fd4SDtyZzxJOq6Ei+6sefQDQQfUUrmeSnBoevpu55F7VgtF6zahoeHLeM4ktNE3YoLN9owDCNbGSrIxj9LvX3kn3Ua3M+pZwGelL93lUXXNc1yiXYVGAfKlqeo6I1mZC7nD+TpgeTvMLqnrj3h6O9WflZKpBDx4ygBPEZy7IjjjuXlE2rvqVSFqaQaEgRm4WiaCjAkzKncT1J9po94TNM0MY7jUaT3VH1JJjhOogc500pilJbQjAjVRks79KwSzDkfq0ul7LF1mudMuyA6lRLZNs7+/pRgyLUdx4lp9MU1Qlqx82wQKG2a/Lg6APo+0DhVXEw0rrY8eXHJ5slzlpcvceun6GYtLWXdEKeew4cfuHm/ZX9zoFm9oFq3aBbc3t1zf9/xzQ8P/PB25O4hcNNpJl8Exkt2mpUAJkKGIaYSBGdxZjnnObNSOaGzwcwAqSyb+RA0hwkOg+ZiqTjXLVYoFqsrlpsNzcJStRWusri6JuYi4+c7UjcRhp5+6PF+wqcJYxRtW1FftEUkILG2FUYJv7SqDbbStIta5tJKgqv3ieF+D2LzKpSn8t/sPiJqIgltFcvLhsW6oVk3rK9XJJ3ESbyP5dnc83Dfcb/tHiWZsxLQx4Hw2HZLRTdYybWT6qRInJV1k0mP5PXOxennACiQoRLk1NwcnavN+blOxxA5B8sZMHL6+yPMpvyblnv7KPhCP450w8ihH4gJSVCdk+Q0ZypXUdua2tQYLEYZjLXEY3tPY0yZVxIEVAPUzqJ0FHxT9MRpwA9OOM9KoYqGaZxE1pAUqCqHq6SDYDiJCWilz3SDZa2KqXi5J3nu7Z2O1joWtpKEeDYXiKKvrBEHDpXLwFaLwEfMUnhonWmqmmgz3kQqZ2icpa0rdN2QKo1TFTFMxBiY8Egn3JOSdBKcawhhzzh6nHXUlcw3K2cw6sSPTioTcsRahdOV3BRt8OWeBfXpDdHfIwjK3VclQEjZGo+BUCeFTVCTCYcDQzeSe09cLkltgyKK+krXF3ULRTeMbHfFMVuJukVVVyiVinI9mKJGnhP044AfR5avntM2Nau2IgUhfcYQqGpHVdcoIzyoaRiJvmRchVeXfgQimRf2WU80z0izJK2k2lHXNcqKuPTM51NZiLc5J1QOZH8g+z3EHc4FVAsBRYxKxH+LukvGk0JHnCa8n8ihJw0H9reviUrD4hKDxlqHTQI6MEaX5Z2Bkj2TUURIQTbKY8UkJsfeS5CZ531SkcnC99NU7h1Hvl6GYnlkpSUzB8GispN0LnNFh1IijyaqNGeBrnRBJQiKpNdc9QJHgE0IAR/8sbo0xlBX1RHBFmMU/8PzzQcYx8hUUnvrLE1bcXF9Tbu+xrZPUM0FNAtwNZYIIdNvB3YPI7t9YP3ll1RVi9UNX7+54+3NwF/+asfb28Sug2GqROAARc6ShKniuhHJ+JyZUiaULBmUkLqPQY9Sf5zmEkrBGDODh36AzUJmM8dDQd0sqZdrqoXDLSpsbanXS0IYCWEkHyJpCoQklX8InmwzrtIYVVOvVzJfIdEYcZYPfsRWkknXjUPEd6QVPk2JXX8AJnIRTBaitOFiuYQIcZIGl7aKZtVQLSuqhWOxasuYIcFdzzBMdIc9290D213HKQrmM7H5xxVhyqm09UtLTecjUKOYQhSzVQlI6iyROn9mH7e95E9yL/QRMKNLYJR22vzrCQdweoui0Tv/qk6V4sfJmPdiTTSOkzx12oCxJdGNghK1lbS0tcVqKx5+IT5Ca4MIH1AqVVcANkoBMZQgaElzt6aMDHzpPCkSdWVFpcuUAFhavaoEwtPTc0rEiZGTu+i8ThWVttS2nAdBOL1lDeeS4c4jpFk6ckqRXCzSGlfjbcTbAJWlrhy1c1JAOIM1NdnWwvPOHTGHoxbzrKYTQmKaAlVd09QNbd1SVUCOpDEQSncmkHDGYSuHM06S1JyJRoRMPvX4dMUYKI7MCDIxz6ruMkOQDFhRY1gbQzCWg480xrJsFry/+8B2f+Dm/oHL1ar4bzX4kBknTz96phgx08RmtaSuF7x6dcmh9ww+sNt3PNw9sH24Z1FX6OtLrq82PH/5iivvud/uOHTSM+/Gw7GKbKoastxco9S5eMuPjznzLHBoozXr1QJsjWREnhw9yhp0mh+jgEoTpIE83GDigauNRa2ugDUwo7FElijFSBg993d3dPsDH96/I46Bsd/z/V//S1b3dzxNkc3TLzDNCm9bktUEq9G5zOlUPm62kES5RSfOw4X3YqXizhznpbpKJc9NBTBUhtAxHL9SCkX+TB+RpOVtsdqinCaoWRtVqC/TlBkGTdsKcCaEQIxekF9nCcZRESZnCex1hbOnrL/vB0GIBY+tpKI5Oy36MTOU9disliyuNrB+wbtDw4dvJtLzAxdPLdcvGvJ2IvcRp5e8+sm/x4uf/jHtxTNe//CeX/7l1/yT//drbj5sub3LYgQehIJRcjysFQGHqmmY+gmyqOSoskOXkZU8H0Y0oK2Cqnwdt58sQXA7ZV4/ZNrGMvJ4M7/b/YDWO9pDzbOrS0zborIixgEfRsZ+z9Tv6cYtXeqwVvH8+TV122CdJQbh/ClrSEpkCff7RN06qkYAZWFShEmzf0jc7nd8++Z72qXDWgWF45fJtMagk0ZFw7peo8wSmooxOcIIahfINCw3LcqODEOHqRTdNDER4T3HPDNFWQfnrTdRntIoNCEAumyqSUSzfZKxyzlHMAP5vIuj5pptpiWp0zxSKWLhpsooIkrSVhJgVV5zHEbmLNlL/jhBnitOxceWX0+eveDyyTOa5RplK5k/FuCSSrrMfivWm0tevHyFq2owhrvbOw6Hg2h3ljOoW0cGXCHZTzpIETB0jNFjgnRzUj7JClqthf5SO6rVEqvAInJ3av7S8yIs58DfdGQSveAY8ijYAZ0JeTreL6Ns8eNQjCHSjQMfHm5omgVN0/L5s88xTU29aKh/IuBCjGIKCZSlNisuLzasFkuy8wxTx2HYst0e6HrPzduBw96TsuZP/ujnPH12yZNnF4RwYBg6dl13RPc2VUvbLFi3C149e8FhGnhz94HoFHbb/41nOx+/x0xwJqie/d3xj6UtVBat0QpnDK2tWC4WbDYrxjSRlDwo2sxiRzLrmEJgGEdcdtS6OlYjtqrpBulF2yLGaq1lf+jJgC8cEyHYBvq+px9Gdgcx0BXZP5FTq6oZuGGOn1fOa96k51RenQawRtM0FckYyJ4cR3IYUFY2MHn/SE4TxB4Ve1QapSVYzrG8HcmU1nGU7CXnTF3XpJToDz1jPxKmnnF/w/79N1TWwuoKtXxCokLjMEYIrkbLjETliC5u8x9Xt8fR9llvKiVxMpBs+NTyVUo2JtFzlMo1Z1G+SKTCCZ3fd6Z6lM1Ml0mMkp8VQiAlMdQ8iqxrXVqmJdilcn21KVOzub0ulA1dZOGOA/GzIwQIFqxWRGUYs+a2S+zvt4zxgL16yueTZ72w9B9uif1AwlKtnqKcZd/Du5vIr77d8uZ2YLcPDF4RJT/AKiWWSRlyQU1GNZuz5GO7TS4ux0A476lKFeHmEhBTKqNzwEfYj5nDmJlUfnRqxk4YM2LJqOlAzkIinuLEED3DsGUaekY/oSsBPTWLFuskWZkFJHQBEKvSyUulYhMZv0RSAiKrG8XV9YrlqsJYLRjJUrXZBFY5rKpZVhvICj1M7KeJbj+x7d+jtcNoS05lXmkt7bJl4WPpV8wnJ+23ucZSJWgdb23prMXMcbQirdA5/p1amPKWH2/lJYCePdOnHal8z9zWOwtk87061ZJlzPPoraWFilJHWyb5XlX4zA3qbBygo2jSxikIBclaFqs1T1+8wDjHMA0cDgf6oRdnkFDeX8tzZZXsN9YYJqXLbE8JhUMBOWGKNpvVIg3n3MnVRMBQ0qnK+fSszjMKBY8Nhj4amwl/c5LOUJ5Is4VV0SVWKguhXc0OHgqyRqmKGBVhAj+BVhVNY3hytYQUxUUG2Q90iIQoIulaG3R2WBraWkHy7J3iYnPBsglcXm9oF420dJVFaYdxbal0dUHBLjCuFd1YZbGmxjlD4xo+9fjkIDgjdlJKIhE0X7iZ2Z1P3XRltJSoixVXlxdcPb3GLeShTUrhhwmVKVzAgBolW1ggAAtjncDCrWN2nagqx2KxIIbA/XbP+9s7huKSoJRmuVwVe5PA3cMDWmkaY1lUUpm0TUvVd1j7eHY2K7xDUVk4tu0UWhkWq5aQLT5PEA7gNViBu0tiOwhB03focEAlL+2XXGaGSs3R8kiatU7c6VOMNM2Ch/stDw877m4/MG7fc+8PVBry+AJtanReykNixGInJ1Fi0AR0EuPMlNOjB/gEQAmPWkjGFPuTs/giDhOGunZHM06SWOOkkDH21E5N6DLnkttuTJkzlTnNNJ3TGiRgSBAUZOQ8H1XKlAdaSUukzIRqZ88cJaTS5fRu+Ag+KZn7Zo33cHs78M3rez7cHRiVYdh/wWcXFR+++YbkI8uL5yzWLzHtir/65mt++c2Bf/GX7/nhdiRMEZMUKoh+pXOWicxEIs8qQDkRchIotwYix7Uu2XPhqJdWsMC5oVJzC1XOYoozsV5I+Mf6WGXqRaBtPC2gpi1p0IzB0MfIEAOH6YEQJelbLURlv9lsiAUdnXKpdKJA/mMqItvF7gxtSWogMmKrmqtlxZNXn7NYOrRRjCGJyknKpGGisi3L5gKdG0KA/X7i/rvvubm/5e27DzgrhqeLZYurNFVjWG02RGV4FI+yNGlVaVGiSvKsisBEmp3+8rETMzuf/Maxxfz/fP53pwB2RH4el58kXZmzRPEMoHb8Y+b4d2fTQlRBv6qPKsG2XVDVDVkZuWYxoSOEMRIHj58iztUsrhd84TSrizXj1HNzd8tuv8M5i5DW5Yfr0h41i1YkG91JM9eWpBujCxISKmto24qqrgQ3oLTogyKJW4gZuRWqoFJL4s48Zz2e4aPrG8IomsN4oU4ljuMcKLgEpUiK0uJ2NPWG4CN+UvRdoLI1y9Waz774gjCN3N19YH9/jw+RxMjB9JAU1SSy5TpXLJsFTgfGVcWiWqDJXD+7wBgRQUnZoHRD2xbRfa2py8zQVjXdmPBB4XRD61pW7t9CJTjGiT7IxXBJHaNxLmTvYqHLROZ2GrjpPIyRWDu8huXFitV6zVMf6fYHohdhWpEGCjLEN4bcNvgpSCY+DITQo/CAwVpF3TQcxom7w8C3795BjBilWTdtSRRFdNdoTTKiK2itQVcV2lqsc49ueyrAmfOhO5SZBVA3DpstJidU2JKGicSBYCiBaETngMkekyeZ0ZXMLR810Uo1lU6wX7TBKMPl9RPa1YarZxNP79dMQ0/fHzjcfk/f7RipaC+fUa8uxZXDZDDIhCpPxLgjjgfiR9qGPggHaPYoPKl1gNIKW1XiN5hmuLTAm1EFoq3scUOZAStaawLz5iG2Wjmkopghlfrc6gwhoDWPYNqqoHZnII7EjFxaZrF8jz4CaMRu67GAdqEUEVXi/c09/c2OH8I9H7YTuz6yWPyaPPVcV5E4HHB1i75ouXm9Z9ff85//v/4Z333zDR9uH5h8gJSoFGw2jtoYknHc9xNdnwQJnUD5xBASBTskiY0WNZGcpYohIdqVZg6OIoMnh2IqM6oA9FEREmTC8d83qwtW6wUmwjhNpCByYr0fGP2IUYGLzYrryycslhe4uqZetpKYhsQ0dbKB+QGXNTH2xLij74bCo7V4k0m1YrUWUQPjNEp5AahkDzLWQpmMNpZcTRy6QFaG6qKlurPUnaGpNDF4+r1nu9uijQjQt03NOH4EPDtWgvKnfBaHJHh9NPctG/RsWk253scW5tm8MZ+txXz2/UeBirIfcOyU5FMxeTYXSWUGN6unZGTdHmPtmc3X6bzAx8i+H+m6gVXfkDdRRD1iEspQmalXdU3dNrSrhsWqZdE3JBWx3hCCK6pPsjfUtUOhCJU9jg60OUmgaSNJ5WIh2rDWCfdXl5m1CIBDyKBT+fmuKollEKWiXDjUmtI2Ph3GVDhbgWpQZf9SWAl4KOpmUbjiCmUsSlu0qYhRLpbVBcxSNSjj0E6xWF/yqlmKt6CtMIjrTGVFFzkroS3lpuZyvZagjaD0Z/CdVoIeTWeNGKUKCr5wtFvgYnVJXVV0D/6j3sBvP34PP0F50lU+tSqyPvu9ykQUnkQfAt00gdLsDz1101EtWshQO0esKqJShGLUqEvQEhPJIpIbIyEFafNQUKJapM1SlumFUogzgdbUVeFR5SQqG1oLWspK9ZWZs6LHmc+xRZcfXy4ZjGe0OT1MhgmdoOxgIlNF+Xzl6wTE1se2wfm4XZWxqkKyM6u1uHw7h+GCcXAYA3sPKQfCuCeOS1LVkG1LxgCeTCDnEcJAihM5+rMNgiNYRetTVTgPsmVccmJ0zS095mG6KQ4FJVOc9xSpmmeXhzlhOElbPd4oymy1PHDz/PG8JXr8L5+y8kfqIOrUhn28DGVjmyZPnwL3h8D2EDlMcPew5/am5cP7OyqrqHLF7W7k3f09H+72fP/DW+7uHkTSL8vaq62iqRS11Qxl1hdiEWQua2rmDKazVt+MIZzr1ZxPv09na0ppdRSXkOWj+Mgbg+iViBek0opVkE0CE1DGUxlF2xhWi4pmIS4R2jkhutvS5i5t6+wFgq+1hfnzalBWgAfG6rKZZlKeN2CRszsSjXUk5JGAAWXRtsZWUNWa5bKWzN9H/OA5zuXyj28XHz1b52tRnV+j3/Jczksz85tb/Of3gDyv0x+zxOZgqVDy3ErZePyM8NFnOUXVx7+WQyQOVQF5+SIIkcrcPx3PTdrRwldOWXhzxlpcJbxnbQ1xmsRhIoSSbCos4tOn0mMd0Kxlr3TOCb2k8PrOqSEohc6zPKJQXSQXOEPc5nwU2T+/N02zZtE2YhFXxAW0nvnGCutqctntjHFoY6lsfWpfZwQQ5BzGSCJd5yW2khmtsRbKva9MuYNqnq8X4RAtSbefpIsxSz1SzuH0gTmCgFJMR/uryjlqVz3uSPyO45ODYGMUS2MgS/kfgaD08WdMOjPkxCEm9tPEfpjQWXN3uyd6TUhKshYDTeXAGbRqjoFv0Yjcz6KpsTP0tqihkBOuqknREEyE6Fk3jj/92ec8u7oW3UpTMQwDwzjSTSNKSfsuhEjwgV3XkVIo5oynIxfeXM4cyeMK2QwAlElYpakUGDWhVMDEEV1ebzSFCylNHUhEMknNTKeS/Sa5awpV3lpz1LjQCldZqusn5HzJ1fMX7DrP6DO972DcEo0hVyIGLi2eUYSu/RbCAZUeV4IiFWeOVZWcrJjtShIhi0iVz4JWWOdwMZUHRx8Dl1wfaY/O5sK2soXHY49VXM65KMKYY8CMMdF1XaFjJKx1VM4Rivfd3GZKyTzyEgSFLe2OxzcMZm0tazQWRQrireeT4mHf8+F+z5u3W548f8Yha37xF7/kX//iV/zwwzvy0KPiSK0SjYZKay5aTVUJgOLmfuTQJ/pRMvMAgn4LeS7wRY2obB7niURCqsKQMp7MBGcblNzrSBbj3TliIpvH6x8OdNtM5WC5qKicoV0EbCUIyrZuqC2gDyI1RkNtFUaLS0e2lsouUe2SbivQofX6Et0CDoLOWF0XJ5IiT+VlznPizolzQzaZ4Ef6bsCYJUo5QkxUTWBzZbnYPJU5Xsi8v3sgpWLkqxz7/eN1+HFy+fGRM4/X6Nn3PKruzv7+pDjz+HUcE8yzivDjnzcHQs6Daiqb6+n9zz/1jFA9+zG0rcgJ5hSYxomh75nG8YjIjtOINZpgNLvtloeHLff3W7yX5GSxXB71W2Nxlej7viSZPBKVqKrq+DnmrpWIh8wBWx1HLUYJRUzr6sgXFKeWQPAnrWGYrcpOIhtaGz57+UdcXKzQWlG5tmjUGlGnykV7WekydxSEtEkckdzHtqsCXYyIF3WS50BxbPuqMsNMKYktW9FflSTMolCkKJWe1gK+CyEwDsPRy9VYe9ZpkpiRglBGKvfp9d0nv/KKNU/0imQV9yoxqsRtDLKkslRpY8rsc6BZJJ7qwHW14nK1YLOoixp4YIiSOSmlaBctzhrx7rKm2JJADJ6YIn23l2rJaNraFUUKxfWlSD2t1g2rdiGqNNnSWY0zqswKg4jVZoqOQSois/ZRhnAMUkgGriUazt0TGYirhCKglSAzi+5E2dxMeakuXzP+Uh8J00ebpPIzTwHofCgv8mpZGbK1NKsaF8GNGWyC1KHDHoVHZy8BMI0S/HIo+oenY3avTimdgmFZ+LPEkTGWuqrQ1qApEkVFEi4EeWBCKKo0ZUAeQuH12VP7+Jy3JZlxQaKe7UEnbdBC8ihKHiAi0DONY35ox+Jz5v1jIVyVZ6QyLKzMbZ+sLD5HYk4M08i72y1/8dc/sLo5kJTm/XZid39gHEYuKgODIoVIpTVOCcfVjxGfM/shMvhMyDNgI+OLmOX8s6Wl9Pj5OJuCSiDM0jCQJKmkOxl8SPRjZjr7/pwztzfiq1nVihAb2tZSO03b1DSupSoo55wyXbdDqR7vA027wloxKyaXEQWUJNDhmYg5knTCKCFvHPlhWahAGalUVBFLtsai7EldJCXZgKyNtK06fo6U4No0sm6yIgXFOJ3d9Iz8DBkCzyd7XC+yoc+cQHWsGmMRds45H1uh8+WSDs3pZzyahB+D4+m6zh2MXM71KNyu1Ek8+ng95BrMvMZHHaKzCiQDfT9QVzVGZaZpYppOAdB7zzQOqCwV4e5hy2F3wE8BAbhqnHVoFTHKkrVBm7Egqk/PwnwO58c8bvA+FAnEoiLFTCQ3HDN4ToFTwC5yr1ORJ5w7MqfLp7lcXbNZrskpUlWt8BO1ZgoTMQVBoZeAS5aEwpYARM7H1i0INmS+vlrLuospHnVvrXbHcVSxRS38ZNFpnqNTzuC0JmojxUep/owtRgBKF5WYTHYRo1WxjPq049NemcF7wzQaApqdSWxV4l0Ix16xweBTZjdmkqpxVcVqsWRVOxbO4MNATIk+JgYfUFrjCi8sK0hBoNFTjGQtw9n9YVeIoA5dTXifmEIEpTDO0DS1yInlfFTTDynRjyMhlpad1sSUGPxESI/BIxnYd56H/XjczBVlg57nWUU+zCqP1b4EwpMkkSrDaK1hhljHOeidc3TOMn/gTKEQZGaSjl6EWRlpZWUIYyAMeyIjY9AYU2FtRY4TOU+Q9pAnHnaPFWPu7++o60o2L2PRRs5j8l4ARCFgraNdtEdFFz95hnHATxNx8scKb+ZZxlLZAVRjLeisunr0QA3OobQS1ZEZPZok756z1gzH/uGsv0qWXNtaQ4yJvheE73a7fXRefYSDhyqDcoqoVWmlZ7TK9GPgw4NUnvW9CDPcPvSYrLFaszIVOSTCWAjJCvqcmRDH8d2Y6D34qIlaKj4fistBlmZ0KGjGuZyY99CUwWeYzspDkzJWSzDNZaN9GBX+bDnknLm73zGNA3ULgZbl5LC6AiQbJyjhT42ew6EnZ0VdD6zWE65q6PoJCow9eQjR00fPMA0E7aHOpw3kmC+dlIsiGVvFgsB2zCjNEITm4n0qiVFiRn/mBEpLgzgFCCHjz0yQM5lp6GUezizgfFb5nVUFSp3adeciHJR/O15oJZv1nIXMCWV540fJyOmDFARyLl4j53H6vI+Xc0E864/+HaZhOL6r3K9bUgw4o3j/QRNCjzMQRvH1G/tOiPMhcNg90O133Nzv2e47+mEQ0YlZSCBHphAZ/KmjIsoycg10PP1cSQrF/UK0ei3WJqxJWCsBapaHm69tjLE8u4EQxiIcEshAN/Mc5QcwDpHBStLjq3J/tGbyEyHK/Z+lFHOUJMNqSSpzTgX5n4/3Y569mnLPfJjE8NkIknNu0/qSNFtnjlWlZvZAFY/KlCSxn/dNU3xSKQn28R4r2O0Pn9QKBVC/q12h1Gl6vCjDVxAusXSlzpsK5TqeLZy5BD5fmOeNBXVctOqsNXd67bzojxto+au5PXJCPPHo31J+XBWd/1umuLGXo6nsj97n8UU4/636Lf/2Gx+9Tzp+e9v67D1nG5dyw0/fMz/Acm6yEcrRtu0RlfnooX/0v7MNpvzrXBWflkX+6PvkN0ftz49P4PxS/MYL+ptO+MdX4PQ58rEqBKh0SR5OHd4ygztVZ3KZTmsmnc2ItDpVHMe98+wTpHwsYI6f/2/o6D069HyrePzr+TGPnPzZMpUHn+O6nn89Dxw/mptyCib5R89imf1IlnX62R99lo9P7UctxPPc7aML8dESkeuWMj6cwDHmTAXotx//vz8//00dOWeiPwX4pm7OArk6tvmOQJ2zB2YOtCepxPlePU7J533zUx4deWzVj37/+Pjxdf14v89ZrLXmY7lYnL3V2WKen8fj3/6oPv8tn/i3HOrxbprP3up3rZbHn39+Nn78uhgTXX9CiOaZ/P2bPsqnBsF/d/y7498d/+74d8e/O/7/8fhdQfD3QIf+juPj7Pcse/3tn+pRfvJR5fFxBgKPMw310c/8cSX58Qc8/yjhLPOx1n5Cpvo3H38TAOBv/j71m5O5+XX8zRlSjP/1n9dv+hz/Vt7wozV07ATAo+G9IM54/E3/TR2f+OM+5WUzjWQ+fp/79RsRlWeZ+u/4xt9QhM/ls/zvUXdmPn6Py/zxeQmC8bdUCr9zUf/O1f57vPa/piPnk3IS51q8Z32Z/Jsro8egH5j3u+M8nfxoHzt7IY/2RiXVfz67j+rs/fOj73t8/05tYPXoPaXtfdo3lPpRv+v03r/v8Zs6ROf/9ildo9/y7b/tx8yvPXYkP+H45CDYPlsJItDqcjEzGbHWscawrBshcFaOLz5/wfX1BT/5yRcyWEVjEAcEkuZwGJi8pxt79sOBbux5ffOOwQ/0fqSy4kvXVo04LcfA7rCX/rJWtMuWpmm4uFizahc0dcVmtaFtW+q6xtqTmaNCeG5Xl1csl0u6fc//9B/+LznsO6y1/ON//I/5+R/9XNRwfueNUI/bQQXoMk4yCB/HiWkKYuY7TcQyP5mCR9qHIpumtMY1FXH0+HHk3fv31HVN2y54+vwZxjhQimkaizHwiJ/kGrji9N7UrdiiaEPdNOx3e968/oH/5H/xP2MYepxz/F/+0/+Un/3sZx8tkYygOmZJqRnyf2Z7pM7ageV8Y5ldhGL2Obe1Z8Tn3G7USokTQGlHHukPZ20gPVss5UyOAoCQmaoqA235HDFEQgy8/uE1//E//IcM/YCrKv6T/83/keevPgcQXUQlv87b0bEdqU7ajPJrEbpWRTfybNY0mx8rZp3J8m/l79T5awtZef65lI1sfg8zXwdA6ZOgePmohQ6k+P6bb/gf/of/IeM40jQN/+gf/SNevXr1G/hoM3xK7l1O6diWM9aiitFzTKGo7iRCnIgx4qeR6MVrsu8HJi+uHcMgLhTT0HPYbwXw0Fa8ePEZ19fP+PKzn+FMJdysElhnJ4Z5fczL6jjyUDJL/+bbr/kf/Uf/EdM0UTUV//H/6n/C+skSjz/69hFVcUcv56jENHo+hAorWIM0t4YBXfh+WYmAsmh76uJ3cUJ8Ht8pyz1TRdBDRMXlis6KYloDqgC2zgLAeeBSKN58+47/7f/8/0CMiaqq+Qf/4H/Mq1df8fLll3TDnkO34/WbX+Ncg9aG27t3ZT6l+Tt//t+irloOu54Pt+/Z7na8fXPLVz/5CT/5yU+5ub2laSpevHqCqyDnwG7/nsP+ga7bsdttUUrjbEXj1gx95K9/9YacR5zT/Nmf/V1SzkzTyG7fidawrY7uMEZbXr16xctXL3lytaHvB968u6HvBr779tf87/93/2tSSjhr+e/9+3+XpkgtzkA2saULMkbSomWcYmaawjGYz9563mdml/CYxwKq81jrCpbCEIqQg2hCCzLXaYU1hqZupLWMKvZwxb9xuSgAP5kN5qIkFFIuwhDlTpX29MNhx3/2z/8Jn3J8chBURqGcmIZqI4sJZCDprBEdO2tpnKNpLU3rWK0bEcvWokVINqgsHlyT99hBo2uFnQz7dEAPijxmnLVyQSpXkIYKr5zYfmlolhVtW9OuG5q2oqkq6pW4aC+KqeSsoC5kUcfV5ZqLzYbDvikKKXLzfv7zn/O3/+xv/2jz+Q1XgLl8zYgSh/eB3fYgeqWHjhQlW5ymSVCWZLquI3tPDp44TmhrqTdL0JoYI8vVmqapaduWZ89fCu+xqui77miXM40ylK7rGudm2oBYGTVNw26/pyryWfN5/fEf/TF/8qd/8tE5ZMiygCTGzX5ghbZ+llEeRa8zZbAuouYxl1mwoiBGT3JUx2Ch5p9VZiGz16FSx8pgfonKYFSRfSpBcEa0hRBYLpdn56X58id/wOdf/cHZzyvDezhyTo8gJ3WCbcu8LYPOx1nasWNRZjrze6jjxi4nIzY3lI2UIx/vnNg/BwNbXi+fIx/xUbOwklYizbYsM1uQ9/njP/5jvvrqq0fVvFwi+QxKFVEB79nttsQihKALEnCapqP26+RHgvcMgyWUIGi1IcRaAA+LhdCFwsRhL04V9XLJV1/9IS9ffVGCoBOaTFkPeY7gHwVBc4zyoh5UVdXxumqteP7VMzbP10yMJQhmiLpo7xZATAmCRTwFW4KgqJ/oYxpg5iBIIuoSBJUuryxm3+W66TwHxGOELnQs4UeaUnQZBShzBKXNFdmJVjRTKk4IV6UUV1dPePXqc376sz/mfnvPfn9PZoSCzjVOHcX6nz1/QV0vWCxHlHM07ZaYHC9ffcFnX/6UdnVB0ziePt9gnYhgtNvMfl/RdS11Ky4SddXS2EsOB8/72xFjPFWtuby6AiUdrnY5ClpXGaqqKuL3mpeff8WXX/2EF0+v6Loe17yh6zpxmTnOFhWbtqWtamY090wX8bOwhtKS/IbMZIRaprQqfqqJUSdms+yUNDkHUrIS3LQQyUJIhJRQxhZdZRFKcdbSNk3xpgRyceDJmUW7EFslYwrmI38UBE/lpDHmmJh/Si34yUEwxAlCRClbILL6yJNTRdA5xcyYPG9ev2f7sGWaRpyxot7ihXzpTA1ZE1KiGzu8igRiIclTIMYTRinC2BfycGn1KSBrxsEXQWp4UNtjFrpZr1kuVyyXy+MilsBhad+85vrqEj/KzZyPHDkqf3D2ED068jEfLsCbyMPNBx7ubnn97Vt22z0Pdw8sqwVKaXyMrJ9e49qa+9dvGO8fGG/vifd7bF2x+OwZ7ZcvsesF7WqJKxqB0zRgraGpa/w0ioSZD0XKLKKahhAi3h+oXI3WAnUO3v/oZucIxFMFMv9vpvRnBOAUspjGJuT6x6SO13uu3mKhSogod35UEJ+DNVJGEGQpkWOQn5YT2Y8iXlA5KEHQFtcIZ52AQsr7few4MWej86FyPnILjzSVdKq45l1blepPojVlo84injgHsjmLTTPaZu4Rle+f/71U/bKhyq8SNM8qYCW0GaHQlIqyfPJzJHBRinusRVmu329u30jlYgEfJqZuy/df/zX7/YFhmFAIiXi33+G92Gf1fc80jRz2h2K8LBWktRbrLHVd07SW9bpGmQmrNG215GJ9zZPL5zhrUXDkz8qJKKmY1OlenZkVnRbU2eglA1MaGZJlzAN5pvEkhUlSwekSAOf1p7Igby0KfWYEqI4twFSCYDoGZglglHUt69NKvoMpyV3WorNptMaUu6PmT5SL72A+PiiPDp00/pEiU6YfOnwM2KomZkXMirqqud/d0A89bXtFjBPBd7z78B7nWlJS+BhQVrNctWir8GFisWxBBR62H4hpR4gDh/0d0yQc53H0VE6XoiAXibKJZmOpa8u3P/yaqqqp6wajKwlGQ4/RK5S1HPqB7aFj23mWnWcYvLx38KIUlE/nFXwQKsI5x7gEwpxL4luc39VxXUi3KKbI6D2n8VtkFph2VniLwziWZC1JkpUgBUhZE7Mi6Ii2guqvnGHyQqnbHnY451guVzxKUpR0XHR+LHry+4yCPp0srzW1cyyXCxF5dVIBHonSIRHIZK0Zi1zU/e0DVeWwxonHmjJ4LbtQTMLpGtLEmCZuDw8MfmT0E5UTSX6tM9bKg1fVVVFgUaKwYi3WVkXRRSqzQz8I1HjyJ0KmkUwyp4mvv9UM3fRoZqGyRmXNrHD16Nqd2vIiMq8VMUe67Y7v/9Uv+PYX/5qv//U39MNE7wPXdkllHaZp0H/0E6qLJV//k3/G+O4D4f0NtpswdUX94imbP/tDFq+e8/RnX6KsRllFs2xo2oZFU+PHSohqyxVaqSNJfZom+n7gJ1/9FGsdh8PhaEN0/tnHkBh8KtdhPo1MLCLKhapOQnzyUk6EYhEUU+EJzm3QAo0Wsi1HAewZbTtbLWUUKVvhIYWASgmdA421VJWlbWtiybiNVtRVRWWdoI7nVt9s63QWgM/zOa0ki59352OrDNnUpGBTJdzLJqqUKq4bZ/c3F+RlLqnBsRKcC55U3MCLWPncSitVnrTS5oqSs0qyVKScgsRRa7X8m8ofm9icqteP/y7FQPATb95+T/ADyY8Mhwf22we+/vYHjKkw2p0JI2gWiyXL5YonT55TV5IELhYtVV2JG7ozJUMfePP6B6bRU9uGhWtZVC05KXz0+HE4Vryurst9ejTtenwWc8JwdmilscoQleVIys9CytfzPSgUCpWLfmuEKksQzEFapyaLIHnOiZA8AWmJ+uwFMuwMU6WJgC90I1AYTlqTc7JptCrczbl9ro57y9nJHOdKWkvwPG4LObPbb9nu7tkdHiB7jKi+s2wvqKo1h+4gdIQQ2e62rNaGZ8++xLlbuk5xODwwTjvu7t7StI4YR7r+Dj8diNETfVHiAZRqydkxTlDZhE+ByQ/0o2jCXi9bgu/p9luev/gpSkEX9vggHNFhnHj34R0exfXln4nalq64vLrk+uHy0b4Rk3iu6iM1QYKgj4kYpeoSkn/kXAvWJ9EvRosYSlXVsjdnSYhXbUtOmd1ud+wsxSSSf8EnsnMka6hsTaB0HmuHq0A5zc39FjUYwsxfNKZom6tjETTPo1NKeP+xJtNvPz49CFrLoqq4WCyKbp3DOns0bz3sR6kGohhlhhgZRinNnc3FWDIR9SmjGMPIGCaGODEOAz4FyAlrLJUz1HVV5IEs+UhKV2QMxorjuTGyKIfyEM7zK3l4DT6INZAf98Kd2vcnkqw8eqj8Y++p3/A4EEPCTxO7uwdufnjDu19+zfe/+GsGH/FKE0xL42qa9Zr2+RXJwO33P+DffiC9u6H1CbtoyZWjvnvAtg3x0ENl5CufJOSkPaipq0rmg14EBGZlCVMcNULwOFc94jYB+JiYjvqc84nMQTAd54EJfWwp+JjwSRGztD9m/8VQqokUk4jqRphrY3IWgd85Y8zyFTOYkslXzlFXTirc8n1KqSPZV0yJT5vO3A79bXJ2as5KSqPqWGnlPCemcs7HqutUfZ0aPxnQp5kdZwFwfrCYW6fS7UCpx3/Hb6j0joHv1CFRnH+ufPz6TcejWZQMQAjec/P+LSmM6OwJU4efevb7B9p2Q12LrYyzThR5qkrUduqGdrGgrmrWm5UY9VqN0hk/DXSHBx7u7yEPonNZjFNTkiRm8iMpR7RWJBKuymjrTmerisfDXLn/6MkBhZilmiIDJpddoZM++d4hDU2TQMeMnSJVyiJRGJVoTaKotFztmCGgCSSGGIpOUyJaXVqac1tAgu2sOiKjGVUUnop7ChwD5rkZmfRppVKcZ1TzkTNMfmTyI96PKJXEUzIGqnqJU5bt7p4UAzknvJfZWNMu8dMe7yUJGYY9Dw+ZyTumqefh4T3T0Al2QPTR0EazWD8BU6NcSzYO5SKuaVAmk5TGuAofBsbgUdbJZVZKBPARjuqh2xNvNftuj8YQUqK15sgRPj+3H0kgquKOUcYf8lV0iediPYmIu7WWpqlZtC0YRY6R6D3WGGIOcGxl5lIRCmcxmUTODjF9Tqc9wlmsduidrMvJjyUeQI6nACi+pqd9I300VvhdxycHwadXlzx5csFnL1+yWi6o6wrjHP04cug7vvnme/phYBgntK0xVUU2NSEp0pSwBFSWfndCyudu6onlv7Z2NEqTteHqasNq2fL0+pqmXmJdhcKVAa1UH85amkUDKhNSZLt7kEWvFZvNhqqqqKqKw+HA5Ef6fsswdDxU+yMv8KP1/huPueTPOfPu9WtuPnzgF//8X3L/q2/Yf+i4udmxj56Dynxb/PsuwlNy94InK8ftbocdRxbGUK+XLF694Pq/8/fRlSYFz7/+v/8/MMsWd7HC/v1EfPGcRd0eJZjgVCXMN3ieQeWc2O8P1LXYSJ2fwz4HHpIv2t1lTmU0ufR/Q0ol8AmvK6bE6CNTEv5aTrO24KzegVQBxRIhp3k+A8YXo1BjAclyNBrXNCwqw/OrNZXTWKcZYhDJsTxvmvk3BrtHN+fR/ZDQLb8v1Z6oI0owOgYd6RAct9icmRk/pe44A8QUJaDjLI/j/O4YvNSp7tHFO1Olk1jz/NBSzkn6ibLLzt87f7+86m+aQcuRUiJME999/WvidMCqQL1saSr48suX/Pxv/TkvX37FeiWK+9rYo9TVuXZoTIFp6hnHjkO3Y/SRwSdJJMloJobhPbf3sLp4LuRqRu7u3jOOI8MYePnZF2wurrC2hqIkos40ZmMUw+HzQyuLVg5DEqDQ8Y4gpP2YMRissrgJGDzhdocfPDpmFs0aq2UWtF62tE3FZr0kEPEpcHu442Hs2XUDYcio2mIWFcrIuGbW4bRKY7LYdIlA/DztpFS4s0ny6RbOwg76KOR1WpKVVdROi5i4scQAh/6e5xfPWSyvef3Dv0ERsE7jXMIYmU/E7JnCyHZ7z/ThLT6M3N9+YOg7uv2WofdCSNeyNVdVzX/3f/AP2Lz8Kc9/8gcM/QE3Dvzp9U/ouz3RT5jKUi0GVusBc3FNihE9TfR+IKdIs1nR9x3bt/f8s3/haOoWYyru94of3r55tG/YIrU2d2FmJKoPkRDTschIKWKtLs+OyHdbo3j67IKLzQWr1YrtbsfQD3RTXzATkbYxVEmC8L4bAI9SE3WtWLSG62uLnwZyDlgHq80lq4trnr58xuEw8O7dDTkFgs+IsHcuFWCRTSsKWZ8yCzye86e8SClYrZdcXG642Gxo6kqEULXCxICxBmO1CPMGja3EMNfVjUhcZcWiaqQfr3TZdCN1aMqYQaEryCqQlef5iyes1kueXD8pYqqiWUrJ8KpKzE7rtkIb8a7b7nfSz06xtIDEpb5fNIxjz/024dsKqx5D0edNDjgCHqTblplh0V134MP7D/zLf/YX3Lx9x/2bD8T7HVNIvOv37MNEpzK7wwPGWp66zKvhwCJdotqa9fIlL5sFF4sGd3mBu76UYXLwqM2DPJU+8vCrX6OGjmVjGcr4SkxtReNv9KLu0Cxa+qGTjHQai1bn6bZnYAgjnR/KOZatR4POoQwMBRQT0rzAI5OP+AQxn1VlxybWRxJLehZNPlVuqpiXaqWoa0fbOBaNdAyUkc14RmqSOSpmMAtrF2Hu8yPP7PVyzAGdAs6RgJcKEOKsKkyyVqQqS6cgpmb5MyXfo05h9PT9j//T6rz6K7O/rI6tNMXpfYw60QzUsap8jEA9VqofHbO0F+Vn5SytyKpyvHhyyWEXGHZ7CAqUEU/BuqFpW6qmKQHppEl7siSSNpcAmqRyMMGXBCOBihiVCNMD/UFRt00BnETu7z7wsN1ye78FLeoj7WKNq2tc3aC0LVkkiK7wR+jWMq87lsfIvIck68n3ojEckiNOCj1FjJd9RMfE1I103uOnifcqcXW5YfGHP6WqpeI15opmallOI7dhxEcxDk7HRGYWHNDHhEUVZOl8E073RB3vw/yrApk5fXTDtFGEOLHbPYCKDFOHsoqQRnzoWC5XhDAS4sR+t6XvR7bbgbvbN2wf7vjum2/w00QIE4f9Hu8npmEsgUfQnXVVY1xD3++Z/IA2iqptMVWFq1piEAeQurKi05vk3qSUWVw8IwSRTbS2Zpp6/DRSby5E1UVJJR1M/WgxzkFwbivO+0AIHl+qNmONuOtYjTEa6xzDMJKR5GDRGtarir4DrxOaCEZ8Zqu6JqSED4kpTlKJZri6XrJZLXj5/CnT2BGDxxhwtUWrwMsXL+n7iRQj93cHpkEkO2dxgpBP+fJvo3n8tuOTK8HFasl6s2a9Xh3nbaEYTmalxNXaSCCUIFhj6/rYylgt19TOUZcLFqKYK7rSVq0WBnFxm/j8y5esNyuunzwhhNmVXRVPO8Nq1eIqg6sNrqrIwP5woO97xlHasrbQCYZFwzA05DyScsIqK9B2Tg+ArHiOFzQjm2+OiWkcub2946/+zb/hn/6T/5Lbt+9psTQ5k2Pm3bBn70dGlXlz/x5lNdOq5mHqeZoiZrVg8+QJn33+OeuqgqpiWC7loQqB+mpDmDzBR7bffAfTwOZqSVyvocgLxRikPTVO2MrStg1d3xFDZPITdfqxgeQYPUMYj/FD2oMZTcJkyTTnuWAsszjvAxFdaBMcN/f5SEfZKdlYZs2/+bqRQBWrl7qpaduKtqlQVoMqzh96xvKVjXnWiTy2PzlWVSk9NkKdg5UGjiCWY4tShuO63MvZvRzmuU9pQx5bpqUaydIem7fAUxDk+Lt5hqUQofQTHvG8SpTPLEG+JFNzAJxRpOX1ubSWz4/5XGOMp/dIYJRBVRUvn19xpw68343kpEFXaNNirMUYizazuavIq0lSccQJlRlgIqOxpsKbGRWYAI9WAT9t6feBxXqNthUow93dB96//8APr9+yXK4EnRcjTV6jrC0BRknVq9JRWPz4WB17xmeVly5dhBzxYw9BQ/S4XuMiLIPGJINKimkc2e92bHcP9N2ely+e8dXnL2kqQ2UqFm1N6xtWkyc/PDDkxOgTwSZSQfTOaxYlSOGsFEqlo5rOLP14qvRk7R3/zI8aEmJEnALb3T3aZKbQo60mxJ5xEr/BYcj4MLB92DKOnq77JTcf3rDfbbl5fydBLM4axyXJVEkE9ZuMczVaW/aHe4Z+S4oj1siIaLlYH2ed9qjKIzNRyt4ktJkM2pCiJwWPL9q/sgY90Z72DoU6im7L2+SjbJkv3xtjwFUtbVthrcJaS93UKJUKyjPT1obVwnFvFd6ANeW515qqrhlDxPjAMMkc2xrHkydrLjdrXr58wjC0+GkkBV8AU4FnTy8Yp8jQj3TbnjEOnPWSSMj80hhzut+feHxyEIwqM+XEwY/SK0+ZfhrZdQe2hx33hx0pZayzaGdQRhNyOhrkXj97xmax4HK5Yho9MUR8yMIfMRbtMiFPhDiybtc0rsboGmUVWYMnUlWWunYsVw2uMtSNpakEenu5WjJ5T/CeaSoeaTGioicbTVOVpk/tfpSGpxjouo7vfv1rHu7v6YlFqVzz/a++4cPbt/z6r35J0zQ8e/Gcr1684Oab73j/7ff0cWJMnmg018+es1qt+JOf/zGvrp5y2SwJP/kKRebX23tu375jnCZG71ksltR1zeWmZVEvaNsFbr0mVy33H/a03lA1mXrtyKbBNoboC6AlJj68fy9VYduwWLT4MD16UJ1GZijqNGfLOWO1wWlH07QiuGsM/RSYQsR2vbQ8jvHlRCXIOcswvOBU5tlBnnfaMkez1dyGCkwhkfuJYZRi12pwTuaAtijAayP2QSAFqi6txZRPXmLn90sTi2PHifeny2zvWAfNAajw9LQ6Vfgn7p4qc745OM5BKh+3w5l/JjMhCcBWmcK/UpIUzLyzY16VjtdCqXItyuePMTB0Pe/evuMXv/jFce6Sc+bh9j3va804DlxeXFFX0rKiADiWrSKvFOpK02VNF+DDduDrX/8193f3fPXVH2BthdKWcNbGmqvBucqeJ6nTMDIO0nKPPmJJPOzvuc/3jKOiahe4pmV/f0e/30LwHLZ33NaOMI0sp54QA8v1RihQzHqijwEJpvhTGjhSRUwEnCIbQ3PV4oeM7yJv3nzAeHherXjSLqmUZv/uAyhF27as1ws2Fyv6oafrtmVGNNG0K6p2wYvVNZ5Mnzx9OBBiJOZMqDTRKrLRR5rV3P48Jj2KszDImQTa6TmYD6UV10+ecbFZ4axnt79lHEdUMty8e8fQT7x+8wP397fc3n7gsN/hp4m+l9l+SkIjoNwjVSyHtJaA41zNH/7sZ6IBmjX/5i/+KW9+/Vf8+p//F1w9fc5iueLy8pq+2zNOPYe+A2VQylK3ogXsqhrXLrBVzXJ9UWyPDNY1oDRKGZwRutpv3fNLB6ofBlIWI++mdVxfb3jx8jnGnmbjROGfhulAZZ5wuWlofvYFu+2ed28dPvSlWs0yEzaGelCs1xtePH/Ck8sNlbPIlMWgVSWJukpgMlp5aqu4vljwvU5EP6LI0tWwlpg4UuJmTdVPPT7plRkY/MRh7HH7rQxec6abRvZ9x67vmLwvmYQl5kRIER0jlbUCd60rXCVf4+AJIdJ1E6b0yyukMsnKMk0ZZSKun1CFbD+Tr4UM7FExYnwSOK+F2lqc1iTn8E44fNM00QGahM6ilJ9/w8A0psQ49ty+e8f7t+/oiEci+M0Pbxm6jqpyXD+9ZrlcsNwsuTeKEH2hF8hDs9ysuLi44PriUlz/hhHlLMM40A8DX394x+Gw57Db0zQtdVWxWi9p64a2amjrmqatWb17y+bykqZdsLm8LC0njU8eZQ2qsiQfsJXjYr1hsVgwDAPn0aIxhtaeHN/nKsuU+Yo1VobJqPJ3AvHWIR65audBkAyp2OzMxpw82jwKUtQWbpxMoggpEWKh5KtMW3ii1ppjMEJLa1YfdZFPm47W583DjCaiVfl85f9aSRWoisPHHABPRO58DCanADivbB6BWDiGieNvmQd7OYsFUtcPxBioaoezhqqyRY+2tM2OUFSYCk9P1pMIAH/48F6SmLO228Pde2on0Ps4DdR1i7MtSmfIgd3DHWPfE9BkJW7bQ9dz++EdQ9+xaBoWyzXtYnWs/hMc79XjKlufRJXLnH3wgf12xzCMjDiWqxXLzQbvZUalFATvGYeeg7EkJVQGpUTtRqPQtmYaDscW7Hy9NdJ+POdKzn3rXJvyGiso6ZTRztIulyyM4+79zfEaict4ElH15Aty02BdxEZprUbkeqko0Jk8JcwCVG2hsYVgXxKhkvjMbe2y8n4Es/8xaldz9eQl10+fsVo2uEqE6fs+cjj0IkwQA3XT0rRLxqE/UZnSjA1SZ/dJC7DETxijadqWr376M7wXU+ntdk/tDHoaqf1AGyxrRsL4wLjfEfY7oa2ZirF3TNqiXIVrWmxVE7od1tU4V+FK0aCMBJzu7v1p3KA4cma1PvdKHLGVrPP1esXFxZrLiws2F0tyTgx9x7Dv0CSqSuOsxqjMZr1EA/3hwDDJful9xOSMjYlp2XB5ueb586esl4Ie3e92BYiXUWhyTuQoc3GUcAkXbcOwmkjxOHBn9lGcyfL64/717zg+OVxuDweSzez2e2xp1wxhoh9HunEgBo/VFktmCr44zitq50ArbOUwlSMpxfZwYLvd8/79HdbVWFexudzgaourDX7b44aR0Y80zULme5UTIvAUUP2AnRST1aRmoqkari6usHVVvKci4zjRKdiSUVFIhdFPhHF4hILJSMZz2O/44euv+f7rb+mR9uphf8BkuLjY8Ed//Ec8+/wFVVMRdjuyyUxhIBJBg3WWZ8+f8eTqCc+ePCFMnru7WwKZ+6Hn3c0Nf/HDN9ze3nLz7p1UOQgs3CqNVZr1ei2BcbHgycUF68WSZ09fcHlxxWa94enza6qmxi4a3KplWbd8/vIz0JphGE49cWBTVVw1jTzUZ6tcGQmoIVNaoAIuscpgmlYqg2LpMlNMjt+rVEGMzm0tQZfpAkKJKZAZyVng0illYoZxnIRzFgMhONq6oq5dUZgRwndSSVzSY9korT3C088PrQKGwm0q1lqC6pyDYHkudJnlHecEheowK748mj3mY3d1njUK2EWqOmkDS0O1HyLvP7xnu93y8uULVqslG+PktYjLhC4BGBK73Y67uzu8F/swqzXfffMt33zzzVkQTLz+/q+Z+vf0fc9tu8a5mrpZIV6ikdu3v8JkT20N2jjG4Nnd33F3+1bm4mHi+YtXvHz5Oa52xwQnZX3kbR3nvFkMm1MSJPcUMuEw8N0Pb7m5ueXJtufq+ppnz58Lmbrch+An+u6A955Dd8A+3JPCIEINSmNcze7hw6NrayiUFlUcGhB7KYUUQ9ppVO3Qi5r9uz1qyLTVgifPnrGuGn749jvSIMIRVSXr/PXr1xgNrrI8efocZQwpK+7f3UpnQkHKI5lAUgF9scCuWmzdEDL4HJlngzqf13/zWjl1P87/fFyD2vDlT/82L189Z9kqUhQxi0Mf6LuBcRz56WFfFJ/EIUUphTuzEsuliZdzohs80zBweLjn4vKKZrFguV4zbLf0+x1v3tySw4CNPS83ay6WDS8ua77dJUwcSL7DWIe1mf3+wJQyY1IoW6GM4cFJ56dpGpwTHIWxlu5w4N0Prx/N4Y3WWKOFL5wC3k8MQ8f1SoLey5cvePLkCc+fPeMPf/5TUvS8fv09oe/ZW8Vi4VjUFmLg6kpGYNM4cuhFIGWagljN5UxVWV68fMbPfvYVRmv2+wNv3r6T/SWDVa6MXCb6rsPahspVPHlyRVVV9P3IOAbGKQrCV810GFXm4592fLKV0m5/wGcvG7YxaKOJFMdkJEubDTv9NMs2TYRhpNt31KZCK0X0nr/6q1/y8LBjt+tQiPuxs66UsobVRcPmYslPf/oZrz57yWazpqodWmeUSbTR4ZyhqR1GWWafQaMtzrrinWYx2rBbrwlEbG+ISqPc4yUfCUzJ0489wzjRdSMP00C7qPn8ixe8ev6M5bJhvV7iVhpxRczkEIjThCbTOsfFes3f/zt/h5eff8bLl5+x3e/phwEbPEsLFyrzp4u/Q9/37O4epLIqvfZY2rhx8qSQ2IfA4f4OdXeH+vZbga8bw6KtZda2WHB9/ZSnT5/y5s/f8OXPfyaJx1lwr1xN7RrZbHIqyKksqFAyUwlmgraVBzOmLGCdGGmtxTlL7RwhxaO7gmTKMIwTk58YugNxRmvqhNYidZSVvN/8lQs02nuPVSKLpvRJ6SPBUZrrGI+YXRBOhx8eCOODzEtUVVqW5qyVOef0STb/An6ZnUVyTGWmOQMlyoYM5ff57Pen2Z5W4H3k+29e80//2T/ll7/6FX/v7/45P/nJV/zJn/6to0JMJtONA+Mw8O133/HDDz/w7bffoFDUdc319TXffPMNr1+/Pt2vnBm7Dwz1yDSMfHjzDZNPoJyAbUk87B5Yr1Y8f/aMVWsYfCAMw1Gm7ub9DWHKdHuZ9cWUmbxnLMjfuXUtldQDOSeMhrYyaBK+GwgodFWxPRwwdc2iHyQRMYaqdqASPkyknPBxQo89D5WiqWsWbcN+F7m7efNoHWotqix5blerojLDSQJNpQgxEPoOPYKtLgjDgE9wfXlBW1mGRYNzqnx/wpbZ8zBN3O7e0Q+e5CGV59IZOb+6MbSDx3ZBErZWvoKeK8JTAnSeFs1zpRmN/ShU5szU37K7T+weAkalIoe4oF1q6rahWRj2+47tdk/TNDIXTpn1Zo2rKg7DQAwCNGnbwId3A+9/+J4/+OIFz59d0mh4v92TDm+xuxuuF4afvmhxeY8OW/wP3zO8v2d4GEDVVK5lvVrz4soRdUVnFmxe/ZRmfclq2ZZZny32Q7K+/TTy67/6S9T/+f90PPfKWZzRhChBUKnEYtFwdbni+skln3/+kqdPn/P8+UvWqzXdYcfYDxiEH7heLamcI6XIr379K4ZhYr/vMJWVtq9WtJWjqh2fff6SzcWKxaLmcOgYp55h6qlcXRzoYRy9IGe7ibapqCrHq89e8CxEhrHj5uaBu9st250/2mqdAGafdnxyJThNHgzYAk4xRngss0PfXEIrIJdyNk6BNEX8GPhwc0tKiX7o+P79W3a7A0M/HVUmVMqSTRrN6r7h4nJFZTVt3RQvqhZjQTtFShViWqtPJFelMdphTUVWGa2k3dc0LbXvsZXFZ4+2cxO7nFcI9EPPw/09u+1eOHhOs1y2PHlyyfMXV9SVo3KabAVEooCcIikEyViMZllVvHzxnM8+e8XV82eED5qwU6gBXFVR1RVPFk+ZYmCzuShmkpnJD/hJVHLGfY/3nmEYmfpB2k9dR57Eq+th0FRVRXtoGLyY4352c8PTz14W/afTEbN86RK0BAQjhqW+oF5P9kPle1NE5YgiHWeKtRH6QMpZ0KpFWcMrhcqJGD0+JWk36ow2sYAFC2czZ3GiRrIzVVpPOaV5sldUTXJ5Pcc2ZM75EdAi58x+d8dhf8lysaauFco4dBbgjRggzy3OOSyeVGjmik5rDUVb9rgW5kh4viMWMIzMPw0pBHYP93x4+5Yfvv2Wz1485WK9ZBp6mtqVTTMx9j277ZYfvv+e7777jm+//gYQ9aLDfs+7d++4v79/FCyC7wTV6D377R39MJGVE53MnLk/dISsaTcJayFEmfMpLaTh+7sHuoPn9nZflHuSzJ+DJ6R4nIHmnPhw8x6jNYtFy8tnT2gqJ7wqY3B1XZ6pgvhFugF1XaPUrNuYC/rWM/YHdI5UFvpuYOi2nIcTaU1LUNG6qOmoEhURwjsUZLAP5CmTp5HhsEONAypHamdwqkUUSESFROVcOJQT+92eh10PSZNDIk0TtQVrFHGUBNJojekWKA22tiQD6bx1kh9tC4+C3o+FDDJ+2NLtE2MY0ClgnGV1eXXk68YYGfodD/d3DHV15BoaC01uhEMdhaak/EToDwy7e4bdA1NjsVaRui1q2NKkjgu35NVly7B9ELT74YHDrufQBQZbkXQEM2HXrrSXRVbR2IqmFSUrpQ2VE5f6lIOIbLTto0fgkb9jQVU3TcVyuWC9XhaxlBprLNM40nUdh/1eUKNKOMHOSbDd7e8Yhol+8DRGMAgi6SaGz1dXlyxXC5qmYb/fF73lQCoz0hQD4zTSDwPjELAm4lzGOSv0DONYdI6hqTh08dhqPsHBPu34dNm0MWCMxrXuiEo6If0yVoFBY4EcIikFhtEziyd2h54pRA7jgE8JtWhZtgtMBpUycRiJPhJ9YHtzS/9wT397i/Ge8OXnfPkHX+JWC9pqwcXmicxjakfjikaeaXF2Re1auclEHIFNGIkO2v4dacxUQ39c0Dln7u4eUEnzX/xn/4Rf/MW/pN/v+A/++/8Bz18+4cnTCzReIPYpkKjJiMAvPpIHT04JpzTrquHVk6e8fP4cd33BLkyMZNIwzLsoy/WGlTXkZzP4A9I4ykNqdBkKC7KrO+zx40i33x8rOR9nqjmCrL24ZPX8mnazKuTQU9vrfddzcehxRgves8T+EETke364tTaFc5hJzM7RmUolXA7ooKiM5Co+SyVFKiAXq4m1hSjw+0QkKXts+Mxk9Mo5ocqQ2VSWygpE3oeTcW+MST6XMsdzyEA3jcf2dUqJ/+ov/j/0+zu+/OKnvHr1FdVifQw+4pJe2rhlM5pd6r33xJTECNg6MTU9zgcLbWOeH5ZjNu3UWqNiJPmR7uGWNHbYOPHh+2+4XFTcvnrGZ5+/wlaOKQTubm54+/Ytf/WLX3B/f0+3P9AdDsQU+dWvfsX+cODhzCw4kxnHLX5MJJ/JoWz+i5aQElMMjD6wOwy8v93hVCMzL20AmaH/5V/+kpvbLR9uHohRgEUxB5kj68xytaCuZYO6uX2Hs5bNesPf/3t/j2dPr2hqzWK1pF221FVD27TUzuELyKBqa3zwhOSP1bVSir7fQZqobGK/u2e3+/Bo/zFFwDnrIJ0cZlanvCzNtI6Y0cNE3o10g+dXr38NKWKD4unVFU+fXLPb3jNNI9PQE7xUpKapOdzv2G0PjF66QXVVy7OUAjfv9zyJgU2KNLWiyRuahUPXBgxEorREs+Ls1p/mgbmoAZ3NmHJO7B++pj9o3t3c0O/uMUbz9MVLSbKBrvd8/+07/vqvvme/3aG0YrFe8OzVU5brJUo1uKalqlv0wx1+94C6f8v/8//2f8U6x1eff8GCkQbPZ4uGL5+t+eoPfsK//hf/itu7LX/5zR3fHxQ3g+Le7+m7d/TdgS8/e0JrNVWcGKr/HNU0fP7FK4x1GOd49vwlZMTs2Grevnl9tg4RtRhmNHPGGsPV9SXPnz/jydNrnDEcdju6fcdu/8Bu98AP33/LulmwaBqMtSyXKxarFd/98F5mpUOPz9KB8V66h2hYrddcXV5wdbXh/uaWHAI5ePpJ7u04jnRdT3cYuL7aE5NB64qu3xPCRMwDSlmur1bsO88wRaaQSAlS+rcwE4whEIMieo1RFrLGGifOBs6xbhaCNtKW0Xsm73lQB0KUbH6cvFQmBdastRBYBaaeicNA8IHgPS5HDJlaJba3W37AYOsFl08iF8ny5GlFbRYs2xZnMs6UB6lAxI9tLDJV3dLGJevNCt1nun3/KOO7efcBvxt48+0PPHv6hMUXL3n15IJl6zBEiCIsmlM+UgdSkQ2KQSDIzjg2ixXO1Sil8QWdOvPOZti+ICkFdaa0kTZR4V7FGKiaBldJ5tZ2LeMwoK2hqqUNULc11lmMFZGC1WLJ8y8/Z3N5QX84PEKHisJLIGQKERxRzFCSIeuCfp01YOV7zDHLdkqajFpnUskMdT7NlKxWUDmM09RHrmAszc0zaxgorXK5z5Up7bECvz7NqaTySIgG6Vy9TdOJ5kHOPNzdc7t4T6NrVs0akxXNclMcEkb2+92xcmmaWjas/R4fpNqUOZl8QlfAORCYd26tJQONMRInX7JWS46Z3W7P2+++xe8PNGhyHxju9tz88Janl2usaiEnjE44q1guGqJfkGPEKiUbwjQe5dY4O69+6OkHA0EdkxORAJwYxgk/eSo7kcaBMA1SBeVIDInJR3Fk6Q/sDocyB5zbyQmlYRpDoV+IWpPWhrqqWbYtq8WSdmGIwZNToKlkdjQDqI7yeUHcTLSVyl5rjfcTOQWhAux37A+7xzNoNTuEGJSKpdooykLI2os546dAt92Ttz2dq4iIGPj+fqAbJoZJXNzHYeDu/TtqJ1xhbbWI0/uJDx+21M2CqydPAU2IkQ93D6jGkTWMOuJzRFWa2l2Qa02epe84GRjPKzef/eH8dimlWG1e0iwW6OYpw+4epTLrywusk/PthwmlNmh7zQ/ffE9KnnZVsV49ZblY8/TZCzZXG9abJT/8xb9gyJl26Hh7/8Awjnz97Wv+6Itrrp8/5dW6Zrmoedgd+O7tLW/e3fL2fmDrNR6Ziz7cG6FzbTsWVvGsTmx3B4aU2d6+O6oJvd78GlB4H2iXC7a7h0cdCZkXC1FeK00iEvx0hFHcbx+YRl/m/J5h6BiGnqeXl6wv1qzWa9YXGzYXF/zZ3/7b3D/seP3mHVXbiJpRvSjPGcIXLFrDy8WS66srvvj8M3GxDxOHw566cjhb0w8TsJc1RSATiXEqAu5aKsgYSVGR9WNO8990fHIQzFk21Ry1WKFohVOahatYLlqeXT4RCyTt2Pcd/TDgQ2KYPFMIjONEVgZtK9pmjXVOtPyQYBVqEYwO08RCK0yK6LFn6Cdub7csb/ZkU2OqFSkYDBWtW6C1x+iix5EVKc1oL6DMGuu6ZrlYAJ5FUx9XdM6Z+5tbfNVz/+GGP/3Tn/HqxROebhYopyHFIn0k6CMp6IocUYzEmFAJnDYs6lYgvVnhpyAK8io94pNxpBQUdKICpbVkNd5jmxpbO1bXF1SNo+oGpnEUpN5qyfWTK+qmoW5bHg4Hmqri8vlT2tVS0FOPwnsi50iMJ6QialbRl7muehQE1Xl+jo7pNGcrXUOtSoali8q/MjhtJexlkY3SaU4VOM7d5P3n3qtwLD6WRZs3zlismeL/l7Y/7bEsy9IzsWePZ7iTTe4eU0ZkZk2ZLFaxqG6J6hbQEvqL0BAkQYCg36h/IbClJtFFlpoo1piVmRGRET6Yu5ldu9OZ9qQP61xz9yg2GQ2IJ2CAp6ebu517zt5rr3e9w2zV9qEHYAG6/ZFds6UynmfXz6mcxzct4zhwOp149/Yt1hratmWzWQFwOOyJUeaaStvZn7QgNk1AmZ14AGsNYZoI08hpd0Ch8M4Tpsh+v+fhzS2xG6i0RYXCeOzZvr1j/PITvEVsAXNEqULlLXXtiaGmJLEfCzEIO++DXbUA4zgwjhadZw9QrQhJfHC7YZw7cJmdpThBkdluSGJaPEyDePFOI2U2llCzyalmtr1KGVR+msEv2pblQr6axszi6khlnVDqtZoNzJnt88TM3WoZNeQsIuoYC10fOJxOnLrTR/uGQOD6SbB+Zu/OUzeMMsQcmMbAcOwox46xikQdCTFye/fIFDIhwrPLFWPfc3+/5WqzoKFGlwo9Q6PH44FYCm26oGCYQmR36lmeOurKkkxBWU3VVDSXS5TVJDVXODUbdv8Hrn/0u0rTrJ6x2lzgl4FpvYOSqZoK50AbWEwJX4/UixeQK0IYqBeadnHFot3w5RdfcfPigovLBadvfofpO2xdc68OpDDysN/x+18+5/LmmpulAWV43B24vXvkzd2O+/3ASRmSKzxfLwhhYvdoOXQj2cJzpwlDRzcG9o8POCt8id3bO7FUK4rNxYZ+HD66tfLBIVTLiYk4ySFsHCb2x8PcnXXUjZ3N/QPOWeqmxleetm1Zb9Zc3nzKdrsjK0OzaPG+ol2sGYZ+jorrBbIfRyrnZab5/Dn9cGKaeqwpc7ScZ+gnMe3W0DYOa3kafyjyXAQzFHN2ZvvR148Xy9eeZlGxaGYqv3dcrJdcX1xwc3XNZ88/w2pLzoW39w/sT0ecr9nuD+yOB/anE0UXrPUUXcAobF3PImSN8Sv8rD9cN41kE1qLbRZYX6HaJY8pc7jtaZd3PDtNaDLWB6wTPZKhoEqSk7vKJBWJaSDnicppSu1YNNVHp7r9/T12ueJiU/OTzy757MUlauiJxRKLQ6cCSqOdnckjiWnO91Mlc1nXLLQm9R3H/R67aKH1pCnMTijzXEUbnPVg9FP0R0FRtQ0pJ/qhZ7p/AK15/mVNKfOmkxMhyYYAgqU3dcOxl4SNbhjYqyP7/eE9mw1oKs+irmSGMudyzVJaQFGSOIaUosl5ZlTpc0yW+EjOx0LK0+SXGWacI4+Q0U4qMtNBg0ecWJ4AlgIlpBn6KowlzrEn77tKYXWBMgo7z8CUUnPnYj56D8OUOOw7Qv+Kptlw6ie+cJ77+3vu7+757a9/TYoRYxTPnt9grRHH/yBFsFlsqHw1+84KDHr2eFRKsV4viePE0HX8/V/9DWGcBOY3DgoMfY/RNZv1c7ytCQO8+u4NF5cNi01LMopXb99x//DId9+/ZugHhmGSTX4c2e4PxAJo/9F9xSCaLGcUrjGQhOmnDTSt5/nzF7T1ks1yjS4QQsRpJb67oaeoiDYF5xUxyVBVGznYaAVOq6f0F197Pv/sE/7sn/1T/vSPfs7lYsF4PDCZmqAKfd/PrjoFXzliVPRjmDvCD0TdQI5a0gOGnu32ke3Dx7NOw5zkwCyRQM2esgJXe+Ppuo53L28ZThM+KdrFiqgj/TSxHbfsbne8vO/4yc0FpInu0LFeL6nqms8+/QzfHHBNS8DweOj4zW9+gzaayltefHLFs4sVV02DN4587Dl++4rLiw1+o9HLiqRlNvlRc8778bB6P2qW/60U9mJF/ewCrxK1+RQ5llvGviPGwKK9IMTMOAZ+8c//DzKjzyOLtqGuHFcLj8oTcTwS9w/c377mt7/9muMQwHqe//SPePbV73H12Seo/Xfcvbvnt9+/4+HuFYdDx9vHgaI1yjrefPcNUxSLtWEa527b8l/+i/+G5fULijazN6x8iT5XUzcVt29e8+f/5l8/PbMQM2pmhLdNTQgjx8Mjf/Fv/q0Yd8fE1dWG589v+PSzz3DWkp9f059Gvvv+ex4e72mWS3528Xu4eoWyjkM/8PkXX5AL/PVf/S273SP90LHeNGwf7viHvz3xybNnNFXF5eUF6X4ihIHFoiEXS0iW71+/En2uK3zy6U+42KzQZI6HnsO+ZxoHUgRlmjma7f/f7FBgsWhZrmpWbUvjPZV3rNuWzWLJxWrFpl2glZy+mqoixEhb1Zxs/94oVwuDMMaRQp4dYKrZ89DNJsqFoXgSHuUbmsUltmkxiyVWJZxOKO1JMdEfjthqwnlN4xxea6xRlGyRCVdkCj0pTigl+LaIQ9XTm3467Kko5BgoiMtEVEICSWUO99QarCVm8dmcQqCkhCqFBoUJkel4Yrd9pNQVOtREhECQU6Jk+bNm9tfUWiDiPM+kzOxuM/UjpDTPVPPHcGEu8+zw7BCi55f2vZPDh8cfZyzeWVQWswFKfoJ7ypn5yBmWe2/0fLZKE2BdilssmYTw+XSW1I1zUCnANMrCc3NRkcLyvtPLKc38J0UsUlLPkTfwART6kQPNf+AqhX6c8F3HoEa+f/WKQ99zTInHhy277ZY3r16Tc0Rr6LujOHukOPugKqx7xDkvBIEzHyjnWe9mePH8hrEf6E4nbm9vCeOELYpl2+Ksk+KltRz3jSGWwmkYePXqFf7BMpB5e7/jcXfg7bt7pinOmlVBQ3bHHrSmH37gcl8kwirkQFECC2sVWVY1zjbc3DzDaI9Slu54Epi4nB3949ypyyw0z+761hqMVlijBAGZebjGSFzX5dWGZVvROM04DFAZilWMIaAxeK0+kLvk2TlHvQ/EnWFtee2MzHXjBynlvHf4OcdenX+NEvKSwRCHyO5hz9CN6HnMglE4YL1cMA4TaZw4no6QAmM/MoyBYQyM3YBKmdZ6nl9do5Rld+yFma3g4mLNetmyamoWi5apZPqS6B73xFLwtQcrBzDKxzkS5z3iPE9//xpmdo+3+DbTLC39JH/OZtjtdgx9T7NYE4IkOIzVDQVNjiNNrCUSKRvKcCIeduwe3rE/PLKPPc2zBX6xxD+rSHWky0fisePh8cDbdw/0w0hMs/VhLuSoGE4HYjHENNsnKhmtL57VrD9bktEYbbHayIEkK0oAXzls/fFhTPYXRYoaCZCWPWAYBsYQ8I1kuV5eroQxbx05ZU7HXvyLVaHrTnR9T2vq2bbNSgB0nr2G5/3OWkuKEylnSaJRimEYAdDGksNEP0xsH/c87ve0Tf3EsL65umAaBvqTdKgpJkrWWCsox/t9/j/dEv64Iqhgs15xeblks1xSO4e3loV3XK5XXK3XrNtWoi0y1M4TfWTRNDweDiikgyhaoXVmmk4QBLNvms2cSOFJRREyjJPFKMeoV9A+w6w32IsNSwcLm/F2oKSJw/0Dvo74WtN6i9PCvtLKkEsipsA4nJjSgIa5CLqnF7pQ2G3vsWEiDoMkWpTIqHmi6ls9xzFZyzQFxikwjqN0GznTJNDDRP+w5d3tGzqdMd2Cdt1inRGHiJAgZZGWOCcD6PP8iyLEHqXIw4SKmTiM4oOaZIZyLkbOycOVQ4MIh8dpJDgJS/3wcftZ3qCLEZutM2X4B6u8zNDHGQbNRb6I8jPnmAjMzhsFLBbDDKPOFkvD6ShGxe1CCnfJM3QW5ximNGe6aZISv5f8BIW+J73Iz/Eeqv7hVYBjd5I/l+EwTGLS/fIl3eFAfzwy7IWdaIzi9o3BGIV1BqUEZgxRDhPWOmJ6z1D1TtzvyZHu1HHYH3jz9i1pnPClYK6vUc2CatWAEhgtG00okWno+c1v78RGa5x4PAwc+5HD8SRdf4ZxikxBoE2ljczSPrxFJUVwCqOwrnPBqsRVW3G13PD555/Tj4n73Ym7U8cwDGQkeDiGOKMNgjhkLbq+ylnqWhJZlnUz6z8jRUNTe66u1yxbhy+ZcDgQSsVUOY79gNMW5ZTMHfN7AwWtzNO+IooosR3Txn3wLn14W+fi94HVnAJd5qQUDFM/cXf7wLDvsK7CGAtWURnFJ88uebzfsRseOR0O5DgxdT3HbqCqPNv7LUZbFsbSvrjE24r9vuf7Y0dJmefXV1y3nnXluLm8ohtHHrqO3e09Zpy4udygtJ2JL/FpiXy0ff6AbJhz5s33/4B1O3y15mG/J8QJcuDN67fs9nt8WzOME8d+YGy+BOUwcaKaO8GvLg0c98TtI7evfsfDbsdB93zy+5+xenZJsI6+PnA39pj7R16/feTlmztSEbavNUkYwjEyHHdEPFPxMmZQGuUy1QtF85UiTGDO7PmiSEOhf8goU4j6o5dQQr01pGgocYYcKU8jhPV6xbNnl3z22XMury6E8RkLr16+oe97UFm8ZrdbMpZpimgt/s6pFIw1LBYLmqbG+gIlEStP1dQoCg/bPSiFtRUp9ez2J75/9YZ39/c8f3bNerPis88+48XNNfdv73j7+oHj/kSYIkoL6a2uKqrp4+L+H7t+dCdorcZVDt9ITIa3InaOStOFKFml83zFai1BtlWFMwKGlCQ0/6z0bMMDxgQGP2HMgYLH2oaqalmsL6grz2azwteWpCK3D2/Y5ok6DTye7lnkyKWKXF4saRc1aVsYPwmEm8DiajGfUjNee3GpN4acA509zyTkGoOQR6q2pVQVk7XsTiembiTETH88EkMkzG4PqiiW2RGGQIqSJmCUwTpH1x9gb1mS6IYOcuH029dyag2JqXGoWk5A2pgnxtn5pG29I+bI/ewmIoUwz7KJnnd37+iGnlUYGcdeRsTGMvRyCjsv1AJsHx64v7vHGUVb1zhrxJFFnWkr75f52YknxyzkzBkOzTlSyKQsXXGYTZiVZKcIfKo10zSijcYG6YQKgC5kU0gK8tlsWya3T7SZDzvAj/MD3282P+xwFRGjkhRgIin03L8bGLueaRgxs7tJRlHPbEjv7VPtNyVhncZXjoQk/2UlETtWKx7u7+i7geOxZxgjKkYcmVpHlibiy8DucOA4JoqrqVvRtI7DyDR1bHc7xlggSTpW0vLZMXs8Wi8zsnGYi8jc+T4eO7JK5CJGEwqFzZpx2DFsR6LfYFzF0lmOxwP77gRGC0EhZaw21L5mtVTyc2tNXXuWi5rKWSxSuJNR+KZitVzQtjVjmhinifvuQNaJmDyPuwO1rzALy2noCEEyLJ0TLW8pswdnUXJf3tI2DYfxyOL48UxQz4QTyTWe4dmnjlKjUiGNgeF4ko2zahljYb8/MEw9eUgsvWZxs8YbKfTOvBBbLGUZg2LoToz9yGQeGXPhYrWm6waUKry+vWNvoLGG7bZjuVqzubjm9t1rxv6BvTZ88tPPWF1KWGt5wkB5v5h+cJVSeNwe0ZVl2x045gOhjASO9GkkVBM5PxB0IlSRVE6obDHJMA1wCoXDULAUjMoMnzqqzy748mJF85VHLyZ8fmDnZP2sJ8shTEx5pCRNjgpdMmHKhKy5XC/pIkzDRE6FrDNcK+6rv+OkfkcfRlI0pKQxRZHGzNglqmjZDrunebxSYJzDasjJE8cy60k1z589w9U1X3z5gpvnl6w3K47HPcfDkVcvb8kZ2mXLT3/2FevNhhADaE3TtvhmgbGOMA6z5tRjjGJ/eMB6x/XzG9rVEigsQph9iQtTVCxWI5vLDuM9N9eXPHt2SY6R/e7Afnvi7vaR21d3pJAxTp6XVgWt/wMP7n/m+vHEGCU0+awgqXlxp8xxHOF0ZNV2khARAlNOklGHnP6VsWLPFAu6xJmpVjAawlDQeiJng/cDuZqQcVTELyqSKejYiMYtjcQwEu4f6EtCOY13FUVZyvaErizaKqq2wjgxKjTGo7RAOQmHUz0ftkMpn08omn4M7I8nHh52jGNgnCL9sZN092nCWie+m9VCfAnPnUuBHBP9fo/2hqXzpF6RQiJsd5RYcCjKqYcYKcbI1+wT9pTVME3ElDiVjHZO5i8pk2Mkotg/bufE+Yk0w7uTtkxKch3fV8HC3d0D6807am/YrFYSfWUUau4itTbyr2ZxAQphIsSAsjIXdKgn969QMiEn+mmEKKf/HCPGigXa6XhEKcWpHyhGvkmbsz5LYA41Q7AlZsqcS/akGXxihxY+ZqoVftgQOqNwBkiJymmUNaQQULOGzBqJdtFGUXmHtcJMLVlYvrWzOO9w3hGLOOdMSXRdORa6IjDbFAIpF0wBYxVeQ2UytZro8gAhMMQAOlE1ljRJWLPVgFXYuRtLSAdtsiJnjUuzH+kPll5MhXg2LkgzlFhm/8Y0kqYBZS2+qslFnEhyVKR4tpg6G8xbTMxPWi9VmOe6cv8acMbMn5MiZoHrRTzOTHZJTAR63TOOAyHKIfBDc2WKgKtGKbSx1HVNVQm7+SO4QX1gvSeTtn/UZolxeObUD1TKop2nFEWcMqfjCacU3mh59lag3JQhF8XuNJJCJmbNse9JSMZe7T2ZzKkbmMgcUeRguM4W327QOHKIPNw+sFgvMUazWM8aOpXPy1rg0PfUtvNNUTVrnFuhiqayCsOEwmLdfNhnmPeWJIeGbDDJUUwGnckkaRaUpvnpJcVAuYTq2mK8RieD1REbI8WMoBRGz/rQOQmEKUDOWKMxaSaXlYJxjvpmhW2d+HtaYVOKfb40B7pNKB9Q9fjxPv/BM3ti+Raoq4Z2teLy6orlosVayxQm+mHg1J242FyxXm+4vrmhaduZb3B2lXKkec7vvKeu/DyrP2CNo64cVV1BKbSLhWiGQ8LYitV6zYsXkes4cbFZsVwumKaJNCXu7x/Z74703YjCPJnc/6PW/T9x/egiOORMlyI6TExaYWMkToGy30GB7XEQ/0vX0I0jY4h0IRKMQ7dLhnIvxtlpZOySMHlSoKTjefVJCrW2mMph64rqasP60y9oL6959sWXoAoxRd49PrIwwOUG7y2T0Ty83dINI93DkbVboFcNZlmhajHMLiGSSqRi4kMhrKRZSLfx/cvXcPuG19++EsH6GHDaYbWhsp7Lyyt0rYkqMIZAH4N0OlNAjYGHX/+OsN3zrFTEEAjjyPT6LbVxXFQt5u5I1IpTjAKTUSTJ2SiSlfiXmBOHHHFXG7SvQFthNqqB7btXaGexbcP1zQ1t01JWEepIfzo+FYxcCv/Tv/tLbu+2rBY1z66vWbSNdOeLBfWiZblcCvtymvi7v/97to+P7A979ByJtXSe1XLJ5eaCYg1jCNw9PtB3vcDCfc+iXbBcLDjs91JEKKjaopyhbmqur665vLxkuVzKPEIZ+sORMAbGYWC5FHKDUoacBf611qIUT2kKH1PT4WLhab3mdOx4fvmCerHgMI083Cv2JVObSmQ7lWO5amV+s9syDQOUzE8++xxX1RhXcRwT/Rjo+050fDGwbi2paGJUpCwFY9FYVl6xtomlOlFMABfph5H+NDLlEcNE7TRfffZcDBBioA+RWAqhFIK84sQo1PPj8UMihqIYj3IN1szykFQkdVsrJiP0/qbSLDcLrJvdNAZhc+Y5fkfYlhqDoAspTHQpMCqFm+flEmJcUDlTYmJK8kO49YpiZ/szZRj6gdP++CQP+NBG7GkOOBtVGOtoFisJ8K3q9/c1f98PMfinLUrNJguAKpqHfYfTFeubG6YYGIfAr7/+FYvac7FquVlWZC8/I9oRcuab12959vwFl1fP2P/211K0w8jyYgVWs+9HxiGQQublm45PdpFuclx/8QI1dPzd3/w1Yzdy/ekVf/IvfgkWEZ7r8zaqxYzhg3swxvJH/+S/5ur6Csg0jZU0tByxRgzDnTGz5rRQ1GxNWJyEWlOIJTyZufO/ljUb4/kQqPGqZkwD03DkfvH/Ymo7NpVnWwQFaVYXjDxA10vSiIJCwmrFYrPm83/+Zzx/8YJ6UXHwt+SsyEVhDGiV0TqhdeaVe4tS/+ppRp9inKVgaX7W8s46X7Fcrbl5/gm+UqAT4yTJ9e2y5Wc//ynPn73gsy8+m1EuIUnmUjBKo8hY67i6vqapayEvlgnvNMu2wluB023VcDx29F2PcZ4vv/qKX/zyl3inZ7egwt3tA4/3O/7+b37Lw/2OYUw0iwXOeVEKZBHd/9jrRxfBcQqc+pGEohsmlFZPc6ucCyFoFtWCy+WFGC0DCYurVixWluXyQKd6umMvmWskMkIbp8jMUE7Icl4pORD6ntD35HairRqB9ErmNhXGEOCwp1wsaEskHg/sppFtN3DxWc8Gy9rVGF1AK0rQpKBJybzvLgqMhxPHokndEf1wL16WWdNmTaO9UOdTgtQTuwNTnOidYxoHsRObzblLARMyekzErocYsGFk6Q4YXaEVLEKDUpZFhBRnM/BcyAaKEUiwT4HH8USzXFO1FetnLwhdT+w7uuOOoidKVyjHt4xVhdqcSIuW06njySqoFPaHjvpxT9/3nE4D3lmZjRn5MtYQY2QYBm5vb+n6nhCDnBK14meff8Fmc83nn3/J9nDg9HDHb7/+HXcPDxyPJ8IgnpG1r7hYr0k5czgesIsa483M6pTT382zZyzbJZfrDUwyZxyGAe/F63W73Up+WYo0dY33jrqWiJe7t++evEpBIMbGa3Tj8aZgiVQqs6w0aulxs3a1qjzOSVZiCAMQMRqszSgVyUlYkPtTz+3tPSkntFK09ZJpygxDpDKKVVVxs2lYNo7GaRqrWHlh1SbvOMTM42lHP40s2oqvPrth3To0kdd37xjzzNScGZLFCbEojh+z10KGIUii8Vl/aotms1lTr1eYyw2pquhy5KwDlq4acirEKTIOI91pIGZ5htpacXjSCm9kPmqMxjuZ68YxkmuP1oZ6tSYXSKmwbhtOJXMYB2IUaLVt26dOMM1dNTnLelVgtaVkRf6BP71WSubdSnP2djVFCbu0SMGoqob1+ooxwOO+5+XtGz55vuL6ectvv/ue0+HEy3dbirpirRxuvcBVNTaDPY6cuj1xPOJNxnpFUoZKz0iDVWQrqFVRmn7suX+4wy0rppxYaM/Dy3ccdo98+vkNy+slzUVDIAoSod7PMc9XypG//Pr/zWq7ku6l9ThvaRaWujI4p4khU7uGhV+xWVzR+CVX1TPpYMmMdoTZZk+bMHf3gSHsSXki5wM6JWweKVUkmUwshsXNCzYXF1z+4vf563/157z5+ncob1E5o40EClT1hk+v/pAXyxsWbUVsPuNsU3AOjj439HbxDYr/59OGOIZOhCx5jtrSWkINUqYbJ/bHjiZZfKXBWHzVsLnULJYLqtqL6Xc/ElJB25aFdrSrCq80VQ3GLUQnmhN+W0k4sfWSlzpOPGy3ErUXIlXlWC8aNpsVcZpIQVDE7cOBt++2vHvYEaYgEK7VYmZvFFZ/IJP7EdeP9g6dQsKOQdz+TQAlXZToujIqeYYqQ/YY71FabM1yMWhd4W3DpDOKIE25kvb9bHJstMwLVJHsKQqkEMkhUaLkAEryQSEqzZgyZRio+o6mRGLf04dIHzL3hwFV1VTLjDMJpRU5FlIQHP3DG5uGkQFN2O9JB4W2hovVxSzkV4SZPp9SJHQGFQLGV8RZEH/O2AOFKaBTJo0jKgVUnHBmRGkoylHljCFTJWn3S8rEnClGUYwS8kycGPuRxZhoEtxULcMQGaPC9ZLKnk1i3A9E5xj6ibxsOE79k0C0AKduYH84MQ6W7tRj5qzHmAXajCnJrLEfOB4PguEjejBrND958Tne1Ww21zwcTvT9xJu3d7x684bdfk8cR7x11N7z1U++JIbA7btb3KLGeotznv1+T9d1PH/xnIvNBZ/cPMdrC5mP2I2vXr0mztlqy7ahqirW6zUAu+3jE9lCIbZ9sonPL3tJOFWovUYVJ7NJq6m8AVVIMUNJaDXDRjoDEr4cQ884njgeH9HG4JwFtSCmiWkcaXVF4y2rtqayBWdEalBbRcoyj839xGOaOPb97H2q2SxrWl84Hu4hSCI3T1pMeUDuY+UHY8i4KUERQhG54LWjWI9pl1BVZGNIs20ZzKSUXIQoOKcUTOMkaQzqnBZicFrj7UyN12LirVHkkClZo52halpikOSFtvbEMHHSWvRiZ9ecM6x5ZhCXwtlcQSGExTJHbZ2v80xQq3MmY5kTFMrTr6vKs1ptKGi6ceLNu7d8/sWay82Ki+tLxpB43HccxoSuCm3WFOT5YhXjJCk2C+8wBmwBN/czxSpGo4hWobIipMDhdGBz6ihGs6paDttbTqcDD7dbXGVZXSxJkkcxA7gf76g5J76/+zvqoYZSaJqGqnZcXLYsFmKxeOonLvwFqnnOxjZYVdN6Q8aQ5lmghAwI+S7liVGNBH0k0jGmUezviEQzEZVAvsur5yw/+5Qv/+kf8btf/Zq3L1+hvUFFZhhTZs/L5pKlW7IwFcouEO63QmOe3oFcNGvzfoZbgBgngtKQw9yta7RxxFwYp0jXj4LTazFM0dZSGy3B2UoxjRNjiGLKHtMciWYwVkzdjTczmWsUop9RWGWZ4igOWYcTU5jIpdC0C5q6oq0rjuMkuvMusNudeHw8cjgNGKWws+OWMcKENh/El/2Y60d3gqGbGBSMdgI9D8a1wvuKumrxtibFwps39+IBmQtTSIyT5Pt1p54YA4SALgFFxlgFZ1/JOXJJacnFQhuKrlBlQQ6e3d0JdKEQWawvCVPFMO745vYWbTW+rmkpLKNh+fDAZyoxmsKmkja6jwJhvn13/7SpFqAbDlQqo5WhrjzaGfqxEyZdCIRRzMDjFFjWFW1V8eLmhjKNqMysSXO4pqZeL/FtTRg7VIqoPEnYoy5oD2FhJcS2GwjDiTRN8GQtp5iCdIZ1ypjfvaW8O9C/PRLHgTJO+O6A1uIAfzgNhJKJ3pKuKzLTewZogbvtjqilSC2aWnL89Jz8ESYed/vZBWhmcGZhGjaVdHZ/8id/xovnz5lC5l//63/D19/9jtev3/H2Ycux67Basb644otPP8NXNafunpffvaIfOuq65n/zX/1XHMKew/2ed6/e0jYt11dXVN5LdJO13N/fiwD94WHufhJGyXKlFCnU4yTvDQCKpl2zXK2eSCUpSap86y2Vn4dgKlPKCLmgdWazbqFEybYzQeCpovBVpk2KzcZjvVipLVaVBJ12mcbCwhnWbQ3xSAiJqFp0UXhVWDFhWoNvLnn4eqAbR76/veNPfu9P+aOvnlPpyLd3W377bjsTfgpWZ0TB/l6knEvh9dt7Vn1N460I6wGcoj9MHM1A63f4psEuFsLUPQNW8zz1LBDWGpbLlqryLJqGympBXmZ7upwicRpJ04ieEnUxeO3RlWKaJUCrRmK+1hcbbt/eEWM468mBMq+fBCTG/ogGTvWO1A2U6WMYSjpRyfLUekZ6ZkkFShImLjZLfv77X/LJ5zc8vHnHv/xX/yNZjfz8p1/wp//sn7D7quPhYc8//OZbXj0c+e5+T04RqxXPL1tao6gMdKe9xPQAhoR2hrr26EXFalEzDglVYIpiLrDeXPBn/+Sf0nzTcrd74Nd/+Q9QChcXG+xCWLZRsqj4cDhdyAz5JTlY0pg4HWu0dry79aw2nqrW7LYnfk/f8IXraNyIMhVvzF9TLjZQeWJ/wq+X+PUSUzKxf2S//R3fPf8dO39g1x3oY2KaEn6w7EPPYSz8/h/+l1z85DNsnaH2lMbTXtUMZYTHCVOPFP2O3eHPmbLBOk2au7h+CFhV4ZyjbWsomu9ub9/P/hDXJxl3HIXNqxXrywuyUoRp4LDfEybH0Bmm0Ekn7y0PDw90x466WbBaX8qMcHOB8xV9N+B8ET6C1RK9NPRcrC9RJUIW6DuGRF1VEiuVM9dXlxht6LuR16/vORw6Hh8PvHz5ju12T04KY4WlLiVeJEDWKKw9R5X8p2vbjy6CORZyKOgiycfFKIw1OBRea+I4kMbEeJwkNWA2M025QC5UpuC1pjgPiJ4lx8IUIjHKZnc2wtbWzzos6PZb2QTLzFQskRJ7CkEw5YXDVhIiGaKQNx6PO2pfWNYwOVBk+iAnlLvd9kmsfX6hI1m0fEnjtGDYKCGRGOfJJlGU//tPdQABAABJREFUoTJKXtjDURIfKGSjybVFrRrMZoFtG1TjJftt0qAbat9SL5e4qwW6KLItKBVRgyJX8wHAOsZYGGOmmwJT7bBOMcWeUiLoBJWWDkhlionivh8DaoyoMj0tVKUUP/3qJzz/5DOck/wtZw3aKA59x2Houby5/sgOK80QZVNVLBdLsjHsu45unHh9d8d2vxeSkxYm7OXFmstnN1w+e8ZmuZTfe/mS7mXHME74quLFixcsl0se7u+oq4rrqyuuLq/QWnM6HiEnKmf56ovPkbonXVvOkkAyThPH/ZHb33wrkVUUhpDopwiKOXJl7opUApXQ5v2CPmfpSdq0pA+kHEWOgMLojHeK9aoWfZS1OKufTpMhTkxRbADN/O9MKRFzpGRhqVbas/QVl8uGPiQedztOnZxmm8ZReY1Rs9uLEoKKpFeE9/1FKXR9wplEnRVLp6iMoa0cdUqo0wm7TZi4xBggzwxcJaiJtYbVesFiLYSE6+fX1FVNXdU4LX++OxwIw0iYIotVy/V6QVsG9PY1ymrZfGIhJrHJY049UGXuONXZm/VMZJJ3p+9O9N3ENAQcgcv2hyHIszxCyecg9mQFMHMBh6q2XN+0fPHVNZSRd9/f8s23b4kjfPniGdMYKSlS1TUxF06HAylOGAXDwmKcRVuD00ZIVzkxxSQevyZhvMDArvaIaNbQDT3KWNrliaEbGI4Dp2PH9sWW3f0ja79BOdE18gMhPUDsMypn0pipnIjPjVbYonDKsFktaINHT0L4GkNPv3ugrJbgLOl4oFpv8BcblLPE1DMMA0cfOVaJflD0MTNOkTIYhqnQJ8B6lLEM3SPTNBFDmj3F5XBidUGbgvEBYzNmzrtkNiVxSmEt1LaQSsCaj/WqikKKkb7vhd3vPE3diEbYaCpryTFymgYKAecMuqoJU0AVRVVLWPhyuRT5RMr0U884xZmUN9H3J8I0UTeekiVCret6QpB9Q2sjM+2YOHYdx2PH96/ecuoGToeOvhcWrNEGM7sRPRn/zGv/PwsxRgb1RcykjQhmrTF4FJWaq3s3ctweZ1hOYZ3HGivziMqKY4UVliKlEIbE8dDJiWeYhFFaFMYllDHgxKZJ7R7Y3b0mzQNPbcA6Q91WrMoShcASKQXGKbM9POJMoHGBk8mokuingSkmto97PozZKKqQSEw5oKM47TvnxS5KGwmJzQXli0C5ORH3e9I4koDoNLZxlHWDuVhi2hrtHUOKnEJEqxZXL2k2G6rrNaoUos0ok1GjhaCgbqCu6ZOmmwLHU4/VktOn8iju+07CRm0uuJLxKUOMqBBQU0Cl9+xQpRW//MUf8sVXP8VaI92XFdPz+8OOx+MBMwfuUgphNpg+Hk9zZ18z5Uz3uBPR+P39HK/jcJWnaHj+6ac8e/GCqxfPeHFzQ9003L5+zes3rwkhUNU1Nzc3VN7x3e++pXKOq8tLfvrlV2it+fabb2jrimkY+eUvfjFT7yWTMMZA13V0Xcfd23f823/5P5CSwFOnIWDdNA/yy5lviDEy7HdPbD5h7pby3rtUK0XKYT4zCmRSOc3FukEpg1ZmZiFqjBHItp803SjPoGiFC1GMftOEVgpvDdoprtdLdv3A9vGB7X7H4bimrqwUQZPFgLtklJI0etTHdnB9n6mN5HRces/KW5k1xYg+HPF5h0sDxou/qxzSmKFNTd0uaJc1q4sFn//kc6qqxtoagxBk3r56zXAcGPuJi82am6WnzR367T2KTHVxw2QalPZkpcW0YGYnl5TJZEpxlKLIRcYgKSa2DwdKyjyYO375s+fcrNyHNfAJCjUzaUfNc2JVyjlIQorg84af/cELUhr49rcv+e3Xt2zvOtwUMNZS0DRtTciJsBWUJutCiJFp7jad9ZQ0M9NjRucCRlPNz9Q2noIhYTn1HVPKQsTYHej2Jx66LffP77i/vaa9anFa2JX5TBP94IHFkzzCOECzFLZxhcUrR208l+uK9b7FDIaoNKnrePzmd5S6pmhF3j1SrS+pNldMq5bihMV5rOBUa6ZYMYTMOCV0r+hHOMVCwZAznI4Hpn4kTok8yd5cSpqhR4WvC96Dd4pFU1NZQ20tzggS44whpIna5Q8OY/O9hUjf9dRe/GOXy1ZGV1rTVp6uO3E6HLBOo6kwM3mPEtBKU1U1y9VKxklBCGTnxB9tLGEaKSmiFhUiSc4cT6L/vbm5Rjfir3w8Hnn37oGXr9/y+s0D4yDIXJwipajZnF0MIsSP9yw7+rhz/09dPz5FIie0+NJItzYLZcMU0CguLldUz65pft+jjWT5eS9VXWjyAuP5ylPXlVCtp8J+doB/+fKW7W7P3faR7X4vUoCpYJ0XYbhST21yThnjDGWoaRtNcRoTgsS75Ei333LQI48+URZWGHcmEeLAmPoP2n+ojKFxHl9bltZRWcuykXDZMAVKSAIzKTH/1hZhiI4Tw3DiMU/oqbDbF/x+SY6B7Czf3L7lYX/gec7UY6AkzRBl1jmeOuIwkKaJ1J3IriNWFfebJaeSOek4n5ZlAcrGgWi/LCy15rKL2DhRYgDfovR7aroCNgvP5apGaUU/DvTHicfjgf3QcxonrBPiCqWIfAAwHpJKnKaef/j+G+kCSuGL3/spn8QApfBZdySmwOb6irqpyaZwGnuGPKEbz/LZDTElVOW4fH7Ns+srrC1sH+759tvf8Ed/8HvcXF9zsVoyjQM5Jtqmnc28lUhxKHO6ROL21WuM1oR5kY4x0IdJ1uv8omulsCVjTZbZspp9ZNNZYiFRS0XBOM5OPgJQoFA01jzNvLTK1F6xXDqC1Whr2I8T2+MJkxPXbU1jE40tXK1aglYcp57hcOBwHHncD/z7v/4V/faWP/jJJWWeVQ0pk2aBeCnAx6NpGEfqxvBJXfGTpWdZWbJOlOmAGgaWVmGnBFPNi7W4n3hb4V01GxMviGUilh5tJ7HbmybCKJZSh+FRkhWMRjhLE3ns8LuX2HGgf/2S6fkXxItnYAWxqLKWWWOMqKKYDiL+3273Is2IiXe3OyCzbCx/+NW1dB0/uM5yD5U+zO8zqJmyr12mdor/+r/9L/jy558RpsK3f/eSV2/ecrWquL7YcHmxxuYRmyImRz7//BOuri7441/+nN3jI7vtI7cv30IGb70wL3XB5cQqJlql3xvHa8vCGYy15NBzfbNksXKUVxNxP/Dyb1+yaBasbtZsPl0TleTqPd1PUWyGDS46xj7CoAk6UepCug90XnG5XrFZtJSLNbsIyTjGX/wEu1iLZGz7yNi2hLZl3G2JOpMqwzY+chr3eK3ROaBjYr8NPO47tuPIMByZhpreHmhqw+VywWHbM3QTqITzlpwDb17d4irpAl9cXaOKwWTNMARyHqVrdIVpmD7qmcahR5NpmpZPPv2UdtGKF29VU3lPu2jnUGyPrytSijw+7Lm+vmS5WFBVDadjx3fffc9ydUlVNTTtgsf9nmEYGfqOuqrwXsYheh5/ZKVwdc2zF58w9D2H3Z5/++f/lte399y+eySrCqMM3lpUkeBfUxs0SQ6WmtkO7pxgP/3oQviji+BZKiMbSEGOcZpzVJL3nrquWS0WWOswxuJd/WT1pZS4vldVRd3UkmiQFMt2Sbfpcb5i/bijWS0wt5au7zl2c8HKQRLNy5w7lmQXKZOWCKbKkaYJY8WUKcWJEKzo6bIUj3OWXvqAvqZ4byZtvJwkvbW4un5KWcC4eQ7nZtFVkQ3VO6KR3/d1zapdUBUwQaKKxnGkH0ei1vKzp5n1V8T3MYZAjgFiIqlI0JopBIKSOYSMXyQc9ZxFJlsHTBRyET2SQFSZ/NH5+3wCl5vsuo79cc93r15xCoE+pbkTPFttyWnKe4GqhckjjF1jDFXbUJVKNElO2IFVU6OtkYSQMJJmjc/Ni+fknMV5wjmME7gKxTzwngNIvcMaiVR6stVTMmfWzPoik6mq94bnM1ouY+QPT3vnOSJnrdT5/55/zXuBfi7vbcLPwalGnaNj5O/xVtPUHqPEDKGfJoZ+RKUkLM9aSWfhLCkrUhTrpnEMhKTZ7TvuPHx501JiFsiGc/amnn8+/bT5KAVtZbhaN3z56RWXXuF14TQFFBGrI03lsV5TvOb6cslCVbT1Yg4q1YSomGIiTwDCWs4pMUwnxnFgmHrSAGWaBeeTMJGvwoBPI2NIMA2UaaIoka1gJOEhzrmCaabLPz6KgUQKiVM3zTPcTD9G/pFl43nNnKOTn/al8v45qELRhfX1kmfhht//xVfs74+M/cjd4x6tLc7VGG1oqkr2GG1mZ6IgWY4XK477nnGcGMdZNkKGIrOmyUWKakQjaizaCkJgrOFqtSFTxPxbGfpdz/Ghw1UVFy8+1Dm+X1ufXdwIYjKW94YOtpBLgJhRnUJ5sYqMqWdKga6MeEYRupcIeYKk6aeOQKKPcJwGJhuwlacEBaOGIBrrKUZS7AnjkcNxK1pdbRh7GSkVhH2bQ+bxbsDVGu8NKjoMFoNhnOBsw+i84nAYeP9QxOJQa4VzjqquJTvQapqmonJefGido21bmkXLMI7s9vtZDiHvxzCOpKKoqpUEHMy2gZIsP1F5i1aOGBNmJrLVTYv3spc+bh/ZPmx58+YNDw97jocOVwPWU2bkQyshMZZ8zghVs80bMO+JP/b60UVQW4WywupKRajZAhd6XNVgqwrXVPhFg3MV1lqcE0zZWY91Xj7YqqLyXuALDJeXV5Rc+OJnX7E/HXnY7fiH33zN3d0D3333kv3jjr7vCYPkyinAojGqoEbFdDyiVGJYeKqmwihPDIoULSkHOX0XsY4OORFS+gjZUErEnMZrvK1x1lNdbiAEVIg47bG+wi+WIigPgdNwIm8fCd5xuWi5vrjky2ef0fYDOUzsQ08cR1KKKFdTrCZZRXSanAqDEtu0kjJOa4IqjEiAZNTnXLMzDb3MXZKBbCkqEZPow0qJYhQepzkM6P2NfWhD9u7dW75/9Yo//4t/ywiEORnAWkszv+iV9ywXS9x8gGnq+mkhVFWF0QJzVZWnlMREmTVEkWMQOvnzTz7h4uYGFCyWC1CKcZDDwBQTRWuGMHEaevHknDH9GOOTML58eB+lSJjzBye6yhkab56kOefvsGLl+b7ofbQGxNpLjj2iG9UzHPokKj9vySXTVpaqskwB+m5ie7enOw6UkOnHRL6o8L7CV7UYY/cdx2PPqcsEKnaHxJ3qOO07Yi7YrNFilyCzmwQlvd9UlVJ8crPgFz9/zn/zL37J4c0tx92B3enAwkaWXnFxvcJcbMiXG35/+QWlWtE0F/RhpBsGvvn2e4apZwoDpnJQFDFlun5P33Wcuj2n3UR/CGhtOdSwXBd+uoKV1SSrGUtADyeKrsFXaF+JaXIQs/VcDNOUefNmy9gPxCng3BJtNJHI7bYT2PijnWMufnzAalUAag7vFXF6UQm3sDz74or//f/xX4iXqU78w1/9jkMXGEb4xc9/yqJZ0tYNb+7ecXh1oMQTv/fTn/Czn3zKol7y+s07fvUPXzOEiRAjXdfROkvrPZ9/2rBeV1w2tVAOvKNuF/zsZ5/Ttg1WW27fPnB3v+Pu+0e0cTz/8jnKa7GLmy9jDf/bf/pPuLi4IGdDfzoRw8QUBx52B06ngdwnqqAwcSAcDwxdx9uHB5rDAqstcTcSa0+sHce+p5sCj8NA4IQ2GbcxpOwoCVyc0EkRpok4PdIfJr7/5mtOjx0la/p+YJplNTpnpmPkzW8GXKPQTvG9PonNoRK2r9FGumUL97e7p+Ul6zngvBfjg8rhG0/beBZVLVmCpx7rHFW7YXV5yf5w4NXtW4YxoHWPUgeqOlHVsFgl0jBy7CeGaZzXecJo8FYTYpodZCqevXiBcxbnHK9evuLv//bv+Oa3XzMFQVB81aAxqJIxRiB2q8T8P+c0F8Ef8nh/3PXjO0ER+AgWa6RrWq7XNHVLXTcoW5FwDFFcYoqaSSWqougKbSswhqys2EjNkTvi1JEYw0gsE8oWrm42VLWnbRe8+v4ljw9b3r15A0mgFD8/UJMLNhc80Ho3aw7Tk1+kr5zMH7WUzpQNIeoPzj0w5sgpDuQSefP4QIwZXlsWztNaR60kU806zxjnVIcYeeg7RqtYLRdUiwW+qbkohtIpwsM9n/WJddRsQqTqR9KhIxkvid+PR0LfUVJEOzOHQPJUCJ5OnXMHqI10pTFL96eULGBrkJOQUZT4MSsvl7nrLYrnL17g6prX797y6v6Bd7s9RamZvRtJDzt5GYx5muFYIwzOynuatpkH5DXnKCTlrcRhec+yafHGUlc1Vc0TMeJ4PNF3HVNI+KrhxaefE7Jif+oZ+wFvJU6L9J6Gf842BPk88sfVDGfBOyjWiBFwORcygdqkM567xdmwl/J+41UfdJX/yBkZZnbp7HOpRIPXD4EpyNwllYlmUNSVZsyaWAy56FkuksnKoW0motn1iaBFFIQyc4yQMIWt10//tFLw+adLnn+6wF96dm8SuxShafGupXKKLZ6pSxxut3SnhuJ6rDuI8fJhz69+9TXtwnNxuWC18WijKSXTtjVGw9hPdMdEyBPjMNAmxdEYtmGCymBu1ihfkb3lkEdUBKfEzNq7c7yVkFqcdYyMTFMiE9HFiIuUMmJEzfuP2GSRLCiR8L9fewp5lzRPovlQAhhYXrf8F/+7P+WTLz5B23/D7t2e3758SeUqLjdrnj+7YhMT/XDk8eHAbf0OU2CzvuT5zQUhfEY3jISZRBWmRAiJv3/1luW+56qb+P2ffUm9WOCahsfdgcN+P6dFKHLKvPzuFROBzedXXH26xir39K6UXNg9HlBJzdrIhFJQu4pPrzz5otCfOsiR14+PbB/EZGL3eMAcD1AU3WFkRDMqTVFipp+1YkyRQmE4dTgrptdqkty8q/UShnt0P/BFs+BbdeAY45MlnVWaT6+uuLpe8snmmkwklUzfR7p+4jBMkh5RWZbLmqatmFJ5IlEqhUQuOYexhnGccN6wXNW42lI7h3aWYZRczIuq5rpp+bPlmn6/JYVJciqrmuVyxcXmAm0dqcCiZHKOxPHIZrlk0TQYXwkPoCRqasah52//5u/4q3//V/zmV7+GxOwWpPBMOMBpyaAVreOZCKjnRBjRcEpM3A8L2P/89b+gCJYZ6tC4ylP5msVyRV031FUjG6gxMrgtklcVi2R0paKeOrJz6O0Z4juf/JXKGF1wVtG2fh50Go77HWHseTRi3KtzodIzI2i+AYMs2EJGpYzVYq/knuKPCiEXpikzTR+recW9IRNy4LE/0Q8TU4KF8yxcRVXew4VjSsJ8BQ7TwFgKNRByZowRZyxKWWw3sRoTLhVJZ9cTUfdEJUUw7jvCOFBm89jk5lSJZqbPf7BZS3KRbNapZHKRPIeSRD9ZSpHUiQ/c+2Xjn+d9wGKxQGnN82fP2Q0jd/uDNJlASVkCj8+xT2XWaiKpF9ZYmrbBe89isaDMQjDtLa6qqOqKvJIgVtXw3vElBsaShf0VIkpb2oWXE2s/MPUD0WfxoC2zubl5b1B1dij5oa//mViimIl+Z0R0/ozKmW0B7zet89+ieBI/y6/fQ6jnj7l8UJnOkHSOYoKdikgyhpjpQmJMhZAE2qyspfGFpDRqTgnpz44v8+OQH2vWw35Q7JVSXG0qmUPqxMPQs+tHaiUVX1WGI4rjmLkfTgzhkWIHtPZsH+/Z7R75/uUrbm4uaBd+Xk/yZa0hOTevTYlZ6vqRDsuQHKegqI2mNZ5sLdEahpjQMVIIaDRG6SfbOUqZY3nE+zYVSfJWRVxCzAdFkAJ5ypQpy/5h9Oxfq2eGdgFm1qxS5CLJMr5xfP7VJywWS77+9S2/Td9y/27Lw36PdZ7nWjqVnCPb/ZbjcWBXH7m4uKaqPMtFI3pmndC2ZnCZPAYe7x4ZlSH7is9zpqEwpcR+36Gy5NHpuRDc7Q64hWe33bO5Wc1F/P3VdyNed2Jxp9QM61XUswGEyomui+yHnt00MsUgopKUSCVzShOnMXMKGVfJqKhuKnTRwoA9BRaVwjjFOEyUUlg0NWnoyB4Wys7WhuWJEGK1Yt00XDQLNn7BMA4SIRYMfRcJhwxGoeuC5I4Y1PDxfUnckugNzwb4ysqsTVtNpUU4n8ZJNIJNy2p9wX1JDKfjU5NknaPyFdo6ItK4lBIJOlF5CQlvl0tCigzjCGRCiHz77e949eo193f3KNRs8KBwKmEQHav+YDh0djM675c5Z8rMBv+x148vgg50Y2jqBZcXN6yWaz59/jnOVljjUEW0TUYrkT2kSJqp4saOuLGn8Z6LRcOqXdF6R22dbOAp0U+e02DZaxi6A5OeiLrD1JmmtVyvFjAlVEhUclgn64K1Fq8tZZrEvkwXLurnbOqG1recugPDFNifBh4edty//Vh8XVlD4y0mF7TrKVERleLlbsfxcCKNAyUnchEzaxD3jXrR4uua2/0jL5uW7zdvWX3xh6wT6GPEHk64YSBpzeB6VHUguAdygT5EMgFKpouZUHtCU5P/yFFqB0aLkz6aFBMxSoGKgA2BPI2c/uENQzcQc8E/WzF4ma+d7+ucGp9mVldT1/zxH/8xUVu6KTFESU031oGWw0IMAZLQ/1OYRKYwDDOcKVli0yhU5uPYz3u64nK5kllNu6CuKpwzeO/mYqZZLFoZqrctb+4enlyC2qalriqWdSPdrq8kSopzUcvCEv7gjR7CSD/OeQSzMFu6ZY0yWqDQ+c+/T6pgnicq0O81oumDLlPPZuAKKaQlKXTROOWoXUPSAyVLAkNEM2TN64cTc8PJ9aqliYnHNFHCQD9l9vmGsWSOeSKOknxh0RR7JujM/7aCnz1bsPCJv/n6G/4/f/kPHB97/tlPvhL7LecZi+W+H/h+v6VXj2QMSjl2+z2HY8ebt1uMtVxebei7Hmv1bAkos/CYMsOUOPaBu/sD7mrN7ifXHNcVVW2ZjGHnag7eMowTekjkMJGHQJ4iYRrEfScm2tri3JLlpuHNu73EPhnDcrWg8pYzKFVKYf/dI9WUwScubi7wdYXxlSTHl0jKQszIBsp8MNAJrq5XXFys+D//P/5b/qc//yvalef2N3eU7QOr5QXGKrSpqZs1IVsej5HVsafre94+Hvjmd68ZxsiiXXNxfcXmasV6EEODjsB3b1+x3RqWKtOYiDeFRbOkqdb8/KsvePtXf8luu+W7b37Hiy+u0R+8hKUUDrsOFZGDbBLZWHX0LNoa5yzDMNANA6cp4BcNzXpJ89N27tBhtzvy+vaR8e2OtnFs1i1ffHLJqZ/o+onXb3cs25rKG7797iXOGC5XC3bbjuEUcDg0huVywRCOswPMHCmnNPSJ4/2JcYqs1mtqHL4Y3r3r2JaBt7Zj0VacPrBblGUyx6flwDAOVFHcl4oqxByxrsZWDp8Lyliy0kRgsd5QVZ7j/igHxWFkmiYMCrSlqh0FQ+wKYRzoS2RzeTU3QROn44nvX77kv/+X/z27u3timFi3S5wpQngzGaUCqsx+y0UQRT1nI6LehwBYa34Ayf/Hrx8dpdQua9qLlkWzYL2RNOq6tTN8Vmaocu5MZsuhlCMpB3QYyKmicSuWzYbr9ZJlXUs697xRhRjox4FDt2SMI0pnDt2BWkM0imAMykgESzU7VmRVKFNCnQbGuy2mUvjWsqoqKmMIY6DrJrphZL8/0Z0GSbg/Y+BAmMX8kYJ3nqU21LYi5cKpH8hBTLZDDELJNRpdeYpShBjph4kYMqXA402HxeLamqaMWF9IJmOKuL4nVchNjbp+xnTck/se83Y/21/lpw9baTWTj9LTwPsM85WcySGRYgHt4HpDWnvR0c2nIUGbxZQ4P3U70NQ1m/WKm+sr3t0/PJnaKq0pSMyV1qAxuMUC5gVhrBVykDakObvwFMYnopFFY2ePyBgjOUfRMEXR/N1vt0LLdm4mxJinyBNvHW1ds1mtuLm84sXVJd5KWkFKCe8rPqyC5/modGllZu7O8wCtnl5+KaRzUTyflufvKT8sjtJCPnlwksVJhWzRCrwzDLqQVJEuJsE0RnbHk5B7UKA1zinW3qNtFm1gjGQlusJY5N/RCOSmfpBiXpPwJQkxxzuMTXSnkWPlsNZRqoZiC76esxHnm7TWzN23QMM5iTm5hILnJ6g9JUhF/COVkrFEpxzH5Yaq9bQeovEQNb6830SaWqBw6yzb3SPTOKDIOKsxs0tIyYmSAn13IgXzwUwXVFCoad64BpE0K6XxxqOMIykhDEWKjEk4w1wFbeDyecPP/vAz4jTxr7f/X8bjyDfff8tq2WC04nA40vQTx34iWUM/jjw8PHK365imRDdpTiFiveNhv8VYQ9VUVP45y7bm0ipqHbAqociQB+IEV5sFbl3T1pWw23/wvLQWGDPE+GQqPo4jxmhBAYaRUhRN1chnZTWVd8ScSamwaht2VYezmv2xh1LYLCrGKTKOsn56DWHS7A4d9Uw8vN91IglIij7IGlsvW/m+KTyRCccp0fcDqWTM0LM/djzuTsIOLULQmqaRvu8+3upLOnP/eHb9BVc3l1xeXJOmkZwiSmmaZkHVrlmuNyhl6IcR3yzmOWIzj3YUd/cP1E3L5uKSFEZyDgz9CVNXKKdFahQjhMjbl695/d0rHu+3qJypq4q69hid0Ur0w6BmwpNkv2oDRp/9QmeqmzLEKD6sP/b68cnyq4rVpmHRNizXDYumwlUzoJmTCLqfYnGEmZVTeZ9EnSNqWbNoPFfrJau6odL2aQaVcmacSRPb05aYRu7uC7Uuklgxp57bnHHz+SGS50SKxBQHmosaX7Ws6gpvLGEK9P1E148cjx3DLBb+8DpLIbI2eCchk6pZcuoHzM6hJgNJcHfvxA6sXrTkKEVm7EdilFy63djTuppVW4MacDGTXSSPUPoikUKrBvtHPyG9fk28fyS/2UkB/GBTRin5HLMYhp/x7XNxyynJKchXmM8/BZvIU/fROpUNMD35TAL4yrNeLbm5umS/PwjbbN6c4X2ivDWK1WopRct5nJPnRJlPiqUw5EiIkrYwnXpyjJQpAJI9F0JknCZZzMP4RHrxtcfNhBxnLNYYKut4fnNDKfDJ9Q113WCMIaaE8/VHz0vS0c9FcJZ2zIVca9lkzx9lVvlMd3kaAZYinyvlfaSW1uqJSSpZhbKoTDFPRdBo5pmeoaTCNCUOpx7nDM5bCpLzuHIOZ8EgmYxZJeQ/YaBa3kO+T/cE2BRxRZIF2rZm7ApDN9E1AesTrnIUo6iaAnmcmdJQecPo3bwRSwTNufhJMVdSBM+vmFIYYyna0ivDqV1RLxu0lXdKpYLDUJToItu2kpSVybJ9fGSaRigRYzzGObSeHWRyZDh1JPfkPC2fbTboJEhDGef/SytsY3CzbVhExhUyDZNQ0lICqMzywvPlz1/QVjV//+9+ze34jjevXzHFS5x17PdHKu+o+pEuRaYQOZ5O7LtAjJkhdOy6E6hElyaquqLoTNtWbDYLrrwRxmYWC8GUxbfyat1i1y1tVaHR8+zgg/syQkWMOVNZC6UwjpPILoBhDHgnB7zaC0xstIRCUwqNc1TWYDUcjgMlZY6rmpAyUxBLwzO763gaSBkWIXOapGCqILFnxmguNmtMP0mqTzcwhSisXZUxRtONA8euY3foxAwhCzowDJp+HN4fCOc1pUpBl8Kzq0tunj1ns7pk/7hlDBmtLFXdUrUrVqs1MRVO/YStG5zRNMslfdfTnwbevbtntZpo24XAwGlk6I40TqFVJUjDFClj4M33r3nz3UuOuz3rRUNTV9S1Q5EFmUplzvmW2aLAQAqcFW/ap9GJQerqfwZ2aNM62oXF2kzKJ8aQGCZxR1dFkYOwD9Q5jXaeVYVxIoZIbRSezKr2LGrPovF4ZWefN7mJKjvqxvPFi+eYkti++p6QEmqcqOdZypnSXyjEMkcNFUmDf75a8+yLT3hxeUXxliEnFs0CpSxxgjwVej6ODlE5oktGG09MA2mKDFMkjCNWKxrvwVnWqwU3N9cs2gWrzYZ3b+/YPuygGHzbsLja0OfCfgy0WE6jUIXjyqOKZFytr9as/vBnfP5//e94+9d/x+G337L71WuJp9IIwQWEjZfP5NDzCWf+eYsgelVdcfH8Ob/3f/k/8fDud7x5/R36X+v55fj4yk8OI3C1WaMUhGHg4XHHm7t7bAXaOOqqxhgwGgqiUUy5YNGoOY3cavGgXDnDeRihUyZNgel0Es/BnBhDlAiemKSjnu9jjBIXlWOknwJpGBmOHaeuJ4bIH3z5U1arFXXbMk0Tzn0svj47figlOjBFQRBT+fs1ep4NSrxQmbvEmXIr3aGWIsg8S1BnN2GY51uzu4auGM1E7Ec6L13ocrVmGjqmOKLrDdpblNPs7h/IObNpahbe0jgLZIKG2jhGI/omh0GrgtXT032VUtidTrRXlpvnS/74ly/YPQT2vxuIWLaHjIoDqjKYpsaqRC5ywKm8oamELa1nKzPx8wSp6lryzzDz7ApqbwQuLYVeaU5KU6YiQcEK6qp6kphUdSsxTwfDmAqH00iaJvrpSDcEUog03nFzscKUQhg+cCBRimazotlsKCWRgjg39V2gbhR1rdlcOpZ1g6uX9CGIlCCciDrP3a7i4mLNprng//Z//+/Y3j7y5rt3vHr9ht1uT1CSq5nJ7E8dvqp59uln/PQP1qAUh9OJ7f6BY3dExUDbNlysV/zxn/5TblYtYXfHafuWsR/xlSWNgRAG1DARy8BWKd6ur3nY9x+tKU15Un+k9MRsIARhabZNPWtCM8dxEmRmGkTMnxK3D0e+v33g1dstl6slN5dLXjy/5OFxT8yJMWbizBu6vr5EKZjGkRgLVWX54ieX3N93nLrAmBLoQlVLF5pKASVMbmcNRsGi9lxftKQkBfrx2MvMbwr/6L50mQ/QYWI49bx++Y7TaU9OiZ9fvaBdrqiXaxZNyxQTY1NzOu5JKbBaNExhYgwD3377DWEK/Lu/+AvqGpra8cmLK9atJL3sH7fsH4+8vb3jr//dX3L75jWLqqJxVuz+tCBkhcIwjrPud45o1kbS43MgR00qkjijjBGT8h9fA398EZTNYoadlAy0U554OmpnKYB6HiDL6SKhSRhVhOFjNN6I0NEagdBEo3WmCxQJ/TSaymq8ApczLidMOUfBygMulI8YgEYr1m3D1eWG5XJBMpo8TXgbiKZgtSXHTJjCRyc6Gbwiat7ZbNmbTGMVg7cMQZio3lvauqaqz6Qd+arqirptaJdL8ddL8/xlJk9MfULFgp2gnbuuGsvCVmRbs1dCWJjX0TzPm0ke80byhOWpIvltzIJy4TRjkGzGD69cynw64qMvZw3LpuJqsyKlyOF45DROZAK1ep8BznuKCh8AeXPhmAU5WlxozjmkIqqO8uLOcKykOCTZ0JRYxDnrUFUhV0LoccbRLJZYX1GUUPvHaaLve7qu/wjfP6ML5x/hieQ5b/pnUs0TsQXmLqnM0OcH5s+op/d6fstnnxlJHxZClHTGdeXwznB1seB4SPRDRDs3fxl501MkxYCuxH4tUzi/WmZ+9mb+JD+cMQGEoknFgHL4GpqFoaw1cSzEIOxOjWQhPj0XJSkMZ7cMgcvff1pP4HiZuzUlHYybcxbV/IlFNANpbnYURtmnN0aetXT2Z3jbzfrQXArLZUtbV9xcrmi8px/Gj/59Yz3GeHJOczpClqw4LR18mjRZJYpOOOXmmVkioEklSVJLEgLXZrkgdYFttWWxrDEWrm5W8hwLTDFT1fUcnHuJMoZT33PqX9ANHY/7wxMD8uqTSy7WLWFZMFViPFWoYST2A8oO2BQlR/N0Yvf2nt3hfceklBJj57PUQusnCz+FaHgrJ0UQCnESfdzhcCIE6VZv7w90/YTRhs2qYbmoUAr6IXDqRnIuMxPa8OnzyzlMWBFCxlrNqq0IY8EaizMaiujtcoYQMlpFYpI0EaM1wxQBeZe9s7SNJ0yJnH5QBI2eEZLE4XAgo7HNxDgOKAX7w5GEJmNomiUqFzRiuFFyxjsL2ZNjZBoHHh8eeXzYsmgt63XLZl3Po5JIGDOPu0dev34lnWbfUTkzmxqA1UJkK0rqReaDIqjEh5a5YDPLbUrOpLnT/bHXjyfGzOJ4rYWOqjWkNMmMKmUUBo3BKvs0d1EU3OwZuvSW1hkqO1sYGY1l1mohJ56sZK5oVMaS8argS8LnxJOOSxVCSRQt9qJazUni3nJ1ueLTT59zeXXBmDPxeKIdEiWAVZZxCBwP3fuXGai8ovKKqBK6JHTJrGuNCg5izf3QY6xms2qoa5Fc9H0vJ75SWLQtzXrF5mJN7jJjSIxT4jhF9oP4ceoMNhVW40TqBvS7HcsuQdK81paEkkilObneai1wCzP3Y2YtCgMsoEthSllo199+TyiPqKn/qLifnXXKR0iOHEZM7fnk2TXWGMYxsP3NN3TjRC4CmXrvqKr66XkrdYbaZhl2mb0kRWhHHgPD6cTu4UEcHATU4tiNdMPIqR9l5mgNq6sNdd2wXCyEiSY/FgtfcdG2KG2YgjDG9vs97+7vP4JrQpANWXvzXjjPzIb84D/9QV6M1udsN86DwY+KBcjGVpBk8TIjGkaBVWCNYrmssUbzxedXvLvL7HYJV9cY79HWk9VWNu0YpWh6w0TGlIJOIuVRFKxWYqj+EYlSEVXFRI1JtVh91Y7li5bT/cDpceR06FDJoRwUfz4Invvfp79o/qzm4+J8EC3MGwVgzsJn72ZfTE1WmqOa15+CTTHzkUfWcQyR7nQijCOkzGLV0iLv1fXNFW1Tc7Fc0FhN+kiqozCmQpuKXIJIXrKMH4wqqAyTV2J6MRoWmw3eery3hDwScmA/HplOI91hFEQiF07HA4vWc/NszZ/+81/O3g6Z42nAWEvdLFhuNpJZh4jic8l8/c13PDw+sn185PlXN1xeLCl5xfJuxXA4cnx7TzgemY4nupzIfWA4Hnj3zbfc7Yan9aWU4mK9Yr1qCaGZOQ0S1TWMIzkn2trPxSQzDCeGvuPNux27U083TtzfH6lrz2a54LPnm1mXGni3PbLddWSE37BZOb745AVNZalnfWwMia6bqF3FFBL7U8eoCxTLMCZCyQyjoDEpFznIWTs7vxia2lLXjnFKGF2emNgKsNZjlBgA3L65xe/2VMsN2grc/93L1yyWBzabI6vVSgIP0oymacWyXZCritpZcpi4f/eWv/2rv2W9rnn+7Iovf/KCvusZhp6uC9y+veVX//ArHrd3hHGkqR3WKmmc3HxQy5mc/LxmNSmd3y7x5M0qPomEc07EyA/ew//49eNTJKZE6IMwoiZJNfdGvl3gPpE4KHMuhzNtVYtMQpVMGHv2j/cclzUqjVA16HmQFUtmSpExTFirxZMxCbU5DwGvRKyvrPRBuUibrDUoo7Ftxfp6w80nz/CNZ+wHpkniOeI0kWNCZzFr+mDPIBGJWfKvSgpYoDaZ0WRqnfC6QA5M3VFYokqzO07ih6zk5dJIwncXAjpEYpoYUuaUCyciGgk1/fr+HS+njl893pOmQBonjksRkPddT/fN78jeoqx9MkRJ6f3m7ZzBxogbRuJpzyKNHP/mL7h63hBVfE9IAEKYmMIkhWCGj3NOGDJOFTaLmrb5hE8+/YyYC9+/fMM3336Lqyt85bm8umS1WnFxcYHz/r2wuYiXagxCa0cpchBHH2MteQqkHOX0Zi1ta7C+mYG6gjaWVMQrUHIlFSUl3MUlvq7FIi8lDscD+92ew/7wURE0Sj/FXIl3IDL7QLw/RYR+7nHOHeGHFWcW5c8HRfU0LJz7psSTXjObGWUwiqayOGuwJmGMkDYy0uXlgpBZSFQ+0ixrFusFjsw0jqh+wMhPDEUS5kv+uHU/jhXf30beffuSw3HEYfjFzTPJZjueuH+9pTQW1zesPllhKitG10+3KwUrp/LBHDDPRIwZmp4C4zAynSZUTkxNRby7J3UnVJjE37dylIVhjFkIGkfoh5Htw5YYJpzVbJat+EZaxeWlGCYv6gqn4Hh6T7kvFBFyjxPGKLxzJKOJMTCOA9MYsboAkVI69O1eirtKuEqjnUZXiu7+xO2rd3z799/xuN1x9/Ytf/Jnv+TTn3zCz37/U4oWb9MpiMF3RuGqaoa5JVFAFQjpiropaDMR6RmyRtuMvVrQrmrayzXxeJJCWDLq8ch0t6eMPanvPiD8FAlODhIGLebg4K1iGGSed+pHvDU4q6nqCjdGhlg4HGVGvlpWLJuKZVsRUua063l4PDIGiWa6WjesVxWLxhHGAaIlR0/jpRh643BeC0tWWYYpMkwTSnliTBxPJ06DhAYceyHpoBTPNounGXjOcFcb/s3TE1No4zEGXCmMw4i2js+ur6Wjd47FUuwOb1+/YdW2LFdr1usN1ggacXW5kXFIJ4dGowSl2yyXXG7WrJcrpjCx3W5ZrK9Yrlc8e/GM+zev6LTsyM5YqspxuVkTwsQwDDijKVoCjcW/FlIU+0xjPVlbUpbfE/nXf4ZOME2JMCZUAmwhaQnuetL8KUXRCWW02IvOEMVZkJZTJIaJvu/o+yNOS+SJ+qAIhjnr7vxASigwz0ClLT4vLTUn5sgpRs8O8U1b07TNDG8Jo/LsAG+0xltL5dxHQkqBacSTVJ0325QgS1do1EzuSZEwDIQCp9OIteKE80RyyJmYIiFFGd7O3VBWzMVC8xgnyiGSftOBMSilUV4zFhhSYux6ymRQ1pDSfArPZ4gUnLeYnDExMpZMH0dWD3fYxSXKf7ipClR1dlQ5Q2XSNsg9OmfxxrH2LZ88u6HvBr7++mv6MDKc5NBCTtSVp/Jenu9ZgjC/ZE8wa5D8O6sNUylia6eZBfcaa9/bb1lj50idOQdMyablrfgCnqG7nNJMGPr4ZT5rNs288TwBt7NL/rlcyqKX2z1bqT31w+eRIO/F9ud7kbZZfv883gZmHdgZd51h5lLmTL8sBxRlcQ6MM2gnR0Ed47k3BdRsTK3+0cwiFsehh5dvTpy6kcZafr4R8otWkGMkTZnUK9q0kDgidYaqeSL5lMJMjhFNWk7v3++YMjFkpikwacPYdYyPO+q+x8WIWtSotiL4mm4M7A49wyAGyNvtI9MkkUqVd/jK4LymqR3eG7zTWISt+uGZY5pGpmnEe9HtGiOpF2GaHW2GICbKEVDiaRniIH9/ZVldLkhDQidQpeCt4WK94NmzS54/v6RdVmSVSRTcfO8xM0trZD5sZieRtnU0jaP2hpIiKQWKVeBE42ydxzojAvGrNTlnTvsjcUyUHySVlzli6ZyXWNRM158PXaduYJjHR6mIKUUu8h5V3rFaejnoa0U/BoYxcOontJr//bairT2Vt4y9xLmNY8Aq/RQgW1cWXxkqVxNTYooR7xpizOyPjsNpoJs1hlMsMt+3RpAmJbKWYazmI6MsJCGegTaGkBIpJhRQ1zV107JcrXicw7jPEWiV80DBWDHbSEU+nxgCOSWM1rR1zaJd0LYtCrFQ3DhLu1hwdXXF5mKDIjP0R0knchbv3YxglCcJiDWiQ9dCmJitHWU2cmaBo36oLv6PXz+6CPZHCYedrBMHFWOILmO1wWqLUgGtE8EkKutEaF0UJQrbqDMW7yyP+y0PO08IHZ0/PmF9WSu0d5jKM8TMMGTGg4T1Gt0wMBFKJJRIpUUcXyGUdWMtF5cXrFZLmrbmNI2kFLBWmHbGWGJIxGcXqJLFE/SDaB4fklDvlabkzOO2o+sl3FZyOzUWzdBP4s8YE0XLR6dzRqeEjok4CQmokHFW0TiLUZbJGQZvOKjEkBOH0AMGbS3LZk12kGvJxSoFcoQpSIBu0Rojgx+m2UfUOMe0bChK8WaM5F2HtmcSzfw2GwvGzEcGMTzHCEkil4TVTjrNceRP/vAPeHF5ycOr7/n269/y7u1bDq8rjtfXhOMO9ZMvWaxWmM1aCjpALuQYRaSfMroUWmMFighZLNd8hfNehvVZ3IF80+CcZdE0rBYtdeUpKeKtpfGW2HdoZ1k2NYbCcFw/bfAKaJyhdRZjZq2gDAJkrgrvSbaK+SAx28fNq1yK5TzXnP/c+VIz5C/m2pKGnrIUSK1lgcWsmEJhGBJtm1EloEriYiWIgFeJZJQ4eVjzJErOc8ENMhwlxA+1TAr0iiEkXt9lui6x8IrDJ4Vl1bC5qbieCj2Rwb5Px3j/7QrnJSE+pUKY0uzOLx1gjIkUYY7zJEyFLvU8xMDd4UQyluus0dcr2LTsSuTN9sBvv7/l8d2RaZyYppGrzYLNUvw369pSNxbvNM7MTjLO4ir7tAGVUnjc3QkpoqlmxrFlvWnoBxhHzd1uR8kKisEoyzB0fP/970h5xFrFH/zBT7m6uOKXv/dH/PKnfwhkio5US4PyiiEO8zl5TntQslYywvAtZGIW+UNBDrY2FRgCxQXQdk6vgYgS02m35Is//Cnb1Tu645HjduRDA20UNM7RVn5+V2YXJSXFQinLd6/e8Xg4sT10M9tZQl9/7yfPaCrZP989HHj7cGB82GOtZdk2OKdxVuO9glJIoQhbeAzsTz27/YCzhuurlmKUzKLjRFU5NssFbd2ilOLmsuVxd2J36Dke5SDS9YF3HKidYVF7ri5qlk3F+yoIqLOnsKakwNj33L58xR/98RUvnj/n+voaTWF7f8dvf/0brLVcbL7jxWefsbnY4J2lP53YbXe8ffOO4+GEs47Ly0ueP3/G8+fPCTmSFbTtAv9JxWqxJI0Db29f8+3Xv5bi7i1l9nyOKcm6VfOYaCa+mFkeZKyZ14N+Mk0xPyRJ/EeuH50sfzqKP6WzSex8jCHWs82OLfO7p7BZJkJWG/I5OgWYcqCfBnanI3ePnn7saap63qA1tmqwpWCV4ftX73jz/S0vb7cc9j1DJy9AIBJUYlU7WqNYW4/VBesdi/UK6/18Ohcbrsp7cggUW1g0nrCsmfr6g41P4a2kbucsH14uIgrNKoNVGKef/mzRBbSwEUuJxDiiVIuac+JCnLApMSpwlWdpDWMpDCnwMA4cSyIgwbmkjAqJY9rP9GlF3TjyLDSNUfQ8Zo6eMmjR6CGMR7JASVclUychZnw4EzwLpFHnRTrfxXyCVfo8OxKyzXrR8r/60z+hplCVwss3r5mcY9zt0J9nrJoL/swONcbIPMAVGlfhjEhMhqtrUopzpIvMEWJMTwwWX9VYa6m9o6483hk0WeB161gulk/2bU3TMPb9UxEEng4r6smA+uwLKteTEcLT90jBO0sKzpuVmpl750/h7EAkTGU1SwokLiimjEG6SzG+fnLxln+fPPurCkLRRehToWo1MRl0Meg8G8/neZ73URUDo4VAZisNo2RZVosFm0XDwjvU5Q3HMLIPHUGHec4nP4KwPo3k1g3CosOJcb2e0YBxiIxDZOgjMRZCkcT1hz4SVCagsV1A2cC7373ldnvg1es7TgfRopUcJGJKa8YpUNVW/n4tiAZzyPbHyHNh6E8MQ4MxmZgcxha8bvDWQ1E0dRJz5THivcX5muVywzh1gFh+7XVHTpraV+JcYgqhS6gRihVGq6RRWGZOE6rMaSHkObJJCBbMZJWURFqls4EkkgClpFQGBW65ZPNC8bOp8LtffcvdY/eD2WsmhsgUIw/7E8du4PbdgzBpU+Fhd6IATdPw4voCReFwksP1sZ/o+1E6wBB5frVhtai52ixFbjXLAFKCIYqTzRgiY8j0fQAFXQh4d5KcRAuVtVReSGcyhmgYx0mkGt5RRdl3nLE4a/FeQgI+7pmUSKKMfH6+yMjjsN/z7vbNvFcUSkpcXFzQ1C3aGJqmBW3px8ibN+943G7Z3j8wTGenqCWpiAG4NoamcpLdqQ3KKuq64frZc7TWxDiRU0CRsJWnBlCa0+EkGthZgyjZs8JezzGShK8LyPy3lP8McOjQR5JSOJexNokqX2l8kVOYCK3V/GuBzbSxKCWQQciRIYwc+hPbo2OII+00znMdS2uko9Q68vL2ntev77i92zEcesZ+4qEfCCpJ5IjVRGWprEGZgnKGdrnAOjuzIsX13hkrESfaCJW8tjS1/ej47+YiOE3qSVyQS6bognYKkyX8UxXQUeA3h57toiTySM2hvCFFyREsCu2EwptzJg6B/TRyLOISYtQHWraU8d6hK2HsRQqlSG5iLuDse75miKKNKUpDThiVWBDxuWZUliepeBHBdMpZikHJTwVjpn/Mr8wslqdQV57f++lX7N/ecry/47uvf8tkLf3hQA4TpFk/REGj8caICboxbNqleIzWjZjknrG+ubCkGEXHZ4wUQSOzEncOIp0ZdtYYqroVNmeRYrxcLj8ugrNv7Nke7Yy4z7c9Q8A8FbqnodmTkQOzPeh7BplSs5TifZMrneXMNHvPslXkIj6gSpunElgAd478SoZ+GGSDrQy5WDR2tseUVPX/0PLUpmALWC/+m8pqqrZlebXhYrnAXin2/Qlz2HK/f0eIwnI+Tzy1Enehvh8IMWOzmVmxmpJ5KjTTlCAWEooxwWOKjEoxKYMfAsqMvHrouHs88HB3YAjyuWkSwyS60JiS4Ata0mFQ+glO+4GmnBhGQhhISTqWbKUTN8Zii6LyLTkNTCQZaxTHol2K21GOhACnfiKmI8tFmU//mhIKyoCv3NO8QCsNFiE3qSjrQWVyMe83TuQ5i140zQeT2S4QRZ7x9appWGhHrTzbu0eq2+1H9zVNkcGMdOPEu4c92/2J717eyQikKKZYWC4kVef6YiGuWH3HcQh0Y+D+8YhWYu94uVlyfbHgxfWarhfzjsOpY0iRKSXS036hmc7s8xCfDr1aJ7wx+DkZpq6quWMTwpDzljplcpGxQ+0dTV1jjBNj/vk6E2OsAXIACjEnhr5je38PpbCoa4pStE1LVQmRzVcVSlumkNjtjtzf3XH/7o5UwPqKxVIis8LsiyrEu0o8iufIvc3lJUorpjDSnfbEMGKdiN+NEVJjKJEU83lxvx9JpDQ/8/l9n5nLP/b68XmCUwaTKUXmO4mMippkMtkmgQyNkCHwCqzGOCcU8XlYPZRI7I6MBGrvWC8bNqs1i8WK1XLB7hR4e/uG/+Ev/oo3373m/vuXxF6iULqUSKZQTCF2meA9C20kMd05VpsNzntyzkxhJMRMmhJE+SpRxLynw/Fp11TAemFYLiw7srhXmCKaRmVI2ghsVUSbNwVNTIVxgtOYGWMhE4hBQ6fY9SfSMHI3DiyMxWsxx36YJk5pYsrvx3JayWxksVzgvMU6I4nY08jpdKIUgzWORduSYiSGSBwm0KCtJqeJHngXLZvZauzDx/5E1zdmNjCQOds5hdmY91KWUgrTKMPq+7t7Hu7umfqRYRBad1aG559+yi//5E8kM8w51suluDr4itZXAosbC7OBtlLqvXP9fMo2xuC8hHramfJtZigtnuOlcibOydYxRraPjx90TQqjHAo7d0Ln2Vd++jdLPmM7545vTruY9aTaCP1fDooCsVjnJDevlCfGpDIGlSU9HOOwlcAu45Rxvma1URSjiGRiLDTFoItjTI7jmIhJcWnX1DridE2cnTqiMoRS6KbAh4QdVzuUUrRNxWHXYbVmdXlJe7GhXrZMI0zDwOu7HceuR6lMu6goGDKGbpyIx477h0K9qFgsKprGMk7SPey2HX0fyAnQijGLpm8vjSlOgY4TPGi6vifGhCqKReXRRuEdXF+tuNwsWF+saBaNIC9KZCWlPH3qT/ekFCwXS5q6AWAYBnE6Kv5p7uq1Q1Vg0IQQKDnTti2bi82MNhT6sWd/6tgdepRWWGOf5FoL72gqR105qov3ZJpAIc8+s1llUoHQj3gFV+sFrTdYUwhTL8Q9FEVb4R5ozUjBV47FzRV/8Ke/IPsGpf8lZNH5/Y///tdYYzl2I1NmlvUkUhZzi88+ec6z6ws+fXbFYXdH1/fz2h459pF+ijzbLHhxteLTZ9e0jeSwXqwclMLFSlxgYspcX1+SS2EKka4fGYaRu+2B++2B3bHjfi+uLxphure1JxX47MUNy3ZBEyJXa3mvLzb1jN6JEYavDE94qJIgdIG4IcZRzDp04Pj4QLd/5OHdW+p2QbNYslhfUrcLfLvCVg0pZ+63L9kfe6ZY+OnP/kBmtNNI398xxcC7+zvW04pFu8BS4VxN5Wsurp7hmxbrHA/3b+m6I6pIwHYcJ5Q5gZGfU8+yMmKaD7azo1aBQpIGIv1nYIfCfOqdw0kzWZiVBdHqFQPFEIrGloTKiojlw4CxiEQwhZSwWLS3+LahXra4umLc9by9e+D27p677SOnaZAsvpwJqjwRY6QzK8L0nE/xZ2JNnCnEMSbiFIlTZBonjoeO4/70kUTifE+KjDEiz1A5C1lDyeacBbClmjvPmApaJYpKmCjZc9qI/osinXACOU0ZYayNSoTNSkmHczY2ds7NchN5uJKFJk4XZvbDi1E0NTHIC2GtSEvKXECScTPs/PElAcQJpcVk96lJnEki+QNmRi6FGCP7/Z7dbsd+J/lgRUunut/tqZqW7niirRusNlS+orIV3npSnNl5JWCcaAeN0ah8lljMm0yG0PcyO7OGYKQLtEY/HUxCCIQQ2O/3jOPI43b7MXT4ZJtmUCrPszr9pPfLsxF4zlk20XlgXmZKvjJGZkc5z4N0Ed6qwqwxyk/kl/wEu2jpeNBMUxQ4W2ms05LWkWZNVily+CqSrTaGIM4xSqGtnUk6cojU5gPjYqWoGiG7GG0xStARa8X4GqWJIXA8dry9vSOXCes0deVEN5mg5CIz0JTJSRGjou/FOaTvAtM0G12rGQMoYhwvuivhn6kYJZhagfeWRmuq2ouurHVcXy7ZrFoWbU3lz++uxqiZrKTtbCP3dGNUdUPTtFinsEZiut5rTs8svjOcK88kpYRJkpyilKIkmTODQmUJ+JZDliIpw1Qk/DrGiPEa21iMF6jNONEhl5Q5PTwS+4E8TlTGYo0llQ9CyFR+ItMIlFpIOlOvF7Sb5Uf3ZYyhaWqqupHvLYLgjKOQe15cL1ktPFYlUoyEEAkxy/sxO0TFJN31dn9knDzjaPGzTi/PKI61WvSZRfJIdcnoGZbUmtmwXAg5zmgWs/wlJTicBsYgxCgz368+9jhrqVzEu0jfDz/Y4xUogzYOq0ElQyxIWHgS27OU4pMTlKsajscjy82FHKxzfmJuO+ehgFWKaVCkGHm4uycMI0Pb43VNVSs8FqWNkA2rRhKH1Eg/ToQpEobAFDNxZn6Xsxb9/JTKB7WBMxL046kx/4tCdc/oUtGZrBVRBXGCSIFiHDk7KBodBQ61yVD0GS6ZQbgiCy5rTbVoWVysWa0v8G3LML37/7H3H7+2bmuaJ/Qb7jPTLLft2cdcFzciIyIzMstkFhKqPl1QtUrQpEkLlegiqoEEDfgnaKNESIgWIEGJyjJRGVmRERkR1x23zbLTfW44Gu/45lxrn3NvnKAyhZByHO2z117TfG6M8brnfR6+ffued++vub+/I/pR+I6VYka+Kw06C7LUlUWSgN5Lfn30kTAJTHkaPGPv6fqBu9sH7m63PNw/hdynJMoMxigUEZUj1qijaoDRQph9VrWl+TuR8oAyBpcyTklKSCmFwQCWYCzRaaJTdGli0gXgkk1RLojHfjw5bi5110F4TKP0QKac6IeOGISKLPoRqyTCVFbSH9S1IOE+SrLFGKQlIp0AIFIOyUck1eN7MPqJ29tbbm9uub+9J6csxkFpdpstrqrZPGy4PLvAKkPrGionAqWHw/7IOO9qh3Uzn6U8H+fccVJ2h56cM4umwmq5v23tcM5RuYppHOn6ntvbWw6HA+8/fHhyrmK0pCY9R7EppSOBwXyvQgzH4+bS1pEyIj6cMxFBn2YlDacClpEexLllQoxFmXRKk9D0YyAk2bpdXYmKdZRFqpWk73JxdLqhx+kiwGudpBATWNIxEi9Li3Z1jg4JhcMoh1WzwZBU3jCMPNxt+OrLb1muKhaLmvWqIYREDDM1YSZFhdYVOYv228NDR3eYmEahUBM6uiBObBYnD4o0TSGlaGpHUzkWTcXFekHb1Fxdrjk/W7Bc1JytW4nyTUEaakErVtbijH30rBSLxZLlco1z5thzKg6MJOu8n47vtaV8ME3T0RhWzpF8Ivl8bPAnlnSXVmSTGaeJMUWm2xFbWdpVw/p8TdVUVAsntHU+8PD2muw9Vila7XC2Zphi0TOc51iWe44gTkci9bqluVg/ui5Yr5Y8v7pgvVphkH67qlLsdx3T5FmvWnHefY+fJqbJM3pxlmKSjMM4eR72B8w7WDSOs0UjPKNaVClWi4a2qdntD2X9S6nBh0CKvqju6NKuZmhry4vLC7TWjGPk+v4gWRidqauaqqq43wYqa1g2NbUz3N9veJxDEgyZRhuHMw5deG9DFmMep4FpHEi7LZGSrrQVz1+9Zrlas1otqYwmWCtKI4DJDpMVcZz48PYdu6ZhsViwbM5pAzRJMi9GO6xtUaYmYtgcJqZ+YOp7+kFUTLQ25OJYn8oR+bivSbeCLkQFP2z8cFFdY1HGkinkpCmRtNQeDBlV4O1WQVAJlSNj8KWp2lApA0o8VT94Jm0Z+8g0JHybaaKSpuIQyONImrzUeGYqNqVL/jcRjCIkkWjqpsy0H/mLX33LuFgRmxVWKcbBs9lteXd7w/Zw4N31A7vdnt3mcMwXZ+DQR+oKjKkgj4Wg1R4pv+qmobE1rWs4MJGSJ6lE5SRl2riWlDQhwnJhRLrGWxaVoObINVoFyF7015LULHPyxJAYDv64gWc/YUlYB0pLY/F+dzjyiFZaUVvHRVtjXMI5xboxLBtb6n+PnhfHaphMlTl3zrxJqzLpEx8+fOD9+/f86X/9X3P94brU1YqxsRV1IcX96suvIWQOmz2NrbFGk1Pml7/8Bbv9nu1uK60BWmGdpXIVrqp4+fyFKHpbx/ZhS4yBpnLHuqBEFhV1LcTlISXGcQSjCkPKacQgOWVnq6P2oDDIzDBpiSbzMBQjLrW45EUJw5doY446AIIPxxqi0gaUOG85SrpFaWn0N8ZQK4uOEylHzs7XUvOMkWlMpKTQ2rBaLITn1kpvZowRVzm0koiT4LH640UqNci+H0qdRNZTCBPTENFp4nLV8Ac//pzFqsJVhnZRMUyR3kycNQ2qtVRVy9X6gpgj+82OYRAawHTcLmavWQgvTEHdVRYap6it5nzVsmgr1ouas/WSpq44Wy0KDZcAGrSR+2GMKawk6hGxwqMNxoogc0qRcRwkWk0FtKMUk++Pz8EWLbvlcknXdQz9QOUqrLFcrC/k+ceEn8YSTSlCorQwZTKOlAx+1AwH8GNg+zDSLCqsc7y+/BQ/jvhx4OZ6iz2M5HWNrQzaiHOqcpY0OKeaclSJqNIR9JNS5uu3N9w9HIQtJkv2qG0qYojklGkaadQXR3akGz27PjAGeRKrhePybMHV2YIXV2uMltrtYilN9rv9wL7zHPrAthvp+pHtoRfqtCi0aj7OwJCMrmU/7KdA7SzL1rFc1tSVpWns8VqmIRz7Rwcv1IWP6yjCEGRICgGvqBrrKuJoiV56r2e8QZz5iceJm29+xaaqefPpZxhbs24btpu9KKcohc2aGODuwy3aaIyr8N6wWl2wPrtktVqhC7nBYTew246g2wKEAj9tSClirUbCKAoCtPBX65lJxlAZhTNP943fNf5OtGmCDEToGyW3c2w8jkHqTcF7Ycw3iFSKFmQ+pqAKlTSu5pjxY2AcJsZ+ojIDQzcwHDqCD0dGf5WLNz6n8+YisdLFKEvK8f3NPavre+qzMy5XS4bBMxwGHrZbHnY77jdbhn5kKMZ1HjFBTEInJE2vJ3qwDNgSdWilS6+iEkIAbSTkNycYvbOCXFJayIGNgsoYgkNSaMkfj6fVnNJNR30sU9hvjDWgxbAN4+kma2WwRrxubXKJpIRlPanMx0OVzz2OmOYxG8OUEpvNhtubW26ur+m6ToxK2RyVNmhjySj6fmDzsMGguL29FSRlSrz78IHdfsd+vwczoxWl1jZHd3XT0DYtQzfIpF20ZGfIVuqDqSwsARZkkTnKPEEbynkX9OYjEEw8tkCIhxiKhmVORXcwCwQ+5VPFagaTzGKk2pxSHblkLhLinAjVmJV6t7GokIiJ0jwvc2OfBmJIxKwk6jHCDJNA1oOe+2lLrGGeXljKZYP3vtS9DIos/bUqoUgs25o3L59TLxzGaqxTDGOkthMXZ2eShTGOGCLeT4zDRPDxyG0piCCOROPys8FoTeMUy9rQVobzlfSorZc1q4VkLJrKFqYnWcOiqGLlOtXM/KQ+flwSDShR/57LFDFE6lqh7BzNlwbn4phYe0pjp5hA5wKGKqxASlCqqhhzge1IujQkhSoqAlILzhgra8iZBoyUEjabntQNTDvNxeWCdlGxqOVZ6kJMSJ6rOU/XVs5ZhKF9LHugrJe6ckIHVyTMVEnRJWTvrEo0nJFo+2y1YL1aCBdySaWHJJ8bfWIU5ga6fuIwCKpUKMeSpAcTJbWf502aqqpISRiJmuhK3VxoFWcyeB8VwaeSBft43yjrYF77hdNY51rIUMhSaoqiOSl6rJFpHEkh0h/2LJaaqqll70kU2jOLyo4QB2l5CCMPmx0+KGLSxJgke2QMXTcwjp560aIT5BDlQRTtypximbu6KMqb0hsu0GCt/w1FgsoI5VLVOJQBVCYRpMYVIISJqDzRTuSqJllHXbdEnYjaYBaOWlsa12DQ2ADdw56tqUlj5vDg+farb/nmq29FJy9IbjnlVDSk5k1KhjaGpm0JydP7kV/94teMZB66A3/0ez8jBs9mu+PDhxvutluu77aySPxHdPDKyCLKmdpUYrR8xBgk7WdrrLZzzz5ZS3RSu4rKOvyUMQpBSVlBfi5Mgy9F7do6lHG4qkbTFTb1SFayIM5bK2z8xrDdBow2tE2NMlZaHDKMRZaorS1VbaUxPkViSkwxMwUlYr+PLkt49aLU6x7VWuYU52wAU0q8f/+er7/+iuv375kOI2mKmKZFWwdGjKDShhgTN7e37DYbur4/Luh3798TQgAFMfpjS8Z8Ol9+/RWLxYLL80su1uesl0uWyyVNJXycRglnYUaSuqVfXdQo4qm9AyCbioDBD56QIjFnvPcU6kacq4WfMvjThpyTHENbWiPGMaVEU0lrh7P26IXuvXi6IWRU0TO0znF2vqCqHf0I4wghKKyGRWVZtQtsyIK+jOmIiFaIB5i1K0jOiCmExk2ljgY+I32h4+QZxpGFbQQxrDIheHKQKsiLy3NeXF2KAdUQiUxTYhg8Olg22wPb3YHrt+/px5Fd1zEWBhytBDWJySX6E+Ml7U6aVWNYt45lY3l+saCtHW1TUTvpb7Mq4bQIt9bWYZzcM6XmjEM+NooflxZgrcMog/cDfiy1MZ+PwqoZTwiBaRKJrvmTdVVJlOWRtoE4PQK0aZq6PunIySMmxAgp42PEVQqHCP1OY2KaAnhNpsZYxy+//oqb7QO//PCWP/kHP+eLz1/z+z/5hFpLijcmXypOAtp5LH2VgbvdHmel11bLhkiKkbNVxXJRsVo1hdghs7SioH65lGtSWqOqlrauqCvHhw/v8T4SInQ+oLRiexilvl3AkCkrmRPGCnVgL21U3keGcSo+jqIfE03taBvD7bYXJ01r1gvH2dJxdXEGtrQAMROQPNoO9ZwR0Ucjo0oLm3KO2tVM08Q4TkQTSCljYyQzEVPm7uYGrQyLRjREU8zkCM1iTc4NPtaCME6J3aHDR4UPcDh0UtrKmdv7e3wIfPr5Z4xdx15rHqqaqTjFPoTiLEkLVlWJEcwJgi/z8N+EEbRW+AzX6xVtW2OdPeb5tVbEOJEKItEqRWUsry6uaF1N4xyNcTRVzbJZUrsKpZBaym7gdj8S8z3vvn3H/d1D6UuSRTsvqjlyISeUsuKdu4rkE6AZx8Dt7QPZGj5/+UrScVWDszW1a1gvItY6UoBv1N38jfjgCWHCmkxUsoEnLSwJVmmck9pWzMINWGvDol7jjEGjOcSRlDQpC2CFJOTWVmtR4LaWKmXGmMnJo3DUlaP34qEtnKTylFJcrhc4Y2ibhqQUY4h0fV9g+UlYOcqfaSwRnj71ST2ZzKrUeYqAbcrC4BJTxsfA3d0dfd+z3+/55S9/ydtvvyWkhLYWrSy6cujKgbXEklL0OaGSQoXIw3ZLRoRpfc5Q0oXJSzF1HAamccSHwGK5RCnNcjEdTy6EQLKalAQlmwqoaYoCWw+lj6vvT+myDOyniBlDaW+YicwrjBZnpLZS2KfSxTsUYJC1pvRUGYIPjMMoz8eYo2ZhzFJT8z5LQX4cMCqDg+Al4o4+EP1A8BNDFwVVuVhQuQqjLY3Sx7RjKmKrs9MhSLaEtmJ4Hy9TH4RY2U8T2rUFYFLYishF2UJaVIIPAl2fBg7dKD1n3YFx7JkmkdKJSVB0TmdMzlgrits55+OaNVpTO0dlNatFxbJxtI1l1ZY0WtmkjTE4K5qCxlpc6fMy1lG2UslqFDTwKXxXWC17gbYNbmFFcDUE6rrCGM3Zai13KxfexxgZ+kEIk1OGLOtsBlfN7UrTNBF9YBgHxmnCB0+7EHShcxWqNAE55wgzcXcq91FZcm7YbSJ/9l/+DV//8gMXF2v+5I9/zidvnvPmzTM+/fSKuqmoreAcDNUT37l2jkUjJQGjNc5azpYN5+uaxcJJucBaibymEUXGuZVE3sbSLC9kE9eKs6UgwwEmL3qDz85j2eRtEdYOTN4fkc6HfmLXDfTDxGYv2onTKEQdwzjRjQmjx3LfoKk0TW1YX2+lzaSSdPR2/5SgfuaGlvKA1K1TDCikd7eqHcY2WOePmqHjNGKCIiMAvqHvORx2WHtGVIopRVLpr/VJnEKtwVaNkIAEAcPlnJi8ZxgGUHDYbRj7gb4/lBKLnE8sTn+MoegGppI5kD7eUFiSfrBt+6FvnAvvJiucstSmpnHi2WgtfXsBz+QzBNksTbLYbLHZQSjpRCNeRs6il3YYeoZpYvSZ92+v2W33x7qPGD441rRK/lUa2jNjyY0PITH6xHZ7IGrNze0DTVtjjGEao3gHWWOxgtJ8tP10w0jlFDFFggoFMBHJWZOzobfC+eGDL+AcRVsbibByZpw8KSlyNvjJC9s+qSwOEdMVcGY6GqWmsoQc8VFYLHIqmCc9twyUwPcR2gnkdykn6Uf00qNovEGbcOIYLZ/YPjzQLlqMNtK7VwrHolcWePf2Hfv9nofNA++++Zb7u1tpoUAU2h9Tz8XgT8AorUnaEKMvKMgEZQ7gLMF7op8Y+56hlxqGyhmrFH1T0dcthszWaqamonayUcSUhBoqzhRf0iS8eXiEDs2Zzb4jZPG6Z29V6lTgdCaFWWS3sOHPyFSy9IshcjfD5Ms9FxHOjMypXSeb6jR6MYI601QaW0mdbvKecewJfmIcjDD2a8fQBYkKjJVm55yE6Luka3JOR+OuDXTDcHyyOWfu77fs+4G+H1jYhqEfuLvfoJTMeXNkzlf4wgRz6HsOXUffj2w2Bw69GMXDIL18PqSSajxR6BXoowj7ohBulXKHsih/DJMvtHslnagNxmaMDdImEqUH2BQcAFCI7xWb3eE4Z+W6NpisSF56xGJKjNEzTCPWGZq6OkalAiTx7Pb7kr0ArRyqAB2OVIwpcdgfGMeRrusYp5HgPWeXl1RVRVU37LtBarhthY8jIU7EKSBYb8vDpufuds/Xv/nAN19e0zQV/T7wk5+8YbsbAMVy0bBoa3JS3N3sn+yHQusX0VZMvtGJRWNpa0PttKTsCnAkhgmloKr0EVwojEeyrxmjRQzAIPRqUTJRldM4Z/ACVRGeZS3lF7CkZFFEJi+ZoeAz0zQSYmIKsoeJE5gwVpDYbeWoa8dy0WKtYrc/XVfO0I2CFlUaXAigMjEErJKUalXASzHBGDLBz2TdsSDzB7A7Aop2YYlJMY6RlCfppQ6jOHVaEc2E9gHNKKCbGJmmiZQiWms+3Nzix4Gh6zhMgteAzOg9MUWGELC+9I3msq8ryeR141PJvN811MfMFU9eVKdCkypsEEeP7LExUfO8z/NfJ6BBeY8q/5tJjWWD5dHfHNNzc63nt54XpUZZQt5jc2Q5pjXmmDp9DICYR4ynuMkcZZE+zvzPx/rbw+r5mj8+znzNx9cpNalyMNmS8qN3y2/mSD6X78zzP8p5zpv6k3uKeIjzqJu6IPG+/7rm+3ICiaSjOvPp5I8HOD7z00tzhH6K1uVgp/Od/56NkaQp9Glu/JaUxYnrU67fT9PxNftRBMV3zuu7Qz39H4/n3JNfU+75o+PPY5a3Ol7X/N0K5taXp2eReXQ3y28eH0f6vuZR19UpZV3ujX3cRvHxyCf+0vn7TnPlaIa+/+E/Os3j+iw3Yr7M+X/qyfvV99zLp1+Zcpb0XBntUVfvyckzH/Tjb3l8TR+d6tP3Pdk7clke6ngd8wefzP9HzyiVNhpfnsFcxz7VmfSTfSHFRN+fNlYzO1nzN37PnJ7X+qzQ8Pi+PX7fY1Wb+fqP+8R3L/qJ83TKkD3dZ75vzN/55FyytGrMo7InMpHTTP6+Z/C4/PL059nxfMxne/qmxyf0/dd3evm0v3/fmvxdQ3iX0+N//9aN/AcbwX87/u34t+Pfjn87/u34/8fxu4zgD06HGj3z7j32D773aKef1Xd9CfF2vte3mz90+vl7DpGPX/vbrikza6op9dRBLx15T9gE5shk/vk73skPHE99p+998d/syAKimMfjaPh73vzf8aQ+8sp+5/P47zoEjTYPbR7Pwaej5Bf+DZ3Hv94hHLf/Pzr494Wn6re/5YeMfPzf01FV1d/xm/6/GN/ZLuZo5F/jIQoAax7Wmo8P+vT43/slPEpcfE9sNWez4Ona/b7rezSe7rJPv/eUqfloZeTT648zb6e99W9fR6epc3r/9x3jybl+T6R+uopTZP/oE78zc/Td6atKgJuJ4YfVBX+QEVRK8R//D/9HvH79CW5xwRATwUf87l5YLoBMJKWADyPj2IOCs/PzIuUiKLu6WbJcXTAFi4+KwySw15wiaRypCrFyHvfEODGNPUlZEhqfZ1Z4uLq6xNQtuV7Te/ndsjZM/Y6x2/Htt19R1w0XF1f040hWGrdYYd2SYRz5P/4f/rdMoxAz/8l/77/P2eUVi8WKVz/6E9aXr6kWV1BSjvrRQ9DlAc6pMaWeTtoiasOsVHB67eQ/qPJ9Wj4i33lMH8j71Uf/Pj7o8j0KjvRnSglzzsPtNf+r/+R/wtB3WGP4X/wn/zNevXou7Q7l8ykVSqEkzfpz7SzlLG0o6O/1cXT5RdKQoidMnu3bt2QtMPerZy+xrhI9wZI+krk8f9lvMVq/y+EoAI67u3v+0//1/45xnDBW8z/4H/8T1heLJ98hMG450ZAGYvKEPADpyaHneztvkN9dcHwXMv7oPfM1zamZUxoqS3oy58KeBDkrAbU8/v2xRzTT7Tz/7P/0VsiuFVy8cNiCxMzE0h4QOTLHJkixwOh9hqyOrQQoCOHU62hK24bW+qirljUC0tEKY8UdVMRHUjXSCqK1QmlRL3eVwRiZa2ZWFwCss6WGK+hAP2X2u8gwBKYxsb2T+7VcLvmn//Sf8vr16+95vPnJHDhNhadpwjkVrlCCVi4vh3EiTh6/PRB8IgbYKcWYE0MKXJ61tJVj2c7zXJCwM9PM7Mc9JlrWeiaWOKVv557av/mbv+E/+o/+I0IItE3Nf/o//59S1xWbwwE/FYKGUWShcsr4MEpbjJH6dUqZEBJtQZU3tcM6Qdkqo+i6juub68LBW/P65YvSkpU5jJ5xFAJ0kuyXcfQFiKSOyOqsBDWvlAhz+0loCIVTORJSEmYtH5n6kRAi1/cb/un/7T87AqZ+8qNPZQ4WLIDS4GonYtoqY40T0omYGUfpca6q6lh79j5yUkBTxzWWsmAYqtqwXDW0bUXbziQZNU0lShnffP2Ovh8JPqCt5mzdcHG55B/8o59xeXnOs2fPmQZNCIoueGKOxJywtpLvaRYsFku+/s03/G/+l//776TWv2/8MCMIPLs459XzZ7jlJYfRM02B7IQuzCgNOpGiZ/SC5sk5sVqfFaLphKkq6mbJanXFFCtCtpzlhjCNRD+hpoHGWRZ1Reg3BD/QdcJbKUZQl74WeH75DNMsyPU5fRD/52xZ0+8f6Pc12+2Wpmk4v3hGNU2gDfXqjLo9ZxjH42QHWKzWrM7OWS5WPHv5kosXn9KsP2GW1DkZm7kXihMEVxcDcTRwpRBdDOFsKI8G8JFxm42gmY1g2ZjnWqd+bETn51C+Z+43mw2iUYrFcn0iw1WK169e8Omnnxzpl2RT9Xgv0OqqqnDOSoN6LC0USZ0QlY+Oq+cNwYAfB3zf43YbfApElblcLWiXKxbrNfFoBMv3nEL3v32ePSqGZmRTquvqibFcXTacP2uZ6fiUKtRROqNMxidFzJaQFRT5myfX8tgIfnwCc235o989Lmk8qcE8MoLycy76gmJgU9kQZ5LmWXsw5VTajDieX9VYrNPiWJU7KJRmAojIURFLz5vXkZwLYfRxTprjBm+MOTphGcPRvylG0FolzEvHpt+MNvloBFMWJS7nMs6awvVqn9SbVE7MWZeUT8b+8e3TWvPzn/+czz///Dub0fx+ifKLLqh+igJNKdHvDmLYtcEthKIshci07wn9RGh2DF1gHAPXrWNSmUklrtYNTWVZNk7aQKxh2TbS7qEFSJGytFPMa66SRl+Ze0/q2CcaM7kuxZuXVywXDduuJUyJGBLTKAQYKSZ88AJGsYaUIzEmxsGzalpqJ+0nphIjmBR0XUddCVq3qStePX8mjDgKei/X13UjoQBn8hREj1NrtBUy9wRHGkarNOM4lQZzKxRwKTEWAIqfAsMwyvVzyrBVhQ4vpYAx8n2LVcuUApGE0VaQmaFw8WpFVVeAGHqUNP2novZwetYKpTKuKj23lRh/qb+CdaKCUjc1OUOw9miAXaU5P19y9eyMFy8uebifGIYIGKbC2KQA46BqDFWjcbX+oQHt36FPMEUInjgeGHcHxnGiVRljnTRvWytM8TlgjMEH2B+Go7xK7UDFzGHwRCzKGpbnl+wf7vDjSGOhcpnaJfKY8FkafhOarISFPARBEPpxIQsvQp4pd7JFJU8KnjANTAqGsSNKZoRMpKoNWj9lEtCASonoO1TYo+OBSidiLh64OkVsxV7J/SiG6un2/hg09LQxW330+ceqM49B5Xr+99wecpyhp+/6zrNR342q5sL/afFmQVSNPUN/wBjLcrmkbRvizM8aYgEGFGM+HzvNqQhF7Eam3Y79+/ds91v23YE8BC5fvqJZLogUarJCAFCWwW8LBj+6jtOimf+O6WnOcPIjozdHgz/T5qlc0I14EvEUgh+3bbmJCX7r2pi96e9PcT3+plKoV48M4KN3H50ANU++8nfxmHL87vHrukYb6bOUpn9pS9KIQYtTxjm5o5OTTTWGVISHRUpn7psCAT7FGI6N/yhpAVdZleyNtIsonVAqoa149SgYhkAMmRQiqjZgNNrUEu2EyG63k141H0U/MoKfIAThMf3dIz86v8Rmsy1RyKk30GgtzFCT5/6bG6wVZ235QjbKYdfT3+yIuxFuOh7u9jxsO379ZkVqDK617HYd1igqI+Ccpql49fyCxhkaqwkZfEzsB1/I3A1X60Wh3ItUlcMaTeXcKZp/dAkxjmgMS4ts/FYzOcM4enyIxCj6qW3jwAg3727Xs24XIkrcOJTRYBRjzFSLhotnz7AIWG8aJ7SRnsJKa7CKYDP9YSSGRFX6jLUxUOjkNPJcZfVmUImkRKhcK4PBgh8xSlG1C1Gl+Yi5KBYNxhhTyQ442sUK5Sd8lBaNnGJh/pH+xVh6Y49Au5IJmctSFKQ9JEJQRc4r4f0gWF1tWC5XKKVJEbQWzdqUA0Pvucueu/u9qN7rJb/4m2/YbA7YtqEbD3RDR9/31HXF+fkZ1llu3z/8IAMIfxcjWDZHY4S1JJUGWq0zot4ukdo4TEIyHAKJgK2s9BcZI9D/FIEA0ZPGA2nYk8Y9YzjApGHSotPlBw79AWNKn1JdQxRdsxxGaRdIifPzK2zVoHJAU5qiyaQUGIYOUzfYyrA8W9I0ljGHRwZDHpDOGpMU3cM9Vles1q+wtiXbhlQESTQzA4RQRM3ujprpevTJtZf3nXTr5pSVnuuPShWeC5685xgJzr+HEpnNp6se/ZhPxlUpPrKBZZyMCQg7SPSZofO0ywpnGy4vnhNCwAfP7rArvT8ea93pFinxSJUWQd6cIqumIk2WMMDdu7fknLl8/Qq9WIoCQ5zFazmFkj90rh09bv0kahdX5kAgyWZeDHNKRUsxQRIJcrJKqCer4BT9zcja79QT1ON/5dPv5qhPFa/2GK2V9ykk3Qlz7lg+E9WRcF6ew4kI4OMyfd+NwrIUAnVjsVZjjDs6IcaIXFNT5KpCiPTdQBYZb2rjjnNoEgJTiRjKJEnlGhSZNGWiteSocaWfMoSxEDlkaSwvz6FXE1oFof4r6S0fDH7KTFNiBhbmLJu3Bvz3ikWV55ol1XZ/v+d+s+Pbd7diBI1mWVfUSrGOhfQ8RobrDVVVEZuGtA8QM3434t/uCNsB/9Ax9iPBT1QEfGPwC8uhtWSjMRbaeqKqHDcPHc4qnBUkaEbhY6Z24sh/uNsKUUJMXJ0tOFs2vLg6+w4SNOfMfrfH5kTyEY0oW6yXLbUz+JA47HuMSuQUcLYSrtY4EpMVgpdsiTGSopBCEAMWEfdWZKYw4TtxOkQLUeZdzvHoXOkS2U/eC82j99gs8kzLRUMoXMdD8OQkPbkpR5wT6TpXVx/VbBV105BzYgpe9iJraNuWiBAHiH6k9A3mZDh56LKHVZXohwafyiRXsjcmmAtn3gsKOiXJFEUSKk9CvjEzhJW9ec6IdIfAg+mJ4Y737+/Y7TpMXRPyREgiLLBctLx8ccWhO6D0R6Qov2P88D5B1JGybE67SBrmUcoPhPE8iicRUkTpjDbCH2dUgabkSI4TaezIfiCHgTD1qJhRQRas98IkIYsnYZC8tFZJUjEpkBOFyUImDSmeoq3C7m+00JA1bY1zhjA9vaqZPSRHGA5brK0I4x6rDdpWPOL+OKaelBKC6hwDVkutwihz+s6cjoZtvk/MNcX593C8f8ffqeN0+qg+WObEo3DqFJE+MrTfNxTHepXQaUWmKbJauyJj0mJtwASLD1OpG8ZyHU9jKcntJ2JOopaRhHli6nqarsNPI65uMNrI5qzmCDCdSmvfd5pzurU8DzEs0sydwlNJlKz8Kbopf8TLfBRFlt660/HnppJHt4SPl4h6cm6nFGiJ6DgZwOMxju95aujy0VhyalsoX1ViyI8iTPA+Clk8stFYK9FujjO1VWmbKJpxSsFkhTFFpQJtL1GpJh8p4qQFRqjldBbdPJAsiqKQnOtMiIngk3joIR/noDitQtumCtG4nK8i+HnDghmHVeK87zziY5RPZvKRh+2BDzcPvP1wT0akvZa1pcmKsYvCwpQyed+TqwrVBOij+M/7ifB2L38PEyl7yJG6G1DRMCbLGC3BapLVDD7jrGdzUBgN1grlnS7zqKkrSftC4fYVZhyjNc9/S03JF2WINEVMyQq5WeE8l0isqHSIMRI9VeedpKSrhgQiNuu91GaTpPRzBp8iow/4KZACwuRiOVIBKi08aEop6c8rRjAnhcryPOdUvC80ZykqlM4oI3szzPma0zCFUEMuolBdOouZDLoovGtt0DqjdS411RMBg7U1Qz9Kb+RMZaZ0oXYr5YNEoUXUJbLMTCqidS7KL+aYlXBOSFqMaQArdecpMI6eNAWSiqAS1brBGotzVkhbHoGY/rbxw/UEs2KKCjUFQhRfeuZs0waMVWg/b0SKFDNDtyOMGldbXj2/wjlZtH3fE/yB7uGeHCdc9PTDhpAjQw4IJV/Jp5ecb221MLjUoh+WMoThwOHhBm0rpqSkgdtPGCgAAE3d1CyWLecX56is8GP35LqmcUffKZLOeD9w2D/QnJ1z/uILzp5btF4WaIKCY1SSOOwfOOwfWDQVy9Watn0hxLQh0HU72ralMg5rhI1k3sSOUQ6P6npqfp1jWuNxhDeDOfIxtjkBduZ6ysdG0BiRYREPNOLHibdv34tSxRj4vd9/ydn5ObPMrrGW588v2e0cXddxOPTHXsIS06GTxqfMIcCfvT2w23oOB8dnb9asbc04HABNtBU5ajKRTCSkeLR/p362zOMIVkpqia4TqStb6Kvu7jfFMJb3qUAqKcJ0vO5Eni0DPDFSjxPWcyz4XQQd5f3fxdHN6dEZ8CM1o/Txpx8ZtjnqOganx5RpzvlIC/ek9phhHD2LZc3FxYqzsxXGGMZpoNt3jJ2AGAY70g8DJ8q7gMkagyYlfUwfxxjIWdJZYQr4mElZY3LCSFhLVJFxiGSjhaIu+HJ9CoVl9ghO4B6P1oWgQBliVKQkKuVCcpwwRmpn7H67ltvkA1+/u+Vf/eIrvvz6A/fbThRZqgrrwPWe9Zf3XCTNQhvaZ0uicaAd/W0PU0J5Rd55cZQ+X1EvK3QDrusF+DEk1OTZWc31qmI/+PJ45zyAkA8YLc3odS28nmeLhtoJS86h72kqQ4hRkPGPh4KmbjG2Zhp6YhLSCx/SkVKs7zth18k1Dw8f2HU9397ecr4cWC6WvHrZYLQDpbm+7VAqs2gsjUsYncUh1Qaqmm4QcoZpmmirBc5V2HYJRsQKQgjkmHDKlP5gOHQjyurCrKXIKpUygWL0gX44oKbAsD88ubQQfYk2izKLlrSqtgoTNcM4FBYj2Q4F8ONZrc5YLFouL59z/eGG6+tbZr1OMCXqzagoQYlWVp5HlPUUghhVyLSNpaoqTGV4/uKK129e8vf+8Odordhut1xtJzKW33z1FZmENrBeLph6z/tvr3n77ls+vLvjt/gv3xl/Byml4hXoTIqjsLFbkclAiZ7fFD3KGmxVg9KMY0eMI2kYCX6c4ympSShRSVBZEo61M5wvhFX9ftvRT4Hd4AHhJxQx1FMTZi7sEmocUD4wRokYVY5UVqOspbYiB2K1wygr4rST6IfNQ1j+J8YQUM5gQsVht6FZi4SRbcrmnVJB/mVCGNhtb9ncfyCulxijODt/RgwTY3/g9v2XXFxcslytqM9fckS0qNP5nyLBXF7KzFXGx3VHwT4cQ8Q54XqKg0oE+SS1nzPvvvmaNPX4GDg/Py+1IlH0ripJcVRFhkkxs+YLpD2nxG6z5e72jg/v35OCl2MqS9cnui7w4SGwPyS6PqKuN+ynntHvcbZBa1u0+EIBBTwyKo/AJCcH4NT0O5ZNfqbg2h36gmAsn0+anMWD1yVtkudol/zIqIqRPdVyijWbf/co6zlH3xzN2yleK08Mk2daJiFEzifc5emLoCS5j4/h+E3Fyh+j8qdri8KlWeFcJfW+mDgcesZhwvtQQDWCxjySb6tUjFRiDGKAUiybYp4js6Nppq4MrTNUxjLFxGGMTFEkw8j2CAI5nepjT59iRIRN02h1RKCqkjWIUY7/vaN4Qd5H3l/fc3O342HbCcLQJhIZPSboPOowMvVBRGKXFUklQvaw78hTRo2gKotqK9Rlja0UymYiAZ0SNsClz9ic2fuMN4o44yQyR35Rr4R1ZPRa1kRV0dSKurKlxvz9l6IQ/tRFXaOSIsUgihiTlwgMVQBdBo2iqRqy0lylzHp9SbtYsbh8jrEVWllCtWToe3aHDVmBU4W8G8meLdqaVDtSakTA2lqqyhIRseBQW6KBHCOLhZQx+mEiIUxRlc3UzoCqyFlLpi54qBL7vn96bVpI/KvaUNUW42DyvThWhCNyNOXCQkU+oUkLLzJIKUPAMTMVvciLPcm2zB6m0GMxU+ehMtYZvvjRFzx7ccmzl89oF4IIX2V49uIFSls+3Nyy3+/oDh3v1TVVJdzKm+2G7cNT4/67xt/BCJ5SdilM+KnHI+wCShumIDRgFJkM0BjtCKEnBuFaVFjIsxBnIqSpqERIeuLybMnPPn3JV+aOzWFgTHspDitNToVEe974kqQR0BNKR4Yp4JzG6JLqKFI+1jiMdhgsvnDUPc5FpRyI0RPCiGsqYhzpux1+HEghluBPHk4udF3T2HHY3rK5e4fJl7TtohjHkaHfcff+KwwTRgUuz5+D0k+6BWaeegHczBNkXl6KE/LxY2Tpd4fAv0/pJpDJ9Pbrrxl2G8bg+dGPf8xqvUYhXIrWGuqmEY5FpQoRthy2qpykzGJke3fHr/76rxn6g0D5qRhDyxQddwfohkQ/RELcs9kl9g9viyKGTOqUIykHUtDHa38c1c3XRolOMhAmiUjm6LYfwxP1i1ORXRbR8dtUPm1ys0HKpx4pQKLFU46X44rMp5QzZJIqKe3yVp0VlLpjEo67Y4Q3H+14JvlkPo+n8iTqy0Xd49F9gAIVd1hrCwAlcNj3RC/0cSrLecWUqErrglLiReesmEI+liFilHkjahFl/mRoKsv5smbRNHRDYAodYxBIu1a21KNK1CB5qjlARnBIcs7isc8yNiX6TqLAF3+bESzPY/KeDzcP3G32bPd9cQgTWUXaMaI7j548cd8zxUwez0kYYgCGkTwlGDJmvUafGTgT3lhNwmuL8oGcI+s+orKirS1Za8kazOeB1OFiykTAG4UPhmktaXtrDYTwOLz/zqiNpXEVCiMAvhQZp6k8VkVd1UeUcF3VQtBuHO35FXW7wq3PMabCaMeqWhA3Dxw2W0nJakVVsiRaKSHUL4mOtrbYwkfrYyZERagsSYukUrtwBfUqGpfkiLOK2lmcq4lhlqOSrFx7aE7rQUk61hiFNhZXGaxV+DCSEF1CawWlnHOgSL0WNGmSWmDwpJKFkLlQVoNKZT/T8zI4pUtOU0vWrBaO0i9+9BkXVxecXZ5R1Y2sN2W4uBRVieViyWG3p9v3TMMo4uZG4b1n6P81p0MzMI2T9KDpDGnCmUxVyWbkg2fbdUwxMvkgKKQ4C486ai033uhcWM2F0qYfOyYPPmR2456QJSe823YMIZS8oBSDxzEgzU5Wwvl+4O5hw9XzCqe1MKpPnpzFKzOlfuG0w2LIUyT2E6GfnqS7xqG0TORAHSMNsGxXNNWCyjYYrQkx0A37o9fX7W/Z3n3L5sNvcGlg0S7xfuLh4Z7N7Vuuv/4rYn/HuH3OyxefYusF2pg5iXnkgYQskkrlXE79hY/69Yq80tEYlgeiOTklpui5zSPlxF/86X/DctEIbjjA1fMXaNtwdnHB2fkl0+gxpkfrLOz0uvBAag1OcXl+Tvf8BbvXn/Ev/vxfsN12DJMU9TOKqB05RFTumboeRo/x/ohMy1oSfwpQ0T26xjllCU/cwjLTjsjQUoT1U/j4LYK/nssM5b4di/Dzm0qt7Bgynw56dD5n1kyTTvc2mSRRnsq4aFGTgt6gg0UhAIjkJqL14n0XS6fKjncyfPOfPD+U06I/ZWuPlwTgfWC72TH5iRil5mSUxlqR7DLW4JzFVdJfO059kdORjUtpjTKgcy7zwhShZwVT4PWLcz59ecnl2Tl3m47AB8bbHXEKkBM5JjmxUu9BZ6xJaJOpGjF6EklwJHX2oVxjqZX+LsrDw6Hn/m7DN1++ZXO/ZRxHmlWDU9DGxMVuoI2Z9fMl0WnwkS6NZOtQTYW1S/IYSZsRvbKkBjaHG8Lcw3bWMkwHtvdv2X+4xkeozz4jvHhJPD/Dq6J/l+LRmXHGohFdzA+3D0zTyDAMvF4vyzP77nVkoPcaO2T6YSTFsaSfZR5KTkcLx+s0cehHklaopmLqO7qh51/8q79gs+3Y7XvOzs5ZtC1n6zW7MdCROK8SrdW44ugI72ymmzrIQtA/y7XpHKX309WF9xiMc5isUUkAfDE5iJrr2/uieZgwlWU/nEASWmkunq2pagGwpOwL2MVRuQZtDNMU2W62eN9jrcJax3K5YhykP/z6+hrv46lMMWdodBYppsJHohQFNzAbUnG0nDP89Cdf8NkXn/HT3/sRtqrQzmCtw3vP6A/EFDDO8LOf/QRypj/09CUVrpQmZUucfmAulL9DJDhNI9M4ghVDkI8pqgJNTwLZDiFJSF1WelVVhTHeFuFTiEnQiFPw7PtAN0Z2cULvBky1R4VCopwiuhxGvFuLUolhGOiGnn4oCgOqEGD7iRi9iH2WjUAWd2Ds9oRxIH8UCcYgckNaizebM1hbiQSMFu84J2naVUlqJ/1hix8OxKlj7LeM3Y6+29N3e7puT3/YcXCycY1DhzYVrmqOQcgM66BEeqe2iqd1w2Od6pERhJIC5eQpzlyHT57XOFJZTbWomHxgGCd0VLhhxLqOd+/esl6vePnq+bG3jKSKNxfEeNqabFqmVDPEwJh0AQ4EkspMQaDOTpVK1yyhlCWxqEoyUHrKTimPMm04roDy78fOydGafDSXH7dPfJzSPH7l/PlTEMiTL5oRHOr0+wxkJUlO0a/U5KmCXuO3GiYBHNgKzMLgWqR1Z44Kcy+N8DMypqQIM3NEWg4936CPRgiCvwtBkXMkkx89W1X0CMup57myON9CeX7JZFTSArRA0lDWOqy2NG3L2dmK1WpJXdfUdaCuHXVlSgN/OEaP1hkoa1iZLALBtZY6DhrvhThZa0lxPQ7UP1K+Kqco6MZhmDh0A92hI4UowsI5o2PChUgzBhrAOCWtGVaTjSIZJU6Rc0XdpCJUiSns+fDuA2bR4JYN5/WCPCVG1fPQfYMfPNYHYmugrki1PrYNzfdN8BcZEvjJ0/Uaq2BqpCVk3hOeTkIYhgmrjfRL51RSfTOETgkJfZRVILp8SlQ9jMYAJkemoWe72Qih+3IpEZAx1PYk4myNkZpuKu0LqWgPRmlviFqcXkHvzy0OSlonkPMXoJsmZUU/DKSYMFpjlRM5qtODwjotcm1K2h+0VtR1TVXVGG2xzhNCzTjVJasn5RU/zZzMCWsVWhX069wXW5xaUaiQzWyeN7qQCtS14/LqnFevX/Lq1UvqugIjosW73Z7gJdAJMaAVPHv2jPvbex7uNvTdWNrBSqEp/xtQkej7A123h8YRpkmKp3lG/UlKM6VMjBlXFodWmeViwfpMUIhgIcHoJ8ZpZPCe2+2Oh/3IoXJsI1wPkVerhkqDLjVDyc/LjTLKsN3vOHQdh1kAVmt8CIzjRPAjdVNhU8RqDckTpo7NXRCk/nQiwc0IJD2GiKkE0ZhjxrlKpGOsMNinlBj6QVBX08D24Zap28J0YNgp9tsbNvfXbDcP7Ldb+sMOkwMqBQ7bB6xraZZnx83QkE5GTc2R36leeELblujmka2Qn+d0qipsHpRG40fzWYlXtVyvSErRe0/2iZCh60Tq5OWrF7SLGqUUzlUQFcMwMPmBoCyTauhZ0udzRgxeJ0JSxOQZwyiQ75hYVBmrM84qlJDnkzCgJLLQgnQWI1cMwzH9+ygyfBwXznjO74BeM0fv8vS7eQOCo73V5XiPDOFT8JA6nktS6XiukYzKFh0dHJaEnWG4htBPqBRom8jqmaFRFU5XJKOIOjPkRM4TOYdjTe14buUfT2zyR9c0p3NQUDcWUyR6NBwVFHJG1DWksHVK92qNLVD3RGKMIh1EyjRa0zY1r66e8ezyjLP1Smqu1rBoa5aLCq0Tk58KwEWxWDqUTmgdj/dSWY21FVoZxjHhp4SfRM8wxSygBy3Oarf/SAOyXPXu0LPZ7DkcDpA1tbPEFDFDoNlPLMdIrYEq4mqH0hZfW7IyZK1RRSHeNg23119z9/Cev/jlf87F52+4aj7lcvUGlRRTNXB9+AXDw5bV/Qd066BqieZM+otTFI3MstYoCh/JJ7p9Ik6eF6uF6FR+1KcqjzSL+kLpdZSm8uKoaEGdGusIEdCBurXFyVKsKtFB3K3XbB72hOC5u7uj6ztiilxdXWBsQ1U3VNZSac3UjUUCDYy2qCzPK85rSwm2QOtExkgJyRhyFlHlMHl0zCgb2fc9isyiEc3K+iNaO+uEKUgcLyEZaNsWZx1aa5po0TqirTSri6A6DIMgUasKVHGWYo4i+xXKpnA0gsXwlnsrLESG1XrJT3/2E376sx/z+pPX5NLj6EPk3du3xJionMP7SQgL3nxCv+8ZupH3724KDsAUR+SHcxL+8JpgjOQQhLkiTOQU8NPA5BODjxyGWUFZpHtUjhinqZtKambJkqISHbaxZ5o8YIgJfIhsY2TCkCrDmRI2gYVVpLEnxYCxFUplYo5sdlsm73FVRdXUuLpCW1OawUeaRmh8pqmnblt0jvh+j9WWlE7AGIU0KTd1BUpogNTuwO3NLbhLolnTXqzpuz27zT1aQfQjQ7eFNFLZDHFk6jZs774ljJ7oe/wwMOSA1TB2G/L5ObVOouNlFG1Bbaac6f0MQNcllSGRtTXu2LROMZozSEYpRBiVkyH8WElZKal7dN2B3W6DjwGlLdfvvqXve6xzQOTwoy8gcox8d4cd/dijrWN/mOgGz6Ef6cYJtManSMwKXTXkINGQsb3UEcr5zCkhOeE8hy9PA7uysctzUEejOANn5No0Tj2NcI/N6Y8tyjHClE0tz3U3Tq9/PE6BtfQUZpXKZq3RwWKnirh1xK0mbBVTN0m7gvG4CLU3XH52iWlq9LLh292v6Pwen/bfPdgPGDllVNE7rK1DaWkTioXqSuSE9FF2Z45kcpQU3NhPJJ2kT1KD1ZpaGS5WSy7PrvgHf/8fyS2LkX7scAvNq1dwdbnER0837jkMA8M0YSoDWRhA0qzv6BMxToAixeJ36Iy1mawhlwhSMjbjk+hplkj66pu3fPXNe3yIuNpgrCEOI/U4Ue977GqJayuqqyXZS7uMJuOywqSMbiwTntvdNb/41X/Fw8M1tJrLN6/47Od/wOXrVyxWLSpN7L79V2yUYtze4+9/ITWs5t+BqkJZy8zME5LoE87p6RASBM831w90U2Ai87MvXsv8fzQLU/BoBa5uSKWLbvLhqCyvtES/2mkRlk1iXLttwhjDZdXwycUlMSjePTyQEdaYqrLoHBhXDh0F9eyDlwg7CRgsJyWlGS3RnfiA8lpIGQGkeXLMaGOonKOqK1zt+OmPPpfIcxzKs3zksORMPw4kJelMVzkqNK02JJXRJvH8xZpneUFMV6SoGAfP/e2WxVLW4IsXz1DKkZPm1798y/4wst8N4iShRIPyWEKQLINzhk/fvOH1J6/4x//+v8fVsysWi5akIpvtln63Zb/t8JNHZ4WrpG0ixciLF88x1vHr33zFdrtnGn1xRn742vvhRpDiMcV43HwVAsceholxBJTBVJoYPJpIW0lzvTGWcUrEIPx5IYQieiiFnRQjPmdMFfEZfFZEFOqRlIxW0m2WUmTyEyGKUVGz/IkxpSdGGvXVNKH6nvU6Cp/ezFH6Ub5GPbq8GCKT9+wPe5ruQNN3qNrQ9wcO+y115cgpkKMXs1XQcilOTMNeWDZKkTyGRPQj0feo5KlMpqkNldEsaklBhZiIJELShKwYxpGUAjkFlstzVOHWkntdWgoe1QJPP3+3RWKuOQY/MnSHotFl2W62HPZ7Lp49k9qCOQEi5lRCzkiKWEFTV1gDJ55JqT0Za483zhT6I3U0xiL/etxdnpyaGKv5ffN/j1+dn4vUdr/Hgn30vhkJOqcg52znk+f70TM/ZUsLuGR2jJJBRQPeEId8/BPGSAqBlCemSsAIZjS4psLpFquqojD+feNpDPh9PZ0nlG95rXCF5qMUWCZruT6dHtVWSo0xJ4HAJxWxVgSdG21Z1A2LpmXRrOj6nv0wYoyjbhS1MygCMQe6acX7uzsetlvhiUwnwzDX+maazZzUEfQhzpkqKdnHaNzTEHHkgYeHLZvtjjmOVwpsjDgfqX3EWIOpa6rlktCPRB/QORbwnCLqyDAduL3/hpv7bzl0Wy5ff87q8pLzZ89ol0sMGX9xxWp9zrR/YDxsyOMDcdfC1IPRKFtzrFfl0z2kZLNCgs2+J+aMrhTPLs/onxhBKZ3PfzKqcGckFAUdmQIxheLsUiLQef/MOFs4RF1dUp5ShhjHkd4oun5EWWn4DyEWYyeIy5NDqUhZkQq7izJFICDJPmg0xxT6PLfmyG8YkfTio366jJRBY2lb0IUnNKOLRibUjcO6GmMgRsXQCc9nu3AYrfnkzUu0cqSoOewnqk2H4oAfBdl8AsaIjmJdOxarls+/+Iw3bz7hzaef0rQN1hlC9OwOByFPmbw4elNgsazQCvquo6prnr94wdn5GZOXbOCMqv+h44erSCipZcUwUVcWZyuapqYfPdM4sNsG0IaqaYl+jzVwef5Mmi9j4uFBEIZKaaboy4SxqJRRIUiDY1WTYmKMkcoYktIobUvtQZCcY8zSaKmkty1lSWG5uibmLGKlKRTRT8f5+bnQFC1q+iEw+unopWZg7HvZcI0RrTrtubn9QLV6xuLiBeN9x3Zzz9tvfs3r159QO4M1iWgQZXQt9D5h3DCNCT/sS6+2cKmGYUOlBq5WlouzBW1tWDdamDlCQt9NdFOmmyLf3rylHzqmsefnv/fHNJXk3WXDS8d6l0KMw2w+vq8mKPyREIaeh+u3YriAcfLEmPjkj36fLz7/lB//+MdoJU3TWE2zb+gPHQ9397y6WHPRVrz7zZ/j8gPXt1sae4a2NbatCUETfKZSFqulOcCUZ6XRx+ZwdaxfnSI/APUIzPKExae8rhXE9HeYzb9jfK/hKTtfRkRLVTZo30JniTtDf9/j94mpi8RJEM6b/RbDEpMVbb1hMYF2DToJLdVj8uVjuvt7kqAfn49zgvjMOTEOI3MvIMwfV6RYBJfLeWsTsUaetTaSDlMmcb5e0piKha64WK5pXc3bbz/w7fv3XN/d8k/+yb/Li+fPeHW5wBqxdpPR/Om/+HP6X/6K67s7chRHNhXx6IgipVDASKrA3wVMIdyi5rjJfzz6fuDdu/e8ffuem5t7MKo0bydWu5HlELhI0qLglg3V5Rk+3eJjkP5Mp0nWEuMDNzdf8ud/+v/gYXuHWy75d/7hv8ebn/8Bzz95Q1NVeKvRKXDx8hPC1NNtbxjGBxgnzOZb4AVUz0mlZmTQUiZJWSL9lJhiYtsfuNnAN3c3nK1buhKtzc9usaioK4X3Q+lRBZQwVZEiEwofJ8YwkYOk6Ky2OGfQRsSthyFw2E3CPZrlOex3B6Z+xEyJy2XDuqlxtrSjKGGjmW12VNJaMnQ9jXPUTU1dOQKRMUwYVzAYeaKfIn2YmHJinCb2+wMqRLYPD0/KBVXVoo3QtlEbyA6SxYdBonxtaZqaxaIm+ExbByrXcrZesVwsePPpaxSWFDVXF2+4udny7de33N0+MAwigjwfsG0bXr58zhc//ox/8o//MVdXV6zXa1wl7Eeb3QMP2x0hIkK5+46H23suLpc0h4r9/sBPf/ZzPvv0M37y05+iteXhfivdB/q3OaTfHT+cNk3NnmfCFFXpjFjzpnG4LojieX8gxQlVWxbNAmusTK7oC7R2jhrk0NpmtMvksSdPjjx2jKPGZsOoweXCHqErVEzoHHDGkJXA/UVVXKjcBMXZ0XWJtmk5OzsXBGOI1FXNwFxPObq4hYIqYGZUhQ5YoyQ37rREnX4khZHhsCNaje/7ItMh4pzTNLB7uGGaFNMgitZal2JwGrEEWpdZVMJlSArstzu6IRBDg0LY4HcP9+x2G/bdji+++Bkrdfak1ndMhz6qE86Gx3xETXaMolQpyCXxqGulUbXj8uKcs/UKWzvx6uWGSDHeWfq+Y7fd8XD/QNs2vPnkNZ9++hmXV5+yWJ6TTUvX7dnvNxyuNbHfErp7bEG7JvSpbncsAn5kiObFV/73FMRe/v39geDRc/+tr3/PeGp48vE/AJ0MOjnM2JCGmjBUqNRhjcctM2q5IKWGdtWyXlUsVhWNq2XzsQ4dJCw78iV+dC1/6yjsInNf3umzhSShKMGno1MkpNeuNhinUToKmMJZLlYram1xwXC2XNPUq6K+PpFCoNKKtnKsFy1NU6OdI1Qtrz/p2fWKfZ8Y+wPTcJAMShakdzw2zs8YgJKdUYmkpSkkFkX4ecQYmcaJ3WZPOgyYfqLJiqCkZrkYAu2YqaIm33bCSdrApCJxZdFNJSlM5xjuH5hUD2pkfXHG4vIZz16+ZrFaC/paHf0FFqszlmcXuLrFTyMpHKj7D6SmIoZzaUQvt3gGNMUUj/yoaCVMU1VNDIlxfAqoqyuHs7q0fhVHriBz5x5DnRUWiyrYAqOFhzWQuTtMbIeJPkxCxJ8L+w+GnDX7zmMw5KR5cbHGGmn/yqYYb2vwSZhgJgRJvI8jo3fElOmnAWMq0IrtYcf9tmNz6GnPVjhjqKylbmvqtnk6DQMCDIuKqQ/EqccPnpQ91sH52ZJx1TKuIiobSddPoFVFXS25PH9J06xwtmG9/JT9ruf2pzuur2847DseNvdIDKO4vDznxavnfPHFZ3zyySfUVUVGwJRKK2wnwVZV1WWf9qQcub+/Q2uomwVXly+4OH9GZSoqW5VnMZeRftj4OxjBU4O20VKbAOmpaWqHNT0+RKZpJOeINRrnKmHenPtKknANzhIwSiHFeAekiRxG0jQyTTVOJ0JUOEUpploUQXLCVkvUWTmagj61nSLGQD/0RX5FcbY+h9JjZZUuheynnmo8/q5EH8YJWrL8SZMnhYkUR8Z+T9SKNI7Cy5gEVBH8KAXiaIjBFyb3gk5NExqP0wlnCklA8Gy3O/bdSKyfEZH2hMNuw3Zzx2b3gJ8G2RDVnO7LRTWgRH/FCBoF1oD76EmqUjRXFBaPXKDzhZ3j4uyM5WqBtrqQ4T4q2OVM13Vst1vu7+85W6+4urrg4vKKN29+wvrsipAMm80dd3fX/CbecHjw7PoHjD5FqyX1/zQ/ycngfpyzOGo459O7nvQSfM/4rWnPR3Rnx1effO/83WK0ddLoYDCjIw01eWzQ2WNMxrYRZypQmmZtWNaKptY0dUPlaipn0V6jU+lpfXycj841PzFwjy/+9PvvKquXRv1y3nP0b63B1RpbaRIR5zSVs6zamgqHQbFeLKmbFdNuwGgtcmXO0ThHW9csVmfYqiW15zx7vuN+H/jm7a1wzMYBlQMzGbgQJss5paNkVIn1k5f18tFlpZTwk6ffddBP2CnitGEqNcd2jNReY6Mi7UcSkWmn4bKBRYVeNai6BucIW09gAhVpV2esLy9ZX1xSNQ2UCDwVo13VDXW7wLoaTQ9xxI0PxOmCFD1KScsSORGzJMRjToIyzxFrKmlKdxUxZoKPx2emlAA5rNGziwLIvlhITwhRSECMMmgtmRatNVlL3W47BKk5RmFxkWBSoj2VNeMU6UxAKcNzLNporD0BkJyzqCByWEbJHjaGJMouOTF6T9toVFZ0w8D13R1vbx54kV6yXi5pzqtiBOtTtj5DCnI9OSn86PF54pASqIh1moe7PWFK+CnjbF3mZkbhcK7lbP2M1fKctl1zcSY1w/2bgevra/b7Pbe3d5AlI/j8+RXPXjzjzZvXR6Lyru8kI6cUSkkpzdmqSPJFlIbD/kCMnqoe2Ww27La7giNQhZ/5aYnlbxs/2Ag65yRMDblI8FRlJ5aFX7stwzCy3z4QssL7ht1+jzMarWDyk+R1p0BlHc46Fssly7MVgza4rScly+EwoquR1rU8uzjnrHI4FLe3HVZJL4laGOp2weWzF/zxP/g5VdXwz/6L/wo/Hni4u4EMbVVROUNTW2qrSX5kOOzpd5snm4xSQmXtgzTPZjUxTR1+OhD9XqBPYYTxwL7foYG2MvRdxzQcsAaW63Mur86omyXTMPJNvy2RmCb7ET8c6PcbpM9RMQXPX/3mHZtdx/q5IqbM5Ceu33/DbvtA1+/oug19fwbBEIKgcV998gpnJCXmHBgNtdM0TpM68wQcMxN0aRS2LLKcMs2iZnl+zo9/+gUvXj0H4rGmNAyemw8fuL295W/+5m9wzvHs2TP+4T/6+1xeXnF2cYHRNSAw/Lu7FR8+WN7/8k/pcoAcIaZic/Rp03jSWj6bJIkXM3M9Mx9/x6PXP0Z55bne+OR9v2UUgI6SDz4KRqUGmLTwF9pYYboK3Tn8h0jyCRUyrWulgSkmFosFdd2yvnyJNQlrM5fnK8zSodeOtX6B8g16rDkMDwy+++hUHsWdHxl2pSgSMxprLMFL07n3pUdSpdKSVPoanaauNWcXDfXSYB0cuglnFI2xuKRpjGO1WPDZ6zecX70gGsd2u6M77PnJF5+xbAX9XFVn2GZFal/y4rUm6Ss228i7b79kOETG/oGcParKx0yQZIBkTh0lczJzq+bT606Z3E/k6y0vQuZMiTST95B9ZBUUTSqz5KIiXlSkFwuqyxV22eCapmRWNMpK6iMBtqpomkbqmjnhi27oeDiwf7hhPGwIQwfa4FNmGD3NsEVNB0ycyM2SpLRwpcaSjjSAtlgsi6alco6mqYkJRv90HkoriaVpGjaHAyhYL1qpG4dE9BGUbOAZioHOhGgZU+YwJqIyuLZiOkwS6St5/k4bkUKaPNM0sWwrLlYNz89alJlTIBPOgNWW6uqCmIQv9HZ7oBs9m0NHYxWLytFWLYvFgtVq4vXrV6zPVpxfLFmtHL56VB3Pmakf0EYTvC+cpuJU1E2FSo676z2b2w5jNBeXV9RNTdtUNPWai7MXvHz+Bc42KO3QZCqXWa0yL168YM40zZJZIfiTM+ekyd9VlbTjTRMhCp6jbVsyEWsVz59foLWnOwhO4xd/81fcXF/z4cMN2+0OZ3XBavzADAx/ByNorcUaQ/BSJI+ptBqXtGRdOSpnMUbRtktWqxXNoiXHSAyeyUsqUgyoxRppUPdTYhrBunOycdi6ZvKJyUeMMpyvV7TOFSh2gGypV+eszs54+ckbPvnkCh8S47hH5UjjLCpDaw2VztIiEcbCFtOTwvjkulIh0NZKxCGruoaY2D/c8db8CqUN/WEn0aCfBHUYFNM4EHzAGstM1l1Zg6pEAUBlSWMeugM3d7d8+c3XtMsO62pcXQvish9ID3dMfmIYevYPt8JWMw344YAf9qRJJKFyDjSfvaCupdeochKpWquojKJ2p91njhTnxvUjxN4o1hdnvPjkEy6fXbFaLjFZmrTHceL27p6vvv2Gmw83LBYrri4v+fSTT7i4ek69aJkydJsH+m7g9vaGm5v3XH/4ltu7G4a+E6DAk6SmLK45i/BxfDPzlsLjVOUjRoknv398dY932zllmh+lR9V3lsCxNTDnwt6T0VmjksHFinxQhF1k2vRCZEzCVKaAH+qi16exxrBYtLSLirOrc1IF3gUW7QXKOemH8wfwmceOwEy8nWcD+JEhbFrptzLaMqJQ6kRBhcq4OcJW0DRQN4p2oXBNaTL2CpNBxUylLbWtaFwxIEqhreXi/IyL9YrLy6uiE5gJOLzX9NuR3d7TjZmmPWe1esbFxYY7PxKiQumZtECUCExpp5BUxYwYFVab48iZMI5ELYwhzarCWYmOYi80XM4KL2XSBl4u0Oc17mxB1TbYqsJWDl2uIedMiKEgy2WkEInjSACyUoSxI0wD0Q/k6LG2ImMICeI0ofxEjp5c+uw0QuN4JG+Yc/M5FxaYka7v6fvhyRxUxqCt4AhW7aKwp5gjwbzSRnAJxpYUsXxfXVuMspydBXwKdGMn/YgxSz7VJDKarHOpm0Y2O8FYnC+FnF7rOSKdV1WSzJUxLNsKVGL0htoaGmewqwUhJ6q64mxRUVtQyaOz5UkuRlEYtxRWW4yCmKJoI2ohIGmqVohIlCL6TDQJ1Whh5jJV2acFXTqOsehnBklvGkNdt4UkW3E4eElDRwFFAQVIaY7vMVrWXFUZcrK0tWMcGyAwTgOT79ntYBwPpOSp6vK5305f+53xg0V1KycqwL7PwsHpS0Ookm7+pq1p/ETdVLx49YKLiwvOLs/pDx3dvjDJaNlAlguHVplx8Iy9p9sljHuGbmvsoqHbvGOaIgbF5dkZ56sF0zRBDhidePn5K66eP+OzL35E3Z5xc7vhcHhA5ci6bbHAurY0OpHiyDQpxgh+6khhOO2wcGzoNNbSNi1N26JS5O76LTc3H1iu1xAjaRoIfiDFyJgDKcr5LJQjp8Q09oKidKIAkAuo4WH7AG+/pfeZZv2C5eqMF6/ecBhG+nHgML6n6/YcDls2d+/wkyw232+YuhVxDOwPW1L0LNzvs2pr2tphjcCAjSoFbYFwHofRCquBJDw1JY/N5YvnfPF7P+HFixc07QqyIk4T/f7AN2+/5W9+8Uvef/jAf/CP/n1+8qMf8Qc//30mlRhC4GGz5e3X33Jzfc1f/eVfcH39jpvrd+h+g1OJtdOFbPyUYZkPPTtMcAS3MbMKPp5puqBBU3GyjP4osXEsTmuevvDxnD2RjUsUph5lZQUWr5PFJIvzNcNuwt95hnuP1hPWDlRmhXWOyrVHsWESLBZLLq/OuXj1nIGJjd+yds+owoKsYNNdl9qePu6pCUF3yqT7OBUKy2VFVQmtYC4pZO8DKJHrEpo0uYbFUtG0muVKSglZQT0ZmBT4RGNqFlVLW7dAJkaPTo7z9Yr1asX5+SUZGKaJMVVMHq53O67vDjxse1x9xtmZJ77wdLsNwwCosWR+RO3AGIGqW2sEuRmE3SeGeHz4GRi7nqrKKJtoL2sIDpst+WEkZ09oMtkqYmvQn51hzhrqswWuchhr0ZU7igTHFIUSaxiPOpNhHPEKchDi/KnfEaeOOHXkOFHXDVlZpqjw44SeRggjOQbICp0zNitBoM7ztYS4oqoxst05dofDE8dKW4Oxlspl2rohZThMnpgDPokOn3UO56yoTaQIKbFsK1TdMuSKMUzcbx+Ik1DjWZ1I1pFQxJyLpJznZrPFGM2zizOWtS1zMR7T1CmKlFBVGbJucZUihpFF7VjUMn9XqwWvQmTKk7R0jAdUI5m9R9NQMkuNAFN8FQgx0Pe5pIYt5+sLTOFxHoae6FNxDiu0dux3g7AMaUffj0JJGSecM1R1xXK5OC5hKZ3lIsKgHylRWOHI1RI1GqsFfWojy6Ym+BZjEsNwIKWBQzfg/YhSibY1Iq7wr9sIQklmZZkgPngikaqumaVaQoi8ePmCf/c/+A/44z/5E549f441mt3mwHaz48//+Z/T7XeM/Z5PPrmEnPj1r74kxZ5p6NlPkUpfsTpf8PKTn+DCnn/+z/8Cpp4vPnvJH//J36NthPF9db6gqiuapeFhc8PdzTvevfuKaexoKkuroDUJEw4o35GIHLqJw25Hd9g+SYdqMipF/BTZ77aMQ4/d92hn0M7Qb9/JZpytpGILKGFGk1TVQhqI+47f/OqXhBDYbB7Em3Kazf0Nfhrp9lvOn+24uHzB2XqJyR6dPO/ffck09Uxjh2HAVtDWDY3y2NRhVaSLW+I04HJHqy2rypVNlqJjJyKkj1epU1JPxYhsSVIa5SyL1Zqrqxdo5aCQUdfNkmXWXJydMU4T3779wP+r/y/50z/7lyzP/p+i8wYsh8jN9TvuN/f8q+uvCZMnhcCrOktkCsyVY1VIno8C79+TnUglXZlmmRUyOZ98UwErPGmLZybinTcs1OM056xMISGhUkKvlrX0W0UEgKCzxSSNPVTkITM89Ay3I2EfIcqcMCmhgsbYhrpqMK7GuArrhLqsqh0X5xf0ccQfpBZOTkz+QE4nPb65RUQdyWPzsQZ0uiZpUtYmM01eEIZZVBmMtaLyXqm5+kC7MNS1wjoRzSUrlFdU1DR1S+uWNNWCpl3QLhqq2vKw27Boa1xVcX71gn5KPNzveThEhpAZoyObFlsn9BRoV5dURrO5f89up9gPN8zpWKlVS6bDOXNMhwYjskyPxyypFnNEOUlp1qpCJ4MykXGCqYHxTGPPKvTCoYtIt3FOcAVaWqnGQo81hMDX1xseYsPzX3/Nq5eXXF2sIWZC1+H3e24fRvabQIyGkDUhwaHfUXV7mkNHrALZaNRMOjC3gSBhoXGS8jRW8XF2LSNE4CnN0kPSXziO0ketsi3sLkmU4PPs8MEw9TgDX3z+GfWipl2v2O8PDH1xzhXMgD1ZF4qsNVHDRMLGEV0o+Fwl2blm4aQHcT9ga0frHFcXFzgtguTKVOjkMYBDMlVN45iGwLDvjxentebzz56xXi9wzpGhiAHvaZZrlssz/ugP/xH7fcft7T3/6q/+Eu8HVuua/X7P5mGD0zdUVYMxFZvNPdM0MEwdi0XLarXk7GyNS5UAAXc7ySgq6Ymd06QpJUIUZ8o6Q9M6Xn1yyTR2aCLaLFksDc6FwkgjAdWsY6iU4uGh46/+8rt7zveNH8wdKnRZiZmZPyfhygtBisbWWM7Pzvm93/spX3zxGevzc/q+R2uHNpWQ1ZZ60dl6hTGa3XbP1SayHxLXHwbiNDD2IxfLCwyRQ++ZYgJtePbqOYvGUFfCqKGNApPp+z273QOHnQjC5pywVuNMxhIxKUDQ+KHHT+OTvpj56qS4L8zqMzuNFY1npjCis8aqClfLcbPiCAWXB6dFwy2Kl7xoF8K4oTIpBsI4MJodfb2hriqmbkfyIzlMDPsN3g+EMKJJMqlrhyai0oQikcKAnzrGfo9vKnLbyCTJoLUp/ZMfJxtng5RQJS3jFkuqusXaipnlR9C6Bmcdy6rBZUUaPd9++xbjLLateNY0XGrLT3JFu70ndlvOh4EcEibCq2WNdpqxVvgQjhJM+VHbg/TLn87x2KZSCiYnTM6JYux7s/rlxSMj/RwYcgoS5XjyurDGzH1pSuqyUZMnTdpmUpcY70em3UQcEzqViDpmcpjI0aIQFLR1FlcJAbk1hrqqCVNGY4k+4qdJyIZz5NGlMwsglxM/neyTkYoHKwCAVDxkY7QozBegldIcezKP2bsERIXKgkBUSbgrXSUkEqqAvJxz0oNVNQLjzyNTlh5VZSqMbbAuooz0gtm8oK4b+l7AIfnRfVTzMzime3ny95MVVhrFyaIgb5zFVBoVNaGx6BZYaJSTFOPc96uNKSKuioyQM4dCIDB0I7nqeNhsubxcY61l1ro+HAY+3PfsdyNLl45lau8nqmmi9gEdpLXKz7XaDEcihlzWcqnTSZ/d4wvL+Em0/nQWsg8fEz7EogUp9IM5SzpxBkRlrYSdygvtV7touby85PLqisNuz9j3JX19MgZCOSZ7TogRnyNWF4aaYz6FY93ROotRiqQtphABZEoLWIYwCqrTGUc/haOuIMi8enZ1xtn5EmclGgwxsGgdy7NLlusLXr9+yd3tA33Xl3YdkfSappG+H+i6Hu8zxgTu7+8Zx55hOjCOC2L0HA4HmiZijGEYh2PKfybyl1phEFTxNJXUO5yfL/Fei/SeVUyLisoJkCmlxDSGookpz0DSqU+Sfr91/OBIcBa5VVpACUpp2nbBftfR9wNnZ2d89tmn/Dv/6B+inMH7nve3HzCqJinFn/23fwFx4uWzFReXl1xdXfD6k0+5ePFrXvzmG9793/+MbRfZ9p7nly+o3BrdXrC+esmz1294/aPPqEzCMKGzJ0bP6Hvub9/x4e3XPNzdM02JnOCiaaksrCqocyD4zLjbMQwjo/c8ji1yigLoQDgbUwKThPdPqUQaRmKGyIiilSbxMiFnuLg1lqZ2olrhKhaLJYfDlnHo8MMWTUKFgeHhA4cU2K7WeJ8I/Uj3cENI4v0bDVVlWCxqiBNp6rAoxm7Hdrvh7btvJH9uLMMoCLflai0Fbf+dKhggaL6qaanaJVevPmWxOidlTYhZPMqytq0yvKrXPDcLzpLhn//qFyQNpnX4qmHRrPiDq89Yqwq1uOAXDeT9hNpP1BcvOCw0Xy8i3z7cchg60uRlMhYi88cbyKlLJXFUcJjP+FgMLLyoHyNIH0eCzBsxj6zgI2M4l6xKhKhRmGjJHfhNZvhyIBw83bYjTCLS3FSVMKilLI6MUcQ4UdsVdV2xOlvQLmuq2tFUDVOIZA+7zZbtcE83bYhJWCvEuIsRPl7l0XCf7ol43B5Cou8LO0hWaC2RkKsEHJWUpL9TlHaFFMXYqahhFIcmAtMQSC00qxZlAJ25uDjj6uqKZ89eoFyLHz37WBGdEBTX2hKR3ty+O5SIKFDVLcY6pmkSHlGdJU0pPUWE6ElJEaKk8J7G7aIzmkNkuNvK+qhr1FmFTgmtImZdoVtQa412RujirBFnwzmsdRIZ5MA0SdorxUg/9QR2vL+54Ysff8b66jkaw+bQ8dW39/wX/+qaw2HPH3++ELotrRj6gfN+4KobCItMX8G1jcJ4A7P0s2S7oqjYK5/R+hLnTn1nOSNpv2zIOdFPpfVKKc5WC9qmZhhGvBcB2FTS21pp0hSZ4sDD/T26XvD8xXP+5E/+Ibc3N3z5619DTBilWS0X7PZ7uq5DW0NKmXGcIHtqp7i4WGCyQk1CrA0GW7cszi5QWmO9J43SEhNywDUVjWnZvxUdxoQmaAiParjGaH720zecny/lPmhNyplpmrh49prV2SUvnr0g58jd3S2LRaGGI9L1B9xuS1NfoJUAfb788kuG4cDoO5bLBefn55yfn7Ner6nrmu12c1QdmaapyL3B4XAo802R8oRSkU/ePIc8kcIgrTkhMvQ7QvDE4AlBgqzgI1VV8eWX18D/e95ofuf44enQkpqKIVE1BuscTV0z9gNGRVbLJZVzdIcN3TQw+UAcEr/+9a/56tdf8+Vf/w1aZ4bDGb/58kfEBJ+9+Zw/+Lni9etneFXxF7+658/++o5uu8O0lhcvXvLpZ5/x6eefsTg7Lwr0Qlbnp8ThMHF9veX29oCtz8hGIpCQNf2Y2GwPoG8ISXF7c2DStmwwp+uavc6TwKvQDsUoyDYhgMgkChON0rSLJU1pUA8hEa0QVsaQaWrHp59+znq9oHKWzcMNwY+EMNEdRoyp8MNAdxjouo40DRijqJxjyEF6MI2hrhxWaw7bHdvNjrv7DV+//cC+D9xvOrIy1M2C11r6DA9DfLIBGa3RxpKz5pPPfsTVy0/48c//kLPzS9ZnZ8IGUVhdEjAmz7fbO/ZhBKtZrZeYJKoav6cdP0Zz7nvW0lNPaJfEakm+1Cz+8T/AP1vy/Krmk+sP7B423P/mW7abDYf9vlDVyb0+isumjFEKI1bqGNnMtaRcFqE2+YkJfWzgZq7cY91RcaRLOtkYqfeYrNHZoPcQ7iL+2tPfjoQhMBW5JqUogAeNdopsHBhTFNXLPEihNFonNJqmark8e843d7+EnPFTTyaitYBFJDDNkg491jPTR44BAsYp4AGpg2q0qSTSj5ppLCTvVtE0Fqssta3JUyYFaEyNzQ6rGs4uLjk7P6dpG8ZxZAqRy8sVqawLVGTwGuyCpm5RyuCUYpoGRq2wBmLIRJULjL5C2SSpTi1UV0oLwCjlgkPOChERP83BlBJfffUNq2zpNz3NeYOpLJGMbgw4A1OFqoReTFsntTRboY2VP2XOgMCMnDG07YJFvWBxtuSzF5dcXlzQrC6ktnr5jBeffManr77msIGLOjMuV0zKomJHVSmMHrm0js5WbElMWYjPVDoZQV3kimZgxhOi6XJtUNq9UiL4wH5/oJ9GnBERWa11EUeeBEGvG7pJZOLPlh5rAzo5MonFaslPfvYzrt+9ZxoGQorYytCqht4HMkJWva4MjdPy7HOhogxTSd1q/JhQOpJLO4G2in7w+JRBBTZdR904FrohK8Pcrz0P2dslklVKeImryrJoKxa1Y7e9Z+z3aBW4ulww+RFlM8F7xmL4c44EH3l4uKPr9gxTx36/Y7/f0TQVV1dXrFYrdrsdIGTb0zRJAiZF+r4j+ICrHClPxDzSNiLqbA0lhZqpncMZDZVjZtciSwlhuxn/9UeCjx++VpKLdkbjylxuqgqrFX2/Z7N9YJwCmiXvv/6Wv/6Lv2R7dydyGcrz9ddvMcbx8tknnJ+tOT9f8Pd3nt3wa/7yl7dM40iuNBfPLrh8dsXlsyuqpsWPmRzGAlmO7HY9m4eO7W7E2JaohNorJImMDv2IcTtCUnTdRKrb7/QxPaYdmzGMOYvkiogBzHWdOfWgqKwTqRJrSu+jRKBRMGKslis+/+wzLi7OuLu7Zug7+v7Aw/0DKQqsmRzJMaDJhQ1CzueUtsvEGDgc9uz3B3b7A7f3G8Yps9+P2KpmuT5jffkSo13RDnt0XVo8aq0dV89f8uazL/j8Rz+jqmuccyhTSH2zyFoNfuR6+8CUIq6uxQiOE4vR84mpeKUNTZiorcEpzaWriauGtFiw/vlPSC/PWT1fsvjwnN39A4vc4N59Cyi6bkdO6ZjimWVWZjb5I3eoEp1I8crl3+oj2rSjs1IM4Fx3e5oOfRQhUuqAaHRUqA7yPpEeAuNhJEylOVqVflStUcYKTE4bKKjQ2UE61i7Lza5cxdnqnMpWGKWIaeJUm5w/QzHwp+s8pjI5vWyUwlk5JkhvbE6KGCH4jDFFbBhhpzFUxBhRIVObGp0dVlcsliva5ZKqqth3B0HrGUvMinESnlQfDcY2uFpq2jaFghgtmqFa5rytjJQBbLmnOs+nJ2nmstFkhdCnPbIVOWdub+4IumYcPGotZ57JJKdRTpMbI7vQLAWlzTEVqkp6bH6okpY21O2S5XrF+nItYJHlCls1ZJVplisunr/g9fNLdsbT2h12scCZFt9LqUQRWFqDMY4qTYX5s3hfR4dK0JCCeH/afkSWfTCTy7lqVFRMMRCHIiOUpGZXq0rIybXGJieZAxLBe0yKKCWgk8VCs1otOex2RUZLNPy00eRJVok1hkUjiE9jHTEaoU3LQY6pTSGrzmQVhWNWK+mFRuSjhjBChCkV4NYTqr+C+NXi9stRNVYbnDYYpTjsd0xjDzmwWlWMPjN6QbFKHVNEtKfJczjsORz2DGPH0BvGYaBtaqZp5Pz8jBgiWlmMqdCj1Jq9HxlHAT41qSHniVR4mpXJKJOKqIK0f6nCr2xMVfZxSadX1vFDxw9Ghy4qWFaZKUJrAo2GJu+hjrgLh1OZOPZs7t/x7ddf0x0mKv2Sd7/8kptff8Xr5YLej2w+3PJ/+T//X1msVvzh3/tT/sP/8N/nD/7e7/FHf/jHfNho/vQvb8g601SaLz57yetXz7m8ukDriqqW+tWm77i52/Hf/jd/yW9+/Z672wNVvabfbzkMA7ZqSJPoHwYLyjgoQr8fa708kS6a53hKsiiV6LNZI32RTS1CtIu2wTiBbu+7LeOUJaIsNYTr9+/4/d/7KW8++YQXL14UGRUlXvnk2e8OvH/3lvu7e5JK3G8e2G639EMvqZ6m4stvvsIYw83NHe/ef2C7P3D+7QecfUCjqGrD5dUzzs+ecXZ+xUfxEkLttOT56zf83h/8Ia8++xGL1ZkALQrCR+DPE5vDnuvrG/70z/6M1lb8/h/8Pu7+DH97R3x3zc/Pn/GJ0qRhT5eFKivplvqnP6X++7/P8h/8AbmtcSpxdfYJxMj4B/8ub7/6De+//g1/9s/+M/bbLV13wJaUR37keMzirzPEPuXSf0Y6omyPV1aQZJkskcejTesIknnkDQg+U6NHhRozXHv0XcQ+KMbDyBg8IUWaphGv3lqisSRlcLYmmopQFoExQpdVOVFhJ8FiueTiasVvbp7R51vS1pdIj1LLUsd5Bo+yvY/mnAJqIwAAtarIyhCToh8Ch71n6AIkIcV2RuO0QWeD30PsQQXNxeoCYyts1XDx/Dnn50vO1zVd3+F9IumKw6QIO48JiWxrzldLmnaBVorY7emtZjTgTCTpRNYR22qqlWF1YcllI40hgJYNaAbGqIIS1PpRjSplvv72A321xo4Za1pquyCFhG4NVJbYGYIqNITFQVC2BmULaCoV/lSYxohyC1789A95dnXJ5eUln/34Z6zWF8QouIXFcs3P/vDv4dLI3duv+M1f/BesLj5F2TPUb/4cayv60GMqQ11VrH0AAioL1+dxpIyzhrPVmqaupUZ22iHwoSfmGrRmvahYNI7KGfpxKi0Fs0CxklRpkkhzUdVkpel3G5pFw1lbc/F7P6bve65vrrl6eY5ykX/xZ78SIXFlWNRLnDacL2rOn6+orCb4yDgVJR8V8HFWmZCatjIZ68qTSOJ8GTJNBSpPbB82LOoF9RN6sczgJ6zX+ClQ2/l5KsZuIE9w8/6OQ98TpwNXFxVTMGwOE0qbIgohrSAhjGy2G3bbLd3hULI0mtvrGy6vLjg/X/Pi+Qsq1+BcS0oBHyZ2+we01jjreP78BSlOhDigksfZTO3Aj57o0yMh6XgEEs1O9rffXP+gKBD+DpHgamG5PKuJC03lEs5CbTzKJXRWTGki+ZE49MRxYDp03NxP3F3f0O/2LKxFJWFl74eeA5kPt3cMAbALtn3iMBYjWyuayrBa1CwXC9p2gZ88Q7el32/wfcew33N3c8vQHYh+pDaZyiFCmo2mwuJSQ9UuSBiySkhj+HcVh2dDaK0tUN1TDSeEeEyNKC3eyn63FfJurfFxJBkLKXJ1ccXZ2YoXL5/jKlcgztOxGbRpGulFrCqqynH17ApXV3z99dd88+23XF9/ICXRztLmBqU1m82OQ9czjZ7dbisRh9YMgwBvbm/eSaNpfNz/KLGUMYbFYokxlhQj9/e3jEPPOIzM0UzOcLvdsd3tIWaunl9xvj7jQ3dH19RM6wWLP/o9FnWL63bgPaConz2j+vEXVJ++QTct2RrqPOtIWlhonr1+jasdm4c7Prx7yzdf/oYwDJDikQMzowSsMtfwCqhlDii+w599fI+kOWeAxvzxXEjNj/14WaOjIewCcRMIHybiLhEHIQg3RhQ9ZkdlRgdSMuEhZ6aUiIinXdc1zlUYbfFjwDpP1U4Mfkvvt0TisQZ5ikbniHf+8buMFvoRCfkUMpNPdIeJvgtMQ6ByxWFQmugzPgXyBDpoTBLNy5Skt2x/2GMtLGpN5SqM01gnjDc+JOHLLKxPVgsyUjTaEs4qmtpCNsSoi9OhcJVB6NsSfr6+YggpNFhCpP30WdmqBmsZc2IcJ+reYRojfZSmUNAU9MYMBGHmTS2OTUyRnBNuuWSpFCvXsKwrquUK5Wpu7x/4cLdBK03T1pyfr1g9e0ZMnrO7H1PbF6jUcpcq8Jo4JeIQhCzBGqxW1MriVSG/L/V+YzTLRUNbi+PzeHWFFJmCR02apmmlP7pxWKdJqcIac2KaCTVaK9rKFDpBTUDh+47t3Q0v33yKqiyrtmFDRufMsmkYxoAPqdCgBR52PWfrilgZwjgRfC5UlGUtK0gpiBHRrkSw4PUozwaFNZqYMmPnqZUn+Y96CdTJhZyZphTQ7Q+MahRd1ZxwzhT1nsQYtIijT0JtNk0Tfd+z3x3Y7w5iBJH50Xc9foocdj1xEgk3a90R+LQ/bIUJrKlZLVdCYWkcKkdI0ncojrH0dsci6jCNIz4Epkmi7r57Slbxu8YPM4IKzlYVV5cNmhrNiCbidMAQRYSym0i+J449jAO+O/D1l++5vb6hPxx4dnmBzpZBKzblpO83W8aoyHbB3cPApgv0U+ZsrWgqzXJRsVguqJuWcRjZ3N5xf/MtC6fod3s2t/eMfUcOE43LtE6Y1hdLTaUdtTI0y1Vp9t2hCGjleRwpzAbQGKFhmynd5sWQUiQnXVKWipwSu932KHZqrSIaoUtrXr/i4uKMTz99Q1U5xmmk73ucE/mQxaLFOcdiseDy8oIQIheXF9RNTUhRUgdDz+HQ4Qs36aEf6PoB7yO77Za6qqisJecRcuTD+29Yr9eiFv2kCJyLIKZwA07TxP3+ntsP73m4uSEFj9IGYyuuH3ZCTmAqnj17xqtXL/mXf/MX+Loini1p/+Tv0V5cYPZ74mYLk6d9+Qr3+SfYN69RlUMpqFM68kjaSnPx/CXry0v6oUPXDdc3N4RpJKWM1YqYS/RX8lACJpHHM5dgPirFHFlJCiTkaPzkTyZT0tha2GjEA9dMG8/4fmB4fyCPGQJoa4WHllyo7oQLVpf7GMkEMlPORDUbwQbnaoyt8GPAVSMxag7DHYfpnqTmQuApNZvzI/7XR9nRx8M8UiSYxsQwRnbbiWkIhClRmQqVtRgnH8lBJJZqqiJgmkgqonXgYbtFqcz5qqGuarStqFwl/WfphDo1RmFUFkBYnNAItV9TO3KyeG+O9HyussxivkeUrwKlNFllPs6mzG+o2gVa1+yzaHLWWtM2S2gymAyzCLeVqC8mKUWoVCTGVFFfSJFmdYZrl7TrS1QQKbVsHO/ff+D6ww21q3n5ySvOn13RXFwRleJi01H5M1SnqYIjqkweM3EIRBdJjfQ5C9EDRw3Buf1huWxoPjKCIIZ5Cp6YoarrAo6ztMqVCNky9Z6hmzBZ7k9bG0LSIjmHZhr2DP2eT169xBjNuqkxOWFS5Gy1IueOEASY142em82ei/OaRTDEYYAkskqzO5mykJhYZam1LfqPSG21PEdrLWkK+NET3EQM3w0K5iEpX1A50437Qg0pjk5dOarGYGJiDJZxzEzRM40jQy9E2fvdnt12L+0fzL5Opj9MNM2B6OfAQ2rMMXr67kC7bFguFzy7fM5y0VJXFZqpKAGVspSS0kBUAIkQJqZRcBYCHDy1fvxt4wdGgorXn73h8x+/YZo8/bDH+wF8h9IDOkduH24Zw8jLFy1VGKl8z83bbxl3W0yKtE6UldumRnU93gce7u64/nDHu3d33G0C4zBSO0VjRxbOs15a6tqRUfzir37BL/78T/nlX/wZn3/+hmEYActysaJymfMzxRgmQvQsW0qkClAzTQmdM5U1jH7iP/+XWloM5qsrUWDO0tdkbYFmo6mciOyKt2eOAIoUPURJv6AkL28tLFcNP/rJj2jbBdoYur5jGPqi62cLhNoKSaySCPPNmzes12v++I/+iMPhwN3dHbd39xwOB65v7nC2ZxxH9rsHBivkt1p7xunAn//LzOAHNJYUH6cOM91hzy/++q9pL694nhNoTbe54f3Xv+Du3TtyBmMr9kNA2YpPPvucD2+/pO+3kvp98Rpef0b65A3h+TNWixoVPColkjFMzjEmaDMYFMZIL2UGVAjifKD47Md/SLu4ZLE8491Xv2IcDriiJBBjZLfZMnS9eG8hlrlbOD1l+p1GAWdAJikBYigttZUZhTkz7qdgCYNi8y7y8OVI/67D9MUQaTHEThvaRYutKqmhWot2DuMqKA3bsvXPFGwWbSqMa2iXZ5hKMcSR7bRjPx0e3/7fuQi/YzI0UpvtJ25vBvo+MvRCqq3QpADBK8Ik6GVjNJWxaGVx1rJaryQlpQ373Y7aaqx9c2RCGQ57mlVDu2xoGgsGUvakEAu5+oRRAWcydaWJXh/JFgS4ZAv/r/A+xii1cF+yCSmWKHH8KH3tHElX9M5xvd8yjQMvyVQ5YFtH3I/k2vD/Ye9Pfmzb8jxP6LO63Z3Gmtu8+1pvwqNLSFGqSsgKqlChkmrOkAnNCAkolapSJZgxQUJCggGCPwBmTGrAFKFSIoGUIARJVkZGRniEu7/+dtadZnerY/Bb+5jZve+5X89mgBTbdd/1e8zs2N5n77V+3bfRzpFiws8z/WEn7WknnZkUIykGnn76E8Ek2Aq/vxNZQu149fqav/jzv0Il+MM/mfj8Z78QU2dTU60vWN04rE88X39M1BqallA3jJUhNBpltMxjK1eEQALTPOOaipA9OYszxMOb60NmnjPWRqbZi2BBY8T+KiTG3uNQbCrLWVcRcmKYJqyTWbM1lv1BDIZ/9cu/EL9UbdhYqM/XdNZyve/ZDRNjNEyT56+/v2OOgfNVxScXq6KLDFgjkpWDJM9t09DUDUPfk1Jitx8EKJMz63qF6QzGaqY5gDo+ul9aidBG1pocRK5v0RFNMdN0a6HdGI210nlxNhLDkXGcefP6JXd3e26ub7l6c4X3QuSnAOL8HOT95oSfXoGKpOxlvpyFbrHZrlivO1bNiqdPn+DcE9GHNgZniggISVrF2ZFSy2pdMc8z4zhirCQbnBrzv/34cAHtsg1oY8FW0jJSsXQsMsN8QI+jWBP5CRNm0iwDVGsypgBLfMqEEmzmfuDVt9/zm8tfE6ozdA5cbBssI1ZNrDoJFN4H+sOR/e0dd1dXrJqGkIpTgTKgRVItzZ7sJ7pNRV1paqfIydBojXnSURlDP9lHLZuFn7JwVKRlIB9LVpnsFHVd0zZtAcB4CYaU4ayS+VRKxScxBFm0JwAIpR8u/MqUIympE3os50xdi7v72dkZ0zSx3W7ZbLbs9weMrbjb7dgfDlzdXgkROwaMlQH7/nDLmzevAZFsenhdJ1Pd21uatmV7cY5RGasEYJRiQkUPRYh3Ot5x3DVolTg/22CrlqpbU3crcBXRCloyZ2nRKBQ6RMk4FkWV0+8XV/SkoKpbuvWWs8tnHPa3VGNN14pSfAa2uz3H/YH9zS37m1v87IlxPoFd3n8YM1kVzqrKUBzQtQKTHSopiIpwhOmYOL7xTHcJPyhIhXNnBJBgnaNtW7SzAgkvwTendK+0s4A/ykpAGZRxuKYh28gcJ+YY8Cn+QHTjwefyWxakFq+4aQxMY2SeIjEUgrwqfnUxE73wzZQ2IlWlBJjRNjXKOrK29P2B2Xv8LK1rpTN5nlgpaJoKiGLzlSMah8rLqEC8LCmeljkGYvDEEE5BLyXxBV38BQXEJZeWYnGReHTpmmwM2TmGMWLTxHjo0ZXw3OIUSVqhUgFIASl4ghcqlsWJEETKaFsJKEJbVJllzdMIKQmAJYlObpxnvA+EmFG2RiuNJbDpLojaEJsWXzm81eTSATh1hJRDaXGyt5WMPKwzuOp9AImrRAVFWcXCRV2qsWkKULilaJmbJiV8zUxmGHqOxyOH44E5iEURynCxrqkr2eybyhJzwnhFnD3j6Lm6PRJD4Mmmo9YaaxTZFLNlpcWtJ0RSGeOQBXiXizac1RZnDVVliTG/Z8adi0fWgkda7NAWz02teeBfqhDREFWq9cAwHOj7PcfjgXma0RrW6xZnNSkWxH4GiPgwsfRbpONW9sfYkFPicNxT146mETyGXcZSZlFiUg/m7hXWafljDV13+MAQ+MFBMDPc3dLvVlCtiMqSnMZUDlNP5LliP70ipMT+9oZ63GPmIzr0VDqga40i4mPgbpqZUmYOkTTs+fP/9z/mzau3/Mm/+fdpdOaPfnLOm6//BpMqLi83JBLHfmA89sz9SDiOvHl5A7YimIo5JeYQ2N/d0r+9xh8OfH72MStX0WgLymErx0+eXWK14e4wYB5471lrsdaeOCpGKZq6FpRigtop2rbj/Oycu92eGCNVVUvmm6I83Fk4Kn0/sNvtubq64jwm6qZFqYyrxfJpkXrSSp+CYozxVBmuVisZqCtFP4wcDgf+5m9+zXcvv+fV69dc3bzG+0AEqlr6/+gdX33zFdOc7v3nkCBEzszDkbcvvwUiTy63rNuKy7MN43VD9GIPU1tNTInDm2+weUbFif/y3/t32D55ztnTFzTWoRMcj4O0BskQIp2xdNaBc2SridwHLa0MSSvR51QZV7Wst+eszi6oVx3Pn12y3m5o2gZCYnd1zZvvX/EX/+S/YHd7y/EQSsn2TlTRWRRg9D0CM+uI1RqjDHVsiB7mfWT//cxw67n+aiIcMnlyWGfEusZB3TW0TcNmuznB/YdpEtLtPFFVDpWtnEHOJ189pR3G1TSrFWMaOQx3DF4I04uYxI8ePxgkQWlLCpnjYWQaE36CRXAdJer+gcxMonEWoxy1a7AKnLVstxts3aBcxeE3O8ZhYr87CN1CW1yusM9gs+m4Poz4mIlRYbsWoxQaT44TYe4J00CYB8I8CrK5H5hGEVSODzhGWokreE6KRHFgeEcSLmXIxqJWKw7jDXGaWM9Ct8ixw/ezVNgdRSZLkZPHT5EYDIq1zLxiJhYzX114vSlG9ldvWdWWn33xCZ2qubw4Jxx3HI8D0xxRboWyHmMzz558jteW2dV809UMtQSPU/BF5NCqyuHamspJdbherziuV6eHWyFar+uzhvV2cyJ8x5yYY8T7yHHs8dYSqUuyp4jaSvUaI999/5Ld4cjh2HOcXhJ8Yp4D/+7f/7tcnG/wVxNWR1aVolXgVcJPnu9f9/R9w6dPLlmdN6xaR8oeRQUpM4/CNw3DTNs1GOtIXcNCzrUgM1CjmY0WDeeH9ytI1WvUoskIcdGKVWBPAT8TgyIGMeDOOZGz53i85Xg4SCI2TWzPVnz22VO225YYI1/+5lt5lmb5ftHm1YxjEAeQmKisaIXe3r4lhJlpGtmsPscah64NEBEZKrH1E2m1mjo5Uqqx1rC5On5oIfjhlWA63hBuHV452qfPqNZr6nXDNI0cDkeOU8RPnv2dSH2pKK7RK2dQtaZaO/w8cLXf0TUdTzdrPl21TEox3F5x9fI7srWEnBmOOw53jjdv3vLkxae03Zqz8zO2mw3dao3H4oNimCNhDvh5Yt7dsLWZ9ZMVH61q6taiKyXAAOuoawUxM5kC3nh4bSkxzwXRVVqhKYlyhzisjwzHgRyzwMmtE7BEzhxnCTwZi3UNzglvb55mae/4kZzcPbimbILv+q7lnPF+JmqhSzgr2f1q1dJ1DU0jgJiY5aHDFcWInJj6I30/v7P5St/cGs3d9RtmP9J0Evhs3VDVNXNK+GlEKYMBap2Y9te8nXr+cQicXT7j8qNPef70GU3T0XUrsq3ISjNOPXPl8G2Nbh0uiyrFckiVK/t4Uolx6rl6+5q/+C/+CYfdLefbNR9/8TlPnz/n048/YXtxydn5JbauefvqJX/1z/4cPw4M87to3rKBqozBYLKhji1pzMQ5cXO9xw+R+RDpzBl109E8S3CepeItMOx5HgQUkxPDOBKygDJiyqBUUZ0QnpgudBNrLUaLWEJKib7fk0yitpbNas1+XjHvr8rM8kEwVA/Rofk+i35wzJPQIIYhEeZcnjXJvIW2IDQPksYqizMVTV3TOkNbV3Rdg6kblK158eJjVE7sduLiYrQmD4GnH30q4ssqME8ju7sDlXqKrhyaGdIMeUYTiX6i3++4u75md7hlmjzBh/LcJpyzaCcjg4Qkc+R7QJPcK0VXiyaq1pnsRE5sHAOmH4XYPyUSI9kGNmcVyoi/5ehldjyOk1SZGYbDAa0UTV1RNy3kxDgMIiXX1GgfOB5v+fLLmVS1aFsLeXx/hzcz05wYG8WxsYytJTotSUJp+c4xkLQiFr6mykb0PpuKTdec7pjSmueffcKLjy7pVg3TOIimae+lTZ4UXdsVEreXNrN1nLVnIv02Trx+dU0/jgyTR1khnU8efv31W65vBmqneXK2YdXV5ACr9g4/z+z6Hq0iX377LcFfcLlds93URV+z5qneQIKUIofdHsgYm2mamrqpaOta7lHOxP2RMM+P9wyKMpEp815EwswZobRYp06z5XHw9OPM9fVBxKsrxdnZGmtVSXw1Xed4/mzDJ59eorXi7KxiHCamKZBixtqKqmr49a++4Xg40vcDH794xtPn51zf3ODngdevep5etsS4pqk2AoQrCNgsqCxpuSJ7jnOWqvpXTJEAIHqyHwWuGs+wOlPVFT4lkp4ZvUjdDP3MttEo5ais8H1sZdg+PecuQ/36hov1im1T8+n5hrd94Bgzw2GPqiqyNQQf6Pue7797yWc/+wPW2y1NU2OLnt0cIkNI7KdIChPZj+gws2krnnYV68rKIq0MVS1zHWvlA7M6PwIuLKhAaSVK5rMY9eQCuRVbGy8wY2PIWJTyAuFGn1opWtmiklJ64En4M8mIoLZx5rQBCghONsnlb6nkcrmp5qTUb7QqpprSBkg5o7VsyM45QYjFdxVjpUlgNMzjQMqJt69f45yVLK/0O3JeBs0KqyD4mdF73n7/NWN/YBp60nCgW63ZbC7QrkEZyxAm4qbDWogxSMsl32/yWWeikmZHTIl5njke9txeXXF3/ZbxcIetKpQyXJ4LeXa1XvH0xUdkMt1XX9JHQbo9OlS5NgUmGUwyKG+JvWceIv3NKOi/GexWEhazzaIClBSESH/MhDCIG0qMYhmzNHxUgdwssFQlm54o24svnNyzxDwOKAfaJrqqoa0abhRFjDmXhEc9uiPqwfk/PKZJBJv9nEQCL1MCoPhFLuo5VlusWf4YmqZm1UqSpJ0jG8vFxQVhnvHDARUjUSmSF6FlrTKSq0Tm6UiOW1RWaCUKLmT5k4JnHgf645GxH4rXXmmD5lxkLu+pLbk4o797XQv5H4R2lI1mJmJDJM8BHyD5RJoCXYyoJEPlefYnkMriNjL3vfghVuI2o5Rimj3GCVExzZ5pmhijpzoXMQHXNqjqSLSKGcVoNENjCU6TrD4F7VyqkJyLVqguvcCUqIp49OkRVNB0LXXX4GpHCBMpldZntgJ7MYKYjEjSoMnUznIMkb6fuNsdCp0iYutMiDD7xM3tkeATH11uMFpTV46mdYQY2fcj9U4LHSBGUaTxnkwtYByroK6KckrC+5mcEq0yKJKIUyx7SEla3vOuVIK21mWmvewPprhmGFO4vBnGaWboJ47HI7Yy1I1ju20JPjD0I9ZoKqdZr2rOzlqqygCBaZyZZxEkqVxDXa24entDKu337XbF5cUZs+/Z7wLDOHI4HmhqQ7joMEY8FXVensX8oMiQ7oS1/xqc5W27Qtc1YXfLbndFn2fOatgfZm7ujux2PS5Gbq5XPPniBdWZ4dlHR+oaupXj3/j3/z3e7AY+/cuvCW+/x4aJZ43lBTWHaPibN7co3+DaNUo53ry54z/7z/5P1N0aDWxWLcM08PX33zPbNXf9zDevbzBzz9pk/t7nZ/zk+YoXlyvqWhyTu+2abI1s9ioT84x7lIErVl1N29bM80xVNCFJHlJCkXBW4PAxJlZtgzaKfsrEMJJSZnu2BVRpiSTIia5rWLXCO7u+nWVclkXhQBtRH4kxkWISK5iCRDWtLZYlkNOSje/x0wQxURtDMhafoF2ds1qtuHj6lGkYcdw86u8vLdeUZKsK08iv/vk/w1VC0yB4CfLasbDpFBlnpVqNfuDu1ZGrb7/iV5WIR1eNKIy4umH15JKf/MEvWJ39ETGGk9RYSomkIFlT5r8ZHTzH3Y67t69xRDqnIXhu3r5l8gFtDGfn51xeXtK2LWdPn/Dis894RWI3TJz6GssCLYoqdWxg1Nx9feT2ZsfhcMQ2mm7dcvHplnqlperpJ6xuqXWFmxriy4mbvWc6jmilqbuWpmvFQJWMQvRgFwFj5xxt19K2jaiHkCF6+psrUbSxmaftGh/P+f7mvgJUxUppoUwg+wosSUg5coarq5EUYBIdbrRWrNpFN1RQoU1ds+5WbNcr6lqCy9nZhsvzLReXZ4SkmaLi048/w08TL7/5Gt/vSbMHKyg/6zSXl2doMndvM85EKiscv96CU1E0fPsDh90Nu9tbDuMBsxJgm9JaAl4WM2Yf/Ak1usxaHx7aZJSKxDChK1Hh6Y1i1KBTIDpNVpHsA/XxQJ0aXF1zuztwt9vx+vqWiCJqjfUDT8+2XG431OtzXNOyunzB4e6Ww90dN9dHQXSrTH1psU1FdebwG4M/Gq43jmHb0D9bnzRDF1JqTokcRHBdGQVG7Jail1b7Q/J1zoj12c5x3GX2t7corXn67IX4E/rE3eEo7jrdGePNtZDA3ZHvXr7k2+/f8OvvXp+S3hASSfD2OJuJoaN2isl7bu96/vQXP+Gzzz/lJ3/wM/bH4tt63RPDjMpRuhM5k5DfHVMEA74E4EZZ5knE7nc3R4yVgKW0ULce7odiu0bRew3kmMW3tLaYyqG0BNBp9lxf7dgfem5ubvnk86ecnzd8+tklEAWcVWtBzLaWuhhRP3u6QikBEwr+osGaFd9+/Q2kGasTT5+s+eijDVXtub1ruLk5cnd7Q06e7aZlvTY4V5DcixWerk8KPdaKIfIHx7YP/cZX+z1Vp5mmmXh7Q+4PdMOBfojs9jN3xz2Nsgwotp9+wfnZhjs0KYwYA5/+4k/pdkeOyfJK7fHHyGRlY3NZsZk0xiqqBvq+4u524Juvv+arr37Ds+dP+MXPf4arHJuzNQOWpDOXvkWNic7C5rwDp+iDx3tDmA3MHpPyqZ2Vgqi9P+yGnp2tWa1ahmFk3XU0rqKtG7xPzHNkGAMgkkTWyGYcwwxFyHbV1ZDFWSPMI4fdDd9+/aUg25RiGIcTInS1Wp2EXVNKpJhFsiuLErp5s1SUihgFUv79998zDCPBe5wxBKsRgXWR1eq6LVZZpv4dvzO1KLGU9h5gFRC8ADhO88kFFAAas2AcBE6tFcoqaTnEGT8m0jxgBoeyijAOWBSkTCKSCi8y5oTXiqQE7HH7+iWvv/2KV999g58GIKGNo2pqsa7SiuA9+/2enBPTOJYKg/cIrxL+FDoZhruBsI/c3uzJGtaXHesnHVVnaTYOVQWyArtW6JzIOZBHjZmhGSr8XUQldVL+8T6grEYbqfitszSuYtW0dE1bCPWWyhqcVgx318Q049XEMd4yjWOpZKSFm4t9hioVplo+2HcoBTnDMERx9UimcKMUTWvuxbKTuMbXzmGNxllD1zlB0a2K1ZipqSpxiiBl4V8ZQVdXTQc50h/3nH90BmlD/+xCbLm0EJxVjiygGe8nxmEgeF8AVFlmqUUwXiEJ1kLFUVbLxqnvRzFKCS1IG4exWiTtLERryMqRtJUy1yawkRATxs8omxlTYMgwVxt8VgQUbvZ4NNqKvY61QtCefOLu6BmPCpcca1dhvUInqRhia/GbiuE8M7c12Vh0Dqd8JBtNVtJG00W7tHZiK9Q2ohrzuLLI+OPIXNUYZZmPmZQDN/mmeK1Gjnc7tDYMpsLGRDaaYZipnOP8bMPPfvIZ/TgxThNDPyDYH8X5dkVbV3zz8hWq6BIbDZcXZzx5dk5MM8Zk1tuGMIpknlUCWlE6U9cWbSuaVYufAsFHxn4ikAhRnOkbp6gax2pTcfQD6nTHpPVuStGQtXREnHGYyqLK+CBGL0nA/oD3ke12zXbbsdk0OAdaBXKe6bpaXExcLd2RAqSSDpfGWVuuT+OcwjlN21XUxS2o7SzabulWa67f3qGU4ubmjqa+KKbuuWjNChpcwFmpVKo/Mnz/gePDgmCG676n6R2KxHA8EI4Zc9gxjonjMbLve1LdMinN+sXHPPvkI+6YGA53JB949tlPqXYHrg9H9m9/yUEdmHIQqDqZ7UaLQWyV2XeWwy7y+s1rXn3/Pa9fveRnP/kcV1s2Z2t0CGgLUa1II7Q6020bksocwow1WtBl44RzCasNVVbMPuIfOEQrYLNu2WxWWKu52JzRNQ2rpmWeI9MYuL49FK060dFLeWkzRDSZphJlC60y8zSwu7vl66+/fLDBFfFmbei6rtAvJJvOKTPP8YQ8nMN8GmDHmPBz4Or6tlQlCVNaraYgMZXSVFWDipG6rh5trIoF3lWoBkp4jTFFog+lhaDR1hYiLYhekvx+rRQYacOGjKC2QmROEaU0rmlI04QtulkpR7yfmaYJHwNTsduJCV5+8zWvvv2at69fkbxo+kk22tCtO+Eu5cQ4ypxumqaTRcoPydypLATx/jAw3E0c+gPryw3rixWXn2wxjYIm4vEC4dYGHRLESHYeExTtXGMQ8WuTLCmK44e1Dl1QfJWRoNPVtdhb1TJ7MUphUmQ8iIP8MR04mB2THzHZUGASnMSzyzXk0hE4LaoHxzwJaR2KiahT1M2iWQpEjbOi0LLoWbZNLQa/bcM4zbjG0Tgn/LkoHpm5IJ27tiHnRN8f+bitMVozP7nA5AQpEXNA5AFkkwvzxFQUjBbFFl2Sq9O8t3AOlRJLpRhBP3JgV7Rtg1ISRPTiIF9rshJ+o3YOjHAFQ8qY6DEp4YlMWhPrFSEr0b4cj0RlS5IioJacEpNP7HtPGA0uG7rcoDzgRWMyVZqwcoxbiLYSTdr0YCay3BuEKuWcFbcap+kaS+XcCThHuYVhmAi1JxtNGEVU424WfmbOienYI+LwhnXXodBMo6euJAj+VFtu90fu9gf2bieIzhg5367QWvPXv/macfSigtNUjNNI3Wh0LZ9/01XMaNKkUOWJU2Tx7Wsqzs/PCCEyz4GX43UR/I8kMpVSVJVls63Z9t2j53Dx9BOepLTg66qSJEcr6VzFwFQ4eUpbnlxesN12rFY1ziK8TxVYdbXYd7kKspYgiCoGwEqqS2PRaKyDqpIxWl1rXKVpWkPTtZxT46fAPM7c3e15/uwCrRzaZFmPRd2LMnpZWvYfenwwWf7ZJx/z+Rcf4YwmIf5g/djz/cs7rt7eEIInNw22rVHrFXndEdcGrRt0yNz1M29ubvnm+28Y4kCsMtk4jFJYMh/XDUrJov/1N7fM8x21BaMihoB2im7bcfnRBdXxjm2uef7xJcp7dArkdGRUGZQm6ISaevRwxCohVRulSTFzdxyJDwALRitqa7HrjXxP2SxVDKg0o2Mvwr2VpR+OTHNgnGamaSDGGfv6jSyQLLqpr9+84e31W+q6Fu5hWgSjU0GBClm+qR05Z96+vSFEUYKfppEQA957mrYTWafNltVadCDnOIkvm4Zp2JPjxJe+x1lD3/ecPAa5p34sD7Ys3ixVqfzrtHCWymXxiDtVkmWD0FkEoA0ZdNF0LATrMI/Co0LjM0Q0MSmO+x1vX7/i6u0rvvqrv2Ae9qTpKGAd5zi7uOTFJx/z7KOPWa1XwjVLIoHkQzjpB757GDQWjcmaqrUYq9h+1lKvK9zKoW0sFArhAcoFZZQNYCNUgXYFzfM1/loT9onh+x7lHSobjKvws2caehptabrMRd1w3nZ0dUMIM6/evGba73FxYLaevpm4ut3TR8/WPsfrkaAmDvPtKakQLcbleH+ByoxNnCuarqapDM4VZ/oMVVXhrAY81lS0teXp+TnOVvikGAI429CdXXL25Cnj8Uj1+hXD7pbkPeeVQ6lEiqNwX01FOj/juNsLxSN4EjLX66cjh2HHvr/FVlCXak0VI+NcZqkhRIwVvmLTtoRQKBbcE7C1Fj1WUWK20qa3FRqLUgaMRRnpOCR7IJmMAdqmY60VRp8xBzHTtXlkU1uIEZ09Os8kH8Uzr16hnrTkMRGGzPSqx86R9ukFOYFxlhB3ZA2GurQBMykEshVOWc5gjaOyFZXNrNuK50821HX1jnZoZjjMjHbEOUmEqWTuVhX91c5UhJjxUUTX/Sydn65u2HYrnl9eEBF/5b4fOBz2vH3zhvV6Rczw008/5rs3t1zf7vnVV9+RUuDyrOP8YktWmd3hDdM4EXxAoQkpEWLC1jXmOLM7evzcE2NgniPOiitHbYQjuB8nbg8933x3wyKyoRS0TUNd18z4Yp1XNJEXgVit8cFz7Pe4OrLerPjFH37C5qyiaQVJe3l5zvEwcf2252yzorIOZ2pqZ7CVoqo0rhLgWYqa4KFrK8K2AZXYbmvWK4fSNVpXGFNh8nN2dwe+++Z70QatGy4uLwhxJsQZ7/0pvzxZYn3g8cHt0Lo5o1s/YVXXaC2tpX44MBwVV83Is8tzzjdnPH96QeUMSgn/zeWEDpmUPNYoLi7OyeMF02hJGqzSWKVxygpXBsf5+R4fImn8nKeX56y6Blc5Vps1l08v0doTE2BqDC2GhA01WSWSygQlSFBi4mTkE7PwK8PjNpSfBfoNBhWjIEiVaNKRIpWVTLdyimmSzD7EKETvEDkcDsXzTONnkSfSuz1N46mcw1pLDCWwNQ2LOk3XdWQy4zffM05eKk8/lyA4i+htalmtO0QiKAkqrnaY0l4V4d1GlG4MjyrBpYRY+I/vvMwCApLiJJ/+vXzHez9TEI/LIQRmaZ3l0s6dQ0kQxondzS03b15x9epbfL8jh0mCaAH9rFZrurajbhp04Xwt2bYCUcDJ+eEJP742DdXKkpLBNApTa5TNJ2Hnh9egSkUm/xBzV4zCnRtMpdGhRo+W7BXDEJmmkXE/sKladMq01hVaAuyur9lfXTHc7dg0Bm8i/Tgx7CfGPJPqhGqQNt/i0fMo6N1LvD08lms1RqoqV0lrjqI7qYuwtdaKrmvp2hajLUpZtHZsL85Zby9oVxuyEhRxyOL8krIQ3AWoIJu1UgW0YkpnglzQsbF4wA3MfgRV3CvUvTh2KgCZGIUTqHXCz/EEnnl0XUkCK4tbizbF6qN0M0rLbelGqJzROWGVotIaL+g1kSksXnspxgLJL/6ERmOsxTeZoGHUmT5PmMnT3R2oDBgiJntiMhA9yjYsyd7JbzIvZHEZBVTW0NU1Vr8PslBKKpvgi3h1ATzFhMy7jBVutJIEVzxYhXaVE2iVsEq6OskZgjU4DU5DpQ2ff/QErTSVNez2e+4OR755+QaA2lnwiRTk/Vxl5XMzYh6dYmQ4HIlxLo4nIoejjVgj+ZjIc2KeIv34UDFG3T+dpSWayOQktmgq6+LsImbD27OWzaZhtXLUtdAa6qpiu9nw7Fng1cUNdVURU5ACoBb956qSWTcKIooUNV3XiJ2chbatcM5QJ4cugtjbbScgn7Y+KUs560g5SAMj36/5xe/yQ48PVoypm0tW6094st3SuIxRkXna40fH7nbmj3+quTw/449//jldrVFpZtuuUNZiYoQ4sF5V/OIPfs5L0zMed4QYqIzCaoU1lQjnmobXB7h48oQ//OJTfv7Tz7i8EFuYiycXhOkznPZ4H0hYIVEaTaVkLlWUGwVVFgURRc74yTOHhHeHojwux7HvC0+wwiqDVQrmfFLT7xqL1nLjptkwh4CP4ZR53d7elgpKZjnGmiLwLDSIs6rCZ/HkWq1WNE3D5eUln3zyAkXml3/1K7z39P0om0gUPqWPgSpHmQ/pjDNwtpWfP9tuBS1qDE1d4WrHbrc/IRflKEg9pU6vq5TuH/MHlZ5oD1L+3L+HtHaWJ0A2hxOSNXhiENmlmII4dYwTh31Pfzzw8rvveP3Nl1y9/Joaj1VZWtiIXuDZxSWr1Zq6qspGKteztGkXyP27D7NSMsdBZ5pGCPeJWP4EdEl7tFIFaPswwBc0ropCkr5UuK14t+m9JR7g7m+uOex37N/e8aTbolNi3TR0VUXOmVdff8Xd1RX9fs/zZ08JOdFPI/u+Z1ATYRtpLwW5SXYUGHBpWeVH5/HwyCmirMFViqqRDcNaLYjFUkVqrXBWc3lxzmbVoTCgLMa1vPjJH1C3K1zd4kNgmD1jCPjTDVTShWhqAX5lWxIyhzECBwoxMQfPod/TD3uG+UBW8YT6vM+LinBxkD+pGO5qo8Tq7MEzGGIo/D+NdU4CbikspBNTgmCZW2sSOiUchkrBmKLMqKwlFNDWgo7MSagazlnp1KxgbjT7tebu7oCeAublFU/OG9raUOcRcWKroGlOaMJcEhVVxhpGZ5zR1NYIwM0YHieYsl+BwU9RpghK/AhDVphEAREh2qyGooYj1XNKGZQ/fZ4pBkG5R49JgVVT8ZMXH/Fku+L7qzX/j3/6z3l7t+P4y5HkPZebNU+6LSqJlGPXrWS2qTLD6JmmmeNBui5KK2xVqD5GMY5lFOIz05Top/DofqW0oKQRMfuUmH3AqQqjLE5bcXtvHBeXHavVim6lcbW065um4dkzS9duePvmhjhHvB9PHbC6UTI31IJyD0qRo2a7XUsgbRTrdUtTO5SqWZKlVdthDex3G4yRsY42BuUpSZGMnYzWD8j8H3Z8mLN8zvxf/q//hH/6F684W695/mTD+ablo6ctmRVffPYHPDmbuDhb84uff8H2bI1yhsk1kqnliM4DVd2y3T6j8j9l7g/EeRLNRAUpaxnim4o/+7efyUC+P7BabWhWDWEeRNX97AzlP5JKTTu0FgCKcH0KV6lkQ94HnJPFnhNMc8Ca2wdGrZk3b2/phxnras5Wa9Ztzfm6pjiKEKMEjhQD1shG4uqK2Qup2s9iUqmNpWsbnNYnUVjnKp5cXgqHaBCAjHOOpm6oqroIwSrm2dP3R1ErKYLFbWVZNxUXZx0X52s2qxVV9VzmaG0nAJsSqNXiefawenv0IOTHr5eF/HBR38+qlk1Mnb60/IxWBamixFzVlo0xBM8UModhIBuFrRxOgc0JmwLWFnAIpR1rNLWzhBAYhkGcLQqN5Pb6mturK/a3t/i5QCV5eKoL/SIQTSg4E2l5a8yj6z5RFB4IWZ8Cv4JEgKzQWZFnSJNiTUu1NlzaMy7WWzZdx7qp0Vk2r4129Bqi8vx6/5UocehMamesCaQ2EJRwoJSOpZJ7cBfK5v9udL+n2ZjCUsjYuqJpa2nPOUtTVXRNzaef/YSubfHTQLc9o11v2JxdkJUlZcUwzIyjJ2fNOAXCOHJ1c0N3d876uOf2+gqtKsJs2O/37A8HfvPVr3n99nuurt/wzbffcHd3AypiK4XBEHU+iXSnIm1nSntUIZu8fs9AWXi6tmro6pasBF4fZiFfG6NRShRHUoJAwuQkv6tU0ToGFn5tMopAZj8ObLzH1RHnKqrK0LSGvQ/ybGiNMVtymtnNAxtv6KrMx+cNvYdD2OEnR9KOjFRHZKisoaksbWOotaKuDG1ti4v740PrVJSwRAYtpsxx6Au6W/aXxQGjqrQgpn2kcqogfisW5HBM93NehcgPxpR5en7OdrPBh5nX17d89/aar9685W4Y4aOKtirUDRVFUsw51m1LCJFDW5dzyIyjx2aNDpnsZU/zOQhW4h0KUkDc6yNJdFa17C8UdafZz6Jzul3Tti1V5fB+lM5DsDjrMLZmva754z/6GWMvHOu2qQr6XhVVGzEinsbEcJTRirWKy4szqqJTe+Jzq6IVahRVZTge94Qwc3FRCz0lFDT/sq/HdAL+fcjx4ejQt7fMXtNWe/b7nouzFSmdUzmDs47NpmKz2dCuVgJPLbqMKlt0hhRmlHNUVrNerfFaE8ahyEJJBoW2ZFNRGSsUgcagtEVrCPMAOVI5x6rtZChvHDkrQQLF0mdIYmMTEc07o0VRQDmBy1dVfbqmnBGyqp6xIdNUNZWzC6idjJBOl7lOKn+jigJEFiSYkEoXAWvFw10uleCii9Fm8J5xHDkeDqU9IlWncxZt1altcHlxxnaz5uJszdlmxXrV0rQdlauo6lqCYAktKDHl5Z24Voqhd8AYS0qvyr9OE4EyPitX8UDp+d7zYGFscRLklc8oyRDez1S2wlqDtVoSHB5Uc6eMe9k4pfWrlCLHSMiZw27HYb9nnkbhPr4TBCmtp6yLU4RCEKoPzlK+bQmE+VHAfxwgC8hhTuhRoybF2q7Iqw66xKpraapagEjl2ZIWVyJoz9EcwWaMU6ATyiR0FSUoIua1y+86dXYfl1T351U+n3wydtWFB1rTVCIo3DUN61VLt9rQFCCUdWJAG1Mm5sDsk7TXfSQl8CGKfVffczgeBLp+cw3Z4SfF26tb7u7u+PKrr7m6fsXd7prdbsc0jSgt87mk5FmJhZOVYr5/ppa/CreRh6g8JRW+NQZnLSFFYhL+LCWAEgu/qzwbOVPkCQUoZFMU0QAltkQmxyIqPpFCA7bG6ExlMgahRyht0LUVlZlRZss5BlqnyVlsd2KcBHRlm1OSoktlqBWltWeF6P/O/ZKuiLT0ZK+V980pUcaqp2vJGZwunaHC+RXxfe75lkpjjKWuRVVFFVS1cYbWWZ5fbIkxsivi0CglurlWY5y5vwmALZJitXPSslYZq2PhmSKt3QzRBzmPB2smA4kk4gc5lYpQ1mvKcm1L217MBqRLFqMnFV70OPU0aGxlOduuqK2R+1jQwVqBjwKuAaniYozFy1OKDFlui1B76TwVJxFjNN57/JwJfianIK1gSjO3nMe7Fmy/7fhwxZiUGMaJt2+PvL25pWktL9+ecb7uuNh0fPzxczoUI0baAQiCK2aDj5lwPJJCROVE3dY4Z+gLT0YpRaV0IbVbbm6vCX4meKnQIHC8eSXlLoq2aSVLVIbZR+HcIRYhMUm2svhMpcmjlOh/ppjeQxuGBHPMJCWzHaVgP2psac+M/YgCrNUcfWLw0gaNOZOQ3lxG4O8pSjAIIWCMLPKvvv5GEJ3G4P3Mbr9nd9jx1VctSoH3I2fbFU+fnrM5W9G2LdvNlqcX56xWLZfna8matSw22VEDsTg/Ky2gnHdv+vLQp0yZC5T7yLvZ+v1+prjfmx6+/uh9xY++6BLKNUfvCXNgGo+05+Jy0XUVrjIs4xRZYEASndfoB6bRyWLOmXma6A9HvvnyS477PX48YpD50MNFLhWfNBdPzvLvbr7lWNrBJxDQaUYINis0FWHMHF977FuoJ8dPzj/DNRbbWHycaLuWGD05zcSUmdPEoI70dg8fB2gSqY0oATpTJwh4IrI4ReZezvrhx/nuPVBZqr958hhlqWxN5SqcrXBVw/NnH3N2tuXJkwucNWQFVbMia8sYAi9fvWLyQumpTEUYR0KAYfQcjz0hz+jWMhN5+foN4xS4uTry5Vffc3V9y1fffk1MIzl7jAtoE4VUr1XZ4BQxa3KA6H1B4D24AKtO1lb310h59kV/M88BckTnGR1mdFCoaeF2OYxKoBNDEauuMlR+KrJukLUm+Yjf3TCu1lQpU19YXJzomGnCkagMymQqo8jaYNSaEHqG48iqUaxsxNYBH3oJ6E5m0hkJ9rFoAG+fbjk7W7Fa1afxxsOj6VrqtiVET/CibWqLXi6Ze/Cd0axsjRJDN4IpVeGcix5rLL9X/ADFxFkxxXsa09OLLauu5vnlFuuagh62tLWTZyFFZh+YpoCzQRK7GJnnmRgT2lLstDR12zHPgf0hF83Yx1cmQ4WAz4GoFkAMRJ8kzSxdoapyQpxHhDAgFsm7ROpEf7brWiorsnzWKrn3GMI8M06D8JVzRqvI+dkKVBYEfgwEIinHE9rTq4QPHuuEPgYBP484owS4RZlV50z2nuR/3B3j3eODTXX/7X/z73B5fsl337xl9j2oxMfPzsVp3Ciur9+iVODp07PTRvry2zf0fc88TXz66Qsxvoyick7xPvPzLG2ms+2pnJVN1RNiFLitziVLk3uWpfwg5HyC3VqjmWfh3TiR2CCGTAi+ZNiRmGAcx1PtA7CkazllxmkqfKkgC08pYgil8lHMCeaUCbMQT3MqxNQMSeniPbig5qJY1mgjm3/OtF1L01Scn23oGhn+fvrxUyH3NzVN2+CspXIVdVUVWyfJvmIE0ctbaqnl5gRp9U7jY0RUCQBLS+EEfjlN/3jwevkRxYlfs/jhPawhQbh/WSlRrDFiRaS8J48jYTiithvhcFXC30Ev5yCVmhTtAihqlSFlCOPE0PccdnuGw4EwjYW0+wNRWJXM+f6kTnPA+/O+5z6ePovTNerTZ6BSljmH7XCVw2VLTBNdVbE+W1M1l4D4X84qMKfAVbxm6iZMq6BLJJtISGUj+8UiNawgL7qUDzZR9QPXBFLJAymIfVcqXM5xEC/JVbcSXz1VxKILV9DnQMiZpK9FFmyObLoNyXuOdzv6aWIOEeMzh+OR6uYGHyJvr275y3/+a968veM4DOyPR7RJGAtVo4XkrrVwHbPCZEUsIBmjjYwIkoCZjFXijpCziF0/OKwCGz36uKMua6bOCZNFlUTFLH6bORZJtyROImrGEoXrV+6lUpGoMhPgj0cGo1l1a3SM1ApWBPmM5owrXQqNRvuJkEf244yIvydJNIzGhlE+azK1dmxcy3Zd8/xiy3rdSRfp/d61+CQawzRORUNaY6pK0NHF5spZmStOPoCCqM2JkpRVgmRQKbHvJ2YfGCePqyy2cCCXRzxFi9WwbopykdZUVYU1Re2qeGjGLA4WCyWrrhtSSsVdRzATVZ4geFSYQOt7381y6MLh004SDkmApK2rsuxnuoiOL/vTssiUKqOtGJjnscyNMzkFwhzFc7Tuigh7xDPLM6Shrs2C5pA5c4JMKkm8jBa0VnSrBudk9KNyKh0TaWnrYrsVk3gTfujxgRQJxZ/+4gs+fvacs7ri5u4K7yeeX24gZ1JI7PY37J2Sdkslc7jbmz13t3cM/cDF+blUSg8yu1DQhCFGVtstmqI4ElNxYriXaCrQDpYoKJVeLuWAPnGXUozoSvz7opJSKKUkWp8p44to9OlYehZZvhZCIHh/8g98gJciKmmBhsLpIaWT8aSAACI6CMTfBGmtNI2cm1aKpq1Zr8RLsGssTWW5OD+nbduTLJyirN6cy4It6upw2kAfxjoJkBn/SAPwvjkorU0e/dzDwHdSNzkBBE63XK56eU0t4BmparSt0GUOGcYRPwyEcSBHD8bKZrbIaC2BVZcpSMoMw4CualHYD4n+KG4X8zgQgy+AJt6bnS1XppC2jtybZTZ1v10t13xfDS4/ff+eioxSBmc3glxDk/KIrQ3r7YrN2RnjOPL27StSmhjzxC4f8I1HV5Br2bRzyZaB+wB4OkcJhkWIj8cncn845yAl5qUFXLJ072dyntnvd8LVVJG6Ft/Luq6YgvAy56gY58Q0JabNDDEy7fZMsxcQV4JpnoXbZSy319d89dVvuNsPgsQGXC1yX9qY4vGmSbm0tJeZZZlZqTKncdYW4roSmtQ77WtnNU5lrJ/QMSIsJnVqX+XkMTHj9H1+ELPwzFAJ82CTXVrwAU2YJubeylrNmUorWiVcwxQSFgF3KG3RwRODZ8pHlgTI2DUqW/AjOYv7R6cyG9dy3tWcbTqapn4HbPbg9mmD8GBTef4U2okzQ4oJn4JI21lHSFm6NUXqThtNzBFMRmVNPI74mPAhYpwlKyUBKhfEblSApSmoe6OFliJrKZ1GGEvLWpeiQOQgM4OfZR/MCZNnmaFFD7p6LylTSqGMCN8vqMvliV7SCmlzq4IsXl6XtWeUhhyJYSbHhEyTMiEqWR2FH52SSLupsndbW5SIWGQD5YqyWnZf0dGVZ39p4qbTmr9f4okYg5gLfODxYZWggi+er/njP3jOzz855+bmmmHoJbtJihAzX345QfR89+VvyPNI0zQ0tuUu7BkOE3/9l3/DalWzPesITy6x1uFHz7HvCSHQNK30/bNkmNbWVEaTtQE03kesc1hbcZw9fp4JPhZ0m+iEbjcbtpstoajdO1dRVw0+BA7Hgwi/ot+5Nski5nE8lftDoTzIoi9zMCU2UgrRyozzRPSeHLzMJTHMc5AOhhI0aNNWfPH5x9ROhutN7bi8OOdP//SPqN1CfNenjVpicS62JPdoilSCbFpkyU79cnloY5RWwX3AevAnI+2e8rm+eyx2TjlL92V5oGJRd1ClkstZIPcJgzGO86cfUXcrgp/59S//kn6cmZJitRLLk69/+Zccbq4wLCjVsoSUTCGncUAbS5g9w+HINAwMx57kSxtR55Ne5kNFi4eZp17ab+pxq3P5nmXTPLV37+86oCE5rO5ots/ZdmdUQeNvvuTi+RN+9oufcLY+4+3VW169+oaXb9/QM8CLRKoCwQaSAp0MdbTkqIvLREQjlBGZDy3bx8OzeD/hWK0aVE5MY6JrDW2r6TpLRuZDr6+/I11l8peZ7dmKqnZUxY5KKQW64th7docZFbXMYUKiay1NZWjWLW0nc8XGGtrK0nU1UxDxAFM5jJGNRlpUGmtrUi7z3nGUbgSKzWrN4mKfs1QPlZW5XtQAU/n8Fc8/ekZrLMPr1/T7PSkl6lWHyQpi5u7mRpLa0+O6ZGpBNE3LWkQJ71WZCtWuyTGJu3qY0NZRVVaI2rnIn1ESk1RQmLYm+EhKgRwjlcmo6Mn9HdoorHM8Pbvg7MkZ588uWbdN0Qn+gSNn9vsdtd1wtmlPXa39cZaHDqleTGPpzjta26CQrpKTvjd340E6NUpxvnXkbCE1pxldzInb45HDccQPnso4uqYlk4U6o63YFc0TEGhXHd16jXG1JM/TxByEz3k4eqm2raE/7mV/ydA4g3nHLDjFiEqSfDgjllLKqkIxU5L0FDUlW9C6KlsRajeCD1mOEERta7F0yhmOh4OADLUpQS8j0TawBMn7Ja5Oa3sZq2gjmsEKTgLaMS+pjWTF2koy96HHhwusEdEm0TSWzaalqpb2jyJliP4jfJALIUdyjKy7lnB5RmU1tspUtRiA+lkU6cd+LuhLzThMZaNehpoZlCiWozXOR5oiFLwEqBADWlvpGfuZmEQW7fr6pkCRI9vtFjKMs7QLfHjsdyaqJPkE2UYJ0lNrUzbgdCrz76kVCmUTJi+bm2yqVRFudc7StjVd17Bdt6y6hq6tqZ1ls1lTF43SUwBkKUbvsyR5LZ8CXi7nmfJ9gLwHeSx8p/fv2oPwUTa4+0V6/z2qDKUek+SXqjSzOIpB063oVhuevXgBwPcvv+PN1RtyVrTbC3Y316Qwc7i5xg+DBLRS1S3+geRM8J5pGIjeM/Y9wUsF9EC3+tG53B+5VH0PrvFB1fe4A3r/36weLC60VGezxaaGs/Ul5+oCFzV7fwvGMk8TsfVM08DN3TWH6cBsfIGKG6xy+GXpqSS0CzglDPe/651tNN9DkR4eRot6f3KKTCQGj58HcmmzosR4VGlIxhO1aNJYLT6IMXv5k2bCLIFAzR5oyNnRzIZxHBicoW0aurbh4xfPyVrRjyNJLW00BAxS6HyS6EecjUU2UJUujHRsKNXGTGnjPrRSUgpXVaxXa5599ilvv/se7z3tekXygVjm9XnpfDxq3VMSp/u6WqFK1VAR00xOGT+O2CrK2sy+VNBFoq6sGVU4hk41RdA+SeWtDAlF3dTUbcvZ+QXrjdh7KS2B94eew0xmnAamuca0lSSnsczPEqgkz3eINUlB3bUADMdelHlyBmUFKFdQkNZo6qYSukRKZJ9wWniCuspFStELdapyrFZrYhSj43meZT1rhatlzjanzBxGJh8LkVz25ZREyUlbxxxg8o9dWowqc1xZJUI9QGTxdFZFSN5grCngPCThKvuZVuZUwS5dO1kmshMtlaHiXryfUp6cRjVLHrT8R6XSgboPiLKmswChlsRA3d8rZd7dN378+GDZNB9nYvJoa2m6GldL627hZGzXLdM0cXd3J89vipxfrOgay3y5ZQ4jC8zVe+H5HfZSMVZVxdCP0ooMocyiJIuJOZOVpml9CZhC+lRaF4V5+VDHcaQfA/0488u//o3oQCr4w1/8gqqq6McCyZ0el8kL8TezVD4GU9WiaWfMqR2qlwwPSNpjUKCNzP6Qiso5i3WWtm3YrFdsNivOtysuL7YC/XWSBNiCmqRsmGQeJACPAQdLIEypoLXyvRrCqUWl79Vhfujewb19z8kFYWnj5iV46GJf94BcXwJzyuVz1rA5v+Dy6Ud88tkXvHn9il/96q+5vnojdldPn3Pz5hXH/R37qys0kZIEQkGiJeTf8zSTQkIrxTROJY1QBQXIfRaoHz/MEsPvN0lOAftBAHxQAT4Miqe2TlaQDHmqsWbF04vnXFRPsEmTpluyEupAWzv2h1teXn3PaCPUiipV2Oyg+EgmAkkXSapT9cejhZweXkJWD9f56TAmC6cMLX56c2boC8Ks0GaM1ZjKYJuMsQnlIsYZjEHEjpX88cETJk/oB5QKxFRhazAqoXPk8nzLetXxs59+wZwjd7s7+kkcxpVW1E2DUrnMWUARqZx0XXKG29udcARjPhHt4+wxhsdBEHBVxebiHG3/gGmeGfuBbrthPBxJqUcjyOplDcgD+aBdX9rwugQ2rUTggDlCysx9D9FinCanUfZc5RZEmKBWK4eqHFV2J9qCda6ICii67YbVdsPF82c0q466bU9C9z+4pDIc+4HNqqGuDSFEok/kCCoJitjPMz545pSpVlItHoejJA85kXWFnyeGaaZ2GWcNq7ZlnkdC8EQfqY1B1RWxciLqMUxM84Q2DWfnNTl7lErMfpTHSinpDpQnf99PjLPo+aakS5JLcfZpOQwD/RAeLSejFa7Yh8WytkSM35yI6ycTcl0SkyyKXFrmHeQcHqCFpbO1NMAlWSjrMksVmHIovz8/+oyFvyzXYkxiAYWcRL6ztEuTzqWdKshlFfMjLvjvOj64EjyOE4dxom0tUWkimXkYhE+nsqiNA0/Oz4W8SMb7A9M84v0sC0vyZFLKaGWpLrqTosnSepD5imQvh8OB27tbhnFG2R2uMtS14w9/+jHOWtq2LYK1IoabsszUD4NnnsVf7+3Nnpgi/+zP/xxrDeO0GIOWD8A5qroVZ+Yy9DVVU3zkTJmHlIFv2ZAbVWPVBqsVbVNRO0tXV2w3a9q25uzsjNVKNB3Pzzrq2lFX1cnG5HR7ik3TKbCdylFOld/Dc4X3FWAEUv2+kes9inJ57wRZhIJVeSSXQ2D5kpGdqqeCbloQpu1qxac//TnPP/2CZrXmr375V+xvrznsbvj5z/8Ioy3j4cDtm5f0+7tS0elCojYsMm5xASL5wDz7+0xRq5J938e25XN/eKgHVdby/x9XgMv/OAXIR5WXMpAsKtRszHMu2k/47NkXNKbBAuvmZzjlsXi+fPkV311/y1j3cObAKa53dyxt8/VZQ3SB2SoGPRSZt0QusNWUBdaf4cFIMJ82jsfHjHWGtq2lDQzkAjlVBlzjUBa0VaBmIp6cFKSAwTJ7hassF082zG1k7icOxKL80pPVjAozOnnevK5IKAYfWXU1tj7nnI5xnJhnv7BBSAnGWVSM5jnIOs8Q5iDOB0U7FCCpVPi374cO6xzrsy0vvviM/tgzDQN6u6FuGnLwzMPIPAylWikGZUajtEEZe9pAH2aH4hKQmI8DcZI2GfOMVRZlLK6r0dbi6vr+HgRZa4mMsQ5jHbbtePrxR6zPtmwvzgvooyj1vNeFWBZMZuyP3N1qwTtsz3DWYA0y24sKFySRutvvePXda6zWaBLbTYtzjiEobvZHbm73PNm2eJ9ReWT0k6z5GDAKOmc4Th4/eW52Qh0KYeKw39G1LW3TMU2RGDKHmzu6tqKuG9arDj97nBGLpegFzOdcRdZa5NTGmXl6jCVorKMuerPiKCPrUpeOgy7ULKWEMaDQOOOwSn5mnqUjtzDLZG9ZTHfVSR1KLI9saauXOZ5kMKcOY0QqvawgRilKnDOnLlZOIuVoMRSonCQDVpPtv+pKEAr0XwjowYuYcvCerATdQ5khiSizlMQxlfbog4nIffUhrs1CWxAQiFLSy41lLqSVxllHsEk285iIXojIInJsTgEkyyeL1ppVJyROsgispjm8Vxksh7EWWwRyHxqoLmW/LbJMzhq0syfVjsoZKmtYtw115ejqis26o65rNpuVkJwrdxrkGm1KQb8g6ESUOBa1A2kRLB9RgaU/qPrePR4GvsftNzlknlb4RKWt+fCbVIk28j6prPklAMKitKIVrJqO1XbL+cUF1hiinxn6AznFIizdEKMIq/tpFGCL1pDTowCUs4hJLy3RhQskLRX9aJO7P88f+HcuWeNyvg8D4+k+P25IyvUqFBaVK1Ru2DSXnHWXrNs1lbFiLGy35LknTpGb/Q37aY/qFGoF2UEcE8wZFSEOGSKY1okrQRaeleL0mJ/+nKrDUgm+WwpWlaKuNXVl79tAZAEpGIR/UWaNOSa5ZimZiSqJfJyRToOOCp0ioXMyckgBH0bmaJmjpZ8GUIY5ZpQRj0Lh7ikSYo8kQVBmXSnKXfRR1l5ISXhkOaPz/Uw7ZYQH+O490xqDpdtu0MXiJoVItJZpvSpgHBFnzjlJALDiWqCMKcEe0SU9JYpSdcRiCaZUgpjRVmOto91ssHVD3TYFZp9OVl8xJQmAVUW72bA+29Jt1tgirCHP0Y9vohmYJ888echG1nPZP8TzXhCvcY74cWYfEs4YusaRggSheRIqFTmfWsLT7Jl8ODl66CISrrUoUdlKgCzGWfFRVBWLH2qMQfbneZa9Rtki7mHJqRZethK5xawWk2aKaPv92tEFp3DaLpb2opaM9BHyuixBaUWWDkfp4p1Qp4+WdeHPlpWsjSk/p4hL0OTh98pry8hK5URMS08H8gI6yxkV0wPsgYIfATT90PFhijFACgk/zqRJAqAgPWeiEajsMtANfhbxaKMBgzPSOopBFtkC/FgsNXwxz0SBKS3I6XgUSSStef7kGVkp9scBbQQ6HL20QlCyeFAIGssaulbxR7/4yalNmHNimib++A9/ClmzOwyn1qFSiqpuSpVKUbBQOGtEGcZZ1p0gNzebFdutCF+vu5q2djRFhspagyt9cjF4uJcsw8hDk5HNYyHI51IBxvC40rvvIrzf9nxY0zz++vKf5fHivm2Rc+ntLz9XbmiZr0ib1yyPFYsm4/J7jdZ8/vnnbM4vuLi44PuXr7jb7VA58+T8jLPthskHjrsdb15+T/TzibCbCyoulftNySCXWYtW6tH1nMBAy0f3bhnI44C3+CM/THAeJTp5aZ/Kqzpr0ZrNHZYzPn/6R1yuPmLbbamszOUU5xzvInf9Ld+8+YZD3rH6uCGsE9FkXNCM1yP+biK8hrptWV1saZwmqJmZPTEVAYdSgCehv50CYY4Ur8L7az0/r+k6aacnhL86TqEkY2XmlyQBzWTQUg0ZJXPxpl5jHNSVwiSZMTm3Ync3M42BkHvmrBmz5RhnlDKkrEgaYlTshol+GJimgWEYShBUYoOjNFgjdI15Fg5Xmce4RTYIjY8R/45V2cOHcntxSbcJVHUj1d84kWMghk0x8R2JPhDGSazIipascFEjfp6E6hSjzJ6SIo4zoSTb1sgoo1ttefL5T2g2a6qmEkpTjDitmb3IitmqompqthfnVE2DeeA/97skt3LOHA4DbdXQViuyF2EEXZmSnGu6phWh95gZxxmcolk1NFljfOB4c4VRjovNiu1qDQiYbp4LTUtltKlQRmHrmnVVU2+3kljmJN2ucSe4hCiqKT4EjvsBPyfqdkajaGpL7daEOhN9ks9KSUdhOB6pHs3OFMZatLWP1qIk5fecQb20PktgxBU9qJSJWpwqsi6JZ3q4xgFklpxzpnUdJEPCEaLQ9DXq0V4mnYF80sNNoeAlykKPOWOyfHbCNxX6UH7g+vG7jg+uBHMqD2KYMUqX/rEpwskJZ+6NWVVReFhK14xUUzlLCV3Z6vS+8ropKg6BFINUhVrMTI21qMJ5ObUq4iA3SUvWlzTF2WFkGuciwZWlxaDAKMVHz54xTZ6YH3u5bTYdZ2dbaWk2Ika93bQ0dSWu3Stxc2jbhqYWS5jKSoa2OL7LXFSfWnin2dND1Mvyd84PNkB1WujLDFTauvlB4OMkcrzsLhkKYVTeA94HkCxV+T3b6J1Cq2Rx9+T45WE15fsW0WVpZc7jyDe/+Q03N9dM48hms2HYJ6bjkcOxZxpH0jwW7k45By2IQXV6rouklFKnOefD830ofqv0QrR+DGlVlKKI++C3oLQlsN7//6ykWsoKTNbobHF5w6p6zvnmM56efcym2eKMomkMVmemIXN3d8dX331DHyeCi1BlfPbEFEX0fS3ZODshJe9u9+QKlM2YukYhVAmtFNkoIcv5cl4pk5XB2cfXvuoqupX4NPoSBZMHgYyLfmiIScTel4xdZbIp9y5nwjwXTlcBVKmArQBl0EC9qqi6CpwY4k6z5zCMDNPE27sbpnkQOcBSCcaoqKtQwF4adMI4hXGuuEgkBLQgGXkkCfT/Bw8lWprWsj7bEtqW4D3NqinamZ5pHIk+EsqGBjD1A2Ge8dOMdqZ0DjJxnkgxYirLwsysXEW7OePs2QsuPnpO3bVlL4gnMMbiVCIav6LOtLTr3zvjHwmGSinWq5WImBtDP87MIdBQkZCqJYwTKQQMooY0x8jdbSZ6hzMGsmWz2rDenKFUZp5HjtOBaZrJIO+NwaCpGsc0B6Z5JEbZW1xlKR87dduQjSEUqTyyxqqKoe/xs6DXT4YqlLGRL/vUe/OGpf2pTxZaD/ejpeqTfUqeNR8F4ESWtngqwtsKWZDKWan8i1pPeRoIZY6e0CjrMFmea0r3KpdRzmm/VPefv3hYlsp9uTBthHKlVKFWfNjxgUEw8/bqlnXbEOYgGVeBLYcsrZGqmMUqkiivWwPaMvsgvLkTTF988UA2kOADMUbmgmDK0iNEa01d12gjbuzW1kVxHsZ+Jw+11tiqJgP7Q8/xeGQcJxFlzpkYJJjqouc5jhP7Y/+4xVhQU7oIYFdWUzsjsHKnqYuZrkKUTkIKZXO6/+wfftzvEdZRCzCqCAcLr+/+50qFl0VseKEy6OIZKBWyKbNApI2YKfD0RXZJcXu3e9QePYxeFB1KQJMKUKLDw1aq0vfCSY8S+CxB0JjMzW5A6Yn9/sjxsJPBfdF79DFx2O9PrhLLacPSasmn1uoSmBWc5p05pdM5PAqEWirF4xjuzyvD8XbGGFlwy3z0FMhLC3ipNE8cIw0mG3SOuORRbaJdKe7MAe8Ce3tL11qsyfSHW778+lt+/eU3XB33hGpCmcSoJqKKRJOFghAVaczEMRP6jHFGZO86TptANmWBakU+TQYypMx0eBAsssyyQ5DnYI7gQ2Ka4ukblMqEILM5+RwzORqMidK+Ch6lwmmGI89/wM+Fb6vAeyHTZzURQ2YcZnbHnn4cud0d8H4iLK4gZa6TImJem+0pg1d6IQ5BLq3SXATlY7h/ilJKfP3114/m2ktgiSEUUWkhq8uMSgJb8KGALDL9/oCfJqFDeMkktFL4cSAV6a2ly1xVNS0a33TE67dUfXOSMHvwWMuzvbT2PhBA8d133z1aG0NJIOaYBOClFW1XCxc2Z/phkDZyyhwOPSknrNF0B+F4xujIukK7jtmPjEPP3e6GqSBmpzlRF//Iqq4Zppnd4Sg6AkrRNOJSsexB0+QZJs+UDXU1M82B3d3uNPMTrVbIi0BFyuz3Bw79cPp0Usq8fr1jmuPp/slYS9CXymisWUKGoFVh4QvKXhNCKAlufpCkqlLkSLt2aa/W9SjnEuK9vOQ7mIclCD6yiSu8a+3sI6EoQQ5LYLx6e/jBhsQPHeq3+S6pk4Ak1JX73QPj08fz4P9kfvfJLJv3D73Pj/6uU9e6vEU+/f0Q3fjohLJk6PP8wO/M6NOgd2nXPaIInAKdevjr/vUcP3ovfuB3P1rcEtSmB4R5Cdz/ak5YLZEtFxh7vv+M8m8973/5IwPhgQqJdfe0kt/v8h7cU6Ry1+qBcuISOHMuGa6g+O77q+/MuvI7fz/+Nb/zyFmkqJZjEWnO73zPOz/1A5d1n0r90ENyekzU0jq+z1Ierpn8zkJ9d+n88PP3Iy89+FrXdT+KXH5v78n5/bd9kNh9wE5yqgx+mF7zL36klOj7/vRvZ9+vHt/dvO//sXSHHq/j0zmWrz9Sb3q093AakZy++s6lvf+1Jbm+f49H31/+u8x9l6Np3DvXtdyTD99NfmhJvPt8PTqpf9Ht43Sd77+cUmaa7vf5/Fus5j84CP7t8bfH3x5/e/zt8bfH/z8evy0IfvBM0Fn72zOrf91V0u97/EAVuPzlH4irWltU4n/k/B9ray5ZdP6tmenDLO5htv3wO3Th2DzMAB80bR5fx7sZk3qclS3v4/09B9I6905FnH/w/i2X8VuT5pLEvZ99Ps5gl8p9qTjeqyYe/c77wZ568F7v/oKMtM6WwywziUdIn/ffb5krSFXwoJI4XYg6fc8jiolSp/t3kkNTFHm8fPI8fFy1qAcV8sMiPZ9+Z2bJ/O/VLOZpun8H/bgDcTqHR9e2fHW55Hz/2qNn4l9+MeADkboAAQAASURBVN4LNKT37+Mj4MLpP0u58uguipWZfq+6eHg97778O87sd3xd3ZdO77z86LUf+p4feKvTb025CEXL4ZzlcdX1+Acfr5V3f/FvOR7sE7+lL/TDP/pj3/cjFddCVwvhMXf6cUvynZZHvq9K/zU2gB6PRt6p6k/P5gNRkX+Z44OCoNaa/+l/9B/yxaefnHq9S59oaR++u0GWrz64qA9oZXC/PwELjuSDjodB6b59wIlcvbzP3W7H/+x/+b+mHwaM1vwn/9H/iM8+/QRbW3wMZDLOViidgMj1zSv8nEhe8/mLz2mbhpBGrne37I77ol6XMSqRQsBqy3Z1jq1qlFHcHd9wHA4c+j3TCEYZOlfzRz//OdvNhuv9DTf7O+4Oe9G7K0r6IYsY8DBPdE1FXVmGw4ihptIrVvUaa0SuqG5bDoee//n/4n/LOE4YY/gH/+l/yvOPXgAFmZuhrivqqsY5yzgKH2mhaIhxcFHK0eoUUHOGMHvpgmtFVddYZzGu4s31W16+fsn3r18CsOpWPD2/ZN2tuHiyJcRITBGnHc466qphGCemaeL27o62a6nrhrZtuLu74/WbN/KclPsfguf25pb/4//+/4D3HmM0/8F/+79J01Yc93vm/ZE0TeShx98diP3E+nxL3dV0m46LZ8/o1muePHvCcXfFcDwwzxFXn9F0TzHNBc1qy9MXn3N1e80cPBeXH9N2K+qm5Ztvf0WOmdp2/PL/8/8kzyP/9T/7r7G/e8vu7oab/Ug0Lbk+5+7mipQSq7YlzANxHhkPt8SpJ4wHAoZ6c8lHv/i7/Bt/9t8g58w/+O/8t5inEW0Uf+fff8K6qWl9w2ern9HVG9SqZu9v6cOBSXka3bG2F9RU+Dzz1r+ibjqaquPji49xymGywdhGNDOtPUHgxWJGkNlLc1KjSEbkIHRekplM1PDy+opfffc1//zbrzjOE8osxqyGrm2LhBJidzXO5NsjeT/gjxPfvromI8/bf/I/+R/zk59+wSeffYq2whudwoCxApIyqin0GEUYJrEWQ5G8oEzbqkU5C9YwhAMpz6Angp+ATG0bctbkrElR3CjqpmMcBTwntl4Oo0XgPmZIWSThIBPCTPSz0H2qZTvMWNeC0kQ0gcxvfv0b/uP/wX9YJB5r/sF//D/k4vISV3cYVz5v5agrcf9YtQatIkZFUhwK0d8BDnjcHlZKfBZF+LnM1GJkmueTnrGxrqw996i9vCR6MoeL7O/uUCqz6hpxrjeaumlFOyDdJ+QpJa5urvnVb37D/+p/8787IbifPHvGZ59/wZ/8yZ+y31+jYs/K3ODijE6B4xz4/mrkmzcD/SSmBMs4RrZf/SAvLc/eCS2PJIEn4E2hrMT0KJA552iahp///Od8+umnfP755yegFMDd3R03Nzf8o3/0jwStvIBtShBKKeG9F8PzDzg+uBJ8/uQJn7x4QcqLpYZc/MNAeH9j3gmCSnLlH82+HvS+H9I78n0iXV748fN7KLX0sLLIpx+T/9809Un9BaW4vDjnydNLlKUEQQSQo4vSqJ3JSWNVx88+/Sld03Acb6hvHO2hxod0QonGacJqx9n2KU3Xoa2mvs7sjxXV3tD3CY2mtY6LyzPOz89wK4ttDabWzH4CldAmiTlojvTjyKqtqZ3jYAcMFbXu2HRbnJHFXTUScB9ynJ49e8bHH39MzjKUVgiirXLiA3Y8Hh8Aa+Rn6tqdssBxHEuQlMH/KQgWoW9bVejKivpY4ci1bcvF+SXb1YpnHz0jpECIkcY1VFZ0XPt+YBhHlLWsuo6mbVmv19SNmK7KzE8Wlfceax9UtEqxvVjTrRtcpeh1IvSQmfGTI6bIpq1oVg2rTcvF+Yr1ds3z5+f0bWI8Wo6HgbpZ0a23VJtnNN0ZZ0+fYtuaEBPPXnwhxq/BUztN1pnGGTZtja4NH3/yMXUFRmUCPcmuyO0TARD4GWcVTieSBUdHsAmvAsm2dBeXPH3xKc8+/oKwIOrKM9usLW3jWE2OxloaZQFLMBXK1LRtTWPXbMw5taoJ2ZOzp2nWNHXH07MnAv6Jihg1CZEc1AWM1FSVeNkpfQp4OQuSL0OhowvCyKvM3g+iwVhZDBFtDbrQNUxbFZCaEoUU68ge/BDw+oF6v1Ks1i3nl1tefPIcWxuyzsyxlxmoUahQY1SFUxXJR8I0c7zbc3dzh58mbNeyOV+xuThjyluRhYtHUUnJgjRXyqKUgSxG1k27KjQPoU0sh4DJBOTTtmuUUgQ/EeaJnBJd7cTmKwTGOaGUwdQNpqkZp/FUAWul+fijj3j+4gXd9oysWzKWlJz4fVrH+bbCKo9mYjxeQ0pYUwNyvu8eqVg4ee9PyOlxHJmmSYKgczRNKx0erR7MswXcE1MUbdKi9btqa3ISNau268gIbccYUfiRABTYrFaP7pc2hqqqWK3XkGdUUmzMQBUUKood066PVG5mCvdNgdIoOW3m9whz6d4Ya05gP2sNWhtSlDU+TfOjIGitxTnHarXi/PycZ8+ePQqCWmtijFRV9Z6E5BILfp/q8PfQDn1Yli09uvtGiQBSHn6/4kQVKOHo/XbNj7cGlqz08aX88PfnR1FSneiY7zcW3v9g5nni2B8YfU8ohOG2bbFW3NN99DTViqfnz7h8ekllDf3rtxibsU4xTgGtHNZURBIJS1QWW3dCEUmK6BVx1szjLA83A29ubwgqoQyEGAhhlsCkMtZBvQyoEySvCEkRBjELTnqi0hPBlGptPHJzu3uEoFqCmSDz5OFumqbold5z99q2Ebh94WAuAWgcRcZuGie0KqTWBCpoEplQRBCqupLquKgEzfPIaOQhDVEQg7qSTWqRE0xRLIKUElGA5WG2xtJ1zUnUe55njk3zqP2mi3hv1VSMRS8wFZkka4uYetF9XdwYMpmqqTE6MQ89zkQq6zlrDMbCtL/FaIdtWjabDV9/+Wu+/JtfcvXtX0OK1Lahv33LqusEWOXFe7IfJqJzYELJPkfurt5isohoWzzRT/gwY7sLbLPGNWv6caYfHqOUxRA0kXzi9vqWQxjBWGLjSW1i9ekGmyuEHCzE7LP6KVXbULkaosb7QBwD19c7xmniMIxUVU1VVTx//oxV29I1dXEmELubkDwii65xVY02lj4Fbm9vePXqFf7YF7PYTNaJYDSTGiUgWoszTrL7Gg5px+Dj/WrLmbfXrzi/3vDkcElDhbYQGfE5QsgMO0PrNmy7SypbMxwn/uZvfsNf/Pl/wc3VW7747AV//Hf+iD/9u3+KdoqYZoZjzziPRYwjUzWOuq4wpgEqdEpE5kLMNvR9zziOZfuo0KrFVg6j7QnJuKg3jcNEfzzyq199hbaWy6fPefLiI1JYtC1lG6qrmu1my+VHzznMFbO3DKNlTooUNEp3wECOkd3NHSl66qoB3bJsuydwWUFKz6XyWzb2YRhO69A5x9RNLAjrhbq0tO9joX7c3tyQU2BvFP1hTwiepl3LujCGtmtPa/z65pbbm+tHG+lyHsfjUUTAUaTijKGjFECLQdgjQYr87h4r+7dYOlV0q+Y0SnDOYY0lhMzx2DNNMw/jyb0c5H3V+PD3PNRUfngs1/X7gqJ+jyDI47jHsjfJxaplvvLgGx8hnH4w7t3Pqd5rp6r7wPoA+PT+cZrD5Hde/rFM4PHrMScyEeMkE86Ajx5fnOr7UegdVb1lmrLoo+73hBhERcboexK/dSjlUNQ4W+MqI9WlytjKorVkfHMMfPf6JTeHHatVQ9/3DP2MjwuZPMMkiy55hc+QNKClWpmnCaWPJ+pEzplj3z9qHdd1jXOuOFvk0+xL9Fk98zxhjCXnpR1xX9E/zErHaZSs08gmKRlnYnd3zThNjOPEbn9gGMU3cj4/J5yd8/lPPiekeOJlkSAQT/zGpdqc55mqUFq6rsWd6CAKlaF21aPb7ipD3dVYq5n2e+I44SPkJMszZUXwkf4wEPM1u/3AECJtbaic4uzykpw1PhyAI03dcPb0DNU9J5mGm/2e3e0V+5vXxGkkh0CMA344kKzwj5SxKFsxDG8x2dCtAllF8WJrWsbjDj8NWCeCX9Y6Li+f0JxtSX7g9de/Zn93+7gFZHVR2VHEPsAgdkhqo7DJsK3OqKoOZ1ao2YkyTQocbif8fOBv3v6K4dAz7A7c3N4xzZ7BzygtzitPLs95+uSSJ5fnrNsacsD7kRxGyAmjDE23xlUN+xR59d133Lx9K1ZBqmx9VqqMMHu0keBhaiurRiuS0SdpwdNq0+Bz4NAfqTcObaVq2fc3TFNPq56hzQrrDHc3O7758hv+4T/8v/Hm+2/pjzu++epXfP3tb/jrX/0l/9bf/7dYb9fiR6oyc9RMfhQCeMy0zpF1ApPopwPTNMlaKZuisQ5rxCBbjJKF1uMqB8nw8uUrvv7qa776zW/4y3/+S1brNX/4x3/Cn12c01T1/e6mNGfr51yuX/C0fU4Tb5jjgUn3+MFBrFlfdoDFx5rjwZBiwpzXGPSp27Xc/5M2cNnYF/rQNM+nIBjKqEaUrQy5cifdZmMdC9VLZbmmlEXabZxG9vuj7HpKzMUXlaj9/sjV1dvHc8gsaNFpmnBWSYWNyJ1ZlbGkIpT9A/OvR7FB0TQN63XHkydP2Gw3GGMIYUYVjdHb231B6r/fIjxxARXvBbuTStgPVHyP5vsfeHx4EDx1bjIPo9GjWZ969BUeB8L354b37/F+IFwi+m9lZJRTeVhlPg6YDwb23Ps9PDxSqRT0ouySEfPfIqwdA6SoUBixIkqJWMxBTwkAIjTc1DVGVzR1W+yLkuiaqkSxHzud73EY8FHIY9M0E3wiJyVKC8UNg5zJSfhjqSipp5zwMTL6GZtM4VyKM/W7n6lSheuTMiwVYKkCYgylPbEsxMc6pA8XZswRnY3oMJILXPwoAgXzzDRNDMPA0B9YOUNXV+RC8hYO6CzmpsU7TBauPfXzF6DSwiFcbuIP3a9cnBSsKwo9RYShKAiTk1APcvbM8YidAtk5ttuG9arh4vyJbDA+kHVEWzg/32DWF8zUfP3yJX1/ZJ6OIvGWsigj+YkYmjL2EDmveZ6wypH9QJwGop/k+fBB5qi6ZPxaPjtrDGEe6e+uOBQLoeUQEXSFRqOCQs2AB1MrTDBUusIoS4yJMEyEGBjUwHHsGYaRt9++ot/t6Xd79ocDcwhMMaJUJdXd8cDQH+mPe842rdg9xRmbZwwZowx+nLB1w60P7G5uGQ4HorYiXVbALRnuhdyBZGMRDReB96TevV9S7fsiBJGVdDPmMDLOPW0bUDqhdeZ4OHB9dc2337zkuN/hx57DfiTlwDgNfPbFp6T8nGcfPRcZMZXxyRSj5uLnaYTaEqIIiffjIOpP1sn+ZBTWGZS+B/6E4PGz57vvvuerr77m17/+Dd98/Q1nZ+c8ffacFISLeVpbCFm9copGJ1Le4dIel24Y54YYGvS4IuTM7CfGUQJYypqHFr25POvvbeolCAbvS2UoFAavJ5KxJCM0oUXx6V7ofylGZPYbo3ijxuRP0rWLz16OiWEYHoGzKOeToniiWuvKvPGBdNkCVHm4Nhfw2WkfLvKVpZ354sULtmdnWKOF8O8D3geOx+G0H7xbST4csf1YEPzB48dGbr/l+P0qwd/y7vKhvN8m/V3H71G1/vAv/YFTev+lHz9v2UBlQK+dBKoUMyEkQkjMKpFjZtwfeL5a41zFWdNydZwZp5FpnjAZTJX4+NknrLotq+1zXt685vrmmpgnkppJxmOcAmXRVtoX4+ypxoj3ER8yWH0KksF7co6FhDyTc5SWa8rEJDqDXkWMNmw2G7pu9ei6hmGg76WdYq3FYEsGmshZyMnGyiJayNVTyZqXD0aASophGrDWsjIr0CUIDj27/YHdfi9BY54IcWYYj/RDxTT1jNPMMEwc7u6wxtHWLdvtGa6ynJ2dcXNzwzAMxCgV4zxNxa4nn8i1fZldnq5rOtLMGqct2oKptMy1UoQSfLyXz3eIAeUchwDjfEbIFX/69/5LoDOTnzgeRlZdw8c//ZyoLtkdA99+8xW3N2+IYWS12pK8p0+3DH5mnKS9pbXBWMfh7gald8S55+rNa7wPGFOXYBiwWSofbS3eT+R+L23joWHc3d0DB6DMlcWPrTE1Vtc45QpQyWKS5bjrubp+yZuvbun7gTu/Y45eNvxDT5w9aZrBCP+1bRq0aZCW4Mg3w7d8/9031E5RV4Z1W/F8XdM6S2Mc+0NPUobv7va8vttxvLolOYeyjqqT2ZhylqhBWYO2BgrQRvnInGeCelcG0JCwoB0+CuDlOPeEHNAuU7cJW3kyI199/SVff/0N05hoqi1ttSbMO26vet68+iuaZsUf/PHP+Xf+vT9D15rKOrIzxCyE7tv9RF1BpiJhQFcYG7GuLr55CmtF/amuWlJIHOaJr77+jpfffc9//n/+z9nv9gzHAYUQwxeJSB4kmZlMVK9IaSbPf001/RV2uMEd3qD2NfPYcD39hEOouPWW2zHgmpYLvX20Jf7Q9rUEhKHvT+1QY4p6S5xPQS/6SdqK1lI5J8+Q0VTOEpC2utFKBLSdKYLhtgQ1Ia1bKzPed7fKECLDMFFVFVprBp/x04QOM8bW0s1Ry7OrQGlEBlRec1ZEvP/wD/+QTz75lD/+4z/i8uIcrTWH/R1Xb99yfXXN7fUtlRUXlPdkIE/78yLleP/isl8sn9epXfqg6/j7xJXfbyb4A///t1Zq7/3cD3z10Szx3Tf67WH9vQzhA37fe++RpNKTbkJGG6icxRrRpUsmUiWI/Y7pzpAMxGOPjpnKOKyaMSljpsDWOrZNRbe2vL32pGmkVQ6jE8bApDPoWMp8MQsVveBM8FGqAI0II5f2RvQeRSVVVEoUi3lMFtCDMxpTWmMPj2EcpJWpVWk/ZA6HA8FP+HkuSDRPPxyljZgSfT+cevCpVHAhlOm3VsQsIsUxibhvDEGcypWmqiqs7DNM88jV9Rt8iMw+MB5GKleT11vW6xVVVbNarej7nnmeTw+xsYYUAiH608wwPKCzQCbPiTglUJ4YMzlrwBI8xD4S/ZGkNVEb3MphXY21HYmaOTruDjN116DrLX43M0yBu92BtlthFTw/X9OmZ/S1obYdx/0dQ39HRFwhiBlNxqgMyRPjzDQoUujJMYrIcwEkGNuI+agz5HmUGek0U9unPKDpkxFb0YRUuqI9KyLrCjHI7Q8Du+Oet2/fcNgfmedATpGmbVFGM2FIsydVM6Z2KGvRdQVYAUfNE0aD1pkURQLu7uixaFZV5mJt0HggoOKMzYlGG8aYSGnGIzq3yhoiSvzlrCFXE0bJ85h9QL1jpaR1hdE1WtcYU2FMxrmGOThCHLnbXxOqSKwyV1evubu7BZSME4wmGIVSDoYj3379GqUNT58/4bM/+JT1do2zNQZDSpqcZqwxpGSwpkNVEXKZrWdN3TRoZZnGmX5/RX8c+P7b7/nlX/4133/7kqura0Fvd2tUyjRNI22/nE7VmByJGG8Z/YFdf0U8fkUa9sTDgXky+ODo70b2vuN2aon1GZWyKB1R2qL44Zad7KXyXCx0jKqq6LpWZOWmgeBFuUopoWkYo6UTVdb5ZtUxT4ZjmIjBM00jIWbqtqVRrax7H5jGUTRVw2M1n7ZtBRio1MlJJ2YBHZnyTK+2DZ+Ycz6r1lhX0XbdCU+QUhTxbm25fPKUzWZzEuq2Wom7xbQmBc/Tp5dFmBv2+wPz7BnH6bQHLUF6qT7v1WPuA+YCcpRKdNnTM78Pxf1foBK8r/buA+DvArqU8PROz/i97/qBsvdHz+RhH/u937R8z/s/8947ZkWO4qSuY8JYxLcta6yCpB0uA1PPtEtiZtr3GGeorcMqjU0ZPQdWWrNxlrZRuBxRs6fDYVXGaEVvZFPLOYg4rtKkIME3xYQuAVBl0QnNOZFjQCuLVhm9VGcSLjFKiVxdTrL5PjimacL7GWtEaT6T6fsjIXhp78XFBDWilABZ9vv7OaMxWmS6/HyyOIopnv42xc9xGie0VRgt8nbMIr21290Wa6vEYX+gqRuc1eQss7PONiek6ikIGsM0T/jZlwzYvMNhUqhgwOvijuFkkzUtnrkY0SaSUSSncJsa4xqMbcjU+GDYHSbWtmbVNvikmObAfnfA2XOcrnhxecZZpZm2G5SpuHptefntr8lak5RCRbkPRoFKsQg/R3KcUEkcsjVCJahqEWGvnINUZMLyTD47F5D8gyUQc0m6EAX+rEtozBpSoj/07PY7bm+v8KMnBdCmoq1bXNvglJIg6D2urVHOoaq6YIMyYS6OAxrGcWCaRvpxxqpAjNC1CUcS67AccUpsdbwXPdI4ZnIBIEVV/DythkqRlBbNxkXY/sGhdblHuip/Z5xtMKZCKcPhuCPOieQ0t7dX7Pc7FJrK1tSuIjmLwpKi5s2rWxKZi2dbtk82NJ3YEhlqsrbkWMlmmDRGl1mlE/NaMtRuRQyBoZ/Y3+65ubnlr//qr/lnf/4XvPr+NeNx4mxzxqpbk2OkrurTzP1+/CFHSqKzeuBXxOEVaRiIoyRmMWsOAxymNYdpg3U1ipV8tvx4m+/hPhWCoIedc6y6jnkamMaj6CunUv0UE2pFQisxH6jaFqPguFtk8yaGyaO0xhlbbLFmhr6X5y3eA5mUUpydnbFdr2mbmrquJAnwDTpllAnYZsVZU9NdNlw8+Yi2W3F2fiaKYkrh55lFv1RR+IYpk2NEGU1b18xdQwwrnjw5F6qMUVhr6ft7WospTvWLJJt8LvdF0VI8LfKKD2eH6cPjH/D7CGjnh2ich7/ltwe+D3vvByi53/GA/NBv+aF68If+ff8wPzhUJuWAn0Zq24I33FwNGBRWKc63W5rKsGoMh6s3eD8SUmT74gVPz8+YrgesDxgmQn/HXIGtM0+iotJr7o4Tk8rMDpqnE0Oc2I97Ufb3kRg1Rlm6qj0puovjhJZgaCps+aNOHBwJUForuq5jGCb2h+nRZ1ZXFdaI5UpMYu1ia4efJ46HAy9ffsc0jEzjQNdtUNrQ9zM5eEgC+nGVcJOePLvEUZMGmfFEwOSM1YbKVfgcSCmQs+d81XK27vjsxXMOw8ih7/GTxVhFJBKSuCFoo3G1ExdvhWTbpf0Z/IwCYpR54X1zXfPp2d/j2ZNnGAt914sn2mpg/mQgHEd8ViLUS+Cu/4qkZnJWp9nkzdUV8zyJf900oVwDYcbmwKpZ8e/+V/9MMmAU3715zS//8p/x61/+OV3X0rY1MJGicMssgnRlnLBRvBjryuHqlqpp+eyLn+CswSiFbTvmkLjaHYHINA/3WVqGcZqoMFSpKYFQgrkPPeGYePVNTzSwOjtjShMkjW23uHWNqR1dW6NSmScXcEPvZ8axl7lQCCwcLWsddW0wpiZMA7spkq/vWDVFM7dt0a6l7c7YTUfmEJhDRBuHMgZjK7IWkNCUvNihzRETRHT7tLRQWFMVvzlDnnOZq7fU6RJtWob6mhDhut9xvb/iONzRmMx21bLq1iSn6Icedzjw5lXg+s2R//s//H+houNnPz/yJ3/6X6FyGmMq0lxWuxZBjJwySle48gSNU+D29pY3r17x/bff8urlK/6///ifnJxcLlYXbNoNm3rDHEcR164UXg1M+fhgJ9HU+ufo3DCOP2NSX5LqHaq+Y54hBUt7+ROs72injrt9QPsOm5uSvv7QHiWVP0WAvioi5VL9CP0hzP7UvhyHnhQDfX/k6dNntK2mqhqMUiVQijzkPAfqqiZHkX3LWUBvmSQgt3TfaWmahv/+f++/y+XlJc5VBT8wMw436BjQOYuhQLui7rZ0q4I6VYoYxJvw6s0bomwhpCyCEIfjHT2Z2RmatiLOM1rBR8+ecnF2zicvPuF2v+dw6Pn+5WvGcURrLdSpui74ivvq7r5tutjpPQKjoFRCv+PO89uOf4mZ4H2w+nE46u/slb7/E78j+P1YBfjDr/2uQCqbRSzw/pwhZYX3GR8iKma6KlIpi22cWLcEZH6XDAZH6D0GcE1FTgk/TRyvdsTDiB4iqhe9y2w1lQ1EbbDeEHUGo9DKknUiR/EnzEqG9SnMZUA9o6QZh7ULKktR1VXhzihCzAT/zpWV1oE4WctwvWtqAf6EwM31NfMwkOaZxtQo4zjsjtjk0TkQkydUjtQ0+NpgQoOOM1hDRpMnj/JefLxKM0+pxKqpOFut2BaXgKGAhlSWbDAFT/LCC/JhwseZeVysrTIpx6LZKdXxotQiX1dcdF/wZP0JzmVGOxLmQGwi8dyT5oDPijlMjNOB6Zs7hulGwCQhoP3McDwKgCBFiJ5YteJ8kSOGzGbVYYyoI62OB5pGWpqmCMT7MIuAeIxCgEYVp4xil5UTVWVxXYNbd/JzWkHbQUoYDVkZcv9O/0JY3JCkr5MyRJWI5X+r1ZpoNVFD2IuJqoCPZghB3CkiEBUxKfH885Ew+wK9n0/LYREZVkphjcUoQ9aBkGFOi7O4oqoUKypqp5l9RBmLNpa2W2Mqh3EV0egihO25+v4t+3R4tPpSDDLfLR6TlbUyQ9cKGyyzP5JiwKdQvCAyunROTAm0SpviJmMJ88z+9sg3X71EZ8dHF1/g3IhWFf00nZKr46GXtv0DJOM0z9zd3nL15g13tzv63UCcE846nHOcbc5o6gZrxGvRWMPmbI22isSDtiEKV59TNRdonhFUh8pHjOsJQybNmqZ7gZ0NZoTd8Q3kkzVwocoXYJGCkItObY5S3eVE5Qw+J0KIzONA8LO0KE9uPVo4jn7kRmvGbiXi28DQH7nb7Rl9IKGkEi2fgXyOSnjIM6gHM0GtNR9/8jGXF5fSpSpBcp5bdBJLtrZtqJuWulnRNG1530y/35NDQudCT7AGYxxWKYIf8dPINCaOhySo8Zxp2462lTFD1bSsup6MYrfbE4pIwDx75mmWZ7bsAQt47l5ZST+IM6VC/FftJ/j7HB8OifnXcPye0Nicpfc+By8O6sgoLkTF1AfCONO4FotBbdYoVQnq0Edi0ORo6O8GbFPRna/ICcbjzPRqIO8D8RhIdx7WFtMYrMkCM9aa5CxGKxQVMchmX9f2hL4M00TOgWHyJGuKorw7DYIF6qwFxOPBvxMEY0zEYoQckscazcX5WRl8J16/ekUaRzqtsJcfobLisDuw1pGGyHy8IRhNqCtGE6FrSV2NrRvQljxDHgaYZ5ROKBUxKnDRtTw723DRdozHnl1MGBm8ksJMnEaCUiRjGMYjx+FIniaqylE1BX1akG75nVmMQvNs88d8cvZTqjrh1yXbz5ZiEYpHMww7djcvefX2rxinA8GPYAU6v7+7JU4jaTjirCFUDSpFVAroHKkrh9KGjNAxqkramdIWg2kSRF2YPQp1Mi/WRhNyok+BprKYTYvetGRrCFrju5ZIRq8d9J50eJy9ksSoV0dNzlIrBBWJBJKCF59+ilfQTzP9275sgANxTGifIWui14RZIXNAQT3Ow8Q0jYzTQAz3gALnHHVdc3G+pa7ElzCqwJQSOQoZ2xjNpq1IyRCqCNpirOPiyTndZsNqs8Wtz2QdjTP/NP5TUoi8PaH9ikfeNDJPI7VzdF0LtHjfMIeW/XBLZhSvzdJmlT1NZqMhij8dWmOsY540x8PE3/zlb9hf97y4+AKrhT+573uqumZ9tuXq7Vu896gCJAG4vb3jsN9zd3tLXVX4MdBVHW3b0jQNHz39iMXSLKaArQzPP36GazTp7kFbXmnq1RO67RdYuyH2PyHmmXoFcRfIY2JzuSZOPePhFvXyGpAgYvI9Sn1BU6bynOfoxUwcIe73YSYmz3F/lHtmDXZpD1aGm5tr+uHI7d2Otvv/EfefzZZkWZoe9mzl6ogrQ6Uq1WJ6MIMewADSQIPR+Jv5gT+CAKEG06q6uiqrKjNDXXGUq634YW0/90Zkd022ETB62a2IjDhxlLvvtde7XrGmW23JSVifHz9+YJ5nFAZXteSSB1i1FZmITY50OglTFlm3tda8fPmSq6urM/wrDNqbUm4k9Nlae944UJio86mHmAizx2qJi1qv10yVhRz44e2O4/HA43FHVTc0bcvX17dUdY1zFZvTiX4c6boV796/53A4Mo4j/enE8Xhku9kKk7RAf+cBnHrGt104fSr+n1cE/yWaSn7Gjvn/S/H7icciLv3kz4xBlVzEMQkxgLVDmQqc4cNxzxAmgfL8hA+J4xhpBg+nmWnKeGuIVAzeoDIcDpHpOOInT1xVpC4R64n9/MC0zJDyYrPU4pVQma220hQQqbJF65rm4gW2/K+1zXlobJTc2DFFVqsNbVDnHW8GmasNI4fDQCTinCHMgeNp4GG3J5FxznJRV6RxYAw9H+/esX1xye3lmssXK3xhRMbTnuNph48Tm+0FddOidUPVn+jGA8FkweEInH74nvePDwxv79j3A8dBdFzWGKyr6H2g14bv9kd+uL9n1/fcbq549fIlX3z9pZCCEvgQi0zl6VxlMmM/Mc+euhURLlnJwp+FJK4C6GSwSkgD2kBSHldJPmRTOXSMzPsjtq0Z9jt+99vfEAL008gQBrKCkCLvPvzAh4c/FpeShMqJ4SiZdzrLpimU+Z3vKpLVpMpyuqiInWK1MdSrNXbV8cPDPeM4Mh57tkEzpPn5VUhlHVZZ8JBCLiGjnnbTsnqx4b/57/477h4f+Id/+AeYT+R+h7WJta1w1nAYEkOfOZwydbsW0hWJ6GeJLSrw0JITF7wXvZ9S1HVFu6pomobKWYE9U4QYUFnyOCUFXBaW4XQSm6+QuG1WNHXL5fWG9WpNXdWf3F8hzGTieUPhnBGmLFEcTbImB02cM0WFxhwzh1PPNAWGFBinWcT/x555nIgx0fcj+/2Bjx/vmAbP1M8krVltVqSc+OH77xkKMzpnCXLdH/b0fc/peOLF7S3aaK6ur/CzCNTbtgUlRcnbge3Nlps3NxhnZB7+7EqcxpF+OKJ04u7ukdl7tNP0fSSEjAqeOB+Z+nsS4dxxLa6AunDcFJkUIjks0epCvJJQZEuKllgio7TSggYZKUbT1BDCxO//+JaYIOcng4xpkoJS1zXTNMnc7BwfZZCkO/tJJ7iskUZLYG4KnlyyYY2tMMbiXIXVMq9zVmDKmGdinJl9zzzuwTbYOtNUlzjboNjSDwesM7hVw2azZbXZ8ObV6zLvE+ZrCjNWiTZ4dBXf//CWh4dHvv397/mzX/2KzWbDdrsliuIYbUS+oXjGEC3n50c6xj9x/OQiKPwX9ae7rUI8UcjsSPFjePOfm/n9/xp5ov45wsuP3lt57Od/vKBQmTLXko8ZTSY7iSdSIbEbdGEDJsaQOQ0B5SZCNPioGX2m9kkSr41jNiOTFR/GYANzlgU2hEiMYIvlmdWaqGThV2X2rpPk3ykKDJc0Omt0EKsqVCaXhGWVoakcq6p6+nSZMxQ6ey9QjpIOcPaecRJ2qCNhyMzTwDgFxrEnxg6jMlebFX429DqyG8T1ZJ57olWQA65KVGGgSROBxaAukE5HxmkiHGf6yTPMXobf1qDcRDQGnxUPdw98vH9g1/dYn1mvOlIIZZcsFk//nB4oxEBKUaAWI91fLinoOSIzlTI011rCRIVlKd9OGMUiK8WE61pIiY8fP6Bsze504uK0I6tMzIn94x3H4wPOCaQms0Why6ssVO2QEsmAN5pUGVJXM9cWVRnGSqEai+5qxgP0KjLGmTrakh25XMAlQFhpwceQzZq2mm675urFLa/ffEEiYxVUKpB1oKsy2zpRVZo4JsYcyCGQQoVa7LGKa87z3XNmmY9n/FxCeiuLqZRsrkwxh06Js/6y6MRyioRhJEbwMdOujsQ2QaeKfvbZ+cry3aOEvRhTIEZPLMHZKDBYjHKSi2grlHGEnJmmmegjQ5gZZ8nKk+xRyToUH1QxjT8eTux3R6pGgrenaeJwODAMIu1JMRJi5NSfGIeBoR/EpKGuqOv6KdN0nkuivaZqa+q2pm4rlNWfLBw5g58npqEnppnjfid5qEozTzKGOGhDCif8dDzLGhaqvyAIYsyxLECLPnCZF2olkplYJBAgXY/kAuoijxAS2zyPjKNn9pLVupz3pmkg5zN/QGddnKJgMc9On7FIJEg3nUN4FYhQviTOy/hFUCaiRSl5LQlSF5IOOZCTJ2fJt3SVY71eYypLEztW6y2rbkXbNpASIcwUmj45JymwzmGMZugHdo8PbNcrxqEn+Jn9bkd/Okn3vHiXnpvBTBE2/oli8OnxrxDL/+lC9bwbXAoh/Ljo/B95PO9M/6Uu9Z998LPD+8g0B5mhFIcTH/fScVlFbgJ9Bt/3mODISTMFzfTxSLWf8TT0wfB2NzIbw2rVcPvlDdMuMfSJfRo5TI/s9/dUzOgMJjuaao2zMkdM3uPHiTqXXQ0Z6x0pFUeYKEG8Bz+VAFIZmBtjWK9aOtNR6WfOKmUdTaSzUFbHJLq9aaKfRvFCTIE5Bcb9gdPsGeYjh6NhX0Uuv77CYPFrS//775jDSBUn3HTAMbNWEdKIyQNzSCUhOqPmkYhizEeO08zoA9tNi7IaayDNAz7B/uOOh7tH7vsBFxXb9Yr+dAAotmf5PKd9foQ4gprpuhVVJTelMZlxiMxJuguIKJUxFrRFUtonj0/w9od3VErTuoq/+MUvyFrz61//mv/lb/+OqBQ3r25KQLXi0lls1ry6vuDh7V2Zu4rbRU6Rw2nPTMK4hoFIVgbb1NDV0FUcak2qFVQatWrQKZL3R3zwnyQSKMCgS5K4wWqLrR1u0/L1r37Bz/7Nr/jiyy85HnYoP/CiA1tXfPmiYdWIlvBbNWHDyHDyhDQSs8x9lEpls2UKYSIJrKQNyighR8yZcFJEpQho2qoVBxsFfvLiUjRHfPDElJhjYqHmfbzbU1WiC3v73VuO+8PTxkXJ92i0NJG73QN9b8oibnHGUNkG1TZU1QXbi1sOD54pvCNPAyplhnFg8pEpyNw5JlnjnGuo6w5rKk6nkffv77i8vsRWlnEceXx85Hg84pw7z5eE7eyZponj6UiXOy62FxyzQG+///3vubi84PLyku5mTXPRQaXRzqDdk3clOTMdDzymzP5w4nH3yFxYtCobNJr5IcqsXAWuri5pm6ZsEuRcN00LC9lw2YXztHZqLbIjU+wOn2volCoh6iVoW0YHUYqJdaXoagleJjDPA0uSh60MaAgpMAwjY0myl4+V8eNIHKcSoKvRxuKaClfGL/vdgf544nQ40tQ1de1Yr1sxDV911E1FjhnvR46nHbaqMK7mzVdfiqOSeTJcSzEy+on+dOR0OBYLtQljBUr92Tdf8/tvf8f7H/7If3x8oHKOVbdmjhHvPcOxR5fN7vIZNIthxmfzoT9x/CvYoc/0GQXzXwaVz+OCnoIilxvhn9fE/IsBlD/hfTz/95/Xtv9sMfzsGMaBU38ipiSkDJ6w8GWXprNQU2I/k4Im0TLPPcZMtFrjQ2J3PHH75orV9QVf/NkXPP76gXE4ct9/ZJxH/DwRmTAKrPLUsRV4I2tsStiY6A/HYmybz7TuxcqNDDHEMifLZ9nDsZ/x6cix78/pzOTMHAdCbtFVKjCGZkoDCY8xER09usBR2iiUyaAiwzTweFQcTkdqq1E5CiTqZy6amqtVy7qpaSqLzRYbLaP3BcnRVE2DMo5BN/i7Bw7jQIwWU1VsuppXL67JtiLfvGD+zbdMbz/gZ/HSPB13mGaNMopEIKSZ+Bw2zJJc7edIGCNWCYFj6eBzhpASPkV8CvgY8T4w9gPTSdiY4Xhk3XU0l1fUpsJWjutuw+n9O4Zp4GAzq7Zh1bX87OVrdILjoYe0eCoe8NPI0B/x0ywEp1jx5sUXdJeXXL5+g3ciH7CmQwXF3Iv1VSKhnMHUFltVn1yHOstipnQi6oA1lpvbS774+gu++sXPaJuKxkGjA6nT1Dhur1bUlehNXybHnDXHkHmcND6LdMHZgCahMswzzElsqlRJ4UaLW5JzGktGx0CYpjMsmHVF1po5Bo79JB3asuFVmtGnAnU7+v5U0safjrp2GKvxfmaeNSA2bq6yOGvpqhU+wZw01sm8eZg9MYBJGT/NpGVzqB2YTCpm7G274vr6hg/vHsgZgZuHGj/PTMPA0Pd4a4lB0kxycUBSSnE6niDzNGtSiv1+T9u2GGu4vLlhvb3CK83kA9P8qV6VGDAEapu4WDtSEj2nUeKMZKy0UUojlmFaiCxqTiSlMU0r3VheOpfi7PRM36bUktpuzlK0XAx4JeVmWXPBaAnSdoYCe0JOgRggI57IWhuq+lrE9jkyTB5r3CdrpzUGa/TZvMEUM2trLBmYx5Hj/sDD3QPWWOq6YtquWXWOVbflxas3PN4/sHvccXj7HVpbXNWgnWx+mqYS5ro1Z4/S3W4v8LePkpahwVVaBPYkDIn792/Faea0J6eMTpmVlo42p4RXy/2fZZMaP5WM/anjXymWzz+CQzOfwZnPit9ykv7ks34Olz573n/Nv3v+n88uI84F7V845lmcXxZWYiZJgSlwkVIaU/Yu0xRIXoOu8ES0UnTb7rxg1KuO9dWWqzdXNO8c6j4whqNo3UpUSlagrAiWEx6VK3RM6JQZhklIMuck9SdoDKVkdnD+aJoUZUc3hYH9Z96hSQWwEdfKLM5YSzYRYzOVU1Qq41Rh4hmDyRptYY6e0ziw70+sKoezIn5PMdDVHduuZdM2YtcW5fvQSRUNj2bdtui6oXctd8cjIUfxdlSOpra8uNpiuzU5Vfz+Yc/b3YF58IzjQN+f2HYrjFEwZbKO5M8cSGJIhDkyjxFnDRhdzq86MzRjSoQk1nAxeMI8i5tKCITjkQpFXm9xylCbim3VokMgnHrmStMhBsmv1ltSTIReHGC8F7P1MI2M/YnoA8lKDNHNxQ03L9/wxZc/p8+BMUcmC3OODHMglYgcVeKItLWfXOwqi4F2JhHxYCrWFx2Xt1fcvLyldhanwelE1xhaU3GxbXElX/EKOPrMRR84xSgGEDisVWglC39MCVXMDyTX0KBtxhiFMwqjMiolwjyTtaRQGGvIGiKGcY70wyRNi5KRx+hFg2YWxOITIpMYIMicyhNCcQdBuhVrLbVr0FFB0ihTkZVh8gEimAK7iWWbRqninqMtrqppmpb1eitGDEnm4N57gveFkDMVMtBCCJL1QCt11qE+37j3fY8PHmMM24tLuvWGhMZHiTj6ZFEqJumVydjWARajwChdZqhWSD5G06w6kf/s9uQpYNEFthdWco5BjBYWOFQ9GUg/se8XwXjkbJSfZR1cWKPVWTxfOqMcyRF8inKOTMZai3UOk9PZcP75CVv0eVrLuTPGCINYa2JOBD8zDgPH/QGtLXVdY5Sma66o64rLqxtOpwGf7nm4fwAU1lXFNMIKctW11FXFMI4cTyf2hz1zyIi7ozgAKaVYrTpurq6IU0887Zj6E76fyvxfWPcx5bIpE+u8shKcMzt/yvHTi2DiCWd9Jov45+DOz7u1/yOP/PmvT1Xh/JhUWif1/IF8+hGWYwoTox9JRb8mg2lNU69o6hXtqsAW84SZE2GEac4YpXBacblpePnqmm9+8QW/+re/ZH3R0lTw5z/7ipuVo9KJ/ugZjwE/HiElVMpUcwvZsD/1+Dni5yj6mvTpZ1k2HjmKJm1ZcFSGxfw6kZmHT50fXv3smjdfvyaTMa5Fa4tGsVnDZRNpHt4Qx5EcPd16RRUzr5QiHk88DhP/8Xd/4GLVcLlpeRx6amNo1ytevHzN1WbN4XhgipE09GJ1lgWK2HQN9XrD7e1L3h8P5A/v2PUDjQWfWl5/8ZLL25dcVVt+fXfPt3f3/PqH3+BazfV+zZs//4qqaTB7wzwromqejzoZR9jvI5qB6xtL21pc01BXCmsUIcA8K7wf8f5ESgOrlcZuWkiw1xlXOxHl+oBTM20/cu0zJmTU45FL2/Bqo7l2Ncc0cjjuGaeBfjjxwx/+gJ9n5nHEWrFFMxi+vHrFN29+wV/96r9k9LPM/Jzm/fDIHw/veLj/KEkGhaQRg//02sxZqONxhJjQxlK3lXQUJGwOVEQandlcXbCqlUSAZZl3dpctyew5ecP7wz1jgKwsbddhjCLFiDF72SSEiNHgjKLbSOcjRRhyCJzmWWaUuoivtWFzcUU/eUYfODzu5a0rUE7YilYbnDGfzJgyFP/LiWkaMFY0uYlAzgFft1hTk1DkAPOUGKfI6CM6ifTEGkfIYmbhgydlgRQ36y1Xl9c0TUfKMAwT2jYiq4npPBOPcT7fEwt1XgGTn87ZfXUtZJ53798XSzDNqy++YnWzwtQW0vSj2RkmYass0K6ppMuyAuNrBdrUoIR0l3XmeDjw/t33MHkqbbhataQsZvrH/QOJjC5d19lAfllj1VOwrNauzPPELm+aJtq2oWtbtNYSgZaeMkLFJjGhlcMYVUhCMss2WpxdntAzRd011KsGrSi2aQZljGgGvcdPk2wqQ2S1vaSuG4xq8ZOYd2y2N2yvB47jwO/+8AeGoSfMM1eXW4FLqxtOp8AwasZ5JivD5uoKZWqmyfP+wx3v379jf9zz7e/goqt5edHw1b/5GWkYGB92Yt6QMseU6aOmT4rBGHzKTDGx6lacTj3fvf/ITzn+dZ2gWn55dnL+mUeeN7h/gvzyLzmAP3+Of7Z/KySYfP71+SsuBKvllMqgNmfO1I2FIr4cIQV89ETvCVEWpraSouGsY/Kz7NTmEatrqsoQgqatHW1TcXmx4uZmy8vX13SrCqsz4+MB1QfsrNCDJh0T034mT2XorTVzscWKM6QoBA9FKDZNIhQ4f+/PivrySBZ4eukuPjMyX1+2XNyuyMgNqbTFGYsLPdXUwdUlp+OB3WkPjaNGs91uOPjANAfujgNjDPRhZoyJqnLYusY1LbZu8ccTQ8wcZ49JGZ3BInonpTJt4wT6qCtOxyPjZJgm0T4qoKkdq1XHar0uULQQKLbXK9abDeuLLdPcYxxnppcCrBULrpgsIRhClJDibMT4vKoidd2w6lZcXmxIec08DdSVWM+dHk/FQWXitN+Rqgo/nKhUZlU7dKVpK0OlM/PQl0w3ie3JKRLnWTrABFdXN2SrobHUrpYds7W4mGT2qy0thiovJBNJ+cvpU4efjCAJsYSpzvNEjoaHw467u49s379juxLbqrqqMarBVgp0hcrCKhRyUDpDfzFCIBCdRWNLBqSVpHdK92cVm/UGWzlZPGcR1edY7pmssDGJXKLIctqm5aAP8l5ThgBJKZJO5Jh+fNMW2FQbi9ZWZpFKQnBjyvjR46MmBE1tKmpTkSOEBCoratvItaUySQUUMp97cfuSm5sXnPqBYZ6ZUmRjbdF4arISjlHK8v7FLUefKfXaiCH9OHmBQF2FsRUJhQ8RY8AY0DrKPfd5ZyH4tcg5ir2YUhZlUhnqSdeqjcGHkeAnxv6A9hG0YehlIxFyJPqBVJJanqelP48NOr/sEmacwzlv0BhNXdjPMXrwci0JsvC0digF0zTKpmGcmebAND/NBJWidGyunDbNmSHDE9lM/jOX7tJSVeInOk0zyhjqquL6+pqvv/6SoT8xDT1d151DvbUM3VlvVtiqpqo7YlIM48QwB8Z5wtUWCNQGooIxRFJI+GxQriZrjbEWGxVVVDRtS8wwx8jl5RW7x90no7o/dfx0dmhhCuVcTvxnEKhaHrQ8/rN//5QK8fSmFnz+ybT5U1LN5090vjAoruYLxFHo8ctjngpHYQsVZhuqpCqc30ImpMAcZvw042dhJ3bVWsgJ1rI/PhDDBH7kxt1gqwo/a9Zdy8Wm48XtlpevLnn1xTV1rUh+4vTuI/44kI+RuE+MDzPHhx4TKHRjTVaJrAQmpOxONRahiAqOvxTClDNkVdhXZahcCmBOCaPFxuv5udredFy+WBWr0QqtLU3TUPsTdX+gvb3mTsNhOqIaR2Ucl14z9RN9P/HxdGI3DTQnRWcVG2OxdYOpG3RVM2bF0Qcex4lGaRyKCtlUZCJNZemaiq6pebh/YBg0wzDhx5lUAmtXq47NdiOLlcooq7h6seX6xS2ainE64SqZXy2Hcx3WteQsRTAGg3MtkEkxM02JnFpy2vLyxQuM2XN3/8C6a7DG8uF7TU6ReZTsP19XzP2RyoIxNa51rBqLM4nhuKMfRsZxEFJJTiQvRUKhefn6DRhF0lA7sdhKZWygMlRoqqyoUkYX+AsSxEgOkoX3/F6ICIx5HCMTkXd3H9h+/0eqruLrNy/QKJqmJSuPdhBVVaw5IvOcmKYgiSQhEEPGZ3WGyJytcM5RVQ5NLPCZ4eLykqppuL+/Z4i5FPgFmlXneB+lFG3TlI7SSH5dKsbl5dpLJv7ovldalexBhzZL3qMDZYhJcziOpGzJ2dG6ls61kBQ+KjKGtlqLUDslSBPWQO0UX735ktcv3/C4P3IYRsYUMc5hnNh3ocRaz+eM1YBRJYPPoNCYrEBb+nHm4uoG5xyuagDNNHuUimgdpLMzMjd/zvY7W9vljJClNGDEzk8jr1MKYQyeeRroT3tcymAsp8MDGDF/D34ga4t2QoZ7npeXUmKe5/N/i/kCQOR0OjEMg8xWu5aL7YbjYU+K/skZKz8VMKWg7wfm2fO4P4IyDMP4yWJrbPkOF6uy8nOObTIGrSDniNJgnKGqG/x0wntPzKJBffnyBc6Is40Q3pZxhehglTKsr25oujXdesswzuIlHCK20gzDlpQmxv7IeDxw6ifS6CGAbVp0XZPWK1QAFzNX1zfC2g2eFy9e0rQ/rQuEf0URjDGX3LnM4uj9Sef2rHX/ZDj12Wzwk7qcc8H65T+1Ku4OZ5ZpLuzuZxDLUvye2kCenjh/9gJnULS0iKA+g2vGccQNjjBFdNZY46hdK3ls00fuj3eQI5XKdLmj0pBwVBVs1xW/+ouvubjeYCz84Te/4fiwY//dW/YPPYfDwG++e8cwzUyFpmyMwSkZYmsMztoikBU9VspSIBdxKoipN0bSLYSwk84FMOdYUsQ/XVSjEuuwmMRpJik4TT0fH+55ePeeb65fMaoKTicOKhJ1ZvWi5XX1gs31ij/+5neM48hpN1JfbdC6QlshwXA68e27D7y93/FhP7KtKhqt6Ei040A1VjQ5cNO1vLm54bd/eMtpDpwmz3Da448darPiYtPy6ovXNG1D7SoabbnsLnhxcUPbrPnw8R135lMCSSITckbHTD94UsrUj0ZswKwYhVeuptpe85d/9pe8/wj3d/9I8p4QIspHjDJUVnM67tCpZd05OuPAKOquxijIYeLhwwd208TpeMAHsUvz44i2FU3V8PLVa5q2pW4bVnWNCoFpOBC9zHhUmujHA8f+iM+eWDY4ldVQUg3O16JWRAWBRDAy3/r1H74lOMXgZ/6Lf/tfSN5kteY3v/2W6EfePQzESTq4/WniMAZ2Q6CpW1mg54TVAYuito5YaULjaNqaumnYXlzy4vUXKKPZ7Y/E3DN7IWTpArcnCtnodCIloa9vNmuOpx7fD5/ea+lTt38Qd6LgPX7yGC25fZWtyaGB7GgqjUbIH1+/vMKGieH/+teoKJZrP//6F9zfPfLh3XuG4wFnFBebhm++/obt5QW//49/Q1KK9eXls82SIqSIT3JXxBhRMZJMxjlF7Sq6upV0FVthXU1V13TrLa5uQGuiH4kesk+gK1J8gq9zzuKH6xzWKLIOWK3I2AIjy2Ifk8CWQz8wDiMhhtK1R95+fFc0ypqTn0lKkw99WWejQOeFzyCbMIklMyVM11pN358ga9abLVfX17x4ccP9416cdUIoUWWRGD3GJrKC9XpFShlX1Uw+Ms6fEpmsMcVRiJL9WcYxyEaoritcVUk+qsrk6JnHnnE4MU8Dx/4B7TS2MnRdhatbNtYxjMt8Fkn1sBUxGfrBM8575uDxxUy/rleA4bBP+OAYgmPurrFrw2a1JsRMQuGcZVOL0UHXdWeZi4Si/3SQ8yc/8jk1f8kI/BziPP/5839XurB5lgU5Pk8/LwP6JUDRKF2EmAsm/ul7OMOaKf+oAOZnsOHnVJvMU2FO8bOol5RIi2VVGSKGEMR/MommiZyISuHjspsXXZezjratMRrG/sTj+3sO94/s7/Y87nqOx5FhlIDefCYTZJEuZKGZP20OCrxCkQU8g5qfxxst7FDOmqJ81vb8+LtaoLaIShCT5jSO7E4npvU1wTpy1VBvDbpxXK4v8JdbxsPA48ePHFJkOA6SNZgRgkCMeK05jiPHceYwBQyaqAAlQaDTPJPmCZWFORiVImSYQsT7ieAnquxZrVqur6+lQ3UVlbHUtqGxDY2rUCETp09ZXovgFyUz0hAi0+hRKlNhli8MbQyb7QWTXwJRM2kWnE1phcqZGDwpiWmBreRnvemgCMVJnrwMamN+Mjo3lEXCo01Lt2qxRozRh/FAKkbSKtekGJ6IO0AkCRrw2X1SLlWRs1hLSnAaB3bHIw+7vXQ8/cTo4f4wMY09c9LEaSZ4YW6GrPBZdubagaszXSNMzKqqiGFmmi11u6LpVnTbS0mvT+n8E5IwSVNBd0IRcT8fX1RVhZs9Rk+EokFcbrHPfTFjEEJSThmdDRoHyRG9hggqydyKlKmtZtPVvHl5Te1q2rrjz//8V3z/h7eQIh+ixxnNZrOm7VqssxxPJ2Y/Pcu/lNeXeKVEUvk8yQkEtDJEHXFVjTEWa52sQWevygZxYkqkEFFRosxC8DxVQc4EtqysQK95+fz6aWNfxhqxWDPGAlfPOeNDQhl53TEEsS7XE75oFqdpgmIkN02jzJFjOq+XIoGQaKjt9pLN9pLV+oKr61uaZkUIgf1+zziO+AjGOpyruLy8RCnNeu3ZHU4cT/0no6fnDiznPFfkfvmE/a+Kt2+YJTZtHJimQZ7PKkxlhGSoFConTqeR2YvbljET2ji07UFLd+5Lxuk4StL8PM/s90emacT7RFKGbCzZOulAlXStXdexWq1EChPkXlsipn7q8ZMf6WfPPE3PyJ9P3drzY4Hrlr+fgmfygfd3OybvRVQqD3zmEm6w1lBbS+ssbRGCCnTzNBP6tPl7uiDVsz/7/N2cH1RgVD9Pn/zbvAjUtRhvpRR53D8K3KEixoqRdY6aYc5EnWlMhTMNtRXSRn86cnr/yLt/+iOHB2ndH08D/TSTEIGrVRnr5C1lxCcwR0g6lGKIQKTn0rW8f3W2xtNakWIpaoqyO5OdZfKfF3f5rjQSeZRzICbD7nji7e6Rtb5ljJm0WvHVn73h4sWWN69eoqbIfBzZP9zzh9/Aw4c9IcA0RZGBbC+wxnAYR3bDxEM/E32mUYlJBdZtReUMx8dHpmEQcbuxzGROc2AYR6Z5pFGRly9vGFbXXF9fcbFesXYtq2pNYztMVAyPPYeP+6eTrhAnDSOIQcpJHHyOmZRqYnTnqyAS2F5cgnrJ7fUth/s9/XAiT5FsIbkAOUBhka6rDav1ijevriHOpHlkdxwYgqLWGu0hT5nkMskkYvR8+PA9mMD16ytUlYhq4u7uO0lbQHO9uZbkclsLeSJ4UhrQSWFF2X8+X3nZrGlNs2qJDnb7E4d+4G534DfffsfhsOPd48APjwOHw5E/fDySoiIlYS43TU23qrm8vBAXmLriciPpHadxBiKj99y8+YqmW1Ov1vT9gbE/0Y8T4+zxvrAgywzTWY2zFmvNudCsVivRC3rPaRhFH/qjOYbcrFM/EMYJkzWN7ahsC75iHqVLqnSUaCc/ouLEqtZ888UNb16/5sXNLf/+3/81f/s3v0GTOTzeYxRsNiuqypFz4u7uAw+PDxyOe9ZO4WNLIp+Do5MqKFSGTCBFgc2rusEYQ9u1pYBnvvzySyCfE1RCMKgE0yhatk/WlqggGpS2ok8oMKjRBqcWK7JESlLUfIiMPolsw3tSWDgNivQMwIpRArtTFl1kRubEy4w2FtJPznBzc8N2u+Wbn/05682a1WrFX/zlCl/W2T/+4Q88PDwQ8nvatmW9XvGrX/1Z0R3Ct7//A5P3sricyYwGYR7ILHVpanJBAWMJ2U450Y9HpnlkdmJA4OeZfpqJUyL38Lg7Ij1x4uFxxzCM7I4ngdGzvJa2FusqQvDC0C9M3KWIWyszRm3kPR6OB7q2o3IVbVOx3azYbC/kukVqymKD91OPfwUcOhN8YUmVKnbu1H40gJQ06cFH9sPAcRy53x3OcGr5BgrTsUT3WGGXVcbQWIe1mqayVNbitNB/1bKl+zHa+tQFlvd27hpjKA8XaLHvh0/eqzioTIUxJ/CEDp4l6dvpqvhSakKS+aNVlikYRq+ZB8mwO92/J889KnueXDqKi4LSRTQsBS7lJMbLCoE286eQ8fM5qbC8BGIhZyIiR/Blxw6ZHNMncA2ISFlpJUyqEMoOOXF5vSb98kvmk7iXJJOoG0tdWwJBzEJqxauvX7HfH1G/+46jn7HHI394+56Vq7lYr8lZF9+wRCTgVWIkcBo97jDyu+/f8ftdzw+7kzRSCvpx5uHQszn2NCnRXax4ve34i1/9ioqEtZZjv8fsNCnOGAMXm80ns2artfgnnskyWUyGC6xT1e4sytVWHhNmT3/s2T0cOJ1GUuNoGrHTku880XUN2+2apmmIc8IHBSohQgPNum6JsyaGiI8n8piZ/I4p9Bz9icsv3lCtWpp1yzx6NJqbmxdcry7p9AUf8hF7yHx/3BG9pNU/P9KYUZWmtTW2rUkWcujEzHme+B/+p/8ZH2UOtLm6xVUN9x/eUtVbtG3AiSnxYQI3CLFjc9WxXVc4p1BO88q+prt+RbN5QQiZ06Hn8f6e4XRiPA7SdZfrRFiFmXGeiSnRmQ6lrcD4WrPKCmUs/uPdOf388wkFgFWWHBT9fkSFI8Z4UhwE2k/gTCCGiXnq+eH77+iPR077PeNxYH/3gFGG77//yDAcGceTZH1a6bq9D+x2D5xOe6ZpwHsxbV8QqwV9WQh8OSbm6PFzBGXwPohkoMR5XV5eyv2ksoxaPdjsyBqcdp8gM5cXl1xeXKCULu4mpujpjKSQFO9TssWHwKnv+XB3x2k4EXyQjlFLA1C5GlMaAchCNCrBsTkXjWNZ18TtSbgQq/WGl69es91egFIcT71kIRaJV4yJ1XrDZrs5F8E3b76gqhwhRO7u72nqJ5u7RZahtBZHnkWTWGbDwQcOhwPTPMNirUZimEfm5AkqoevqvFmKKRR00GBdjcsKN0dsWRNd0cqmjKRZRHkPVSURZJDOjdLYD3g/4yfPNEig8OX1lSAaUdxxlvMo2ab/h3eCmVQ0VzEuUNxTW6yUKuQNeaxAZ4nHfuTh1LMfBvanXtwS1FP+PGcxaMmO0hpnDIOxWKOpnaFxwmpsa4dRQlZ6fp/pQtLR8tLl3S46P9mxy+xazF6nef7kCWLZecgNkVFZ5AfiYq9RQRZckxWxhJ3GJMUooiUQd/TMx74Y4Ja5KQuOXq4XrYr11nnPL8GqeYFhlwtRLpDzn6Wni5NyY+uzafrSIafzBXt+nmIZFimznZwwwGrTol7d8PhDLze6TlKgjTiMZEBXmoubS7rtClMb5hQ5ThN3uz1324Ow98qOlJRIURFVYiYxzAEzTqjHHXeHgd1xLIYYitknjv3EsR+ZYsRWlnXT8eUXr4lDj8mBeR7phwPTPOCMDPyfTZ6fDKvL96GUzEpDiOcbSCHhsYsBd4yReZyZ+gk/zTgrmrlQ5ixCLlJUToTCGEOyuixmCp0ytXbUJhGSZL2F5PExMsWJUxzxraVLGzZVYhgHdDZgFE1d0TrL2lXstZGNTAwioXh+h3mFtoamanCxkhjBrqNtarQxfPfDW7JSoI2kOCjF48f34uXoGnxOTLNn9jM+CPxe16LNdJUiaAO1o0kV2I14b+6ODMeB/ngiTL6MCiTBIWcNajFQzsJ2LPeqLRYw2hh2hyPkzDw/MRE/OQLEOTH1MzqNaJXEOzTKRtXaSPAj03jiw7t7TscDY39EJekinat5fOw5HgUaM7pGaRlryEghFQcaU6YZedEZyT2iS+pC5snSLS/NT8b7oi0MQaQS5cE5IkbiVFitcM9F5UrRtS1t2wGSyqGNLokcCxMVKWAxcup7DscD++OBfhwIMWK1QxthsxpTCcSutcDDC5T6zMVKllclrj0FFm2ahu1mS900MoaYxElpkVlsNlu0NtR1Tde1bDYrLi4uzppI0RN+dr7KqnK2IZN5SpH1RCHW+CCFsmzoQ4jMcS5yrSUXs6CCWpVkj0bMG2rJSURr6roipWLrpiQxJJfmA6BuquIepemTbB5SFK21sZaqbVjPE957mqbBOXeeW/9r5Hk/HQ4dB+bhKMPSsivRRvBvrfWT239G5kXDyG++f8dh9PRzlIux7MjMcmEuO4/yq16EmuUDpARWqSLGdFTO4JwRMXkpFhLMqllVCqfEC1OjSVkGzHNIZAyqXqEzTPOni09aYkwobFXAGSs7n+A5DiNaO5qqY1UZsslM6SN6dUlz49DakqMl9BqCKwVnJhc6vDbP6llxm9KIAFWdNwTy/0YDxSD7aQyYxdD2sw74eeWUovDp+VKUQogqzDZFpaB9ecHty0tsuye9/ciHwx0hTsTsqTqHyopcJW6/fMntuzsubi44fNwTp5n3Dx6bvmPTNDzGwGkcSGR8jtI7aMXdOHGMgUc/82HwPIyekOScDhHePh4xq5bueKQee1Sz4q//y3/H3Q8/8P6Pf2AKI0yZftzz9fVX3L64fnZBl2ioYv0lQmxEMxUWeLGTwjePxP6R02FHjonoJY9NJACCS+8OEuNjSfSHR9oKVo3BWEOzumCbLcP0iB8+SLyNUVTWwiiL5uPuUUKWP3zA15br9JLt19ckL4v0nBNqGmD0zPt7fH9AAYMXU+hPjmBoTcfry1eEPpBj5vb6kovbF7TrDf/x7/9J5h1Gs24bklXMV9sycx9onSGnQAge67TMAesK7cQq62q7pUsVY6yYg9g/jF3LW++Zx+mM0Gil2GzWZeakmeeAMZb1ZnOe3VvrzgQ1HyKHw4G7uzsJan7WCuYM9+9POHVAhxXbbY3RgXEUqYNSBtcYfIgMQ+LD/YnD/sA8HLl/PGK04m/+/p/Ep9Rn9vsHjL4AIsNwxFYV/+E//HseDz3708jp4z1t0cuZQvCwTX0ekGcfWHcbLi+u+PKrbwC4v7/He8/pdKLve8mNbFrCrIizo2ILNtLYh0/uuaZpxXAbeK7rs6VzsU7z+PjA+/dv+R//P/8jb9+95cPdHcpKB4iSyKSUIqlERi1FOQbP7CfxWU2hGBBIRzXOMzllKldRty2rrSAlIQT6vifGeO5uATabDa9fv2KzWbNer+i6FQ8P9/z617/mn/7pt3z/3ffn9l2KmicTWWK2SJmUQjHjP3B3/yhuVkozeUmpGaeR4+nANI8M00hV1zR1w/XFBU3TsO46ptmLhMKJVWQu35sUQE2sIyF4fAn8lmZBo5U4zQzTdJaELPf/5D1kIRAuhR/EVSp+xv34U8dPl0johC7W+WrZWulUOhfpkJYL36iEM5nWwTAE0jQyzKF0fppQIl1iiueLR/Pkcu6spaocm/UabSWxfPYZpR3KKB52O5lR+kBVObqm4mevr3G1uNSTNYaMzQYVEjlrspXO6/MuWWyCDMknVKKYlOVzhxhLgYpZPBm1jkQSgYnALPMQJQuDshblfYHZ0lmWsXwxy/Kg1VPhT6lAw4oyE1LnrnGBmRepjqDI6gzVgiZnoWI7V51hw5zhdDpyOBzOG5ScctHwyXsY5hEfZ2F2K+kW5zBDhBSESWYrS7fqONwfCGTmBEFB0IqgIFcancSJXhuNsVoICVbTXFzQVROjm9n7kZgzc4bdONKeevannuq4x7qaq5sLsp/xfQ9K/CyTSoxhZPD9J+frOUFDqWKQrWWGkZJ4Chob0CaiVBKKOwsqEEvsUcmnMxa0I2vHFDKjTzTrLa4Sivh+dyQGSYzQOaGzRLS0lZh3z9NI1JlkNcHPAsmFEXTEaNEMzn6iPz5y3D0yj0cciYrMs+xZ+SxZPA+9jyhlaLqaL7/6kma9RTlHWzu8D2giziica1n94hccjkeGQUhK1miaqmLdWZyDcRro2oqqMjSuZpokbLZcchITVTtcZUlzwGIBQ7da4yqHs5Z5FhPkpm3PqE2MEvKacsY6dyYifK7BzTkzj5mpT0xDJDSRbBTzOEKBA6N2xBQIJLJRZKPxlDzPnOjHgRiyIC7zLIYWCmYv917TNLRRkBkzb+nqSuJ5ChGkqsVEWmVwjeH66ppXr95we3tLCIFTYb2mQgxKxczaz5kcNZVdoXSicu2zk4XIJsqi+3wjj1KEGNkfHvnuuz/yT//0jxINdDwUPgDncdAyYxunkRgs0RjCNEkGY/lJ5fNKWkMu5uOL8Ye8V2uF+LQU5UVWYa09w4nyY8v5S/T9wDhNzHN4/rGkqSkkBEH65Jz2fc9+vycUclxKiWny+OALkxXquuH65pamrqhcBVFYs+REXVUYbcSnOQYhFcmglqhL7mPRWy4EpxhlbrzYgC6b4RDkPZ9OJw6HA23bsd1uzzCo95968/7njp9cBLXOGCOi6DPGfs79eoIXEhlrRcC4bgynY0KHieFwAqWwznHqhSk0zuP5uZ4PE5qmYrNes22FimuNkEEyjqwUu8OB46nneBioa8d23fLl7QWmq0qLvTgsgC12PLFQdpx76rKAYglk0Hh0lkw3sjDDvPdgDEonUg7EPBNSJJLxaWBKIyFHspbPpZ1B+SeYQIpOYSyeP5+SonHePRYNzxIgqmQzYLRhsQGjEA+SWizdlugQuQG00lTOP9unZg6HA4fD/pxBqIAUJhJyw/fjzBTm842ZcmaaJyEOhEQioa0MmbNWQt3HELUmWSMyDiW2atY5XBlg50GEv6urS+ZmwlcD73YjKWQ8it04U58GdqeB6rCnrmq+vvoGFSSdQBV7pqQTgx84jad/FrKRrl2E10ZpGdjHhA8eVMCYUgBVySYsbD8RfotuDVN+bM2UYIrQbK+oKqGIB/8t0QesEgdXTUKrhKsrWm3o+4GgEqkSFuQ8D0yzFG1rHcY6xv7Ew2MpgmHE1YaaRHr+mbIUQdE5zjS2o2lXfP3zX4CxzCHS1jW60OudUWxWHS9vv+Ld2+94fLyHhwdiMiTlaDs5L8N4IgRJ3ra2Io/CustaFjdXWarGUTWOkGaJUFKKbiURPFVdn+3CRBRdFSjtJF1fEvbvUgT9P2NaPI2JaUhMQyCsItmI/yRKZkrR1EJkIaOsQVlNQDrpFAPTOMimLGRsOe9aa6Z5JpTEgco5mhrqraJ2jqpuRCbjauq6PaeKrOqWm9tbvvjiC1arLcMwUNc14yRWcAuDM6VM8JkUDc6u0CbibHve0C7RPc9tzZb7OaWE955379/zu29/x9/87d/w8eNHQvTY2p51pCwjpCSdlFeGaIwETqdEzFIAU4poI5vLECVfceE8yPuNOOfKDFBYpbEYTC85ipKtV2w2lORizpMnlBzTT9Z5tcwqZX1ZNp193/O43wtppbzuNI94L5aHVTFS+NnX31A7h9Waj+/fAQmVElXb4hzMXmQMvrj1iDWlOhdArZcswCXlQmzvzpsMOLNtU0ocDgfquuHq6qrcd1Ys8/7PKIKuqqmaDoU9D08Xo1YFZxhEUT6o90RdUdUrLrZrNh8+UjcNl1fX/Kdff8v9455hkughrcVgN5b5Q0fL9WbDf/vv/pJ11+KcI8ZUdu+atc30w0g/Tqy6hq6t+erNC9qmpq5cydbK5+7rE/eFHM8UYPlCcymaBmcsWilCSBjthJjTVfI5gaaqqayjco73Hz9w7HuuGstl5diuWpreErxGFcNasXp3ok1chsyqXIxaFxPsdJ7xWb1AJaYUQbl2VZkjJi0Xus9B5lZaOkGtDJMLPIdrpqnndNoXx32BXn0cRYKChjmhvISa+SDzJPpJQni95CGejieGU0/OSVhaqxWma6ku1vz5r96gnAGrqVrJOTQovvv73+CPPc3lhheXF6x95Nu390SnsE3L7DQD4pOaxgH8QNU5Xn35htubV9zne05+z/F4Yh4n7naPz4hM6rzbW3bRAMYsvoowDiM5TVRVoOsq5rEuMNOMn2faVuJ2xnHi1I9obfGqor58xebVKza3rxhPew6Pdxwe94TJc7Fec7EWr0llDNpUZGW5XG85TCce+z12sPRHzd3HdzS2RjdbdMqk0TM+HAk/3GPiyPW65noWSPR5M5hjJvmEJ3Kx7mhXG0zVMEwz/TBytV0zWpiGE1+/ecmXX37Ff/t/+W959/4dH+/e87/8T/9vpqHHTxPDOJKyRxO5WK/Zbi/px5lxEEgJHYuObKDbNCizxVZieZaBrC2xEMG61ebsw6iLT2s4JXyOhJywzuIqR1VVZ4r6GYrKMM+B0zBgdjuyUmVuI91EztCpLcpKVqExIqhPGfppKmG8A04bKuuoTc2qXXOxfsE4Rfpdz9//5h+LTqzlizevUMoQIqw2L0l6w3pzQQwS69O1FU17gTYNf/zuHf04cpwjGcnpQ8lcr3ZiLhEj7I8DxkX6afpkHGFKEVwKjw+B4/HI+w8fuH+45ze/+QceHx94fLzH1TWVrrFWC9yZImGeUUo2cbOfJP3AGJHn5CxM8bJmhRDxUZi9c/BCKkmRh/0j7z584OtvfiZEkctLxnE8C+vPXAtj0cpANlhT8erlF/z3//2aq+sXVO0K/p//LxYWkVIWrWzhL6TCwA58vL/nu+++43G/l1BdV4n+UinatuXm+pr1as3VZsvpeODhsGMaTrRdy9XVJVW7JsZcOsFImuczbLl030opnKtwrmbRJAoi5/n48QN93xOCWMUtOsjHx0dCiKxWK+aiM9ztduz3+59a2v4VM8EYCTHhbOECq/IryMB+sflSkNWSxaBo2k5a3BixVcV6veJquz1Df7kMrHsyJiViztgy65nniVmqqtBMnEVZg1GZ2hmMaVmva7q6xpU5Y0pi56PyonFZiuDThfF89ZHWO0r3tkzMtULlRarOE5t1cYEwVvLJxp4/vP2BabXCbNboojtaDG11VGQlBANKx0fp9JRedjbLfLTMSotZ8JmeXEItFU+O83HZeeYMWW5G+3z4yJP5rQznZQ4ac8SUbtNhEDdRXTpBVRhh8nHHfqI/DZyOJ9arlto5Xl9fY3NiTpF2u8KuakzjSsyKQiWF7Vr8PBOyOHi01qGtQilHe7HGqEgyhlM/MB8OzHXF4/6Rdb1l1W1o48isJkKKBO85Tf0nbN7lpl6CRp9Z5qLUEhcUUMQyL3bMkyRPxBBpWtnE5RgI80yYxcotB1mY7t9/oN8/cHj4wDSN5CwdU1PL4myripg1IUHbVMx5xswGUb1EicSqNMoGQn8kHA/E44lqmrDZc5EcF1lMt5873eUoIbUBYbpKDqQQAOq6pnIWKoelYtU2dK2kKNy+fEPdrfjw7i39/pHxuON0ktl9ynJNhRDpB3+mkWeEjDBMvWwKddF1eclnE+Nnc950LkXwPMM2T5vgZfZWVVWx8DLnIpgBv8QXzVMxqk/nWVBMiSqUNBAr8x+FMBNTfIInU4nVWhZqrS3jaWR/GHh8PLBdR5zRWOfIWaBpa2vaWrNdX5OSF1iRmZwV0+SZ5sDsAyGJJEIV8ae1lqapMU663nGaqXV+4jw8O1IRZ+/3e4Zh4HG34+P9HY+Pjzw8PjAMvRDSzEKWUThr0FERfNEwkcghkpQSEwjO3lfL6nMmd4Vlc1HOQ9/3PO4e2R/2rFdr2qahKozL536jShUzbyWdoHOOzWbDarWmbdpPNmPl6cvr5nNX2fc9h+ORvu+p6galLXXTQpZrKcXEPE/iFRo82ii6dUfXiQ2itlXhYyznkfP7NEYs+aSLDs/+3J7lIEsnuEDyyzW0FEVhKEsNmqap6Cx/2vHTimCGfpg4jSOrxhZYtASAKvVUKMqJG8PM6GdOk2fVdFxur9msVwXDVnz55hWXF1v2/ZXYUk0zDw+cNTBt7Ugp8Ic//lG8F9FiTttW6NbRn0bJnFp1bDtHUxlyCPhyQqqmYXFeP1OlWQx0P/1oMfhyQ0aUKbCXsbJAZZk9yJzSiZu9sWhXEXNk8jP/69/9LV9eX6O+/oZXbUPdtDhrcUZR2cyUdCmvSYpruRmMMVitJQWh7P7E1kl2YueLUSHu/ggLlCRwkdUlYglV6NXPT6Wi60TE7afAMA4CExqBRqx2tFYxmkpCVJdNTRa/y6w1h/2Jx/tH7j/e8Zd/9ee8uL3hF199zW/+/u/Zn/a4TUNztabetmjnyooHelWTjoZ+HFhXNXVdoSuNa1qu39xSjQOozP39I5Axw4BTDV+8+obVl5dsukuCDYSPiXHu2U+HMxyqQOY8zkmie07imakWWNTIQpsCWnmatsa5mv1+Yugn/Bxwl7VIVUIgDCe8gTCemE+PnO4Cf/P4lv6w43R4lAXCOFxl6Loa6xyrzZbTMNIPE+t1Q9SRPkwEZVBBEU4TOjuM8fQfv2f8eE+4u2M9TziTeKM1rcpyTp9diyEkvI/MyfO426OMw4dI261Yrdd8+GNFpSK2M6w7KYrjHLi8fc3166+ZhoH9h+/Zv/+O8bQn+MDopdCcDkfuDzNBV2hTkTL4MLPbPYhvbham7hwCc4woa8VFpWmgKGGWazFnMM5hFweTLBvJruvOpIUnKCozzSPjNOBGS185Yoq0bYsPUpSbOaCVwxmDxqIxpCQLcC4vLMkgiydqJiXN4+OJD/ePPDzsIUbcsjBGkeJYV7MyLS9fvEEY3YEPH/6I95Hd/ijM4CxuOIvdmtLSfWw3W1RboYzhdDphrONHZhTlc+73e7799lt2+z0PDw8Mk3ze06knpoCxFmMWVnOWDFGTGIJYocUYyUFA0qAUzha/TnhGPgr4suDLJlw2yI+7HUobvvvuO968fsN6tToXCeee2KzzLDPqBYWSv5e5nbWOHx2lRkUf8LOnHwd2+x0PDw/0w8hqlXGu5tXLWyBz2O04Ho4c9jvCPLJad6xW3ZmIc3F5yTgHIn0hT6VzIdTFk9YYmQUejydCiOfPLnFL/XljVVVVQQbjuZN8XgCNMYzjyDAMP/5c/8Lx04qgAlc1WFsTC7NHpSw0YEC84JbqLqweZyuuL7cyoDaWqrqUqKKUqZtGaLXlxC7VPJWC5XSmMpqutkKlLo7yafFprDuM1QJbKCN0boo59kKcUAplVHGXAZWlr/ucOhtDYSWFABqMsigCwUeCD5K1Vjmsc2AhqMjJ9yKTSIk0T6SP4nTw17/8Bdu64fbNF/h3D8z7E2kOhcEHhtKsqWUIrbDKkJUp4uSl94zFqFYVT9DF/rvMX3WxVMuC4VsrGW3PZ4J+nolzmecocbL3KZCTJiuDqTR1W7HZtKy7lrquCDkRpsx4nPm7//Rrjo97br98yV//9/8VV9dXmKjg25ppn5mniZoGU5cdZtIoZ2mqllB11MZgqwrVODG8NYaZxOpFi86J9x+ODI8j0dwzzInjIF3CxYstYxxQypLQfL7/zsUyb5kPQEllN5JmMPsjUOKUgiJETcqOmDQxK+q2oqsNq0pDCmxXDa9eXKLCxP6uZ3f/UTIfTXEzMpGklu4SyPksp8jGiDQkeuoiJ9H7AI8PDGrP275Ch8SGme2bFxiVaLSS7jPxyY7YuA5lajyQxhEeH/ntP/2WL7/+kuubK25uL/GjI4w9682WtlvhA4RoaOqWX/zFv2N3dc3ddsOH3/+O3cM9hw8/0KPwGJJZYY3DuZZ+8sxT4PHxgHUiTbLWinFLCKiUsUbT1o1IjKBII2TG5OcAMcvcLidslFl7XTav/cJlygjBo9zjy5xq0bDJbCcVkk4lHTr5PFt8HicUQsBj8H4uobgnjqdjiU8KxBCpXM3sBx4e7/FhpKoaNtu6aM4y4/jIPE+cTkcwWohTGoxVGKsKauLx80i7akgZxn5ks3ZFK8x5nbm7/8Bud8d333/Px48fmaeZrODm5hJbvcSHid3ukd3ukbathf1ZGEmSUuMYSwcTUyyyBI05+x49oTJltSCTS8afwRjFNI/s9o/89rf/SIwTdaW4vr7FGPGxXdxwjE44J3IMeU4pME+ytuf3VyAGYaDO40jfn3j/7h13H+94fHjEh8x2e8Xt7Utev37DNA483t9x/3Avwvkws523zCGBqdAuscEQ/ECYJ/x4IkwT0QtRMsWED5IrOE0TfT+cZ8wxVuScynXVnqUPwzCcZ4JKCdHn7u4OpZDvGum+f+rxk+FQY5YvcQGg+ASmeo7FmeIUr5vCxNOmvJQMO5ek5FRiPVKJBAIpqJaAQgTnPohLSo6ZqDJRJSonFb9uLJVV5wTkTC7kEn1u/xMlViTZou/89Ms5dxkLS7IUm0V7p5V76rSU2F6FZ1BNCF40lH7ii+MNaMVNu8HVPc7N0Ptz6rr4MRatQ7nCzZKKrM35ewVhhBotC/y5x1aKmJ7FWBWZiOSsnUusHCWSyWhD5YQARJiwxcQ4W4VzhqYWiYnR4u4zniLHx5H7j/eolLh6ccX1mxs2F2sOd0eRBGTwXnay2kiRRRtUtFgjEKRRWlKl2wZTVWDEz7G+XGGB093IaTcw+p7tcc/utOPx+IBdwcy8eA3w2Qb8fNMug3v5HkSuY8qcFZB5odJo7aiqDm1cIWZpmsaxbkXE3TaW2hmSn/DzxOP9PW0jkOPTXFnsn5KGEMScWKFoG0dMiX6qaLRBJ6APqODJKAb3gVVds6ormlWLyok0TIQkRJ1noBBV1WFdRZrFzo1h4P27D7SrVuDYrqWyimA17WpN3XYYWxVT6oqLy1tUCqQw8fj+PVntOJ16Rm2IxmFX67M3b/SBeZoY+oH1eoUqmXFaycYqpygBsVqRsymMb33OntPKlB99hvmNVdgCwT8/VQsr9zlZY6HAL0jNJ/lv+WldeTKMEAJH1iVwOknSxjROhBjOhBZTCqbA2AGlEsZmXCWfu25qYgxM04g2FlQuCe2FYEwiRM88j3TlNvWTP2+kz58rZyEj5cz79++YpxGUomk6Li63dKsVj483xOg5HHbAIg0zMo5YzrrK5BzIOZJZNrwi6eLZd/Ccw/CpbZlnmgbuH+7Ybjt2+0suLi9BVWUDITIUa1P5/ZOpyVM01CcfrECvQcYDfmaeJ/peEt9n70mFTm6MFVN0LeL9aZZZ9BwiIWVigpQ1KSsxywiB4GfJUS2En4Q+d3Qh+LPGzzl35jHI/WzKf8sGpKqq8yxRED513hxN0yQyHvd0Hf7njp9YBEUCYJ0jx1QMVp93Vqq4HSxHKgzLgLEyQ1OLR99yUssX/uTaUXBgrTEFi0xkQlKkZT5XyCWZ5UIo7EoKk1c9FQv5feIwHvA+oEItcIH99MuxlaNua1rTnndGCnGI0FVF127ks9cOjyfEWXRpZRcbYmTKidMc+Y/f/iMvL6/5D7/6K+rtiivreNj3sttJ0iNnA05D1EmkD6aI8hX4BFoljIamsjgjllXCnNEkZej7UVzuEWhZaVuoz88/l6JyDW29wpqKqqpRWtHPvaRjYBjiQPSerrHoHAlT5HDc8/b393z8/oHjac+bNy/4q7/6c1ITeRwf+OHuHad5IADH40A3zyiV2aw6VDaEmTNxxWTYXmxZvbphe3nBMM/M3vP651+w7hqq3LL7T7/ntNtRdRXaZeZ44t27XlLhJ0+eMsp/2rkv14p0JU9QvFz4mqqWOdYcE65as91e8Yuf/Yr7Hz6wf9xRV4ZV57jYNlRaSFfjcJAUkUniZYzWVC5SN40UJmXJqmIcPXf3D+TCLv3izWvq+sg0Dxzu7yEGOmfo2pqmsuTjjq655MubLQDTOPN4nPCDvM5yaK149eoLFPD+7QfmaabvJw6Hv+Xth/fcvrzh//F//+/Y3NzSOcvtizdsL654/eWXWNdirEPpxOXtK5q24bf/+Bv6OfHuwx2rmyvqpqapnDAMp4ndxw/iDbvbc3V5yaoVGK2tZ2FFTyNz5Qi+w9j6zHy0ZROseJrli4ZNWMqucrjwqdfr87nS4XBgmqYyt1LnhW5Z44URmrDGEsKi+fLkGCFG1m0noa9aMw5jEdDPn7ABc06EMKN0RGnPOD2C6jBGoruUKqZ6KZBUxjqFNhlUBBUZhgMfs2f74pacNFM/E6ZI8k/FIqbI3/3d36AV3D888POf/4zLyytevnzJ9Ytb2q7j6nrLP/zDP4hEpu8x2oj7URJrsHHqQSVCmonF05TCrZBLfPHpNGiTUeeZZJkj5kiIssbd3b+jqsA5ePHihUCGVsAlXaCnZe09z2uXBuYZMpYRW8l5HIghMs2TbDb8BAZc7QDD5CfevX9H3XSCokVBu4wxXF5ecX19w+XlNavVBusqJh85DQP90JeZfSSlwDCJvjfEyNXVDV3XcXV1fS529/d3nE5HjsfjeV1ZSHFd13E8nsomTFE3Trp5khi8Hzf81OOnO8akpf3k/Ovznfin2sQFlkzFgWXp68UizJRuTHBgdS6Ei8wilwKXU3FWEfrpeZeYVRmW6jKoWPDFZVGkmCunwPE44EOgc/ZHuD5A3dY0ncwQF1aVLVErOuviA6iKC8XTTy7U3ZiCMDuNYj/2pEf429//jot6Q6UcunYC5cbAPCeSSWgcVucS76LPLU9pRtEaqkpTO8uqW2GqGm0rUJbHxz3jOHMMI1ASrI2Vne2zo20XMa+IylGFWYkkwY/9iaE/4f2EVRuscWyqlu/nif605/bVJa++uOHlF9fUtRBB1hcrVhdr4iyhrxKkaiFJgoU2BrR0TlOYcbXh8kpsm+J+zzSN1E3Dxc0l9i8r3t8fOE4jzhnapmKzbUlTxEWDqjTRdKQufbITRi3hovrcuUtTIRCPsdK1N3WFUwqbweZcXD9KLl5Vs1mtSfMIKTONE/MojLOua9msOrabFXVVoYwjosmpMNLK0F+ZxNCfgMT15YaXN1ucMdL1WQlYXdWaTePYtI7jcSDNE9HPhaTx/FpUbDZbyJnHak+cE1EJa/Fxf6CfZ/7X//3veHV7w1evX9JtRqp65HR8pKqEBKKMws8j4zhzd//Iw+Oeyc80YSaFmeRH2a1PifsP7+gPB5xGJAVVYV+XXfjC8JznmarMpxdYcrnfTYFHg5wSgdsKAvTJSpAWJup8nvEs3o4iuZBC6b2XNUA/yQ2W1xIymZa0kbo5y4dkFLC4rpiyHmRC9Jz6A/2wJ+Op6xZna7RyZd2R1i8nhY5Q1ZbKWYbxhEoenRN+9iRt8JNsyIJ/lv+YMg+PD9Tlu7q8vOTlyxe8fvOGtuuwleTxjV9/g589f/zjH9AoXtzcEOYZP4+8fTeeIWaZGWqsLsnwSp9JfEqJu5FadvgF6TLFIkyY9Z6+H9jvD2cNX84FrkY2iKlkTH4SXfe0hJffi0vP2DRnuFRpVXR4l0xToO02VJVwH3a7HcFPzGNxy0qB5D1WG9qmQ1uJiQsxsehCjTUwzxKOnBOucqyqNZvNFmMMx8OR4+nIMPTsdg+M48A4jufGaynk8vPkF3pzfcvV1RUvbl8CisPhU33xnzp+Mhy6MCyXE5Tzp0nm8uuzb7XcMDlL/dJngkq50BfmklbkJGLnhTmUlp1mlo5xYUbmQqUV2HMpf0+sp3NRRpUbKzL0EyFGGpdYrMye3rjsblxTFWcEmTe5rLAYbNaQnyzY8rMiuMQZSWinQlnDaR6ZQyAF+PLmCy66LcpaMAkIMvBNGqMS0QnUZ404feSYMEpTkEW0KdBdW+PaNa5uSdkwzqGQYMR8bSmAyjyxQ5USy6G6qYgxFwhYrJ9MBpUzY98zDj1+nrBK0zpHTi0qJuZp4PblJS9eX3P76pJgIzpENts1q+2KMISz5ZPTBsq1IBep0LvnMGEqw3q7Yr3p6MeBU3+gqis2FxvWzZbL3/yBDw8PWKOpKsNqJYSjFKG2LWSIbfzkpl2YtGemLRSLJ7kmjdZYJyJwo0TkrlLCKlU8R3Wx4WuYY0k2CeFs4NB1Lau1DPWNMWQFuaRVyCA+gRYofBh6XGW5WK+4vrmgrWtWdYPTCqdg3YBNHh1nDsGTvEDn4jjxVASVglWzIudE5SpmG0hCs+Z4GvCHI3/797/hdOypq4qrq2ucdTS7O6p6xNoG4yph5/Un7u8fedztmb0nBE/0M2GeGE+ew3Hi8f4jo59xVotPo7WEMJU5kj0vMiK4rp82Hc/udzFHsOdNyeLbKPf107gzFjh0sSdbmIbCKK0LRJfORRAoTlTLSS6zO6Vp6oaqrliSDpbXtVYML5QuJJro6YcjIXhO/Y666qhcw2p1Rds0rFadbLKjxhhwxYlq6A/oFLCIqUDWmeADYRaOwHktRHS4sW3RxrBarbm4vOL6+rqMYcDoNS9ub4XhfDyikC7NTyND3/Px4/un2CJymd3Z81hjyQ7UWjOHcCa1LCkVWi/fd0lz8CJaT+dA5FzMSHL5Dp82Fc81js/XwyWxYp4mqbelINd1w3q1YZ4DV9cvUEog8t1uxzz1zFNPClNhR4/klLHGIRppJVabWp9NKoTvIB1c5Ryr1Yq2bQkh8LB/5OPdB47HPfv9TqRNwdM2raw3zskctVyjy9q/3V5wdXXD9fWtSCa6h3+plP3o+MlF0BoxuC7TwB/9vXrG935+AVNw6ByfFrNMliTls2ZOlTQV0fGFYm0mneQTi0g6PnUWicYo2WfLzfC0a5S5QwqJdbMmk1k3HQr1yY4OIOSEL44q54lciUlJUbLrkgKsIqhAVhm0OFRgLJV1Mr9ThQOa4ZQyf9jveN9PVNFSo6mblhgHQsoMkzjqWK2o6o7aSidpqoakYdaB92/fEh4D2zHg2glta+4/3jENA2PfozAYiV3HVBUuNOf3D+BcxNqZGGegAgwqK+bR4/uJ+493jH1PCBPruuH64hpjKr6/ecvh/si//a/+DW++eMHX37zm0B/wIeKvoH83YnONNRUGi8kaHWNJ3chEiYYlxUDSGdMYVuuW9djiQ0vTVtSrCnPhuP76mpf9gXHoGYYDczjxxYsvaeyK2m7wg6f1q/McN7NIWmRnKU46i9tOLunsnopIbSFNR6bjPfuPb0nzQKXATzNDP3I89NxcXmGtGO0ZW9jDKRDnmeOx5/DhnskH+smDsijjuLoqmrtRom622zXdxYa2Fo/J/njgommonEEdeyY/4eeRw+HE7AOuteSsaYN7QqIyRB9Ll9rQW0+OWchaSqNJ/Pof/pH3P/zAH3//LY8fvuP2asv1pmMOhqwrXn/955xOPY8PD/xv//vfctx9BDKHU88we+LDwLsPe95/3PFw7GlXK16+fkW3WuGqmlM/nIvgwkg8no64qjnn9D0ZUwv8aXRL8jXB6KLjNU/3aZlkBx+Y9BNrdHmNJfNtiS6aJ/GelFlrS4pB1pQc8HMmxpm6lmDkcRzPjMD1asV6taZbdThjySnR9ydZnP0oSIdpsLbhJmpyjkLi6RzaOTb1Gq3Bh4lTfyC7GpPFnQYtUV5+kkL4/FiYpTmlwhYe5XyVDuztu3c8PNzz8cNHSBLv46zl5uo15ER/2nN/X1FZgyoIWSrh4DnLd70YcyfEA9gGiYBaNsEKEZhfXl3z6uULvvriS+qqhayYxhljnkZQMcYfddghxmI3uFyHmTAN5Djjqlq0yCmjlCVjUMqx3V6W51XsH+4YTwfu3v/A/Ye3TNPE3Yc79vsjx+PIq69+Rtt1rNctVdWgkeQL0fR5hlHS6Pu+59tvf88wjJyOPa6S7ni73TIMPafTidOpP7Npz8HoahklGVIUF6Wbm9eMw0DXffxkBvqnjp88E9T6aTf4BCc/faGfpx6c/+VzWOPZny3Q57ISKK3RKZG0YYFTz4t6zmUIms+vsbBFngvhl+dehJcSq/HM5HZRpX/6ydBKk/XTcyWViOUjxLyg8NL1yURSF+szvWS/l0w/2VelLBZHISecViQUMaligIycvDJDadqW2mkqq+m2l0SjmHTkj2/fs+9HjvEBXfWgLXf396QQICUuuwsRiWdJAZdXeTpS9KQ4k+IsbjZKkZVl9iP9caA/9oR5RutM8pE4RzARFRVOW17cvGDdbYghsX844EPAOnHN0FrTnwYOuwPdnWW9qrFVha5s0UBqtJUuKqYERuFqy2bbUVVCGY8EqtbSbTt0EDuofjzi24nWdqybFVFnNt3hEzj0nNCRUqGdy0wtlu8+p0SOMnfxYcD7gRjns86rrmrqusHVtXTQZZYqpA5QWTOlSJrhNEi80DAHjE1Yp2j1Exwr+j2HRSBXp6ByGh0nca+JgzgjeS8LpqI4Fmnq+tNbT2VVUAf15LOodJmRW4YsiSjRz4ynA70OqP6eoBqU69hev+HxYc/bt++Z5yBEiyLujyj2p4FDP3AaJ4yrcU1L1XbPkJ2ne2hBPlJcFp34ibPIsvtWxSlJxiVe3Jc+8yWM0RPiMq+WgXvOCWMUrjI4a4hRiuVyTivnmK0lBiOIS4hEH872XwKBywKgEAKPsxZbZozBl9SUJN9nKrrDFEvA7zyBjmhr0JUWQ/MQUNqiSjdmjdzbKQYhvn3mRSkG0upskH08nfA+SNoLxUoNVb7HSNTiatQ0cs3c3tzK884jwc/ySbTFGEdGMc/TmQ2ZsgQSaD2WtVA2C846mqbhyy++4vWrV3z1xRfikKN0OW+cz68I3N25EC5r6CfIaEYMCuZZvgtliAlxqgmJuaRIOFedEy+M1pLrt9pSN4HN+pK6ac7rZcqpMKENWjvaboOrOjFW2B0lvLzYrjVNw8X2oow2Io+PD4VIKNmtqsDiCyLjYxCXGWOom5aqrkWSYt2ZxPVTjp/ODtWL+PrTyvocKllupKXdlj9L58f9c0UTFhcVJGk5Z/Iyf8ilo1igymevsTzP8xv3/JyZ846zrktcR1kEnxdoEKab0Q4Uxdk/EXUm66WsiBm3zP9kRmkKHIHS5BiFuVoc+LOR0FzpGAWujCEzpUzSGl0unLqtaFcd3WZNXVnqynL7+guys4wa/ue/+Xs+9iOnh0ci0pHuTkesMTRNw2qzpXEaHQMqhfNcbvn8YZ4I80D0M6ZqcdZArtlNOw6PR44l62u9qpnHmeEoZPowRQyW1y++QNvMcXfgu9/9QIiBi9tb2RHnzMPdA4mJmAa++vIF3WZN23ZoLR2TqWqy0vgYySpRN46uu6BtxTG/nydc49hcrPG7kRgCu+Mjh2pHo1tWTYdylsv18dPhfYrlOy/2Z6UjLFcSxEgKUiwmfWTyR0KeZQOjFJv1ms1mw3q7BS0LXU5ZQAsrpgPee1Cavp8ZZs8UEq5WZK1JJIkAs47txZa2chjApESFZtU5wuERX0TDU8z0IYkQWkPjNMk62vlThx+VCxqSMv1pYBhHutWaxlqshslaGmdpnCFPJ0Z6pnmPbm+w3TXjMPBw/8D3370loyS9W0VM1RBRPBzu2PcTvU9c3FzQbbbU3YaMfmJml/8t99qy8w4Fjlvur7NXphIilCJLCouzVM/0aTlLh7VE+1jjihm8bCiaRlh/KXr8NEPKGKVo6xo/OaI3haXoCfOMc1LolnersuRyai2QZlU8X/3sIWkUBqtrQKzjcoqEeWI4wewNtnZ0bsM8C0xrXYNzNdY1VK4qLF4xXY9xiTMq56vc/zEFHh53NG3LNM80VYU1mq5pOBXZRygZjZJU0bDZrCW7MAZ839MfDwIXVg3tai1p82dP04yxDp8SRtuzlhoLzjnW6zV/+Zd/xZvXb/ji9RtyKtFiQfIjc5Zg5KXzXgrrcyjxfL7IDP2JYRhIymFxzD4zzYlpDvTDyNt372jriqapUMhMr1tt6do11lpub19QdyshqKnF8SZj0Chds97csPAvhul78u7A8Xik7Ro26w0///kvOR7F9/j+/kEKKJqubcUftevE89TP7I8HchZexHorsiHZgFvhJ/zE419hoK143mU9vxmed3nP/35hN+VMoegu/BV1nu2U3gkWHY6SRh+FzAq1RamEUk9Y9zm5mR8X16eB0VIw5fQuJzl9QkgAkRfYs4BdZgoF9lUKssGUnWStc3l/0g+mBHEMErmSDFbLe4/zyBAjsz4y2wadDDppqiCBm04runXLarvCq8xptyNMI5v1FW69pupWpGSZPRzHAa8iUWXoHMkYZqMYCLjs6YwmxBnv509B6lmRBoUfIqMPTAzc7x7Y3+84PPbcXL6EHFEqcPfhgf3DkRA1j/dH5iHz8Yc9/Xjg3bs/8v6HH0hkdruB3Q9H+t3IcOpRypPCiCHSblZ0/cR+f2KaI+t2i6sa0AYfZrTJXGxbaqeotKJ2lu12TRwyD8cHQDPnme8/fo+fIq8uf8a6ucK65/pHSiiqXDKiuNJoVfIZsyIHT1Qj03DCpYF5HPGzL5lsC0FLnHymyaOVoqmLy49SErKqxch5te+pfCIpi60rYk784bu3rNdrtusNr19dsm0rLmqLiZ4cJ6aHR8IwShEOipAgZkXroDKZziTqy2uiqT+9b4zGVY7NdourPjB5j9bSJeUgTj8GMDkxnI6EITH1j7z6+Rua9SX3D/c87h/o+z3dqiUlYevqek1McJrvUK5jc7Hi8vqWtmsxlZOinqFuxNE0pigbAr2kjyylMZYFqSA0WbSYzhiy1hATVj1pCcunKl24IeeFmJZLF5GIcSZG0cpJByIU92E8MgyDdCRKU7uaWptzbNA4T6QUMEqx2Wy4vNiy3m549+GO93cPHPtZvrcsGlytxBnpNEiMUQyBdtXKhg1D8Ip5ltF9tV5zc/OKi4srxmnCGvH99H4+ryMAc4iF9JE5DRP7Y89udyB1LW1dcbG9oGs7Xr96zdXVNdM4PG0ychZyUF1TO0dyFVXbcvniJeuLS4wVyPcM+2ao7+45jZ5TfyJnRV01fPPNz/jmm2/4s1/9OdvNBevVpnznkbqey+xM5Gdt29J13dlRBjgnhTythYpme022Lft+4nR35HF/4G/+7jf89re/4eHhnuvrC25vrnj54pbWWaqmY6ssddPRNC1vvvySOURmP/Pb33+HMZa2XfH65QtWXcuq7sp1EKnqlsurG5Q2VNaScuK7777jhx9+4OHhgd1ud+56j6cT6XggffyArSSGqmkFZm3bFV989RWr1YphmkWbXj3lJP7njp/eM352fC46/5dgzwXxPBfAc0GiGNuWKbpaAoXU00hBLc9XfDafwTZ/sgiysAbzZ+8lfzbOVCxRUKZkpOWStFCEF6hsyFoMxIWQmEk5sMQiGV2dmaPmTNjI0tHGiM8zGoPOBnc2YsviuVg7koJhGiVIdBjQdSNkEzQpw5wiUUeSBmUFUo0KxjBTKY23ws5cjI6X79YPibkP+CFiUwA002kSZieG9Wotwtg4MY0zQ54Yhsjh0DMOM3cfHzmddnx4e09/GFEGzHFgnrwYUReCDSkxjxMYTbJCgY8pFbcRLW4XfsZaRbducNZIVmPWtHXF3HX4NmBrSSnx88zsJ3z05+7tk6PUsfMP+Wmzk7NQ0IMnziM+jfhxIsXEYs80e4FfTscBP43CzqNDZU2KBltlMhZTNVxeX+N9IiaZyUyzBzJdU3N5sWLTVXSVobYZnYrBehhRaYYYmKIVkhfgVKIxsKkNrnG087M+UFECSimemrJR01pDWuZwYu8XvKcfFM4kQsgoU2OqmsOpp+9PjFPPqpUcNkpXrpTCuJpKK1y2tN2KqpaZ5ML0XlIRfnz/LpmMC4u7vK8CS1tdLP4W4tqPkiSe4NRsnmDMGAOzn2liJMSAD74kJ/jSmYXCWHUoI4uBdbJZ9cEXHoCYtldVhbGW3eHA4XhiDpHz9GRhkGfF5CWBQaVMXTcFplNPPyiMdlhbF2OBJI5Hn6FHZLGDAyRNfRg4nnp2h4N0p0nijCrnqKuK25sbjscj+4Ms6jGIDMeVx6Sqom1attst3WaNtY62bUVMPwtUOsfE+u6RsdiBrVZrbq5vefniFd1qTeVqhABDsSETnWBKkmq/zBefm35TxlJP5wpZg7ShH2YeDifuH/Z8//33vP/wkf1+JykeyHhp0zYoJPnEJ8UcoT30jNPMMI5898N7lNZ07RqlLRdbmXGbQnxyVU3TdZIaEyLTJLKXvu+Z5/n8nq0xjKOMFsSKLpfaoWnbjsvLKzabDVVVE2OiqmtJD/mJx08vgs/mgD+i2fL0dz/6vZJvN2WJRtE5k8scZnEHV1oJdKjKBStPcG7XlxtnMVqlQDZp2d3Dj27gT2HbZ9XhszmpzBkczj0xVG22qKQhL240heRTmKEhjYTSUV6sL0k+EQaP02LmbGuIWdK25+DPYagLWyrnhKsddVcTFez7no8fPnJ//xGc4/LmJVlLYsOkAqbSmEouaNFmZR5Oe3w1UW0UTZAE8DPTK2d270+0Vuyp0spjrcYmw7rpWLuWSlsR2849H+8/cDgeefvukbu7E33v+U//29/hw0B/fGTVOqxxpCBG2qqpcFZxcVlzcdWC8vhpZHpM9OOJnDPr6xUhRR7vHzgc9lxdr3nx6oq2rYSVmCObVUeVWy7SK5LtSc0BtMdWtoTWekL4tMPN5dwuGs3Se8umJWVymEhxIOQDe3/P4X5HmhO1q4htEOun45GP7z4Q/Uhb13zx6rWkS1jL5krRrFra9SV/9V/8khA8j/cPfP/DWwIzX795zS9+9iVfvXlBZT157pl2exqdcTniXGT0kUFnvJb5jtZQ655tZfjytmNoHK6Pz9FQslYM08j3P3zP6XQixkzlLCkkQhEjawI7nSFWNJVjs75EVxcot+L9/bfiW1ky97TWRIQ0Y63lxcs3+JCJUXFxeQEkfJzwSZI1ns+LFicOlCo09Czn4dkYgjKuuOwasrGQM9M4Mvaf2lUto4wYI8YIqSmEwKk/4faWtrlimkeO/YHZzyVdZmKahfjStCs8mjiNUGbMsWgNV6sVVeUKwxL++MNb3t7dMxR4VwHEjFIZnRJjmLF4Rj2xWm3JSRFDRiuLtQqH+GyGkIghYY3j5uYG01BsyJ4QptOpRykYhgFlLLP3bNdrbi+3XKxXDH3PqutYrVa8vL2lbWr64STkICXXauVqLjYXtLaiXa+5urrGlLlWXdfn2SzaULcrhikyjBNKKX72s5/z85//gm+++RkKmSMHn6RwWE1V1edopXGazw3I83P4+QqeYuA3f/u/0tQN4zTx4eHAw+HE737/gf3+yDiOHI8Hdo97Pn64o6slG3CaZqYQyUqz3lwSYmYOkfvdEW0tbbfmVz985MXNDb/6xTfc3lyy3azoNsKeTynz7vvvOR4P7Pd7iccqnav8vqEfek79iQ8fP3IaBglCjonLqyt+8YtfcnV9g9aGefKiU7z6yE89/pUSiWc3AZ8WnmW39M91hJlc9FHFZqkWdlOIU4kxEgPdT4qgPEthJymetIlPkOvy5wBLoOcivv3nhv3Pf6W80nZzwcVWLN2meSCngNYZa2qcrmncBSrrwsUYSckzp6p0CIqX119L0vrkJQBYa6ra4eOIjxOPh4fS0mdc0FiUaNackyG496io0ElxOh7pLkQ/5FY1bl2TTgmdFaREmsURQJEYi//lhxm2eSWxSOcPplitt1xcXgPFZs5YtghzLcYgHo1kYtrgdSI7R32aWc0KYwN1XdF1hout483rG7p1x+rygsePe/pdz+7ujpsXa1683uDTJOkKdcem3pJ85uJ2Q1UZ4hRwytC6mvWqK/OIwDSeIMlO+OVXP2cKO07TW5TybLsLVqu1DMM/tz9SCaXFCcQUH4GYE2AFRUgBP58Y9x8I/XtO+0faquXqIrNadTSVOKNoNKaqJXmk61ivtzRtx/XNLShFIvPx7h4/TwzDiRe3l1h9iQmeba0w06EYkAO2IkWBQONph8HSOYvTlpOPDNPMlY2o2rG+3tDPmvmZIXNKmXdvhV339t07cuJsEh7jLDFHVpEUjD6gB4O2Hbevf87m8pamXUsUUBYjdB8TOknsTuoHVCFQ2AAxyTUakiS9aIrmzwql3Whb7gz5mSaBARc3j8UwO8VADAFfmXMuqPeeaZ4/ucdCjNgkPIG6qkWHmBLTOHHqT4zzSMgeZRWJRMhRCA/WigFDTGSEEFFXNXVVI8kpQjQ5Ho9065bGNxz7EyFG2tWqSFGWCCgx+miqmlprWmv5+ssvWG82bG+vmbygDl3d0FhHayt8mFHF2Bm7pJUv6xmM04QCxnHi/v5e/HnnmYvNinXX8uLmhtvrG16/fEnXSDF6dfuK9aajqhwqJhpXc3lxJYhEXdNdXpJKh2asORP/UJqb28wvA7SNyDJ++ctf8vLFK5q6Fcgzz/ismMOMMQbvbLGiEzcVpUX3vDKyPhilRe+qn9bbEAJ/95/+E5eXW7rVmq+/+IpvqoZf/BvD9z+84+PdA//0j7/BzzPv376TAVaRYqANSmv604mUxdlqnGS2Pp32/Hrq+b5b8e67P/Dll6958eKWb75+jTO6BBSL9+w4jvR9f2b/Lq4xizPQHMO5SLbdmhcvX/Hi5SvqpoQp24qY87lT/ynHTy6CS4Dj53NB+M9Dowrw+UlbJzCk3PxaPS+snxbA5c8/f70FL10K4bnglYKonkGny3Eugp+1gm27om3XomEKMjvSBVKwxrFqtmgsRAj+QEwzJilMzsSouVzfQFb4ORQtmqWpW6bQM4WBySdCmEjJo8vg3xmJbLHLvCwpDEasiWZZdHRl0JV9gvlikZkUyCOkzJQSBw9WW0IO50+mgKbt6NZrFPKetBEtVIyBmAIpP9mxrYYTc860qwMhaKoqsVq31LWirtd89c0XbLYbtlfXvKs/8NAI8WN7sebFi2umKEWwatZ09YrgI21tyUkT5kRXtbRVQ+0qSCIHCF6s0YyquLy6Zhw05CPaRFb1hrpuMSzOROezWObDMo8RTZQiK9FCykxwZh5P9Id75uMDU3/EGce664gkKmfFRzZpnEk0jaPtWjYXF3TrDReXl8yzwDnH45EYZ3LyXG4vWFUVZjyiiaipR9tGmLlKMxdnlDANVHWHMRIiGnzgOHvCKqGsot608AjxM6Ti4eGBcRzZ7/esV9uzADwWVxAlQRVMIWENZNNwdfuG1eYS17QLDk9GEaJQukIIBEaUjVi7Qht1hpcXQXzKCnHpWsYOVq6xYt2wLEYh+HN3oo0p3qyiT8sxorSEN8fPHWPis1ifQsyQ8OOZaTLMQeBzbdSZURizkMcUSpiciLG7BOVaYpDgZblnn+QSMUr23mazOvuPWicbgLqqWHUdjTG0xnBze81qvWZ1sWUqaMP19gIt2gdmP6OTRjsZhzwvFlCi48piezydGKeRYRhZdTVdU3M4HJhnT9O0OO1omprrq2varsFag865QKAXBD9LakfT4J95ej7p+BTrjeJVNiITsZavvvyKzXYrOY6la04xn2HtEExJsDCE5CXwANES6+LPIc1+Pi+7KSV++P47gp94oTWXlxdsr27R62suL694+/Y9+4dH7u/ueDgemUeRvmQkXNkYwzSOck8CsURzTcB4PGBtxf7hgf504Hg4sOoq1l3Hqq1R5X+xyHMWE2xftK4Z0Zcb63BVRWsd20uxidtsL1DGsEy7jqeTuMn8xOMnO8aEINEjz+FQbZYYoCeGqNZPXnf6aXAnyQjaonQuf68kkmMpZMuQjaWj/PQdfFrwlpu2wKXlBKbiMZjK+1ww8POnyLn4mJ6fFafXNPYCpz1+msh5JviBMQRmHejaF7RVy6re8Pg4kaaAVRL+mbVhGuaS1QVRa7RyVKstcTbE2eDqjpgTYZ5wSWYvna3Zuo6tabnrD9RKnGFCFlhzOJyYB08YE8yO7MUcwGLOIaYqg44KfMKsFWplPtlC2K0ld7A/7AmTfB+bzUqkIrUIcGPMhJS4eHNJe9WhtCJ6hcqWn3/zFVWtcC6yWgsssb24xo+BeZzpxx5b3XL76hWYjDGO2nVM12LO3B+PWN2gqfi//dcdKE/cBXrXoxtIOuGnCQJMc491lturV7hkaas1tV6Tim/s09mC2mV0Hul3H/jtD78n58iv/uLfUlnxSzztf+Dh4+/44Y9/B+ERRaKqaqw2KOt48eIlXbtm1W4I0xHrFNvLjrptUMrw7u6PDKcjY3/k5uaCzeqSFxdr9HAkjTuO9+/I1pKsI2kR0Y/jTCKgYsRkRW2diLpHTe8D7449v7xyRGugbWDM8IygkFLmu+++L/eP5uJiy+XlFd988zPu7t7x+PCR98PAHAI5B169+RlvvvkF/+av/xtsUzPHSLveUB0O2Lrm0A/Fgszj04GYwAfJQTS2ZnO1FcmT0hLflDJkjdGO7MBacfU32jEnIVgMQ3++D43WkqgCnE7HorHlTFf/dOUom1wUwXsJBa5kVjzPntNwwJiKqnOEB0mYr5qGEOS77Sdxe1qvVjRti0Kx2+34/vvv2e12bLYigWq7lm++/oKoICoIPpylAeJfXGRNKaJCwLlMPzzy7dt/4uP9PSln/pv/6r+mMRabNfsdaOuo2xbXGaZhfBq9KMXLl6+YxrFA1wLN9+PEx8eIMZrTMLG9vGazveSrL79hveroVi1a2HNlc1CYnuexpWIuWYvTMMhsNIi1WNManGt58eJliXsSV5dpnM5BujGmMxt+nj1KifTn+voSWxuqyhKzR1zoAiHOMuNbRl2AK45LxtYMY6SaIjeXjn/z53/OX/zZX/CrX/ySb7/9Hb/+9T/wD3/3dxyPR07HnrwEEofncUlSwMuKTpgVj+OR6XTP93/4Lfd379isVmzXHX7qMVrz8sVLfvnLX+KcY7/fczjs2e12TGUm2I8Th8OJEOGv/t1fs7m4xlUtH+4eeXx85PvvvucPv/8jf/j2tz9SMvxLx0/vBNNTiOm5COZUsGWF0unM+HzyCH0iq6jlW0YIAArEGoUnEkqx/njWSX7qT3om1eTFVujZrsnkcyEM4flj86eF+7Mht9GSGD57j0WTdS1J4rrC6EY8RFHCwIwzPnqZMWiLNrXAprbC2RpVjLatrtHMKCyVqcEGtIuYLMyltutkxpbg4bDnMPT0YWZNEqF58rRVy7rd8v9l709+JMv2/E7sc6Y72eRzRGRGvsx89d6r4mMVhyKrIS2lhgT1qqGVVgIEaaeF/gettdZfQDSghSBAUAtgLwQ1KAoSQXRzKpI1vTEzY3Z3G+90Ji3OuWbmkZnFrOYgCeBNeLqHubmZ3en8pu/QVEskHikCWiXVE6M1QfgjAAchpkOXjyds2zX1XnFoD8esOIgRpXPboh9wPomBKyHxo2PXrhHRoGXBdv+AGUHrwHb3iNKG2XrN69fveHxY51a4IpLV8UleeNEFRIgoNIWqMKrh7lISwgCiQ0fAQTQJSSgowAdkTCCjulxQFTOEKEhwlKfZkBYdkg1+eE+3+yXeD4yHGl1VKKOoyw3iQlDI5ygWSDxaiaxvmBTpiwKMGnFxwA6e7WOLapNtko+JyL2qNbUaKa3FP+5gHMBZSpkmkCF4+v0O5wPWZRuuGJIPpdLJXmpU2ACHIXHlAgJ0gSwFqnqKXosxDfRvb265vbvj+uqaL774gouLJev1NV3Xp1ZjiLz8/HNefvEFi6uLBLwZhgSEERlMZZMbgrdjcmrxgW6IKFWijUVXJrlHaJkWy+xhmAKjykjuVAmexAni0a2DfC8LKZKSSr4Hlc4JWh4ZAkdlqHPZNa1VCvRXS16+/AQX4NCNCcQioCgLpIooleZNWifx7oRQT8ojFxcXlGXBfNFwcXnBfD6DscMGjw2ezS5V1uMwHrtC3jm0FJRKEn/0GS54vn71NVIrmtmMxaIC63Fdj7UC4T1BgGnmJ4FvQEjJJy8+oetauq5LZq+TggnTSKRE6yKdE3LnZkyjD0Qk+MgkFxlizNgcgYspmNijSP/UhcvrpEgjomEYs8t7kqTzmfjufAL/jOOIdw4hBSF6yjIBiMw2Sb0573h4XLM/HE69MSGo6ioZH3tP33Xo7RaEpJmvMGXF1dVl6pYpRdu23L//gPevsdad9umY+Jz6btP1EENgHFIr1o49vtSEkNSOpBR0XZJW894nzmhRMp/PU3dOa5CKGklRVXRdz9dffcPbt/d0XeqivHv7lrdv3vLh3Zt/90Fwkj86Ha/zwCI4ed6II/onBcHJUFJl1GVCFUpxquJiFJyq8lPQOwFwpqB4+jyTHUiibGaidK4EJ9TbpKEHZ9ymj2ZMWkYkDj/0aBRKNxAbtKowuqZSSYllsC2DS3M+LZOBZ6lrClNTVTNmzSKZggpBUvnokVFTqgplIkUUOJ+IpvPFAqUNLkTerR85DC2jH7kkppvAj8zqOReLK5azDTAihKMwSbXHaM0wDqn9FBPfLBDPAmFkvf2AWXj6rme0aV4YGDLJFza75KnmQkSERJhvN4cUuHTJu/sBrUGrSNsmnVJTNWwedgztgDLJqNN5shVMJIie6CLRR2TUGFlRFzOaiwXBDzi3wzHgg0eLEqENUiW7IaEUCkNTXlAWDQLz5HxP+2XkDh0VYXzF0P4FzraM+5pK1ihlWDZrLueG8rPfwcgRISyEjr7tGXrL4TAgcIjoCHbHOA5s1j2imCOLhovrT1ktG27niuHDLwndjn63pRTJkaMqFvSAC55uu0uStvlmTc1IidAaWRTEXmM9HDqLdSUBCUWFqiWqrp9e0CI5dN9e3/HJJ59ye3vHT37yU9q2Zbvd8OrVmyRAHAI//tnP+PLLL1hcrpJTd4iJt0aaxdjcprTjmESQrefQ2hwER0ydJPWqukzmo1FQVSNlWeTuiT7eq5PbC+Qgp/UE3GZSHSGm7o7Raf52nrBOnOFJ1xMRKcuC65srXnz6nJ/87HfY7TvevLtHqgwiKmtiFDgXGQaLUSQivEzuMFprnj27w3tPMyu5ublmuVjgdhZGh7UD929f8fj4yMPDw1Gure866qpkOW+oG4WPgd9+9Qu++PJLLi4aLi8XtNsd/SEFQYTGxchSzJMLSz5fSkp+9PmP6NoUBN+9e5foDM5Brr4W82VuH8NoHVKOWJ8Vp8SZBGTMwS5OINVMGvPhuIaFQEIoT0opBMIwMtrxaFPl8/dxHImRLKPWEUKgHzrKsjh6DCaRccf79x/YbHZP1vT5fEZZFonHd9gTvGe327C6umW2WHHzyWeUVcXl9Q3bzZayLNntd+x2e3wY80gqwpMAeAImRjzeSbxTCJKsm1JJVMFanxTDvKcoCsqiQElJUzfJlF0XmKKiqGq0Mey3O7767WvWmz1DN9B2LZvNhv1uy2G3+Xjx+N7tBwfBc725byMxT5GeOMGucwUocrU4KbKE7EIhkxpGSilzmMuIM0E2oVRPnYQ/VqaBCSJ/+g7iKP10HgjdBGnOVVE66XA1u+Zu+YKaFmVSNecGT1PW1FWNx9INex52awodKXRBU61YNrc01SVGrzC6pCzrPJdKx6DQgtEVeLfGSoElIF1kPi+4+/Q5hxhody1vDx1CCoxpGKyn7zu6wwMax6xS3DxbEelB2iSxNLlm9D7ZmogCqSPeDkwLj0Dw2fNbXv7oWcoQbZoXVkWZqkbAhVNm2XWJq2V7i5EGJTVayoTiNJoYwPnAobeslhe4wTMzNWVt2Kx3+JBaxJUpWc0WSKO4327oQ4fwmtXskrJoqMprfBjwOMbgKUyFlIZuB8F6Qu8prtLNGvEE6Yjqqcbm5y/e8/mnCsSSP/zDPyJ4i8ZTGEGhA1pdYJSiVIqx2yWqxMHTRssYRwafqzaj4PISWRWUF1eYm8/QzQoTS+7/4l/y4c//Bfu3v8HIyGqWjIOjkKmdY90Rhq+y04ewLUJ4pLCYWUl5uaAKGlWsiSHxPINUUFZIp5DFUwh3N/bcNc/5vd//OX/td3/O1eU1TTPj6voa7z5hyK0xIQV/42/+LS4vLzFFyTA4gvdJVJoE4PJB5JmvxrqBcUzXgB09oh9AOIqyoK4rBptg657AcjGnqmpQJz6b8y5VglISRQLnSMhJS8Fhu4F8TwfB0RD2dNImtw+PY6QoC55/PuN3/+YLfvyTL7i+vePhfk8Qnme3S4S44Pd+93cpyxkuwL/44z9DxkCp4fLFDUPf092/47F9YOx71E4QxIh1LV+9/Zqu7zj0Lb/8xS/Y7Q4EH3LEjkgVmS0q7l7csbhc4kPk5vqOv/7X/oCXn37Ku1eviVmuTehE73BhIHqXjK+nXZKCm+trihcvuLq64t27dxwOB7bbLVIrtDE8v73j8uqafrQ87naUw5CEAWJIYhrHBD1mb4CkTPyxnudpjU3qN+KM5zyJ2CtjiBmFm4SzU1AtqrxOSo2PAnEEGab73/lTd29a469unnN1dU0zX1GY5Pa+3+9ouxZlCgbvMGWDLmr+6I/+E373d3+Pn/zkp/yrf/WvePv2Da9fvcK58UTGn4JCzKmTkMxmNauLFbOqhOBYP97TdYeEERkGHh/Xed2yyeWnLLl7dsf15SW3t3fUzYwIvH3zlte//S3f/MVfsF6vs3h4phSN/beO4fdtf6UgOLn/fhwEIftTxWNHMwekqdmc+iNT6e98SN5rasoO8/ww8z+SdBlkgaQ0oJYqC2/LU9CctlPieXpITPYh0wwxHKvHJ88LERGyOp9Iqi8hOnzU+KAZfcfoOqzr02eR4ih8a4wi0QsDEZuy8Zi9s2LPGFocLY4OLzuMDhSFpGlKNm3PEBzSFBRGJ+1JbVKlFhxEBziU9llUW2KOGtkxIwElKIUWAWXODoCAuqqYN02Wvkr7bMxpeBxFzmyFpCwKnPNJv1Ik9GQylk2yVhNsXLU9ha4INjDTdbJ4UZEY0t9opdHaIKWiKMps5CkxRRK0LkqN86CCBu8pVIlSGqccHggitW58GHEioYfH0HMK7pFGbVkWM4QpkPNEvHX9DiVSa1urBDpQ0eLCQPAD0lvk0CK6A8YnT0WtCmRlMI1hfjmnuF6h6hWhtWyx+HZHGEaiTiWRD0lOb3CO0bvU/soXu5YBJSJGBgojKeoC09TIHVnGKS1U0qS5OEod5dqmraoqmgypv7y65PLyMnVKAjjluLm9Ta3CwrBcrqiqGinTrEtJlaHxOldKqRpLnL7U2pz0FmP0DJ1MsxvvcUSEVPR9S2GSMfHkcDHpsUZOwLTJ8UGKNOsJEyBt6hJ9DJJj0h11COmR2rC4qDFVJMoeFzuk9jQzw/MXVxilefnyGqUrnBc8rJ/hxgROMnVBP/bs+z3r7UOq7IqCrpvTDzXOWoZxyAatI9Y6iDJrUUqkkswXc1aXK4RWaATX1zdURU0M8Piwpio0dVWgtEyeeM4l+59c8Z4tMBRFwWKxwDlHXdeUZXkUiV7Ml2hTYL2nzVXi0yCYq6UpgecUAOOT4zcdUwHxrIsVT7XWiSubujA+eFwISfIxRnAJle9yYJqATnYShD9bM+erSxYXl1TNgrEbGIaR7XaLDR6hNYurW+pZpEIyny2OQKekcJSslXa7Le1hn7Wd46kDSFrH6rpmOV+wWMwzgGskZHyIMQXWpqr2cDgQQ6TPhutSKZpZQ5Er7KaqKIxGykjfHU6BVyaD5B+6/RX8BNWxwvpujiAn0ArnyMycDWSeeMzowHA0AMzP0xMEORHWiZlb6H1+PFWESqoE7RUiE9xPJxCmNun555JImd7DfXTSidDuNxzqgnbYEcckgt31FtOXGF0yuhHvLaPL4r+YPHsc8bFPF1SU2JCgvpFIO7asD+849Gt29jUh9ER6bk3DrIRZrXnsPU4GLq9vmdc1i7rGqIGqKkCADx3O7xnDhqaUVKVMPEuRRycTok8pjEpE7/PTUhUVdVnj3SScy7FNHGOErFEYEAkGno+diPnYZgNWKQRKFoQA9cLhxkh0UKJRQjDZM8aQnACKskIJyfVtgZIFWlbUsxKlFVFBJLmHq4xODdFT1AIwIDSD29NnAaHR9ez6+/PGCvX4mnqIRGZUzSVSG5xOfLroAnG0jENHt9/St1uCHVHjyOHdNwzrNbpW6PmMsl4iRaCQJqm4SIuIHZvdA+HwiBgOFDLJqPkIdrQ4Ir3PACwR6W261hSCpYG6gGZR0lzOMZcr9LanqCuapqZZzCmaJs3BlU7cuukalYIXLz7h5uYGrXVSpFmuANjudgzdwPJimf3WLqnrpKgvkRhdUJUli9mMuqwSonmWZirDMGCtx7lIjIfjue/aA6NSDEMHRme0ZzKh9cFSVdWxYpiSRyllluSyDMNAWaT3JcPhnR+Ptj3n2+QWP9ghmWEXgtsXlxzGDb/+Zs/tMKBVw3LV8Lf+9s+oCsPd7SXdGBjGwM9//3d4fFzz+LhGVZphPfL24Q2//vqXdG3Li5s7Lq+WXLoVVV3S28QxFNKgdCCGhJSu65KqETx7cceLl58SAKMLfvaT38MNnm9+85pXX33F8+c3zOfPkVoxWEc3dMlZYTxRkGJM/MDCJCGC1WrFfD5ntVoRM1jPZJeVfnSE7S51s9QpwZjut/OfnzYRP9oyevcYLHP7LZ5e4MlrhWPCH49f0/owuXq0fXK9OZ0rxbNPP2d1cYEQmq8efsnD/T1f/fa32OBQxnBxc5fbrwGjDWVZ8cWXX3L3/I7NZsNyteDXv/oV33z9Fdv1+jhCm7p4RVFwsbrg2bPnvPzkZQ6gLZEEYlouV8di693799w/PHB/f884JjcJBAxZa3S1WnJze8PD4zNevXmV5oj5v+89jt+x/RVk007t0POdmn6eDn6MZxnNhHwioHLloY3C5jmWTH+cTppLUXwSpk6dupgXb4GQp7acCNPNpp4oXZxzAROx/rxVKzLH6UzbkMhj+4A4wOj7o7h1QCbNwjBgyirNrWSiScggiS6y3T6y3a+Jx3avQKuCSKS3B3q7ZXAtXbdHEjAi0sxnVHWBY8sY7rF0NBcGowNRj1gR8YVBNReU8zWl6wjrHj2fUdUVWpMURJwn9A7nYeg9aHCnggmAkE14oxSJewQQfQ48qVr1IWK9Rwh1bEdPbZpCm0Q+D4lMHEkSYNELCAIhCipToGUBMeJFyOakSWNTFwnijnAMoUc4ASFi7UCMAYFMLtsCvHQknl/BOCR5KSEFYxjZj+vjDR5j5O2rHVUoiUWPKPbJ4SA4Zk0KAn6w9H3LYXegns8pa0HpB8LDexCO2A9EEgFdlxXjpmN9v0fOXiGqBjNfMi8Ez1++4MOrtySxZ31sQ5dKMDiHdZ7ee5yPRO9YyJHGaK6ub6gvZsR5jRcdyiiaumK2mFPNZ1AUmFhQVCVTBqek5G/+9T/g+YvUirKjZbvdEIWk7zucc9l6qEh8v0kMnlQZMpvxox/96Kjz+e7dO9o8r5rP55RlebQwGobheK/44FExonRq1Q5dj4hQaIPgdF8m78yYqqIIm8d1UkMpS6wdcXak7xO/1X7k0jIxXGKIXF/dcXW5ZL+zvH2/xvmB4aeGWT1nVi3w/QAx8Pbdr5G6ImLYtpKxd3hveXh8YLNeM45jkgDTBqMNwzDwuH5kjOOxLTkMQ6JrCJX1dktublY0sxneOz7cP2J0iZEVD+8fOOwOdIcNxkiqyjDYBHjqeovWmvV6fVo3YmS33SWRdmuPiYL3kbyIJJT8UY1KndYxwqmNdR64OAXCU9109nNGw08Ntm+DPs6D4ClQJqBKOP0+kmgoeb1N7erTK/RWUPQBYs+H+w/sthuquuRidkU1m1E3CWFO9HRtmhkaY5g3M+qy5O/87b/D9dU1Nze3/Ot/+S/Z77bsdzsCqWU+m89BCtq+49e//TVGa4zRzJcLpNIc2i4pyIQAQlI3M26k4kdffMFivsCY8nhk7GjznDhbnjGZPH/X8fn+7YcFwRh5eNxQV2XqsedNTHQIcaz30hB9qgbDqbhPC0k6kXZMsNx0MHPbJfer0/xves2YuSwpCAY/ZTchtSV1QhLJjEad3vNkt/GULB+JbLf7hCjN2/uHd4xxwEd71EcVYhruC+pmkTILJ4g2VUqdPuCCxUVHkGnGmC54RYwhyXWFLkmSZRJ9oRSbsEBEUK8jrx8/sO9HlJijpUSJpErfDj1SGe4f1zxu1qzv1xAT/8oUIkHfrWXoPNZGui6ilaBtuxMCK8Lrb+5R2hy191LR7Y43rIspobDWpQspV+VT26fQac7gXTgiBaNQKQBGQSkK6qKkKap8/hLJWZskYiyCYOKaSVXk+zhVDOTKIgEaU/uZmIJgPxxSkJQSGyxvXr87nq8Y4ZdfPdC2gViUxHydSGC1XDKfzXD9yDB07A9bLq6vKApNFUc2m4520+G9TeCQIgkmT9J+sSgRZcn8xSe4YWAcAm87T/AOPeQ2uRBYJJ219M6zOaQgUhGxfc/OFYidpfhwwHdrvnm35sN6x74bebcdUPcH1Nf37GzBm/frJ90Sk++Fw/7Aq2++wRQFCJWVMRxSCNpDQvo+0eGNELzPLgbJk89aeyQem1ytTInqZKkDEH3M7YGIze3N6DylNimZCYGx7xOPT4gjsf6w3TFozVAWEDzOjXRtmwn+/skCNBmfitwT8zby4d2ax/UHuuGAMQ3z2ZxZPcd1A8FZRttTVjOEKmkHRfSC4AXROR4fHhItIbd7RmvZ7napOyMDm93m6GDvXRJWSDQDS8wGyuv1hvfv3mN0QWka3rx6y367g2CTpZaMDKPDuoB1CRC43+/P1pLA23dvqarqWxXiFASP+5y7KccRTk6mvrXEnrVG04TofMYjjt+PQfA7XuP8g5wqxkTFOA+CE4CwKEu2u91xv0IIvH13z+HQEaPjzbt3dO0erQSyrFA+sN5s0LpDqST2UZYVh/2eqq6ytGVAq6R4I2QyH3DOHcFVIQS6rkUKyW63SaIcdY2bOI3WY11yn5dKpTGNT+jlJKc2JFP2mBCy6/WGtj3gnM3SkTH5QLof3g4Vf1nEFGcmgWby6/v/1vYdc7/vvJrOt+/YtUh8kq1OquqR84vzu17444vyL880vqsclyLTSdK6w9OML7+ymPiQ0wwn8SfFd7xtPN4N6RfnJz5pQ/53OV8f33h/+fbE5oj45N//ttvUVh+HE5gpgYO+qx0vnv5lPD0mzmYmfO9nzKvUsWp+Krbw9HMdnzL9ZUrYckdgmuGEY0ci2f0cf0+GuY+n/Zr8MMXxRH/HJ5w+28f7f7aIfayUdHrK9+/P9JrHpfYJ6C2ePUV867Gzd2C6Dp+Mzs4G+CdE+anqkWeJ9Nk6fVYHffu8nkAlZ+f4STdq+gzplY5V2PH4nfZBnllJnX/G8/eb/u3OWoeTVNsPrzf+f3ObRBOmbUKQQj7O+efpuvxYtOT0+PR6pxb6cfSSg/s5Xe67X+f7tydUu+NnzwH9rIM1vd7HZybG+L3v8IOD4H/c/uP2H7f/uP3H7T9u//+4/WVB8AfPBE02rPzOmCm+N3d90to+tkiPWdfZM88yuniWYk/Pf0pyj9/6HOfyaR+/1okwn7KH84pJ6uo0uDi+1rf24nv+8Zc876+4/eXtjW///uP9jzES3Um8uKqm9sSp/eG9Y9JWFR9n2B9/mLNfT9kc8O2dPP7hRxdBjN99QKaXSin2KSM8G/If/2xqoZ214FVR8l2V4HdtT/lJ399D+r5X+zfV+seM89+YKn7HDRIjwZ8qwbquf/h+5f+FXNme9i8dQ63U8SL+D9m7iTFVo8Nwgqdr/X1LTL6Hp0oiPfSdH/h8vfiuBsrHlf3x+pp+PjtBp6pTnD/7+NP3XwvxySjoP9Qm8r06tTf//b/fd5sj/JC/g/8wn/GHbh8zAb5v+0FBUAjB//J/9T/n7u6Gru+YXJPTL9NJmpQkQoyozO+bNQ0ytxuHfmC73fDu3Vvef3hHCI7lasE49ITouLu7YbFoWC0attsNIYAxJb/65W/Z7w/8+MefZX6hYL87MAyWvh8wxlAUhsvLFW/fvuPx8YGLi4Sgm80avv76DdZ6Fos5s1mFc57/4r/4rxiGRGn4vf/Z/4nZ7e8iZciCzAJjQEnQSlDoCXoPWpKdzHNrU6afhUhxVH7UUfvWcTz7ebpUEjco37NxWkgiLiT+kPMTpwdGG3Fe4HzyP/NeMFpwFobta/78//A/Idg9xhj+3t/7e3zxxec45/iLX/5Lvv7mF/zX//D/yGJRsbqYs1hUSaHCB+zos/STw2X91KbOiNEQePtmyzAkPzlTCbQRgMSOkXGIECTaKOpFgZBpRrV73GIaMFVCA0sURhjaQ6DvHY/rPZdXS+bzGqWh7wYO+y65Q6sEa6+qirEP/N/+yz/Bu4AyBf/Z//Z/x/LZC0TISjVP5h85IGR+qOstbnT0mwPtrmXsRkKfjZERKEn2kZSJBxZCBu+llqU7toPyDU6amX6sozu5sE9crXzTJEGB3AJVKoG4jNZApNs98E//r/97gk/w+r//9/8+L1++BMBGstQZRxGJukrXQD9C2zsOXc+bNx8SctH2HLbvaPdbunbP//A//R+zWl0hZIVSoBU01fm06t/tFgEHvHr1yJ/+yZ/xv/lf/y+wNlnh/Of/0/+cWd0QXZLJCzEpHY3e4UJAaIVEIzFJdk4omqJEak0UsO73WR90oCkLAoE+jAkdK1VeazRSqKM1UgiCfujSrLJP0PkYAkonQey6qRGuTEhoH/DC46Jn1+6T232RqE/H8x5hv93x//6v/9Fxka9VmpHmzvdxC9PX+WMhZgH+pwH3fDt1uuXxb5aLhvmsZrlYcDh0fLh/ZLT2ZBTAv13QKYq0/A953KCU4qc//ekTKtz0HhMY6/z9iqI4CqpP98I0l/6YT+6ceyJecv7ZpZTH++acEz79+1wy85z7fR7kpvebeOWHw4E//uM//kHH4QcHwdVqwcXlCrGBpmnQWnM4HJg0OqcdDyFk80ZzPEhCCMaqIOLZH7Zc+iUxBppZhXXJbHN1MWcxr1kua6RMjshFUXP/oQE8s1kSnk1cwaQkobWkKAxlWXB5uaLvD1jb0TQls1nFarVgvd5irWOxaFiuZklK6BilBM3lp8xuv0SrFASVEhSGpFKhoMwB0ah4DIJa5VnGR4FQHi/k84P37R/z5IRImg2eguB0oiM25O8erJ+CIFgvsFYwjCKhQ0ewIynZOKuWF4sFq9UF3jtubq4Y7APXt3OWy4rVasbyosH7wKEbGAaZHasFPqQLfTFrSOCIyP4wIJXF2kiz0JRVulAPe0+MnkJWKCMwJSgjiVFiR0PZCIoqOX0bZaiLCmUsupPYWDFblszmJdqAKSXKSLTRySeuVFR1Qd/60/EUgtnNLYscBJm4qZzPvGK27/H40RNGx7DoqHYHhnbAHSwqgEEwKxK9pCw0ISatSZmDYETQjRbrAqObZKwSwTjkmUcMIQdAfxSKnnQ0ESI5IeQgmIQfJEYbEOQ5zKlj8fLlS7788ktCjIx+CngBSbqu5k1S5+ktvH/YwP0j/fAVu20KfPvtI0PXYscegWI2W3B39ylKp+t1VolMr/l2KnY+P3vym3jal28Fz6n6zM+1AWDG/Yf1k+u/mZXMZxW4iDVpcY0SunFg9A5TlihpMLJE+oCWikU9Szq2RPwhYp3GOkOdeXbK68S7zImFklkjOEzpjUYdJOOokphfBuuYQlPXFbOmQYQCGSUqCMaYuJ8DDmMkRaET/1eAIN0PdhyfjJqmZPekj8zxWJ4nwoJ4JEUk7MG3loXjdn7cCqN4cXfNJy9ueXb3jA/3a7T+irfv3if5vLPXTO/77WD4rYruybA2U8w+en7y5SueIO5DFkM49yL8eBY7PVdKeTze+gxHch4jpm2i2513/yZw5HkQnGLIJJJ+HgTP918IcXzPc8DSv2n7K1kpWWvZbDbZx6vgm2++yZVYQdd1WcTV8+LFc5SS7HbbJwez7xPnppk1uTXp8wGPQMC6ka4D5x0iQ3fTTkbatk1ybFLkVkY8HsiT5UayehnHgaIwTw7ydEA5F/gVYEooKzAajMmBT6cgaBSJ2yRjCopTEJSJ7jXxD0WuDj86Yqe3eRIU02KUAl+6QY50HmLmZsUj2dX5VBE4n97X+cioJFKm4Chlqmj8R0bKr1+9weiCEAOHwwGIXF0taWaGZlbSNCVtN9APPX2uBAVJhV8bRVFUKSuNAhEf8c4yDIEZiZhdlIJ2H2kPlmpWgAu0uz3FzIASeAQxKIRXRBeyw3TJaNPSOQ8zilInIQAtKOoCqQsgopSgqhVFlThoT45pyF9xajHG46KT7qP8uEzkaFFqqtpQzQy2H+gf9xQ+UEf4dNHQFIZ5U+DxRBGPKkYhRB62B3ad5WE7sDlYBhcJWRAhxogfkiyVn9rrcVoM8kfNZr6JSxfwPpHfpUzC1d91j3kfcDZiHXS9QwkwSiTt0dzCu394x69/9Sv+wf/9v+Ltq1dsHh9pDy1SQGE0n7z4jJ//vOXZsxeovFDHAMhzNcfz6zRXtpnYnu7Gs7FF/DaY4TjWyLmI8xE7WOwZiAkiLhzwIb1GmFq4IWlb9nZAGYPRgqKQyCAptKaZlUc7HCEiUqWkhZgquugCpNOAz840MgiSFZRA6+T96YM/rkkQkbLIEo4CJUNydBEFwQmczeopPsmpSBJ1xSiTFl4ffkDbezqa3/HgvwFwdkTXhwSimtcFv/97X/J3//bv8+Pf+Rm/+s03NM1/m7SA1za5kPxbdB5TwFDfemy5XDKfz6nr+hhoxnF8YkYwVX+bzQZrLeM4PlHmmqq+87X/44D88YjrPJhOFd4USNXZmn0O4jn/23OE6/n3H7L9cMWY7F6wWCzw3nM4JIPIsiyZzWYYY+i67khslFKwXj/mSK65urpMlVwOGCF6uv6AMcniR+kEB3/sDyghMaaAMgXK5I5sCSEFwSS3leSYlNI5SxFJgUBA27ZJyFrrrIMXORwOaPO0JQGpTTRvoNBJ2F9rqHRaeIxMrgVKCnRuiSqZgpGYMsWz6u/JtX48Qx/PFONpoeRpJXjkWQbwQX4rCJZapMrQRQqVFspewqAEsTq9TwiBP/2zP+X9hw+07Z4XnyyROuB9pO8Tr8m6pKjhrSPYZNYrhKAsKgpTJKoEibVelIZZEBSlpKp0FrrtOBx6dtsWe7CUtWJ+pSmqpKRTScXYObqtxzlFqQxlWbNcaYrKYeMhS9KNSFMiVPLfc84ijaBZVMToiDzN6GSIyJgC33SZT2i04wgwH0+RRRekgHmpkVqwKCULJbksNFezikKn9vdEs5ETHysKumdL+tGxaQdev9/xuBv46l1LayODD4TMA0NlW6fpFJ8TxkXmvkpx1MtVUn3kZnJ2sZA0M60LOBcQShBk8gq0WQP0n/83/4Q//ZN/zZ/+q39GsEn5JQaLjzAEy//z//EPub9/4PrZMz7//CXL5Zx26PHW4Z3FuVQtO+9RWqB0UvGYKC0x04+MlkyiZ1OAfPJRz7+TuacfWynFligUWhm8UsQAQ/YWjRGGocc7ix06LucrjJYYIxgOHf3QM4wdyIBUEEebjrN1xLIAEfECZAxIIoWcgl9yUFBKHtcBIF3XIbI77KiNptAldVmDE4SY9tsTiN5T6uSIUVUV1iau4PfBQY/33Xec0XxFfuvPzluL8WwNKAvNYlbzh7//O/zsJ1/w6ctPuL77hCArBif45W++ZrCO7W7/37m1LaTIa9q3g2BVVczncxaLxZPu3sc6zEopdpma8nHVNSFMzwNf4oCfBcSP2ptTtQenwmYyYP64jfpdrdKPqTn/foJgFq5NBNkkJRRjROULRSl1zALSV/q3zMK5SXEmyTJFAsG7pFghCpQqkJJk6+JcUuCfdjZn+5Mwtowy+6ylrHkqn2PkaDEyEXeTtJlhHB1t2zMMmpM7/XTCoCpI2pNFRKtIbVIANCoFnuNM8DwIiqdBkGMl8p1x8HinpJ9PC/ZpJpjmBunnpNYRgsCmNRbvSReuj2leEafv6TVHzZMFqc28rcfHey6vCsomYsdEevbeYd00z4rImGDqWhqMKilUSSD5GkaSNYwxGqUNEHE2zfXGIWXZh77DR025bCAm5YmiLIhDcoZ3QyC45DohVUBqn+Zr0UP0IAxCpmCVxKBJogrWPeF0TsdvqvxEPEs8Ik9PQAjgHcSADJ4iBioRuak1F6Xhqi5ZNAVGCZSKR/qKzBqNIJhFw2AddSmSwHqwPJo0/3OE1H3LWV0M+eR91FJMrySOn/l7aoXj848dgqPAckqYUrWVFpyH+3vu33+g3e+nsHX00nRO8PbtW2aLpNyhteRwWDD2B+w4MA42VT0+CSXoIp3bJGElgeShVzclq8WMRV0moA1PF7XTLkxz2EhwIZHrn+yeJ0aXq1iZSM0x0VSUEISQpNuIIZ1QmVrZziWBaB9sOnZy4iAnzIHMw7iYPQhFjAidathzbuK52beQMifVjqAjMSoQAR89Lrh8vgRIEndWycxHflqN/GXbeb13Op+nB3O9863KaOJDz5uK68sFX7x8zu3NFYv5nLqesVwGbm9vqOv6yK8+vtFH2/cCW3JXLSUI33bUmegSR5/IsxM5tSGBY8szKXD5J9zT6f0//vfTQuDcHOFUFX5Xa/c8UE7vfR6MPwbyTN2/f/dkeVJ0L8uSsix5/fo1+/2B2SxpHa5Wq2x+mALjOA4oJZjNJrdfRd1UjHagrAoOhw1dd+Dh4QPL1RwhZoSwwmhFoWuMTjqaRw+wfLNorSiKgrpu8P5A1x6Yz5Nr+jgOZ+3PhGadzxfMZgecczw+drQH8SQICk6VYJkrQaOgNhGjocjzwSnwmRwE1XkAnL44XZchnPfqYVIb+VYj6qyjEXO76ASOycoueT7kvGBwaZEbbaSUqUI0AgoFoXzK87q6uqJpZjw+3uNDZBw868eBEEek9FS1pioNs1lFWWiUNFTFMml/asXImkAirSod0VEghWGz29L1PV2bCOVVVfBhu6O1FqdlIqAvJIvZClUKChdZP/yWzkSGPtC5nrbvWG8fKEpFWWlMkVwLQnApSB6NQWG0TzM6mSuq44IFROTpoBER3qOtg3aDtAPKtsykZKEVz69WLMqC5aykKNI1lrpCp5leOg0B4QJaOCost0VPVXX0TcubEMBHNr4giiQKMB37Iwo13/hPLpEYCd7hQjIV/taWL4hsNJCduyGG1M601rE/dHgb0NJwvbphu9vQ9x3nq2LwjjevX/Ff/p//L1xcX2EKw2b9wNANjMNI8IoQJT4qlFGYwnCxnGdZQknZVHz22Qt+/vOf8kd/+PtcLOfob7X7zz52iLgRht4xDB8pxgSBtwFr+5xcKAqlUKbAScUBl4S5tcQJx+gGdnvHvj3QDQPOO8hAMULAGMOqWSRT3+CxYZJYzPdYCFjrUzJGcsQI0RNExEWLUum+ljoSpWMMHYdux74biDE5FVRVgdGghMAHR1GUSUqOp+3kJwt7/PiB08Pnzz0mxOLUfVBSYIyiqQw/+/IFn7244z/5Gz/n4u4Zs2ZOU9cMo6WuqixGoY6v/VevBlNnIlXI6kmSGULg1atXfPjw4RjoII+RcrDRGTTzsXzmJHaS1kXxrQA1BTPnfG5Jy9PzpDzOVqdKcXr8ybGcgnT+XHVVHavS5J7xlBnwQ7cfLpsmksv027fv+PDhHmstL148xxhD3/dHPbpxHOm6DiGgrovkYm5zT14JtEkXpQvu2LJQWmHdQFQSnYeaqTVQAwkif//hnuVqeUSDGpNQdungnGD0E9LI+zREVUodhZzTZ3x6k8q82EyanLm7hZHJRqhQAp2RounG4DQ0F08v6pA/TAxZZUWQF8mP+uGcot8xHJ+1RPL9nvYrJESo92lW5nT6LBLBqNLfSgl9wZM74ub6iuubG5qmpKojbf9I340oHZCFIkaJc4G+HdDCYzTMjEbHAhUUnReMztOPHd5FvIt0Y8fQO6yNCBR1o2lmgmGMDL3j8X2PijvcReSzn76kqQ1WCn7jv8aNkbH3BNJspSwN8/mMZlYxm1fHmd4waGKIbHdbEKA+ukJlABkFXk4xI83cyF5+wjvk2KEOO3h8A2OH8g4zm2GaGsUSlW9COTmUKPIMOp2AKcsduwNj39Ptd9jDmjC0NGLDSkWiBmcbBhQDiiAkyHNh5Mjkh3kUpI6T6MF399XSopEq4TRzPiVdSiRVmaquuL27oWt37LbvMYVkvzfs94ccewWff/4ZRVnh7MBvfvlLRjtix4FJOERiiGRjXJmAO7t7kyosqdCFZrd+YLde8+z6gvDpc57dXj9tN8VUxMt82IgcgQvnm7eSWGiUFIw+XfF1dgH3wEAkaokwGhst0Tn6PgGR0lwy4GN6Xes9BWCip4wSjWIMHoTKLb504JTUDNEDDu0dvXVJd1LrNOczyWBWIBhjiwstPgyIoJKYvg95YZDTxPRb5+tI3j6fk37XOQUmioPkFCCKqqAuC+rKMK9L6sqwmpf87He+4MXdHXd3dzTLJVVZYNsHDvdvuf/6F/h2iww205/id77n928ij8vlcYQUz8ZDE8jx3LlnCl4yf26tFPHs8emaULlqTutRwj0omakdYfKaFVnlKevhnBUjp2IgrY1yapmGBEQ7tkBzIJ6emwj9AqMNE00oBfp/D0EQkg3Rw8Mju13yoFosFmitj1E4SdwEhiEZJDazguhSQJoUA5SSGV0XkDoFQKUkzlmICqEk0fscRNIMJcbIer1FG8NisTyeKMQ0RzuVy9O8beLFnYJgkjRL1er5ZTHFjpibQTGDCUCfzQILPVWCgglUJfLzptR9skIhJJ8shCTKKQSe1QrHHt7Z4zGrxJDaoNOs0Pv0/iGkBce6/J4hBb8YUwwozdNKcLGYcXt7xWIxY7t7w6ELGbQgMUYSg8C7yOBHvIzgNaKR6KBRQhGdZOg8u0OPswk40LUjY26pKqUoK0VRKg77iHc9929bVATtNfXv1ShV4aSAIAku4m0CNCgpKYyhrmqauqEs5UkeTySJpH27p64LpD6bK5CCoAhJW/RJFzR/Ce9RQ49pN8T1O+jbJPelIqpUyATZOd7Uk7h4QvsCMaE9o/fYvmNsD/S7NWO7JQw9VTywlBGhBHupEMHgZWqh+iiTtuuUnBGZ1GpEzP3bf1PunrqGORBmS7Fc/WqlEhL66oLD/pK3b2Y4NyJESgJjjAgpefnyUwSCb1694f3bN2y22/z3Gq00ZuLG5mA8AXK0yoa6WrLfbtmut/ydv/X7NHXNs9vrtE9Ty3dab+J096Q28cdcumAlhOSsELPvpJEKQgKBaRHwudXi8AQXcO2I0DpViCSQmA2BPiR8wBg8dZQoJDqKlGgKmTtHGqFLvHPEKFF6JIwd1jtkFBSInHinJNraAR97YhyR1Ekv1wfQp4qes3nzx6fqOA+J+ayLlJhNR2V6UOQDJmXSMp3XJct5zXJRc71qmNeGy2XNly+fcXtzy3K1oihLpBT0m/fsPnzDw+tfE4cDOjqUlLjgz2LzD1n0T1WWzuba4qMdm0AvUwCMMeKsy24l8klb2Fmb2vAioo9BcLqPOQZZH5IMohCglTwmr0lD+HSMp9nG1OeRJISwiHldB+L5Z546NyJilMprQVLakuIvaV18tP3gINi1fTIOFSkQFYXm+uaKtu3Z7g40dU1VN1xd37LfPmL7gZfPbxmkxAbP6AM2Dy/rWYkuI9VcU1XJcqfLmnBGKaqyQhea2bxhsViy33f89jevqKuRYenoO4t3UJWzo6RWVRXHDN6YAiEEXddSVhoha1arOV3X03VP21AigghkHk8WCvaSKFMwinmVFaRsXGWA6bFyPG+LAhAQDGnegELo4nSjPNlyfpkXxhgFEZu+B4WPySV84p4FD9JJjBMYLTAZGKO1QI8w1k/76PePv2ax8szmc4aH97Tdu3RhB4EbIn3bURSK+bxgzGn90I3pfMQC4xbs3u/4s1+8p57VCCnxMRAAoQQXVzO0DgjlqZtAfwgIG/FDwI+BWhrc6HHdSHAeGQ2NmjO/vMCSHM/Xjzvev1vz7JMZVWWoaoOQCVyzKmdoLYlhPF9ncNuOsBpQ84pAxHPKCPEeNbTQ7mD9AbYPiHFIPpFjDYMhdDuCkYQq8WAimigk3qebzztL37a0hx3t7pHhsOOwfk+/3xGspYqRQkkuG0kj9+y9ZutLNr6m95JdJzOdIh4rGQRIZY6zihDCEx+305UTsQRGH3GBLFovUAqEDGgtqMqCi+UVb9Ub/uLPfkndVKyWS/6Tv/t38kIR+NnPfpfd/sBuv6MqNK0k2ffEJPgtMlgndU1yteUdMWanlqg57AK2H/lv/vE/pWs7vvz8U7SWJ+6YEKAyJy5nIdaPWJdmyeeblJLCFHjhIAqU0IzWYYPLSU6C3qgUWxm8xUyweB/y4qmpjU4aqm1LaWqMUAQf0EKjpabUBQJNDJKmrLDS0g95bhojYzciHAirCDOLUgHkmBIkJXGBo+WQ8uQERCdD24+C+8d39ARYjx89mDoC+S9iTB2QWc3Pf/ops0pTl5p5pagKybySFBwIo2G3fou1jmHo+dWf/TMe333g3TdvWAqLmxtaF+n68egC8UPqQcEEFpIYrVAiXXMfn6vzLwEUSp8QfM6fZnohIEjONlqEowRd4rYKirz/LvUdUsUnIEhJRCKkORYsU3csmQz7FCClQovcCuF070wuQuebmn4fc8L4bbj+924/OAgGH1BScbG6RMgFZZk4O0KK5BdXJQeFy+tLCpOCxXK1YPCOIQ+q7WDZb/cEmYKhHW2yI9GCqqyP7YKYPcwSkCa5SihlSI7Xir639P3IOLpjS2CaRx4XmdwOlXmIbozJpNCnB8+I9KVE8jFQkZy9T33s5G0YfMSfDdmnEvIYAI9k+YiUIQn+5uE/4lwbb/pBgphqhvQiYYKohxMTIARB8IkCFSboQu6ZCsCF5O6gdTwLFoFvXv8SoQ9cXCx4WL/h0N6DiPSd5eBSpR1nklmjcX7Eu4ENW2pVUZsGGTSug+2DpW5WiVirPaN1x0VzEghQCspCsliUmDIhfUGhlMAUkapIYtuVmRHcmOY7CKIHN0banYeQeHRFlbLoEALBCtzAkyy837UMu5ZSyXTiRIJUx1y9KTsgxh7ft8SxR7gRJSEOB0IX6TYSLQKlMRTZwyzm8xljAmwFb4neJdcEZ/HeEuxAsCOI5OIuhKJKKSpSDCgd6ITBG0NLChIpUY+56vnIguyjVctFsCEmo1Mfsz1fuobStZDmUVII5osFV9c3fPryM4yRNHXJy5cvMDrZjC0WM7qupW3bhKqOU2U6wccdUaSFKLW0AiFkNHCUufJJi9K7d++5ub3i0LbMZ3Xq5IS0XyFfizGmbN9ax3hmWj0tcCHPcYJLlYsTMjkFEDFS44VINIrcWhFKHj33nHeEaaygUguT3P2IEqTPdl8IRAAIROcRMVUSRmlKkzjK1lvI3ZWQTApSNal17o6cifHHkGeYMgezH95e4ziti8dikpgShrpMbc+7qxmFSsGjkA7pwfeR9vE9sd8T+2R95bxj3HyAcUejAzdLgy41ey94IBK7iPXhybTyO6vW/PF1Nsquq+IMqHXazgEq00xPIYnCH2kxWQE5V4UTV/rMySdX5tNrqBxQpw8hSQbNTOvd1F6N6do/5yNOxzK99llP7byRlruBx98xNX5/2PbDg2CMaF3w7O4FzUxhCsFoB5CCZlZTNRWmNEgjGS9maCm4urliDJ7Be/qDpe8G7t/dM7/QBBz77T5fpCXL+TK7Fjis7TNBM2WDIgrKoqYwFUoVdF1P2yaIflEkU8fJMT7xGR1au+MNjiDzEUdifJohlBKqaQ4oRIKE5wUgDYDBE3GAEJGQW5BKZv5VVh0RMfGZUmDM5WWYYPGnGeI0RJzkm6bSP3fW0wkU5AonLX5eCrxPJ9UBNpWtCAEupgy20E/VE/7sz/8p692Sq+s5MVr6YUSIyGHfslkfaOoVgoJwoemHA8H32H1gVa9YzSRqCoIfLC8/n9HMSor5yG53YBySdU4RNKZQaCWoG8P1jcIRKEoNQqONppaaxaxhVtc05Yp1+5bODTnrk0Sv2K1TG02bQFk5hIzEwWFHGA5nGXiMHB62lM02iQM0KYGKwSfjU+tQQ0fsD8RuTxw7pB9RMuC7iA0de7dH+IFCK+r5EikVQajcrgo4OxLcFAQtwbn81eFdjycmPqPSFNGhCdR4ZspyEDVjsSSg8S5zHHOLxzufaBJni8z5NvjI4CLWCZxLC7WQ4IQgCok+zvMil5dXDC9H/ubf/kPsuEfLyBeff0JTlxSlYXsYCG8968dHhn44zsZTkpiNdEVy+jiJbod0PqIkBI8MiZP35s1rLi8XbDZbyjIJU0SmMUReuEJyWxiGkaEfnizC0xxeMGAzclRMzuoi2TY5kVuFQhIVqMIkpHj+20AKeGWuBNNsSRKnFh0ShYCs2uNtFp2PUJkCqNFasG/3ia/oSZ0VKUBpjIZYBoKXx+o7psuBGGSeF39fZRG//U9x7M6n+/lYIUoWs4Lbi4ZPbhbge/zYEewA1jE4y/rwwF5GHhWoQiO1granIVIvDY0oWY4woNNx9wHbPSXy/2WbMYq6MiwX1ZE/eb5N88AjAlMkukz0goA/JlRSSKTWTJJGx0taTMfwFAS1VPk5OThmx5wc3hAi8WhDFp+Q6kzUPH+8CWMhxUd0iPT2qaIV339//WXbDwqCMUa+/vorDocDXeuoG01ZKWbLZJeBFFRlRYiRYRjYbtbE6Nl0jxRVhdaGYd3x+PYdhw9rhK9BROzWYZVjDJZDTAg3Hxxag6hSBjaOI4MdWV2sMEXyDnPO03Udw9Axn19SlmVWr0kHfr9vcc4f6RvaGC4urrGjYL+zT7qTjbYszIhWAqXSvEBnMIwSgoKIAgykFklI8W1KR6JILTkhIkEnbzbVSLxLM9LgYp7pxKOyDFKc2qmS9IL5M0lSKEz+0ykYegRepMvGCoFUiqDT0H6SdjPmKYTb2ryARE9ZKnRR8vzFgro2LJYzHj4M2Z5kQBWJA/X+/QNXyztm9SWX8ytWzYbGXHCxuGa+LJDNhr5vGXrLbtcd5wdNWXCxarhdzdgcWowuQRpG5xmHAUWkMgUXzQVfv/mG3TBiVoZgR7r9wOvHHZc3FVIuqCuFlBF7cIydZNieboQYYWx7Dg9bxm6kWNSo0kAlCSIt5LbvUX0HQwvWEr1lcJaD7XFGE+wMUy9SgJosorxj7Fvc0LK+/4Zuv6M77BgOO4K3ORh6go/E6PLfWrr9AT+O2L5HLxxVueL54gLtDcZp7reW4GL6uxDSdaNAZSPb0/0F601kscnMDptsjKRwjFKBVLSDwjuPG0Y6K6nmF/ztP/ojoj0gGLlazFAahIz4GFjMK25urhnHEZOFI+w4Yq0leJcTMpHV9yHNtNM8TOa5vA+O/WHL/rBnHG3m2Mp0VeY5WQjJTHloW0ToMGI8y8Yn9xkYxiHRJCIMGYwhBBRi4val9ocQAlOVhG4A79FKJuWYfKB8cDjnGY1BaZWQ6f2AHQYkEiEMSpZJRi0mn0QvFTFq5k2dE9sUomKUxJAsgQoCvpiqjvT5Q4BhGKlKgxRP76/jDk7ncBpvkDo4U7WSujUSoyQXM8mnc8nzyjE8vmXZKBYzSSUV3kHXQ7f3+DEkf06fcBMzI1G1RhcFDZqqD2ytYNOOtIOn6zOV6InfQTz7mLmik1AYSWEkpZGILJv2ffFz6oR5kSUFSSyBYwIv08zWB3/cdxctGomUyVR4mh2H4MiGOMeOBOS5/NReFSf5zY9RpeeKMtPj5/8OGXwz/TxZN/2Q7QdXgu/ev+PQthy2lmZuqJuC27BEqBR5C5MyRO89m+1jMjc0MGtmVGWNP/S4YUAjCEM2enQgnER4Sb/rCTHdeItVk4AEuUWilEKbpBuahvguz+/CkQto7UjiJJ3fnA6lHEJITFNgTJGJnKer13Udtu0yB0ITJz6EhJjVTKISCC2S/6RI348Sk2ffUyxLZX5SEoGcoByRflOlKHIg9JkbNZHoE57wdEP5mGkdgcyVgGgjrvc4D8GKdBzHgfNLuT0MtIeCvk/CAkJAWWnKylB0AR9aRpuC4MzoxKES0A4D292B68Uds3rG5WpFXTWURhGVSQNnARNcOUagMBgtWTY1VjiUNKjCHFsjTV3SlAWF0gydo+9H9DLNDojQHSz1zGCHyNAFtIz4IYITyHMRRlJb3o+OGHtijMhCI8fkWo+IiG6AYUSMFhEi0UdGa/HW0WuFqeo0N1D6JDUXI96OjH3Lbv2BYb9jOOxxY0rMkt9iJuiHyKQQ6caRse8Z25a66JC6ZFEEnNTIWNJ5yWg9zga89ekyye0++ZFk1TjCMEyoy4AIgYBPbVUhCIjE3+rHpO/qA9oYQlREB+v1GqkiUkWcSwv5crVgvd5graMf+uN5mzw/BScwVlq0Q77uUq9QiEg/9OwOe95ndHZV1blFm+c9PllC7Xdr/HiAcBLPRpC8MqXISjGJ9xjysRQkkMOxMZYv3xNqV1IWBhsDIoYMBIsEPD56fAwUpsD1A946nE4iBEpFRIyoCCqK/CUx0iQZNfLoJH8egcyJauYdC0EI6eaNJzmnJ9v0vAmgJZ/s19TwSR0fnxf4wiiaUjGvNJUWzOuSi1VFKSxjPxJch1USEUPSts3rgTEKZTS60JRRYgPMKk1VJKGRYxQ7nsjzxSkfU5HnfUJmHEpMeAIln0TBjxW2RN6HaeYpzgLWJH3l83jkOOsWyfjckuaESqonQurTc6fgfP5+HwuuT23Rj3VCz8/Dd/ELv7Mn/D3bD64E/+Uf/zEg2a4HlhdJh/Ov+S/TYFukPrGUScvzcbvBBcfOtiyaOU1Z03iJioFnl1ds25bRepQ3VLKmlg3v3rxjsD3WDyxmP0bloXfTNCxXS8bxPrd/T3M/IQRNU9M0NQ+PPcPQY62lqipMYYgkpOq0lWVJ0zSncjlGXv/2Hc6+oqkqdFEhlUpaliqBTgqjMFpRVYa6SrMDo6abhiyim2eEJt1UddSprTuC7fMJPvbZz9PElH0H6YkyooVCpWbakYcRhUo3a4gw9gz9SHtoeXjcM7oAQqcW7/2b44mPMfLN1x/o+x4hFPVMUhSSskpz3NEO9F3H0IP3A8XsirIuWN1csd4fOGy/4otPfsz19SV//ec/ZjVfIJRn9GUSOxaaqpqy6rzAV7BaVVg5IGXBfDln6AaEFHz+2R11WaKFp9t37A4tZpUq7qYu8XaPHwXeatb3Di0iJkiqoqApPm43RQiBOFoObZfErBWoQqGNpBoeUe0W33VIF3Fj5HHTEzPPtLl5DuWMenGBKSqUNqnCciP9Yc/9N78l9C2MAwiP0ApV1iglECiiT4i4kGfabTew33cEvaVRimdlx8XqiqFaIpeRbWfZtD1jmyspD0obpFVP1ilrYRgSEhJvEdEhsakjEBX9SDbLbXFjix127NZf0e4f6A8bXn39FdpI6rrgk08/w4fIJy+fs9ntGOzIvt3jvMf5UyWUD2j6lhe542RJJrrEerPlq69f8Q/+4f8LpEQoQzlbJtCIkLTDwONmw69/+SfsN1/h7fuz6RRUQlIphdBpHh0DCKVTC1MIwmhB5cXYxYSmlgKjJVpo5rOSwVs6N4Imyc/hsWFAO41UJiPSW0TREPFI4VHRIQIUTuCsIDiB1gYvwEnL4C2BE9pRhBSwZUaZjqFPB0QFpPBITtQPQQLqKHUKghMgRMGxZZgGl4KAQ+mkJVvPGlYXS7747Jrr21sub64Ztmu2j4/sdl8zawwxSoyMxAwTNlWRjW4iMjiMgHmtaSpDXejjujglN2mdSaF4WuoSB1QhhCR4GPqRslCYc/R1DihT8NNap+MB2HhybT+/FX2IjC6w2eyOyjpVVVIUkWg9ZVmyWq24uLqiMAZnLd3hkLit0WcAThJWOa8EgY8MiycUsz+2Q88D8jmd41w96odsP7gS9LnclBlGnVTbA8GmzKwoCqy17Pd7qqZiVjQUs5IYPG23xw+gncAoydD1DNYmtXIfcaMj+kh0kWAj+/UeJSXNrMFow2q1ZL9rMbnqS1mBeKJYEIJACI0xIp8EQ1kVaWiuJMPYYgrJfF6fAUgiv/zzP+Hd+47SGFRRI7VBKHXWGk1BsKwK5k2ZnMpLk2gaUj7hGFYzyXyl+fFszn43ctg5Nm/bRHwWZAReUqgwSmKU4LKWSB1AetbbA0YKVrVGaA1C4qNEiYDEo9yB4fGBD29e8/C4JoRAndV62K7ToCNvdVNjCsN+3wElwRuUNAwttHtPjEn9xdpAP9ikKxk1j+st427Dn/7FL6nrik8+uUIaSZQeo1aUZoM2PdZ5tE5Z/rysqMsSYkCbpPYyX824vLxABtg/CMZhoO92gEWpSFFoClNRV5HZbI+znnevNxgdqIzictYwvy1oqqdIsEksOYYIPimUDGOPEGmxiu4eNW5R7YYwjAkxHATL1SXL1ZJPfvQl13cvaGbzZHRLng1N3YPBMrQjYzeAFpSVZtkUVEVFDJ4YPOMwYseRQzvQ9pbD4CnHEd0d6B9fUyrNXAZ+cv2MjS14HCs2ncP5iHNJgqzVHU/7aRNAJaTzGD3RW4I/EIND2AHpLNqOCNejYo8wHVKNCGnRwcIYGf3I49vXaeYyjFzPDSUXXC8KnM33S0K25HziJB5xXGhCEveOQqB1SV1KXv3qL/jj5Yzt44Yvf/ZzirLAGE3fdnx4/8Cvfv01X/36L/jqq98cQQpEcOOIHxVRB4K1eA8x6hTkctCZ+LVEmSUDPWN2g/e9O86TCl2k+agKGKExIdBt14joaGY1pq4BSchIX0mqIBQpoFo3IhUUxhBlzBggjxst3gW8zcowUiJVDm4iELzF+9OCLKTk7tO7JLStJoQtmS1y7moCMQqcTSOYQgtsOedQLKle/JTlpy+5fv6C4fENunlFu9tjh30GYrkTyni0WdRBJLd7S5KQJM2NhQQtJEJoFosGYzSFUVR1lUROihNy3o5DRrjnjs5HVe4UiE7o0FTtJerMKeAoKck6lIxRcHP3LFW9wbNZr7l/8w7bDxitaZp7Ps8FTfAesqWbMeXxuksqYEmOc6r6yrLKILx0bxw5lkXxUcA7gWmmffhYDecv2/4KPMFUwaQXT+0N53wCg8g0uPTBsdsdaOYNVVkitcQODjc43MFRCU2hKkY7Mg6W2XyWkHOZXxRyEOzbHlNotpstsqioqio5UpzxVKYSOd3UafFP+nMq65FqisLkmRtYO1AWNVVdnMpnIu/efMVml1wOdNEgtCHKZNOSyKHyqFQzn1cUxlBVxfFC0VImZKmExdJwNTS8/ElF3w8cDiPv3hwgRKRIAuQheKx3NIWmLiSzC40xEaUD7et7Si1TO1hrIskpQuqEOJVuR/jwhvbrXzBsN+lGXybHjrI9JK3MvFV1iTaaYbA0dYUIGi1qiPu8EGfepE+IT6ElBMF237F/6Pnt19/w4sUdn332Iuk1SklRasqiRusCpfqE/tSJu2YymVGKVEGXlWFezSl1gQof2G48290GRLpmlFIYXVAWgrpOyN31wx4tBXVhqKiIV+JbsxiZ21A+gykSn28APyLCQBE2BLcnDB1+tIQoEKpktlhydXPL1e0zFpmDlQr4cIQlQWq3jmOg7QOxUHglqIOkLAskIfHLhtR+G0ZLP1oG67HO4ezAuH+gqBuMUdxcXFMWBaY2yCow+Ih1aRYVbPVkNj31s0TMo4KYEa+uJ7iWOG6JdkwIVTci/EgROwoGrHAUKld4PjDsd0e3lVWjmRUz/LJ6osmYKDkiCXkfF+wsguzzjCeSQCFCMOwfef/qa2SMXFzeUDUVRVlg+5HdesP79w/89pu3fPP6/ZMsPM1SHUKRIPX5hUW+h3N3fQL5keT0Ai4GbPAMQ58DgELHEqJERIWOoGJg7PZImdqm0pg0U8285GmokBZFQbQRoRJPVeuEuE68QE90HvwE2BBIndqbISNnE3r2tMhe3FxSV0W24Upz6zj19yHbbWXlJ2eP3OhYNYymQc5v0Ms7zMUzCI6yaynrihDGtLbGgAsBFyJu9CgViShGG7DuOKLL4h3JPKAsDDfXK6oqke8XyzllVTJrGoZhpO8HHh4eIXiUzM4Q9ml193E1KCBJK8oE0Dq2RHOykNq+kuXqAmMMh8OB+/sH1usNY9+jpOLQdlxcXmYeuadQqRjSSqXzHU7X3aFtE6pfSBaLZVICci7PsVM1n5LFiLXjsSo8D4IhhKf6vf+G7QcHwflihkAydC373YFx6Lm+W3D77Iqbuyu8Czw8rLn/sKGZ1Un/bzugpEag+PDhnllRIZeX7PuW4ALX9RWm0kgjaIeWoe2xvcUNC7pDz+tXb1hcXqHLMlEwsqTVJBtkrT06JFTVxDk0HA77lF1ISVkrQvTsd3vmixqhzGmnYqTb/Al+eEthSrTJgI5MGDdCU6rU7o0qsi/TTMzZQO8DNkLT1FSFYlZqrq4XdP0z/nq4ZnBbDv2aX/zinzIOHcGPRxV97yx3qzlX85IXoWa2rJjPCtruLXWhueCCsUsarLFtubxcsJjX7A9b6u4tTfeWF6uCutJcXMLVdcO2lyg1tVwFFzdzykqyXu/45O4Znzx7xtXtc8riz7HeMXRr+mGkGxxtFxlHz/b+wIc3G3aPHf9g/U/4yZefE4Tgxc0Fy9Wcu5fPGOiJKmLtPkGjdUoIBYFxP+CDJcQR73aMLhJjweP2kUO7Y6CHIs8LbESXEVUI7u4aXn+z4fVvN0ihKIuC4QCjTY4Wk+h5whSdFHiCiEQRENGDH5C2BbtFhA4VHXVTYUxJOb/g8y8/59mL5zx7/oyqqtPCbwcm0riSoLRGFDNcoWnHms3g0DayHTZ89uKCWaUwZUHftThv6buevrcMNiVjzo60mw1j26Jff8VVe6Ba3nG7ekYwBX2hGKRBmgoV508q3KqApgSnwblMyQkBEZKI+Pb+K/abezb37xGZZCwIx5nNj18s0wxqgqmTMmx5VR1h6unBBKYXIinmTJE4ns1oiDFVNeIcJOITJSp8zcOfPWJMgTYF2qzYPxxod3v2+45DOzydzZCqFC0VpkxOGNHUOOfox5GmqfPsLCP8pmxepzZfGCyut/RjT2kVwkfGtkWUBqEloxKYWY0wNWNMnpc+BIQf0kxQZyUfn2a4KoIWKUlGpmPXyAJ8xI2JBCy0ZAwtLrizbtOJUSeV5PMvP0uWTOJ4CHPVkgX/rTsGE5kXemtHmqahbGq+etixd7/hzfv3mP4D7vBIFz1fP7Tsdy1d22OkSCC9wiBV0jDuxogN0PmAlIKq1Cxrw/XNik8/veHHP/mc+bxhNm9YLldUdUVVzRjH5L/67l0ak3Rtix0tH+7XwK+YgrfMyJ4Q3FFQIOKP4FhnfUbOSvq+xTpPN4588aMf0TQzHt5/oD0caA8HdA7OQsD9/XseHx/Y7/epo5dBi8YYCmNQWjP0Pa9fv0FrzXKx5D/9H/0tiiLxrO/fv08IcmVouwNd1/Lu3ev0mbO+a7pOs/70vw+y/GzWIKWi3SdLJOscjw8b5ssZAnEEqggB42DTEHhRorRBINkbRRDQ+xF0Ojgym+8qpbDOMTqHy27bMcA4jIzDAEJQlqlaGIcR5zz9OOIDDKNFG5vUNbIGX9d1WGuRUlI1M7SUhJBe2/uPDo4bwPapYiSRZ3XUlNpQSUMjJZPI7kJXKC1xJmKVIShNsyoxSlIoaGYBYyxDu2cYtji35+q2zGAQS9f3eB9QKuDjlkMn+dVbuOwaLhY1dRGYNYJF6fGFwAfNWFQ0tUbrBGIwZcHF5QUXtaYsJE2duFPePoU7+xAYxsh+OzIOHhElL55d8+bd1xgTkTIJCXftyH6baASH3cg4phbMoRt4++GeP/vFr4j+U6KIvJSfcDtbIdzIYfOO3ieybgiJw1UVDX0/Ej0MXY+KBkyk73tGb4k6MluUUCTitveWiKIoU9VudIEdA8Pg2e46ZovUfn6yqGZ1npiRjNEnoWwVHAqPISSln6hpmoaqmXF1+4zrmxtWF5dIZQhRMI4uIfCEyOTbmNq4iyVD6Gj9QHtYE9xA1/bMa0FoNJWwOOtSC4zU5jYyqQulfCng+p4wWvrH96ltV85YmopKGgZlCFJggz2dLwFNrWhqlayyBol3AhskXlZ5n1VayIfuKM02VbBpHj9l8acW50TUPgbBeKq4UjYvT6CuaVSNgOP8a5ptxVyhpV/bbkc1W1DPL6kXS+p5wXK1pq4biqLgXGNTkoJgpQukSy3hYWpjAf5stpP5EyglKWWJloZYRiwDLmZwjXcEO2IJxKApFnMiMgtxJGhKFGRBbIGQkSCSDJoqJFloCuscMc/0qyjRQmKKkiS2lgBdIneelEiqVucoyqOcVzwdHzi1lqVKgKukvavTGlFqqrKkLA2FcMR+Q+829O0jttvTHgZ6FxmjxGe6VDIIjiCS1dfoIj7CGBK3EpL2aFK5ilSlpmlKFosZ83lDWVUURcJIlGWJ1voob9n3fXJwEScciRQJkCPEiePsgz+2TgOghERpQ7890HYd292O92/fMZvN2G5SBUiuzrROc2ql0vpl7Zhm+tpQVjBaxzimtdo5j1CaxfKCy6trlC5QukBIwXy+ynZWycEmIbXPOjjh1C79qwieww821YXFfIbWhnbnsXagHyz3H9Zc3Vzm0tQRMh/JjiPjqLibXaTsGsm6KiBA50ZkKZGo48xNScVoLdZZrE+CuhHwo2Pse4QQlLOG0VoGO9C1A23XJ+uY0aH0iDFJekvp5CospSZGwc3tHKUFISbXdGfPL2UQziLVmAje0adBtFDMdMlcw8IkCLeLluvaUFQSrwQ0FaJuaK4XJIKuQylJVTra3SP9YY13LS9eLugOgt0+cH/f4Z1HAsEe2A2Oh/WBq13D9XLGH372gvlMsaiTwkhEYH2JkJoJk1hUFVd3tyyLpGVqdJIvavf2tGZBJi5HNuuew37EuciL51f84jcVSnuEdHhvOexHlBlQ0tHtBtyYWmG9tbz9cE/XH5LJsFGoAM9mK+ZS8Pb9b/lw2LKzI96D1JpZPWc/Hoge+kOXgqCLtP2QFn0Ny4sG1cN+3OEcyBiyMXJJWVSMfY91gd2uY7EvKauzS1RMyNncwstEbLxDRofGUciYbFWFYTabsVhd8MnLT7h59ozF6oKIxtmYqoIcPIyWCAJFqVhcXtDHgoM70L26p+964rDlogwwaEQlsWNCZ4qY2lGFFsmSSSXyuO0HXAi0j++Z6ZJqtmQZPF4ZBlViY2Sw/flu0dSS+UxinUJFiRUKETU+KIQ0uYMhwDmcnxQ2Mkk4KytMAe+oAhMm6s0UBBN44zg7keLYizxKgMcIMc2uieeyXHlhl4LgPYsrUMUF5fyGuXRcXu2ZLxapyj5HG5J4YpU2qcwIgc5laTkBLgZUFHkum90lVOrCREAEQY+kz+hLoiO6njEYQowsTYUXGVCUTBOJAnxMFYvSiiiz+EahEzJbROw4JM6nEFSyRIs0o+69TaT6nFRoodOXnmxa0nF3zqVZH5xmU8SjmokQZG9Il+TotKaqS8pCUxWaSo6IscPuW/rdlmEYaPc9gyMFQF0kEFCIdC4mf8UQso90cpmxPmUlUwIfnUcqgSkUTVNRVgVFYdBGo7SmLAXL1TKd5hDYtx1BnO0Xif+npcqJU2pXRiWy5m8m/yuF0sk6b7vZ8v7DexazObPZjM3jI2PfJ6yEzLzEukRrlStqmzoOUiGkxjlH27bHO0GbkqubO549e0ZA4CPIIKibReKAjo7g0/o2AbyESM71Ktv9fRfK9C/bfvAzi6LIL+wpioQy3O9b3rx5jykUi2XDmJXxfRCEIHL2FAnBUtYldnTsB8vdyzsKaYj7wG53IPo91vlkT6MjY3DIkIR83TAigcvrC7q+xUfHervGjh5TFEhpgKQinlRjPGVVYwfHZr1Hyk+p64KL1QXORXa79snMQhAheNzQ43M7aF7WqKCR0UNhqOua+XLO8y9vaS4azFXD6AKD9ew2j9hxxPmBgGa9Gfjn//gf4TJ3xpQVVVlxfXuHi4q+a+n2e7xQBBE5yArbWvb9Iy9LKMcZn5gLVFGCVPRB09pI75Jqvg8W53vK5TUCwYcPB96/3fDV6/d4f8rcN/c7QohsH0c+vN3zpnngt7/8ms39njBqnFWMg2A4BLYhJRrjMOa2V0QqhQuB7aHjz371FbtDS2UKfvKjF1wsG3732ZdcrB95u1kzKyqW9Zybq1s63zG4Ebvv6a3CKUt76FN7SfWUJs3VCt/gQmJ9qDynmRZwSPJg8/mCqjII8fZ4k5alygIJ4ThPsOMAYUSEEZFdEaqqYHX7gtXlFVd3n1I1c0JUvPrtK4a+Z+w6ZrOGuqm4vL6gagymWnB79wJ4IFjPXS2xyqAWK25WDbNSEmybuFMZ5WgEFIWkLnUic8ekqGJtoG9bTL/DDxsUFikSoKwxFWP3+ES3MQ3+I4WROJPYokKWdIcdYdhi/JbLueTiy08QqjjOrtLFHDnz4eI4mYpJvfR4peeZ31TxiSkCinQfBO8J3uNdclQgB8IUEF16vkgVma5nmCoSCEitubi8YbFY0TTN04VDKXyEvu2xnU2KIFEkbqrWyLI4zp68c/gc1EVI7hF+HJBAUZZEN2S6Ugbc+Mj6sWNxNWc1rzk4m012ZUJVZw4v+dry3mZBf0fUCQw1Dg5XGIJO1l6jG+nHDlVLjNQkTVKTW4P5SApB3VQ0syYt9hkfoLTOc83Ei/PBp+qG1L523lNFgQwev9/hhg7fd6m75RMvWBeaIESa+2X0uMXhQsT6SG9jakEOlt4GhmxYjZCMPrLdHtL+ZvHvEAK1SAjMicQuhERow8poVovF09l0PFVY02xNK42PSRihaQoWiwuWixW//vXXSKm4vrrm4uKCpmlS1yebKCRMhsyegOlYOGex1mGd46c//Rnee/b7Pb/+9a9xzrFYLPnkkxd8/vnn/Ot//a95+/Yt796946c//ilXl1d88uwFfd8fUf9CyJwEnEx6J/7y03Ln+7e/kp8ggPM2ExETkbJvezbrLfNFQ4yCcXCk+0Ac/f6IEW001noGO4JIbYagJEpoooiUVQkKrJdH474koJtmBUolcEpZlsdBt1KaokjcPyFO3J+6qiCOuLZnHC3aTHJk8dhKOl7QwUNIqtRKJHUILRKHxlSaixfXNPOGxXJOc72gXJQUixr6kSAGvB3xdiQGB1rig2W3HhDZlFMohdMaFwKmKDICcUxBMqYLWIZIDB43OsZhoN13VKVDKEMs6iyjFbNOaYTgMEanRWDX8fZhz5v73RNbFJGvADc69vuO9eOOV1+94f79lsPO0rceO0QIkuggJoIiMROCJiFy5yOb3QEpJH/yi19TaIV1VxQUrMwMaijrirqqKbRJGV9IZGs3jDg8u+0OF3tEYRE6oESkUOm8hxiTn59KrauplS9Esg8KnidJSzOrqGcVIUCXlMQTeMGnRUeUEl2UVLM5zWJFM19SNQ1SabwPrNcbdpsNu+2W1WrBfDFHFxpllsiqoKxrmqZi3pRcz8tkV6Ui81lJpQVRBVwUuCgpywHv0mIqJ6H0eEJdWjdixx47tAm9l1HVUoD2H2nYkgTchUwiw8QkgTbsLcEeEGFEyYgqC4QqIEtPxTgBW8JxZnqC6JPlqdIF8aRFmj0Tc7RM97NP6FfvZALmkEjSkZDuEaaXFYjCIEXEjR3O5mCh0tf5ZmMSv1ZSpAAHlEojlAalkgJTrlanvlz0ZERJAOeSQ4QSuJhlDH0O5DHSdx0LW1EgkEWZA4VHZKcVo1NlmGbxIs9aPTGo7IEYsSoyikAUFh/TWMHIJCKdhC2e2voIIbi5uWWxaFL1misnKcUR/OGCO5K203FNABspkrxfdzgQx4EwDljrk1tMSK3OmMOmi4mHObqYf5/uRxcCLleBSknKjFYPMbLdtviQuiQxCJrGEi4SItaYbE+XzYYFgqIwT85XyqtOM+VUsKdiRgqRbKXKmrqeYUxBXdWU1Yrr62tms1kOepa23SNESlInQ4UJyZ8QwuKICq2q6omH4XK55Pr6ml/84hdst1tev37N5eoKrTS3VzfHOe0pDogjenQ6Pwh+8PZXcpEIIaRh6OjwLiCloutGPrxf86MvPkMIx3bbsViuAMkwjHkwKinLkr4bORxaDocOWUvm9Zy6bFBC0fmRw9DSDh2mqZFGJ0NNKQlKIbVh1swBxcOHNaOyKJUQUFVZ4F0P2R7o4uISY1qsdazXW7pOE6JFyYLJkf64+RGpksqBKXTyPjSCem6YXc/42X//b9As5+jKYG1HjGmu4Z3Hj5Z+u0uLr4SiKYnAYddSNlVGLAX6rqPvRqp6jpQafKSPAQcYBCYoqpgy5L51vPNb6kIku5VblblLoE0SGujdQGMkB+/46s0HfvnNW7568+4sCApmTZWyXOt4fFxjROCf/CPL6/sNbz7s+fCuYxwDWhRoYRACpBGMdsQddSuBENlsDmy3LV+/fc/9bsePX37C3/3yJddmwSdXF+jlDC+Tq7mWSQBAOsfYHxhGz6vfvkYoy2KpqOcaWShMqdMCqEEXHm0iSnuMmW4QaLNrxTkq7+7uhnp5jRAPHA5b6AI+uGShFBxSG6pmzuXtHRc3dyxXK+pmjnMeO3a8ev2aN69f8/rVK66uLrm+vkytsrLAFAVFM2O+7PG248tPL/HOYnTSRk0dxRllP1D1A4N1jH2H65NAMBm1NiEwx3Ggaw/o3YYiJH6gVnkh8ieNTUhk6+nKLE2iEAAcYodv3yNsR4yeKBXR++MQz+dZPCGZSkudfh/zfE0okWWJOClzTBVkmKrHDKKRAqLCFBUxS5ulLeZgGM+qhjRiaLfvaV3BYPN7THBsUut6Z0e0L0AXKdGUklXZIIwiaskumxMHkXACMQq8J6nr+EB0I0KnpLm3Hjt47ADLQqOFYLNZE2Yl5XLGy+s72nHk7XpDkGmBrHOlGWMEn4Rox2iJYyB6QbTQkirAwoQjP64xNVJIvEjaweeQe6UUf/A3/oDlaoG14xGu751lf9ilasjaU2C0qQoaR0voHnFDx4fHdXI0iQHvk3uT9Se9YCcl3ZCwBMM4KbIIXJZylEpRGkklBNQlyihigDffvEdpyWxWcXm5Zr6Y8fLlM5bLWfZeTceyMGVC1WaawvGs5iz06MqDQEYFuYtRVzOaZs58tmI+WzCfzfn05XNevnzJfD7He38EJ262D0nxKwfBaWyWdJ0D+/2euq6p6/ooryel5Pb2ls8++xF/8id/ghCC7XbL/cM9ZVEmpbDc9ZMy2WJprYjRHteI6ZL9odsPDIKCojSUpeHlj57x9s0ju12LGCZodSKiO5ugrd55vAsUpqRtB4a+Yzaf0zQNL57fMnQWN+yIjSaS0IDzxYx62eAFlHWZy+SBKAXSB6wNOQMUXF1eHiG/Egje07cj3gXG0TGfz48Xc3JTDzg/UBUno8jjAVCRQkdmtSJkOSjrPWVzzdXzK+bXC6SW7HYb+u2WYC1GidzqcFifvkIMDJuYIeUaQYGiwGAIMfXv20NPDAHvItZ63GAJ45AgyKQ2so8SGyUqSBTJckUHGFyg3XeUpaK6u2B1VePXB9p2RwwjWp4q3DTDLYlNyfPnK6oKhBwpTIIseOfph4HowWhDaVLG248+d9ZCJsWDFBoXskakhT//5Tc8fNjQDB2fPb/h5Ys76ioRmtf7Hc62gKOpDX3nsKPjcrFkPit4+fwSVUaGOPKb7QeGYaC3DlOUXN3OEDzj69880h4sfec57FuSldbxMqSsipSBExmHFoljfy/BiawxmRB+MstqJYBCwA4DXduyaw/supZ939LYGS5EiqJMnDIfsSIgCk21aJitZgmI4gaCCCd0r4CoRJoxRY2mSHMim5w47GhxPqB9wDuHG0bK2qNIYgv1rKa27kmyelTuVwFBNr11lugHnD0QlUolk5CcdCw979594PFxy7t3GxaLhqurFc9uryhKk0BceXZiczUSM0dQCDKdRWXKgMwLtePdu3v2bcf+0PLs5pLFvOH2+pJJ2B6RFFmkEBB6hI9Ym3ioJgMtpi1IgVOC0SSBeBEg+gEVJcIJQqEg+32mpDHp8To74q3NFU8K8mPvGXpP10Y0jqYyfPHFLS8/vePTF8/4az//Gc6NPKzvk7alUtRVc6xAhrFndJbB9fzyq3epi3J/QOkaqcyRFqWkojIliCTTNgR7lhDkdmhdspw3RJoEBIoR6yxlUdK2B96+/pqu3TN0e4qiRGtDU5a0rWMYR9a78WjW7V1y9xhtQGkDIoniC6UpKoku4lF3VmZkTzLbDnluGJi8/nx2whkOLd/se6RUvHv7gbu7S+5uL/n8dzQLIaHxWaj/bL9I10NS1cqBUGSHm5iqq3H0DNnA4PLyiqoq+fGPv+DmJrneA7x//56Hh3u228cEZhHg3Jn0GekYz2YzyrJEKcXd3R3GGP7G3/ibfPHFF9ze3vAHf/AHx89W6BJi5NXrVwRvs5XeqWN18jI8N/P9YQ3Rv4KpLhSl5upqyX7XMQyW4OXRPmPi0ymlcssiYExJ8ANdZynKNCC+uFjyMGzxDkYfGLxDeEU9axBagU5IUR/6pNHoPcpO/eRAcIG6qo/Bdiq5J5+7ECKzGbnMLvA+HEV+vQ5HJfTj4iOSTJdROcuKyQFCGUk9q9BG472j3+9p1xv8ME7OHkl9pevTHEIkMIvUAlVUKJ3mlQqZOEMhISIJaf5lZGr5OkgosjjphIIluVkUId0kZQYudNFlO52GqtaYVhCjS59fP+0AFDlDur6ZU2koSoEpRFK80clfD5lnWUUCISRXqlQZKJn867RWxCHxtnyAzfZAGEceHy+4uZyDTDeOi47gRqK3EAOFlliZEGs3l1dcLmf86NkdfdyzG/bI3QPBB5yzFIWhmRkUC3abASk6nO2PziDH4E5qFRaFZjarmc9qxr5Dm+R/SJBEmVptk6hDQi4nWSdnT64jk+fblCxN1VEgDf5NVVLPaqwC24fkTh5TW8pn7pgQ08xEHPmudhwzufes0xgCklRpaaMpyoqick9OmJQcEZ5KJZsa6x3BDvixm+7CfCRy5hkDu92ed+/v+c1v37Nczen6pHVZ1xVFqekHj3WeYRizQ0YWsZbpHBdF4nhWtcmLm+WbNx/Y7A6st3ukUEQkdzcqzyDTgAKRi9FgIQhiSARq8y0X5CQ76JUgRAk+MliP9iFJ4hn5VPIy7+exPTiZvoaAGz12DPQ20rtIJeD5iyvubi+5vl7y4u6SGCyrhiyfpqnqOeM4Ym1CmidDb4/rPY3Z473CBYMPifA8OZVrWXCUzj+OUc62MCKiTSOUDMYRSlIahdcK4S1+aBn2G1RdI8sSZSR4i3eOfgwpCKoUYEIUBCHRKqEhtZQonSp0n/m3UknMJHEYE7LS5gpTTmhWn6qtbrAcupRE7NtDSiqcZXV5BVFQlonv+7QoEPm4nenaApMYXIy53ZvFAWazBbNZxXK5YrFYUGXhjouLCy4vL1ICm++0c3K7zDJqs9mMuq7RWvP8+XOapuEnP/kpV1dXzGYzPnnxCdvtlv1+T3/oE4d3GJEyO0xM9J4YOWKbjwHwh28/MAgmWsRsXvDJp3c4F6nqhvYA/bBndB1SBYpScnG5SFnXYLlYXbNd97T7xA+5u7viJz/5EfPygrH3dH2gtw7nOz7//MdIrfBR8Itf/ZrDYc84WEJwDHqgvWrpuo720DKfN1TGEEpzHKgf2gElFcZErq4Es2bG5eWS16/fYHubDFqFRgh/dpASOVlEjyBJy8cQsTHNR0SE9v0Gezhw+PVvaddr+q7lfrvBmKSWst9tKOqSxeWSzz//MeVyxWhm2CGpPgTbZy3ImHhrSlJpMBeG6Ebu1we6tmfoew5Ko4WijCQHBEbsdo2uEvFd0lKUFYurJaqWqEqyuqyR0SLi+OTkCwKLRc3v/8FLBB4tBU2jeaYbzCJCMSKCpCkahE/2VO1hlzzGtKQpDYvFjIuLJV9984ZuGMFDrWFWaubLmmZeUc5KiB4VPTMtKWzm6cSAVpKqrvkf/K2/zcVqxvWq4Z/86T9m43bUTUVrR2xW46iqisvFEhFgu+l4/c0j2237Lf9HCRgpEaXh4mKBiIHHqyX9NuA70M0SVS8wpiaSfAL7fsTbiBaaF3fPMCKZqy4XDReLhrJQk0odRhsUSULr5tmnjEPH4bDhsN8nROGwpT90tIcd++0WETyFCKhATnQCyVZLUpgKo4tkZmsMZV0zv7xldnFLF0vOo6BUHOehiDQ/2W/es1+/o11/SEARpj9JAdvHwMPDhtdvHvjmzQOv3j7yi1+94s3bD8yairqpWK8PtO1A27a4o1pMapVKKSirgrIouLycs9/3HA49b97d0w+OzjoEibbx5WfPOUFuTll9dAeiteAClYBGnqN5BVWVgGHGKFxMCZKQPLHlmVRsrA2EkFqyPoILYL1AehAx4nrHMFj21iIHWFzM+e/90R8yKw2V0eCTEP71xTLPKBV1NWez2eAHz/b+A0YnM+e//ju/wxgEP+8df/IXr3h3v8W6bC0VAjpmibZhJITkeD9twTve/8V/i30/hyjwIdFNqlmN9xLrAsrtKUKHiyOhHRg6id1tkgXckCTbvI+MXjBbzmnqitliQVUWuZoJxzni5nGTWqo+omQ8UsGs9TjreHjcJWCNc4DEBZHQ4aNlsI7doWW73vPb37zhw7sHbu+u+eLLH/HJZ59xOGzOzhdok5CwU1sTIUgj2hSovQsoaZjPVgiROgjjaFFKU9c1ZVlydXnJzfVNWrPsFKjyvRGhqitWqwt+9rOfcXNzw+XlJbvdjqqq+elPfsrl5SVVWfLpp58yn8/4nR//mO7Q8/jwyC///Bfc37+j7w9JhSu4LGRwsnJKPpnfrfn6XdtfqRIUMiKEpyw1dV3QHjqUltSFoW4M2kjmi4bNepeHnMmRvO8d/bCnLDX73RajV8hK0tuOyWH+9uaGKAR9b1FRUOqCq9WSskqyPze3t+x3e7Z6i5TgvU3iutYzDpbd9pB6xNrQtkvKUjNfzOC1wLm0MGttMpr06RYhm3cmBwM3WnaPW1795hv22w7hLGHzSL/fMQxpzjMqiVDQHfaEcaAgIIeOWl5w88mntN3AMIzQ7hiGjq7vUCJSGsn1suS2qakUdE5yv2153B7o1x/wo2XoO16ogdIB9yDmDbKquKkSkVfayLgbYYjcXKwoAwT7NAhqneTelIkYrdFSElWkmhmu9IzAHQRBIQukN3StZXfo2XYdvXM8v51zcbnk5vaSoDsObUfbO1aV5mJWcff8gsVqhlaS9cM9ITiMEIgh4gdHu29p5pfcXK94fneHkJ7H3SObbktrO4qqoBqrhDoTDikiMQ40jUAKg2DF5VXNYT/y8Pj6VL3nDFBkGHRRFtRNjetbXN9xGEbq/qTiMlqL6nr8aLHjiJaSMrempLf4/kC3vWecV5SlRhfJcihEgTQlBkGNYLQB5yKj9YzZ8ihGlSo8kb44WiUlEEzR1JSzhnI+Y7a6oJqvmK2uqJoF5vBR0pKD0rHyjY5+/4jrdoShYxLAiGTQEKSK9NxHLSaH+A8fNmzMAaUVfT9mRxGbRxcTpy39Uds7lOrZ7XvsmJF7NpCAxieEoXf2rCoPJ/SpSHJo2IDvdrh+z9nLo0RyfzcelAPhJQUqZTMCrMhydTFpZQpyi04IyDB6Achs6hpjwAbLGBSjC3SHnjiMjFKgdZFmotmnUynFrBEcDiNt63i/6RCxp1A9QUikMVSLOYUMFCqmSjm4hFJ1CS2aaCUngYa024HX37ym2zVopbATX68wDGPEuoCzLXboE+VnTGL3NqRORmqn1piyoKhKlqslRVkk8rhKOIL9oc08as1quaDtBna7lnFMi/s4ujR28iGDYtJYSElBVWjEaoYuNKP1WJs5lBHevFvTdpZ+dEhTsdv3H6HlT24O6TpLx0FIgYzJCqkoCuq64eFxzaHrOQwdy9VlUuQyJUokRZiYKz+lJhkzQRCB+XzBzc0Nt7e3XF5eMpvNmM/nmSZVpNGQdUTvKE3B5WqFEoq+646C9zEEfDxJ6k2zzKOm6L8PxZgsrEHEURRJUDqEPUqDKVMQNFYxn9dsHrcJPOPTBTGOnsOhpd5pdtsdd5c3GF0gDiNCKaRRXN9cE3xkv2splUFXipcvXnBxsaRuEuFzU20pi4K222OtIPiRYegYh4H9vgVSa6ttO5YXs6RGgUxtKutQWmXY8wk2nsr85N8mtcwK8iPb9Rbn4O1v31HKyFJFbN9ibZLKmtCMQ9circLiiO2eInjubu7Y9QPdMBL2hu6wS8HVerSC5azk5W3BZVOgqkteb1pe3e/45/9sz6G3bA4dq8qyChEeB4QfUb7m6nLOKEVKKmKP6xwXizml87i+O4p0QxL41VogpMcUOs8LImWtqZqUtRESB0eHkvbgeFi3qK3kMPY8/3TJze2KZ88vcWLPdqfZ7nouZxVX84bb5xcslzOMMbxdv0GIwGrZgI2EPrDfHri6es6zu2dcXVywax/5+t0H1t2W1vUUTUVtKyKC0fWIGAhhoKoSYq1pSpyN7HYD/+KP3xxVY9JpS/MJpRWm0NRNTbczICRdP9L2icc6jrld5SPeOvxoUQiKbP/k+h2uP9Bu7xmuVkkKzJfHtrqSGoyklBp96BBqxLqE0vMx2fak2VpEyICIWQBYJl5YmYNgNZ/TLC+oFytmywt00WCK/dP7S0wTjClz9vSHDbY/EG2feCTT9SpEgs9PMxYhMmgnLWLr9eHU9j1PHs6W8hjJsmCJX7rmkBZ8JkTppPaR58g2O8YfgaeZkI/FjwrhIq7fY7uWJ+hrEj1QOxBeIAOUqNQilWAzmjWh/U4OJVOiI5VCxWPMJGnvWmyQDC6w2x4YZfJXRlZErXEicQqlkiz65CIy9I4P24FoA8ILfBhpmopPK5NEFmRgIBDjiPcj3p32QvA0CIYQef3qHf2+pqkLRh+P8+J95xhtYD7TyY5qtGkOaAOHwXOxqqjrgno2Z7acM18sWF6sMDrNRRXJmmq775IcmpLM5nNCFOx2Hc5NWpouyRlmQJOUAuFT8Pn/sPcfz7Zte34n9BlumuW2PfaaZ/KlXjqlpCoKJFVAqUdHCNGBCFrVFB1a1YG/hg4RNGhApxqiggiICqogQCpVKTOfN9ces91y0w1H4zfW2mufd9/L+0SlgAjNG/vuc/ZZe5k5xxw/9zXKCaiuchYfZC4/TIFhDNzd79juBrb7nqtnz0WEnydR8AgCkiAoIwsJNlrEIZwI6A9TYN/tGcPAq5evWcyXnM1nGCXt3GP1x6NLfdKa5XLJ9fUzrq+vWSwWVFXFfD7HWXeUX0zBk2LEWYOzc6ZRxLmPazpJcNZGo20xGyjr8+Dw8d/6TFDsgTKjn3CNYpYMU9wzayrOzucYKzddO7PYShND4N3bd+SYuDg7o6k1i3lFDJnL8wu0rnl/v8PoiqapOb+4xKBZzif+/n/372Od5fVHL3n16iXzxZxxHEVzdBr56s0XvH33hp/85K/oBk9MmWEc8QVF9e79nLqtefHCo40Qjbf7HYt9h7HuZCaYGacRsmT/dd2gtMHHibR7YOz3tNZCVRMWC6I2UGvaphECrILGGCodqXPi7Y//iuFhTbN8wdV3v8+rjz/G1a+J00Do9/y//sW/ZrusdcNCAAEAAElEQVTb8qOffUbul7y8XPCD78357ssrPn31Cfv7Gz7/IvHzN+/5wdkVplXM01fY+w3qIRO6j1jHiq+mim2/lVlP0ly2Nebq/Gkl6BTaSrtgGhVRGyrbkMoQeTGfS4Y/jcyblvPrho+++32+eP+Wh+2aTz5dcXG55NnzM86ea/Zdz+gTl8tzVs2c57Nznl+84vr8BevNlugHXF1Rt0uGSXHz9pbLsyv8xQWff/4L7na3/OrdL9mNHVElWmO5PD/j/EzRjT3Be+I0EnyCrNFK1opz/dMe/xFST7ElMlJVzlrCOKffPvCw7aje32I0tJVDhVjmf9LmrGrHfLmgSx0petZ375mvljIfdI04N6TEFCdiiCL+ICKuLK6e0V5cE0Pk7usvSP2W1N8TQyBpBc0M1zS4pmVx/YzF5TWr6xesrp5TtfOCvIQYnqJDZTXm8v9MCBPbhxv8MACKbCoBe+gijYagSVfLJednA7/mrSD5lMw0D3SIUz1L9fhC5bviYDKd9aFKlDagTdL5WS1alosGa4oZ7QebiiFjSVgmSFJJHV8mw34/UZkKXdW0yWGR+tKnRERTNSt0jugwEoNQgcQ6KWMQlRfBfAltyWgJiEEltn7kRz//kmVdM6tqHsaKhGKMIvOljaZtRUpRK01SMyJi1zZ1Pfv9A+u7W/oorT6HISmZYQYVjxXSAXxyOGLK/OyLDZ+8svzh+QXnjWPygV99dct2NzGOkfUO0fJNgTEojHGszs94+dElZ+dzlssFB4PZpmkKJSjSDSPTOBJjYBwl0NVOEtj5YkbdtBL4iHS7HUM/sNnscUYxqzTnc0NE8dAFccJRBmsMTSvI9bPR431kGAI/+sufMEzhSSV4mH0etJFRGVMrxmkSS66hw/uRlANaQ9/3/OyXP+PF9SWV1VwV2og1RmbsKRJixLmmoPlnfPe73+eHP/whr169EnEV7/FeRMKXywupAlMSzdNhoOs6bm9vuL+/F8qbsdRVLStJ5cKnBI1Gu0cu5LeMgb+Pi4QQhENMzBY1detoWpG8ck6Y/ylmnDNUlcWryDQNwo3RmlnTMm9blrMVq8WCnCVTspWlqmoRU3YVTTVDKysLuGlp6pa6biAjA/zakXlFSpHPPvu1tFyT3JohCoBmu+3Y7wfGKXCo9IZhZN8NGHPiSkwhtUZPioYQdPEsFIVaRcJaMFjySdvJSOlIUo/WLjFGYj8wrje8/9WvqBcrmnbG+cUFZl7DsuXs/JwYAg9v77ndVlhneTUMLO2Mpk48v1yx22wF2egqrMtor1BFGmzY79iFivXouN2umUIkoqjzjOnUT7AM0bXWR04OWWNtXQAYivl8UVQtPI1tcLZlPr/GzBxXwxXPrucsVw1n53OGEBgmcY5YtgtqU+G3AXRFM7/g4uqK6Afq2nL1fMK4Ge++fMf9/T31l47FxYpd2DH4kaRAaYPVBusqsdVxVnwCvSBnCz2MMAXch1ZKRY2DEgyUVhhrsM5hq4pJG3yUdtLDg2V0DkPGWRnGm7bBOEvVNgx7Czngg2caR6ZxKFmvOozdoIhZizlsg6mcmB3HxDTs8Q4mRnII5KwwylG1La5pmZ1d0i7PqedLtJVKNZbWop9GTnefE/xPUcIJ+LGXNuRBoFlLmxV1cK2UrP3Q+jk+m8ondQtHAIHMwDlGQ+Hwlt9VFKWmDFnL6INMXVuqSrw8yVk8M5WsqQxFUgyB+6v8pGICCslagTKl8hT3Bg4tK23RmeIoc2pXJK9/aONR2lxGFypJ2WC3u04UZ5TFjp4MTCGirXAWJ6XIsQgsF3PjVMx7U8z0w8BkHLEAgLRSOOPIWotPXhIS+2mg0Epx/eyaZy/EDsk6zTgFznuoZ4nRJ1FNURmrMz4KpWHetKzOz1idLVgt5wyjZxwFTSwi/Y5YKqDKSRs4EI56pJQ5u1LFw7WRwFZPQrXIOeGDGOzmmI5v+oiUzFBbgy0AprEf2O2H05UjRsTphHN3TI7kv3HseVjf8fbNVyKUX1uMynTdju12jQ8BpQ1104pamBJgWlVVzNoFl5cveP3Ra16/fnUUVsg5H8VODiC2FBNOiVPQMMho6UCL0OrgdAER2afJuSSJj1Kc3/b41kEwRJhCIunEq0+WNLXlpz9tMdbhKkvfDyh0aWU1jGNgmsQFnpyYt3Muz8559ewVzy6uGKdA9J552zJr5ljjmM8XzNoZy7Pz4p02MPmAHX2xI8k4Y/j0k0/JwI9+/BNy1gQv2nYhRoZx4vZux9l5x37vBXWZM7t9T7XeFDm1x4seoydpA0kzTY+SbRqNVZG5czgrsxcFqJQheCGy5szgB6IRYIgJLWGz5Rf/8l+gqxqlNR9//zmLWU1TaV59/BEhJj775a/46n5kTIbnz7aQNRWJ77x4xthP/Gj5BU1T4aqIVha8IYXEZrfm3ltuBsfX6y3DNOHHEcYZOYXj5zpAnUXCTqMwaF3RNiu0zmgDl1fXOGukWpwSVldcnF9z/foFGIPTlSQ0taGeXRDSRGTCZk2aIr/44hdcX1rq2RUff/JdUhxQJlPPL7m9eeDN52/56ssvePf2K15++jG04NsoAgJWWpJi99QwSyCMyZpcrsN6vWXYdzh7smujyrytSIAV4q9xBtdUuKZBuYopTNyvt+RpoHGGeV0xn89o2pp20WC0o7Ga3aYWsnSIEgSLeod1Dm3NYfyI0ZnZrJHAWzekrES6LI30a8teR3IQ0rRtFri2xTUNZ9cvaGZLmsUKtIgbpGFgGgeG/fZJknoQfEEJ9y/4Cd/tCONECgldIS0f644GtDlHIYI/lnbHeV8uC+HDoHTkzsNxjnJ6ZBQ6Cy4y6UzTVtRthTLqiOI8tG4PUHeTElqN2IJ2fPJ8SpG1AeMIhVNnFGC0iGQrI8jqHBBhwHxsPyogHTZ1JHkzWlNpUXbIIbLd7ahsRVVlhmkqLVNBIGoF2YvMHEDynhwjKgUqq4nJsPcRjyGU9p3REoy8BlLAqyiCHScXy1jDn//dP+bTj655+ey8mBZ77OySqCpCUnz19Vsqo2kbS0iSVOcQuLq+ZnW2YDGfc3d7z3azZ+xFSPx8tcAoSdhS4c0F79ntNtIp8Z66rtFGkop23lI1FdY5+q6n3+1Z7/rD1RexcgRodNAecAZpx7Ytn311y37bPwb4LAmYBF1xmVBZjNIPQiP7/Y4vv/yM9XrD3/rhH3N+NuNs2dB3G27vKgY/YirH4uyMpq7pOsvoB+bzBddXz/ijH/4pf/TDH/L973+fxWJxRIB3XUdOojMcJn9MiPq+Z1ewGClG4cIag47CEdTFYFnyKlV8Hh9pHt/m+NZB0B5tgyKV0czahu9852OG0RNCpOsGrHG4qmK1EnJy09Z0uw3dfkcYe64vV7x88ZLXr18x+cgnH7/i448+5uXLl7z+6CNmbUvlKm5u7siZo9BrSokw9XTdjmHo+aO//adi1rg6Z9Y0OGtJ3qNzxmmNLqiubrPHoHDK4IdAnjLKfXCTMkFWWG1LtqmZzVfkGCB4YhhKi8mx7/YE70neFxSb4nzZ0mjLzFj23cS0m7jtb3i333P50x/x+uMLqk9ecv7qCms04zDw2a9/gav/mNnqil5dcteNTONbLs9e8Z3Xr/gP/vbA9c1nNLuNiESrmkiN0xPGT8TNBrpInjzTfsugRvJB7xEAxdnqksuLBePMU1dzmrrl+fVLmSXkQNO0NLOW5dmS7IVeoHKDbSq0c+h8kFkyzGpLzBM+dsRhYhr2rB82vH/3li/mvyJu7tnu1nz97g0Xly/Q2vDxx6+4vb1nu90yTIF23rBaLfBqJOuEVaqALRLt/JK2vWA+v2aaBNByfj4SJs9ydfeEqHwC6zhmfa5y2MphG4dtG8KQGfuRu82G2ijyYkYiMUXPFLzMUFAlzTXsu4H3797Rdx22XdC0c6q2RZe2nFZC/9HWYpoWjCWjsVoxXDynu3qFH0eZAYWIqStsVdGuLrBVg9KOaQykMBKmganfsb1796S8UEUYHE5RphpdVZjUoq0FbY74zBgju/2ON29u+Pqr9xI6Tpb2N/35ONn6HZiBUmSjkiR597cPNFbx+rwlJVljByCCqKjUKC0enouzcxbb4emLwxFogTXkpEg2F2mvA3Q+Ppqqal0y/MfZYC5o7aatyTkx7Gt8GnExoFPEELE6i/oTYo2kbYUFKi00qJwz/bQnTJ4weciijoNShJyYkswkrVI4LfN0ExU+Fm/HJ9dKsTq/ZEqOn/zyhnHysm8sWx5u12w2Ox7Wu2JF9jHOGbabLT/76c/4/LOvaJqK73/vU5wxPL++otvv0DrT79aMQy+yY9udEMtDxCmxJjMzg7NAzow+0RdUbe00qq2wSrHbb1FkZpVmCo+KM0ZptFWMQagmSY00jWOxaEplL3dXKJzKx3tOHZG2KMX5xZJhmLi9/Zof/dUoKjI2MfRbNmvN3fqOuq55/voVL1+/IubI12++PIKBfvCHP+Cjjz7i4uLyCGKpqoqzszPpkj08cPv+hr7rODs7O97jy+WSafKE+LjPCTewiDQcZ+mHJPDbIUPh9/QTlKGoOQ4ez8/P2O06dntRRHEu09Q1rhLiqUgoJUL0Ah5Q0oZbLpekBJ989JqPPnrF8+fPmc/nok9qDsAV+RC+GIGGcWDzsOZhfc8Pfvi30EpL0KwqKmuxRhchZLmJUwwMfY/WglDjoK+bPvxcImXkSvtHK7lxUsrE0o6JMUAYiGEkJQG3ZKVQWTOvLJVVWJPpJoGVb3c9my+/YDf1vPv8c1aLmutnZ6Rit7Pfbggxk5RjUnM6H8jTnsuzSFsbnp8vaG4z+AkfRVk9azELjWEijh1xyiRfDCqTIX8gdjyfr1itzpimSFsvaOqW8/NrYhqJcUQpQ123tLMVORhyVKRJMijjDqK6h956jcqKlD0xSWUSw0S333B/9w41bNlut9zePbBYXtI2rRgh7zs2uz3eB6okhFdtFFlFVJYbOOaMNY66amhnc7QJuCiO1CmKPusppeUAWoEDIEQQdMY6rHUY50jBE7QmTEkErWMQXdtSZYkvpRYxKG1IyjD6iBkn+r4TGSxjxApGFRfxYvdiXQVGAlJKZ8IHNRVDtyVME37sMZUEQVVEl2Mq/EE/EcaOaeiEhP/BjSoB6oCCLIHCGKmYtHmUQEPa8F03sNv17Pb96TM8Pp96/K6Of38Mlh9aHp3+XgZMhL4b6fc90+SFRpRTAU8YFIZspG9ijBaj3bp+8i6Os8kDKlWJIPOBz5VPHldK12MFfgieh2BYuQrqxKJt6fqAzSXwJZEiS0EqzRgiyUJWBqKVVm9KxKknjhNhmo6vp40pa1wduWeqnKwDQf2baGcZoUJsdj0+CFldWc1mveHhQe5vci6+mZa6rpjNWvw0Mg4j281WcAXl+UXEQigkWkFdmfK5ShtYFe9ARLBA5yTGu5MkXikJEAglYvBGi/VSOvRySx9cRNNlROScjLI+XINP/ATVwbUdtBGRgJQi05Tp9uujCtFuF4gp8NXXX3N+cUHTNNgi1WatvE5VV0VjdF7M0eXqW2tFbEUb+ml/dLio65qmaajr+vg8RyWbY7tW5C5TVk/X2m9Z2990fOsgmOKI1i1Xl2cowE+eFy+uqaotKcPXX3+FtY7rK0HMWadLSSvWQa7SNE3NfD7n4uKS5UIQQocPV1UVoAqxdzyKpMYooIZht+GzX3/G55//ij/9sz8lK8XF+Tmr5ZzVcsbVxZJxlEWBEsWJu9v3XF1eHnvgIruZngQLTcAay6JW1MahUUxTJyCDQh4L0TPsBua1o523vLh8jtaVUCrwhDAxTAN3m5HOR6Iz7NZ37HYP/N/+0/+Ucfcfsjg/Y7/dMHZbmHY4q3HNnN48J0yabvS0t1+R/cRCiRXJlOB27Gmdw2jDw27PZrun268Z92UeWFsWqzkHqPBhgbx48TEff/wCsFSupbI1q+UZ49QzTR3jOFBVLVrPyfpgRtygLCiTUbpH6QwqMcVACoEcxMYmh5GmCnT7t/z611vqaMRgGUPlHPNZQ/PyGcPkGabIZtehWsd5EmK+MgmUZz/s8d5jjUMbQyahTcY4I3NglZni8LhpA52P9EESEZSQopuqxTeeFCLTOGA16CxGpBaBd09+IgRPVvoYzNq6KmLbc4yFZA3bbiSiCSkxn7ci0t402DJLtFXNQby6zgajKww1YRiJeFAFZWyttDTTKEo0fSfdhRggDqj8gXaoVsfK6OCwgNFoZyHLWlMgm6JWBB94f7NmvenoukeQzRExWv5/YhRxNCQ+TBCTosxTHluxhxaqVgqrNP12z8ZpttuO2km7Uykl1kQ5g83HoKGtE8UTDtdLVFSGcWCvDCpF4Y5WDVKLC8E+ZhHdiIguaAgCShGz3IqpdITmdcOyabhoGr7+8jPCOIAfmfo9nVZMgy2+fZ40iWNDro3Mm2Ok26xLJTiRlMPVDavLa6JAKiBrjNIiWpETEY52b/qUQJ4yN+9uBMEcBWU6jRM//osfcXt7zzAMfPrdTxiHPZ9/9hnOGpbLGf/g7/8d3r59y/phzS9/8XOmURD03/nOa+bzhrpyzJolzhmWi5q721vu7x64udkKWGYacMX2K2VY3+952I08bEba1rFc1EIkz6JIpYoMc4rhuGaXq5msf+sYhomuP5mdKYUr+szWFu1U6aaisqhi1VXFcjVDacX9+xt22x03b9/xsOsICX7x+ee8fPmaq6trHh4eALi4uODs7IzV6ozVaoVzMpKKMZQA6aiqihQjtXWkEOjmEijruqZuGrp+ApSoWZXkEASAo6xi8pGjfF2pZr/t8XtUggmtsmTQJVUchqEEK89+31NXj9qJOWemorhgHbRNxWzWMJ8vMGVetVrMpCdclCxEdFg4TaeCqjklbm9uePvma7764kvubu+omwZNmRFUjmcvr/FTFI5T8jRNRdVYhJJkuLg6p6oqfIhPEmZjpc2rydIqLM9plbCXXUE5RRWZVY62qqhMhdKi9v7Q9aJG4SfGkJhSYlAJbRRWZz7/xS9plguyc3RYFJnLqxfM2zmVrcDUBDsnxBW/+vovCfsd/f2WszGx0C2rdiFKIjnz5X7kYfQkldFWCNm5remNIabHyZBSitXymrOzF4K0LCoQ2tRYJ1msUrVUMbkmpkKGNUgbNCPApRRROoqTNzWVgTHvSDFgrCaFwLjvWawuaRrDfLngxbNz5rOWbRy5vjxDpczdbovJivX7LfPLBVVbM18tCcmg1ITRLVq1GDUj6wLqSFpem2IhhGzUX7y5oVcicO3KeclGglPdRpqhlWsZPUZHHJGm0uQUyVnUNWJKhJjQxpCcRhmHdQZdWUxVY+oa11TYpsFWFaaWWSMHPlaRpgo+EqYRP3bS2gVc1WJsLSjjqYBaYirCxTXWtsTJ0LT735jXlatXACuQYyKFg3i6ZMAZUEW5QzQqAyFKdXuEiFNme1oEmQ/ix+RDG/NQcQlQ5pQxdKjAckb4djmVmZzM8IyVzUcpdZyfp9LSjNE/QYcqpLUonZhHu6GsREdS5SQAs4IqTUVpRyuFthZNFmcOBIVqjMJZS9us6NcLegW7/Y4QI10/UFW1nJdxZLN1VE7TVvrRHSMGcnG6t1WLcRV1XZGywSKapTGLd2hEFVf50nk4BTEBOUzUbsb5+RXOGcZhYLNa0DQVOcN3vvsJMQTGoWM3ekgRaz5lOZ9DjNw6i0EAVn7s2IWBfiM8VGU0V5dnVBZWqyWz+Yxp6Bl2GwyyH04hMIQkYCljmc8qzpctRidRVulFDDyGyDhGfEj4mNmu91grUpXGGtwHgue2gEpONWClJ6SPHUD5DhcX5yzncy6WS97frdl2PbfrDb/sBj779Wfs1jdoDU1TcXlxyfX1NU3TFMBeKPe0nFddDI7rpub84lyqZu/FpKHv2O429N2uEONz6UYUHuOJt+Cxev2bMNUVVGF+dPbOuQTBsVRukwy5EbJrPgj7qoRzmqapaNtGlMaVtADqyol/VwriohCS6C4WlfBQVBpiCKzXD9zf3nF7c8P64YHlanVEB9rKcnF9Jj5xIeP9iLWKqrZghEezPFuQcsZ3TzMEZ4RDJ602Ab8YYx85MSU7digqY0pQlJs/58xmFAPfGCI+RUJMTCpQG5E8e//mLbZtidrw7Pt/QA6Bs7MLnHaolIv9UUXUM27ud4wPd3S39wS3IGnLhWmIRHIKvO08/RTE8kk6H0RnGJXGf1D+t+2KxeyizJCK8JG2QjfDoFTEaIPKrggpi/2VzpIR52SL/5sEQY3BaZnphChZprSpM+7K0TRigHu2nDFra/zGcr6aY5GZy5gi/aanaheS4VUrRh/JecDoFqMbtGqQKyHZtlYRdbJEc868u10T3IJxNWfWVsKLQqxhbFVLBZkiyddoAo5I5RQpio3Pwfw5x4RtWrDgtAVXoasKW+Z5tipB0MnzautEzD1JGzeFUKoKmfMdNn9bNeKQgMZ7j8oZlTJVUeKo24boNFW7+YbZ3OMYAFVcNIocGyf3tCh3lIAei/XVN7R/BBWcjiAZXSo4ig6k4uAoISOEQzfh0A49PIacyVqjjbTKH0nzJViXdZySbMCn78Qqg1W6VJsHrWF1RLzqVPaKo2WTID112WgrI8HQIA4jzliWdcNiNiP7iX2/Exm2YaSuZ8eAuOs0zigaR9FLzYWDFiFGWmWps4A/qqzRWTFlERsIOYp2a5Zznw+jlMczSwqByhguzs9QJJxRLJctK7XAOserl89ZP9zTbe/Zb8SGTCtDXTXExlNZEVpIRpG8mFmHMPGw7RG/wMyL6zNWF3Pa1jL1NbsaVJS9se8Hdn3Ax0xEs5jVLOYtbSUJ0gZBOntVtI5DJsdAN04ShOrAYjXjFMekeNSwPSJKlXxefaAcHPaSLDQrvVBwdo6tGuqHDW9vHnh4WNP3A4ZI2za0jbQ127rhUcbwccyhSnvdaE3tLLNZS105drs9w9AzjAUPMhYReVXcZgqY6al7zmOb9Nse395Zvq2Z1RWEQPSaFODh/oG3b+948/YOox113WCtYb/fMk2TOB03jvPVjIuza569eMbV1bVQFvoOH8fjcDQWUMGhCjwEwGEYGIeeh/sH9vsOP0x8+dnnPHv+nNX5uSijNA7XNIBBNnhIxXdvKv3/2dVMbrJGHVNfBVy2c1rjSJMgwTLlxgwjRJjVM5wx1NYy+kAf9nx1s6EpLYOb/T1KiTHrlEZ8iozTxHxe01hLt+v59U9+zpsvv+KH7++o2xkzHF/+6K948/Nfsn/Ycv7yI5bX1+zqj9lozU13z93uHW0IfKkDdT1H6Zq/fG+wWJZVYgR8yvS7PYuqetLiBYVOLSa30mJjAiTgaAoJ2VpyVMQBrG4eM7+gyFHh6jPInpwHkpeKxA+BbgfbdWBz36GCwiHQZ+cMw7hjvb7BTzW2jlxetlyuWtrljPV+4N16T/aOODqcOWPWWCrnWcyf0dQrarNimEQoPY0J0yp06nmsBDO393v2+Y6v3t4xn9fUteV82VJphTMGN5uhjUDp0z5jkseYJColyQnnzwdGLybLrq6oZy2r8xXLecvZ2VykxJqadrHEOUdTSWWXc2IaOpnFjAPDbo2fBsZhjzYW7Wq0bZlCIKRICpHqoGozn+PqmnrWkvzIrPe/UQmeupJrI4jKlMSFQKlHt3elMsFPkBQaoZucJvQHorBSYDBlc8vH2ZNW6QiwOTRPD+3SQyWIkuzaGYNzFVU9l2SgKOpQgBSPFIbHGfLpp2q1pTUVtRN0qE6KPBwwoGBryBaigSAUd2wwhQsHM2PJWpGdJjkhYaM0tq3RY81m+yCz8SHQBkvIisEbhm1PThFD4ny1YDFrMD4Tp4QfA1cuYYJsoOMwMIVIUJYhTPRhKudefCFV0pjTrTJn9rstIVwxny2IviMHzbJ1hJhRKhHHHpMnljV0akTlCZ8oe9tUgDmiZdy2c+pK0dQZ++YdMWauLpcsly1N7aitpV4uWc5nxGFfXGmGozzlet2z2Qy8u9nx7HKOBvwogL6UhCva+8h659kVXnVOW55NoZDlDytLZs2ptBy1fpwNSr5TRA2irBPxhZYVtFic0zRLKtfysF6zXm/4+s3XjNPEF1+8oe/+S37xs1+hleGP/uiP+eSTT2nbGQeps/1uS06ip6qQxKrvi1Rm1zH0O/zUARNKC4pYKK6CWs6F1mGMmPf+HriYbx8E67qiakTcNYYgg3Mj2nHWGuYzUWjRWgau5Z1R11IB1nWFtdJ/7ro9JmccogJgrD22K8QR3YuIdpSAMo7TEYE2X8x5eLjH1RWL1QprDdZZpgMpOCcJgoVEqWyBW2u5UdUH9JHWWlqjZVhOKrQpWQghZKyeUNnRWMMUJkKGbvKgElW2xGkoXMcW6xUBqJMu3noVynqpcvuO7v17fN0QkuJhtyMpI+2e4JlZw2J1CcA4DYyffc40bhl2npi2xKS57TOOyG4aiWoSwd62wWr9m621Y0V30obisGgMRgkXKmeDfaKig4ACsj0CBXwSv8SYFClqUtBMXcAkEXu21opXIkFAIGGiLrOIJOZwVFXF+VnNiEMpS98HtBWhBGsatBbXb2tVQQaqIsz89GOJPJTAtfthIpQF74pqiIkeYkYpi7IC6KHIsulssEZ8LUVIOhV0Wl0k9axw2spmbkuXQNouhzXh8X7CTyNh7Al+FM1Go4vSRThKmdWNyEDVdUPdtlJVOkfWCls1TyrBwwhBTr9Ga4s2TjorSeaaJRE/apPWtdBYjC4N0CPw5XE1HBRfVJn/yWNORavL3PAkAJ6uBZnXyEz6wCuUV8uleyA8wIQFJZY7T65X+S+ShYiOcI3ltR4RiAKuKVT6aMSLFI6qNRlFMI+DS1sJCAcj3YmcISqFcRXLeYMZGmIMqOSpl9KCJ3pCykz9gI+Z0Qd23Z5uSvgE2Wp8ysSsUAfR7JxlPKKfttfE0DkQ/QhpFFeLSjP6Yo5rNIvFgrbS7PcDzinGYUQpEbevKw05MnkZM1lraVrHcjEjxiRFx6xlNm8wOTKNka6fGPcDYz8wTRJEtcpH6T6nEaQoMI2KyXtSFAFwRT66VpAzgcx+PzL6pxiJw/U/XQcCjHlMlw7txsO/C69Q7N6WxVOwqWsyka7r2G53gtG4u+G/+a//FeM4cH93x6vXwgiYzWdS+Rcg2gEdfRQzPwEWnZ2dMQzdsUg6JF2ZR0DPobD6tse3tlJq2oa2bdAWfBCl/KbM+RaLlouLM6qqEg+vtqFKjhgDs3nDvG0lCGpRktlv9hAjMwez+Zwa+XnwMgcchlEquJSO7VbnRAv04uqC9zc3aGt5+eoV1gotowudtGEFy0HO0mIxTh83m5wy+QM7wZk1tEbTTRFdvMMSmSknfIyMfsQo0KpimnqGlBhCwJiMUo7se2zV0jaWKhiyzlg0jWuoXMUQNTp5dAr0794yoOkmz9uHO6aU0P2WNiYum5bLP/8h8/Mz7Lzll/c71ruR9fqB9Xqg6yYWrUZnj447jJ2YNY5PF3Maawvk/+SDZS2ITw76kiJcrNCAxehGhsvF5FWV4H+43mRB/xnjUMiGkFIgJ0tOjnEfcGRcLRtl3TRoKwjSKUy0dYUfRoZxYPIK6+Y8P3/G7c4zpcx2M3B+uWTWzjCmLjedp3KWWASWKfOPpytRNkxyEk1GH+m6CatEXLg2UGlFq8UzTWVDZkTrjEkJZx2DKjyknEVrsZGqHqXwMVOJJ9ORa4lC0IdR9EensccPHdOwJ5b2FGUUEKO0YrQxLJYLqrqhaSQAamNkpmYtrm6fLMQYDxl4aQdqh7EVKFH9keaZJHQa2RwWixlNZbFGFS5ckc+CR07fMTjK+ZSfCzVAH8/o0wB4KivY1DVtXZdEiyceg7mYQcdkScqBEtTs6RFyJGRx3Eil9TrFUNwKZNSW0ZAt1sq5y9EinQukDa1EDyQqEb9IWVPVDXUbMFVNCmIgi9Y08xnnV8/phoEQAyEMLOctbe2IQ4+PkbDZ4mOiHyfuHtZ4LFEZNFJJZswRTa6Ps8inQVABOQamYUulJ6yaWLYaoyWgCl96yax9zsP9PVkZhn7PrFE0TcW8dZAOaydibE3bzjhbLckpspjVrBZzlqs5037Dfhe4vd2yflgTplFkxVLGKnA6UznFvDWcLx0xZrpO0xe93JySCEYYaJzCl8Cx24/0Y3jEElDmsfpRjPoQWFKpWgU0pR+1RfPjfSrof3GGSOfnzGY12+2G9zc33N3dsVnf81/8F/85X331BR999Al/58//Li9evODVq1dcXV7iqgpjNH4Sib6DgEDOicViXmyeLDc3N3R9R/DhRNnmadD70Nz5dx3ffiZYW1QtFZ7SNTpnbFZcPb9ieX524sWl8MEUN3iNU6LXOW9mqJR5uHnHmy/ekkLg4mzF649EaksjiLftbicLNUvfeIqBMXj6OEKlaM9bdt2e2bDHE4u7tqLf95hTdFoCkipu3ofujUKnp9Yt+7AjZ01mYlGLFch9P1FZjdGWMAwAzOo5N/2e/TjRJ11Q8pG2FR5daywzrDiI14ZhiuyHjqASi8Zw0bQsrfgZdrFj1hhmpuLZfEazvyH/+l+x+s6M+tlzvvfv/T3UkHh3+SVN/TPGX/yY9f4Nv755IMURlXo+uV6wmre8XJ2hvGff9U+H9zqSzGHWUtCflcXaGmtqtBJ3cpUVwZeZloqiiaoUw7hFZU1Mmqp1VG3F7HyB1SOtg1/rRgIy0PUDPmWGccDaIDSPqytiUGgPbm6o2gtWV6+J1Y79OLAbHnDOMp/NSGSUjmCT8LMSYCORgHJPFS3KnSpoSSU7VfSBKMMmupzEfkrDWWNotEYp0XjN08C+G+l70XW1uw6lHQ/3a8Zuoqoc56s5zloWC3XUZISCsAueHCbiODD2Hd1uL+AJMiZblC0WTG1L1TQsFotS/Qn15yh5lrNQLU4OmalxfIwxjtl8RXf/jhTjMWCBIimx47o8W3B1uWIYBr5+e3dEmB5mi4e5tUJAMAU6w7FvoMAooQUZo+T+MUJgl0FY5NnzSy4vzjBaNv2U8vHZc874mPDZEqlBj5wS8BWwnFUs5g3W1QyTJKnWcNwvAkVRJhe1lJDwfS9KMkboKyDV8MhBJUfRLlYoU1Hf3qILuVtZg3YWW1ectY28xzRhVcIoaBctRimmfmLwHtWNbPYD9WJJ1dS0y8UxLRmHgXgA52n1dHamFM8vZ1wsNHXeMXeayjnaq+dsdgOjT6yuz6jqGucsF6sZGc35oqFykKJmuWzRRuFqx8XlirpyaBLdvmPoRzZdwDjLbNGy7wfevL3hX//Fz0nRoxW0jWMcIykpFq1lNms4W864WJ4RUmKKifOzBh8ib99uydOOECfOZhUpw+gTuzEe+0PHz6YfK7wD2vLR0uyRm3saBGMhsR9syShro24UVxdLnl1fiB1S33P/sMZPI7/82U/57Be/5Pz8nBcvXvAP/+E/5Orqitl8Jp0opaicO9IdDjSJ1WqFNprNZsO7t++OHZTD9xgjVVWVrsK3O779I6W7RipzdWm7GKpa41x1InAsljWZLOgjDFY7FvOFiFvv99ze3RCmAGQunz0jIb3ycRrpx57RDyL/E6W1NsWJIQwEFVFOE1Ui5MDgR1Gb0OKqfEDGHSZ+Cn3okJ7MMR6PDOyniWw0LifhYSkBfoiji8EnT8RhnCYhQ/MpCfHaJU1lJL2NQVQhdM4ok4mjZwoRWymx7rGWxml8QXZWyqKMgwxx2DOt35Pu3qLbivr5My6fXRFDYnh/g6stmYl9v4HscSoxrxvOZ3MuZjPy0BG8PwmCGWxCuYQ+GGIiiTomkU0kq1BaCQZFEpV+nVAqS+vPyL+jLUbXxaHD0sxq2rbBlIzQFp5VRpOyZhzFHrvrA+OUmCL0U0DVGWUtrqnFccOLJrQxqkhTSRvKaNHlUi6JzZV5etEOpN6nVW8+WvMcwBckGLwiG6FRkDQ5ayY0vsDgx+Ax48C+69DKHI1dT4OfAEsUKQZiOABhRoIfBb1W/l3ZLOr5pTNRVdXRlsZae9w0Du/3w3ZNzgd6Q0Fiak3dzuRmTplEOS8n81FnBWG9P1+Knc5hnsMj/04wbFnwMBQ0Xan8ykilPAaadkY7W9C0C2IYCeOexWJOUwLKgb+o1aM4d8yQ0GRdgTJPgiAKWudonUVZS8i5aNfmYxCUOYUqPNTwaLGkZHYdy5QjZUil25EQaTPrEtZVZC1teKVNEVUXFRWtgGRRKaBJLGYtvp9oZjOG3ZaQxOx1Zg1NW7GYVcUjEWwKeC0t5FAQtqeHUxEdR+KQwdYYWzNrFsXVIWGN6HvmhNC/lMYaLQj0KHuftYYKLY4SVqMR1/XJB7QaBHAXxcdT5Ofisds1lnleiBlXOZG3q2WdJSRRiAXYoqTEfiL/Jg7zgmL/cCF+E9/utDX69OH5Cafw8QucbaisEQ9Qaxhb0Vze7Xr6bmC369hvDQ/OcXd7AznR93MJxIUecwDqHNqfrtDFzGH9lONQrR4C898ITzDmJGoCIZaTIrI1CgXmUXMuxVjI1pQ3XTNvZnzy6ScYLHfrO37+2S8Z+oHd1HH56jnn6Ypp6Ljfrrlb37Lebo6q8t3QMQwdd7t7IKFMws4rgsncrO9JGpHMck4ylEMWozQoc5z9HFQQPpwxvdvsaJzhrKnRQYSDd5MnW8BqdtFTkaBxqMqS/cToPV1UqJCwrmaKnvVmwzTJPEBnxTT1BB+4qGvmKlOjWc0bppxZa4/2ipwMm91Ii6eJW+7/4r9mvHvP0O/45JMfcHWxZP/V1zTzBm0Sw7CmNZrVbM6fffwJ33t2yR9cXvLV25Eb75/cproN2HlG60okk3Ii0zHlvSAvtRiHVmYuXSwF2mTGcYcPIzS5RM2KSAJTUdWautU0tUHFSOUM82bGbD7HuIasHHc3HdvNnhjeoLS0Yr98v+FsMlRXH6MbR+syHot1kawGUrbEqBknQ67EmTyZcqGcetK+JiWI0h5ThVj+OBMo2po5M6XIfReodMZXGqdbjHMMTWDKihADu2nEkzH3twJ0cnPOL1bMZg3GaFIKhFxmLEPPNPTsN/cMnRDex3EUkAEKXSUsMgqoKyfCDQXFZowq7RlBRX6TmkXM8rFiKlWbsSwvn3H3ZUsulA65sFoQp+Xzvnx+xWIxY7vreFjv2Oz2HFqcorMoG1iSbqmIPMCBk4/O0iZJJK5efsTH3/1DPv7O9+k2N9x9/QvOV+IFmI6vWSTEykYTlCNSo0yLsbvj56S8i4umZVm3BGVJJfmobCpJgRZgclYYpcghoFTAOgVGzJEnL63UDCgns4yYwWqDMhVV00LwQppXhqptmC8XNFUt5PLghTOqMov5DJMM05h4O05klYkolosZl5dLzldzcojkKTDYhsFHNoNmP0zofIIqz5mwvWFUOzZbg748g9WSy/MzmsqhdWboNoyyY1E3Lco4Uo6Muw1jv6PbduiqZjZfsDo7Q5MIw058BrO0qn2IDGMgqZrF8ozvfOcVm/WWvh95WHeMPpAyPDs7o2oc2lkiAR8DfR9Z72Tmp41FWQfK8bAXfIV1hnxU/SkfCxGTiPFDfeV0DEby8fMTHp4Yqadj1WiNQRvNrG6I1qJzpjKGPJ/z8tkz/JTxU+Tu7h7xRnTc39yw22zQxoqxdfl527bMZrNjIumcY7/fM47jE33Qg6jCgSd6CJrf5vjWQVBgwqUNBVIxhVgi8kGxRKqv5WKJq8SQ8WyxYjVbYipLv++5ubmhTyOpAtVabvcP5C9/xfvbG7quY7ff0fc9OWeMteUDeZI9yT4N7P3Ar998KTOTUo4fLphkA4L8OoiyyqBVINCnW5DPBhUN26AZd6P0ygcvShg6MgWwY+TNpmMfEh6FT4l+msgpMmsryIXjSCCTsSguV47KtLyaOxGLNgadAn6cuL+/p60WWN0wec99rwh4qi/eYe7XpC9+xR/+9z1uecHq/IL27DnV6iXn8xuWVvFi0bByBhMC79+85X69YduPj4WgQsTNZ1JlCUfOE/NWiqisMLYRVQkzkEMmhcjQdQzdToTPdcBWNVWzoGpAqcAw7Hnz2c9598vP6TdbqrMlVdVStwuq2YJ6Zdh0Pb5T6NkC7YTmMNzeoP2am82XNLMGazWrZy26Ac9E1omkBH0Z4iSC2JW4lhwrssNil4KVnAP5yATXaFNmXqWqyUoEk32Sto9VIq3VNAvOVktW9cdczCpmdc3ZasXl1SWLxZzVco61Cmuki5CKGkkoYJih7xj7jrHQg1LmaPnjqoq6clTO4KzGamk3qiLyK5fnKS7z8ThYeiXxXzOG2eqSarbCVjP63VourNHFVkhI085JNfjDH3yH+4ctN/dr1ps9fjpIDiKoaBSxtEq0UgVMITgWU2Thxr5jt77BdxdY5Tk/m2N0Pm4uhyCoysAsJ4i6JuoKtJXznk4wpwpmTU1bV/RRMWtaklYYE45hEmUIITFOAZ1MaekmjCuZfoocIPTWCrm+UhYr6CDhnQUjqMsk1XFbW5YzoduMey8zLCWJoLWG1WKOe/0MqzJnizmrtqZWitD3cg/sO87Oz6kqRzO3bHeGoT9Vwsk4FdC52P04Q12LWlEcdvhxIPiAshXKNdROE1Ogv3/DbvtAmCbOLq6oZ3Pq2bxQGBI+ZJTRIspgLNPgWT9s6PuR7XZP101stz0xRc4v5rimwbiaumpQKaBi0WbVmrOzGf0wMQ6e3X4SUYpKRPRjFJHvfor046loucK56tjaPP68zAW1/hAMo77xcQcivB+V8PoEGye+jFpTOU1lBSNy+J3ayWvGFAnp4KQi7eiu646zP2MM4zjiPwhyH76P3wMX83sEwcOgXekjBPuA8hJB35JhalH7aJoGpRXLxZLlYknMiX7sud8+4AlifmnhYb9miCNfff2VfLhplNaeAmudZBg5kU1+rDYNjMkz7R6oXH0c5h6yEa318bHivSU8oYQEwdMjZU3ImjHCGMTCYwxJ+ERa3K33IXLb9fQ+MsVMTJmpOE3EnIvhaQIdpW2iFfOmZVE1XLSHNpgWXqD39N2e1rQYnQkxsJ8yU87Mbh/gITJ81XP5vU9ZPvsYY2pcO6deXLCcLViZzHnj0CkyDj3v9nsexkEIuScbq9ERo6K0kE1EHdznM6is0cpIcywm4uAJo2e/3jB0e6axJxpP1bbkhRflHDTjfuL27VfcfPUlfhhhtcLaBlu3VLMFyjYo15DMgG4aTC2zSd0Yko2MaSu2O7aiXbZSoahQQkIq5GTRgNXGFZL20+ulKQLPOR/obk84QceJmBbeY8oKHxPaynx4vmi4Xra8vFjw8nzBrKlYzmcsVkuqukK0mQsEOxfXgRiL0aqXYOin8j2IvqcWFRpnraCVSwvIaAGN6oJcVuVzljvqgztM3vnBfVspjavnuGaGqRvS5gFI5ZwVwAuP6LkXz68KWlSUPoZhpNuLobQgOB9b5Qeys1HgjLxXMTPNxKknjHusSTSNIxYpslSqEw4cwXwAtVRkVZXZ/JOxNKCO50SnJEoh1ogS0WE/0SKgbZKCbEkmoXTCOmlPp5CO7WltLVYbau3ERaKgjg+BPQeP1QqrFU1lpJrqslyLUqE6Z1nOWxbNGU4rlk3DvKmFb5rFx26aBiqnsLWjtQqrFJtt8+RKGZWxpZ1f146qroqwu8i3ESaUluBrrUJ5MRYP454YM7P5nGY2p25bwrSXdntMso4qJ3iEEOi7gX3f0/XiiiOVMczmLfPViqppSUERx4EQRcc5g7RHnUj/BV80X4s/YcwI2X5MxaT38YMd5n2nbdDTAHjaJv0w8By/l/FEyKnQQIBUHpukK2KsyMkdCioZqWQJgGVfPXDFdWmNH74fZt2n1eoh+P6+rVD4fYAxymC0EIadcygUXddDmTkdevzWWrQyonPpI9vtjmny7Hdbuv2edbfGtCJDdN/dc7u7I6VE1+1LxZKorCsosU6IwoBtbJEyiqKniNxH/dTLCEzpJ2X8YQaolT1C5UII4jhxco6sNWil8f6YruCKA73Sji4oujHxy3d3pSefCSFhgGAyo4eqsSwXgqDLZLxPVNpBNmynxHJWczafMfSPm2BbZWZNhnHifr9nM/Rsug5NhNTz8H/4P9KeXbD86IeYHPnupy9Y7s6ofceCiX/5s58whUjfe9p2TkQ/Kt3nzObXn7E1EdvOsI3FOIWrDUUTiuF+zbDvGe63PHz9nm6z4+7dPX4cxU7IBHFGP1uyfPaMjGaz7njzsy/ZvnvAaUPTtLTLM9xsiZ0v0VXLqDR9hubyHGUjWXv+9N//E1xlmc1qlBaNw6qWdnXOim4/EKJkwlWTyNniffEZm9bHnVWBzFeVImZpbWeliqmmDAJzesxsnRVB5LnVfHS14vn5nO+9Omc1bzlbtMybSoxLVT7avkxe+FcpRhT6GAhjEJuuA4J5CoFsjbhgLJYsVufMF3OquqYqdKBD9ipzspP5CgfTzyd3GAdGfCwqJTE66vkVq2cf028eZJakFfqDFhY509aO9vklL59f8gfDa7z39EPPMAS8FynCVHzxlBJBfGctdSWBczZrqeuaqnIwrYnA0XfpMGIo/5NwrsnGgFuBqsklwYzhNGnJZBPIaiIRAIsYIU3HlrDG4Wzh/7UzUspMfhLqhzE413KwDrK6jF+y8IqTyyxWLeOg8eOE7iM2R9KwRc1dCZ6B2WwhSTkKlRtUmtFW5zTOspovGIeRFBPtrGEYVvT9QDurj+hEfe2O60MuoKJuKy6vz7i6uuTZy9fYqiFEjyHQ2MxqeYGqZuBmbO9vSCqzqOecnc2JWYF2hHHPevdA9L2cDWO5OF8VFZkJYxQ+jGy2W7puZEqK+cW5JD6zOdq2qGzw/ZrkB4LvWT+MMn9sai7PF5ytpGJ8f7uhu99CyvRDYL0d2XYT4/RUPOTpqD2fgF2eBh4ROzfHxx9JOblgmJURulDKxAgp6ePzS9s0idODemzdZxS2eHXplEvrvRQz3hch+0PIEqPsfPJeD21aKZz+BmaCh8G9qJykQqyV7CoV6OxBwmYafclAZfPo+l4c4KdR/J+0AZUZ/SDQexlOFKBG0Us8OaEgQrTKaEzWYrxKRusidlJO1OHiHKpCeJohnG40h0MrEVL2RxfkjLKFF1M21EzGx8NQWm7fBISU2fV94TdOJYNSTD5RW4czho3JnIeEz4rgDfsQwVo8mTEFxpDopoFt34lUVE7kMLALX2Pv7phtB66vn7NaLDDLlmrMtFPmYTuwnTy3Y+LCZhGmPlkQD7/6jNvQY5uWat5gK4utM2kMpNEz7XpiPxF3PcP7B6b9QF7vIARMSjRzi8Fj0p6uE5WayWdyN6CTZOqurqjaGtc01O2cerbio08/ZbVboZ0ja5lvOV2yYZUErZpAiihLRuFDf5ytZSZiiviYhZA+bp/M0MT17QQAo0CljDXCgzJWeHNWKxqnqK3ifFbx6mLJ5WrGs/MlbV3RNhVVZYsC0qHtWsAfpfpTStbgYdbhvfAStXHUjaHSFltVNO1M4N1FVUa+jCAttdwnKecDaLO85tN16H3C+wRWzllOon5kqjnN6grRu0snAI1vpjUooHYWV2D9szqU4NQe1+/hvjBaqlatRfPVlOr1AAYqq6lUpgdAjmT6qqowdYs14g06ec9pwDzci3XjWCxbmqUhlXboNHGc8xrjED6rluCWEtOk0UahjaKqqyI5JyL4Kck8Sa6NZjYXOb9gNa3TLJdzLlYLLgvKdzWrmLUzQRdai8oBkofcl4pRBP9FpDtT6wbtjPj5Hc4tT9trSinOn73i/NkFZ+dLbOlGqRTQuoh+KyNdCyX0spxEMUYdzGmnnpwyhoQWuCzKVhgrG/k+TOQ4EaKirkQ6UJdK7uDikUIofGqhCrTzOSgrakLBM+SAD8Kn9V6AhtMU6MfA6MXsNqSnQfCUr3ra/jzdYw/n4NASNQXRTPnZY/JUklN10tF5ImdWrJ5QxfZJ4owERcrcsTiLqAM/UR0LHFOoEbm818Oc0hgBznzb4/cIghLgQGZ/utzccnJKqyfL/CHniYNC/IGvlxEScdaCnswkJj8eT6hA8w86l/K2YkroclK1EesaFIQg1Z+8B45Z6DejlA6zlnhcPKeHKRtSzoGQZNZhjDn6Bx4+e0ypEL/l4qXSutrs9/SjZm8MaCdecwEqV4nahs7sfWCICaMq+hDJ1jLlTA6eznu6sWffdwILJ5P9xLDZkFXC3ryn+ZM/5+X8U1bLGc5mXAr8fErs+8hNBJsUtX6SwnH/y18y295h6oZmMRcNzErjux7f96S+R/uE9Zlp3RFHjx6LMj+Ks7aGSZT3H/p7Agpcgx4mTAbjLK6pqGaNcOHaGfPVOd/9/h+w69bcb76UzTTnwl1LxBSIWZR4Yo5Y61BK4/1ehvTGkJjISTFNE9PQ0Y+bJ9dLU4SbC9IQwOpEY6GxiqayR83I1cwyayzPzhdcrxYsZ40ohxhTNBIf1U3EM1aCTE5SCeqDLFNMog87TUxBuIaVrXDtrLSvKhHaNqZAzI1Qcw6OKIqjNJ0+qFyc7Ko5wzQGpimikHZRihB8wtRzmtW1aJdGT44esn4SAE+fSGZC4qtWVR8EztOhMciGdJown7Y64fE7lI1NHx9mbYWZLXFqSQqA98f3dHo0bcXqfEHVzMhVTciJ7S4e37vWFrIhJyPyaykxjvoojdW0dRHUMKUKF6oKStwDFouG5DSpsuRUs1wueXax4vriQDsQ/rJzjrZpIAfII/v9PSknjHI0VoJM1++orKZqKzRa3s8kc/5TvqrWmsuXH3P54oLVvJYOVooCwNGaZCw5y76jcqZpWzl32RLSluATYexKEuLQpkIZg7aVtO+9Z+q2ogubErPZkroRb0djRCS87wZy9PgiFu2amnbW4NzAOAyM9/f0Q6IfE7vdwDAK0rTvvQTB6TB7e3r9U3wcKX04Azy2QA8B71gM2TIykT27DI7lsdocmjSPa44yFS8VfubRrzHFdKRp+OjL+zDH5/sw2J2UoqTy578xU93DRY3FvV3uJ1VIr4ZHfkkuyByFtUIgpoAVjDXUrjpSFg4zvJREfkkZyaCzNlJ5khHYdT6CJKzWAurPuZhkJnLhWMUyvM9l7mGsPbZnRBFE45w/7gEg1YgxltZV7Ps9PhQOkWjIC8CifAZTCNGHTCST6b2nD4fLeqg0DS5GrLHklLjvB7562NBWwonr4si269BK+DrdMDEET+4H2rrh7OwZJqxAKeazJWFf8fbzLcP9HjPusOOOm8GzCYneK8YoGeJxQWeYTYFZP5IGT9h0eCXACJUjJifOZ7Us3JyhkmpYJJMoFYu4QmhjWLYtIWWGBKo4IcwvVzRthTaycFOQuVnVOFpV89ApVDSQdZklib5qzFmUZabpOL+KaRKDmKSoo0UrTcoeHwe8f+rADoIWtIc1pTKrSvHqquHZecXFyokZsLVUZRbUWMOsgtokIdUfqslSfSoFIQVRxfGTzP5CwBh13AD7rme33bHd7pgvzzBNQ7s4x1iDtiIunZUi5lxk00QM3pgDsViXiqLAWj5IxvqSoStlsQfkZQqYak69VCwuX9M/vGN4eIdSSTok6XEOc9iwtNZPzldWJ84SBx/Fk3WSSlV9RJwe7gt1fMhx/idWu3KvGregXnxM3ztxz0Bx4BYeDqUUy9UVy8UFShna+QxlFOdtXeZAZcaUKBJlooXqGwHaaaVYLcRiraoqJi9V7XgmM66YEv3ZQoyHfWDWtkdT1SoHTEg4qxm3O3Yh0DkR8zAaumlPLPewNrG0Tu1x8x8n4cBWriLG8Uk3QmnD1Xf/hPMXF1Q6k/woQgrDnth35Glk6Pb4bkva3qOMIcZMvx8Zh46cAmdni+Kg40hlTjdGQZu7pubs/Pw4fqnqioNghcoSMEKI7Pc9Xd/z7t1bYvAM3Z7aiY7v+eWKvB4Zw8DN/ZbtbmTfebohEGImAq8/eUEGfvrjzw8X7AkK9ODkfhpUDgHSGAOm9Avyh4pHxeKsJHvKudKmLPv8ybU/bbemlMp5fuw+5AJuPKzvw+srpY4elIfArMuo7m9sJigfNBeKxOHm06A59uyhtE3TQfH+4JUlsOZypqVaLMLAOR9aUJmEaCSqQ4Z6SA+ySKClmB6fp9zAh/5v5rFM1jkfwTIUt+pD9v0hzyUV49RD9oKS3zgoIxzmOWTRbAS5aaVqLSG38Hfk42XJvqMu7boidhwyPhmyyoxxpGB/iullJKSEjZGMwtgKUz5PDLDbd+TJs11vYOpQfs/95NnHTIqGcfTieH86KEbOAymVJEzafkalAtiQ4JRVxs0c2cAYJoHMI27taH3kHCkgTQGVZZ7UNLW0fWIkeU8YJ6ahJ6SRlDzWKrIy5MiR+0WWNpFcj3hy46RjxZRzLBtzfKxMTg7jjHA2Q8aoTGUUF/OKi5njcuY4a8wjOKWIN7jSRVCqXJ4sgKDjLZcTKU7E6BmHXhy9o7ifyLqPTN5LK9Q6bNXgmpaqbiUImtKmOprEpmOyJF9KHNfVY1vtw/v0MAc8XEahtWSyshjb0C4uiEPHoG6ON8Bh2eb8QVtUPQ108qEPP/5wFll+cuwtn/x6pgw5Dr9V1r+2YGu0m5N7/5gU/iYypswAjSCSURhlUK4+Js0H94uUMtOoCTHijcYo4V3OquJHV6S4Us6EKpXtITFWjmkYCT5IpVfOuyZDCoQp4ccRHzwEX5SPEj574cDWbaEJPPI4U4RpirJuGotS8Sn5WimaxRnN4gKrsqi3RI+uZuD26HEgofFjTx674jwinRDpahkJ7HUtHYSqImKoksY2M4x1tM2cXFqJpgjZZz/hh6E4ZXhSrEBlFos50zjix1G8MJXCuBplIllpRh+PXyHKz6qm4vXHr0EpfvaTLx4D2En1dwqK+abAIglXuX8O9/KTLlxRGcoHLmE+jq9OwTW/8dz5MeX4kKf4dG2dzqofEz2h1P7m43/b8XtVgilmxvFRqsYYVaDL9vhhvPfE0md2mOPs0Hu5wWNOxQTy0cFYKUUswJToE3VdPkRBdR7ioY8w5UBRvJJZTckwwJRWbMYgVZ8+qrEeNtrfPDlT8Bhh8BYunRzGWIwWj0PJOjOodDztrnhyUUyDU8hlnpKFaxOUSKjVLT4k+hCYUg+FBpBzsQQpcljAMZDqonITYmCzWdPv12iVudvdMPmeKQxlYzI4Gna7LZ3iyWKSm0HUcSotsyk0qOKqgXMklckq0czmWO/p44DOoltZLRpxxJgCSct7GccepUUTc7la4ozG9z1+v0crS4iJgS1RjcxmljAp4gT9vhDKTQJTSM06knMQXUwllADjnCQ8SVC6wrF7lL1DKepFQzWvmfYjDnHQ/uT5gudLy8XMUJNQSWbXxlB0H2VGcAS6RLHLCVGqCR8jY78n+JFh2B2H6+1sAYh4QjcM+JhYnF2wPL9kvjqnXazKPQBSoXIMgCpnIpEYDcZkECcmVD6dUT8eKcoay1kRo3Q6fCzX0TacX39M9iPb268QGvQhQTjMBiVZ+dBC5pgcyko+/YfSpj0u+BMlpcNzH9InQElrOCtFrueoaompVsT4/tieOgSY0082TYkwJqGcBAE7OOXQSvaNxWIumyOBYRgI3uO9py2Bj6JWkmMSBxptRDHHWjLg/UTf9wzjWPYpGY34aSRME91+d6RJJQz7bmCz2WObima24OLZNZWbSVdCyfON40jXjbR1hV1VIoPYPGq9KqVoFpe0q+dYc0DFShU8DTv82OHW7xj3W/rdmqHbg5pwbca1kjw28wV129C0LbOzS7SrUdVMQGZ1Q9suMdaIW8u4Iww7pu0D9+++Yr/fs9/1GK1om5oXL1/Sdx3dfkvfS7WcsiFpT9Ky906T3MsxBaq2ZnW15O/99/49wPB//T//P45rxJwEvaqQ1Y0xR2efGAV9qg7VYHpE7ecsAf4QCG0Rsw4hHHnch78f7rEPUaZGmzJ6Sk/3s9POwcnMshDnZCSniges5m+qHSrAlaqqxDXYWqZJjEGPRPR8gF8fMt50bKNaY0lZKsnDCQg+ikyWUehT4d1iqUIJlClnydYO2XwJrNYotLGlTfuYXRgrPeQQwzEwHkt086QhxBQmlPKghMtDzoIMVQatLbGQeRWOlD0gM8PKVVSuZholcKgyQwDJUCpXY1yNcQ3knlDI85I6iTVOVgIGoGTSEAh+YLfbMMWEj4Gu36B1RBEZ/SA9dx5bnxlPzI+uAIcF066WzC+XpJCPQ2VlBECjjBLtKiXVrdWKFCrC5Bn2I36KGOdQBrI2hJjJSmOdp3YGZSva0k6N3jPsNkwxEPZrVBPRVaZ1RmgKBy5fFhcHU4lfWlbjMZnR2hRdTU0ufDqtxSeybiQROXyu81dXLC6eMXUjNnganbGzimRhklxGYOnkY+sxZRiniew99GNJqg5Zq9zMdVPQkU0jOrZRsv+UMzkEFufXEhjbGe1sRt204mSv1VF94xCYDpczp+Iq7wMxgbUZ59Sxsjwud5AKyAfGSRCJB/QdZcSgTaZqa5aXl6gwHsE8x/blBzSRw/k6bd1LQpmOL5rzY1jUJ78osPYkJsDFSV4pTUSTlSUtLjF1SyZJ0Bon/OhJwcvvnByz1jFrDDpL23CcBvZDL9fdaNTLFzRtTd1WJGdFXaSqqIpUWUzmSHUxrkEhSVI/DcVDM5OtxqpKiNrWMqtrAY2MEzFEjJWkqqlbmnbObL6iaiqaZsbVs8vHBFFD3wWZO+saVztMayAoUXc6OcNKO7Stitt5RObfXuTwcqJqVxjXUi8uZIaZRQHpAJyZta2IgFfumOgppTB1XcyJD3qYCm0EqY42hJQZx5Gbmzv6MeJDBm0Yp5Fh6FlvRBrw3d2O4D39MKEU2NrSGsMPfvh9Xn30mr/1J3/Mn/753+X9+7vHyg9wlSsG54+B5ODrqpSiruvjWYghHCsw5x7Pj4g0aLk+5fcOX6eP+5DbfapKo3gE3hzbrxyQpZIYKWQvOwU/piAt+xD/Bsjy281e2l9kohe05iH7emxrlFI3iXGtdY/EeqAoX5QM4KAuYEUj8HTwfDhRB9+p44alFWghzD6etEeS8QExaK0psUZmVZRyXPhT05OOTUyPYJfS/ylHPs6DHmkXIkQtLdVcFv5jS/fwe4csW5UqVB5Tgvmx7tPHV6H8LKWADyNdvyPkTIiRyfegPI/tQ9m8jq0popjiftABe7+fsNVAuRQoRBFGGYUymqQP7zHjtCKHyH7n2W9HpsGz0x0J8DEVKafIZj8SlUHZTH7YgxlAGwb7BaqqmRSYmcLWmvmqIkRFCIp+n8u5C9hmQpkgQTD44pAA2gpJWLROQdCTmZv368eMMGfGbo2tLGGYiF5mibdRM9rMRkNjUgGGaKyryrUqvFGK40HOJbEyJaFxx+To0KEIpbpJOeGLuDsK2tFTdT3WORTSEhdOYGmX51RamtL6CSHhQxRgkpEAl1LkzZs3j+sqJT7/7JfEUMyga4cCpskf1113+2v6zXt2d+tjEHxKCk6nS49j5/Px1B3XrwRqhfhiltWoHn9RAmAUd4xDNai0gMG0JfU11V7h1hNff/WOcRBPxe3te+7XDyevmXl3e0sKHhUlYMUY2XRdqfQV+36gnbW085bJTxyI/VbiEqGcz5QUc9egciakicFPkhDqxzGMDx7rnABRktAM7t7fFdWoTFuPx1ltNU7UQyApQzw0xjUMxbon5YRxjod9Bz7z9bvb4+dKKfGrX31O3484a8k5lL0giKReGBm7fbm+Yh6sFY9jCK2petDWl2qqBEkS2jWgHVjR9s0oSBNh2DPu1ty+fcNmveaLz9/SDQEfEto6fAhMfpQguB95e7NBKcRUdwqEmES9pm2ZLRYsV2f4KdJ34+O+kTOb7bYEvQOyPjFN/thKP7jCp/QIFlJIkaO0KCId9+ajlGE6OtVrrY+KLqcqL6p4zIKSugDpKmolHb0D2jOlhA9eZoPl37TWZTRWEKw5s9k8BdT9rkP9riGiUo8NEmlbfuvn/VbH46zh396R4cg9/LdznOxEv9fv8G/we4+HO7aCT571rznZshSO9eQ3/Hs+PtGxBlHf/MQfoh/Ln37zaf+6RZBz4XDKoa19fO3yBKd/V8fX+Le3sg6AsN848gc/VSdzjDI6OBzOVce56Dde/Xyo5PLvukS//fi9l9Jv+wV1cn7VyfymvL98mliCs/abr/nh2Q7P9dd9lvy4zJ4s09944qcn8GlL7eR3jw87eeHjZTydg55crxOVkqqqTmas33Tt8+++TN+4Rv/am+GYcOdDdf/Br+fj98d/e3oOHgFUpyOsw/H7tBH/f/n4kNKRDwLK33B86yD4745/d/y7498d/+74d8f/Px6/Kwj+Xqa6orL/+Ct/Xb3y+yB0ft/jIIp9HK4eX/P0QY8Z0QH8Rs5MJ5WFMe6xXfsbn+RpzfG7P85hHsjTZDQ/DnX16euUWc0T5ZDjL6vjix35jTyez8xvntucM33fH/8uDtw8uTiHdvVplvrh8SQ7LpXLIYM+Pc+nxd1pUfiNmaz6pr+qI5Hc2IMapswZjm2N8v7jieq5qwr37oPPcDhH+TT9Prn+h9b5k+v05B19wxk5/fHp8x8qkmOb/OnvPuXuyXv48P1JBv6h/uFvvoXHN8Lpm/ltDzx5nHryXo4I7tPldqjof8sLn44cBKdWRgQfzHlOr3zKiaHvjs8xny9+Q//1/7tHPraGD62608v3WCXpJ9cxpcR+vz/+fbFY/Le+v0n3ND+9177pdjp53f9P38OHn6su82j4zXX9297Dk7b7b/7oWLk++XdV5tjqsG44Vu6qdJeeopmfbmQZiunx4c/pCLBzzhUVssd1+LuObxUEtVb8r/+T/5g//bM/5W/9yX+ANi1a2WLCk4uUWM9B99AWAWBXrY4butbm2Dn7pnP7113M4+CTDCly+/4tP/6r/4qf/+hfs324x+nMrLE0tcCrU5SZYzcItD2EABp2/cj/5n//nzNOYhX0H/xH/2Pa5TVDMHRhS4gTKSWMajFGfOGMylgVqHXCGlHkOKCSDlBslT1aeazJLFqLKy7J0zCyWiy4PD/n2fU1iszQdXT3Dygf+fTqGocooSgHup1hzy5hec7eB/7yJz9m3lTMm4qryyuy0oQEzrYFUGKpK8v9wx3/7H/xz+i6Dq0U/+hP/4TWiXWVq6TV1nUjD5utOD2feISVUIdSrswsFChN2zQs5nMWbUOIgZu7W6pG4O37oSMWmbumbkTkQIuQ9HFzLDMRW/hZSmlxo1eGtp5z/clLLl9e87f//b+DMwYVIv/V/+X/ztuv3/CLL34NJjPEwL/49a9IBfD0P/uP/4eszmcE7+n6sQhOO5arObNZw7DZC2jKVuz2YqLqc2R5vqKZNYRxwMRIFSJtWwuazVgBUUQZoGYL2SiyMagEJijxw9SGfhqhMiSr2G32TP3IuOtJWslsU0PtnADBoipSfYGL5RkK8P0IMfLwsOV/+7/754TiyrI6W0iSqRSnhpeyKavjfZNzxtm6+GRqvJ+IMRSxCbmWh8+htGG5WtG0c1arlyyXZ8wWC3wQcfT54ozFfImrapq6OewmjH7C1TPaxYrzy2dUdYOzFfc3b+i2a/bbLcvlirPzS549f4l1ruRumq+//DX/q//l/xzvJ+bzBf/8n/9zXr9+/bi5/cam9tc1m/5Ner6ZnMvQuwjMSlMr4KeeYdjx9u3n7DYPvH/3VXEDiVjXcH7xjKurF3zyyfepmzmumpFy4sc//jH/5B//j/Des1wu+c/+T/8Zz1+8ePI21fEtZFC+JAygkisPyI/vRXmKJx0xgp9g6BV9NzJOE9v1RgSvnaZphbM3jAOzdo5zjqZpjh57jxzRp2CSb3P85V/+Jf/kn/wTQgjM2pr/5J/9T1m0jQhEjMLDTikzhoBPGW0NTVOL04o9qNiIPm9MmVBA7wrBanjv6bue/eDxITMGjTIRW8EP/uSKdlZRtZb3X4/sdxPrhx2r5w2zVUU1X6IQrWBro8SPrPC7yLSfePfre6YBpjHz2RdvOb+85A9++AP+6f/kn3Jzc8M//af/9ElL9Lcd39pZftbUork4n1HPzrGuxRlBRoUYGfsHcgzkFMVKpqqZn7+WXUFrjNLHbPrf9DgEQT8OkDyuDFsPAAFBBkrwQQtYx2jRNs1GCQXhAy8lq4RwLYPxcjqMRtsWa2eiDq8SJkNrM7VVLGcNKcUi+BzFwT0I8EOrDDkAYvtyWJjjNJJzxDnH2WpJC9iYeHV9hc3ieo5T6HaGubjCXFyx84E3N++Y1Y55W/H8+TVZGUJSVK4tRH9H07jHCqkcVVXR1g3WiGN6TpkYMvOmLdJ3sUjWHVmRaO2w5gCRhqZpWLRtuSkieTlhKgda46whJqnEq6o6DvxtgUI750R0+gCpVoKKs8rRtjM++eR7fPwHn/Ls9Qtev35FpTU2ZvpP3jLHcn9/y5g9aRo4SOcpBdeX55xfzBmmAbvZ40NAG8fyfM5iORcLo6wwygqAJiWiSiwuzmhmDX7ocEGCYFNXMmO0jr7r8TGgrCEbyAYR7c0KGw1t3aC0ofE1yWmiKADgK0tbOQEmKaFBOCOSWeMQcFaRKstyNROLLVuQkqdzaQXOFaFppUCl48/1sSIpm4yPoqSUAV0ARyoLEb4ACg/SWtoYrBVtVD/1dJ0iJU9WioYEqUWrJM7kOhMmEQfvtmva2YLWaVpzTWWB7Andlm59J6Rsq0jzGcRJ3ofKYApIpBxaK169fsXHn3x8BEyc3M0ffP9tx7/JjpGBRzWcTCTnyDBsGQaw+5Fub0geNB218SSdZP/Ie3LYYNREW89ZnS9wrub+7vbkc2k+ev0Rr16/Pr5UySCOa1WCIAWAdBqQE5lEjGOxFIJunyEGJkZRKwqB4DV13dA2Dc+ezZn8yHa3wRVE6sFiqK4r6ro+EtofPTEPWp2/+/zd3d09nmmlOJvNqJ2h2+1prcbUjrZp2Y89Y/BkrXHO4ipdUNEFHV2c7rFyI+SiKR0MOEQWMARRqdE6YxTMlhWLVU27qNhsJnzM1CMszxyLy6aYHCt0VjgnxtIqK7bs2XvxiBRcvqySyjnOz895/fr1bzhh/K7jWwbBzG6z4/79HW8+/5yz60i7OKOezYiTxw8j+4f3JN+TfM98vqCdr5ifvZKWl9Fovl1m8rsOpeQD98Gz2W759edf8OXX79huHnA609aGxhYSeC4STNOBkxLx0bPrhieK+sP2PSmMbCbFqA1RG7ANWQW0nSCO5DSRQoeuFU5ZliYT8cQQ6MvG4ccBHzpSCqyzxxonCjO5OAOoxHa/4fxsxavrZzirqYvCvonp2BJICXKIWIQkqxFkaYiBI5K0IGVVqT6+qaM1JUU0jna2oO97/DSRlaFtZzR1cxwcHxR7JEi5I4orxoizlrauaZxD4aiqq4KyhPmslQCTitJPqf5EhLlisVgwDAPDMLDb7Qo82lI3cy6fveC/84/+B/zghz/gxYtn3H79JVWENhvSd3/ATFX84rNfcdutSfmxxatQLOsZy3qOSpk9e6aYiAQimaQ1WEMMiRi9EOuVRRlVEgWDSlKl1weeq5JgFwGfwVamnGcxGhbRXifO9UnoHSmLALD3HjK4SjwXlQJnBGnrJ8/mYYPWBmcropfgk1Lhxp6YmSqgqg58W1DKHK/pYTOLMRCngA+eGOR1RZGp9JGUVO/WaGyp5o1R5DQx9p779/dF9i/TLhYsFiuGZy/w3TVt3TCzlt3mgf12w5dffs5ydcbLl69owpqqmbHvRn7yV3/B+7dvmOLE5dVznr94xcP7V6LMlIRWc3t3+ygcAcc1dmiJPV2n+eTrG+/4D774rY997C4dWnmxnMtMjD0h9Ly/+YLd7o71+ob17Vfc37zlVz/5V1RWkuWYFe9sg63m7Na3XD//iO987484v3xBmPoPXpBHjuXJbCCVZE0CXlEGUoGcIaWDK06g70a268B2HXn3ZWa/27Pevme9fU/f71k/7Hn+4pJnzy9YLr/DOI7c360ZxxGlYDabsVwumc1mrFYrqqqirivm85mchRPKwbc+MvgxMGx3fP3Vl5wtlywXC55dnBNST0yeKSe6LhHWSRR2jKaqDH4UJZzaWnLWBYVdtJmS5uFuzcN+5HYfMVWkXSi+1y9QbYvONffbN2w3PWM/sgqFyKNbyUaRRE6rjI6wXd9x+27N2zc3+FHjJ3h4uGe+WvzOFu5vO771THAaPPvNlpsvf804DLSrC1bXL+UiJ4VyS3I2+HFit+8IWXERRVBWA0k9nSfJe32EeJ+qFHzj9RHcPGTY73asHx64vblju+/ohlGUG6IhHGgZWfrEgw+FryIw//3w1EXionJUlWPyAatrkqnR7YqmNtSVwoU7ahtYtPDdV1dcrOY8vzjj4f6BzWbNu5sdvRqJakRpDyZTu4pZO6eqGpp6jo+BcRJFkv1uy42Cy9mMqm4IRGxlqXQtpqPa4iePHwZiikebppQTCYESg/imHdzKjT60ex4PP034aRIfQ62FBtDk0kd/JLc+fj1tVYuGq2SuYxjKdSt2VCkxBo/IyqmiMlPgy94fWzIHV4/q4FTQzvjzv/8P+Oj73+fP/sE/pEkwDRN3P/4c0080PtEuGl49v+Tv/d0/40e//hnhTXwyYDhvVzxfXXC+PGPqJ1LY0AXP1I/0tqO2Dm2kmOp2AlM3bQWFlvM4bi18UhTjNDGlyJSFmylyLZEGg3EGN6sYw0SIiUAUQeqYSKMnR7F3UkahckFGhggp0dY1lWtoi5xXDIFhHNFA1w8na5uTToGV0YEunY8oSvkpisqSaIvKfNlaVdzrNcbpQ9PleJNlEt6PxJAZJ9G1FEqIpW0qFm3N+cwyrw1zpzEj0CdMDrQGrhY1f/Tpc9rZnPc3d7z9JdwzEpXob4bQ0XcPQnEqc8J+c/dk3iEKUPHx/v0tx8mo9a/96Tcdh1brYfuY/MA09awf3rG+/5r97pa79Tu0AVdpLi5qVrPnXMz/LpTkc5hEuD1EaOc1Skd2uwdcVdMP26cvaMTg+ymiGkALrzFpQoj4EFiv7xn6yG5tub2d2G1H7m7u6Laefh/wY+TyWcUf/e0z/uziNdYZur1nv+8YpwljLOvNlp/+5KeEKAIT0zQdz/nz58958eI5r1+/4sWLF8znc87Ozr7VeXtytnNm2/WEoWOz25FSYPQ96MC2WzOEAW0d4xgZOo9Smvm85dXrZ7haCU8rTiQMJMVXX71lnCLew30f6XymH4FpxKfMdrdG2YGQLfcP7+j3E045jA1YF0lpxOgKrR05efpu4OH9ms9//jV37/as78bCI9WEPJFyKBSb3+/4dkEwQwqJME2M3RZbVWSimDqaGm0cMhBxYJxw24aBfugxIRytjw7PJZWMxtmqgCMAnkqvPcEWlI1ahL+yGIaOQg71IRTX7cSkEA3EA3AiiyTZYcOfQsLHp9DlubM0lWE3RbQyxKIl6lTCZI/NPZXOzGvH5VnD9fmCl9dnmOwhDtw/gA8ZrWWTsUaxnLeiXF+1NM1CFN1Hx+QnUooMQ4evHME54ShphbIGo1wh0EOKkZRC+cSH/0p1IEm/fB1O6smNmBG+W4qB4IUXqbKQ5rOWjrA2j+2iR5KqerQiSeKTmMnEUkFkpQhJkgqfApw0Iw6N7lT4ddM4Hi+iMeIxeXZ+zqff+y4ff//7nF9dEtc7pn1PHgJ+25P2Paa+wDrFixdXvN285XbXPiZIgE4KqyzGWWpX44wlTyNh8vhhoqr1cU6arDhSkPPRE9CUeYm2GXPwVhu9VLU5QSyk5iJKQMrF8DQQciSpIqYeknQty3BeaZn55CSkea3EoaCuD/O2TDwBNn2YseYsbbLjn4tjwmEdH+SmpGIvkmNW4Zw5BsFDIpQLDzLFRPCJGLJUoMqilATBpnLMm4pVWzNva1aVhalF+YnlbMaiiI3PGhEhnxYNZ8uW5aLFBE9ViaZvCB5dZsOQCdPA0+OgJ3oa0NSTbx/88fEnJxXW44k6/aUPwFFZ3k8II5vtDfv9mrdf/4rN/dfs97fshy1NW7NYzlmcz6lnlkadM05ZXE2mLJxbNGfnF7SzFVXlMEbxYW6ekielwjlOWoQRoog+h5gZh8A4eYZ+5Pb2nm6f2Nw7bm48u+3Ew+2WsctMfUabwHJlOTtrePHykqapGfrIm7c3PKyF75dSYppGJi8Gufv9nq7rxHGi/Nn7ibqupa1ZguCHqiu/68ggbjmAMpp+GgjZoy2Mfk9MHle3hEmEHcSjsaKqFHlK0naO4heKcwQvFe9u5xmwhKSIIQNe2vreE3wRjx8HQgjUrcNYGStoU/Y6BWEUb8W7d/c83O3YPfRMY+GIk0gplA7E32AlmFOGEMl+z7Ae8f0tcexx7QLXLJgtrlDWUK+uuH/3OfvtHj77HCVlCqHMj1LI4jbdtrx4/kJmSNaWvvrjfO+3XSZFZhp6xr5nGkfZdIuYrs+JHA8gArlBJp+OiD5fkvRTiNL1yrFcVugqcz9o+pjJeOK4YerWLNqeuXWcz1ZcrxwvrmZ8+skL6tpQ1ZabzZqoAkNUnC0XLGYNHz2/lo1aGTSWKTaMQRwnfJgYx4FdLzJNkz7HaVFtqV2DQktgTIEUPFoJ2CimSFa5CP2asuHJPC+rxw0USvMoR5If6aYBfUwwxAhZRGgPuoCGprQwZ+2McRyKbFQnlV+OstmUKmmYJnwMxJxRSaOyxhbhYWctSUkF3nlf3BUc2hguLi/54R//CX/nz/8uz169ZpoyoffkIXJ9fs12DKxvb+ju9pi549Unl2z8CyY1oP/Lx+Ro97ChmzXUyzlN1TBvZ2x2G8K+px8DuR45Pzvj8sUV1+2SyU+8e7hl6HrSNHJ1cUbtFE3OGGcIk2c/DowhEmRHw2kh51bWkULi7vYObyIYcI0jxwwx06qKSGAKHm1kf44xinh35WjbOUaLv6afJjCZ+XwOOTMMj9wspaCqDa4Yzk4+lgAWy78fkgyRoqqqUqVrsPag9n+48mLBFHxiHAMxyOxXozCIePjFYs6z8wUvLxd85+UlZ/M5l8sF28tLNtsddTOnnbVcXV/RdR1KwcXlkh/84fdZnq94/7AT4rwyx1Y9ZT7uw2kQzBIs8lTu3cP05qAf+ySEffD1+HnkKEmJpEIcepH5cAJRpKln/fCeN1/+gp/+9F9y8/5LfvmLv0ClAa0izbxlsVpxcXnFefWcuq1xtaXrEl0Po1qyOrvi4vIZP/hbP2TWtlSVCP6/v70/+VSZbrhnPzg0idgv8aNlfau536zZ7Pe8+fqW7WbPZrPj4W5kGgxjtyImXZJ8j04LdFrSriYWs5br5yvOLmZFWDsw21r6AZSOzOY1L1+94ObmVsYb3nN/f89ms+Ht27f87Gc/ZTZr+cf/+B/z/e9/n1evXv22TfR3H9bQNAuu7Wt++fMfMz303O/uWCxq2qbCKENtDaapmS8qVmctF+cVm9sHpqnH6IrLywsWZ8/48s2eXX/H7XpLNZ+L1BoTOU3omLA5o2MiT5kwiq7rYtnQzirqxlHNWnEXCZrNpuPm6zt++hefs3nnmfag0oykEoGAjyMhTb8ht/atPvK3fmRpGYQIOSSS8oTuAXxH6tfkqUO7GlO17DZrdvs9v/rylkyRrUqxoIY033t9zfXlBVfLOVotju2f3xb8lFKP7bsYGPqecRjwkyeFSApZKlVdtEopNyWyGRxOio+RaSzSZeVDWSZqPbGoApPvUWlkGDe0TaSaZRaVodKRcX+PSgNOBazO2DIQzjlijGIxq/nk1XPOVwtePLsiDJ7oA2lK2CCKEZ3OosOfE8Mkvf273QaWmqpuiAQyGq2QGVKYUEWSzhQtUJBKISURMhf4+WFzeDyquqJqamJITIO0TpqmhhSIaSpC1gatVUmuFc7WpJjJEUY9llmm6FZkJS2/5MTWKiuFwWKzxWpBlVp72MgVMQW0cxjnWJxf8NH3vs8P/87fY35+KbZAIaKcRTU1eV7DsiafOb5a/xy/8bjdgtvtA1PqOc33x2Fiu9mz7gSd6oylrWrGKdD3Hf2+R2VYzedcLldoq7Ebg4pB5kQUSSaVpEL3niEGmadpQWc6pamUZt7OmaZA103F7/IgCXise5myEjWU4uRh65rKOqwxpBBF1qmo0RxE3VXmN/zOmrrCWk2MUjmJGofIBx4UOozRWGOoa5nFxIJ8SymX1yg6ljFDVJikmdc1dVVxfXlO5Rx1XfG9Tz/h6uKcVy+ecblcUFlLHHu0zsxnLX/+t/+Udj5ntVphKnFlcFXN2dk5IWU6H+lHz+ilNScdwdJmj083oJQnUhYFEmMO4LhwslwPoJGTPx8DHUj4TqCK6TUl2yiP0yozTQNj3/GLH/0r3nz5S37xk/+G/fYd47gndx3aZLQBkwy5D4z3W/xaEWPNbD6nMhV1VaF1zfnZBc+fv5aKqliq5SfBWAqCu5tIGHo2d3v2DzeMfWDz0LMbeoYhsr1b4ifDNC0YRydz9xQwOaN0pJq/J/sLso8oHWV96QmlE9pIu0eZjNIZpSJN43j2/BprLe/f3/DjH/8YgLZtWS4XzOcyG3TO/bUV3287MtD7xJQC+27PlDxJZ6rGMZ81zGctZ8sLQlCMEzgn3qvd0LHvOsLkuVieYasWaxvAoLQVaThtSEqhU2meBPBdYNTSabFK6BlXqzMxJA8ZFQUfkUbP+y/fcfP1Pfu1J4yanKTA0CpiVUYhHZyDG8rvc/weQVC2oogWtFPK4mYcR+Ik/WNTtbj2nL6Tmd0vv7iV9kKGofDdrLYszUijA757jTIWtKWyHyoVHOZUhxZSLqipib7vGPpenIWj3HghKLKWHqEuGqA5i6xQQeoTU7GBOnkVTcSqQGMirYnkmAl+YG4rFnOHVRoVJ/zQo9OEIaAJcmOWKZmzirqqefHsgsuzFZfn5+w2O4aul4BTEgijpJLNOTN6gVCvuz1N07JQEZOlz67QEDP4gIoZZSkzziwoNg5OHekYGD9c99ZZuSHIDHkihoNTQ3HrKJJZMsQ/8HgMCvHCU1pLu0/lEjZE8imRRSvVaBwVDvckCNoSBEOcUM5iqoqrZ895+dEnfPz9H1DPF1DcJ5S1qMrBvIG+QS0rbr5+z37cobYNQWXG+LSyCCEyDhN9nKhmBmcMbV0zDZ6xH/Ep4Kxl13Vcnp1htCA1SblY0ahiLwRjjAxBPNmcEQSzyWCzwqCpq1rmHIcWfulS2OIgopQiE/FlfWkl2ojOOYzS+N7LmVOSnGVVzEH5gB+KtDWNFo+2A6Dj9DGRjDFisNo2NUorpqLDK9xPVZCIoJJ8Fmcdl6sFq8Wc73zymrquaKqKT1+/4my54PrinMqKNNZut4escZXl5UefMJsvmC0W7Pqu0FMsy+WSkBI3Dxt8TAzTRCjtYPkUpYV7cqQcSDlA1qR8aPgc/LoeKz6lpFZ8KuL92O7PKp0868ncPweG/Yb1/Q0///F/xee/+gk//ct/SW0zVhcJRStw3+ylIzX4HdM6kFSDmUFtDUkZKtuyWi65OL8oXpfCUVUnWr3I7cLmPtGvJ7781Zb17ZZx6Oj6Oyaf8MEwPvwAsgVscdrxWNNDzmgzsVjd4HuYegtakbGkPJWp+6GzI0Ewk6gqy2q1IifY7zseHh44OztjNpvRtg3n5+dcX19R1/URTPWhOPW3OaaU0SnQjz2JhLbQNI5Z27CYtZydrZiCwo6ZEHtSjuy7nn4YRaLSOBKaELOsDaVKN0iLsH3Oci/6zLiP4pBBptbirLFoWkzWpCmTq0ieMrH3PLy/Z32zYdhHcrCoLLNz0Xw9yFH+/q1Q+D2CoMDfK9z8ClXXGGvIJhLDSA4jatyRQ0+atoTuhmH3wOe//iXBZ2JSBAXOGprakT5pSVt4+9lPYX6FmZ/zne9+F+eqUl0dJmCQ4+NQ/WH9wM379/z4Jz/hi6++LA7gmZw0ytVksyDpFuUfULFDx20RMc5lxqJI8XEWBqCSwURDkwMLPVBXgetzS7OoqOcz3t3ek3zA+Qkbe4zf0t1+Sf+wY9zvqZTnfFnx4nrFH76+pKpqHvZ73t/est3ssMmLKaizqDyS0sQwDShjGWIgvr9hioEx9nx0eYbFQAClGnTILLBsdiP77YAzFbPFjMVygVaHStBwcNs4PWIUFmPT1gyDF3++kCBrKlNLcFBSF/kp4KeO3X4SiHYWzz5VwEyuODVba6lzOqrGW6VxBwsarcQV3YmlkHY1s/mCxdkZ/+F/9I94/Z3v8uLjj4k5MYWCdHUWs5hx8b2PsCtDbCfWfxW52+7JWZwytv34hM+4WKyYzeaM24jRFltpvvvJJ/wqf8lm17EfB1S3pVnfUs0kGO37XgxElaLf9yRnwWl6H+hDoI8BkgxLfd+TK4duakhZnA5mc0IVyDqDysxqoZ9UtWGcPPuxJZgs81ZXpKhiZL/b09QN1cyxXCwJKbDeRKKPH/CXMv1+wDkBMS3mtVR1KReRccWsXbBaLbm6usIY0ZXd7fYCnhhHxmEsfpCJ8/mSy9UZf/jpx/zxH3yHF9eXfPTyOVXdYG0lnn3jSL/fsO+2DOPAw3YHtsK4mhBGYqxJKbJYLNHWUTctrm6ZLVbcbtaEHOmnvnRa5NqUafTJp+IIIhIPUJmpZvMomCy1XyGnc3pOyuA7AwQgSGBQWjJ+FMEPfPnrH/Gjf/3/5Kc/+ld8+au/ZOr3uNTR6BajLKFX7AJMMZNVh40jTep4qWqqV0suFhUXZ6+4mr+kvfgBs8UV7byW91neRkYdDVtBbrf3XyrGbeYv/mVAm3ua+ZpX331DTivitOKLO0smYlxP1h1Vu+bixS/IvsY6+PTPbnj7eeDrXylyXhJTxX6/p9o84FxDv08M4yCGvfmpXFpd13zve98rFAkRtP7oo9d873vf5erq6t8IFHM4jDboPGHyxLNlRVMZPnp2zvlqznw+Z/X8mt2oSPvAr3/6nmnYYuKOeW2oKsvb9Zov3m8Yx8wXX74hZcXlecsYJgEHhgdCSIRJ8eXP1zx/ecZHn17x3WdL0Yvd9gy9IjnDOM/EYSLsBvaf3TI+jDAKz9hUjvlsBiqQmNjvKxptcOnbQqkej28dBCU7FaNa08wxVU1ti6GkH8i5ODAXLp7Khe8SktzIRhFVxPvEOHZ03Zbtw3t0yFQxkdLHDGPG+15uGmtYLmd0ZfY37Pe8ffOGr77+iq+/+pr1/QalxCYn2Yy2FUOEFDwmF8SeMmgdhaagSkvv9EPlR9CNIO9kk/N+YlgH4mbDetdR6cxFLWalOUb80BGnAcLIvHEs5g1nixarIYaJzXrD/cOazWbHotKsFjMWbc3ZUtpAD7v/N23/1aNJluZ5Yr8jTbzSVYjMSFGiq8Wo3ekeNHeGYjFXCy54R/ITcL/fEMSCIECC3AsuZ3ZU97Sq7pKpQrv7q0wdyYtjr4dHVYPMWmAs4BkiPcLdzM45j/qLrnDsgMEHdqcTmci6UbSmopIVIhch6LauODnHNAy8e/eOxdDivWO7KTwio+2Hm3l8a7FwypwbigOErahrWwbNFJ5gTIkQPDH6UkVQ5n+CjDYSNbc35YyqBGaqS3EDCTmQCZjKoI2mWi5ZbNbYqsY2DZvtls3FFS+++ILN1dVcXT7KBslFLNcoJhK7caCfIqPLRSQdyPFjak3XTyitGIeBqmmxUrNZLmmbmqoy2GxQRhFzZpimUr0KyvvPFM+1HAFdumozn5SYith4yshcao1xHAkpMcUJkedqWBQBdyUERhtEJch1Zporyn4YELOq/TlZCDEw+RLMC6ldoX+D1xlnKy4ty9xPa01T1SwWG5aLNZ999iWb7ZaryyukzDPS+MRhf6DrOg7744yChpvLKy42a37wyTOeXaxZtRXLRYuxxdnkjCxkEAW57D395IiTBzlRvXnDenJEBBfXT7C2wlY1QhyJKdJ1PX3f0Q89KZd5pZzNr88dkrIIM8EPTOOB5AN13ZT5//zsSxs+IkgFECYeKSMJWfZjhiziDBwCciJHx2n/nuP+Pb/8m/+J737519y++hXZjRgB1tZYXQEKD/Sd59QHptCxNoG6CZiU0akqbWZdoe2Cpl2ibT3TGj6MTM6t3kfHBiEUsfsQOzYrR91OhPgeoyOyilTLb1AmULUDWRyp2iOXT98gQo3WguXqjnt7gcQSUkOKFSEoYgAp5uQ+n9vC5w9BZS2bzYaf/OQnhZ+rFCklLi62WGsfKE6cn+PjM+H/D0hGAKtKYJWmTg0qglXQKoEGckzc35+4PXhe3w8cTyNaZDarls1KY62CHBndRD+OpDyhZEXT1NTS4oMjioGhd3gX8X2HDGs21Zp1q9GakuAZTdQKtGV0HZ2PyGhROaFlnlHOEq0LBSWj5xHE929sPr5+t78lBFJrbL3ENEuaShO9w7uRMPbkNCFyj6Tw28RsGZ1TRmqByCUr7Iee40kjM9QuFCRkcPQucb8fmUZH29Ys1guOxyOH+x3vX77i66+/4quvv+L1q9dM04ASCqMNEoG1FamPeNejRZyDoEbPRNY8//cs1fNwzWroMaaHNtlxGLk/HLk/nkAqLpYNV8+2xZs0Rqa+I04jIjjWbcV61bBdL5A5Moye97e3vHn3nsPhBBdL1quWzWoBSiOk5OW7W0IBIpKIeH/kcDxwtarIyxXVwiJyRgvJatFyezyWILh7T7tcMAw9RtVIIWmb9u/PfDIEFzj2A3W7pKpr1usVZjab7YeBvh8Yxz3T5OcDWs+AGYGRhfDe1BVhHCCd0bllg4XgydFD9shqRVUZ1teXPHnxgvVmy9X1E65vnnB1dcMnX36J0AbPI/fyRwpASQmO3vF6t+M4RMZJ0M5u2uKRU3nOmfvDiZgi43Bks6mwSrFZLlkuGpqmIuqMrixIQT+NaCnJWpJ9SdCmYSAlTRaWXBULJ6M1+EAKCZvBCImRir47MaXI0U00psYojUbO6vUC3WqU0Eg0Yt+T+4Hx2BWHCaXmFnyZPcY+lfmgLhZJtjYfvasYMkmBrBRaFX7mk5sbvvzyx3z24kv+9E//l1xcXLDdXsyKSJ6hP3L3/pbD4cDr1+9mtxXJ8+fPWS8XPL3c4Ls9aRoJ44RUFVJZlAHpHUlKXIwM3tNPE8NUVD26wXF13ZGQXD55Opu/ViWxGEfu7u+43xVQhqmaIoKgCh9TiPjRIvTuxNAp+uOO6+unSJYIsSrAGoqP4BnU81j5RJ4JsDI+jGLIonSGnOPtt3/Lm5e/5C/+7f+d27dvON7vWC2Wxb5MWoTWxCjIInA6nXh323EaBsRW8UlT0SiBlYGUBagaWS2xzQqpa9LZSJtzh+U3QTwU82gCUu/YXntsPdJNr9BmwtYdi8sDdRtYXUwksadqBq5vjujUoJXAtnuM+hwRW3JYkUJLCoYUJUnx0OIGWRIMJDkLmqahaVtubm4+fC8hzJzSx1J2v3l0/6Z5bSZ/fEtIAZctLGpLajaY3ECMxHFCxIQbPa/fv+O7t0e+erWnqgWXlxVPnm24ulQYkzkcTpyGHh/3SJ2wpmbZLqiXmiQ8djmxuz3SHQfC2GFS5rq94OZ6ja0UQidyVZOUpneJ3XjHlDNGLAq/1xTnjWKWXWbhoKm1xarfFNf/ftfvEATn7nyYyLlA95WyRT5NWaJuIHpEGJmm13QnR3Dhgd8Ux0SWEmHUQ9VldaKSE1U+MR3vuLs98dVXbzne37NZr9iKwKtf/oq3r17z13/+l3THI92pRzmPDQE81MESk0KMYH2ZD8p85rlVcxBMRAHCx9+6KykKEnOcBvromFKk847jMHLse2qjCUYQnGMcJ4Z+JClHGB0qRi6XDYvaYkicDjt2x47vXn7HN9++Y3foOO0b3DiiUuT5p5+ipGR3uOD17YFu8mBqRhc4+Ymff/2S682K8CRysbygsqUPvzpVrEbLrrvndPAMY4ebAtvtBV98/gMWi5ppGHiYowjBi08+Y7va4DNsrq65uLzkn/7xP6WyFq0V3anj7v6Or7/+mr/5T3/F/vaO6XiaTWIL91BnwIcym1QSU1f83h/9I66ePMelTAgjMY08//Q5m4srnn7yOcvNBbZqsLbCGIs2GmEKapScSSRSLnSF8yZUCERKZBdYmgWqzhhbkbJHBv+w9hBFO1SXl8r1xQ0X2zWnux6ZJNvVmlasCt3EFL5lFIK2bmffyYSPDqElpragZ8PdGkKcQCbahcFYgzGaidKKikKQjYLZXxKrEJUhmGJNZZThQq5ZLxYsqpbJ+SLLF4uEm8iCaRyJORKzp2mKbdDjYZqtDNZqJIKFXXC1vuRP/sGf8A/+4T/mhz/8Mc8/+2LO8sGPJ2QOLI2mub7i2XbLD198hjAWZS3GVsxlL8J7EprKlmcglCKNGWkNoqpI2hCVwueMi6VVnUZHNUychoHudCjUGSH5+ruX/OIXv+CXv/yKU98VGa9FgzGqqERpxTh0Hx2ugh7XT9y//BWVzKT1Ne3V+oFaAucKpZjhFjqAfFC/Kf9GqR79OHC8f8M3v/iPvPnqL9m/f820v0cnSVut8S4RREapjJFlzUXpqVrDRar54pOKF1eS33uuWW7ALBrM4gmL7SfU2+fFxgg5E9pnj8AZgR3DBzQvZELaI+uR5dO/YYqC4dCxP3ast4btk4hdv0IrjTGWfvCMfeIv/0ywWuyp6szisOV4kAh9QooJa5e0daJuDhhr0PKarivybwlNIlIGYOWhpUcAkEJVKM/wzNU9nbrid6kUxuiHTt6HW5jLgvxx+3pKEZtV8Y1MEIPndBoYdjtGn3h9n9h3kf4UicGzqA0xQbNcsNlYPvliw+/94bZwA99l+pPguBPc3k+4MUKseXJtsc8yyzrx5EpR65csGok2LVOyHA8DLkBVb2j0lsu15fryQGWPtO1p9o4FJcusWkqNkeoRCv53u75/O3Tm+nk3ot1ItBUx2IeHL6RECA2iImSJDzNnJxY/MJFBqAxSlFmSUlRaI3MguYHD3Tvu3x64ffWa+9tbul3Dpq357ptveP/uPW/fvcVNnuiL31zMBaSTpCYiiZECV5GSmMrBWrpu7sPI9O/JklIKxOBw00iWeVZjaDgNHUpklMgYLWiqCjkTwKdYqldBptIKLUGkQN+V9tT93T3744ljN6BFornTNEayWq9BarbrJbvjwOgDwXsm53GT43a3J8dIrTTETFu31E2RWFKitMsSpSU79B1KKnb3d5BXuGl4OHyEEHz64jOe3TxFWEuzXLNar3n26QvatsC+x3Hi4rCn3Wxwo+f9q9fcf/fqgWsW4gRzvt60Laaq2Nw84dMvf8CzF5+TjSmgqDTx5MkNy9WGi6tnVPUCpW3J5Gf0QuQDMf/s5QcfACKSdIbkYJUhKItEFO5efIzmLUmLNZLlolAklDR0p3tyBKttObRm6kdIvujaulD8CqUi5HK6ZlF4ZXJuf2qpirKRVqSccSHgZMSTyKp46UVAZUpbXaqPKGtKSZQWLJqWnIeyBmWpBs+JYE6pgLN8KEnAo3WopCgfCBZVy3a14dn1U55c3XB1cUVbtyDKelVnkI0yBeVrQWiDtBWyqgBB9B4XA0pphE6oWSUgn9UCzhJ3xqC0mU2NFToXpGpVGbRWxBjw3iG853Q6sT8c5jnkSIieFDVJFkRxlh8fqgCCwqmcTgfcMGBqTyPlb7QYz/PEc3WSSDMoq2C5Su/0tHvP7v133L7+FYe715z2t7huQCFZVhppSqBIIoNOhFjQo4tGUSvL043g2VawXYNpLapeoZsLTLVG25bM2Zw1zh8fZpwfVVE5E6JHqQlVdQixQGaJVgYhE1lOmMrhnWTYK4a+pj9Fbl9r8vWeNgSmUDM5j67vqLWiWfRAwPuRLCxS1CCKBN2jxuw8shEPM30xaxSfn18x+S1+esfjCe8DVWWxttB2loslWusHkYWPUK85cxwcMUmiG8neE52nOxb/xslHuiETo8QaQVMJ6qr8WogIImBrNfu9KqxR9CdBXQuE1nS9AzVilKfSiettxWqlUdITgyNlST8lDoeewUUWC0l0BVS4WCwRApQuJtUpFtNsIUqFLOcfj0zevvf1/YKgAGsbhFR0u/cIrSE5dHDF1FCpGakjwWqClLiU6fuBkIvTr6VC6GISuagsm3bB5XLJaRzp9re8+4s/582bA19/9Y73d/eElPizP/tPjG7Ch1AUSoQsDEogKUlShlEpXIz0LhAQRKkIEUwO1NmRc6FoBEAIOQt5f3hMfhwY8ZwO91SbJavFgqsXzzE648cjtRTcrFu++OQZdVWRU6YbBhAFRWmUQBGJzvPuzUtevrnjq6+/5vYU6H05+Lrjke++/jXOO548ecpnX/6Aw6nHec+rux2D82Uuc3Lc2lvu3r3h6XbLom5YLNZkaclC09YWlEJqXWaPu3t+3ve8ePHJzNWaK0Ep+Rf/m/+aP/z9P2B1ccXheCpO5lpRr9asNxu01rwA/vC/+C/47NPPefP1t/z03/57vJ9wzvH27SvGccS5keefPefi5povf/8P+Mk/+S95+uIzFpcXJCIxh4J8TIIUJCT1QGCNDwRWyLkEgDNvDSkpIGeBzolaalplaExNVA4XPV1/4ng6fFxZ5JFlu+Xik2dU1QI3Zd6+2ZF1EZbuxw5pBFIrFAYfPPeHe9bbNc2iQSpLEoLReSyQfSL1HiM1Ukt8CvOGd6RKkY2G2uLz2YeyKBaiVOk6pELPSVEiU5nZjM4jvUcJRZgTnaILaoBMcoW28jgKagVWgpWCZ1fXfPH8Uz7/9DMuNhdUM9m+PNiIFgpm3cYzL0rWFcIYhDYlQZu1RJUtfFRJcbmPMRJSQkhZ9CcXC0JM1PURow3kokCy3my5uFgjoseNA1FYjocDh/0ON02FFC1lmWHKQqPRUmDmWe7D+0qKNHnG+4Fu61BVZKNsMZKlgLs+vs7J0mzYKkEkD87xy7/5t9y//TXvX/4F/f7EcBw4vjlwc9lwfVXz6Y8bgsycpsD7O0fXCcY+c3FlWdqKH1xrauupqwmzeY7Zfk61+RGmvUGbJSE+Msnm48D3mxXTOCZMFZEm0totRiSWq3uk6jh2E1IsePPNiq9/tiWMnxI9TMM92nxLFh33365ZbQ+sb3Z88lyz3QrGSdLdfYvSLRfLlpg2IFqyCCQRyRRQmpKSplnMiYosx6IoOrNKGfp+5Be/+BX/r//n/8A333xD09RcX265ub7gH/+Tf8Lm4oLLqysuLjYz1apcMWd+/fYEeE6HW6ZuIMeEYgbfUUA57VJxUVmurpZcXFguLpY417PfO5T12CpjbGZ9tWZ7teSzz69wU83pNPG3P5s47N7iphNPP/mcxrQoUXF31+FCz76LvHl7y6kbWbSXWN1iTcPFxZr1quZ0kgTvimJTAjdFhiGgcjlPHtrov8P1/WXTYsR5j4oDx7u3dMcdr4KggMkVTb0o2UWtOdzeMh77ImScJVk8sl6R0PU9u/2ePPpCdsyZd6eel++OvHx/z8GXcblIrmQ7ShOFmofpBSmaM7ME2BzSZEJoiotBEhAFOQRSKCCQKWfKEfQxkLYfOlRSJD9gZUNrMpsanl+06HjN9WrN1XbLp0+uqHRp5RaSgkJQetCSjCIR3IR3EyEE0oxu8wms0Sir2R87tLkvSv7TgIiOsdsz+sQUIil5YlBYmahEZhxOdN2JxfqKerGmqQwhQ0gJOS/MaeiJ4eMDFSBpjahr2osNwlqmcWJ/PJLiue30waro2acv2G4vePbsOSEGvPe8/O7bok4RAp98+YLVZsPlkxva1QZla0JIM5BBzwfxDMkmzvOHGSBxnqnkc/vrnH2WqiRTOgYuToxhYnCOwXlC9Mik0MJ+tKRj8CgpuLracnt7x/F04nC4Q1YSYSVkD8kAGmMEOcFhGBiMJgPV1ZKUI9M4UusKOYttS6E4nyYyJUiBYXJE58ijY1G3WKXxCe6He7r3B6wtlIKmtrTCIhJMo+fUdwyjKzwnHwmDKy1UKbGzSlKlqo9fmABTVVysNnz64gUvvviCm08+YXlxgWkbpNFkUpGE0wXIQ4gFKSnyjBIuM+voHNG74gyfMygBQsPs4K20xocJ5z1919N1J6ZpepBl67oOW9WkWGZNYpZfc+PI2I8MfQ8io61k2dYsmpr1coGUCi2PH51BKUlCFIxTcXVJMaBIJHio9nhUyZTrw885w/72LXevvuNXf/3vGY7vSVPPd9/uOB2GcvjrjNKJ+7uOKQQOvSOEGp0Vz9aKlYGFgbYRmKZCLzasP/0j2qvPaa4+B9MSkpgDQv57yda/9UdKM4yWVy8buuZr2tpzdRnp95Z0bxCqJg4L1u0GsQwoHagqz2rbY6sDzWLAVhVVYxHmjpAlp86iao+pclE6EhGpE0IGCvc5Yq2lrhuur6/ndnEixAnvHP004lwoY5th4Oc//xl/9Vd/TaaI4ddNzf/w//n3XD+54Uc//iH/u//2vyk0hoclKJBCk2LAuYALrgRBqTg7m6mcaavEeguffbZhsbAoqbl/Bz4khhNsLgXrC0GwI6RE8oHgG4LPXF9VXFxckHODtYmMJ6TA7f2OYYgcTx4lFetlzfFwS88OKTR11SByJocwo5cjISTcmPFjKnzx9Nvv7ftc3zsIJgQxZ6Irmytk2J0mZJSorFgutphKYxrF8X7H0HXkcFa4Z85iyr/TDwP7g2QSE8ZqUJLDfuJwLCToXrbzDClRm1JeZynKv0F8UIRJM+cLIWcyt5xxVAoRUpEzoowfYsqljfTRkZpx04gTmpwKCb7WgkbD5arGcMGziyvWywXLtib6QEwZhHr4KMG9qKJLSqdJK4XRkkhxTrCVYbG0SCkJ0dOdDoRpIIXCP/QhFTWbVJTRRyfpNQV4khLNco0UGa3k3FpLaGnIuQzFYygKLh+5SUtAK3RTU+XSvjsNA0KKR5DrkhAsNxuW6zWX10/IuRxWy+srfCgSaU8/+4Rm0dK0Lf4sQ3d2BZirwAf8gPgQ+B44Xg9fD85E5zMBOecC9/feM7lhRlkGckpIodDSfHSoCgRaSeq6oh9O7Pd3uDBitEYn/UhuKSOMJCVJTkXQWmpHrSQixFLFKdCz/mnOpUshzvw/IfB+fq4ikYQlKQihSMq5DE1bQbRYqUgqQcy4ccSNE955NKagYWMiB4nUBV2r5h8fr8QiL9e0S7YXV1xcXbPaXlC1LVLrB0EDRHGHQMjiXFKiVHmaM3o5eU8OgTxbKiFK5SqTBJlRToEQxeZmGOiHgWmaKJ02ST+MNOOI874ItwdFiKVD4F2R7ZLzPKbSmrauWS+X5AzD4Pg4E5dz18Q8+l5nYvPDin0U/OZ2vMhlGhecY3f3ju+++jl3b78mDCeMhN39wPEwsq4kLiamEOh2vugD9w5rFEZnlnVBPLZGYCqJbheYzRXN1RfUFy/Q7ZYsbBFIf1jEj97L3Lr/+BIgFT5UnA4LZH4LYuQSi+sVbpLoqrTmViuBtAPGelabAa0CUgascGX2LjTODUXfVwusFGVum85jpjy3GgtCVs6CDjHMsmUiM/QD0zQyDCO3t/c45/E+8u79e16+fInzEaE0Umuqr15yc3PN7f7AP//n/wv8b+AkrDFEGdFGE4MkqzIqSDNfVptEXSdWq8jllaGyhhxh6ARDLxApo7TA1gCFQjf2ATcMpCRQKtMsK7TVODeRY8D7ic4NDGNkmiKLdoWUmmk4zntSkZIryOwkSNEXRS0fiWEGlc3awP9zru9tpdSsNshqwctX33LqR/px4vW7e0yAKilWm80s+jtxPOwYhpHh4DjrhKbZ5sNNkm+/fct7azj0gWfXGzarBZGix5eVJEpZYP0KTKUwUhQ+UlEQwwIuwdFHXMrElEkIjC7anY1QZJcgarLSiFSgmPFBaeXDNbiRShu0lbSNYdFqGgIX11uqT56idIFRH7quAAukhKqCB25TCa7GGG6ePEFWLX2quT1OdFOktpbPPrnix1885XLdMo0DL799yfH+Pbt9R9+fGCJMqRzASmSGSZCaAtB4/uwJq/WKqjZ8d3skZkEWmsVmhZSKYRjZn/YcDsdSGc9XcFP5SIFkBFLXPFt+9pB4F7Rd+dwkKK1sXRRkKiH48mI9v/pzolEOBPkYsXD+N87JvDj/UflxbtU9BEE+fNHMWcQ74t3I7nDHy5ffMIUDQgca2yBShXiE+BIILi6vaJdLhvHEsXtPP+3YXNYslkXqy+uZVSYSQikqr3DDlnEKDOOI7QYqa9gs17RJIH0m5FCI5ykxiYgLE8FPMDg0Eqs0OjpydpxOPRerNZvVivV6g20sdVshx6KeUklNLTVZlDmntoZKlHliBpIrGWwYP54JTqPHLzLSVCwvLtlePWG9ucBoQ46R4GYgTc5IrQvIKMV5Pc8qM3M1np0rjhdQhA+UxFRnDVZBTiN9f+J4PPHm3Ttu7+64vb0FqZHK0PSek0/0UXB56jC2RtqW+92erh8egqCUiRwjMmcqrRld+C3/NmEM69WaH/3BH7F8ckW1qgmx44FJ8VAFPv518a1LPvD13/2Uv/nz/4m/+g//I9dtJqfI3V3PcAqMfabvMu8PPfqbhNSZxlrWzZLLhaYyQBpJpiWalnz1Ce2zL7n67PdZv/gn6GpNwpavnh7rKc4r9dGa/S2tVzRKbKjkP+Jie6Kp7xhPif4gCT7zydN3tJv3LNa/wjlLiokUJypWiPiU2/t3uLHGjSvuD7Bcaz7/YsnV847ldoVfmTKNFAGyhlTmlDFGXr1+zb/6V/8XfvSjH7DdbvhPf/lnaKWpqpr//r//v6K15o//+J/hpom2XZCHEVEtkfWCpBS3feA//OXP+fmvv8OqD/eppOTzz26wVaYfWrw7IUhUuiIFWVRv8i2rZWS7SVxuJkiR00lQm5agM2/f3JcEXEqef3rJ0Ae++/rIT//iNX3nubhY8+XvXfLs0yVVG3DxxOBeg05US8tyc010gXGY2B3uWK7WXGxW5ATReYbB0WpJJaBWZQ0mkUrsoEhL/q7X964EbdWQhOab1/dMPuBDJKOQKaCdQw8jUkmSBGVaJBblJ2RKyJzwKZFDER/e7wZOemLXe5qmpqoKx0tKNetizh6Ecob9nh2IpUBkOc8JSmWZ52rm7ARdPl+RtSYbgxBlRjU5j8gF0fR4QSutUbo0aLQqepG1sTSmyE35NEtYC0HIiRwzMQW01GilUKpG6eKOvlyuyNLwZVBsjkXL0lrNs5s1Ty5qVrViUoZps+TNuzsEERc9Pkp8ligE3ie63jO2Bt/YwtdTBRDi3MBpipx8JgtTyM/G0A09++Pho1nGm+++48nVNduba1RTI5T+qKXzmIBeNnV++N3HXMr8EIIef95v/juPA93Zpun8+8dO1VIUI+Y8W0TFGDge9xyPe7r+iDGAUqizRZR81K4RsL28YLleEnNkuVqgrWC1WmKrCmUNp1CySxcTMXhiyjTLBVk6fIiEyVMrXYKV1oXbNoJPDmLJYk0WxCRQpkZJTW1qcijVr6kbtqsV2/WmPNOUGUdHFTIiZbSU1MaihCTFTFKJpFVJ1FIixPSo6vhwxVns2vs0i2UXMfJAKudgDOXdCBCx0Cu8K+AlKN3RB9rJrKc7b4yZb1Y+BGeD1onj6cT+cGS32/P+bkee59zNFBlDwmeJzwJtRny45/39Pd3YY2uD0YK6Kh2UcRy4u7/Hxcyx6/gQTMoowzYNm6tLpNVAJPgBokSkUn2XEces+jHPtmKITP3Ar3/2d9y9fQMxkrLC+8zhOOJd0UX1PpBsmftXypBRZAlCSZTVNOsVqydPWV1fcfnDH9BePKW++gzMgiQ0BdHzeC+c1/nj9S1+e91HyEEivMWKLZWGSgRiHQk6UNVLggvcvknEsCB4xdBldNpCNBxPCu8qvK/pwwpdCYQRVG1F3SwJsSYmS8bM3RnPOHRMg+Pdu/f89Kc/ZbFoUVLiJseUJ/p+4Pb2PVobdrsdQgiquqIbPbZuqdaX6LqZC/LIoRvQj/wfM5luOOFjYhhPTMOx+KM2ido21EpBUrSNZLkoRYcUEqEsSjdsJzicJprViLYO73tiLKCxprVIqVhvapQqXRbpi5yYVZKmLllRVWf6Y+GuTqFnKVuaVrNarXGD492374l+ghyptcYqgdegtJgtJH93Ee3fLQii+Or1LXmugrZtgyZhoqMaHVIbkrWYqsZUYNOA8g4VHHmcysEYMof9RJZw249cXmxYrzJNo0vQUxHJXAlKZq3G89os5MhzUMwPQfDMLTo7LCjQmWwtQikygmlypBQI4RE0nRlybw0hxOIeoQ2NraiNxWqLn+aMXSlCjMRUnMLRBiVssbPRGqUNi6XAVA22arjueqbJo7TiYlNzszJYBV5q8uWaX9cGJcHHgEuSgIJUeFtpmhhXNT40M/qrzNq8GzidRt4fJyKKdrni6vqa09CzO+w/bNSc+fbrr7jcbHj22QtWNzeYRpFyfMBQlSyzLH0K+I7MLHKci3hAnisPrWbZL4os1t8/Mymz1/N1/vXZ5fr8d9Ks78csDRWT5353x35/z6k70NgZ3xVT4XA9VhERgu3VluVmyem0Y71dscot69UKZHnPY5dwKRFSYvSlNdcsFiQ0YipoN2yFlZrKVoizgskcBI2WpCzJWc6ka0NjG4ZhFnGwFZfrNdvtmoMfi3O9dyjUg9xaYy1Wa5z3D+erD5EYMzJEnAsfY9hyaekEn/AuFIX9UBK2FOYqL565YBJUqfqcnx6k3IrIeYYH/cRz4lFai3lOZrLIuOAZxpHD8cRuf+B+d+D2bkfIkIVi6RKDi6U7YVukGjkcTtzudvTjSNVWNFbRVpqcIsNQVGuikBxPw0fxXUqFrRvaywuG0JFywPsOkS0CXUYWsxi8ULNJqtQE7+mOB37x079hON6ipSRGwThl9ocJ78LsbBMQ0sxemDXaKqSV6Fpj2oblxRO2n/6Y7ScvePqTn6DqFaJak6iKEmk+Ux8EeRb2/pAOngEx8rfO1ewzeAFBo9lSCUWtPakeCMlhjWS/d7x/OwEX+Klmf2cgbMlB45wg5FiID/VzVjGjqp5mKajbJT7WxFyR0ZB8kYvsTkxT4vWr1/zs737G55+9YLtZk0Jk8g7nHIfDAa0N+/2+gJ/qBiF6bNWy2FzRrLeQSyJy7Eay6z7aw4fjAa0Dw7hj6PYoATkmqotipEsSVJWmqYoBt9KKZlmzuWiJSdINRcg6MhFCR04aawwXFy0hJK6uW2wl8MGhXEQhqLSBJpVZ+aokinJw+DQgTaRZap6/uKY/DOzf3eOnglw1ZolVkmAkygikgqIY/btd3zsIjs7hk6YL0NQ1la2oFg0hRHbhQH+a0LLCNBvcpkbaisvVBTZHTI70rp83qaSqDJOfeHv4Bp8sQVqqeoWdJrTKGGFBSCqdUbKAOLIoffgsFKZdIlJmbR3H04D3AVRCi4QSoExFVposJVkoYoLBBSbX49zEh66hYLncsKg17uAQWSLRrNY3WF2Eg1UV8KlUbLvDnhAc2Tu0ljQaKlOhtSYLjZKJ2koaa7hcFmCBmwaUjPjuiDQWrSzPnjzhxacnRjR/8fKOTCKkAn+XOZJzoPcjh6Hjzf09vfNUVT3PrjLeTXzz9VcIpVm8XBWj4X54FGgyv/zrv8HvDrz55jv+wZ/8CU8/+4xnX/6ArIvG6rl1+hGSDwr3BB7Nk4qeZf571Cd+swp8/BFjfPA7/OjvFaYgzPYn4zTwV3/1l/zy737K1998zaYtAaS2FUpIQnSPsvBM5w/c95G7u/fle00wnIr6TgDeHO5xOeJEpp9GpJBs1roosCiN7zMMni7uqTYrDAo9Cy6ImLFZUekapClIR1EEr3Vdnr+2BkTm1HfsxhM+lcpOqgqLwKRcDHBTIss0gx9qximQUiZlOJx6lP4wE8zANAX6YeJ07DgdjhwPB7rTsbiIkMgpzsAFgRTFGHrynnP7MFOsr8ps6YwILepHmYybSkKVgcN+z36/536353g8FU/OYSTlIsOn13C1XPGjz7/kn/2zP6WuG+7u7vg3OvMrOXEaPJVRNJWhNma26JIkKbDxcXjPgEdIh7QBMR4hBVABWS2QpiqJTp5FGOKc7abAr//u53z766949/pbFIlKWV5+d8c0unJmCEg2UTWWZ8+e8uTpNZ/94BNWq4rNtkLjsVqz3Vxz8eIHLK6eknRLiIo0TCB94STOUmwgyFLPwa7MvzLnMWHRwn18W8ILZIoYM/L2W8HeNGyWV5xGzxQC330biEkTQ0WOG7JUYDNduGVMB4b+HcqU9bTerBGLhjFB32/JNHS+wiiF1RJCw3i64+3rt0wusb+/R4mivhSc56tf/5osSlerCCYIQgi0TYNfR/TtDlvZQrRfroFM9DW390dcd//RPu6PhQzUnRx37w+kFLlfBd6tO+rGsKgElYbGZq5vKjYXNZ98oTBqQJnM1cIRUiDEXCTVMJirBZcXyzLPNnPwTxktFVYaarVk0U5EAhNH6oXmQkr+6Z9+MQuDewb/HZHE9bOa3lr8kHBhANugtS1O9/pxwfT9r+8PjMlF7ioLhdIGbUp564VgBIYQUECVe5wUZGupbF1UWICYAme9EJ8yLhTLmMlHRp+KtiNn1QM1Iw9LZpsfkISFf6KqBpkhCUM3OLIP5XujqO0TSysoZVGEXGMs8PxHLtfnSyrzUC0iJEJqpK5IQpQxgdZlICtAG4PNGcXsFWcrtDJIqee/q5BZoFVGqKJXqrMo7a1Uslw9Wxet10s2xw5jJGLu95f7By0LkjHESDcMSKUJM7gHMpJcWn2+KPlXRuOmR23enBnHkcP9jpfiaxbrDX3fEyggmHa5wtr6AQACueARxEN5/WFMc57z/QZs/DeD4Pnnx23Q8/W4+5fzhz+IocDv79694bDbESaHU4VcXmJN+mjGlDOc+g6lEodjh8yyPG8iSQhChnEMOJEIqox5sixVmJrRmcREjIUYPhlLlArlJc57vPcYq1GAEgo7S3mleRYqVEFWCgpKlZRnGTgKEV5IKqlKQhMjIUakVuhU4O1IUEIXHtWD++2H/RVCZJxGTqcDx+Oe0/FQhLWV4Cx0lOb/ppRx3pV9NT+nIrd2biULyJFp7AnBo6dxFquX9H3PMA5Mk8P5QAhxXltzN0CANoqmqdhuNrRNQ3SO1aIpALFsiouKFiRiAbxFCliHj2edcTgwHTOT2zPs35CiR/crRLVCmDJiEcqglMWYmpjg1E+8/vZrXn3zNdF7UorELBiHQPAglGWxqVHacHl1wc2TS66vtzz99IL1esHF1Zo4jUWkY5a7DzkX0X8iIadZ3YZHHREBMvAA3Hq8XhOEMPG4HEwpg0hI7YmpxvmKYawJaU7zRBGT1rrCjYsSBE3G2IDKgvXmutgF1RVBW6jgOHlevR/QOtK5wPXlisv1AhKkEIuMX5gVYubUMMXIbleMjaumfqB4xBiRSmGN+dAlYxZuLy+YcZwY++HRRhUUuZpIcgKZLZCIQXE6+aIoVBu0zFiRivKQVfgUEbmYCihZ+kk5G6T0BZmsU5EmFOUcC2Npd9amrKEs85n5hsgJU0mkNDx/foGtDFWtGYcRZQWrrUWmiskkhuM4u5gklCkjqQ8OL9//+t5BMObSKtOmxlQttq5AJJwQHISkyxEZAnYcGI9vyVKwaGusNBipZ4i4QkiLFIrJOfpxYt+NLE4j3cbhYyy8QxQZWRTcc6KwzpiDqMI2q2LU2gTuDsdZ1ivN7b5Ezm4edicG5xm8J+ZCuhbiDKWbF7PSZKUKJUMZpK7IqmaYCeyLpS3IKqmom4SxAUlmWVU0tkKbuqD1pETKiMgRLVPpl2eFCgIfIIQS3OwsRXZ9ecFxCrS1ofOhHA66KJWsmiIkHlPkcDwRk6CqY2mNQAEAaYUfHXd3eypri+zbo+BTzDZPdMcj+30hxX/+1a/5wU9+whc//CHPPvkMqQyJD3qNSnwMYpHikT0PH1d6f3+wyw9Bq+gXio+NYXPmcQ7ixp5+f8frb37N8fY9MiX8lGfjW1OEDD4Kgpl37+8Yhor793u00GihaFRNnq1aXAAvS1UoddFKdX7CaIlSmuA8KSZiAp1E4dxFwenYEUOgmknjSmv07NAR8gf/RaMNIUZCilgUWpZ5X3SBKCV61eCCJ/hUBKYJhHn9SqWLKoySKPlxyhpzxkVHNxx59/41q0XNmydXrFYrmrahaWx5hiHPzzUxuIlpmvDBU1zpDXVdY40CATF6jvs7hqEvm93WiHledDic6PqhBMGUHpHoBSiB1BJjDe2yoakbjJGslg0X2yVRdKSZUD54XwQAUkJZzfgoWOSUGN9+w36Ew92v8KfXEB3GtiSzJeolqV5TNQsWqxXr1Q1uinzz61f89D/+B777+lsU0J8mjoeeooOgMFXFzfOnXN1c88d//MfUDVRVRtUjq9WGm+sXnPYjx+OJX339c3x9ywhsrp4Sc6EtIR+ri5SA9uF3DwivQuPImXE6fXwexgR4ZD0i4gU5azoPsjaYSlEtZzqLFOzva3yURC25ulmyXEU+u37GerWkbRf8u799RT8d+G73np994/FO4MOa//If/oRVu4LoiX5iGnugehhJ5Fx0ad++eYutLKvthhgiSUWmaSryfNaWkVJOiOgLYGoWk+j6geHUfQjtAoxqIEik12zbKzKZKCWHfs8UBjprkTmjc8bUAl1rxuARyiFiIgRNzgaRJUoUi6gkZ35tVEyDYnffc+oHLi4EsQrIOlNVCVRxSrHGoGTL8tPL2Tw88vrlG7JRtJsVTb2iP0n60BOnQEgCW2vMvLf+s1WCwUdy8kz9SJcg9RZbS/LokRGE1KAzUiQ2TYXWilZlcsik6DgOcxCVA2l2EiCNjOnIIRiOvmJKRTYq5oCLmW4akdkhUsSnyNmf6l0qyzT4wP7dW/qxL63EmGax59LekggYR1SM1EYRMQRgeDToProJbWuq1QrVtiRjeXccCBG8z7zuDnMmGtBGYozmcr1C1A2qqhDGFs86LRFakKMj+h5Bybp0XZcIGCKqMghd5g6LpuFiveJytaQfPV03orOiVoJl02BlQmaYxglraoyp0KaoMFRW040eKSV10wAlU3x8uRSLhyPA1DPtAt2f/Xv2t2959+3X/Ff/q3/JYn2BaVbzwsnFz3COUFoV/p/MxT4p5fygcvJB0X4OlvJshiwexHuVUrO5xYfAl+dDJeVSZft+YDoeaGVCrhdc1Z+AXCCURFtRuGviw+CeDHdvd0xtxel4YrNYohSMbmB0gTEERgtUCtVarLEl2IhAXRmaqkaPEVxEukgcPFkErKpKFTpNdGTksqWxmpgCQkq0Lo7sAokIGSsVVmvSMM4VlKCyDUoJXExkqVDGIoPDh4TrO0JOaGNZ6SI2XtfVR5t1saipK4OUkW+++RWn/R13715zc3PD5eUlP/rRj0qAsxUii9mNw89O5gHnpoeEYawMikyceu5v33M67unHnrpdYZoFb2/vePP2Ha/fvuVwOjKM08PzzTkTvcePI2N34u2331FXFfu7O06HE103MjhfqiAJ6DNJWaC0wk7leXxIWQ1aVVT1U379t7/muLtDJIlddphmRbXZUbWWfmH56V3g7n3HT//qG9wwEnyBwQdfXA2ePF2xXDVcXm+4uN6yWDU09o44dRy6juPpNcpUfN1esFp/itINV1dXJRm4f0fd1EhTo3ShRKTSaEKKM13j3AGZ26GPc7xHqPJMSbSqpmajb6jMdgbmDWQtQQmUiaXqTIEUDqSoyKqluVixXGZ8+AX73YHjTnN7+3PGIEFvifmKpAzCSFRToypJjqEoCdUVky8C7kab4qYgFBfbLSFHQgjUdUVV1UWyrbYopcseCI40HplOLWgNUnE6wnjqHnV9BKtFBQHiyc783UA/ObyPxChAF0OAq/WC589atluLSBV+kvgc2Y+J06mAdC4uVhgrsDYRHQyd59e/uGd32DG5gfqPWuxckQuhCCFxfwhM4wmy49NP1hgr0Aa224vSRbMtVRNpToIxOg77TNrnYtdFKhq2v6N02vevBH2cZ2ZlXhSFm32dculPK4UUxRDS1qbMdXIoHLBcpG7O+LRAaU9aUao2lwZ6NxC8IQZDCBkfItMwopNHpoifib4CST/rDsYQi2nmNCLCiIgJGUuwlTPaUjmHjsUtXKiiaPJ4MY8hMKWSRXtgDJF9P5JQxCQ4dg7nJyY/UteWurYY22AMGKuworRLSyAwZJEJXhQqA2CMnU1xQ5Gem3l61miaumLVNDTmiCajcpG3slphEGghZteC2cpIlQ9r1KzvKbDWEmL8mLZAgZ2cnRNDDKQpMYwdWkpSCPzgh7/PpYtc3BhMZWcj3KLTKQTkWQA6pfQhCD6u/v6eKvC8kco1HyuZR9Xj/DH/3k8TbhhQZGqr0bbF52r2FfDMZmGP3lcmjIGgVAGMxAwi451jGCcG54kULVuVc2kNKjl3f3SRAdMGYhFt8MNY2HqVKS3pGHHTRKxL1RVTkcYTDx6aCT+54heoVTEITQV4Is3M30plMC/mlmdxXo9M0RctyxQRSqH0h/sSoqwlrSWZxG5/zzQM+HGk606cTkdWqxXL5ZLFcokSxZ085ISPoVgjhVAoNkrhZiR0GAoR/ng8sD/sqYYJ0wy8v73j9v6O/fHAOE1FbYizWbAgp1Tk/Iae+9t3WGO53+04Ho8MQyFkC1kcMaTiQYnJKI1W4UMLHYghFaszVTN5QT9Ekp+oMVQpgR7wk6Q7SH75iwNv35742d+8ZNk2VNZASigZaWzmYgPrLVxfZRZrh606/PSKaTgyjkf2+1eAYm9bpitP3V7QLK4Y3YBPkbqpMfUC2xQUd5mazhJ6j0Yv50B47tyL85jg0RVExhqFkjWqtkg5g8tkfgCapXMl2UZEyEBAVxJpEqf7e+IYcUPi/duviXJJvb4E0YCskCYitEJqwCeUhMpaQpwr1kdJqLWG5DLBh6KZPPcWjdEPCSvRF//XoQNtQWn6PjINPecoKABjCuKztoopB2IuIgKzlCpKKuq6Yr1e0DYVldWIbEhREGLg7vZYOJzHHm1WtEuBqiQuRMYpstt1dL0r+ySV87zQbRTJR46HibEv7dXpQsz2d5l2WVFZRV0rcq6QGtb9ipA80zTPxkUxSPhdG6LfOwj23YlFu+DpzRXFzDWRVEZLS6thuj8iRfELlJUpYtleIr1DpMQweIgF+aMNWC3ZNA1aJ2TueX/7hjRaQmc4jg7vA3l0ZWMJST63nWJiFPsiESQ1Nwh01NjbDmKAHAroIyWEj7y3gqOG3qiCoBMfV0yHaUI5yaKWfPP2La/vD1w/Bdus0HbB+5PncOx4e/sGckAryc1mxZeffMKnT2/48WfPQQoMpd2VRSYrjUsFpWerGpVjAQRkSKK0ea1RrNuaF9dXHA8ndrc7Uk7olNBAay2Nrbi6uKRZrLB1U1BTc5txd5pISHRTMQzTTHr98PKVVA82K6VCU1Sm5rA/sbs/cjr9n/nyRz/mn/1X/4KbZ8+Knqh3hTIyU0VSKrykKD4EuceXmLULfzsAFomxIm2USDM/M8YyMC+fnjmcTtzu7piip6hjZPpuh/dFtcaa9uF+oSRQ67pm0zY0UlIpBTEy9D2T94whYL1E6YT2mcpYqqamWdaImAkhMbmIDBEVE/3xiEZQC0WjFarSdF2HqzS+sbPyTyaPgsrWkOG4P7JcLGmbYieWiPhYaAJSimLiOgd/IYujtpCGvi/rPxILTUR+/DytkQiRy9xl9Fh5YuxOnI573r9+xXG/Z315weXNDeu6wRiDbeoHwFIh0ANCsNsHUvBM3Z53d3fsd/e8eft61j8VfPv2llPXc384EkJJbJRSDx+IzDj13L5/zd8mTwiRb1++4m6/px8HIrG0qZRAG4k2mqZWVLYh6EewmJy5v72l1h6hPU9eXLG+VAS3AzmCPJJi4u1Lx69/PfLn/65jvw9MY2K96VgsJE9uKm6e1jx/XvPkyQlj7snpFxzeZ2LMTEPCu0h0CWVm1LjIfP3zv0SIFZvtjxEiISR8+9OW1eaay+tP2M46t9ViQxHbkHNydo6BBRSFkAitEOnxDDfjVCYT2fsI+R1CeYQ6IrKGpAi+QhmJrhTVRY1Kmb4fSbJjHCf+7s//gu9++YZXX92yFzsunv+IP/jjH6MXlFmbyaAiyIAQDmsE61XL6HpijBxPJ8ZxwHlXKC/DQD/NIg3a4LxjqdZYKwjeEdxIHDuOQyJKTZSafFKEYf8be9pjTWbVGgSOLMCoom2bs2a9bNksK1YrRfQ90TVoLkmAnwb+01/+Dbv7I30/sby4IBtLtYYpHRijI7Hn6smKxeKK1aqlbgTWgrUtfT/y6psdSi6o7AI/rggu0IuxvJdFpmkSq+2S5XZNu75hsezQ5sjf/Xw/U6p+14ng9wyCOWcmN2KtoTFFlUOITE4eaSlQWVV8rCprCTMAJSRPaBXBWiojSS6QXEDaOZM2qqil7Dw1A3by1INC+xkQEROWItsTVw150ULdYGX5tmPMmBgR4wDv3uDIOJEZiYgUkdETY5kVRZ0I8+zi8eVyZggRd5oYxx0pw3f3HaZqUbZmnDzjOHI8HUvGriVEz6KtqWrDs5ttQQYaU2qvGEgpkFOYKQAJIRVKmRm6XmgCEonVkhdPrsrBO07cdwN1ZaiVoNISo4EciH7CizPMvsw9jRL4WMARKZVWwEf35R3OleekdXln1tQoGQkx8fb1a8iJyij+8B/9YzaXV9hmMVe1ihQK8TmG2Qg1fwCPQ6k4hBIP1InzOsnngyTNVWRKhOiKf2J0xFTmlWM38Ob1S7799ltO3YASgWxkUSWZg2BOGjd9IDELAZv1isvtktE54uSIPtKuBCpDO1fOWWSii/jJIbSiFssi+zWMfPPtd7RSsbU169UaqyQLa5GpPFe5bKmqUr1ra2Yncg/eQc6kHAne48SIiJlIkbEj5Acrm/Ncws+JQJSFfgNwPB4RKE796aPqYnJxVgIpSOEgM1YF6mliUII3b1+xO+15t3vPulkURwmlHuY7dVs/uNprWUAx0Y2cDjvGocelzOgcow8cu55xKrq6pbtSWtpy1qWVWuNT5NAdmdyID4H39/eAoKottiozaznzY5VSGGtp64bgH1XuKfLuq7/FuJp67dD5SFs5komzF99IcBrfR4Z9JLlCvK+r0vKva83TZw1XV5LVKhLDiRQ94Ar/kXI4C5MLN1CX55nItHUiM5LCy1KtZhi7Gtffc//mJUK01IstTz//Cc9efM5qs31Qn0IUfEARGlAoU9Es1h82lwClHFUD28UCbTNCBhLFQCBlTd9llJZoqxCqUGN8HBESUgx0x0QYFDJYFBGb9rT65wjxLSIZVGjRcYFON/goCVmQhGR7eYE0hn/+L/6UH/zwC1brFf3Qczid6IexiHLbOCe/pSoNqXj8iaHHiQIyighGUROn4SMgU85FnlJqhSAjc6JWimmmToV5hpwAbVukrPCDRFuLlYanTz9jsx0JMXB1c0XbgmSkqQVa1Pz491eo2d0iJIcPGp8q+r1nd4g4r7nYXLJeXmLtFoQnZcvxcIubQCRoqqZ83RGM0izXFar5FkwRfP9duYLfuxL0fiKFilqrYmYoi3ipMBJpDYtFAY9oYxiGET95Rg/BGpLI1EYRXMCPDl01aKVRSjCFjjCNxDChnGc5Stbn1owAEWfZNWVhuUFcXFIpXSpC51DOkYVgyjCR8QJ6SjmtUsBHiKKgw2JOvxUEQ4YxJJKfuL27L/MRfY+yFjmTnmNMOB/mVqRGktgcioddNw0YK6iTJCcPyRf1glQQXCnnIpSlqhIZcpmtQsRIeHZ9wfHUMfQjkfdopbG6BEgjIQVHEJKUYpEso5T7RbR4fi8p8aDV+fC+PF6XueEZrKJ1QbIKEbl7+xo/DSQ/st6siMFz9ewzjNIInYsWX0ykGGa1kzlFFh+4f+f6DD60ZtJs7JrOQTCmEqizJyZHignvPLvbO96+fsWrl98xDCNaZkRSOBcIYeZiJo/3H5CqCMFqvWS73TB5T3c44iePripqpYlKgYs4N9GNPX50oDUpi6KA30189/o126rBbC94+uQJC2OwFE1CScZYjTW6gAWVgpBnoYfCJ8vkeQbnIBXH8SSKHJ5UAmPNfI4KfEwFDi4i2mpSjnRdB0LSDd2j5AGcC+VdxVzEqUXCmVDmfl6y293BaQ/3mlW7QElJDKWNpLRiuV7OQbDYLUkBakZ2xBTxKdNPnmPf0w9TEbs4z79mNGlp0xa7pZASx6FndygC9l0/sFqsaOqG1Wo1g4dUaYlKidIGayzDEHjMsbt//UsaoVhHR9VUGCVLlZMmop9IXhBcIoyFpWe1QBlN21qWS8OTJw3rdaRtHVM/kLOnmD6XVre28kMuMbvBp7kVnlIihNti1SUEQ68ZTpnTPnE4RNrVNaNTbC5v2F7elHGJnFHeegbEGYMyFVWzfLgvAKEctoaqaahqATISoyLLmoghirGMMExxmEcElEkImckhMnaZ6CQqW3ROWI406tdkkQAF4Qk6/QCdBSFJQipOJpuLDevthnbRcnl5MZtjxzlR7wgxUodZuH7GOsUUi3XXNBBFIGYIMeF0IPnpYX9litZrVrMrEAlJppaKQSgixag7hLKepLTkbBi7RJ0NUlbcXH9ClgmhM9utRUkHMWKUoVpINquGEBI+RG7f3xbPhalmv/ccDh7nNdqsaJoLpFiAcAjgeIq4KaEwpFqjlcW5jMDQLqoiAqB0CYL5P0MQFMAqBlbOsd/3JFuGv3UAaYq3WraWRGZ0pY+ctcG2CivKJrvYbBGyVIxVEZZjmkbSmzd0+0weHSpCI0QBqGiJ1xIGTwoZ1w8E2xGtQcQStFx/QkRPchOdihxIHIkcCSAT0kIQAQ84X0RWi7TGh8vHAqPWVY3PgsEH3OCRWhXpplg8EUNIVFVNXVtaK/F+IkTH/WGPDwPjZGlVRBPQeULJ4gz/odsuQaTZ72ssB31MXGxbPvvkBqkV02zfsmxrZC5aeIfjgSwGmBOMonBjybnYDPXdCWYQy0cvVhVFG0RBEjrnOOxOWGPRSrNuW0KY+OZXf4v3IzfPPuWP//m/5JNPP+Xq6qoEkHPAO0unUXQEpSrcOqSY6S8fLIJCKHw4gSzQ+xhwoSfGCR9Hjrs7jvd3/Owv/opf/fwXvHv9hopSrQ+jYBg1GYnUBq0bTP4w7xTA+mLN9sklPiYW6/WM0hOchpFhmhAq0ufM2PecTj1DCGRjOe07TocT/RQRccRw4MX1DZu65ul2w9B19H3Hr77+msk79DTNtIvMlCLbzQotJYMPs1hDkZHKD8INmRgSXdejdanQnI8ILTC6om0MKRdxcefCI67qvA4nD/msTQpGK0IrMfWC1cWG9aplcBO77sg4DoSQ2O27Mu9NRTxeqdKO3axa1qslnzx5ypPrpyht+Nkvf8VhuOfd3T0hFrHhmBMxzOAmpRFJlwNBUEjtMWBqMMqwuq4xRpcK0JZKLIriAyFFMcNKXjK6D4oxAqia0t4bXea+O5EBW1mmSeC9xo8Ou8h8+fsK29b0fWYcJJ98suXyquHFc8jxSBh7RNZUdsF6vaVeVHMgTDNakqIUlTIhzgGAjBCJcQx0neerX+457eG4F3z73R3VwhObr/m9P/5T7GaNskWJJ1NoWpmzHVv+DbnFTEgj/XCgH9w8PUyIOBapQApQUEqFl6pYwKVMzJJUGUgnAgf23Te8fvcKu+oLanl6T1OVmXYKeypGdA5EMREYCdFxcbGmbRd8+ulTpFQ45/jf/rf/Df/23/1H/t//479hGkcGYxjHoaAsa4WQxfopjoIsxoeZ59SlIr7/KAqe+olkShfOmKoIy+dEZWIJnj6w33fkFNi936OkQArFcnmJrVvqzZpmWVHXhuN9jxsjwwHy2ZpNd4QA3idevbxHoLD6RM4a7xK7+57j+69p6vdcXm1pGmhauHmmWS4q1ssl3X1i7I7c3h7QdQPGQqoQmN8l9n04K7/PJ2VgPPRMQeDdVOSGUuFoySyRKc/uCpki8KtLxiM/uEcYWzzLzk7eGYhJY6uKUNWkKTFmOMyGu5E8B4USGMZpIJwEITtEnNuObiSTSMHTE+lI9DkzzHMByPM8KpOSmF0nPn5KhSekqOqKxWJBTAl/6gs0OEVyOvP35IPjtRCCECPjOLE/HPDO4CbDpBOVLoK90pSMMuXZN0EUnmXKkRAdIUVSzrPxrETrIkME8wHoIjGmknEpgZC6eMeJQsdIMN/bDMX+jWGwsQZtTAlKOUGMTNGRQkarWKS1Umk/nfYHjG05HI7cPPEPUHkpS6srnYWPhZi9wkr1F2fQjA++fK/e470r2WlI+OAJ3uNDj3cD/XDg7s0rjnf33L1+hTt1yJDIQhNzJuRM8KWFo7UmxQKseIDcA8M40PUdLiSCiwgkbbvAhtmmSWWMLmowgwjEmBn7kb4fCiFcFt7YECP7fqCxFVxK6rZBKFmshVIqkmfi7GhenqmQYtbHnUEzysxVbwnE8MjlmwJMEKrMTbWc310jgAmjx4834rmNywwpyvmh7ZSFpKprIqCHgeKhlopaTTzrdWZUFkQiPhpC8LMXoCfGYmvmZ3J93Rqg8B+nofz/0uIu1JvKaIyRWHvGUAhMZeaEqNg+iTMXcf5JnYOO/NhyaPKBYxdxvePYe0KkCELP2boxBZWpLVzdVKwduFFw80SzWUvaOpNzRY5rfMizy0GFkUtqa2haPQepTAgfOhIhuDlwBZLSOKkZux39MdMdoOsiWWeQungxakMS8qGdL+c7KK39+FuaqLn0RDHKzjJ1ESUNMRbruHbRouZAOOniqxpSJoQd0b3DLg4sLycup0y7XbK5rEnMlKCUSWkg5wnE3P7NxUVC5NKdGfpixTaNI01VcbHZ8PTpE77++ltyKmjXYrVkZh/Lwp0+C3BLIfFuJHj36J4KAtwRSZOjkgokuNCDEGhjMFXhTAcX8Uh8huA9p9Mt2nZcRBimmqrTnA63+HHAdUeMjmgtqFcGsiEGzXCS5CQZZAEDpiggStxYzqmM4+p6wXqz5Ppmy2rVsF2vqKRkrEHQELJgChkZFDKpmdb1u13fux26f7uDxjFMI0kYMBoxq3qrJMH7AidXpUVSnmpGzshJbfRDQDzjj7UUtE1d+GEucRg9xxAIORT7lRDLbCokxpjw455wR5n3ibJpkyqbeSTgcsTnCDPHLuZQoM0CsjAPcl2P04WYi+HjcrXGh4CxpqjjxFhmd/KsSWrmgGXQWs8vvuPde0llFLUVrCvBsjHY6zXGNoiZh5fIhb9IJGXPFIZZsUWibY2UZchu5lmS0Qo3lkMuJlBVhakX8xuToGRxxsgl8VCiiAw8vmxTYyqLO8XSEpvnugo/8/BGtM7UxjL1E8fdif39DudDEdJGoXVxxs7keQ6iHlBpLhRovo+BcZoDn49M04D3jm44lXmSd2Q30HUH7m/f8uqrX9Pd7zi9vSW54iMYvCjtGR9wKc3KDwbvI9PkPuQtOXN3e0dKgcH5kkXaisVyjdGGZCLBRyprWa6WTNPAkCPDseN0OnHqe9CaiGCImdd3OySCz58/YbVY0CwWXHU9h8OR06nj3Or9sFxKaz6ESEiCWivynAQoVdpyMZRqTwtJu2hKkpZjcbDXiqZdIEXPoZoeEhcBVEbPHJKMjxEhM27WQPUpY21DytCYqrgoyXnyIQRZCaQWJeCaWVZwtvbqjidSFtzf73DeYaxic7Wc3cgFu/sON3q8Kw4ClbUs2oqqUtS1QNqShGmjyLF4QRaEfUGEn1Gl5T4KUIYPr4t9NzI5wd1x5H7vGcfEOELTGOpGc/NMUNfQNJlnL1qUUBBh0SrqKtPWAmNWGHPFNEmmMXLcj8j1gkYvuL7cEOfq73TsChJYKU7HAyFNhDChs8QQGA5w3EUO9zD0UK007WqDqRrQBp/CbH6cZ6PaghQVonhjPk7GktToqqZqNng/QU5YGfGxJBo3N09Qqsy+ptHjg2cYe3Z3P6M7/ZzF1Tu0cVw+aVhdXKErRZSJKXlE8BAMIQ8gJ2BCZIeY8QGD93z38iW73Y5hGFBKc3V1wR/94R/w5vWbsk6FoG1bjDHEUAQREJ6qqsrYSkmGocNN04dDI5fkgeCZTj3VZgVK0E0TWWqMqdhs2kK38CPMtIbjyXPodiTgEx/QlUUoyXdff0V0DhkT241msbI8UVusbiHXuOFEDBKoUIsaScLiSL60b/u+Z7P9hO3lDV/+8FO2myWLtmW4rHGDoj9E3r8/8O7tDuk1MiiU1Odm/PcNbb8DT3BRk9ZLdKOLqIAUMzS6bIaS9Ui00o+WygcO2ZlcnVIqvKu5NaqtRYXAbfRMbizQ8OxJQs78slLNuRxmR/I8HySCmKCfFTHcTLRXSmOMIUiHi6lkd0ISTYv0XcnazocbxZ0+ZElI0C6WSCV5/fZdqdRimrVLizZp09Q0VRH8PpPRjRZUVjNZTfYQY2C9qNHWIrTG9RNShTJINxCDYxjH+V4kOmmGacQ5R1OXDN3MqXbKiTGWKjamwkUMKZLGidMw0I0jzrviKvAb19APyAgxgpmlrUYXyGdFDzHNKiXl/RklWbYtTbukalbkWbxcqw+qGkUDFMhnV8YiKh1D0bz040TfH5jGnt3hHePYM409w37HcXfP+9cvOd3u8KMnj7lUWwky5eDWVhJdgJQY+/JMunF8VL0LGlNTC0M3jYzDSEeP8sU9pLQDiypKMorr5YIheF7t7qisJUvFcn2BHybcaeDrd+8ZxpHr7ZIXT5+wXi558fmXpXLseu52O7pxIHaHorYhBXVT058co5uIogckWZzBR2KmZJQ1ox8Mp02plIruA6SZcnG+Kym4ebrGmFlom0I7qK1hvWiwFYxTTwwRqy2ojNSJdlA0yxZbW5qlRWuJNpJKKGSG6B1v3r1k8pGkHBcXC5aba6pFocQIBJvtAjcGur1D5MI705l55ADClsGSVoLIPC/SZxfXs1vI/IqEIDxCX+eceXfnkQnevHOc+ohziWnwKD2glODNa83FRcuzJ0sWn7c0C0m7TGzXSxZ1TdNWaG1RypJDQ3ecCMMdcZRMOqOSJc9V0jQdSKmoR929PzD0E8fDRHCacYCpi/gx4lyZ0a62G374e7/Her0t51coFVOOiShmmTo5J9TxA19VADJH/HDi2E2lW5QT5IBDEoXk/ckxD1tLpZ0SInlCf0SmwI9+9CUaiUqGY98whUTnR4T0c4K/RsstkgY3acIk8C5xOhyxVcXVxZbtekVKxdJqvX2PsTX/5t/8G3wodnPPnz3l8vKSf/gP/oi7+3tOx9OMANZU1tJbRd8rjsf7hxuzlUEjiVPN4Mrsu3NTcfNREiU0IUz4bmLbrNF1xXZ7w7v9KyKOL368nClXiq4HomZdNXz+xSXLVYWqMvd3Y0FZbzJN3bBeb9nf3yNz5vnT6wKoWTa4PHBxveD66YZujLjYo/aJ+3cnxh6Skxzu9ty/35NcQCRQ4jdcbr7H9b2DoGwssq2KqrgoaKxza/AMlFBibp/NJquPr5QzeUYLnonk5QAow2hPZsyRIUZ8KEHw7HeXyYQHzRhBkoW9lXLhR/kQSIK5winSVqREMqX0TkKRTEuOjsz08TcmysDZhYAW5RDVRuNjKBEk54fNoKR6EIMupo4C5z7oN1ZSYLVk8h4fIjpEJuceEIPbVVM2WCpSbjlFRld0G52bSiWIQM0CeDlDSKCyQM4ivmU+6XBxloHL5yf08RVDJOnC7fwAaAklq9cgcnx4l1JGhAzzYQICPbdDUzErLgOWeXGde4MzGjTlAqAJkeA8bhyZxp6pOzIOJ6ah43h3y3F3z+nuDtePpJAQWT1UmEIW/ilZYNLslp4CIhfG4Id3BY2paWxNrRwuR1JITN2IrC26MpjKQM6o4FlUFSoE6r7DA1kFhK6IPuJCZDoNGOD+cORis6FuGjbLJVKUpG5/6pFimtvo50rHgAzEmQMrz/sgg8jFNuw8P805IYTCaDW314owd04fca8BWK4b6trwAZ4vMErRGIs1ipSLkEGRvZqVNSpNu6ho2prFughUKC1RKZN8EfbuZ3k0ZKZqNMt1g670LMxe0LSuCoikyB5EEtRGo7RAnVX553cvVFmXRXJMzAjNGYwCCJGLLdeja/ICQubUZcYJgp8/wtkrL9PWkhgqYlTEKOdOaUleEYaUFcSi6ytlQquKlCLBF0GLJMo6DH7C+4kYPKdTT3dy3L4bcJNiGgTTmAixdE9W2zWXN5fcPH2Crey8z5ntDAVpbmsS8wxK+3Bu5Ax+6lFjR4i6CNwLEDKhpS1z+OA+oKTLNkOlhM4Go1bcXH6OkQqJIb4T5DEypXPVB0oukWKJwJKjIkZB9MU6S1IAOWIuMLTWjD5wf+hKspeLV+Fi0XJ5eclPfvJj3r17z+5+V6pcKTHGMAwD+8OBV69ekuez1ViDzhKvLTF7fCpteTUjZ6UoAvwyCyqtsbXFNAuOzuCSRwiPMhptFOttg8qwbRZsti1VrRhDT4yeEBxVZVisNBdXFTkXgZPr7QXbyw1VU9M5gdQVLki6QaCCQEo4joJpKqDJ3hVHFEGp3v+zyqYtLla0mzVdiKh5zmEeeui5oD2lxCjF5Oc5FEXkNedMyCU7UUoWKR9Z9BozpaWTtHyY1xTx4Ywo+Dag6HUqpZHaEFMRiHXjQPQTIifqqiqEaKMxSpEri1WCql6SpGWfavrk8L8BCRbaErJgdzjR2IKIWizaBxRgGbhLtDaklPHec+o6NDVWCSbnCTHiQqCSFqMCp36iqh0hZ16+fDk7Kgj+8Cc/oDICKAPtcXS8u3vH7f2R/f7EcrUqvLK5rx1zxieQaBCGYRrKRp+60q6lQJnLW/94ZiFzQs0bO4yzwocZaFeGxcIw9qFsNiAxkDmw272kO56YuoxqJEJnpHQgNDnL2b+uVPTee7zzuMkTfMS7UIby3ZGh39Pf3+GGDtd37F59R386EvpTcQqxxUCYXHQ37cwtSylTWUGMME2OplaltTKTlQWCq/UF15cXNGbFrdwxDiPRR0xtaE1Lu14zBMc0HFlebFgCWUneHg7shp4+RIZx5O7dLdOuI7nIN2/vWK5WSKOpqgYpJKaqmbynn6bSkk0ghS7gJJOQJoEsKim1KbJ1IUakEWQNSBjHkUbU6KrCGgsIfMhkn4iTf0hdhBA8fXbBctXMoBvxkCgWnRpgKnPcQr8pgWm9XrBYNlStpa7NjNQUzJrbJGDyE4Nz6HWDqgy6KgpHcu5uLFaGpkloqcFlZBRcLy+KGoxI3MVjmT0rgZDmoTMCPKgAPaRgM6L7fGUE2Swoip2+BG+TH4KOBJYLy3q5YL1eME0lOB5PkW4aaNvAYhGLaXOAWh4RWaErRUIwRsHtbiySaSox9mdxgBOHAxwPkV/+cs/pUNqfoxP4LKDW/MM/+cf8+I/+gB//5EfUtca5rpxXMxDMhRHnRvqxI+fMfn/3kNjnFLl7+Ss2lx1Vu6WpK2pr2GxaZFWDNoV/mfJDEMxJECZJjj+m0l/yo08twQeG0fHu/T1aZBql5tZqIeJr8RzJhhxuiZNg7B3WWBaLBYvFYu4+FC1nFzL748B6tUKpHqMUy0XLJ8+f8n/8P/zv2e/2RaHmeCD4IrDgnOPlq5f85V//1WySDctliyKTg6fvOxKeKGWZnSqDRFNpWxLGRUPV1qhFxdu9oO8cv/zVr7m4vmB7ecGPf/wllVa02uCmE4d9x7evXhODhmRoF5b11nB5I7l+8oxKN1yuP+P93Y7bw5Fff/cKWy9oV2t++JNL2naJrRuibZFagh8Iu24WOynJ/f8cZMz3DoJiFiIDHhRStJQPFZ5Sc2t0BkyEUMw+H/aEyHP7VMzIxjRzwRJSwHq1xAqDkRWjmgE2MyFeSIUxCm0txtiHAfZ5UCyloDK28KaERDKhFEXKbMj0U+CX7w84EZh+gywvlS4cvvP3nItjc2UNOdXElJCyoOJKRpiQRMYR5KwJaaymwjJ6sFaSlWF3HIi7Az/9+S+x2rBYtDj3GUZbhLJ0/Y7d/sh3r97Tj57JBZpFUaxIs+bp5GOZZSqJVBLXl/aEm/wDKtIagzWaKD4OglWbaBZA1gUgIEDXkqYBU2eEqMrcb+Zc2loy9G/Z77/j7v6KC31Tqo70YHpEjPkhEDo3MY4DQ9+xu93Rdx37+1v67pbgO0gnYEDICWk81UKgq2au6QNT9HMCJdByToZSJszAGukDUibiGM/p+bwOFVoYmqohxztSSKwWK9pmQVU1hUKiFKapcbGg8iQSmUEmMFIW2TOpGZPAe+h9YoiFL7rbH4rJslB0Y8/op3L/CVIUZK3QxlK1RQzbSI1VhsGNBW1JaW8ii5Rd7hPBeVbLFVoXoehKW2pTfQRmEkIhhXqosBCzLdg8P88Eci5BEFH4cW1TY+0si3XmZ84Vq1QSWxmaui5IXSidhJzOZRvnmVcWGV0JlFEYCgpPIskp8W63Y8oOr2LZ3zMvUcwzs/xgrVXaDd4/8qfLmdtdh6bMKmtbzom2Kfq5ldVUFazWNc1aFdWVnHExsz9kum7i/euRnGYvR21JCSZXMAQgWC4amkZiq6L/Oo4T/RAYR804aYLcUF9U1JcGbWqqpqVZLfnxP/xDrp89oe93eEb0aKjbBUIUibziRRlnsYf5uZ9HPEryo8+fsVxucQHG/p7TYeTNr+5p2hpbWQTl7Cp6wBapDFq1RYQdxWk/z5CjYb1e0rQJFyAEhciZyhqaJpPyiXE60fUnjqeOcXQ0jadp8gxcK+Cbtl1wc33Ns6dPOJ1ObDdrzCz2brSmrmrSoigghZmHe6JQbB7eF9D3A5riaiLVnPRpQUyhWIapFVVTYxuJbRSyyiQ1kVUmCYkbYH8fCK5nbKBSilo6YnD4kInTiqZaUVUtzaKmbWq0aulOPffDgb/9m//EsZvK2acaTLWlss8h3ZBSTUwGqRqkzExTByQQiRhHUnT8ZjHwfa7vHwRzEVM+Z6h6bn2KB5ko8YBqSzNk/iF0zsoiZxHiczUR40zqRLBqGyppqUzDIGOxrRESrWuU1tjKYqsKW9Xl6wk5k/RLsDRKzfqOAvIeayRNY3Hve/a7E2/u7zjK9NGBCkWQVyqFRJNiIM9zQKMVVHa+jzIDSSmRY3Etn7xAiYSQkpTLjGUK4IImZMHpWLLSb16+YdU23OT84ClHFvSD43Ds2O2PuFC883woB36IidFHXMwwE6KFFPhYWr/Of1D9t0ZjtS7K+Oc0XIBtM9USpFDMqljYVmGMRJuMyCWrM9IiZVHbmKYdx9MbdvuXrC8WZG0hKWIuBsYxFVpGjBHvfQGe9CcO+ztOxwO7+3dM4z0pDdQ6EJlIYgTj0SpjpSGSihRTiHOKLApRPpVkKqviip5NmBOa8NH7KvQngVEGYibHTFO3VHWLrSocgSwFSmp6V/hwwXlyiMX01hSroVobDlkQUmb0iSlmxpjo+p5kKiqlmbwr6EtRFNpiyqgI4kxXEfMI4BxMZuNcSQE4pZSYgsOPHqMrqqoIZytVQEcfX2dlHPGhonpYq5k8pyLkgogt1apB6TmxJEEuMlPnYKq1xhpbvAoJ8/wuF0NqwUyWL4FHGNCiyACaSiOzhFAMen0MBJ8fyPnqjPou39pHiOvHnpJkOJ5GaqORSrBYGKpKslwY1qsFTVujVMBahakEqYyD8V7ipkCOAX8aH4KRrgwhphLk+kSMAqMNbVsUa1IcCdHjfCq8uqQQtqVZlgpisVywWq/YXl3wyedPqduG4+EWMWiUMWwuL7G2Rpuq8M1mtSVSef7nSwnJF58+Y7FYcjz2vJl2DP093/78r2jbiroyGF0Sdmsr2maBtjW6XSGrlhgq7t43JZmQxTRbqIw0kRzzDJQSwMAw3nM83XM6HTidSpV7BuflDFUF2hQk8sV2w6effsrxeOTi4qIIeMwSeGeKx9lAIJ+J78F/eF25GERrSoerqjXKaGyl5/0Z5mq+qE9JK0AnopoQRiCMJo2Jacgk74nDhBWSSsiCsM2ZlBvsYsOyXVO3lsoqctZ03ZH7uxM//7vXTD6T0Vw9+ZzlqkawwrsaIQ1JFIcZci56uSEgiKTkZv3Q/4yVYIyF+FwWQQEpGiULXjpnuq5/CGphhowbJWlqWzQAZ16VDyULOauJABileLFZ0HyypNlsEIsKpCIEjTRLkIboHe2ipV0s0cY8gHBOXcc4jtzf3rNYrGiahlV1S2sk69ry6pfveNPc8u7uJVNfM401PaeHR1Vg4UXeKsZIyrFooWqFlZkUJSkVm57wkDRlYg5MIZEH8N4RwkhjlwgFX7/e8c3X3/L+/S3H447PP9G8qA1u6jnGkdOxmJieuhHTtEzDSD86fv3qbbF/SoKQDVJZmtUKYxXgH/iFBeSa0FLStg1NVeHdh8NHAJvncHFRzlEh7Axjl6QIycN4zOQocDmXjURkcre8fP3nDOktFzcVOV8j0iVjKlVOpsjWxRjp+47d3S33t6+5e1cscpomkuKAc0d24x3eT/gwMdhjqV60pNgARfJIgUHHhJu6ohLjPCfviPlM7leM8bFJZuZ46mireq5Ky8GcpcA2lna9oB/vGdzEYer41bffcDp2TPcDpmnQdcPy2RVqu8V8mrm/O+CT5/3hyH6cWPiIThE/+KLjqopqjM9FNaa0FwMuBHwserMKyRSn4rogNdIqkopkkZHWkEIq6OYpENKEj4JudEWF5tGVciiC3cAsZllEyGEOqrJQC0jzLK/YbpUpbSbOGqZiVq0ppK5EHD1h9HgRCFMRpC7IFz4CEBRN33KI7IcDMklkLOtFCo2p63J4PhoLl2z0cZXOR9J5GQi9R7SStqr5k3/yBdttxTjskbKY6k4+4SfPbj/g3VR8LFMg+kT0ienkGYcyOlis1+VcQDGFoke5P+5LYi4Fy4WiqiRN09BuGhZtw+WTa9bbLXW7IItEiJ7R3/N3P7st3L0AfedIWfD8s+d88uIFn774jKq9xhpLvWhx/kRj7XyHRYf2v/6X/2ueP3tOyvA3f/bv+Ys//zP+H/+3f8Wh6xldoGkW88xOFbs1LdG1oGpKcGzsJdYYjNWYZbElKiC7In4tpeLbXy1obcW7b15zOvbsDx3fvHyFNcUbcLvdsFqv+OKLH/Ds+XOev/iU/+6/+z/hnafrO6qqYr/f8a//9b9mt9tzPHWcjkfGcaLrCrH+cDx+kETMMDnJEDz7fcezZs1y1bC5/IzTaWSaPPend7hGE5WlMKxLAnbxYs06beiPAXdKuD4Su6LFPHn/oRCyAntpWDQlKRmmkW+/PvDVV7fc3x/47pt7tC1gqJzfMfae3e2O1998VVDjMlJXYhbXOKGCQziHygFFwTXM9fr/74D26Pr+wJjzLCDPm1BK5AM4ogAjYozEHMnImU8ksaboiSppHrQvx2niTLs5c4/a2rBZt2yvt1TbBVlohknhsyUEwSH4Yg1iDHIu88lxJrN7VAzoyWEQJDcxaoiD43js6LuB4MpmrvTso/fw4vNcyUqUMQgSYZr914V6cGEXyBmwAlImjJBoIZFzZp3n4D70MPW33N4fOXYjQlm0rsrAOkYiJZmobE1sBUPoSWlkGKdCokaShcFUFVobqroikfDTUIb+j8SypRBYrUsrmPhRJaiqjGlyAb8QEZQWWphK8hFFLPquZIRRoItx8On0jqwSt+9ekrYCuW5wpFINEvHO4YPneNhxOt0zdAequsxLY3L004Guu6Mb7kolFRyTn4pPopUzHSTP89VIdBHXebyPeOfxhmJLJQQ+CdzjwiLD/niitnYGTgjUzDlNMxVhHIfiKjEOJBfARbILBOGKL9wwYpTi4uqCy6tNATYpye50KmhOa1lqQ6sNcgZn+ZgYxpEYIqvFAglF3FxKYkiMzpfnqzRtNeudSlGAMqrIT4WY8MlxGouF2KHrP4IzxZm8LlLhIgrEDHo6A8iKvVFdG7JPMIsppDx/nblLU7wNZ4TjbFc2DBO+EiWBSalU0/LD/E4gZt/Pwn/s4wBekKbMME5ElVHRzDzJPB8vj5q5Yo6LZ4Lko8v5SAgJ2QjqOSHuT8UeaZpGRhcLSjhkonMIkTC6II5jKGMTLWFZK5amoDp1U3Nw0E2BY99hZh9FreeRjFQzfzOS0oR3HVJFhCrdpxgD/WkkRhBJQyjC9WG4p9sp3pvIapOwdUu1aOmGO7rjPY+rjFJlF3L9ubJfLpd4NNkEspw9ShGMoytt6C5gjEZLjZFTQfNqkHVJuIw2VKadsQ+Z9aKmrTWHd/eMo6PvPXU7PcwC7w8HFm3LOBV/PlvXD2hW74ta1DiOvH3zhtv7PfvDkffv3hcfSTdR1XVxanlUyecsSJmHNY9QLGYec84CIsXFXUvSDERBaSSFE+2nCe8S0WdUVojzOhVl3WhpUIBIqaDJTx3373cMx544emqjyzQgx2K1BuQYEMmhVUkEg81YnalNAfeJHFEqz2LuH63M73V9/yCoSksuzTM8dW5/ztJYMZQ2WSDNhOrSrqyMpq0NRrczdLcAYoRMyFRMXaUQD0HwyfWW9dWGJDT7Ho5dpus93rkH4ncOYUaHObr+gBtGxDggXUR2mi7u8TLiZOb45o7D/sQ4RiT2Qa3mw0ufkX9KoU1FlgLXF7HBGQtHpmjticJKR4iIlQItC3BBlOY+wXn85Nnv9+z3J5zzbLctddXQVDXESM5iBgQs0abm0AdiyPTdyGmaQCi0aTGNRFlLXTecuhN93zGNA1DmAOes2xhDXVXFu+vxi60ypi0K/OeEQyuIFO5lkA6BJCuLqCqEzkyhxx88g+t59d2vSF5h9QVBChKJkMoccJoG7u/e0x/uGLp7rm9uiFmy7w8c+lvu7l9zOu2ZnMfNCjLKgK1E2UBCIoVlHAPT4On3UwHXhIi9qGfRaV14gj599K7udweMVBhdUHhFlirPG3+i649008A4jsiQMbNOYhg9zkXGQwEgbW6uePbJE05dz+Qj7+/3DH2PXa/R6zVLY+cgCN4FkvcErdksWowsCZFUmuAn+mkgxlC8Ilc1WhZhiOL+VcYA3TQx+SJbNkyO2/3hIwR1TAW5KGWeE7Ayq8tzgFFSoKymEhJ36Gcx+Yh8GFPID/PDnIkhMo0Tp25g6CeErvCz0a9WH9Cs8pGvYTrTkUZHGCLu5OmmAVFJjLcFVXwOtqJQpGAGwGTOQ8zHm4tpioSqtPm1Km3cqU+8e9ez34/0LqCVodI1RIfWmeVCEQu9lUymrhULo9jUibqF1VXDm0Fw33nuDtDUhrYqsoRSFXeVFMv5MA4nyBNuKm3eUs0Khu5IjoJKLWmswRiJiie6vce7HX6EerFmGTfc71+xu3vJ4yCYZ3S3yILgPUIILq+eIBeO2sUS+FImpkw37XFTYJoG1DlpSR1CRKQsbpNSaLRoqcyinLU6sForFo3Cd0NJomJmuXZIpQghPGjFvru9Z3SOuqkf2uwpJTabDcMw8Pr1a97f7rjbHfjq66+LJCGCm5ub38KVF9R9WY/HU4cPEluVGZyxAonCVhptdUEHC4mWFSFC8pHT4f/L3p/E2rateX7Qb1SzWMUuTn2Ld18RkRkvMjMynKTTcoIxRlgWCCsl2jRo0EFCSRMJSwgJIVqAEPSQW+6hbNMAy0ADyyZNODKyePEiXrwb771bnnuKXa215pyjpPGNOdfa55z73rmhDFuWYlyte/ZeexWzGGN81f/7/yfyBMVLH61SSgSGs+z9rXXoAiUEhui5fX3Dy69fMt6NEBPnq07KRjkRxh1DFCCgKV5aNEomN5nkoDtvRGlDg3ag7Wm/9Psbwvc2gq4Rr2VKExwKw2hqXlrqMj5KzrfAUt7IRTH4RMaz3XbEkok5LvU9o6x42N5z93pP2T9nej7g1x0qZZrrA9M+cpgCf757zUBiUhmbqZ5uYO9HiIHLMXERMptUuGstcbsmPbpg+OYFwzCx84FswDhz7wLNEG+0QWlwzvD97/8WxY+kaSTcDbVfcY4eC4WAc2Ctou9aUAIcOUwjIWZi0hjj6DvL00eXfPDkCR8+eUzfOCiZZBwo6f3LxQjlFJaYgxhjbdGmQSnDOHpu7+643d0Sol9ACTO8POeINiLFc3rbg1f4saD1tEzzqERJQenCxaOGkjI5jCQ1kmKFrQdIE/zxH/0hdx/v0dlg2p4xTHz9/HN2ww1j2HPYX+OAThnCdsXN4Zqf/Pk/4euvf85+dyN8kLHSchWDNuBHUCahVAH25CC6gtYJDVzOkTKJ+KZyLS6CS/fThvvJM/iEc0akcZwsyHG/Y393xbS/w2jFg9Wah25DiYXyJPPq9prbw47Xt3e0xlC2W77/vQ85DCPfvLrh5vo14/6O8xB5sNqw3mzpY2SKCSJ0XcOma3l4doZSQlZwSAnrHM2m59Wr56KyriKlWEqC/X5adOuGKN767TCyO4zc7I8SNoBEwiFijCxgpaowcS28qZRwRdHW+6EoDCkRQ4bixeuuqdCSMikEhjiRdKI0BdtrkkoM00CrmsqApBeYvaqJVcmMZJLKJF1qv5xUJIuajZxCKGNkLck7hGorndASKgUPzzONnrh6kfiP/z8/YXu24qNPLrDnhrbRfP2rG1x2JB0q8xEM+4jJDlUsAQ0q0dpIf2bZbg0XDyNOnbMdNC9vJygRpTOblciwDYeILo5UNO2Y6VeGftXy8OFF5Sw2PLp8QC4KbVouH1yyXq/o1+Ko+5Aqcfo3vP7Vp7x4+RWf/fL5vYhJdlwLBXwq+JQx1nDWO9aNwbcwTYFxnFidt+TcUjhnmiIhZnL00rKkNZcPnuBsS2M69ruhCo57hn1kOmTypNDGYBrDy9c3S410tVqx6ldcPNB89fw50z/+x0zjiAK6tmW1XjN5z5//4pfc3h3YDx5rW4xpgMI4zjJa817Iok8ZUma3n/BBYWzi7LxltWroL9dYbbHaUIwQdxQcu5s9h93IcJ0gKlTWjHbEUGqaMkE2R1T5OIlzf7sjjxOdUri+4/x8g6/34OXNDTEnUhoIo3QGdM7iUqIxsHUdXd9gG8f17o5+3dF2bVWeef/a4HdAh5YFzBJKJOsTrbgTjThmcJuSHr+UIaaaDgqR0fv6eZIjzknEIHc3e6KaGF8fOGw6VIj0r25I+8jkIzdhxy579sljE0ItFBJDmiAlckz4kLlLmbu2JY0bIbKuuW9qJPs2qY6qHo2qQAHDerVGtx2qCxzyrWiipVlNPaONwHvb3mKtFnaDUepaPiRiksVhtOJiu+Z8u2K96rG6ULLCWkcIwhHpo3h4qagqjye0tapKCI2jqIdP07SAiIrOFWhEpZXLb3NR+sw0JZSqvYRF+mhyFKFjqxSUBAj8v2RFSUakpnJkOtwx7q8Z7l4xXmX2w54vvvolQ7zDp4HJ37Ftemy7IQdPHEfubq4Yp4GQPCICIp5wyaCzeJhKV1WIUlDFiAI1FmuKCA5nhU6mAk4MeZFSkhMMaWbSyQvFVZ5bNsJATglrHH3T0LkGXTSlE7YebQq3V1J3Cn6i7xrZMJo9e6NISdG1La5xGOsqQMXgXMOq71itOpqm6tAVGFLGVJDNerui5CTQbSXnPowTobL1yEYZhe6t9oqe7qkzBZ4YwHJSrqtrqhSKEpWH7WZbpWvuiDqJiaqozzIbwSzOnbEKW0RLMxVh5WHRv1T3BJHrahAgSKlRaF3fMYkieUGRJYdQzZ/8P9X1H+MJOEHB2dbilOGwN9xeHxinSLdp8MUzJWmxKRqs9tJ/WM+6t4J6Ptuu6EzAGU92iqAMhxBJNqO1YbPZMo57Qhhkn4mFEEBlMczTFAlBjEbrGtrWSS2ubQS7blo25xv6laCLlQ8kJg77G4Zxz+3uNXe3L9nvbu7vqzO6Vs0RNKArV7KFzjl2ZU8YR9lftJRppS26gJaat3OWs+2WxrVY06Ao2EkRcyClIHqV2lQAnyZNgmLPpVL71ftz2B+IwePH4Z4RDDUjEGpbxKxmMje2vEkHN7foCCuUEJQchsJq4+p8EHBcSQkRkGMBqJUEKkvhWBXpAiiqgMriJNW1Gmdg3TgSfIBUsFWoetUKCXzjEvupYZg80XtC8liNONQUdFGiHwtClGElJS4dAt9ixL5lfAdgTCQm0RYrStSYZ69vBrmo2taga1N5ygVTIKMZvecwHLi+vpaLjcIoy+Q9cZzYffYcNQUIid3lBkKg/fIb9BDJsXDlYCQxFUH51WuLJ5FUZiDwvDbiD9GQwzVl/5KH55e0bU/XrygxEMZ07yJp6gZQ6zupKLp2zXnfs2kaXtvn+GGqfHYBpaBfW559/JjLh2dc313x4tULrm5fcxgGppCI0aJSpm0s33t6ybOH55xv1+Sq+NAZw+7mwBAiN/uRwxSZEvgkqVKboShDQnFzfc3dfsc0jsIhqqStQIiwtRAGhEn6kuoqLQV2dx5T6eukzy3TaEtJBTK0zmN0wpoAxcgjGorKZBNx2kO4Zbz6nH/+05/x6vo131x/CW1E2QQmkDaPaM+ekcc9adgz3d1BzmhTlStyomipQ8WsKJMS5owiigHOKNE9VJamKhgUn6Aupk2/YuqObTbiqWamILytaIVLEZstcRoJYURZaRvZrlZc2DUGTYyRtjdshxW344hPievr13z8+AnaGdZGkdc9et3xwx98woOzC5QxtUVFc3Z2wZNH56z7pqpBVOcuZ7S1dOue1dkH5JKEgmwoTD7w8vqGKUZCzqAtC2GM0ktqfRllRlaXxYmkpIXZRRuLcx1du+aHP/gdMbSff8oh7PBpIhNkMxbsDCBKC6t1h4sRbzQxFg6HwDicgKhOyC50bXNqtEUnAeTkIq0ew2EiOFPLIGKUKRUcWkE2JWeGYVxshVaKjz7asHYt09DwJz97ycvnd3zzake7sthGM9xEki0oX0gmC8+81fQPWzYXDb/74x8R/cB02LG/mbgbPHk/0HQ7tOl5+ugpXz3/ipubO8ZDJCdNChZ0wbhIMQHrFMaA+VjTtQ2bTc+ZsWSlCTh00xOVIU4J7yNT8Ly8+pz94Ybd7jW3t3ccDvt7qcNqKwDhe01KUbTU5xtr6doWeMnN9Z5YCT3GaRIDlsWx6boV2+2Wi/NLnBWD0jRW6nRKSO9jjDjbiMEqGWddrRfLz844Ukzc3k7EOAqlJAJcXJ+dS11SaWl3sKpGSTKE5/ZU3Flhm6ZiO+AwTCiVyATWW0fTKqb9WA1eoVtvMG2LXQk9ZmcbeivGklyVbqpDlxCe4aTAx4CaCvvDoZKNULENhsZoYU5qwZdMvrlhNx0Yw4hSRWTPtBCKhGGgaQzF6UoUYe5lT953vLcRnKaIc1Lf0bqCtitxb1nAZseLPKcOrLU0TSPF2CngfTzxLg+CrAwRnBTuQ/TcXr0ghUhJA0VliimMWpGoaunm6KEmJRd37mNUKKJGmFE0pMZRGgeNQZMWw3AcUvtDq0o/Vnj16hq1TbjthtXmjMYFRn3AVrDP9qJjvRa03PMXL3h1/ZrdOBByrO0Eisfna6HjerzlYiNtCMUotBZy4nCzZ+c9X79+zdXdnpuDbM69belWG+HSPBx4ffWaECcB/yBeTsoapZFG7a4FZg/89Kw0JasjACUmAqIQTRYdQmeh7zIlRUoSaZ6oJMo8W23JaSD5Pc9f/ooXr19yc7iiXQucXdnErtzSpJYffaxodUNPy24U/WBnW1zTQCPipzEWvM9QLEYbVq6jbVppdYgKqzXOGlQ/orX0Xhqr2A0nTksp3Nzt2fRrHp6dU4qS1ElJInPVbMh6gtaSdCHkIIX+HGgazZnt+ejZU14f9rw87Mk+YlE8XPf0VpDCz548wirD4Cc+/fwzDuOEUhrXaaLu8dWYxQw34x6XHcVCu1EIk1JhipH96Hl5fUsCKcYi9bhhnNBV/f10nc4/zs3a4pfJTqu0AGzQmqI1/XaLNZbLu8ek24I/RCY/CINQLuisUAUsWvpS0dzshhqtzSWAY2+fqkZsTpGumpbeNKxdR/IFnwO3fqzySZrGCTGGMUp4grUcI4Xaa3s8L+8dnTEYk3j6pGezt3xzPVB8JqXC5Xol9U6tmFQS1Q4PL17ccdgHnj4558HDNY+ePeP5l9dMw8A4FA7+QJkOKD2xPSt0qwdcvbjDj4kxeqwTGr6u1bROY9G8fPGKuztH2zlcv0JZB6Yj5DvQjovzhxiT6RoDORImz+3twOvXE9fXR6LpZdTUcKm7iBgah2samq7HOCegOm3oVw2Pnzxlmiacc/y9f+1fI3jPMBz48ssvORwGUIquE8rFtm0qsKf2yeZMSYm+a7BmxWazkf1XISTYCowWOjSjOCrR5+VAaxRXW9hmdLO93ydY5rR3JXzXRtN1PTlrgs84XY2pVaJM4QvZIt4dGWeT1J5jFnalOo+LgqIVySiCAqMKpm3QqZDHgKslMmsbkpasEdaSlcLnxDiNhAJtv6LD4FDEWEihIHKqEoEeU9Z/CTXBGEVHakmBLkZQLp9S+r4RrGmcWYkgBDGAKUqPW86JKXhiDDVdqfBWMerM3WHAh0ggkXVljzGmggTqpKuhda6/a45kvsLlKHpjpT6UkX4q82uUh5XWkEUOpzeiNr5uVrKgXKxCt6YiNuEwjVzd3HC72zF6Lz1YWqDO52cdjy83XGwaulbLLFGIsTWOWApjDNzs99wNI0MIEh0Yi2sFxOC9Z384ABGlJAWQs15utNLSC/autEZeUpHC0ZmSiLWWIpFgln8wSdKjpfKMBhJoJRFhjqQ4cne45vZwxTDtUc4BBp0yYx65y3tKBoOhNx06aIovlTxYPPBkEkFlSFGMIJbebWldg7OWXMnDG2ekTmKL9CbpjH3jvHwIpJxpmhZlBNE4xrHWaAzJiMZfIldWGihFqJxaZTnbbjikSNndCSrQaFbOYU3BOstmvSJMkf1w4NX1NYdxwljHo/EM0ypyJeBOBcYYyKrggsFm0REsFGIWWPh+HMFojG4pWfhV9/uDKKfEN3jT3uG4lsLRAZA1LixBxuKalu36jOvdFTmJtmfKkia2RWOVkU25MWhtybuDtKGkms4qSzW8fv5MDaihB9sZIeNG2iT2g2QitNG0beUprVylSiuUFTqt0z7BghLJJAONKVxsHY1T3OwmSQtHaNpOyotkjLbSDRkT+4Mn+MzNzQ0XDzrWZz3dzlMMhOKJcaLkiNYjm9UK26xIk2ewAWM8ptG4RtH3hraphNGHkRACoze0GdEMtInBi5JJ36/oO0PjJCOTU2a/89zdeva7+BYV5DystXRdx8XFhfRPzwjVug6V1qzXG374ox8yDiNd1/Gv//2/z831NS+++YYvv/yKUAUIdO0Jts5ioyXlJFSUURCzzjm6ruXBg0v8JFqPKcWKE6hQviVKVaiTe6yUWlqcQgg0TSP3++SOnc4LpTXGKrpemIxSAqd0xS1oQhhlTUePwaCVkKtHn4SCUWCDS2ksl0IoudJlCBuUTQU3RWxVvTHV2ZtTzKkUYi74HEFHxpQIRbJkOZXlUbKSB7DwFr7neH9R3Vpzy1mUihXpXl1h5rsECbO11rSuFYQdqrIdTFL/SomUo6Tp5pxKY1G6QZuMVZHsNWEUlJxWkuLKtfYoFaIqk1SlllRi8aBtVcc2zkHRNcUkC9jY+xjunDQpKnQErNQGD3Hiy1cDL15/w7Mnz3BVADhZUGRevrplP9yyH3e8uHqB9wNTGOkaQ992PHvwgB8+e8SzB2f0FlQJAhwyTia6MxxS4nb0vL69Y5giPsODRxes1mt0Y3j58hXDYeAQhGjYaI3KGWWkF2xBtWpB+t1PhBd2u1uMbmmaBshojRCfK3EWUoZUIsPdiK46d9YJvbY2Ftu1RApXd7dMNhC6Qi4aHyCFjAW8HtnZG253E045Pjz/iG8+f87uZo9RLU0n0XjTQ2oSk42U0KJp6eyZNMdPGYul0VLHO5SRgGc/7XCdZdT3jUXXdZydnfH0g6d0fUtIgZ/94lOeX19x8APdw4b1qgenUDlgCwKsMB3GOBpjaY2h1QanFK5uE33X03QNhcx+OvDy9opDHNmnCcrE89vXHMrIJnUitqs0WRc0kWkaUYeAMoqUFVP0TMmTVMJYg2st40GQsvuDJ1VE4enQVLozpN2DOr+pAr0NDRrNwMir61ecrc94fH7JF19+zuF24vXtXoBFJdM3Hauupz87Y7NaUQrsc2I3DhzGif0uCN/rXEhWqhqiIiThTcfF2QUfPXnGNzdX3OzueP3VgXEQB0Spw5KytTUV1TSWrmuYxhMjmAtf/fKOctnz8bMNF2eKsE7EEPjy+Z6rm4mXUSR2XKPZPrsQObEQ2d8MDD7yk59+TlaZ1daxWYt8Umst7eMH2Erf2DQO6yyffHQutSY/knKQ/QGFNa62HawERmgasmlIZLy/Yz8eSCXjrkaePHrA+facs82K169u+eqLgW++GXn1+g3OYVgoIz/55BMuLy/5rR/+iJ/96Z/y+Wef8emnn3Jze8vusOfBgwf8zo9/zD/8n/1DfAgYY/idH/+Yb775hs8/+4yf/dmfcXt7i2scTdOQc2YcR6xzrFKibVvGw4Hb6ysU4KzmwcWW1eopAP/sn/0zRh+IOQldooausWzOAmhDLgVjDG1rGMexSmedpN2P2wYpeVIciWmg7Q2bTcdHn3xIiHtSnJj8hFEaZzvGHEhForFV60RHcyswqpQreLCi6GOMjDFzG67J6hLrOh4+uOD8opAfJ9I4YVDYtsUYS4mJ29s9h0MgRk0Cpli4Phw4byy9cRQtXLPBS7YrxkLKmhnX/77j/SPB2mM057OFmWUmwlYnxXUlzdBFXhNjpAyjADy8x4cgauVJIkvrBGXaWKGwMjpRsjBJpOJrnlf6A0XHrRrB6rGUCpCYuSWVkr4dbYzQZ9WemhnSfZoDB5Y0ailaUmcIyf9hmkh+JL7IWGNx2ixtISkFvB/xcSIWKLWesuo7Nl3L2arBmULJgWmaSFgsBhqDQsjFRx84jCM+RLS1rF0r3KHGsDvs2Q8HxmmUKFlJy8Yi8pmlVqSNqte/3J/MQDVnpCzqCsZaspq3V1GITlUOaWYAcspx2W7YdFuenT0DdIX2e1LKaOUwymKVojFAcQI8yRnXNjx+9IQH548pWbHabGtv1rwwLbZtwLZoHA6ReMkI+YJWhZQjScuRTyUTU2SI4ejL1nS0tRrbCOQ9+8RYnZApTaiksEFQtSpkdClS01hB46pnjqYxQmxtUcQiXmUMgvCdkieazOZ8jQlS48iqCKhrV+i6rkZDtcXAKECIn2NIGKtpVw3rsw5tLE1ryDETo8PaRqiq9P309cyiNHvO87/M9bZUSCRiCdze3ZBjZO06wjRAipW4okEbRd92rPs16+2GRw8fyZyaRtzdDXq/E5q9KOTzIuIrQBpZh5Z119N3Arc/DAfu9ju891U/st4KNYPlBAEcQyGEIkwvZa5NF/ZD5NpMQo14rlAGsBrXtbReM+4D05TRY8KrO5TVJA3+IIZ62MGrb275/M+/pGtXxJDZ3Y6VeUfTdI6+b+i6hq7KUWk0IVbSiVSgVaAsbbPFuBbTNkQ9EnMipAlURKuED1cMY2Z3mIhpIBeJsEytN33bOD8/p+97zrZnjKMQWvzRH/0R3nu6ruPZs2d88NGHPH7yhFKE7mzuK3z69Cn/jX/j36hGz/LVl1+y3+85r4TubSP1wK+++Jw/+eO9cO06i1YiuTbrnMYY2B2ES7lxBnO+FQo7LTSWQlJy7B/cbDbMHMCnI8VAKQmtC9ttz3rTgRZ5r8kH0n6CqGko5GhIqhC9LIGkJbo2babJipIEqKW0wdoGSyKihBUoFQ6DxxojpBBaC4AsS6vUGCLDFAixAHOtTxNTYZwiAwavWpqssWVG2WsKc3/m+4/3N4JZUG2lpiB1Tc2oWngV0mdV+fAKGonQgg94AuM04qu6Qo6BnKRO1TQO1zi2ncF7sEreZ0wkZnBNi7WS68653INgz2ACSo0QVaV107p6t/qosM5sBO+3ElA0qggqcemfN5oxBfbDjtthJ8TgtsYMpTagF9kEXKPBGHQxrLqOzapj3VmsyuQ0MQ4NrmSKcmIUlKhfj5NnGCcBbax71tstm+2WKQSurl6zH/Z476koJKkHKbWkobVWFcggzsibiCilIiih0WqbVvrWlEQXqaL4MgmfEsZZuYc0XHYPeXr2mA/OP+B22HO9ey4k4SHRqLY2+hr6RpGTgWJROQmS79ETHj14isLQbxtSnkhlquTJwh+pS4MqFpMtQRXS3HytRP06m0JU4AWRwZhOqZDqOVtdU3MKZURweYoTPgd0MtgoRjBOQsEWiydh6VsjYtAIyYA1QnmW0KQgqOfDNDBlOY6zyw1titKIHzNTCMQQsdrSOTGCknKH2QgGP2GspV9rzs56STMaQwxF0n9NhzaIlMLJyHM9T5clw8JsBOf6d0mEEri5uyb4kWBb/HRAkWidxTiDbSxd27PuV6w3ax49fkzbdNzu92Alei29pGOnyTOMnhgFENJYR+MatqvVYgTvdjtu727xwUNd65zckZRmBywzjRHv70fuw5S4KhOH0XMRNG1vcH2L6xq6ZLm99pVsOrMbbtFW4XorjezApOHqxS2fqZFV1xBD4fYmkoqkHNfbns2mZb1ueXyxxRpBX06HTIiZkAQ53aDZmDVN29GsWvZhJOdATCNKJzSZGG8YB8+d3eNjpOCl/NFa2vaN9PWyzhTb7Xb5/XAQjb5xHFFKcXFxwfc++YTvfe97XF5eVvSiZHL6vsc8fsy/+W/9txaJuf/0P/lPePniBVprHj95wvnFOdM48ZN/3vPFZ7+gpFgZuPIi3WWMFmm3w4EwHui6hu1GkKFoFp5m6V3MSzbl+vqacA9LINy9OQWMKWzPevpVRy5iAA+DZ9qN6GTodUeKmqgKUwbIWKvplEM30GDw+6HaB+HbdSSRt8uiLL/bj7RNQ993RCX91mOIjCFyGAPD6Akxyx6tpS8xJOGCdkUz2ZYmiZ5tKpqMoaj3NmnL+A7pUEE4lWpsqJHfzDyhlDBcJO+JVR1C73ZLvUrkhYRVRc3wdoRKq20MfWfROpGLw/pCMpq2KWy2W9pWvOcFPbcUeo+tGaXke9JOb07Uo/TLm+UXUa8HS1EarS3deisCqOue/e01MQRGP1RbpETRumtpG0cukZwMKUpT9hQTBx9Z9x1JOcYEKimaqLHJVDBHEAqrVFhvt2jXglJcXd0yThPX13fCHpOp4qcGhQjdilKH1B2s1vRdS04Jb+9HFpttz/asEQNfm75NrQXakmlXjowlbRt8ilUXcOAw7rhTPa3u6Uh0scF5sD6DiriVpW8159sWZ1qc7cBeg4PVg4f87t/5PofDIw77K0LyxOQZ/LQI8OYs0XRnIWdHSobd3UFIEMgkPYJONJ1Ca1M5UiXapRTu9jtevn7Jp7/6BWfnGyE1T1EK6UqTivSngsi55BQ4HA5MV9dYfeByfSnXP0YGP9E5x2q75ub2Gj+OXN2JE6Sd5vLB+XLch/1I8BE/TAzDgFaF7fmWrAS1V8aJkjPTGOjWUrd5+OASHySS7lpDybBaN4QUUP50Fs7tEscIapm31XkTR0fm8Ivrlxit6LTFF0+3aXGqXdakMxaDAQ9pnwhTgEOmHALpMOE6i2kM1nZYZwSy7iObfsWmX/Oj739C3/a0TYdpLMpoXGtqvyyVNLseeVGLdJoqNfI6rjxYWYLR+FQo0eC8prGF3BZsU3jgGsKUCEPGVmkvSd87lJIoAZAa1oWi6w1d1/DNN3uGQeD/L74WpLopGWsMrbO1di4IEddZbGvR9ks2Z2suHm65fKYxTnYDERFWdO1E8AdePn+Fnzo65/jbf+O3+OKrW7r2FqVe3e8V5JgSncf3vvcJTx4/4fHjJ5LGbhyPHj1itVot5zLf26ZppMbXz3zIiv/+o393UZ0xtdn8+vUVn3zyPf723/4b/L/+n/8RV69eiYOy3y3pX9c2PHr8hMuLrewPFMYggJLJC0NMqI7Xer3ir/213+Lly1e8vnrN69cvAIkYv/zyK4xVlJIYDpEQBq6vbrm5u2MYRhgTg0vsWr+gPSeVaVcrqWOiFuR+mjxWaVpbnbxS0CWxu71lv9uB0ljX0nY9iowqGZJnGAOjD7za72sLmeSvdDaYYnl+c+Cagf3dxHa/YrXuGMmUs1y5BP+SIsFcC6r3CHKRSFBuWaJU1FGIwt82ehFuzVkijxQjOaUlnZkp1CY1Uo0OBVk/e8W6ejqSEpXa3n0jqBYj+EYaCe5NzpnerbwBtCilKr6Tazq0IjjbHms15Jo2jL6irjRtY4Uk1wljSEoQtRh6SuYwWUKBog0YJxIkpgEMuWhiqrpmWUK4lIXiCp/wXjboXMmlS0V3SZFa6i+SBjlBESrpGTwdIp6pZ/y6wOy1kITnXGZ6StDQKChZgUqM+cCNv+XmcEdJmbN2zVm/quS5mlXn6FqHdRptCspE7oaXFDNCMzHlgagmfL6TXqcSKVpwvblIVKWUpjRaegQL2FQlhhawyHwfTaV9O5lvRhNz4W6/R1lBZGojyhJlbj2YUyJKo1TGGEeOSGOwVmhnap+YEL0XK6jOrIoYKGVAG0oWPUlnDG3ToItiGkZ8DOgJurwSGDiiIVmSbCQpZrTKrDpRlp/CCKqgtHjvucC78Fkz2Gs+ZSV2X1LhuaYfKRUsgDTmw0kTO7VkkAjRM0wHrm+vcMbh40Qh1ei1XptaUjC5sju1Fmc1YRpJPnKT74hBNCjbriGlGpGmkx7N2TCWSkRxCkpQhWIKWDBOoxp54ArGFKkXrxrimPGHiFO167BIxmQ2gn0vBlPrIuTdrtC4ImnYLPtOpvbP1QxTjGIEtTHYmNBjIGfPYTewu9uR1ZbNecPFw47WFpyWpu5YgkRbzuF0hzMb7naR9eod6NB3jKYRsNcPfviDJe25Wq8w+o10ar1MM7PWPDYnUSWIw5HPEtZq+t7xn//BH7DbSctUiBHvJ3xMuKZl3a159PASReZwd0eepKXNGEPX93R9x3q94YMPPuAHP/wB3/vke3zzzTf85Cf/XLJv2vD97/+Avm9RqrA9W6O1IiXP/jAwTQEVC6229NaJAwgEXXBdi7ZW1FpQwgoTI0ZrGmNRyD6fkxdHFUVRBmMbbNNKIFUyKnlGH/AhcfZklL0yl7qPCgmJiwlX4Nw5+lVL17dEVXj49AMuLx9ia7vJ+47v0CcoTe2yQecFTp11kkgIkcYJ82uQ5uDZ+Iy12XvuJywIx2HKUUhso6QvJp+knybnxbOdEadaqXtN4UoJiqsAKPOWlwb3UZPvMuKFKIKlKaC0oEy1NjSNozErNps15EjJUZoytcLZ2v9C4TDsCcHgJ8X1izsOJaFL4sHlJck26G6NaRtM24rCfS6Mk4CMQizEVJimkZBGQWzWRmVJI0iEq5QoD7RNU8E3tlJQIXREHDNn8xBuV1GnN1bUCxyOHAw5RqY4UgRjRSf85iQyt/trrg8Hnn7zC56tH/Ph5RM+fvKU9aEl5kjbCkEARVQiQj7w2cs9za2lv+6kfSZnDru9XPsCqhEEayyZVIQ+ydiIMQ1aWbpGUwaYdoESa0N2NijlqLnG+YaLmKg13O73FXiicW2L1i1Zw+SSGPusa9re0fVrpmEip4JuLK1dsXGGNA34HHHJi2I64LMXtpIEw/6AUorVqsf1Pd5a7u5uGf3IGEe6szWNamhMw2EcKDHjdIMfAyUqNtstKUd2h2sykaJE9y4n6ZO8Nw8r6lrmq16uI7X2XnJF7emCbuVO+1wFfU/vfMkEAiFOTHFgN9xilBFnz0ijulK6ovUy+mTDcJUp96uvvmS/G7m6uWMyBWMsm94uBPmp9rqlnAkhLVFht+oIoR5zxRhGFehbxWbd0W8MpgHTZnQDplFsthvSlAg7T2dAU6S/tLI0Na1FS8K6OvkZY2C90bgGpKRlJcPipT7pfSZMkZzAZFXBPIVhyPh4hQ8B7z/m408e8Fvffwr6hsJAiXt0iTRacXF2Qc49w9ByOAtcbaZ3VprezjoBxvLw8aN3vPodowLc7j1V/zeDBtebDevNikePH7C9uEQ//4bb/WumaRJdUh94eH7Jhx99j0cPLvDjwBcVYe99oOt7+r5ns1nz27/9W3z00Uf8zb/5t/jhD3/I559/xj/6R/9XUhID/g/+wT/g4uLiBGEqkbnUjsUx00XyUjOONM89GbNhZ25Wk/lptcYquYckT0hZsBRKwC1oh2CJM4a0kElIVmdGstdUvLLY2n/eIBgBbcSerLYbHj59yqrv3+/a1/F+RrDA9c01wzBwdXVVI7OjAKWe6wRFWh9SXbBz07REgmFBNMq6LlASKjWEwUKuvWQhczccyAVc46RRdRjQ1ciVspTml2M7LcQv6dqT3+XlVYjX3yeMdVbIYWevRuClDRhFmQVKMdTlSUbEdiuEBqVF1NKWjLKWMCVe3+54eXOHaRqMcRxCZFfroVOIXO1GXtzccDcMojBfJMI1NdpTUBUtoKSE1lm4+5y0ETTWsFl1OOvIKVSQ0qlxVxIxNoaUCs7VSDFOKJNRZPpOaOymEKkVbNquEYRj1Hzx6pfEaUCTuXhwht0oXt+9BBUJZAY/1f41RcqBYZy4Hm/IOZBzxB+knqmUol31UguxVuZxgd1QpZK0oWlasBnXR/AOZRra7TnOdezuPEr9s+WOnz94wIMHl3TOcH4paeub21tM69DO8NXtN1jtcE3LWbcWQgEziURNgdb20leYOqa9Fog34EsQYE1Q9NbSNY7tWnqxdAHbiKbket3joycWEVL2KZF3e3TOgppDMQyeIQeKFhLuy8tzbDsyDpEcA0ErifyPy6saJUmMUNmYKEcDU4q4eyoXCGUuzZFEJqTC46tjqASslRWMJaCqKn2aO2rrHFNFmF/mXbdkJPXrA3s/sBsHcBpMJZHXUg5o+ip0XPu5ZgLvtm8ZByGUKBU09+zDjvMzw2olWQPp5EaOQWvCIZKmTJqE+MJoaJxeos4pRcjiWF486+h7y3rtaF5rxjGTgqARcymEZAmhMI6Foj1+LPgxooog1FOSUqyf4O564PZsx+HwGu1uQA/kNKCxGNOwXp3hveXmxnO+3fLoMler8Laj/cZWWSPs09XId07R8dZXSW3tw4+/x+ura/6zP/jPj1J0xvLbf+13+O/82/8OTx8/pOTIzevXFTFaMK6tQrwrLi8vRGlnu6XvV3g/Lfulc46/96/+6zx+/EgyF4vZn9nB5DjUG4c3p3Krv7a8a36/QqFnpec6p2XOz6nLI5rzCHrkOPeX75HXyd08cjvPotvaGlzT0Hb3+aF/03gvI1go3Nzc4pzl9u62EhwfU4/6xOjM9YH5jOaaXU7pjelTgERJE5MVJpNYH4dpBBSucsjpGtXIzXhjei2bxLHHZUmXzjWW+upctbVOxzQeJMWGRVmHMpY0tUy6Mh5kMRIlR3F2VK4adzM1h/B3xug5HCJhCuxj4OsXd8RkmCaBK7eNxYfIFCI3u4HnL265vh0YhyT8kkg6WxweYa+R65bxOqIIDAdP8ZnsMpQJa2KVSpKN9xglF27vqn5cSliXZWMKNc2aC6YRD3nyEh0rrWh7YXZJKXN384pxV4iTITSeMQau70YB1uTEfpyk5cQYtBUpoBBHUgqUHIljpedSmrYvmLltxdTmbPJSq+26vgrqZmLoMBZ6Is4G9vsTvTOQPrvGMRpN1kLsfnNzi2sbdGO4en2D0ZbJRdIqCOTe1uuEgjQQSsaXyLS/o6SIyYmb3S1TGIWtpPeses+wn2Te5iTp01K4vTtITTElQpB+phADjTFYZeisJ4yJnGA3BlSTUS6xH0amMXLYBcIUpb5ycmL7w1QjFqkDq+ooHjcfveyjxpzQqtW+2Jk3dAaGKVV7YmdrudTPM9aZY6x2EnEIGX5h2k0cDhO7wyCKLUbBVBbtQmftAsoqlYhQvjcRptM+QQi5MMWMiokc6uamJU0JijBADoU8ZUxJtcnbEJMcSylItqNkbBdZecWUNdd7oQXMcTaCmZgNIcA0ZXYh40NmGHPdNjUhiBjv5DMvr0dMb/jFZ6/Qbgd6IieP1YLsnqY93mu+ebFHm5br23HZlXPO/PSnP+XVq1f8ZY9y0hJWSuHFi5dc39zy6vXVkuVq2xYfpIwyeS8GwhpaY2nR2KYVybUaIY3jyDRJy8cXX3yx7Kk5Z169es07LDD380x/oTNhjipPzeT9z/8LfseJNS5IC9nPf/7zd2YG3zXUr3uhUseCzLvqbIvtPjoMHE3P+w11/CAWp/Tt4/jNH3Ty3b/ZX/t1n62+5V68xyfeuzbq+FH3enHKvXTHmzb9nV9375Bmj//tr88nueI3a4TfeYh7hzq5muXeH3/D2++lrX/Tq08n0GlNSTy8++elj+9Q9yYecGSMUPcm1juP8B3He/ycOZr5Te97e5zOPnXy1Mk1PHHclnf9xuN9j6/9DU+Vb3n+zb/fO8i/wJe/eV7vFQS9uQ7e8fVHw3//72+99DSC+LbTqGtK6Tfn0Lx25/1u/nNZhKxBoqb/Msbs7J+WdU6p75b97CQa/XX755HvVcZ/Wef1L3u8eV6lfHsH/Xsbwb8afzX+avzV+KvxV+O/iuPXGcH3l1JytvbZnX7w8tO9utwxvHvDs6+vfff4C3jBb4VR7xGFFu55UZv1SpqX/ysz3ozK6rO5sNvvl9+NPVIWAW9HNu/yDsu7f3lXlHJab33z7zPw6a0Yu/ZHFY5pHvHIZ7qj8la0UuBeCrvtmqUGfVqIf+f41r+Xdx7f8U2/KWaCb5/Hb7/srTi6QCmZcTzWBfu+r8ohf9nj29IN/3JGzplxGE6+7u1z+naX/Nc9+e137NePN/uH35wPc4tHqeCjb/uYUzwCi/jrjJJ9Z8Q73/vym/clVfuaj7JWvON03zHT3yPtde/Pp8kJJHs0jEc2nK5f/Zq94VtyQW/98nZ0/V/sEN3b4Mff/FLe0wgqpfi3/3v/Fo8fP2LV90udz/sJMYDC8Td5L2KmlVBX6xnleBqin+Y8Ty6SXvBELDDrpc5XU31V/0wQlNKOUWqqedb7E5h5rs+VeceprQYF7wP//A//BSklrLX8B/+X/zN//bd+tExkVG2anzfrNzWKmNM9s9Gfz+T+wjo55WPqZTnrd0yOd12We3OxSpNUh2NBCxapuz7/5gX/w//x/4T9QRCNf/df+7tsz7a0jaNvO6zR+JBBmQpNtsyLaqnbnvRgqjShSoKSaNoGbR3adZQizbd3r14LqnUaWW/XwqhhDG3bCwNERbjO/KbOOZq24TBIPeLm+pq262icUEWFGEUyahwpRdoe2qZhmib+w//7/4MUI9Za/qf/8H/E48cPay2y9rBZt4ipmopLm4EbQt/n5ptLTgLcSSkt1yDnUlUBVIWry/2dKf9KKUvPVsnC2JNSIua4tKhUIKz0YSpxQFKqyOMcSCGSjWhPOwABAABJREFUchEEdPA8/+Yl/8f/w39ACJGmafn3/tf/Wx4+flzX2zyH1MnPx3+XVPs8VzmCBN6cOLlysRUUFKmNqep2SHPTcdLdTwkf53o5mSPH2Sj/m98z/+n5V1/xv/lf/i/EcVGK9aOP0cbdc8JOhU9PVQ1mY8V83krAafNR6ePF4UgmcATlqWqkVMn1voDRhXXvWHUNH3/wlL7r6bsVY10Lrul5/vU3XF/f8PzVq4pKLPMUkDYVCtEPvPz8z4BC5yz/8//B36c1it1hYBwGrIHHD7bMybNZOs77IEwrlbh6Zt6KqTCTjDhj2Gy3PP3wQ84fPqJppW9QVwmlReVDq7fZr06d22UTm++oWvaweUtVM5GIkf7QP//8K/69/92/T8qZfr3lH/6v/k+sNhtiCsBxH01BugJmB7aUAjEKgrmC+8rJMch9nN+f57vHItGlTuZTOT1mlmCrlLw8tDYo5JyPAdf9drfl3LXlmy9/xT/69//371UXfH9leQVKF4QvBlBlUWQo6NrQLhvR3ECvzZEGSl43M7ycHvi8oOQEBfJtmJlRxAhVuIsWctWUovRjpbQYj1IZX2YE6fyzQO7KYsyMPebNFfD08WM++vDDitCcn+XkQp/+fIo+Pfk7M1bp7VHkUp389qYRVCcv/PX3YKbWevNBmefePNMVbdeyWq/YrFasuh6jDT4IlVpW9xtK57aMnGrTVy7EaUIVMErT9w3GNijXkitwJvSdnHUpdHOPkLX3jODcRmOqTJIYO1kwZ5VqqmkajDF47xeR5pk9o+s6WRAsp8X5+ZbLB+dYJ+rt2mhhw6/kz0YbtKrfacwC5ADpsvPTSMqRnGLtKRSSB+m7pL729M7MVHySCQGpx+SU8EkUxW3V2StFFLln1UofJtG0TJUhKWV8FOqqcTp6qUrB+cUFlw8e1N+PG/1v+nmehEcjWOdi/bOI4VbpsepgzvNXMRtIlp30XZvG3NP7bRvK6fPDcDjNCaC0kcdJBHZ67Lo2kJ+eG7Do2qGPFNCnRvK40erl+KXhujA7BdYoVp3jycNzHl6e8bd+93fYbDasVxt8BLSladf86he/4uuvn3P4p/+UYfJMMdZNRcn+QSGfiM8qBWe9o7MaXRKuBKyB8/6o1RdTwpsiIuBIM3+MQi2WsiLqhDFCGr3qevr1mnUrTFOqRBSKRhuc07iqJWicxbnmnnG8ZwSPV12er3MyL6Eq9X5I1Blz5urm7uR9IjxgrfAoLZmbnEUPdEFy1r1HCUtSqa1c5XR/KwUqa9jRNteavaYGG/Om94b6g5AhV4N31HkV+2AWm3G0DSzk4QW5r84175yr7xrvbQR3uzuc09zNTOdzQF13eBF8LThr6xytBK36fvP6/aL5/YmvtcFoKwSqhaUnkVLE6xdFSrxXoq68yDbVvXtWSXjLQMzfK424p8bGWodzbplI78wEFBb5kfl4Z2821yg08/Ybjw7a0QjfcwKWtxwjx+Xfd3yGVhUxWkptpC5L36WIwB7fq5XGGceq37DqOow2WBuIRTzdsIifKgTpWoBcBYojh9trtCo0TtN3TpjdYyBlvdDnLUX6qhF4zzAv16lqC8bIVAqHw4GUEn3fs16vaZpGiBRSZcuv11kpxbs2XVMbkEVAU3qEnK26hNpU5XAxuI1191RNcsk1eyGGolTPVsR+1dJ0rqqTkJMg2bQS2qYlgpmjkNova6wY5FKKOBqlNqwXkcLJKUpXlcrCR6mLSBB9i9MzO1qn1/H0528bi0M2r6tSm5JLwZLR5Lpc62ahTt7Iuw3gPI5u33cZp/PhyCx1Oub5+9Z5SUjwjgM5ee1izI/HqOr5KAVWGy7ONnzyvY/4wScf8t/+N/+bnJ+fs92ckbFo7Wi6NX/y0z/l00//nJ//8heUmxt8DMtnf9s5C/EG1P9JRialxdFPSfopZ45kiQzTcc2khHXSm3d2fo6xlmEcuRknchEDsF2tWPU9nbNYJ+oyrMAYJ33KJ4wLxy1FnVy3YxRNTbfeu5Tz6+d7UTI3L7/CH1ak6Cs9pJxHWVq5ToMEIR9JMUr2jQwqL/3gpYTFCBrjAElTCnXcsc9wOYc3jblxNfJkiQK10tiZem5OvyhEkJvqsyjHeLjjfcf7E63lQklJvIpcqjdBhegXjJbpaPQxlYEq9yf4PSN4svEzh/vi4ZQs7s3s3c9efEqJGDwKMOoklVK9Hdno6sWsQeCbnkayJ9yh6rhRn24A9w21XkLy6+trUkpsNhuaRpjpReFcSXRV3k4pHT+qLtGijre+vPE6xbyTLZ+xIEhLbT+Zo7ZyVGpWiMrH6YxyTYtzLQrNfjcQY2RfHZnGGlYriQ61VlAjsBg8wzQwjQd6nRZZmzFEjHKSKtQGq7QYLx8WjyyGSIkRa9wS/c1otVTnjT7ZyGfyBa014zjivV9g2zPThrz//jQMecLHgZCNSK9kQyngLJQq3VQQIzR6Me65OgspZ6axRoI5LouwFIUKEg2mtpeIrhRiCGhlaFyLNlEiTF1p72IQcVClpBkdcQwWB4FCimG5xznKHEpJ5mV8M80+r5lyXDOnhuGdhuL4R5kp1RvWqghpefEwCQNKCJ5sHco10PaSeXnzEL7F8ZiJ8edt602n9p6RfsOyl1xECu2NSHZ+/7ueX36u1+Q3GeA5VlSzI1cym/Wahw8v+fv/6u/zycdP+fDpI37w8Yc4J72b2vUY29B2K37vb/4Oz5485F/8yU/46c/+jP2f/3l1qN/tvMt5RWHaT0ma+4GSU5UAKoQQiXFmrxEtyZAyJUlPY4wZRUAxEtaeKXj23nNzmAg501lHOD8nb7bkrqdpG3LM6KwxxqO1Pc6ZGmUxR9vq5FEjKlVLSYYCpQYxKUvkcDwp4vCaqEZySoRlL8onJShRKqGclGOyGMBCJmdPWNo1RqBUJ1L2A2tczQzIvj73Zx97CuX4RAjY3XMqxLkXIhOtNSXV6FOBrfzHIE76MF7/mhlzf7y3EZy9OLnukuasUe3JxV9evYS/9xavut8ScDrpTe0bWiabkmhmjuRCCDWsLwuQpcyOTQFVlFByifNyzC6q+3yh+g1R3aNT+e4N5jifarN98LhJGqetdSw8qhz1FU+9MVUNm2yQSa7bQpOkjv9Xs+0T5+ENW3r/eEvN9qujaMibQF6txFvK9XtDjbZUjqhQyLos6WVtrHxGbUo2JEzriLnIwi3SFF0Qwl5VhNdT0pz1XtSIcAYZzM/lqgYxX2OlpCakFJVmL5JiEG9yVlI4cXDe3HxCCkxRhG4zBZPtsvHM2m2mZFKpyvb1uFKpagkxSnRWYnWc5iGLVRsrIsAp4YcBYyy5p2rEKYzS+BgIKSxcjIpary2FheqUUj1V2ShijIsDI8f05v09SfXdu9/qnf/e/7t8qWQGEip7SvIUv4fhQPaeYZpQ3Qq6NU1Vm1DVcbtnlO6t4/nKfGvQuhjAb1s/b7YBvWXovuU8l42l/vyuT18SpQXmgkSp+8Vms+bh5QUfffgBD87X9K0jRU8KEzFE+vUFTZfp2o6+dZydbfjexx/y4vUrPvvqC8ZR+ne/LRYsOVdDeUohJwZO9FIzOYnTOqt0pJRrBDkL29ZHFjHhcZoYp4mUC43SEn1lkZ5SWpRgovWUbFEqnwQbJ9fujWzGUmYyRhRVjOzb6GO97eRmkcIgBAT5yMssSY+6h0Uv5y48frK3CWNCNZLCURpjJISjUyvEGLXentTRCAIRmf0KxFGeU59LDTLXvXPe1xK61gbnNGwqFbiJEXWQ8H6gGPgORtBag3NmidpUraHIjThGLcwnNHuP70CHLbv+/KuSgm/wIj3kmg5jDNYYMTwxcH11Rdd393j4JASfFzCULKm3xCz+y0LSOKcrjXnH8bx5eMvilPMRxQKLNpCnyM3tNbaxrDYrbGWySSky1RpR23ScSjYJn2pgv7tFG8N2e34kGODo5R/TiWrxgDjxRqmbLcvPRyPRuHJvp8pFGP69D5QiQqybszPG2yt211dMV0e6tWa1xbgG5RqMKvSrntV6yxQSd4cJXCuGUgnpr1aGYb+j6VqMEW28lFPdBMQn9NEv3vw4Huq9grZrRdlDK3IJhCmQ/ESuKRdODJMACU6brwu34w53EG/e2hatrUhd1RRp63sW1pSawok5kZFUVWttjarFOBaK1CgqBVksECbPNIzsr65wbcPZ44dCEFH/CzlV4dypSnnl4zy2DUbNdeeaEoupGkyN0Y5SMlO4X9T/NiNzfz5+6ytknk2eaRhI+1eU4YZ8/QX57jVxPHBz8LB9ij5/xrMfb2n7FW17VIRQ9z/qW79mIaL4NanT0/GbXnZqQH9ttDsfwvzae4dVowSOWYBPPv6IH/7gE77/8UdM+9e8/uYr/vhww+Fw4Pbmjk9+8Ne4ePCIDz/6BB8jk4/8nd//WwzjwItXL/ns8+eMOVCWuunJiZTKhYyuJRtZSyHEZV6leEyHzkK2qUaC4vyJAG0pipAiMUumq9EabQ2Pz884P1uzXrcMg6cE0QSUenesIci8/x6BYPOTqkoPCepUavLaGKxGVG+0Ilcu5+W0KKTpQLYzGCrXVKpZtFxDnMSJfBcHc8nVSIrjp7V74zVy3U7LUzEXQhH5K13AVnFlrQ3ZNDW+rAFGtRN5nJjBQalqaKoRhGu2IZHwfnjv9P17G0GjRCBRoZjZbrR+056denNqiUbeTLO8ywNMSW5ITglXaztaaw7TnmEYKTkLGWtjl/SgKjPkWQARknaItT5Z7ccMi64Os14Oev5+XSPLd3mzNYasaKXz83OapuH65lo4JIeB1XrNjFQVCrAjbU8pcn4pJ0zUXF15fBD2mcvLS9q2JcRE8IEQIuvNeomAjgjX47FIblzSPeUEuVVKpm3cGxuD3IOZiFxQlA0qrjA5crh6IXRUSuETJKXZh8y6b+j7BtVticWQTUvbrnBtS79eo8qc3swSEY0jVmuM0mANbdfTdG313iIxRXxMcrwKip/QKmBEFl4ot4pMpqbtJK2rTpp27xUNwKfMlCIKcCiMTqQSiUVjsmYIU6XyUzVdgtw/ndGqUEqo91kL+XKRTUlj0GhyUIRxwh8Gpv2OlBrc1GGNrQLGShZlqdJPlZS9VBp5XQqtaeT1HB31omSjmJGpY3iDkFmdRFXvsESnEdq9vy559OMj7HdM1y84fPEpL169Yj9MxGJw3YFm/Zp+fcbZo6e0zz58+/N+zZgTk6dG8z1NobzvW1Otx5+X/WL+Ty4chXKfKKF+7BGSdmRWUQqhCVutiGEi+gk/Dnx1e83LFy/56suvubh4jFKau9sdX3z5JePk+fHv/yt89OEzfu9v/g2ubvbkspPNP5W3IvSSskiT1YiOgjg7qXJfVhRoSkXWeK0NzpFTyAWTM67uL43TnGtNKBJk9H1DSoG7nejqNa4BFMbYeg2OkaCuwCyV6xwpx4ki6plKhAtUrYMrRY6JVEUNTofPGV1T93nJocV79JeSsZkNYY2C6zoKMUhGRZ20eiz3H2GUqrOm1AhZjs1QFEQSKQfZl0taMjxFzXupvu9Z1SjcaIkSs6nK9fH9CM/hO6FDFW/iAuZF/uYymp/Xs4cy5/Xrwc9CvHCcW0KTlCgpL5uYVqpGhyMKMFphjZHWiCV9JJ93RBa+sYUsByqPNwPTb6tJLGdSAT4oWK17jNHsD3uC90zjCEoiFu89bdvJpDQS6s//Sb0TlCqk6NlPI9t1j7OaFCN+mvBT4Oxsg7WmfqaC8o5NphzrHpJpq2hKY+/dh/nazzVDKSg7aFpU27HP9XorUWz2qXC1H0kXG4pWrGIho0nKoG0jkidtRwyxtqYUUqVpMk2DqmhM6xzWtcsiKike4fVavGVQGISvVBWFM64Cd+zipMwgmTdvSSyFUEFY5CDqH5IIJ2ZFYUIhactcc5PaWIwRBYLCjDQzixeZSRgl55sjcj+GET8MFJXwcSQXK7UMWY0UqLXFvGwKKIWKGqvEY9d6yYbKfSoSV4SaTr1/b+/PQYVaKPGO24l649Vv/KQkTZ1jII0Dw81rXrx6xc1hJKuOxg707S2Pnn0Pay3nj5/CG/I+x2lW3vENLOtuNoTfDh05efN9q738/xQ5/tZ7yvHlc93nzU9R8wI5+gfL3tN3HX11xnKtZV1dXfHN8+d8/tlnTNPIeDhwffucn/zkJwzjxI//9u/x4PKcH/7w+/zhP/0J4+jxPoqayBtDoiG9INgzZVHQmY2gkIzL3jaLF8/Zm7kVI9XrbI2mc5aIRHDWGmLwTFNgDGIMrHH03epEMu54bQWAqJfrVqrizrw/qYp7kHaJIgLbMVXw13xRCzFnwhy1qmoEixCmlzyDXkR+Ls0CCdRSSM4EH2oWzx6zgAWKmg1mWu7fDOwrKYmKC6BUzeIphVWShhUeebnJZQmoloUln6dl7usiotzS4vF+472NoNy8eiD5GAmWt50kjhZnPsg5zSd/TScTd17sJRVKTSGESrattBic/X7P2fmZ1OCKSNeUGiZrpMAfQlg2Z61YwBBqPtA3j62OnPNyM98FRkAdr7QxltXK8MknnzBV/r3b21uurq746uuv0dqw2WxQKM7Ozuk7ASAIclHz7OkzXr9+yU//+J/z5Ze/rB6bpe83rDdbHj1+jMAPMnMP0KnXK5uBohSph835cvnvfsN/13U0TSMLMwQBeSCRl2latucXpDCR/Mh08KSQKEn4PjGO0UdigtFHTNuRlaboA6Z6k8YYcilM04TOCWMdRnVHvkdqEds6+q6vkW3hcBhIKaKUYdWvadsOa1yNkiTCnLf8ruvup10US9Q5p1eLygSyRJRKYSuMWinJXhQyRXmyCLzQaFcNUjrxSguxBDHKBHY3N+xe3JCnkaQLjZ/wuorKFtBaDKLUqeXqt07UGXIGHzwxBoyBnCSSGMMkm14WgdPBD8u8+nXjLRvyjt/nYZqGvunoP/6EsLGc8RptMy+vrvmzFxO3NwNTuCb0P+WTCBcf/ZB2vUJbM6PI3nscDWHdE74l76lO5/AbdesCtUG8/vlNPuUlE6JPzvkeEF+i7HpEuX6eM46z7YrLizMeXj5krws7Cp/94lf4kGnbNZcXl7R9yy8//zmff/Ept7e3fPX5p5w/eMLv/c5f54++/8do5djtPgOV7130AkQfiFlXZKQQm6cYRXMyJULISxQjaylXGbnatpDA+whMxHGkVx1n644hQyiZ6TASq7M0ZkUqHsqBrukpTa7crUf9VLUAJI5Bx/27NZdPWHqMU0zkeD8dehj3hBIlup3BfjXzohQYbcg5EZPHh7G24FDRsJH94YA1BmcsjTmJBWtUOmMXSgFj1OLIx+SXQ51tRcpBSkMZQpI9PZJxzkpPsNKEUKp0WcJYEe+NRTFO02/Oxdfx/nqCKDFes1Omjt7gvYtewRoaFi9BlRm9Kcc1qzVorTEZgdenowJySuJVk7MI8WZpbFZ1xs8Ag9kwFCCWmo7ShpBDTUVUoV0Kygji0Nk3O/reXpjM5/WuTUoJHL9p2mMalUJOgevrG/yw55ef/pzLy0vW6w1nZ+dS24qRw+GWq+tX3N1e8+LFC7yfOD97zMcff5/Hj5+hkE10vs7LgZTjsl+iv7l+mKta/Ftow/ugpGXDMQaKkftZjyvGQI4Jo8AZQY9aLeTQklaU1EmMAdd2GGPFeNWeupgyRUV0iuiSsRRBszlDSgZSqLWzQn/eCKvIJES/0U8kHZfN8p5CSUWZnnpZujoV5KqqvfSRKXnOWazWNNouabUpepljeTa0pf6bmFtqdOWm9yEyDgPDbo/TEhmOY6TtpCVDJrHA41M+1oCsTjXdqsTTrmnjeUHLnExSO5U48p3r7Hj/4F1b2fHnU7Ow3GHJjLQ9bnPO5tEzvhd3PFgZtLnlm5vAq33kbr/n7m7PNE40qw6FFX/sjZLFO9OXp99575dvO40ixk/dm9DHN8+76/Izy2sVQLlfvji6/6efN8/xUssbBecMbePYbLZ0TrFZddxevWSz3vDw4iGXF5doo+i6Bmug5MDrF8/p2hWb9QUfPH3Kbjfyi199Kc5SuR8x5ZRIKi9KDqgiaNCUqh6qrMmYRCIuVXLyuSyTMmI4a6STM7U1wmCVIiLZHKMNptbRvJ8Yp5FS8tJfuyDkl2tYr4WqDsictpsPvYJ3ZsTlvZpggclL9iPnBLNKexYNHaUKJWtSDsTkmfxBrrlWy7pQJVJSIpaIqbcol1IDnxnwJtchlTlDeCQ6ObYCQSmmGm2FD4VUMj4lUnKLoZ1ll3L26GQwKZOVxoe/hEgwF0XKMxIMlBIknKRrZrV5ufpGV6/sjeLpjOKL1SswWW6OzkAu5CAq2DElyVuXgs+JWDLaSpPnjK4SA1D79BDwg63s9rkiseZQWxdwxuJsA02Vaphv/MkEuBd5UVsZZnfz3oulZuWco+9b1quO7brlZ9PAzfU1P/vjL7h88ICzszM+/t73CSEyjiO7/S13uxturl/zsz/9Y+5u7/it3/pdPnj2ERfn54AmJcSgnWwsc7QyG0BBQx1RaWVmoHhjzICjuRhtjJGzqgtU6pGeGDw5SUHaGZFrck76X1IylBxJAZJR2NWatmnxXYdzThQvckIl6rVONLpw3ncSoWYHcaqN+LA9O6MUePX6iv3hwDiOZNTS3G5rAX8+dgGkHO+JUVrqiUbYXeYWmhnSbpWl0YbWWpw1NRUWFnTboomXI6i8zKN5L5nGkXF/YLjd4bbnpACH/YRreqQVU+algBuCLMAiaV7RcFTk2oQf/bQYi5A9KUdCDMIoo/Jb9+td4zeDYk5tUXUJ2x6rLuiefszT9gAPHNvO8PMXE+ZV4LPbgdvdnuFwYH2xrRvO/En3DSF8e5T3fidQj6xUMeXZZbkX8lGb46lOdM14nKRLJbN2sgDvfcUxGapUwVRHrmsd52fnWH1GiRNx2jEeJsIYefTwISF6tpuevncYU3j5zdecnz3g0aMP+OTDD7nbDQvxw5ti3CklEurYy6wgxLIgPqV+VskR4rxR5yVFGIsYwVznX8rgk6r90IasNRaHKpE0HqT0MI0chgM5pRPWnZOrcWoI7z2Wy7wATKhI6RRPz6swTnsyvqb3HUppTCUvmWdYLoGUPNO0A4TNxVqpgWtV+2RjISJzJ6ZETNK/aq1FeshFDkwqlqZeB7l+ArwU/EbJEgl6XwgpMYRASh3OWoqtALeSmbwgxrWNKGX/coyg9LvEJQ0ltS51rP2dhOazo5d1Pnoi5divFUuRhl4tn2Oq+rkwHBTZ6LQWL6UuwPm9c4FWCtaZYZCNZrVdCSgmZGHjKKCMwIxBLmhOqfJQzolkCKEwBTEmFVBVka/Hmt782mWyFcVSJlAaZTraFZydnTPsdnz9+ad8+tN/QvSezXZL2/d0/ZrVuucwHPjsV7/grO/43ocf8O/8d/9dLh8+ZXt+JtkH8lKmOd38qd7SjKxXJww5CdD63UZw7rlb8ugUIBMmzzh6DsPE4GUzL6pACpgc6HSh6yybrqHURvJCJE6DaPDlQNcYHpxvWHctbetYr1acnZ3R9T1nZ/2S879cNVBrk33fY4zltz75kNdX19zc7Xh1dcsweYZxkrZ9BTGrJRW+bMJF+hZVzrTGoJMEDY1rCDkQc2AcAl5pRm1ZrzqsUfSuJcfqoc4F87rpixaexlhRkh9f72hC5mG/4vUh8erFC375j1/wd/7eX+PZB5c8fnYuAKwgklEU0EUTwkRUUrPW2qKVQSsj0V+K5JirgHIhaZEIe49s6JKmf58xvyopQ3Er2H6I1QfW64b/+jpzuTnQtQOfvrpit9+zu7njweNHqPa+8fvLGvfPRfYKPfcSMyt2grjI87+nTE6nEeD9GFB+qmCxnMW5815S/KWgS6JvDHjhCt3f3EjdKMJ/7ff/Lv53Pdb2WGU43N3xg+9/zOAnnjw+44sv9gw53vvuGCMW6VVLKZKUVKNDkvsc6sOHjK+RYMm5pgMVuSiaRtHMqHtVGKeBcNjLGtfShqW0Io0DUxCATEqFvu1wxkA7J4reDTq6l0sv4ojkNNclhThC1sPxvSVHaX3MiVIE4V3MTLGoKCWKOnyeyGmQyBIDyaA0WD3XPvPiIMSUSElkt2IwaCMlK23EyKV4dLZySYhsmETF816fMBItp8w0TngVmPSEmmOanNDW0mhL1zqMf381jO8QCUoqSWih7vcBohW6yJTVCL1OnZJLtuOYChVjOJ/0MuYme6jRXr63+fFGP0vJc0Pq3GUiCNMQQq0rqFobZElfLBDek/Oa6dekmVuji0IX4SxcADxv5oBOo7QativTslqfsd7e0TYt39ztuHr9EvU1nJ2fc/ngIY+fPmYaB/Z3tzjnsNrQr1Y0bSsio+rUpy3zD8vanxvOC2KwiqoeqirC7vCOcSQDqEwsKUAKNQ0aCbng60TTCFtMiRGN1C/Qlb9RgTIGZxRGA06jVh2tymz6lsY52q6hax2NVcxVSyUQ0BpVF+lfI+NMx3bV4Yyma1qu73a8vLrl4D0pZYyti/YND1zNGyM1FYxClYJGFlasqL1YIKZAoRIh5CRpTFXbOMq8R8i8ixUpp3yk14pu5Xg9BfwUuL0+cP3qjr5zXDxcH2uXCmbfIy3Ruj72i9bcVykFDZgayZZyXx7qu4x3GcR3PqMsxfbQbtBq4Cw3rNuJzlLT95KdKPmE6+i/GFu4gDpUjRLn547nMiOhQalaIz557ZJMLXO69Xjgc1dwilIHJ8uaT35g3O+YhpEwCao5U2htw4fPPkRrw37v6foVGlhvVlycbzg/W/P8uX7LyMxO+WKASiYlVSPBIhmdWSG9Zl4ErHL/OhijcdbgrFmc31wEuCKkQlIPLTnjQ0SVCY0ihICxVpy3pdQk1+J4Jd+IFstMf3fcY/MJabjgLPJSny8lV4HmXJ1wEFSoEDCURfRZzpkkTnxZ0KMVeJYEsFgqqj2ngtagkyYlRYwsWS6KpLTntPZcRlMVcdos5RItmaDKGmVUQ9u0rNcbNqsNt7r51pT+m+M9jaAsmlTbDxbjV6NBWfji0WWtjwKm9SbP1e5MQrJiAmSgJOYuf2UVyoghCF7SV3IHJASbG0/nyZNzYhxHYhTR3Vwy4zgyjgPn5+eApCxmerFpOICCafJLdFmgqs17ck4YLeej7bHoXL/wZFIdH/IpdXtTjodPP8G5nhc/es5nn33Bi5fXvPjmOc+ePaHkxONH56gcCdPE3e0d1nV88+IFuunpNmc0rkaeeXYfahRcJ6Ze2O6z8LiWUlFVGavfTq/NBlAbUZgfxwGdJlScCNMo9a8MYxEUliuF6D1xGilxApMoKWBcg3MN/aql61uMteQWzLbD6Qd0TtcMS6ZU9hQ/7JYNYhyH2gIj80drTdetuXzwgIdPHnL54BFfPH/Jn376K3722WeEGDGtwer79UAAqyRiKCUu9GrZzmkw4WWUsEJQnSUWxilijcxF6zQxFzwZW8TRsTpzdzsSDoHWJ571hscPHL+682gKBss3X7wkTiPbRz3OSQ+rNsLaIW0NcyuOkvppKlg7E4kXGqNxWlJLk/e/sST4Fx1HAj9DUT2pOSNrjw4NGfAx4AyQE/u7O4HIl3Iy2+6Pf1k28e1WCGqNqYI8To2iotLwgZY8B294ove2eD0/QY0si3DE+mmEnNjdXLG/fcUXv/qU6BMkw/jkQ1zbcrG54KPvf8J6c8aLl6/wIRMzXF5sefzwnA+ePuRXv/oVp2QUBSoTTv23clgmJAUaU8GnSqJdI8M0l3Dq8RsjaU/rGlZ9y6q1dE4v2a6YWdCjPjoOU2T0UQBsuTAMB4wVsopUMqpIm8+91XJMXS3fPdfAKSc/n7xeSgW1ZxdBiKrF6BW52lnavNRcQ0IRfKxBUqqp/tN649x8D6mKIENBK0NMiilIOQsUBoPTFqML2iWUEbS9M6UiyBuctRhjaFpH09SyVNOyXZ/x+NEHXJ5d8vVXX/Mf/d/+w/eam+9Pm3YytJ5BC0pyt+rETlRko6QVjwTKRzaQhK4e0SnjjEYsutYCUbc50616jNK1z0UWt7WWWI3BNE0La8vN9RVKK7q+xTVyWjopxmGqBlpjK7/kPBQIQa0thHGYQ1W0dmhlUcqijONt2NocDc4xST1vq1mfb/nx7/0eX379Ofvxjq+ef8nN3TVffW14/OQBGchaM8XM7WHgz3/xC8aQGSbP48ePaBtpRVAcyZ1zkfTJDAaCWeniCCHOVSVjHkLYLPWvUNMSVmsMFlUSMReK0jTdiqAm4e5MGbSp9d+Cs4rGNbRtJwTYGmyJmFIwVmGUEgRmqijJUohZjsmHsBzDMA7i2avq6KAYhonbm2ucdfzohz9CK8sPPn7G9X7HMAXa1arSqJ3K8oC28shkXG3gnxtqC2J4tAZrZS7lOtcKsqmYGjgIoEZSyWR48XzH/urA0xKJRmNajU0DrS5sLs5YdS2NaZl2Eb3WmF5V1inZtGQj15XlX4MtFVxQ6iYt0YQpBVc0rnlDvusvaG3u9Q/W+bHkE1Qlq1Caa2/Z0xObyKrzOFUYdjtiSAswdEHu3ft8Fkdk3uzfOubvkNadHbO50HAsWcn8PJ6M/DFVVhayRAPz183RzjEzcBL/qEKOkRQ8OUeGYc/t7TW3d68pCaxqMVbRdS1N29C4BqM16/Ua5yMhFrrGsVn1PHn0kFXfLyCweUhm7EhwAdSWCGkzSJlaByxHRqMimSdQwlxV+3djhlg0WTdi2IAcAin42oYUmYL02yotdchxHGm6Dts0Ev1VZ1dYU44b8nKLat2ynBi/nCoA5o0xI+pT9EAkFi9KETlLG1FlkPJeUvpKJ0KIpJjwIVSxBchxzuQJ2G/eA6Q+L719ShfaRtFpIcBw2gomwRjavhVuZ9sIYb62GCOqM841bLcXtG1D07T0bc96fc7DB89YrdakZP5lR4IwX1e5Pmp5KMWiGnFvNSgWFYF50kv7gqr9J3XRzZ+7yC9poQlSSrhDlXjr+aS2pyr9WowRawXyPk4j/aqrnJ5zymnu55HNUak5xXkcxoA1kEpl+6eAbik4mULW3s83LRf19AKXJVNqrOP8wQM2Z2e0/UqQkOPI7c01V9dXaOukrVqJYdsfBm5urrFNgzGF9WrD2fYcYzuUMuJF1okrPX8ZVcFBUmeV9EOM9yPBXEEgSqlaB8iCDK2LIxVB/C58rWVOYWpSgZgStlSmIGsk7YCgv3QWmLKujkyszfMzJDymTPTTknKVfsrar4cFCnHyhEmkuM7WK9bnl6zOLlm1LaCwriH4yFtamHXhKCqVmVILsrFQm2br3LRakVXBGlNTs/Odk7/FysU6hczdzcDu9YHHZ67e14xVmdZp1usVm76ld45wiALRdoWsdOUBzWJUi1yPhVx97mfkuIHPc13PQJDjcjkun1M7sPyq5ql2/z3qdE5yL0VYaqq3AGOEgAXT0TmLUQU/DoJgzQJSqDkzSX3du+jvMHzzV7/j+d80TnsftTr2//ZdWzNLpV4fMYIhBGknKG8awHquS3O4GEStZho7oR0LYWKc9ozTQVoJ5ujCuePn5STzxWhKdRhb13C23dK2LbbSOi4p2XKCZqxRljCb1ObuzBHBPf+9HNN7pfJ5SharMlppi7KWuW2MmCjEhYBbmsYF1T15z+Q91rmFg1ebtNRYBVVbr1C531ay4CxqOvTbRiFBDqQ8UqKsFWHjNRQMMdSIzkAMUYBAU5RUp1aUuexdar+yUst2OveIWqNxjcY6i9GC+OwaYYBardY0rqVxHcY52auMpWk6mqbl4uIxbdtK4NCtWK22XF48oWlaVutX7z0f358xxmiMlZrZDM3ViyLPSaqgUG+ExtboSyuNsvK7KRCDrwCYumnVSWlbS9M27G/2sskp0YqzxnM47Fn3HUZroqr1vRAZyyDvbUTOqV+1KITGzIeRyY+ooujbdmlYX4YSA2hUQuUBP9xJLWFqULYF29GuH4GygozN88RXy7nO02We6H4a+ebrL/nZn/45P//ZLygxEXLkJkV+8pN/get6mvUF3/vhBzz94GN++8c/Znd3xy9/8Sl/9If/Xy4uLvjt3/odLh98jHErhkEmvjykrqVKQr2Rwnr1errX6jUrM9i5tQSJ7vzkSeMgxfuYGL1nSgJg6puWmAv7YcLtB1CGvu8RwEEh+YTOFmUNRbeEnGuN+GSUgkGaf3MCSq71OlVZHWTTjTkS/Mg4jvzB/+8/4+LhY55+9DFxnCgJpr0nBaDcj5hCTTdpkS8UJHKeN/2CT/X9vvBoe45zlt6smPxESBFdZoOkCBQOw8RXX1zz8usb4m7i8vuf4FaKwRTOLiC3Gb2CH37wCGcNv/r6BXlK+LPM+eNzgtKEHJnGgFIBo0W/0BhNyIFc0YJt06CKFhSgj/gpvGU45vtZfcOT/90zg2/9/Obn5NO/J1HwyOOBJke2VvOgNXgiw+1rpnEkhETTmmpQTt2Fd3/+m8d8+hL1bS+EeylRPYPPSmHV95yfb/nhj75P13WUkun7fjFSn3/+Bb/45S+5vdnXcszMHDMf6RHsYayicRbnpDc35cgUBobpgE8DRlmsstjWoRvHNE3sdzeMB804eGKChOYsRFrX8OzxUy7Ozlmv1scbVEsSZeGvrMhjymJYUhbawpBm7lq5L3NvuiieGBpryVpTtEU3HdQsl3MNIWZyzAxBnFwhBRFv5fXtLYdpom1bhsOBtuvounaRGDPGLSnXOW0/RzGlTpLJC3H3/VudamwdgUDOohEbJkF8ZkVNwzrJIghaBe8zMRamkGp28AQ4WQMNaxWblZz3nNps2471as2qXwu1m9bCUOU6Vt0ZbdPTtR1F6xqxWJwVcYCz7SOxD87iTFM1SzuJiPWvm4n3x3saQQnfZ97NYx8Xi6cpV7AsF1saswU4YJVmtVoRY2QoBfIJ4GEuiCuFNZa2admVnYTWk0crhTOWw3hgmia894SQ8D4wjiP9aoVxhtW6p2ncstBikn6vuX0jxoB1jns+boGcpKYpaU9DUVK7ooBKGa93oBsKrl6uBZaxeFdz3RIlG78fJ8I4kLyIbRotUa2fPMoYep3YblrOtz2l1i2FL8+haMjFEmJNuSSWdB81135MAc1350SZog5jNM6JJt/cLpJSAKMpWldvMrD3kxDYKo3NBUikHCXN4CwxrAgaspYdYM4Kh5jqYp+ZIAolp1onEJUPZwyqcZW9piyLIqtCqlG/UpphCujdDv3yFXsPWTlU42icNNqfbq2tdbTGgSoLM4uzdR4qKL6m3oqS1Ip2ZC2etdWGmOPCS2tiIuqESk5Sqy6ScsInzZQ1Tx6cc541D6Nh3bV4H9nvBg4xYofA9vJsKQekCjCwdeIrlVFFV3KHI4NPKQalAt9mLt4ybur0+bcJ1e5FY299SsEQsSXgdGKlI1sTOXOFXYzsxgNhHIh+ounWdW7PKdX7h/Hmd70ZgL7vlnM/ClScbTc8ffqE73//E77//e+xWvVYJ4LK1hps0/Lk8SMuLs75wz/8p+z3Q41AqJJR96WUrDZ0XVeFls0xWzOnApnbuQSc4ZwjZ0md3t3uQFmUbQQkhWLVrWibViTiTs7/1PiVWpY4qi0co8QyR4b1vdVfQyshfnC1pSvExOQlTa2QkrEPgcmLao41hlXb0loFJeMnzzQO5DCxV5nYNsTG4Rq3GD5djaDS9kSfsaryFMU4ecb97l44H8IEKkp7W41oU0C0EGM15FFSoKKMUpb3WyuGbs50NFZKZtYpjNVYo1h3FmsarGlomhVtKxFc32+EhEJpun5D0/Rs1pd0TUfb9iKWIHUMjGkwxrFanWFrfVAk+Iy0bdWyxPuO96dN00eQy7ygZUOc2dvLsmaXPH+WeohVmu1qTfCe7EUxIMHS60Y1KdpINAdUIzjVaNASJi833ntiEAj0MA60XYfWVRrIHAEaKQbGccCZFqUlpXJUbziOlISPLitH1o6iEqXIHS85k8Y9ykS0WaGUEUOnZlGZckzH5FIXnSJMnhw8RI8zVMUJQ4qBnCzWZLbrlrNtT4kJowxd09F1W5p2jdIdOdclV+bUTgZV8/e1Z1vqqnNa7f42ZOuE6CvrSsqZcUwQDcVoEXj1kcNhJFf2CZNnbkvDqmtYdY1ExioL4KmmwlEKFQQZtrSulESOAVu9wMa5Wp+TVJdIeySJ0lEETZXdkvpo3o8kdU3WLcpmWrum6Rv6nBZsjELRWUdnHalkDEaMrbV1bVem/iI1ZqcdVovEinLSeJx9wRpL1zhU8UwYTHG01qEbIWufosVXI4hxBBy7w8RrH0SW6jBh9p5PfjujnciIhQoSEN5EyTjoopcNT1fEG0jrxEyA/JvGMQ5kLqrc+/u3cfLKNMmYEnEl0JnCyiQ2OnDmpHfrZjwQhkHS0mrDyX721me/6/fjH97jJN6R3DUKLs/P+d5HH/G3/ubf4OOPPmK97is9odyvfrXi8cOHXJyd8+c//wXBSwYBNdO3HT9vNhZ97WGdN8J7/MU1HT07dM45QYsGz353h3YtrlWkKKTYfdfTNg3OvbFV5urhnxjDI1qUxQguKVGq+1pkHktmRGGNWur2wziBFQcyFoUPAe+lpctZgzUaqwSkOESRA0tkhuJJzpKcXYygUhplrNQdtZ3z8JLFkdZ3QsiMh9292+j9CFgUUr8uSRFnI5hmOkdxlktNn0uaU2TMWitZPaM1faexVtM0RkgsjKFvHdb0WNPR9xe0zZq2O6PrN5V8RNP3ZzTdirPNA9q2o2s78pweqRyhWltc01cDeAzO5szXzLH6PuM7GEGNmmWIqpVT8+Su1m+u+82eVioepxu2bctHFw+5u71j9/yKPCB1mPFAKpJlXnUtnXX0puFstZEII2YaZTFWsXYdOsO4HxmGibvbHcMw8cFHK84vznGNWybjdAgMdxOH64GQ9qA1rm24vDwXZeKTm/75N3e02x0xJZzd0NgtD7ZrGqtprJZG6wIxy2RSymHcfFMK2sw1FCEM17bQtYp1W9i2mdyLF2Qa4ars28LFRnG+sVxsW54+eUK/OqPtNrWGU7+nbpLSCC80XIfDgCCRBVGrtaFtWoxSdG1z9IiV4uJ8zXbdEsKIsaaK424Y7hL7eGCKgVQUjdsgaJFCwmMbR981OCtita2VK5WSIL/avqNpLApDqMjalD2Q0SoTqmZxDL6qRWgR5az1txh91YUMtY4QGKYJt9qyfvCEhCiRt53DuYKf7hfunda0MyqzrgqtpMnfaEV2RiIZpUhQo/oitHy53qss7B5ff3XLYZ/QpWHVr/EKvn55jXu85cFZiyme4TDy1c3Iz7++4eXNgW9ud5xdrllvLJuVQztFmzQWoXjqmlaiwJyFucQautZy8GPlfwTVtgxd/5ZBe9c4Mmi8f3pnzg0oBbpEDJGmdazaxLaLXPQWHwuvs2d//Yq7zZaLp4+WTbqu6vccc4zzrYcCtSFaoZdUr9WaVd/xN373d/jxj/86/8rf/j0ePnxA37VsN2tKnluaAq35iMvthn/6T/4IozR/tvslR+DH3GMomSejZH6Ymo2iZJxxdG6Fyo04rDGR/YRKka5f0zpHaHvudiMxFWKYCN5TErSupevaxTmfR4qJRF7S3QvhvSRr3rgsZWGEWbK5SuGMpneGGD1jmNjt7ti0jsZZ4T41YDvLsBOGXGV1ZXkRqrDGQKM1nTO0ztA6TWMAVQn2c5Lk0VK3UpRQ6cdSkQhvNx4PtBSm/UDymqIKOokzcLirahpKwHK2coM6J9RnroXOdTirabWouRhrabseZ1vatqdpVhjjsK1Ef3OkZ22Hcytc06K0JpNpmh5rG6wVxRljregg1ms5yzJppe7tefeAV99hvbw/d6ha+CgQGLY61i7gjXUw1xVKRXZl4jASh4nsI9MhMIbAOCVC9OSSULHgVhq7sbRtW1XcVe2Dsay6HqssKSTGYWSa/EL+qvSx0T6nwmE/sLs7cHezF/ohZ1g7Q0x58RrmMcuohJjJxQCWojq0c9jGYstEyhkdSy0I19ReNYL1LOudkCh0POyJfoQUWbVWyKWd1INWreFi27HpLH1j6BpH3/d0qw2qyjLlNPfwJEKZiGEQyqRxJ+gsHNo0FBRN4d7mtdyMGT46m4rqeSqtpbiOEhmTKKrwShesVcutlD4iUfVAzfWMLKi7aNFaFNOl/1Lg1Ll+r1JK8vLI5MwxVpTcCZHwTH2HgGCMa9BWUtLWOlarHjiCTOaRUibWGqYyUn/WM2tMdcgECi73VsBQLGmpXDKTTwzDxMsXdxz2ieAlwm+6DkeWYzG2otwK2UC2oFrN9uGai8stlw+2tK1bEM0lSxagsS0lx9r0GzHKYJTBKluprAymQYzl+y4+ODqX7zNmX7UUVBGy9VSvC0XEqq3JOJOZDnuG3V0lU59zOSd5WH5NBPgdxr04sBScc6z6nkePHvLw4UMePLjk8uKcrm3pu5ZU+VdT8Dit6BvHw8sLXr++wlld7Ywc50ykPa/LBXNUZv5ggzUOo22lzyuLhiWwKH60XUcZpdwifMK6AsNsBcYcx4wOnWuAc1QEx/l3BM0c09Ma6fXVpRxRlEGALz4mwoy2n0seKkt7Wq1FzmK9Sssa01aRlCEUJTnUqjO4kK+rSkJQjeCcoVMpy+vzUUWiFIhezlt2OwU545yqXKWF1qna12hxTqK/xim6doUzjtY2guh0Dc1qg3MdbbPCuVbqlE1P065oXMdqdYYxrRg718g+UzKuSrcZbVA1+6hO5uWp8Zuv+qkRnOfb+47v1Cy/pC/V7JlK+8J8eAv8Vs+0OIpcEsFPvPr6G4bDRDx49tcDex8ZSmKYRmKaUFNiZTrai47cHzn5nKvAjlwIKeJ9ZHe7YxiGWucS1WaXLDkVYki8fnnD1esrXnz9GtM5mlVLd7YWIEdJ91M+VdQ1+ETJClIhJofSIh9kbS/pihDr4ihkfPX6xOjO6C6tM+Mw8PLFC+5ubpnGgfNtL3BoJUvgYuP46NEZl+uGbWNoGyNpw5mVIRe0luNMeWI4XLHb3XIYDhwOB7JuUGaNa9c4B9bKwvLxpOm1wOSlfjprPwrlHAj5tiWhmWJidzhIS4HTbDZNpXuKlQxY9B21cUJjl6UWoUrBOSE7zylQiDUdlGpBXKOdQ9e+T1/5YOe6ybypFhRFGVabc5p+jdKCCuu6ngcPHjAMA4f9/mSyF8YqEAuKrhcjZLXgWmMSnb+jkjxSY3CGVJ8LRPa7iZvXA59++oLDIUCGZ88ecrbdcrnasF5X1XojjkNzrjkrGXPRccEFl+dbzrcbNqsOKISoaY10FGrVEPwkyvOVFipHhdNtTRtZVGOZVvclbN41yhLnvP84bgJiAOem5SkVYS6J0ptrDHQNHO6usVdrUgioiqD+jdHdr/v+N4536UWTmok8mWHVd1xcXPDBB8949vQpjx494tHlRVV+h+EAJSdSmMhhghT48Oljdne3/LxvGKdAzNI+JNGBuKhay4OKmk4xVQk2gdWXGMg5MY0Dkx/pUsQ52YjPNltKucOHg/DpKou1lsZJffroIBxp06QRXNaePjnf07aIglocZkPBUsTZquYxxCgUhiERrDSBawqpiKPp/URIwgQ0Tp5SMs4KkNA4wwGDSgUdE32iYjcRVLdWmFIW5Yl6U+rceNPBKYLujFKCaVtJ156dWZpW4azMmcY2tLatQDep/XfNJc71OLemaTtc09FuL3Cuo2s31eArbNPiXI+xDdZ2lS5R6rcAqSRMNYAnk2qpU4tRryNnAcwo/R1Xyf3x3s3yM7PKab9PVoJc1MwwXPHIcs17G6NIJTMGz+uvXzGNgeEwEZLUipzSOLcG1dNqjTFWRFRrnyG5MIwTMSbudnuRoInCcK6UTOzD/iDefj6TMD9Erq/viFlx+fgpD59c0q061mc91li8D/cu2Jz2DEE8ZGUEURaSJ0QWOjjXSmQoAdYx8kqzZIdSQkG2v+bPfvYnvHjxiv1+ApVpraG1hot1y6Yo4tVrppffMLkV7vteTJLOdXkInMH7gcM08vzla16+eMnt3R39aiv0a9szmmaDMZYQxBPjjQh3ljOZN59c+ViVMhVi7DAGoh9oGosuFgvkGAkUUt9I82tKaCvgFmbquSitE1plrCmEKGi5nCrHqzoqw+ciGmQCaBI2n1LnSEwFjGOz2pKV5vXVNavVCpA0jHOOtutOFi9MMTJEL45YgmKgLYkpJInqg69CGIpDjJUWrUUriWxfvtzx+uWB51/d8fpqR6g0eg/jBWDRTU9uDJOzlLMOqwtd9mwbg/WBgmLdyfW72w+iRpEDyrSgJA2XVQSTaCtCz2m7RNJTmFAkfJq+82L9dWOp0ctvcziPjhMqjOQYoCQB8SSJdFKKvH75nDErbl9fsT4/o+277xT5/UZzmUVrMau6loymay0XF2c8ffqY8/NzVut1BajIXKEIwYIfD5CjNGqnwIOLLR88ecgPPv6Ar77+ht3+gE+iValKpnGK1knfGYgBiVGAUF3Xs1mtmcYD0U+EmIhBUuUpRrSCrnGMzjJoTQoBnJb07HbDerNZ5m4NyurPcyH1mBabATtaaxoFpgJIVM6omkI8XzlWnfDbDmNg8IHJR2kJiYXNqmUKiXHKvNxP+Cj9nJaC05JK1UnKIlEXpgRTUtwNIzontjrxcOVYOSP93IvArloSRVNUot95nDVsV9C2Im/W9wZbSyKuAlusa7GmxZquglEsjW3ZbB5KytN28hrX0KxWAoKxrfR5K4W1rhpAQXVKi5aqtI+KkoQbWFeiDKVEw3PJtxUoFUtg3Ny+lmqp6i9mCr+DlNKxKfToycv/Ba9YlnmweLBKiLRTKRwmSWEOk6coaQY3SqGdFeRpRYsee2JEhNL7INIzw1Dph46UVLMa/XgY8Y0s3hAj4+TR2rLabDg7P6dbtTSthQIx3K8xaW0wxlZGEfFAZJJX5vdy1DZcYLcnWn/S1C5bwf72mquXz/nqiy/Y7XZLbw+lImSto9UGpgm/u2G4ecV49xple3S7BmPJRUsrQwQfBOo/+swwJZTJYDLWZ5pW2lQkFXMEHByHWu5Vqj2WMzhAa0PXtXSNw1lBNFpVgSr1ns5tFUd0nVrYd4RMWAArIhdEvdf3UxLHvJx8jvQzybxIGTKaojTGOkLMjNOEsRbrHMMwYN3b/H+nm24qwksYThS8M5VoSEFWQveUqmJEipGb64Gbm5Hd3YT3Qh+HEh007yOjT4ytYcoarx3RZkqJuM6RrSCJrbMoowheoqycIyhB4JVaFFI1TSto2HrsqqrZFzHY37LQuAe5Vm/++e0a4QxEk19qTFIQbz9J/2vJaaEinIFSKSeG/Y5sr7m7vsF1HW3fL+n9GmPdu/b3hnrXk/efKHk+JonOlFI4Z1itOjabNX3f0zQNWptFkuiUNtFovfR9rvuOi7MNTx8/ZBgOKAqHyZNLpBRoG0XbaJpGlGOEsiste4V1juA1pcyOgDxmgVphtxR/MqUoNSwnaNO+CmXP40hgcYLMPqmnzmDGohSu9mDqet+s02z7hr4qnqQQiT4I6MdoghF0u2S5Ss1CCW+zM5J9s8ViSkbnKs9UjeftENE5om1iaxWOQso1nah03cMk2Rqyvqc+o5Ris7KsekdjNf2qwdUgoanAFmN7SWGaHq0dRjtc07FZX9A0Pdq2S3uG6+a0ZrMQdQuaU/6u51a70zTmXNebgTxqJsiXdaHmHLJSVQ5Nase229xbK98lj/HeRlApFk9omQhz3np+oirPaymxLG9SaDYXZ5TbHbeHAZMDAqQQQziLQfoQ2R0GDoeD8FpWAdcYA1evr2nalrZrcbal1HTeMHhiSDhmlJXo53WrltV2Q9P1Ql8Va+rU32cXV26Dbc9ZW/GSrVE461AYkQ2JAumXSC0tsGeqPp0YikyOE3/wH/+/+Rf/9J/wh//4P8UQFl2zs77hvG+47CytVbQJ7r76jPH6iskrnv3o93nyg9+je/AjQjYcxsxh7whhTdN9j4tHZ7j1Hc+/+ZKb4TXPX+34+CNYrTZsVj25WOb2jXlIn6Sv3KpHJ2bGJX709CHrRuEYiUFqZ70TFQ5rDX3fLYi404z8YgSzNI1b6/BaC6DA5Ep9J5uM1YbGOTarFaoUpnFcIMxTTJQixvAwTFVyJqIOB7z37PZ7zs/P8d7fM8Rd37Hq1xSk7SX4hB93lTnC0ZmGIgR9NEaRdSYT2d9N7G8nPv2zV5SccU7QyCRNipnrqxumceT5N5kPn1wSnz0guUh2ib05gHaYxpKTpmhIJJQrqAwlaAFKlUwhymaPpIenLKwl1lU2FFM4HDy7YX/cPOG+dZ/Th+9ILX67t3us4xXEYTFpJPqBcRqwPshmGyJjlIePmbu7a/Y+8ulPf4J2lvV2y2lUoxZy+/LWd71ro3nzuZlebG5kN9pwtllxcb7l8vJCpI7aDq2OnKsaKkMIdI3BVLWS87OtzL1SuLw44/r6hhevXjFNB3wYaWxis+o533ZYC7P6uarEEnNLDlBr7KM412GSjdQafPDCQRsmMMKSdH625fzi/P76OnkcKRFENs7kzArh/jRKUVzt681grKbtHN97eEnbC7LdhgE7jjgfUHgKLSl0Uq7LiXVJdCWiSqJXGqcUXc6Y2iTfaoOpB9OGAU2hdxpTAiqlWgKZHzI/RDDbEvLxnhmj+eHHz1ithEGn69c0Tct6dSbIdddh3QxaaUDJPmmMwTUt2pgqByVAKOdmR9pU365iBWofn34je0VZ7BtAZemRrFus6vPKVgBNSVzfvGTc3+DHAz/6rb9F0+hlnXwXM/j+RpAjzHghelYsgBkJt8vRLdUsbC8ahXOOrutYbzZMdxM5BQEtFSOsEZURIBcYJ0mdzakMbSz9as1mu2G72TJFz+1ux9XtHTlPAsMvsgEXJemGnAO7u9vqGbpKQ5qZxpNNFXBW0TWiNG61GMG+EaJoTZZ8m5qvQGWr0UUkPooAN+5ubnn+xa/4F//kD/j5n/4Ulb00c1slCC5baFyWZnc0pihcCZi4Z/fNL3hpO1JUPPrrDyhug88txXQoHK5YNq6l7S9QupdIOWZc00MxHA7CLH84+HseisQjpRJ8syTUS00ttKsVZzmgwgN2d3tCyiQtNTZjZ4miQgi+MlsY0GZB1+4PA6teUiN940T2KhYh26bqj5VMGEfGw0D0vs6jmRzXoJ1CFw2pOkta49q23nPh2ByG4eR+laqGnUAbqXHmgi8BmzQla2zjSEURYxEARVYkn3j96sCrF3tSiLSNYd03HHYjpMR+9ByGHSlOGKd4+apA8rSbJ5iVJrei0qGYWXEUWSucUYRSSDnWjIKht5YpCHinkAkp4MMEvjY7a1dlpb7FmM336mjTvsOY+9EKioQhUigkCYvFUan8rTkXfJKIMEwjX33xGQ+efcjZg4dsztZS31q29jkifMM4/6Z9Zq7jFHEZVc02tI2jawV1KWl5YfTRc9RHoWkaARtlu1B1PX7ylNV6Q9t1rDYb7m5v+fr5c+52r9nvb4hhR+ssbatRSiJ47z2kiTBNlT5Q0OipOsvBe/wk6WJj9SJjJpGzqNj0fc9mvTqmm4v0BRYt89xVajynpOY989hKa4xkauQ/QQdbk2C8JaWRYgyb4mltJKmMdQmnA2rYoWLBxMwDl8kGdKmtCFrRuFp7r42qTmcalWGV0cC216ysEq1Ejow2MRdSFeB9kzBNKcX52QXrVY/WmtX6jGZu3XKdAFiaTiJk0xzl22ZEu5Im+LkdSNUUZ1XUrQU9fZw+70q7V3YZsZq6RtqgtdDDlVS4ubtiGO54/uWnMp+cq9vb0ah+FyzXd+AOFasuNm6mSzvxD6sBLNXhlSIwMzAaZSxN27JeZ8JuJOeIqv0oCqlhqZre8yEuqaquE62qfrXi7Oycy4tLdsOeKQQpJMcJBbQWmrbBOJEmyWSGYcd+5wgV5ADUvpuTC2AUTaOrmKyqF1VX401F2B0Jw+drkJd+oMLd7Q2//PTP+Pmf/DFffvYLVEl1U9RYXXCm4JwU76mbqVOgi2e8fc6t6UnJ0j/7ffSqIbqOohu0KljlcGpFXwrWrQjB4/2EtY1Qfo3C0Xk4TG/56kVxQkJez6HOr6brMCXRpBFyZpgC+2SlnlZTFCXnhcJOG4txLaka0hCmeq1Et61UByFXkVudZ2o0z3gYCFFqbzI5lTT0ait9db7SkJeCdW7haQwxVv7Q45hT4rpmD0DId1OJ5GLQ2lGDBawWPcExZK6vR1682EPONMax6Ru6xhAnKDkS/CTRLY79bo9KgQ/HM1rnKK4iEMmUmgaTNLkGMrFkGoQBqXUGHyV9k8n4FBmDJ6WAUZrOQcrf0iNYTv79jgbwiMaeveCMqdoWCeGWjSkv6SORuBGey5g8L1885+r1Kx7e3LHZbkQ95N42+Uaa8313mVKYV45QpEnfWNs42sZhrUNUTkp1toSPFi07SCl2oYe4RNGv1rimoV+t2O93dK3j9ZXj+kazv5PIqHF6cXpD8JToCV54OOdevpjSQuvnp7G29GjQBmXskpKlZEGs9v2901KqYJSAcObOu1ZVZ6kUnD7uk/OV0krOzZiMmnaUaMlKs1aebDNY0DajdERNB3QsmJQ5c6WmUyvlmxa6sZmvVowgNKpgO3HMu1bXPtwjevxILzz3ON+/qwrFut+wWa9AGbabS5zraNp1TWG62psnPxfvj2lKZVCqNuirmbM2L99Q5v3zHfP6/lw65WOtGFk18wBLf/j/n70/+bUt29I8od8s11p771Pfe602e/7e8+ce4VEkEZkSKRGigVICJUWDVv4FdOjRoEH2aCSCHqLHX4CygYQylQ2kRAKhiJAiAgFJuPtzf7WVtzjVLlYxKxpjrrX3uXbNnj1PDwmkWKZ77dx9ztl7FbMY4xvf+L7Hh1seH9/w8uvfcn52gbu4lrP/N1sTfFrr0XU3V1oMGssS8i0/jlJgM3gjvSOBSOs87sKj+olpHIkoohbm6TgMzNlWqHXBMUSUjhRluLl+xovnz3l2fcNf/eoXpFhrkMpgnebi+QWXl+es1yv2+4HDvmf7uCf0B6ZDvZlZnOtnvTwFrDvYrES+zVkZOGZ+VvOmteDVtj60jHXSzvDV737Jn/8//zn/5//sP+Xl579gOuxovabrLI3XqDzgreZss+J8c4FKkfB4J7ZL64aiPVHvKbsv6F+/wpx7yuYM5QvKIG0aVUN1s+lqPJcqU3Pk9vUb7u7e8PXLL75l/Dk/EO8bERJALWo9Uh/PuLRmv92RUmbE4K3GGcV4OJCtJjvxItQmUaaEqYuWUZGhv+PVNy95dn1N17as1yuq4jFjzNy9uef+7oHt4x7jDO26I8SDbKjtWti3TUubLeM4MfT9InhQCnjviG1zvJwyN+wWtMkYFSVQcJZM5jAOYsBsdI1IC+M08btf33L7cs+wDVyfWzadY9N6bJX+syZzc7Xh/EyIF0YltEp065akC/vDgc5Lz1JnPDHnCsWnGmHLoliIHIZHDv0gaEbJNSPXoC2pQD9OaGVJvG0R9e1yww895m1P1o2CIqJUwppCUZqIYRgTfRT2rCKIsPQggeQU4TC+5re//GtyUTx7/hzXekRBqfzNTurJCcp7GC29p23rWK0aVqsWa4ws1DlLN6GWDWwOo3Mp+NZgXYMyDm0dUxCCkm88KfQYNWAYiOMtWmu6ztM0kmGO44guM0R9DGSl3CKqU+M4kuKEbhq0Nt9aTFerjs16vfxbK/hoDZdeL64VqgZBWgnUZ7SubTm5movPbVUV9TABpRMohW7maiR1/CuMjiRbSLoQtRFIEZY6mjSFS3P5rCeac6J1gtSYGkgWNTs4zDZKshvGKkxeTizqCpByJKaIohDGAFlDNhgTMSYc5diMBJilyDVbKyoup96lYp00l7hPxNHn+6iPsnNCUFPYWlPU2jJOsTJHLfthy+PjG778/OccdrdQIh9//CnPn33I9fX7T/gDp/yVH3L8gQLa9QFWyHMuskrdTy1grlI1MSyCgTtjmLLUJIYpSCRjrdDmK7GBotHaiiqCE3iNEBa6u9bSKDqMI/2hZ5omlBIR3KaznF1u2JyvWK1WWGtpG0/rPYf9xBQS4xiJOX9rPkvh1yzRldFHuu1J04Fc5/zPateTc+Lrrz7nm69+y/2rr9AliKyR0eLA4DWrzrHuPJuVx1ThZ7tq8Y0U3YtS5BQI4UAe32DimsbcELKuJBlDtmIrVMe3YPpFBrhxDucbfNMcwXTm+y89h5gsk6H2jJUstRm8x3Uruq6VDLwUfG28VWVaaoEKJIBIQTQMtaZttGTi48Tr2wca33PWD9UBXBGyYteP9CERchbzzNqLVqit01UPsCjJmo2Ee8t9V5h36KrUyZVBRVmAkq5OEQoOo0T03grTeLcduHuzEzeRFAEjTcqN5/riDAXsdo9Qr7VxhnXX0LWCPoxlogxHB2uVJLsLKdXivdyfVKGzrBK5RKC2kNRIFlR1eSgUUv353z/tvksR5kiQgUVkfPl27Q2tWXnOhSFmQi5kpTA1izGzizsFcmF7f8urr77g4f6Ws4sL2s1KIMHvObfft9iUDBV9E+lHp2gai/cW70RMfelJLYmcFcocA27Z2LU4+NS2A5RkQ0abqi3sqkaxlXnixH3AaE1KsZZp9LK4gibEwDiNjGGohtIGjAUlda2SMjolyGnxgTw+B9h4zVmrjz2JsPSvzbCvbIK1jEKtrNYNvmhhdkpippZnKLrMos6lc8Hkgp37oBUiLaZVbak69scqrarKVK0dzwQ/6jpWx94JoLUgWafjq23P6NoVYFitLnC+xfsVqpYomm6DdQ3Geny1t5O6YCW7LBtbXvaFUuHwJySrUpgmQbUOh34pe9jqGKGUZhhFzKDt1tzdfsNhf8/u8Zau83TdBTfPPmB9doXzjWS6dZ/4lkXU7zn+AGKMqsQXav2PJeMrpz9TL1wDuhSc1njr2KY9Qz9w/7hlZRzWGIaYmKaJMSWMbUXpwTl8loVSj6MsPlki6n4YyCnz8PBIX7MG11pWm5brZxes1x1N41m3LTkW8mXi7v7A/jBye/cort4pHh+FAm9l0dfqBF5ghjuPLbmcFJBLDbtjDPzyr/+Cz3/z1+xuv6K14ngeiiijN15zed5weSHF+sNhQmvF+eU5TSvU5UyGOJHTjjJ8iYkNa/+M7dSQouEQRQ/Qukx2AJXdV5UjrG9Yn51zfnH1lDFYF7YSE1nXBbraoYBkh840OHPGsHmUXsM0iCmu95L16WNNtxSxuMoZlNZ4uyFGgVHv7g9YrVh3XtotjCFpzzQlpgRZGzLCeNNoDLP2rBZZp3Js5s9Z+vaMMXV6P82YiqpixSHiJtnsp5LQjUFZxf4wYLUiecf+MHJ/d+Cbr++JoxAkSnFYq9msWj76wOMbz9cvXws9PwQ0mcuzFS+eXzKtDWXawVBIyEYeswg9TDGy6jrZzJXI8gWkHl2qaHhOSaBTpZfNUgylEyE/heX/5of61ma6zMf5vufCISRChlx7BK0peFMwWv6QMo+3r5imwKuvvkBp6M7WT9+wftwfcsxWX6AwVuG8ols5ulbgUKogw9zOIEuIqbZAioIwtAtC549Vs3bmDjljhVKPwWCxxtH6Fd41tYVowjhZnK1rMFMANTKOA9o43LDHaoH3lG1kxBVFChGM+Ojp2tN3egsuOsNFZ6qYw5wAaGZSlIKqo1uWjVFxKiajSFlE7VU5jnJjxV3CGoOdN/35U7UGVZ1glEiflXpvZpsmpYWEUpA2pFLqxluh3Zm3IQ4oNYiZS3tKcX72jM1mQymaq4sX+HaF7zayaWuFc75my1IHzFUZXLSPxV2+FBn3Cn0kO5fTcSqfe+j3PD4+8vrVa+7u7oVM58yiUf3weODy8oqrm2d88+WvSHFAE/j4oz/j2YsPePbiMyrovCj2zDfybS3l7zt+8CY4M6FAFiKlVLVwPDbMzyc0U4HRsA8Dh2ni119+yePjjtu7Let2hbMO13gSAtnMkKq2Cq8c6MIUfE3jE69vX1OSqKpvdw+gMtdXK56/d83mvOP8rEHpQkqT3HClwCpW5w1uZenORFat7yd+8esvl1aLWdCpXtgyQGXsCQupVP+sufFVlUI87Nlt7/j1L3/O61dfUVTC1FqiUwZnC85k1puWZtWiWs/V5pquaXh2tuJh95LHw45vvt7ilKN1HW74KzZZcdV8xNX1p0S95mEoTBOEAHGMYlOUMjGkOoANzp7TdjecrlBhEmftk8APawwxJ+m1zJBqTaP1Fr1qQBmUqVHYIJHaEAMHcyAX6KdELuJb5mwdeNoJ+SMWppxwyWG9ozm7oOiMcYEYXy3af7oGUNM4MpVH4qEn4+Tm5sR601UNQdkYKU83QbIiJ4glkbURjq7xqKwgFqw2y3P+5st7Hu4OjP0AWeA4aLFa0XnL9eU5F5sVu+2eu8ct237im9tHrq8vWW/OSHmQ981iSpyVCDOkLBAQGZQRpCMUanO21CxjSYzTiFLSCJyKuGynHFFKM8X8dpXtD4JwTo9l7hWBnYpSFAziArKiSWsempYpFuIUMQY6p7jsDEMErTJDSaQ4Mu7u+et//f8mpsDF9dViIfQ3BkRPKiWNt6w6MZH13mA0VatWyiPWaHJ26K5bHnsIqfpSRvrDXlwvpon+cGAaeqZpYBpHxgoza2XrjJbaUogRRZasVy209ToPJoaxxztxZ1AhMvPdY7UNsvstY3+otl/HG954Q9fUthhmsoeCohd4dKkQzeLVivo9VTdBaaaPMS8cgyWQ1ctfooQ0IwrKLMjCvE6lGmSoWgJYhlFt4l/aIIoSicR6ns5kQZeUPCetDdcvfsTZ2YacM5uza2mA96vFr3O2oitFBLOLzmICUBvWJYiR3r6cRWptThrm1FRbS9GKYQiMYyQlxeXVM1HbGgf6w5b+sOWr3/2au67h9fmG62c33Dy/4YP3P+Hq5j26bk0KaWm5yvMmWxP277OIevv44X2ClGV3zaXyLRDG0kyWmTfBZcooCDmRo5AD+ikwTBNKWVyGzoqw6+xHeEqbNbXwSy1kT9O0iGjHOOG9EDLONh3rTScTqMySXNLPBdJ0b60CVUVkTyi4JxdXYYlZEvsogitqMEU0O/PxPo8hMgwju+2u9jCWhfgkVlJ143EO4xtMs6JbXbFupXH/YbhnyPCwH1m7glOGsH9FPNygwoHGK4q3JJ0ZNfKHVCEXBflUjFcsWU6vay76i4llnUZaLQMy5UxUslhTB7e3hqI0WRmcb1FaS902VSsYpReF+pyhqGqnZZxMRmXQyqGUI2Kl5lFZXrNSTCEv8FcKEyFlsqrMSaXwVoS3JZHNvO2IIn2ZmpRno2VZdCqShTWmmntmto8924eDqKFQhFhQ5LOcNWxWLaXA9fUFU1XxP1uv2KxausZzv9+Jek4u5CimpClX6Kooka8qSqxwqqFqrvWfWT+3ILWawlFMOVdhgSdD8IQBO29rMxP7tDXiu7RE1fKb9fyU5C/aWLR1WOfRJoiPXYXrWqtonCJmRYjUBnq4ffUNl8+ecdjuODs7E2H4eU6/Y235/Zu3kMKcNTgnDeKzMk3O0r8ZY2AKwsRumqbC4pJhpxiFEDaKpmeMkRgmQpiIQdaDGEO9L/pJ2aaUIk7pldk7A+2UWcYvoivvgBAkw1KyQRE14zgQwkRKT/s67Wwtp48Mxvk5UBOBOftfFoP5dea1k6pqdEIGqW+Ul40OZtgTNW+Exw2woMhqXvvnzVI+Yc4G5Vfqgq0q+QjpGTTWHMeOUqzWl6w2G1KKNO1aGt9tK/ewPufZb1NXpIHZSWYZf8drPEKudW/QUvZC6eok0bFaCxs4pQRqy9DvKjowSKCZPednGy4uLrm4uqFtRYd0trNSy5r9Nwskf7hiTJVNk0eiochyJA+6PoLKQJw3wlRKLbRmjHc0XUM7dpJGa0WoGK4u4lHlvMU3ri48RmS3KrYeUqzN8xNKZ4ytxfWuofNelCXyDBVKva5kKYDnuvgqpSlvEYPFB0xYf6RCUdWmpcwMv6puUhBorxSsUoxToR8j+8PEoQ/0UyKmyn6zsiFmFDQbzOaG5voFZxcfsWo8TasJb16xjw/cbQN2ZcAmvvndr5hKR/f8Dc+uEr7TXGygdIacDcOkiFH0N+OUSWlmuWW2D08n6TAMNI3HGEPOebEPKiVXmp6wBXfDCGOEkNBkIrKbXH3wiZBvQuD2zR0lJVbW0XWtbFJCq6TkTHfW1MZ3i2salLFMKaNyQmXx2FMlk4IsRsZkqc/kjNGJSBIYvGk4q7R5rYXUMJ1u7krhTIdWLSkV9o8jMUwoAu3a0K4MFxcNUw4cxsCbL++5v3skV5gNa8TpQsG6bVg1DqMVn374nFXbQC78+//OT7m+WHG28fzizQPbfkcfJkp1P9LaijFrUQzDhDOWFBtSnhZpwa6RTKdtR8aQGUOP1WKWarRhCt82QZ7nWZ1I8wU/2QB/3xydf0raGzRTsXTKYq1nvdkwhYTpJ0BjdWblFRetxagsGVdOTHngy9/+Gu9bbi7f46d/9qe069XT83vnv9596KrnqVSmbS1d66vlmfSShjAyTo5+sEvA4GvLTUpJMr5R4MvDfleF1yfCNBCmnv1+y6HfMQyHilBRN/mqOlIUKUVCEqeIlFPtXdBLcJ2zQpvIOE01aLA1gxI93GEcCPHou6dQGC+KJ0Lq4aTmVtdAe0KwKWV5pEcavxBVVAGLBPvkWcpM1pxUWciqIm4SxM+9yUf0r6hjti21cfl5pWXjJx3VVsRrUNNiSUrTxJMSitKcXT7j4uKcECe8a2W8K4NKxz5OQTQUStV+2JPNR863LAHvYtyrfTXEbVDaAYabm4arKynroDLjNPDq9SvCtCeMO54/33Bz84z33/uIz37y92i7Dca1qNpTqudMTMk9PnUy+kOOH14TrE+mMKeB5ZgClyMaoOfIo56K0RrljCjEdwPeN/T9JNJnugjcmFjqXCmJSkwMsqlprcEeU2yrNTGJduThMHB//8gUJrqNXVqvinjPVsHkwqz+oot8zunkTTJGTixP1En7wzwQNTFnQpLXQ8nkbNBuw+byfd68esUwKZSXSTflRKaQteahj6ySJ9srzPoFynuiyTweLI9bWK9asjLc95k4FeIlfFBatvuCI1FsQlUj3VRE8d1qUKZgVEariCbhzfTk4Tt3ytSS6x5jPBoYOyOZyqSrpqgwymyzQjUr2otL0TTsB/y5FMCd80c9yhiXOFVQjlIlmaQ/0HsnVM6kRPpskb9KlAw6Z7wWJR2UorGWVdOIG4SCHAONNbI5LXA11bkiMg6Jb765Y7ftiWPm7Krl7KJF6TP6w8jrlw/c3d2xezzQOIPzrtY4hQzVNQ6FbPydt1yuW4xWfHh9jnLQx4mRTNZKlEZiqiw7ap1UmIEAKQrhK8bIGBP6wqBaLSahOaJSXmrNKWZSEgbeWxOsXmadxu+Yyd+5Iaon/1uOWCyhOEpxxzqTsxRlarCXMJrF81IbhSqKGCce7275/Je/5IPPPsI0DuV9HYfL0+aYf373UbIslM6o2hphavuNyJWNwyABmp0p/YW+F4eSFBOHw56+7+n3e8bhIG0OKTAOB6ZxoO93jGNPiGMtqRRsVR2y1pKiCGjESeq4OUsrhsz9IkLdiWqXFjHWYYqjoNBG5N5yjk+vU4G2TmqNc3ZWijjBz3XAuVdOaVQRebZcRSaOeqpV3H3JJFXdGmUTnFMpsUaaEZUTbgIz8nTcBOfX5z+5ZpsxK2KFaTunaTuPtnZRM5oP6xqca1HKnrgxiNg3utYY56FacbOlX1HN63isWd38+QVjNEUbYoESAqWEWi/OxJi4vf2a7faer776DaHfonPg009+zNX1c26evS/kHGUqWlfHYMm13lyWvadkyYS/zZT/7uOHs0Pn3Jb6YEp5ugHyNBqZf0kMDjXrTbMoAOTySJgkQyw5y42qfTsiERQIMTCLdWtd646+pv2jZBTjOLHfiZC2stVPUOsaqddwqop+S+r81s2pG2DM1RS1Qp+nxpgpCxU4ZUVMc+lK+gAxLavz5/jugli89H8VGcZZFdCJbR+4DIpEB/6c7Cz72LPdF/b7RNN40lTYjZnBeFbBM5aGfQ+mRLSLGKXRQFITSs2iynWylYhQ4p9O1NlsMsbjpIsx4qrGnrWSFWaliEj0ap1Ftw267bBtS5oCKmXcag0FvHWE4UAKojtpKkvv2A8lsalC46xeFkqPhSSLRSg10ckFV2dwNobGWbrG46wskrlkvPNLI+x8yDip2e/2wP3dln4/McY1U5jYXGiGw8DD3SOH/UEWWd3iiq2IkkzIxlso0hBtVKHzcg5n65ZDHDgMowQ7SrwRU0LaGvIs3QSzaMQssD6FRD9F1quM91Tfs9kPUVaE2R0kl/zkeR0n2XdMv+/JCJ8SomYQbW6XFzUhVYlfxhqKNiLcoBRaibLJDJkJNCdyaq+++ZLDbk93dob3Ihb+fbDod5w4SolDibMiFi/rqgS9U5hwkyVMluAdSsMwGKYKe/b9gcN+x363Yxr6ivBIk3uYBsZpYAoiWK4rcc9U53KtNUnJfI5VaF+Yk6pagIo3X9KqohYZW0TBtyiNLllg5ZI4bSUAquallWxE6zry4xN3FFVpYAImp7ocyZqk5pqSOkKpx43w2INcAUZmRv58T09LXlqpKhc4C4qf9DEzS7wJQUlr8FZx3lms92yneFxPVJWOtI5ZVGP+vCPSUOuddU1ZYEjqurRkshXKRwRMSg12Yy0FlJqMxBCZhpE3b77m4eE1t68/p3OWVdvy/NkHXFw94/ziGcZ4uRM5S2seLNczk/UWVmo5kXv8AccP9xNUGqM0peSjIkA5bninkcj8bzmPXH8m4rzm8mrNFAYOKhP2Y40GCrvdnmkKjONUIQpJpT0C6Wmla5RUji4BKXN3u2W7PTAMA23X0DQNq9WqLlAZlRHrkCwZylLYq4/40Cced0Gc0usDPD5yVWs4IpSdcmVhpYyymlwsn/zsH3IYJn7z299yePMVKk2sG0sKgZQTL1/f055tubjpuf5A8XA48Nc//wt++Vef0z+84U8+W7MdIvd7eP6TfwSXf8ZtfI+XryeUucPZAwYrzQK6gK41x3q3ReQ6cvvYc/rcrZLAIQfxFNNaiSt7badoGk9SiqAPBG9RxdBdnBG1I5JFQaUGn6tVK3MkF4YwMRz25LFn1bZ03YoSFSEWxhDBSNO8qw7Qqmji3U7IKKsVIVSGak6VsWfZXF7RrVasNxtx8C4QvTjbpxhPBlZhChOpypD5tcJN4k15/7jj/uHA/jBQcmQ67ElRA45xrOpEWhibViu6tuH27o7t7sDtyzva1QrdrNmGwFePW35794ZhSDjl2LRrdoiIccyidJ+z2HKVksWySRtECD8xhYIZM0ZbSJkcA8PU141MWHpv1zr/9g9FUZpJn5HtRK62NNYpijUko8XcuFr4iL6qjHtjYRgf+PKrgS9/91tQho9+erlsgW8zdr//LHK935a2saLik0S8WoyxD9IBoRXaGmldGEemaZRSRgzs9488Pt4TBhHZKDlRKpR6ODwwTXtiGmmMlyBxXnwKi2i3WPKwoEUCRWZiUWKdnhXUDF1HyZaNtWSqfVF6m80rJBulYMQxZcMDjmwcWWmB6VPCpMjKWkFyYpASAJlGJUEITkyhal5FQRHLbFqrMLUfUBKIBXk9boQ1iJHkct6E9bEmBwtU6KyhdYZ1a3Bes/L6aUK/JDsFOLbxCAog903X7ldVMtMw8vrNa6ZxgALX11e4ZoVrNpTa4mSAmKPo/Ia4OEQ8bu+5f/Oal199wcsvfo5SkWfPLvjwgx9xefmczeZa+h2Vkladuu/M3oZosbNSNUkQgpKpn/jDjz9ANm3+o49KKmpBq0/w75MbWn9RMhEhFhgrfUIhiJnkNCVSLGjtxCsupkXvUmu9ZDIKRYnS3CmZXYUrq1TRYT8K9DEmIeEbBaqIOWauTaFaU8LTjOl+O3J7NxBSWgaMsKlkZKl5MGXpw5mpyKKoUXDtBRc3H/HRH/1dfnXoGfd3jHESQgqFcQwcdjvub1/zVz//S3a7Az//yz9nuLvDpkRSDWZ9Rdec0d38FLr3eTgUoh5AgTUHDEIBR4vZJSofx20RN/i77XiM0pnj0NnBWtfsp8EaJ9CFSIhQoiiZiMSRvKlAhlbo20hWphRY48irFVYVdsOjlBYryaSUzDjFKg2mabwT0lQuhJIoSIOtt+JO0W+3lCTQltdFLJGswVVqvDUa56VdQx13wYVkYoymXTWsU0cGwgBxKuy2IyUF4hRErLpG1dOUKGXk7n7L7d0Dt7e3PD4+cjiMTGNP13mMyvTTwG4c2A4jMUr7QEmiPoNVlFgtt7RMzJwzMWe548bQdR3WWkEuqoFvTmIOqxR4b6H2pP7h1Yv6bN/KCEtFZZ4wo5Q06SfdUMwKTEcxPWiLshZlEuhpmbBzSw1FVJS0LhiT2N695vHsnI/Sj5iFsOec4G1F0XeeK4IWOCe2PjNyMAu7L9JlzjJNQo6Y1CRelVk0ecM0MI4DMYyLnFlOkTiNjKNkgeJpeTwEjkvLRqG0sJpzFpg1pSRefuXYq4eSdgKNtCpQs5xUA83j/Va8iYYSRI5xyJqpaA5Fi+qGMsTsUNmgsSSjZMNII8bINudJqJzQJVVjs4wuCZmGssupCovLuiTPJteNcf77eP9PlHnqU5o7Ba0qlJnAWsRQ+tCPuJjphxO5xfrZc/KyQLt5XsPLImxeSmaaRvaHPW/ubukPewk6jObypqX1HeMUj2SwVNfgnOnHkWkc+fKLX7N9eM3Dm6/ZrFpWq44PPvyI84sbmtVGeiG1rhlnPuK/IE4ScwMqJxlphXz/jWSCi/6fPj4A+aMW2uzpzy73dPkqVbjF0DSuWuuMDFW1wpoOY0QuLdcswXu/sBvJZRGjn7NHkeyRNPiwm5jGiHMTVpvqbqwIo0wcTUEZQwknTcoFbu8HXr45SCP3fKapLmpK6mcz83WW48ozXEHB+jMun3/CH/3Jf4MvfvMrDn1PH0eptaCYhsDuccvtq2/49Zevubt/5Jd//Ve8t9Zcn3mi6nDnH3HefcLq+Z+imkse9plITyGjVY8udRNUhqxqHZWyLEqQuX0cnkAkgjSqaqAqtkqtb2pfnoKUxVttGrGVlZtyAV0wpdA5R8wFpcRwWCtN2zQ4tSE4w+H26wX6dsaSK8vUaKGbt83ctpDZ5wymoKzFtStSijw83BGjwmpYk0WuzegKh8qM9d7T+OG0JCielmSMsazWnTRiW824z0yHzP3rR1IQ54SZGq6UXurNr9MDL1+/4eXLlwz9UBfhAV02aBL74cBuGNiNI7rI/Ug6ibqMETKXmI1CLFG0N1PGKOmT2jQiPqxBekuT6FTGINqU1np0kXv2t36UIzRTikBQ0XRSl3Rrij2AOUjdy8Sj8hPV4LVkSAXnLcaCdYXt7TesVyuYBpQzNfh72vj8fYdS1X3cWWF86yOEnFJlfVbRduc9xsRF0QgKcZoYx4Fx6Mkx1IU0keLENA2MY183wbwsSiKLJuawucKNi3hzEHWVmFLNbOY87AhNFkArB0qk72bbqXnhyMDXwdGPDg0cghDnolbVgFYRk2yGWimybaBk+tgvjvemJBQRnRMrNeFyxJUBn+cNsczZBbN1W6XS1NOYJ0VNC+s/ZQNUoHI1cZYxrIBEZcSGwnabMTaw3T+VW2TeABfRilIh3COzeTYR6IeB7W7Hy9ev2e+2KKRvuT17xqVrCeNe1tKSKDEuAcx++8jDwz2/+eVfMB7uiMM9f/qzv8/Ns/f58NOfHFtK6jYve3FeAgKlQGXJCLU+BitF1XNV5YfD9fwhsmnUTRBVXRSOigDvLFUsafgMh5YFPuhWDUrBzbNrbt88sNsN0qyudJUEOjKrRE6HZRNUqEX6rORCybOWHqLkMQTiOOGcwbeORlW9PhTKZNSJlVIBcZiuUIeqJ109ygHQRR9bN6pr0uKdKL+EvjrDqx/x648/wjLx+uXAfuhl8uiJyD1DKHz1akvfD4yHHbHpUHbF+5/9XTbv/Rndsz8hmeeAQRepYeSSyalbBl3KIicXUiZUk9pSfy70TwW0ZxhKNw3eilntatXWzSCj44SOEVMy1jlRWxlGupWlbRydl0zQKphqRthYhe825HXD7ReOFCPbxy1XL87wXqG1pWkanPO03kEs5BLx1uKbltXmjO7sgilM3L56JRDcNNWMUmBeY6QGEmOipJqxngQtu8MBv/O0Vvz6dCcLsjeRyUfCYIkj5KB5cXPJZt3w6fvPyNqRi2L38JLPnp9zfdayeX7JFDNf3O/IKXDod9g40KhI5xQxKVIpsiFWcp6tklipwP3DuCiY+K5ufpUIkoE+jExpAp1xnfSujVNEJ0MZ/4BZejpg6yL/gxijpRBVg7IFt36fEgoMkdYPJJ+Z/EjnDCVnhknQDq0ESGuMZt15TNiSdi95/OZXrG+e4VYbIn45nd93OCv+fl1n8N5WXoBAotL2cECRa+YpAfUUAt45tFZMQ89ht+ew35PCIOUYMtM4ME09+8OjGOCmKGWLKMawlANKDaQk7TiliNVWyIkxRvLQL3q4VHvbXOFqFaMEEClhUmIcRHv09DE8REOYtDjC17pa22ggoaOsRQrxxtumqf5eJlb6qgiKe7RTRLOpjhNgxj0mjqzGBxoyTmV03dBk1Z1Xnre0lJZ9UMhFkrDI72mVMUVaosYpkEoh5EJE8bgNT96kzBAbHImQs7xafb0U0et9+foVr2/fcPf4IC4S1vL64ZHw219xe39Hs+6IIXDYbSGOxGnk4f6W+/s37PcPdDbx4fMrXjz/E158+COadgPa8TacuNQc52b/utEpJaS+U9IQs0rSW8jA9x1/kGLMHPmXmbpUOO666ghazWn8ctOW10uNDAXq2mzWhCmhMISpvjlFGpHrZF8ij1rLU0U8ByU9F5ouaCIz5FrZi7UA7q1Z3rcU5H2Wa4LN2nN5fmQgljnyqd/XWi1agMtDqddeEGbUuIKVj1ycr9mtV/SbMxKaYZzY9QFjA8b29P1AnAIKhW9WrM6ueP7hZ1x88Amrmw8IeSORThZ2VZmVYWr/Y0oiehzSTGkv1bE9k6buycIoCvYCKYsDtHgGKl2bqkMWwWothImsRATAN56ua2kbcZk3xuCtk3P2nrYRlY+Ly0v2uz3DMMgmZgxdI4w8aw2+2qqkKNZU3nnapsE3HhQ458lhpJRZVfBkss0RYE6VmXc8cirkkEm5yu8pkc7KtpBsRltwRaOM4Wy95nzTcXV+zursAt809PsN1ytDt5J2jMMUwR4Yx0BMEymMqBSxChEDzyw1aKXBqmp8XAPAY+Ahq5RAZ1RqfVic7s3y80cM5fT4/g3liAPNBIT5lW/93nw+c0aoNEo5ij8DuwW7XjQevbN4Zwgp4Yy4YtRyElZLAETqicMD9y+/AA1dzujukqNf2nef+ZwFWiPowKLIpCqRJGdSjCQTCMEQphGllLRBVQ1Y6Q2sf8IIRVCdqZJiYpxIMdUyiZQyQowoJcLvYQpQ+wQVuc6ZQgkRXcDXjLygRXFl5jCMGmMiNlliiKQYn9Tci7ZkJRLluT77mHLdSIsEmkoIciHFZS3RprqoVMKgUpqkrcgnKk20BaNsXQMmXJ6w6pgUvp2AL7XAeZQcF2FRelLiMl80mCyMcrKsKZJUv4OlzNwtPS/vNRNEAr4QA4d+YIyBrBTdqiPmzBQntrsd+8Oe29vXXD97RkqRw+6ROOwJ48Dj/S0pjUDi6vKG66sbbq7fo+s2GFuDq3fGd0fofRazPw49JWv/Ij93Oi9///GH1QTVyVSsEe/pXjj/wLIYVyLN6TaIElipaTwvXjxjvdpwOAx8/fVrplGMJbVzgJLewHmRr9TMUpl4UjdyCMusUMLJAHYSDUVTKFovZqc5Z3J1k56v6cefXvOnP3khi8vcaDtfysmfZZM/eUAC72rGoWf3uOJf3mwYthua5mNe3e+53x743W9+zRAnElacmBux4bl67yM+/KOf8Gf/7r/P2dVHtJvnlGLrueWFZSaBQL0HdQKnJP5aKUnGlGLhyy/Nk43aGkQNIisMBUNE5QoVa83UR9F2bVqK0WStaJ3j4uqci4sLmqatrUtFdAG19G2uOiHJxH7gd7/9DZ//7ndQIk4bXCc+j8ZousYSiiYo6LqObr3i/OwM7T0a2Gw2sBNBc0dBl0TJkZTsoskZK1x2utDqpCgBwhgwXiC2zjZknZiY0LZgjWFjW67X56zbFovnjz/+kE8+esFq7ej3j2zvX7MmEsqBQxy5221RKTHsHuWac4U4jfhjhiRsNlOtkEoptM4xpSStM7n20qbIFCIhZYYpoVSW/cJSWb7C1FT67Uj+dNKezJfT1+oAPN0A1Vu/cZyv8t2MbIK5uSK3I3QBZd/gXGDVOvrJUIrmcJB547QSpRRV0DlQwpbD/cQv/j8j720fuHj+AS9+9KfopkPZ5vdmg95rmkbTNALJi/al8CVLicQwylmqMitpCtKTGozWDH1PP/TiND/uoWSMLgzDgTANTNMgZZGiRJ82ZsYx1JW0MOz3iyVS07VCBMqZMiV0SmSl0doBujpsVHhxGEQrs2kYp0KcTt1MxA6qadxS44wx04e4NJWbedNTimmSDdl7h3PCWnVGhCZ0DSRyfVY0GyIw+hVl9wCHHesy4VWhtdDoJwI0CzIqT3ted+X/ZoaCTZVzK4VipC9aI+ugVen0so7rHcfaovw9r+OK3X7HNy9fEQt0mw2fnp/zq1//Nfd3r/ny6y8J+4k8ZX70kx+jdSEMBx7v3jCNB+Kw5ZNPP+bDDz/k7/69f0TXbvBuxQJ7qnQS5L09wmuERg2Ua8ABipL0Qv5Bw9v94N93/OBm+SeVgFLZQcu5FWa620zGQKlF965QH5zWaGWXTFJrODtbsVp1lJJ4uN9yd/tIDBMiXVT925hbFxI5JqYwLU7Rud6cUDdljWSaJity0sQIyhRmJbUncY8CZ6HxM9Y8N9tKNCEDTdVBppj/Po3BCzLYrBEKvvOe984v+Dv/8EOwDf/Ff/6fo8pE6+B604rSRNH8o3/vn/DHf+fvsrn5Ec51qARapePD1keadFEiuFtKQfZJhRhVHmujw6F5wtT1zkldrhgoSSC6FHBKsruiC8VqlG5ktzSaxlrONhvONhvWqzUgZKDNZiMq8dXzMabIw+0967tbmrahdRrvpAVCW4+1jpV39MGSjWZ9fkbTdVhvpeaoCo03HFSRDdQJUURBhZ7NIqY9M8nmC/NNh287oZirGq+eMOnaTpwsphgZ44AaEi9fT7z4uqPVgbNn5zw8PvLmzS3vd5bDOJJyYQiFkgpjgNX6nI8vVvzm1SuGODImaTBXyjCVWIXfJRCTDCBxGCa0Njhrl2eotTQnW6twRiAqW2E39LcZlt9WgynHNaDG5svCUAd0qf88zQqfjk75O6qWsn6B1h12vyfqlzAMnHUFp1v64LHZEItsVqUSrlKBHBVpP/Dlr37Jm6+/YfvwyObmBWc3LxZXA6MtzhtMGp5cU9t5McJ2HmOt9KIaUSkRdCOhjSamyDiNUGQTVEoa3sdxqExR6cMsORLJouQyDYs1mkIv/WkpBUIUJvB29yB9nUqhrGx0lJrdl4yagkCgShFCWjIe0bY1lQAC4wkcChBSweVCRuO8x3lZP8ZJRObtTAJSVexaUceGPI8hCKQ4RuhaVc1kgwRJKJS2xGZD1J4QJ3RK6BRZl4RXiZVKtU84Lw+8lGoczNw6UJ//koeUGnBkrJZ1w6inLWNz4F3mclO9T/PrWimmceTh8ZGXd3eEJMTFN69fsd8/YhUcxj37+wd+8a8fJRgoEecLq67h07/zMz7+5Me8ePEhXXuB1oaUZ75IHb3lJKN9shHKv0/jxZwrurgQ+2oQ88MTwT8kE5z7Q6RmME+v002hbh1HWFQJzECRBlVRSxDcnCLEDVEMV5yfr4hhYrc1hCmTKgw17/+C8x795FQt6stDKhXKkLWhii9IjSDLg5trOm/fm6rhvNz4ZSN5kpKrk+s8vnaMk+qj0uKwfHV9w4//+Kd0m3P+1b/6Fwz7B0gDZ+cbVk3Lqtnw2R/9jI8/+xmuvUAXwba1ru+k5sVwtumRje+IExwXSSHeZtrGHl9WVONSj8piLEs5ekuLgwOgDNpalKtuz97SeE/jPF3bMdOy16u1OH1bLwFNCPiuwbcNvvUCdxmRYDLOYZwXPU0j6vu2W+HbBmNFqFA8HB3WSKboqoqFLDy1md9Y2QTfYlHOih7MsHYp4t+odPWqa6Q9JY0id5cTwxDZPt5z3ypGE3nYbnl998C5OmOKsoGGmmUPsbBSjnWzRpXX5JgJMWC00O+nMrMTT1ptShFVJDReGYlm1ex0MJNBxMpHKZYe299/nG5lLEHnvFaosiQ8T4aFrHdPc8SEpbg1Sjns5hozDqAdbWPRJrOOLQ5HUo7ON0xhoj/sRI5MKbR1jMOBMA40bYNWojlrvQNj0NZJjSv2xxmiEJk075a+VTHO1ct9m1uhck6EUO9lEDPtYvJirp2qfmjOCbLIrIU4E1akljm/T0ri955iZBj7yo42uNRUCbW5GV3aH7SeM9B4ZETWTRAKU2CRZZuPWJGZUmofcyWElaJQSljS88Ju64OZzYOXGn8BUqlrVKmN8/MD1iTrSdoStYUYoQQKgYaEKZN4WBZh8s6LUDnCcsv8eLoxSPYk5ZLyrVadZQOsDNBlUyxHmblDf2D7+MDt69cCiZZC3x/IKUrNnITKI+OuEgRVYd2u2awbPvzwI9578QFX1y8w2tf7Idqjpah66uU4fn/vRDle65Ed+ocdfwA7NKOz6MUZU6PzVHs3jOG4VNUbPqfUNQM0i6NwrmlsVe9SBa0Vz26upB8mKb755o48ipsAtVEeYwUOTUcHAu89/TBIW0UqVXFCSV0mqzohZAszSjQm346/czGkXLURl4F0HACChKplMVY1qpzVQzKZcZJMwHdnXD4r/Phnf8If/fRPWK3P+NM/+WO+/uLXvP76d6y6jg8++Ih/5x/+N/nTv/ePuX7vQ6YgD8EqaQFRp9H/CcC/UH4r5l3y8TxzYfFXlJ9WPH92w4uba+Ik8jklJ+J0oMSJPPU4pdDO4dtuZqWgjJJWAKBrW6gwz3DoyaWvgrhS98pE/MpzfnNOUaKR+uLFC9A12k0SDTtr2Vxe1A2zFa1N36BCYuoHSiqsujWuadHWijWOdbTeowqsuuFJQDIrF+VSRL5Ng48F2hZnDL0ZGCfNoSRW52d0RuPjjjd333D/+DXbX2umKRLHwLPmj6tbBcRS6GPkVw87nquGq+KJYyJPBSIoK02/h7BfFrgQJMizVoIJoy1WGdlolLgmJAopSoOwRmBRsn4LkqjzZv7qJCM8kmAWDuMRkcGIb+lpKvitOG2u5WiKsWAc588+wqbM8Oo1q9Ul2IbVJx7VdBjfcr4+4/7+lq+/+h13t69x3vHZj/+I3e1Lxv2W1ox040v0bY/frCRbVwrbeczujtM6qa+wofceZ7206CyekZK1qSDrQa6tTNIiJVJ//dgv2aD0D0fIoWp6josPnVZ5MWA2RpGyiG5sdw/imOIcahCHhVklqpRMSRnFRCmFcRTofb7nWmuMscRk6IdheUaFwhQS3shqEmJFpmxX2aGWfhiZN1OFbJKCcNTgaG0XUY5jVGNBS805RtkYnNEYZ6uySmaIgZAjpIEu9TQ50Fb/P5mb0ht9XJClr68UkcXTC56gamnkrbQgCfuWklAlLeWYlDMhRn7z29/wm9/8hr/+xV/xm1/+kpgyrlvzwUcfcHV2ybT7hvMXHVxfcbE6qz3lhRcffMr51TM+/uzPcLYR3kc5IRuVgppPetaBWwbxdx9zoHEkYEJVRfne3zs9fnizPCKtZIyc5EwgMVpVdf7j5JxrhZJ96ToQ5LVSm4y1Ek/B43qvaBpX7ZB2clOSwA2lKMpJAXeOKkGgk2kKxFzEFxADViaZtHPI5+acRMLtaWYtElxjEOLDST1wjpq0KlXge25YhVjU0tN4GEWJfr8/cHZ5je/WtOfXRCyHKdKt11jriCFxcXXD8/c+4oNPf0K7Pkfp6qdGWTQCZdzWiXEazVfVByoBgHLsYcpFiu+nhxiYGpy2pBTIEXKEkCJh6ilZV+ikVPUbhbYCucYp0B8OlFwV7peWFJHZKko8zprGsVqtiL3UZZxzGCdi2v12J0LV3tGtOqz3GOvRRXLRpunYrM8BjfctXbumWYlv2WzVZSvk/WQc1qxeVHxKzYyknmSUjEVnaouGEdjRGJkkMSSGVIkRFEIMZLKo7mjAKKYc2YcBM2wZwiSN5FGhq4G0qRmC1pripGG45KrJqKXlQ1NtsmKu4tCZEpLA5kVBKbUN52Qolll8+xQOfXsjpAZxMgcV0ou4MAXVvFHOkbFiJhCoEuTcVaEtI8YruFjTXN+gmhXZeiGuGEPTaFa+YeOu2F7KfHvxYsW4viaNK2yesLMoty+AEFM8GV+Om4XMVQkSxEH+mAnOqEsu4spBoGZ6khkGayjZkmKoUGggxImSI6rEpW0h1x7NokTuTRvFNBliEoHtKYwVYSuYIHJouQqMLyLUNcOcpvEkAJG1S+tAKq7WppenImPUyDoWo+wdh3FaIFDf+OU26LrxOSOZpVpgJ7lVSks5QllXYSkJ9uZNMsXKzFRKkJuimAzoBGSLST26ZHT1fiwUYjoJpkvVED1BFmbqy7fMFqosomgx5+X8YyqM48Avf/lLvvnma7aPjzLvVKakA6m/JeFZ+0y7uaBtXnB9+aI+d83m4pq222Ctq+trPp5JzXznBGpZf+cvZpLXnOlVJuhRh7VedU0IzDvVmL77+MGb4EzhtYtpIWLQqRVWqwV/1vMF1sXiKMUkpBaKNDpbrasclUwgrRWNExp/24hCuIqFmLXIHsX5fqjKQBT5rxiC+NwpMEr0IYWApqrNxnyDWLK6422TTXQcJ3EkUGpph1g281rLKVr6i0qRBHiaImOI3D1sGaeBcTywubhhVTJ+dc6YYEwTTbfGWGFaXlw+4+bFhzz74BNcu2Lm+RUKibkF5DT2qT9RMS+BX2a6sizsKdUo7e1NUCFRpLWEkIlkglLkFAnTiNaOUmwVPZhZpAaqpuNhf5D7HtICueYUKstPYaym8Z6u67jfHaoWrMZ7sazaVdFq5xxt16Kto1Ttm4LBNx2bzTnGWrzvaLs1m7OzhQiVU8JUma35jiiOrFeFQObMJATk/6b2DWrlRFGiLrbkuc2kLghaMcWIm9Uv6huHHOnDAKNmjKG2oiDjw6gayWsJHJxB5URJMqalzQe00RStmXKAAjEkUkhYpWiUPE9Z2I7P6/sU8N8es4l5A2SWrph/clksy8nPQ8bkCaMKVmW6MtK6THu5xjy/RK/WWOPRJaJIKJ05byw3q3PGKyGWbc4cnF9A2lDCWMXqC7HkGpBIjcpz1LBVitobaU7gULPMM6UksJoXsBDCEgyEKsAgm91xIywlomtWkysJRS5ZLMKUFtRGssSJKQzzgEEHJ5tf0Wjr6n2XxvoUA9MT8ovczll0P0raf/K96qiiFSQ5536c8E5cULz3QkSp652prV8pxWX9XJ6PNvJetWY4l4+omsexWgaZem0KzZRlHpZsMSFhU8CW2l9YarCUj60FuVS1LWZkdNZIfjvTysycj1qMEbQkiN/mF198wcP9PeMw0jSulpxG8nBHUY7N1YbLqysuLp/z7L3PcL4aHBvP0sYwt9hxBDDewnOP2P8pCgbvnCOSTdf0t14n3zGX3nX84E3Qao2vhf9U2xasc7XWpyosIYu2sUYeop4B0grfVCaVM9K35r0lRWrNClZtgzOWh7t7ySL7BKE6YlcXYzhmgtJIX+uB5hhdqop7LzW+GZ/XWgbJyRGmQBinpcA6b7LC7BLjyZAj49TzuNszTIF9P7E7DBz6gVe39xht6dqWZzcfU3Lmq29e4bcT2moun73HR8MBrTIffPoTzq7eZzcUhjKibcK5I2Va1eyTIk2gSqs6GY6NqhIJ5AoBHFU39sNTxRhdMipHMglFEvNUa1GuAZ/wVtesvkelDMqhrMXXdoYcAtM00Q8D1ti6aFUjzlTIIWJSYWUdDykyHPa8/uYlV8+eobXm1e0tXdexWq0Ezk6JmCLKN2hv2Nxc0F6sybmw3qyhZIbhwFSvw2jDqnU4b95CRCpck6oZK4Wkcl38pN6oERHumEamFHlz6HmxPuO8O+MsJx6GHfe7LX0uKGVxfoPRE4oiDg91kzzbrBhDZHsYRQxbi5iyUgWlC61xpGJwSdNPEyEHQgrVYw7GMBJizW6SBDoD4kq/G/onz2uO+uewCJ5ufjAvXtRnSlUXSXJ/K9qiyOgkHnqqZMgBcsSFLZ1TtBZa/YhqJ3jWYNqJQqafpqrLmdBZoUvB58zKKCCRt68lAEN6c3MRcfmY59qfqcHU03NuW0fbNnjvxTXculr/ZVmsYq3LzpsaIFl6TiKOHSZSDggZr9QV5Zg1yzwo0kJBrgSVg2ycKaCTkWcyDmjt0MYtm1AuhSmKYEI/9MxkuFQDdsmgPIfDSVN5KfTjhNEs6JI2mvWqW7Lwvu+XjatbndV6oPyu9PbO5RTR5NVWFKGmYZAA0MwSldI/Kc+9IG3OciaDdkzGMZgGGwMujLTTI5aEoapr5bxo/c5Ep3kTjMoxvZVNZYKgJ9bgtRfo3yhWnef8vON/9N//73E47Oj3e4Zxz/bhFb/79f8Lz4R3hucvPuTi+mPOrz6kW18uQU9ZoNXM29vu9x1lDupOMllB7QRmfdozW52CZoWwH3j8AXCoQEFWVwhmhjNrWp/ijOKVSgbQZMVy8jPzR5g88n8Niz8WSujC1ojfmCj+n2jV55kt+TQaUJVIYazUKo3RVZlCL5ua1qrqj6o5HVx+P6VCSKX6u8n7TicN9RRRlxinid2hZ5zEpucwDAzjJK4CiEitLA4Czg1joIzS05aLBu3ox8zuMPHweMCvwHlHWypMpMqyCUr/4Yx3zyFjWSANNaeMRQKImMXp4tuZRT4yxOq901rIK7bKWJm60erKGioVjtKn2buZIeWIquIBlIKzlq5dcXZ+yezcLaLCmm61pm07mrZDVT1ErTKz8ruqWapVouQh0GGqmQEoY6SxPx4VfgpyfqVmisuifJJFHx9bEVksBSOFQ8romAlZ3EBigaIM2nrWvsXZLVpPNVMQPzljDTqVI2xWpAZMUagshIS59WYOXFPORxirst68taIggvSNpRq4PTnq/KCO2fli1MmfGcRSpeqPlrIokNTQH1UyNk/ouhCSJ1SOuLTDK41Fo/KEIglckCZKDpQ4VjH7Iu7ZFXIXzkVeAt9SqkB8vSfLib6Vgc6Hc9I3Kv6gc/1mzt6OGfDcDzwf8/OdQqjqL8J8lDreyfyfx2lFRWaVpxhnp44jqUNUoaqSSz0BET+XgC8Gcac51iclKYqlMIxP2aExRkKUjL9pPNbI+pPS7B8p6IrzAgcK6iSSbSXnGW+VQL4OLs1MP6SaAcv6l58s6GoJiiOSZ0SMID3aEZXHEvFV65MiYVJWlqRN/VraMULRDDo8eW+7MJzBGSfra7XCyhnef3HNOK4Yh56h33PfKfrtJWXcY7Rm1W7oujVN22GMXSDLpdm+HOfrPKePR+bYVneCfryV2KlKIHxawjqBTPluVOVdxx8goF0hNq2FxIEs4tSBHINaapG2pu2xHI0s571HbDeOsKk+wf+ss7Wx2mC1ku9X6Cen+cJKLRrLL1ljBIJwlsbbIxvNiKGuPoEitEakvE6OKSmx0wsCw5UsbMBYrVxildsaJ5mMAj1KVoOC87MNShusNgzDQCmFtlnxuN/TDz1pd2C7nTgMma9e3jOklmTecH6V6FYdF0phdZFaJiz1C8lknyzr9WEfv55/oRRHwT3Z3EutO+WUa5+kwFdS82lpGre4QLA4btcerZzF2BSY7VRKlki7GGE8quoN1rQrXLOW8eAcTdOgleL99z9eYLDljLWWRYBEybJoaCOZbk6ZGKIkupVJHEJkrASD+U1ynMWXA8WwoA05HdtpUs2QXa1xZWe5C4HtlNDAlBJJaYr2uGbNZr2me33PdhjRpiEVTYgZ5y0YUY2JuaBLIXCSZSH07jGMaGNRWhwHxhiIOaJ0wVlP0zT0Klbhg0gqR0m35SkuUPzxuS6vqbldB1TOkKVpXOUotloVTiRHVArYNOBUprEFEwdUDtgyoaOBZKRZuVR8ZjwwE1TmzCRVyF/QkdlZ3ixtBQLIFkEvpClr2ZzyWxuUbyy+OW6EIltbXVhQ79wAQdpOiMI6THWuWWvJSUTUSw3EtTYoJINNCBqFgqkK2B8VRAo5BkyR8W5lklQ7p57hsGcajszWGgMAmn4M7PfHftUC4n5hlLQArTvx7rSGKfRM04SzDte0rFbSAxdDEPPtulF776X+rbVI8UVVe1IFBh4O/cIsnn/HzDR2qGpSJ6tDEUVSzAqbEm0O+KLQJVKMIZtqHGYbIoYJxZDgsT8sq4lS0HhP2zZ1A6+ZYI6UPJJz5sWzM3JakVOiP+w5XynG/Yds7+/IsbDqLmjbNU0l1lHXnUX5+9tFyJMdru4hSnNKjinLX/LMJYiYkwUpB0hcIaWKTPo3oRgj0eZRRfMkk6N+uFJiHyRPRDa/fCLqagwz8WO20WCpL1aIIydy1rIJWk0IEyULdJFilPizakzOmqLOSaSlrXjkWWtwzlS9TNHNnIk774oQdoeBh10vorp1MgrsZ8lophTFj0sVtHVoCrpkbEqV1i2ZwDgF+lChPOvxxoOHV31kGArTZJmSE2Fd06CUg2KZJoiqNrJyzGy0mns6JOVT9X4xj4V6PTnJ52/34WmGzNySUjfDKj+mlUYZcL6rELL4uJUiBqPzvZ0hZ2vt0s81BeTanMO3Lab2Dl7cyIKmitQh4UTurn4dowgeh7o4pRiX999s1gvcHUJCKfEcnKbA4XB4EgXGKOaoQxipvDs63y4KO2FIErzEKAu4KtIAXhIjCatEwu2s2YDR5JJxKM5WHVOK5GKIUTFNGZS4mjNn1AqKKtX0ObKLk9yXHPCNr04nta8qF5GRs55Ns0KXkSlGhjFWxOLp1DNMWDWIrVElvZiCiJBDdToQ0WWpISRIktGpkigpoooQuUrJRFVQsaDDHp0DWYn+f1GKnCTVk4xT0p2Uj3MjUxfZMlPopblGdmJT53sdjnlmemuoQefpYa1a3DVyDlV/19bo3ixkoKN3nbzXFCdiCKK2gnhdaub644wKyWIpfqGhokkGrT3GlspulBaKtKhMaZS2hGkk5Uzf9xz2e8Z+T57GBXNW2ojbvHJstwOP+3EZhwrJcFerhrZtsc6RCvSHgXGQMdE0HdoY0IacJskMja1BtejzOqNxzpNKbRvRlkwiqQzGk0plhE6pEr6O66mqBJy64DL78qWkIGsOWaOLB0TTdt6HQmCRTTsME2/2T/s6Te3jLSRSmkilkMPEOOyIYZRgqSJnu/2O/a7H2jVNM5JtQjlZ39BN5WQVhDh15IS8bf21lMvqtRyb4eeXCtKnq5Yd6K0aycI9ka+f5AO/9/jBzfL3jweMNng/MEep2pplg56zIGDp/csno0ZXKvoMW2mlcc4uKuNQfcCM4e7xwHY3sD+MFeaAsbKzlAZb4aRZCHeuuchGmquTQSQaTdJJGIUVkp2bO+XeFn7xq18yTGHZTArSpDtHjyHEqtZS2aXUknGS6HMK0pcUQyROMqCcb8goUox889VXbB/ueLx/pPnmFf2UyXjWmzW+8XhvljrCDO/JAjWLiZ32XZ7AZBUGyvX6X795XSFdGVRfvXrDFOIiqyaTpAoNU2i7Yannai3R1TSOlfVn6bpOMq/6GTFFhr6nrY3PTdOKELNzaCeyauRj39Hs/lGKOHmEGEXvMYaljjm73a/X66W+GUNcxkKhcP/wuOD7pRRubx/oh4F+GlB2Vqdpax9Z5tBHckqkHPFO6olTjovwgtGW1jtS2/DVN2/YtXv2qwNv3txz3x+YsvR7OavxXjHFxO4gIuNKQVKCBsRciBPEnEglLpugQtU6YMEay+QnQhvox4kQAv04MIXIfntYJnkumS8+/y1DvxMTgjKTXmaGNbW2l+YUTb7OEUpEzSbHiipmL4uK0QUde1QOGLWAqScsVCqxtBxFkoGsZohSNnKW7tK5bDGTqYAKSc2lhq9eP7J0EZfCdntAKc00ZWmYN9KLOkNlAn8f3SXmssgURpHn2u8X2UJFIcWJ/rAnx1jFMwLTNBDjiDYJYwuN18QcpQ5Fqtcm8K2xE9YFtJGWn2EYOGx3hHGgxLBgb0pbUIaiM7v9QN8f4dBcCo/7gYKiiwWtpcViqM+45MyQoOkDTTOR07QgHeMkSIv3HucdzntymeehZYrioBHDVNeYzDAFtBIbpNm9Z24zoa6pqc7RVFW1ZHxINiTuGfI8QsqClJTCMEw87IZlHKaU+OUvfsnrN29kY6p8hBQmxn5PCGOtG8smeDgcGMee7eMdY/8owfjwmm4H3f34xHGixLjUKI+N8XWTLAVm5EsdfUhP9x8Qub2FESqDfdn8TkmYSim+/PKrHwyJqu/7QbUUpOay3fdvr8ek9od88ve8T81//wBY9/vf/q37efq2pxHo3+h48n5v34HjYgBH3Pq/1ud9zyFNwnJo/fZAOj0/3vG9p8e3gdjv/v473+odv/Q3e5xlqWvC3+69W+rUcFy45+89OYPvPLW33vAdry9Z+7e/dzr3fv84/Fu7oT/8/ZfjD7nn5QnUO9/j+W1+/zu9PXeefufbZ/l9o/O7rukd8+J7Lv+dn3Aydt71c3+jUfruC/yD1tVvr0W//xdOf3LpAf897/7t3316lu+o5PytDNfvXnee3p3TlqP67++8fT94E/y3x789/u3xb49/e/zb4/8fj+/bBH8wMcZ7X8VeT964/l0KSxTz+3qdJIBSy/9nCOZ7Tv74WeWtuOP7vvet7+flu6enuFpvJPpRT8/xnVH5EtC+2070NC78fd//2z5yzjw8PCzXvDm7WOSpjp//t3D8wKzv+771riHydqa0ZGY5c9g/Hj9eH39svp+nfpbl5LffNRafPNdyMhZOYOZ3PfoZnTh5p3f+3LufcTmWBuYPKywtPwDdavNtUe3/Xzre+Yy/nQrnnBj6/fKKqbqZ78qOZyWm2UJtgedPijoz7A8sGpa5Qn5Ljf9Jdv3WuT2578eHfQq/nkqDfdd4LaUsZt8AvmlP8s0yX9Y7b9RR7anKR8qFnfzOfFqqQoPqKC0HSy3t9L1OIcCU4rIGnqoL/ZDUK5ey6K8CrNddvecVWgSo/ZylcgMW9KTe+mX9r+d3rN0dT+IJune6/n/rXtXfWX7+u+eyVnO/6Qk0jIiblFwYp2/JMr3z+EGboFKK//A/+O9yc/MMtFlqCyFnpkkUW4xxxBjZ7w4yWEpZGIZKCbPLGo23mq7thM3ZeIZhZIoBY3wlDwlGnOugm2Ii5lzpxUJ7FjuUKHWDEMgpMYnwIAVhhuVKZhBX74nD7p6SR0qa2O0OgKT+//P/5H/Fj37yU6H1eouzmtZLr5x30ny/LJDVz0QrtdQyn2zuelZ8ma9jvoF10p/U4GbW65EMeLJ8HgFz5oHw+46vvvyS/8F/5z9gt9thneM/+d/9H/jksx9jWODz2oYhJBzKrLowMw/rFF7quO9YzpfB+9brpU67cpwYKUGZzUALiw9aqFqJqToxFCAWLQX4nAlxdsco5BR58+oL/jf/8X/ENPagYP2hRxlFChlvW7zzXJxdMPePxRKZkpCUhmlYnAFkoVV4L0Ljqiimw0iMiWkKWO+w3tBtGpw32EZRlNQ/SirEIIQGaVwWxvHZ+RqUELpIoJVl7VeVYZtw3pApxJJ47LditZNBYckj3P9iC0VqyP+T/8X/lsubFwL9anke//WQ36dwYplfWWrwx4Vm/ol3qi7ORJGSn/x8OXnmx+GhefPyS/73/+v/GSkGtDb86d/7BxjrmGKsHnrgFKw7x7r1/PSzj3j27Dnvv/8Bz977iNV6w+XVNb5pMM6ijSFmaYS/vbtju33k5cuXvLl7LaSWXU8K0ieYY61/x0SMg7iSRHGpoCTSNGK1wVvL+8+fL7XJx+0j+77nfvtAApJSxCrNN/MBto97/uk/+1eUUnDe8z/8H/9HNE0jPYy1P8wpKLmHPOGrN6JCsds/CjEsigVZ17V4Z6qCj7R36apvmnPCWMfVi08pypBK4e7rX5PjhCJz2O8oBZr2jA8/+RGbswt++Vf/FdM4kOL0VJy8Nsun2l9cClVFS4KMVDSvb+/5P/1n/yWlFNbrjv/lf/w/pdtcMqSGTdfgDLSm55uvfsWbN9/w13/5F1jd0PgVDzsRAt8PogSYkV7fVduxatt632Wej4eRECL7fsI1LbZp6dbnpFKYojCOqTXDMI7EEMjTxDiMDMPAvu+roTaUEjFGc3N9xcXVJecX5/jO4GymMYHLC8Pd3Y7/9P/4//hBJbUfngk6R+M8uWqFohQmpcUM1xhH0JbYZKwVNfaFuamFwGCNMD+bphH2obVYV0BpnGtFCLv2B0lR3qDdzGqqDMNZTDcnrLPL69pMSzSHNgujMWlpLHfOk0IkppOJrhStb1i3DZtG07TCMF15R+eqfmXTycZfN0OF9BzO/ShLP928Ic7F3VPa91uboBT5ZUOZdSjf3mCWs3wSGb37KMAwjicbsuLs4prL6xezLOii0zr3mc06ffMGOEd9b9cvT4/vqmmeEo3mTS8mUaMouRJ4qESjqrka01EWSjZBIToJ0SgTowQ64zg8+TzTaJRRzGphWWdiCTgrJBujNGUqjGEUvVgNWZ2oAXm1bIImGWEVe4f1tirUHPvZjBXD2YywVVWhMlWkNSBW14FU+yedkTEPhZxkKEgvpqHFiylyLqjiSCeVBqUUq7ML1udXQgI6iZT/Zvvg6YZWg5McmUkIZSZdFVgMpEtZgtu330se6jEbWZ71ybcBFJr9bncSzMm8M86JryEsvpbeS+vIatXRtQ3eSxO9tYam8azWKyGONU21qJK2DKNgu31EPxhSLlXWbt4E87GPtCDXUlsxtFI0bcO6W3G+OePZzQ1GG0KM9AgZLg8DMUnQHXNZBPhzFoH142UpuioKrydDChMUMcBVxaEKNPPYiZkcJ3KYIIl7Q+MM63Un1lpKnfQ4G1JKWNdweXlBrvZOcduRk0U6kKWX7uz8hpubZ2zOznj11ZrgDSl6CdpL3QTLsV91maeyEKGUJhbFoR+eXNd61dKuWkpwNF1L6xQbb5nGSygjr87XaAzOOEIS0YiYC8moun5bVivPxWbF5fkajbSmxEls8h53Pdo2KOvBtIwx0Q8TIYnii3YWqzXJOeLcRpNhjBGWvUFX+UQl3QDe4r3D20znNGcbx/QDs0D4AzbBFKNIEcWCaxp0fWA5RWltQIvahGJpgM85Vxr8sdhaEIpujpE8TcQYyamgTSLGtETRc0ahra/qCU8noLRKNMy9ITA3uJalT+y4c1hhYcX+aVZVCsNuS3//hsYVWFuyNzjj8b4l+4Z88YxiG4qRxU1RnS/m99F1wMW0UPmUsccN+eRQembS6eoJphaBcaWfLnin/5oXqeMLT6GU04E+H5lZmOh43yXzLHURPzKp5shQwfG64OT7T493vbY0PVd8RBexYaq41/K+0uqkalfkrKAvAz1xVO1R6qm/43xop9FOyfXlRMiZXb9lvVphfYvTlhgCZcqYLFCJ0ZpEpqgCVu5EyQXrLNppGt8Ke1mLUklJiVTKIrpeaqM9GUxR5CR9SfthFKQhRaw2tC2wqWISWRFjwHhp0+l8pbznAhhCmVV/jpDXLNrOvFjVhfz3k4GOG9IpRCddtjWLq7qbpSTwHYUqH1aRAfm46mn3rvdfmp2ffqa8rBZxgrfH4Sljb/694zflLacwsd1uiRj2vSjpXMTLpcdO1Rulc6bEwHA4cHd3z+39A7d3j6RYyHFGIgpkcUsRt4TqWm8V15szNs+e894HH7Jab4gpsbt/oLc9ex24CzCOkWms61LO1Uy30B+ethKUE2g2J2HqJkasFtk5oy0hJMah5+HNK8I0YIHNuqXxFn22XqDf0+BSL2iRrnNeVGOY54UxaONYnV3Qdiuc9xityeap4EY9SUGkTiHfOkiU1piiFni2hrHSgzn27Ecxvs3e0hmLcx3d+oy2W4noeJH7Kg4ciYIly2DDOc167fjpjz7EKohTjy6FFBNv7rakYolZ83iI7A6ZWCLDNFCApm3x3oG1DLMTUAIzDktmPh9ilCCOLjnK/FZOY7UTF5ofePzgn2wah7eGceyJk/S8+LaRSUapcCQ0zhF0Whh9c09UzqlmQmbRfEzVGDbnzHa7k6hnptJW7Ni4tDQiz4NDG12zCBYYwVY5t5wz3jli/XnvZXP2prDNIzlNT+oMOvV0h2/4oP8FnYkYA2O3ERd2pbDXPyOvbogX7xOrZuF+kjYRUGAsY4jsh4liGprVhg8++dGCU+fyVmxdodJcszJdRQFUnuEvtdRL5n/Nvz+//nYd4TQQOH29zGoNSi2nO7uBH/H+2jsIGI735fQeFY6L6/cvyHXzQlW7u6q5Wu+BqpmiPDsFRfr8rG+ZwsQU+iPMBtXT7OknCNwF05QosaCKIiIGweTM+fqM1rU8v7Q8PD6KJFaIIv2mFHo6UqmtFxjKGkMIiTgmpmnAN1YMU1OB6iOIngMEvVzDOIalNqUUFFvQWaOihqiYhogaQTlNUpmiqb2ZdaN8euue3PPj/azowg/Bdd5+Q/EUl3+GwOHxFYfdHZfvf4z2a7BrWbwQ0eFlpL71WQW9vHzsta1Z/4mSk6pZfjk5g3mh1/o0kC01KxQnGO8bfOPJJTHFkXHseXjIHPZb3rz6BmvEgxDAAddnZ9yenxFj4PH+UXpHR+lfK5K+oVRCafBOc77pONus+Mlnn3F5c8PVs+fsx4k4jERlyNZTtGcMMIyZcYjkGJjp/dRE+Hg/6nqntfS9hhGVJ4waKVY2FslYEjEGnAHrFJ0p6DySpl7WOMnrBOqqjzilTEgjv/71b0XpSonBM5SqqKSw3rM+vyKlxH67JYZR+kTVMRGY2ZEzujajUcuDOT6Kk+uCRMZpsF5TVCYj5+maFeuS+ejjT9g93rN9uEerIHZMwKZtsN7R3qy5Ol9xfbHhk/cu6bzD6YKzhhgTL1/d49szjOt4fdfz5Tev+OWvf8cUe2IS2Npog7IW6yErQ9GWZhpglCBF6xNVnzrgNBqrFa3VdE1H66a3LvS7jx+uHbpkdPJwmS1BtCZpjSrVQ8uY6oEnS5meJblmP8GlD6Q8KazKhpiWGk4pRcxkVUIXnrgJSCN5EQ3FuqAtizcsGqFaa5xzlGIgT7UB/OlmkZUmKU0EcpwwMQIB6TEqaL1GTwd0kZtfCpSYKdpStCUWw36YuN8dGCI03Q7XrAQuabzAY/OGUzf2ecQVNY//qqqzRHFCvUFRY/njHjbXJJUS5fx3xNfy7yLF7fJk5Nev5lNQJ5vayaZ7+l6nNcLvOmYYdIHe5udKzUbnsXiS+UjzrvR39ruHCt2coL9l7ph8+8rU8uaqfj3LuOVcszZlaH3DoTb651gWoekcgNlI1FQprlyYRqnZ5JxRRaGVIcdQr+B4D5SRrFbNYub18mbR9VksO4VErqLvIsFcpAyrM0oXUsinV7SEOm+rBM3CErN4wrcewwz7LZA0y4iZx0cpQAqEfsfh8Zbu4gKHQdsNcx/qslyUd++3s+/7UxJJzao5/fpbT6sGhEfNSDUHYUpVc2yLbxpylToUMYUgfbi9ODuoIuWRKQTGw54cg2Qj0pGLKqn2IGdEW1eg08ZqWmvpnON8tWLTdXSN5zBOEsMaMRE2xiLmCYLqlCSZ7wIsv3VT8qxdW/VWVRWdVlX8jBooKFUFJIrGGdE+zTk9jSzrF8d5U4OJmMjkip7MMmEaYx2r9QaS9EjmVNeqKtawoFD1z+mYmQNtKeN8R91/UUEpskYYizEN1iW69ZoYBqbBYm3ExILRhYtVy3qz4mKzYtN5zlYNm8bReIuvsGU0ibNVS3e2wTVrlGmIKbDdbenjJL20KS/3RWmDMQXrwFpXyyPHe7YoDRWWspxSQJJ14IceP3gTbL2jcVZUYqp2odUS6S/6hVqURoiaWT3J1IzMGsfcDDmz4BRAiPPqv8ALcFKULgGd8xPmnIhnQ0xheeBzU/gShdbaW9d1QOGQJ5xztWZzPEZ/xsP6A37dnXE+vKQLD6zDS8RPK2Jf/xXaOMyrP8eur0jNGWbzAdFdEn3H9pB5OBz4+uU9X3zxJblkfvnXf82Pf/JT3nv/fT748COU0RUHfAp4ykWzbCDLIlNfnyNmuZ7jdS3i2ifwSSpPt4t5Dsj6eGTCzUdmHjyKuUJYTn55PpfjS2UJNsrJgnv8HfnfXIcIuQqjzxNczT5uilQhjhQmxvHAX/zlf8XZxQ3vffAjShH91VQqpPvWLNVVOcKqgrYGjRIFG+8wxjJNE40VJY+V7zAYDIFSVXH2+8OJ7J1AksqINZZCsdls0MpglGW/P1BIaJurmLlapM2KBjXJImK1oXUeayz7/Z5pGEkxieu6EqmyMIsE5AmlMznkp+tqrdOIL5o8jRIHYhAFEtes0NqiFuf6kzF0fErLOJq1PUspkDJ56jk8vuHh1RfgGtZXhYvu2fKMlo20Dponz3nBe44Bjyw++TQ6q+WAt/rMauCrq3rPYrempfYqyisrLq8umXIBpcglIYpKmfu719zf3XN/e0cYgwgVlMwhjowxYIh4ndEWtNWoooGM0eI3eXG+Zu0d68Zx7iwbo2kVmBxwJbPpWuIYSFMUIfmYIIhd0zFjVeinqSAhBJHoikESAFIV1J43GGGCWufomoZiCiZPUGZ5SQWzDtcijCGKWM56rq7eYxp7prEnj5OQyWLCupb1+pwX73/Aq68/53DYiUi4Fq6GiumIpCDrhqnvf0SDxM2DDOZkhxR0xKKt3EOxVzP4ZoWEF4p2tYYyYU1gCgUIDIfATz96wYfvveDD967JSTLplVaolMgx0/dS+jDa4K2hbcXLdLVpuLy5YP2L3/L69p7fff6V1BhLBq3R1uO0ONGUkuh7vUCiUo6TGrDVVq4lR/bbPbvH/ck8+f7jh7FDEXand04yQu/FkYCjb12ytsrDZcF00YzTtATu4h0oE8loWyMOYZqiRLFEJJZEwHVW4o9JitzamIUEo5SqWZroBj5J6esNnOHUYRjIRQw3jbV03epp5qg02Xj27orRtdg80aSPcTliS8SjMMZimxa72qCbFru5RPsO5Rouzizt5QsuX3zE2cUVb1695Od/+edsHx64ef6c/9Y/+W+z2qxpuo4lFq4agGqO4k9m1wIHniRgc1Y9R/Ul5wUmnUVk44mVktw3IRFJkMKiBm+oUGwtLOciZIVc4F1tsk9IP/Ak8z7OH7WkErKpzmdBrRnVaTnbW6Egi/lxiQNp2JFXa0qJlOIXmO3pLjtfm4wXZSuxyGi6c2EbG6uZtiMlgR4UTeuwXpP3kTEGkUXzWZCFYrCqCgRbfYTvvSHrQkhTVeWQYMo1Vvz2nGKaonhYTomSQJXKli6JGHqUymCFlVdCgEkxVm3arDK2UZyW3sp8n7JAxEppcgrsH16z390zjj037/0xvlnjlBWlf5kBzBPsGAId23eWzSoLzJiLwGKHw4TtokC39XezmhnLc/BTHeeeRDnzaK1YzlLPknNRy6g8/kbCoIumlFRVmzJGZYH5lGTx1jnRoFXVb69ENKKE42xEq4mSR3b7HSEmQi4kLSKDbWPxVlPajMrzJlvE0dxoVo2mayxt61AlkaaeaadoS8ZZzcpZ+odHVAwn/oTH8OIkRHhy5EUonZqN1oytZmJLkGqqcHoWA19vDM77GhvIPe2HQQT69wfQim694eaDn3FxfoY1it3Da/rDju3DA9o1NF2Lc+K1OA4Hgfutweby1vysSMnJRrdYi73jKEUcNVScmCJiEaYUMSWcdxiz4qHqMLet4/JijTOBPPXcrBw3nWGtsjCfrGiQFoQJfrt7JKZCs1rjnMc5yzgMOKd4dnPGzd0FUzgmNaWWHiqIiPcNKUWcc1IKqcL1uSpFqRrECUkqP6kd/r7jh/sJVoanc8LE0cZWbUG1uDeQZOL7xoAyYkRZaxqLvA2q9uUpFGYZSJi0QJVd21SRZs0wTgJToUm1HULJWvHO+tQxmwSUqLHnJMamWom55+khdStLcJaJFijYkjA5YnLCIPJXvmlpV52YxLYrnHM1MGhp1orzSyhJaL6Hf7Xni+lzHh+3/P1/8A8x1tC07QIZzQWGt7OoZeNYNujjq+r43eXFBVE5veZ6zBZTSzhYNyelpFUCrWUDPMkg3xU3nTI/v635N3+llheXYFMpZo2801qQFPxrzbBISug1WFWOGeiSJb8Dm6sfMBsmKwu2tVgvRrbDPkEqjJNivVlh0NhJM+ZMyUnMZJVE4E6bxYVkFno3s4i1KiKKrDTeetquwTiDdoV+HOiHkTgIrJlDJSDkQippESYvOYnOYi5MU336mqNrx5P7PAc4AmPmXBj2Ow7be4Zxz8VNxNiCzoZCePK85BYJVHq8W0+Rhoyq8JKVdo9QatZUiSzzpHryfOenXE7f8eRZH7eKpzXsk42wqJN3KIu+qfyp41FrMVu1HnImhYQuCUrAqIRRCaWEmDeGREygvLCEvTUoK20Bs+moVrkaLGtap2i8iOsrlSlxIg4Fp0T0XlmNLRlSIGfhIyzXt0ywty+eCr8mgcar5ZF++2fU0Y5tZq+21dA3lVSXgcz+0LPbi3QfVnM2JqZpYr1qWa/aavpsmELCOi/OLEpMhIehJ8W5VDMHt/robPLWOPuO/W85UhJd3JwhaU3KkZiCyCkqA6Vmx1qx7jw6a+I6cdZa1k7jqdwPbWi9pRQlHJFURe1rIqUVpDShtaGtsKlbCJQ1lJHIWSBl54hR1twQ9CIFOf+ZAXmWr35YFgg/cBMsUDMwzc31JQsxIKZlE+z7sYruZpxVGKvkZGv2l7JEQStjsc6TlaIPQTJKrTHOiNuDMaxaj7OGrvU8PG4ZxpEYFbuSKSlyGA9V0b5OPK1x1kskgVDMZ6w4Z0H1rW2IUyScOocgE8VrcXKIpZaBjSPHSo8eJ6xVJAtxithcmErBV8eKzkecNXjr+PQnn3L94hKM4p/903/G7778gp///Of85Kc/5fzykuOCot+6uyfb2ykEyQx5Ps3CTtl2p7XQdz+503/OM/uYbc5sWl0K6eQzODmXpy0Qb7tbzJ+jlq+WMuJ8tbXuVnKulaVMSgGTEyvr+Hf/4T9mwnLIRuxhtPhXTnNt5h3XVEohZqmZTHFEeY82Ft0q0pTYj3usEtp5s24oTuFTolmvZNF0hlQL/zklSkyQCyYXNk3HWbfi+fk1m27Ns4trNmdiALwfB+4eH7l/fORz/wWPj1tu39wxpUAiEU2stYnqmaelvhOqR2TbNpxdrNAKbtXhZJyKO7kkYYoYC/ePe5SydJsLmvVGKOVFgkdFeTKK5jt+WpUrQFQaZRs2zz8DVbBW89AbTA0G1UnmOD8wySTkHWqid7INlm+NvdPPf/tfXme8yiSV0Qhs6JDXnC6SFZIwJBorUOEwTZQwUNKEKwOtjWwaxbhxhCjMQuMkkE4p4moP8jT2omVKqQLfmsY7zjcdm/WKrlFQJuLhAIiTu+k2EPaUcIA0oEqtAy/1XrWQ8o7XVbg0W5yeGFKiEAXYVKZuOk8Z649D4XBITFOhdwe6qBmKoSix7Pr885c8bg/c3W5pHDTe8dWX93z62cd89NEH/Nnf+1Ounr3g/Oo53jUYaxkOI7evX/Pqmy/J4yDlDNc8YeIvbPmZZzE/FTXX3arv6xLRFiIDTjmUs/hGo01hNz1A0egcGXePjLtHpsMjZ03D2drx8eYZzzYWryMpDFjvsVZzftahtPQ6jiEQQmJztiLlkXF7gKGnH8Wb9Ytf/47X9ztpT9IOlS0lzHB0wfmWVArtaiN94JUgF0MiTIGYJ1pjaNaKpgXbfGtyfOfxgzPBWAvV666VZSwXymFgnKQ4G8ZeSB7aiPhrNiglDawxF7RKKGOJTpoqcykMUxVx1qLsbrSwPJ2Vm1iKwJWiDC/WHk3OZJ2IKRHC6QMWDz5jDCWlhRSgauKVKoT61JursHt8YPv4wNmmRTknWa11TEHMdvvtA963uLZFRb0s8DmL4zMJorNklyum3vDxZ5/x/Je/Yrff8+bNG56/eEEMAe0EBi7qLZiv4pyz9ufcW3mqXvJdrMzv0yNd0ER1hMtmuPVJc3ydILmU37sJftfn8Nb7i5Dv3ItWoUuk3pNzYhh2jPsdcRpYtQ3FaXEhxxBroDHFwBjDk2TQVMcQjZJysqK21Eg0aLyYbeYiYtcA1ltcrTFQ6fw5JiY1QWW3dY2ntY7r1RnPL294cXXD84trVr5j06wQVnPmfrejMx1X60tssbz0rwj9xP1BhKNXbbeMaQk0JMNydrbQcXhvv2UpM7e0HLN+hTJO3NFTJOQkdHQld3KWH/jW0yhHd5d50RMWoiVEGMaIwiyKKafJTh1IEu2/zaYop2f37bEww+NPNguluL5Y0zYNKU6oElBIr9y6a1ivWhGQ15kUB/bbkZwjcdijKpP74e6Ow/5AHAe8qf2uqZCzNKiXLDCk0ZbW2jmKqCUDiflyDIRxoN89VCKMBgwpJPbDwH57z3jYYkjSUqHyyQz59mavKHSMNGR0CUzI+2XsMtZLrdPHlNiPmcOomKLF9IHEAVsdJcYQub3bMtS6n1bSGnF/d0dBBOTHGLi6vOTm5oYP3z/DO0cYe3HCiBFv7dHb9eTeay1ZqKr125Jri4HAQd/KXAFpezBIycAqrIZSAv1+oowD/XaHSonWNmyaFm8sjbFLJieZakPTttL8X0T0xDtT17W6ZqeINxrlHRr45IP3WG8uwD3y5nFi14tQ/SzSoLTGGGl1s8aStDip5JQFxi4R4yxn5yu6ZqLb99++uO84fvAmKLZGkVV3Lr5pWcwoZ3PFcehR2tB0K6ZprDU/S0qREMTOBGNxZAiSdh/6Pb5tBSP3vkbPenF2TylSqv8XWmxH0ArlMlOIFKpnWCmVUKCw1R5nGQhFiblvUsvPzUcB7u9uObs9x+sr/OYMaxzGOEoqTP3A9u6Odr3m7OpKHCuK9OTFaLBaU1LBOUNyUTJS5/j4s0/54KMPeXh85M2b1zw83DNNI50T+G2pi58ckm2HugmJwv6pS/fT2sxbGRsscmLv+vkZLntCnVlID/L13MrxpE/wrTGQc178u44fXd//5P1ymenLEhWf/pdLIqeJvt+y320J/UCcVvi1oTmXTXD2bBzCxDCd+AnC0tCeFwqbbIICiWRsa6RelCNBib2Osx22OEo4tuSkWBh1j9LQOc/ZquVydcZP3v+Ezz78lB999AnPzq6w2mCz4vFxz6EfKBNcbxwYQ2tbGuPZPjyyHw+oAutug7IajCJTex3RRC9eiVYrvLPk+HZfp6qoidSKs1IY13IYdoyTqCo5ndFWWH36JOs77cFTqMWw9Cgxp4jFMEyJ/WHEtc3SbPw00V4irpON8Jj9LTVG5mzxZKwsCAVPXntxc8F61ZHiBDnIlqxLhSiF0KRUZgoH9jtxh1A5ovJEDiN3r18zTdLm4lyDKWLOPNWWCFNEBEIrEfSYD2nZyqgMcZoYc2CbBrq2xa3XoAtxitzePbB9uKXfb6thlDyFegtOpsvTjXCtJhoCKg9AR1SeVMT+qtToO+XMGAK7PrPvISmHOkhppqHQDxO7fuL+YSQVadh3zmE07Hdb7rdb8m8/5+tXr/n004/5sz/7O3zy0cd479k93JPCREkJv15VYZKnmyBKVfNgIaKB2IxRmZQCpD2d5SLuIBuD1QqjC0RJBqbdlmG7Y9U4zlYbLlYt3hoao2m9MOF929CtOtquQ2slikxhrH6SsgrVAjiNszTOseo63OqKh/2E8m+Iv33JGLciuJEkaJ2VdXzTYKxD19LY3FaXS8Q6xcXlmtY7Hnf9MnZ/3/HDWySsQunCMB6qISZYo+haD6zrnde41rPfj+LAPgzLAM4I7dwYW+tYImcmbQSeth3x3tI2ljTuUChizIRCZQsaskpC6rCWxhi87zjs9ozjSIqBVOFYZpmg+pDFfqRgncalE/pHKQz9yOuXb/jiN7/i+YvnnJ2fc/PsPV59/TWvvvqSUjJX1VhUjH0zFENIkQikmLHWMnlxLG8az8XZmo8++QSlFP/in/9zvvnma37761/xk5/9DKcbUsqLFcox64KHu3uMMWw2m0Vt5+0C77FMJhHv/Ptv9SiTklD+JX7QC8Y+1wWPY6MsOP+T6XAKn8wLgJY+JqVmBqN8P8/QGfMCME9CeRQxZwyVeJNEUOH86kwslGLBa0/fT2xfvkE5R6BwCAI73j7eP1mMUgqUqEixFr+1jA/xqpOFOSaR2VJFUwwY64gkkkrsgnCstVbcbFY8v77mH/6dv8vHzz7ganPB9eqSi80F55vzmpoVSi4YvcfqyKabaFca2xpgQ2M/wBvD5tdnPOx37IYtygp7FzcTKCJNIy7tTit0juTwVNGiMGu31CBJGy7OLjjs3hDGnhh7rG3RbDg2zhxLPscNq5Kg6rPQ1SbeAo3WeGNlEal9d0Iymx95WTbQdx1vw+JPHswCkR5f0lrz05/+EZcX55K5LbluXsQi2rZhyoWvX7/i7vYNOUacUjgtggq2WaN9xueMMlrMdvsBhRaoPM9mtBnnDLoqUZlxImchqFhbQBUe9jv2fc/jbodrGqaYuXvYMsRA1hrbNKgMJYxLX+DbjOj5UoXWkChhL8GlyeA75vo7NQPq+35puNfW4Eym1YUbN1EshM4yHB7ZjonHEczmPZzr4KxQDgfi0POrX/2O7XbHYb/jZz/5IxqnCeOervFcXl4ttbS5N3A+cpmb45X4MRbzpFxxihDNozAMPetNx2a1Ynd/Sxon2PfcdB0Xbcf5hx9zc3XJ+89vWBktRutKcRgCwzTx+s0dXT/QrUdsIyUK61tiGo9MzupVOpMcnbGcKY/KmhfnK162mr0rdKszQqoJT5jQkyIn8e5McWKq1nApBVkXSkJbxXrTsV533z2Q3zp+8CY4TCP7w0HYQ7W5OqVS2ZuZVJL0cewih35imqLU8upuDeJsHGI6gSwSSunKittTkkFlgy1SuJumRChaHJExFF0kT0fIMQZplRCGqoZYzU859o/Mg2P5x1PsB+skskipsH14ZBoGxsOBNy9f8ebVK87PL4ghiiKGkSbvpUeqiIoFKUFQC6PVO0vbivSRdZZh6Pnm5Td89OlnaGNPVy6UVsQpMk0T24dHmsazWa+X7x9JIk+fR6l0crmX6m10bdkoF8KFYmGCzlDPaT3xyVT4Vp3xuMC9M7aaM011AsIpfQTPlIKYKCGIx53WWNeQLVVezBEKsnDFgTyNjNsth21Pvz88mag5y8JXSl60XHMu1Ww1Lz8TY8KUhC6abOoYRWoh0tLg+OTZ+3z03nv8ycc/5ubsipXvaHVDazy2NjLnInCLsQrfGFbZYnxB64BzmfXG8/zFFWMMnO92vLx7zVgmQglMcSLlSEoBXZ+XUscg8O1DnfytlRKNRWvkTGIUJRRdIMUqfZdBmUVtSFW39ZISwp0MshlXRESDLFqV1MW3Qp9Tcv3Jo+XYCXhKOzhZS2sgdeIhimwg6/WazdmZkE6W38rLHMoUQhgZhp5935NjxBtDay1O60pnNEj/QYXVnREyi8hTVZjNoMwMxSdBobWq7UlK2lIKTCkRUsYViLkQsjSDUxu0lQ5VXOK4frxTOakyQXPJoh9qNKaTu7FkxIvijGz6xhq8UXhTUPmoBHPWSAlgFyJJ+oiwHFtSZt/D+4cH9vs9m1VLSoIa2ao7+i6Fqm+NL12JOksGqJ48/lIghpE4jRQzsH+4J0+BDZqV91yu1pjVirP1Cm89OUURK3GelCdCEGPyVBOXvLRmSVYsQihUpE8Qv1JRo1yNor2G1ik6r5mMqm03TtSkKKQo7U85RuIoKj65wrwpiQ5wyf6tcf39xw/eBF/d3hNTZeHUxcZZyeKsdez2Bw79wMN2J+LJSL+M0bNiu8CUU47Lgq1Q6FigJPJ0R3HApHBpRc6K/SFwSIVQFCgPRhhh2kh/izdWonQljFI1yMYaikhQzVHRUeHilPIt9+ni6hnXz15gjebV179j+3jH9u6Ww/7ANEz8nT/7B5xfiUiu0U7m3ckCFqtGn+gNipB3igHvHVc3V1xdX9L3B/7yL/6cH//kj9HG0nQr5txLa80w9Ny9ueWrzz/n4vKS999/f1m85qTv7Qh92QPlCr9FIDnOWXWEKBFxgLmipOp7zz+VOV2IC8yk+bfG09ykLr+lTn5+lv6Cuf9pNlstYSTePbK+3GC9w6w9Y5bseUgG5wybznH4zS/Id7fsfvE5j9nw+Hj3JL1IKYGVhl5tRP1F6g4JFWPNFjJhzFgy2iRCCQSVSQh0fd6teba54J/8g3+Pzz78mL/7k58xHgbCGOj3A1Ma0ElhvCemyGHc4xqDb1tWmzP2447DtCeZie7M8NHmmvc/esGhH/nV51/w+Vdf8frulsfbHVMYCGnA6oK3BmdalPr2tNPUhof5ViqNX29oG09nQQ0R1WSMBZ0GyFUw3rSgRcZP13aEaTqQck/mAdueYYyHZFBV1nC16miapia60pZgSiUJLQ0ux21uyRQrJJrVEwkBGZ8I9Hb6ugK69YbVRtRd8rL5UbViE4fDgcMwsX18ZOgHyJmgNao1FKsJY0AbWeyVUWDANB5Xe9AUVEUpSywiezeOAbT0whldWYcKlGuYxpFxGjExC+NcaYp1KKdQzoENi+KLlM7UW/NpvhdyD0IubPcHsilcnAvCYKwWDU+y1EJVwVrFqrVsGuh0Zgx7ZtmQ5+drjJl47HeUcSQl0NbIxgB0XYfWhu12z8uXr7AKSAHhQRyF8E//LydZ+61rqUjula5w+yy+oJ/8/LjfsrdQdge++e3neGX4+LMf8cGzG55fXeO1Zux7dts9h+0jjXM8u77hYXvgMAwo7bCuxbcrbOOkzJOKcEfGkRgi6/Va+nhXHeMohgbj2DOEhC4jZ41mXDtux4iqHI1sNMlajNaUFPDW0O+2MpZSJqXMOEw8Pjxy1gl55oceP4wdWgo//+Wv6dpWbvqcnFR8GaU5DNIOYZ3n4uxSWHCrFV3b0DaO1llKjuQ4EJPcmH5MONfgrOPm8hneZbwLlJiYpoRVimnXM4wR9IocZz3KjNGKyWqR2KkPXmtF45wU12Mi1uioqMq8y6L7eHoYa2nXG9pVx/nVFUO/45vPf8fLL7/k9Tdfc3d3S7Na8Xh/z8XVFcZL1DUzxuasWKBuWYz7nMlOothPP/0RX3/1FV999SX/8l/+S9577z1++sc/Q3tLKZn7+ztev37N/Zs7rq9EFb3tVqiiRJOw4hcz4YSTWtCpRVb+zihwvgciUFtyJimWuscMWy6RZF2FywKRHiWv5prgMeKco0ldg0pDWRgeapFtUjpTbCKvQV14CoWHLz5nmiZSztiLaxwaE2E7JaaYGa0iJ6o24fFQWWO1oeksBSEfTOOEzgWdC8aJapG1DhccNhryJELbRRcuujU/efExf/+P/pS//+M/Y7Na83C3JU6RkjPGW8Y4sr/fM8VATIkpTqw7jzEQ08iXb17z9d0rfvPyK6lVNw1du6FgCCrTnXku1QW7YeBxC4fdQFCJyWRCLGhlSNMJnKgkqLPzQgYSAJmWtl2xblbE/R3ZO+zFhrj9ijgdGEOivf4I211QxpGCpijNFB/oD/fcv/41l9ef0q0u0I2jqBFlM+1mje1agpojKVXbJXTFs/M8oGv0NUPmT+FP+dETpOItmKBQ2D4+YsjEac8YAzFnxqKrQHUhTT1xPBDGHbHfY7VmfX7OxabDu4a9LkfZNUTMuoSMJ2NNEcEOlckExoPIGXptaboGbSxFaRprsEax8hfs9z0P2y3bfU/MIsullKX1jhfXl1xfnIt4vzLyuUqg7fuHB37+879a5skwBFRJPPaZV48jWM3mRaFRGqs1qjLZcwikOBETjIMlGY/yGus83iicVVw3K66HyKpt+Wqv2AeRblMlY430AKdkoCi++fobchjpnGSgC4FuXgdO4FAlT/RJD/Gxdqtrn+fJ8yqFfvdICD37MTPtezZnF3z8/ntYBYf9jskYcspgLc36DGctuIZ2c4ZpO4yzGGcpSgnqhUKVzPX1DaVkwhQYhoFD3y+tcr5t6OMBrRTP2nO69ZptP/Kv/vK3POwnDocRyrE0dH5xQdc27B7uBamJiRQ1KWly0oQJ4vT9WfHp8YMzwdv7B5zbV7UWiR5SCAsDcQwJ5xvOzy8ricXRdR3nmxWbVcumtegS0MkyTeIWcL+bZLHynvdvzmiagveR/eOWYYCSLLtJ7HdiEaJFScIuLbXokXWuA0Eeu9Yi8SaLtj5KFC0Zy1OQT1QdPL6xtKuGMJ2Ro9Bux16aWPf7PdvHR1brtcBIcwF6GUFq2QRzKcSK1CgF51c3PGx3KGP58ssvGIaerhMyUKHw5u6W7XZLf+j5+OOPWa3XmKqDOtdojoSEGZCaP/cYqee34LUZbhIotCwb5nGzKzXXO9GFLMcWiJIFrHra8zVLN500486b4DzmclXEqMy9UjIlB1LoCeOeaWwpMfD6t79j6A/EHDl7v8dYj1aO6bBnmgYiqcryPQ1aFLIwOWdlIU3VMqv+MQjsY6zFJCtwaC2EaqW47DZ8cPWcn3zwKc8ubtBac3d3J+NEFRrXEHOiH3sO/UHg/BxRpcMYTUiB13c7Pv/mll/87ksw0K1aNuszrPVo2xGSKMzMbiFZui9qE29EIb2Fb4ESSxa+bIVa6t5tsyKHPWXaoUJPHu5J44GYpEaiCpQY59iDQiCnkWF7z+SvsMUw4UglgjPYpkU5T1w+uS4E8xhBw6IleoQF511u5pXOfx/Hx/H95n+P/YHBFuK4o48TIRX2SRFSrS+HEdIIcYQUMUaUqbw1OKtOHFDUIotXQsQ58EbVTSIxpUyaBpSxogxlhVgWq7CERnRKY0w0o+dx1wupohSUM2gN67pxGutwzouLTfU69N4/GYfTFMViqY889hHTpAXh0kpRKrynS8YoiBX2jVETjUO1Hus1jdOcr1taL/P3kCZyjoxTwhqF0ZY4SBN5LoW7u3s0iauNw1Y2/JHEVAPlExi3QBUrKMtrM2Sb34J5C0WY/nFkuxvxxeKN5mKzrmJwhZBSbWVrKonQULTG+nrvvK3rUQ3YtUJjaLxHo4heLPHCNC2bmtZSSlEamsbSrtasp8TZb7/h0AfSNKG0W1TEutZjtaZbrRjHoYqdQ06KlFR1oPk3sAnOhccjjV6zWq0WDT3vDG3Xcn55TdduaH3H2WrNzfmaq4sV7114zlvFTQtv3tyx3/W8uss13TU8u7ZcXK64ul7xy1/8ir43PL845+xizeNh4tXdRD8pxqCYYqp+gZEUE7FIYXQ25nRenCeMVoyjeLgZjagqGPNkohor1HwUWO/xbcP11RUffvwx92/e8C//+T9nt9/z8z//12IsrBXd2VkF/+qgpy5dFU/MCvpRvHS682s+/pHj/PKaf/p/+7/w13/15/xf/8v/Qu5R23Hz3nv80U9+wk//+Kd88uM/omlahhAwnEidlSPIVObMjpOBXoqogZwsqylLLfbYY1jJM0o2t1nySmtpN8ggvnj1WqTHSN7ybfr1/LnLJljkwgXfL1XtJWOK1OXi9o7DV1/x9V/8Ja/WK/a7Pf/y//7PeH13y2EY+NGPfsTZ5RnnNxfo1jEWRRyhPbvhbOWeoLFGW4y2aKVJYZJWlimizSzmYLBK4zuLc23NSBONtXSt5x//8Z/xp5/+lD/+6EeUojkMgUMIEjVr0FEWt0Wxvt7XcRzJpbAbR3779T1/9dtX/PlvXpJLkbae2TbMaLyTvrUwRmIcpSQQHTkUhkNt50j5reLqDIjmIwSeCuv1DW1RfPPVvyAdJrhThLuvSWT89QusaVGlIaX/L23/9WRZlp15Yr+tjrrCVYiMFKWA6kYP2IJN2vBhyEeS/TA0479Jo5GvY0MaCc4Yp9nT7J6eaYhCoVBIUSkiQ7i44ogt+bD2ve6RhYYlzIhjFhWVEeHu996zz95rfesTEzFPxDKz2q7RZY13A2p/y3S8Z9dCsQ6zviE3l2AGIoaToKKQQMUTeF6Rg1PhpaCI8FlectXinV/341XKh/Dacf+eRvXoPMm9SIXjMTHNFe1JwnrtTOZis2LoOy63a2JYWKYDh/t7uqah7zqIkbwshMOO9vklQ9eitRTou4c9i4+0/YqhcUS/sOQseaXVLnGz2dC2Lc+ed8ypcDiOTA8HSBltHav1hvV2y2Z7wbDengkcSin6vns8aArsjjOH2fPb7/bsfWFFe66JSynM0xGdI5ddw3S9ZTcufH+/413OTF3PxeUrTG8ZOk3bZIa+cHPRkM2B73cL/m3hYmgYGssX7w6gFN4Hvvj8K+7fr/iDnzzn+nJDN3SPY586NymlkNNpnwarHlNtVPU/Fn6A/jAovRTCNIIqzIeZZzcfcb1es+k6hs0G4xruHh7ou571sEYB0QcO+71YrMlQFh+8ENOMmISsVqtKT63EwYsL+r7H+0W8YL0n5iABvyFx0Xf03cBPX70g+ML793uKFs/qaR5xTmON4ubZDbe3d+x2O2IoBJ+Yp8CyOLz/B3CMWa+qVU9KUHV7fb/CqBNb0GFcT9M4XM0OHBrLujNc9HC1yqyconeKziSKS1wP0HYyDzJqRvuM3wVaVcAo5hy4Ghx927BaOXbHyMMYuN/PlXBjCWERTVopVVdS0PE0hD/5jGZKSuLq8HthCwVUrmbdUu3EUnD9wMUzzc9++Y/Y7x7Y3d/yzddfcxyP/OTnP8e1rUQq1YcinzaMLAa5yogQNSWxhdpst3z22U/QqvCnX33B5QW4puFnP/85H736mO3FBSlmvAoYk7HanGnsqmq6xmkkBMHWtxcXmGolV0ohev8hpTtn0dCc97GCOltXASXXB6LAaU5YbZdOD1JFwc7d31MmoaqVaSmgDSg0JdcNsRQUAcJCDgv5sCe+v2X+8hu+HffsD3vuX/8OvcxsUmT13be0+wG9u+N7oziahofuimf9Dc42Hwxkok/oBawTBw6yUMs1Ig3QRYT/SlHnWxKldDEMXG02fPriFZebS5QW4/MlLEw+0DZOyBXn0Oia8KEUWhVSWvApcLu7Zzc/MKWJZtWJm1HKjNXoGaUeNVNGiivXWEKpGlAjhUP+QVEhJJwn409VUCmjTIvpNwybK1pjUWlGVeN4rYUWL3VOopDEiSQUTIa+Ncx+T4iF5K7o3AXt6gZUB8UKBPq4bIEnlmvnLrB2ECezYgqlJGTFiwE6lQBhNKgy89iKQOtgaA1Otxxm8f4kRTGHDlJAK6ewzuCsQyvFNE1SkOXCdrvBaY0zQuhwWtFaSR4p2uBTRtuebqWxHWjnKIgpt1Xg3KNT0xLEeQWtadtOWOcJfLVmTMHLr+jJKZyTXgQZeaRf51J4t184Tp67w0zRrj4H1SAhZ/ziJSJOWy63W1wXKbalNS19M3B5+SmDVVhTGOd3aOVpXWDdFtLKcJgbGmdrysxjBufiAzFF2raRKLCqKc7llB4hm7+xFYpSqqJJVSN9MpEqioypzkvn20WjRd/dWcvNdsuzyy3rvqVfDZi2qwWjYvEz035P8B4/TWw3W9qhxxgxOfdR5EkqJ3LwxCp1cs6hyBgjay1lQd02bSf7nFFAImbP5eWWq93I5f2eKUNTCsOqFSlMydjRoZ0BoyhF/IB99MTc/h4y9nddP/oQ3Kw62tZJ3IWqtjhtT+OgsWBsT8bhi9jiOKMZWsu602w7xbbPdEbhlKLRgWwiui+s15q2NYx+piwLcxxpAIwixci2b9jalht7wbvdjHuY8Kmw+EQI+QOh9Gl2lp7M/Yw+MVljPQTLB0QPya0751qQiyKkLHElbcdPfvEHvH39HXe373n9+jWH44Hr62tW260Y02oBFeU7CAU4Bo8pDdqIKZUxlma15tPPPmOeRvb7I32/Bm34+c9/wcXVFavVWtKUc8Y6J4a7WmPRZ3f4cRyZp4lpHFkNKxQQ5oUCBP/hIPjEmMpQES2ZnZxCdgG0UOhQuuoqf0D/PJ2fp0NQ5jKn73fK88pYqm1VUfUhTFg8yY+k8Uja7fHvb5m++Y6vfvcFu3HHHEe2WrHWmou7W8x0RB12fB88u3ZgemG5/IjfMzyPIaKWgnGPB7ytnaHMtjh3M7mKDpyyXPRrXl5c8+rZS7brLRnNuIyMfmFcQp3RyAYpTFqBZzQKZSBkzxIn7g7vOMx7ljTTrTuZcRwXliUQoxg3hNYK0WfVgQbjjNj2nboqrYUQpvjgEDodhHIvADLGOFS7pt/c4LJHFY9WmawsqJNzTEQRyFk2yLR4VIz0rWYKM4EIPMc2F/TDS6ChZCGynA62E53+EWmow90qVtaV9CT/XXMJSxJdH1ToUKGZz4+XAlqn6TtDZxVLCKLDS1GMqlP9emPFALnKNqZpwmqFNYbLi8tqr5fJRog9Rm/OrzmUgGlWrNyKosQKMGVxGDkF1foQCSniQ5B7Wr0olTKUojhOI4uP9RBcSEFCcE+MRKXUmaQCUhe8PywcR8/u6OkH+7jXVDRkmQNkiQXablb0SeHaNZqG1vZcXrzCFlApsts9YBAd5dqBXWmOviEWWKosKxUFsRCr1KTrW5x9/MxUFsQjK133tApV1zGRsKqRQOpz0fODThBFowzaQG8z19sN19sNq66h63tU29E0YzW+n7i7fUv0HlLi5vqS1brHnoxGYmAcRyiJFBb8PELJaHoJWNZyMKeUiCGwGXqsM2AVcxAUa3u55nK34fJiDYsHpWm7jpwTMXhsK4egROslUg7EZAUl/Lu0Pj+4frxOsFvRrwecdex24nCQ0xGz6rBdT9cPZBwqWi42jst1w/Prlo9frvnoZkDnW2lnY0J34LSWHDgXUFah44IPmWVMrDc34iyvDIc44wmoodCsBy6fr0g68bBbuL2dsc5VnZCqBrhiqAofMqaMNuQamfJBDV7Eef7USVBJI6WaHV9cXTL0Pc9ubvjyt3/N7uGef/0nf8LlxQXbiy0f/+QnDJstq4uLc6bh8bCnaXtJ1e6HuugU6/UF2+0V6+0l/faCdrMhW8cUMmE/YkyDMYamiTSNrSnRmd3DA/vdnjdv3rDdbnn18iNu37wlBM9+t+fZs2cnNez5yjGRgmSMJS0DaPPkEFS1ezJak2uWoSFh8klndnqASjWoBYkBEnioBIEmtVbktpGN03t2331HnkeeXTiWd3dM7+548+23vP3qd3z1/dd4f2Bt4Y9evOCqv2TVDBxoeZ8Wvo1H/vJ3e+KoeXZRD/Knd6sgdnYhsUxBwouVjP81GqNkSpUrPOyKpcVy06z42eULfvryY26unmGanqOCN7s9h+ORh4d7bONo+pZQxOJpmidCNcM2TWHyC+MysZ+O4tpfkY6oLW2xpKNniYlQIC1ZIpuyx1qNbTRuUzBGVdjUkebC8fXx3Pmloogn2zQeSQ0BRVEOs/5HmPQA4S3R3OGXhfmrvyFu39N1HbYE5v2Bd7s9OzJ903K9vWa4+QWNcbjhGda0kPeU+Z4Tg6nk+kNzOpdyWledafKUIgxMa8RaUKtCTp4c5cBoSqRxlu1qVcXPb3mK87bGoGJkPO4Zd3shuUUgBUgJS0GlQPQL03HBNY6hH9hcXLJabXj16hNyLsSQKNVIo+8Gvv/+e6ZxxBS4vrxgs9kQg8iyDoeDzPqNZVgN5+Jgt5fswRgj/nCQNZuTWK0VkXFJl+rRZBqjGPoOgL59LMZyKXx7OxFCJKTCoE6RUE42aB948/5AKfKM/KNPLlhvB375asAoYaxuNh0xanyA/fwxKdwzxsRFD21bWC+Rr9/t+P7hIKxLZcAVPnr+ER+/uOFivfpwXq5kn3PuyZZ+4m6c+AUKTokVWmlipnaCddarhIlqjCIlzeVmw6brSMeRuViSOfL2629QCCFJlcJ66Lm+vmKz3WKt43g8it3kExKd955lEYSg7TvmZWZeAodxYhwn9rsDYZkZVj0vP/mYYiIqgVI924s1N88uWOOqdEVz2O8ZS8FaWStpm+qeJdmf3dDTzf8AyfJFGYqyQj02FmszTqvqCN6gjIgxnZG5m7NGrKialrZpybGXHYxMM2iUXZi9B2PBiKFsLieKvcCaaHEDCSkw3r2lmJ6kO0oS9xqlwBhdK7oaMVMKRZ+IJI/XSUtk9N+WlVA7iFOHdGI71kUjIZYXXL94iWvbOsiF4/HA6+++Zdjt2Oz3bDdbceEIEVzF5PVpdlZomo71ZisPdikso0gjtheZ1XpDKXKQBz+zi54UAvN4FKHsOLIsMykEkUrXyB3nHPas+zrdrEKKMoA+LXKBM+vMQJ0IMQVSpigtqdTIHFCfPoPzp1OrtnxSGCpIUIJYoMWHe5bxwO7tt6hpRufM/Z3j4ZvveHj9Pe/ev+fh/XsO08SmHVh3Da+e35DdwFE7/uZ+z/tp5O205+ClSFKVkKCf5j+qOte1qtpbyeGeS9XBFYWpf1YAlwyNsmy6FTeXNzy7fk5Gix1enDiOR+ZZoLcUoyTSp0xMpzga0V/mkAgxVzKHMJRLVgQfSCERlwBJSEwn6IlSiDFjnUBz7UocM4wpAhv/0DEmSycnsNuJmStp2acIoqIUWWtM2+EAhcflHWrZ4WfP/LBnvL/H9AbjLtluP4Ji8Eqj1YLJAZ0VWktHJyxqmcUYwOoaAKAUkcCSReeoFHTaYqmRWyqRlCepBa0SjbZ0WtNpS6vnp2WLWBbGwjJ7/OIJSyB6IbhoCkPrpFtsZe9wxtA0jQj6rQPboDEYK/cZrQm6IZke8b0oRNXiiyMWiGSybiQf0BjQDSllYo6SQBEiIYR6PzMhVh1zljDuxmoaY0gx4pelbimFaTx+cL98EPJFqXuLqSOgZV6YxpnjtNC5lm3fc73Zcrlas16tSKEK50sANEXXvMFsKNmidKojCFhiZlpEf20tWFO42Ky42K5EMiKfMFTiT/lBWttJq1ug7qtVEqEEP3g8FM9fgHFW9ItUeUgR5yyjHNk4Gi2We9ZqrF7TOEvb9aA0sRSSUlX+ks8cgRgjhScJMpw01XKoaWNZgkdNisNxJBtDRgIYpAM/rVdIObIsS703RZoG10DVnBtr6YeB/h/iEMzKkjD4WDDW0WnN0Dj6vqPpOgoWlKWpC9Box6pd0TUD1g5kg9xZl2kNBD9zmCM0lmwVIT8Q6w6cUoISIcOyLBynI198955muKRdXQuNdymgLMao6mDzeACi0qO7+BPGo6m6oafX6bbUNcDJsPrpkFsZg+tXfPqLXxC95/7uDW9ef8ftu7e8/vWvaYxjM6z49LOfMKxWNMOAGjgP1k8U2q4fePbsBf/0j/8pf/Fnf8bdm/d8+Zu/5tOf/kx0W/XwWqaJt99+y+7hntfffFM3ZXj2/Dm7uwe+++ZbhvWKi4tL/vEf/RH9Zl1NDB7fl49BDMVN7XJrfFOph2DJiaIrg1MbKRyQ2Sk5405J7ErVPLIn+V0oCJCnkXg8Mn/5Fe+/+Zy/+g//b37685+yXm+4m+Db3/w133/xBWNJ+FxYUuaXP/kFH11d89lPPuFXMfA3y8T/48vfcry/Z7nfgVmx6qu7v7NY9+EQt+tbbFsjVuommHIS+6ykcEg3bZyFDL1pubq44uOPP+WTT3/KEiO748jdfs/+YUdKCasVKS4ss4Yi4na0ZKnFlJnmwOILPkDMlpws2WuO9wdCiJJHFwRu0kUOzqIk/03rln7Vc/msRZtCiAvz6Cnqw4c0pZkcj+dCsZxgTl2h+qLIZSFohVttafuWLh2I0zvivOfh7cju7p6Hu1u2n14yNI6PX/WoByFwBD9iSsKSGVqR54TomeaJnBONs/RW0xpNbzULgWOcWEpCo1irDqMsWhuKVmQCCV8JZ4VWT7Ta0Krpg404RvA+M46eaVyYFs88i4OUVYbLizV9YxlaCZ+2lVVubENGsySZ/+m2JYaCTxk/RiZagtWQM3uvmLInxygHXjS0pqEoyxw18yyRQ7vDAe8Dy+Ll34XI7KOMEkpmtRlYdQ1D55j9wjzNhHArM8C378+FtYwCpCgEKcyaymi9mxbe3+/ZHSbW1ys+vrzhZy9ecrndsB469g/3TJMgCgqwKFobUKUQi5G5bk6MITOGwhiKHIJG0djCi2eXvHh2hTX2PAZS9bA6S6bOn/8jEnbagx4PQVW1xj8gvDlxsIhZUIIUI8f9HhdBNy2Xq9W5sFM1agytxNkrJrJxxJiJFT2KKbMsouuz1ohzmHVYNEoHrGvpV4rdw3uWGInfv2F1cYVpO/b7kWk6CEoQPCHKTPTh4YFlkRm8s5aubfHzjFaS6rO9uCQk2cN/0Av9rdeP7wRR4rAQxPC4dR3tGcMPYASq6NoWrVpKdvgF9ruEJvDik0+4u7vjy7/+G6wVKuzNs1/Qd5bGwuXNC3JKMleJ1ei4KA5vAvkwM+0Xbt+/Zg7fcwiGpFtoLjglMogpcMFYSRE43fyn+VRan6KUng6DE4qIwqJK1eCVRwHq6V+eZcTOMFzf8MlqzYtPf8J098BynJh2B968/h5FxrWW9XpDPwxcXF6dkzIedg+CdV9d84tf/iH7/Y7Pf/tXvP3+G9Z/LvDGMi88PDwwHkZSzrimY3N5xXp7Qb9a0w8D682amxfPWa0G2tWKVGD2HxpNi74tCEoaJNmgsZItKP6AiaI1xQg3VPw+o5BLkHlsrp+bQZ1JOr5S56+GXswP5gP/7t/83zm+fY89Fv78P/4NYy7c+wU1z1jgf/bP/jk+J97s7vmPux1/7kc+dprf3u34dn/keDeRksYMG0gOYxoprHIhLv6DdRiCwKCNa+gbh7Gi3TuheiXIusEK5NM0LTcvX+C6Hp8yb79/w+F45HDY4/2CsYbVdoMmUbInRk8IC94vhLAQYmD2E1np6oTBOfcv142iKMmwFGZsXZK1g8tFOnKt1pALy7QQZkUKTw/3zCZ+ycbvydFXyLrQtwI7ieeiqlC/R/cWqxWD27AcRuaD53B/5PKqw61vMEYzzjNff/sFd7sDIWS2w4bWGjqraXKN2gmenBZSzugMYUzEnFhMJqkiEgplsI1j2xliKsQc2E81d68IeUiFyHGOOKO5e5jOh0Uu8PbgeZg193eeccxIA2wY2oa+dVyuVtVEQEvSgE+k3YgLGucL6FuMazG2EUlMOW1cBesUFEMpieQD8zSJn7FfzuhLSYlpfGCZj8zLTCyKlA0xIRFrS2JlHX2jeL5taTuDNYW7+5H7/cibt+/QxvCw23OCeRXgmhYdtXgqK5gXzxe/+47v37xjvz/yYnvBLz56wR//7FNePr+ka1ugEGLgMB755v07UhREy2mDqwkbb+8O7CbP794ExjlijSOlhNEN61UnaRi9EFDECvFkCCLwZKlz/cdcQVC5UCqvQFWUqygEmrUfHgGxyBrbLyOv72/R1vDy5pp9GAnTgSUm1psN24tLlJIw6tl7yZh1jn69EalYyuKQ0/WstlucRtinPhCjZ/FR9JjG0g0WZSuCYiyxKHKE65trIoo5Bl7/5ncsPqKUIYajSIOUwqiIM5liC1qLYcnThKEfc/09DsFSW9IkycBtg86FJUSW4HGdq27uQrsuaFISUakxim1QLEEzB43ygZQVV6ohJPnerXEUY9BKInIoAskZY2RorjU5LixjwLgN1Lb7tCxVTawX1w3p7066tkcLtd8vC+q/5AnvsZYPFSc///4Ij7qmpXENZVgzuI5pf8Roy8M7cUQo88RIEUZUiuLTqBTvbm9RWjGsNzL7GHru37/j/XTk9o1koKUYmRdPKQrjGtYXPevNlovLK1brDf1qxXq7YVitabq2xpRE5ifEGKlUZUGU+rqzypgq/D3nB5QMRT7HE2NUP+mNc65WZEqLoXNS1f4qy8qZ96jDPcvtW8LDjpaO/X7PbvHM0TM4Rz/0fPSLnxFKQr1/z9tf/5bDvHB4/55v7w7cHiayFyhDGYtWotEyxj6RKDxeRotBglbmvNEJEUA2gsdfFaaylrZrSdV6ajoeWY5HlnEk5YhWjcy7jLBII8LujbGSTHI6d88ZyacMUaQ5ssOUczLWCUIVRm91fKJCyUHYefMYST6LmPfJ/G+lDqyVJalQCxXojcIZ0UqWAlkVoso129PQN0Yq99axWreY3tLljru7A8dx4vXr18zzDLmwVoAzFGdIUWysUgjk4B9NDVISNp8pZCWju2KczO1KksDqVFiCl2dOazFozkBOJKNZnnqilsJ+nLFWcz/GKmDWtFbhjKNzDa0VTZ/RihDlZ4QQSGXE+0TMki6urRhUcxpRnFix5dEya1mkaPGLPxe+OacaPLuwBNl3UkqkKBCbzonGaHor5B1NJsfANM0cjyMPDwecs0zT8oS5q3BNI69Fy/ebloW37+/Y70eijzy72PL88oKbyy2rE2vSP8bNpZyYw4z3E9u2AQqxRO4OC7fHmYcx4VOdiychprVNI+z7GjAgh+BJ51vOkqan8iWFPPtPI9jE37fQtB1t+6HHZgFhyWvFbh4ZpgOHsOC9wMmzTyhncV2H0bYGKaTKF6j7pVLVTETS7k9M3ZLzOSQ9pSJQaGW/t7qT16UblpApPrDaGLquYbPuMDpLs6IKSiWUSnXElYVNb0BpWQuhjjR+7PWjD0FqFpyzmot1z6rveLjbs9/teXe/4+WrVgbvSCOVVcHnwN1hYnfI7HKmbTpe/fQPuH/3mhQnvvv2e+b5QIgT1lbPzaYhh4XGWi4u1hin2W4HPv7oOav9yPo4s775lP0CX3xf89ioclgFaBkOhxDODuOlFCHG1OSLp4eh1rL5KXXeyx576MI5vFfXYbKss/r3Boaray4ur3j5ycfc3z5nPu7Z3b4jLAs+eL777muCFwxbuo/A7BdeffIZq/WG58+e89WXn/P1V19itebq+pqf/uIPePbRx2wuLnnx6lOG9YauH2TuVxfx5COjj6BGrLXc3u8+cI0JMYhWxxoJ/SxajLvrXOe08Z1np7qGI9eur7XuvLlgFKQss4GwYKLnEPaU19+Rv/4dv1AtD/2K75aZpnhuSuCXumVzfcPmoxf88f/+fwPWcXy/Yz//1/z6t1/wr3/1V1jlsMrSoMFainIM6zWr9YWYXodEysv5figUV9sttrXEnJjDkewDRUe0rhtlTW+IKdI1Pa5tKDnzcPse/7AjzzNpHonTSLEacDhn2WxW9H3H/UNgWUpN9IC2bRianuMSmMKBu4c7Hvb37I+iXdJKSCPBnxid0DQW6wzKFpw1qAz37wWKu799wGhLiU+0nhRe9DMv1jMpiYxHawnzFRmDIobKIFWgdZ1/hwVVwNmWP/jDT0hK4XPh//Yn/573798x/upLtn3Hqmtpx5HZahoDqcKtuYj4+TQ6aK2jsYa+d/iYGL0nYOi6jk1rWTL4rJgjKGslHLVwZpBS4KlGOZfC16+/r4fITN80tM7S9x3DILBj4yxWy7M7dC0hJg7jzO3dA9MSCFiydhTjwDRCeLEGlSYoAXIUTbAxZyZxQQ4OyW/s6VYtnR7QeymA8vGBNi44VVg1lk3T0rWGkiJHnxnjzJt3D+wOE4uPGNsgU9N6v5Rie3HFNB057u85Hiemceb29sCLiy0vLrf8z3/5B3zy4gXPb67ZbjbElNgfDxQtDimfvXjBN++/4266lzl0iBz3E3/57Y73R88+yuspWVInjLFsNhv6tqF15lyaPy3rfy9Nvc4zldLkLDFGygr7OSnF81efkfTwASSqjME2hv7mkjfTjuNbT7aF7WpD5zpKUcTpwJQiQ7OibVo2qw1YkYT5alxOla+oLP7SgYLRiqbtUbNA6a5pa3EpiIdtGi4un/NXv/mcu7tbmq6hcYqXL6/5+ONr9ocj87xQsmJZADJGJbwSprIxiVwi++OR/XH8UVAo/D0OwdZputbSKo2zQrLQ1tANA9ui6XthQyolGhDjNKax6CIam29fv8FZx6rvudh0bLoVm3XD4fDAPI/kJGnSikBWmTFG9m/fEJKXYXYO9Kuefr3Ba0NZPCHMdXFqnvIntLacosUEohKyQa7auQ8+gBrkq/VTn0z1e53jSUFVUfYzomoqJo7WrFZrnDHoUs4msapkjvs9Dw93+C+/JJYRFQIxBHwNw2ysZbPe0LUtL16+4qc//wOeffwJ3WpDO6wx1pKVwtfZIEgQLkVekw+BcZo+AMBTFt2MImG0E0JMEV/NnLKwu5S8D50zxELwnqgec8godUZ4f4eJiQHFSitMDLz76895+9UXfP/lF4T3O4w2fLxe8YuPXtA0jutQKEMHq4F3f/FXjFnxdjfzzbv33I4j1jRY02C0JRVF41r6tse1nRAjzGOF++QmoK2EnfllJuIpJNGB1vuWcj6jB30/VJGzQF+pFGElZrFk315esb265LPPPqNtHSBFUiFLtqGtxBsU43hgf5iYZi/zDlWLLqWrt7OjFIUuVmQzKpOKxL3M00wMwnD0U8RZ4Af2fTFBSIpcJJ6LpFhSrN1MFT+jhASWpXuLIUhHmhWDFTTgMC7MR4k2W5YAbYOl4HTGlohOueoXFUVDqxUpK7zPOJWwCkzRYj6eEsELQeu7t2D7AWxDUhpVxJVG12b4BCz4/GGOm6rMyca5c5Zi27Y4Jxl4KcrmpXWh1YViM0MPA5q51dwfJyZ/ZBwzYzoFHWlhOmtwVg4/5xyNPsUJaUISdvR+3pHq0CMvEzZ6Vmrhcii0VrPuHboRct7kE/slspsz4+zF+ceIM9HTzkJrzc9+/gvu797zcPsW72dQms3Q8rNXL/nFq1f84tNPuL7Yst6scG0DIWCdYwmB4zThlGHTbXl1qfny3ffsJs/9IbDziiUp/DxSisi/tDYMfc/N9RVd22BNDak+s0PVB1peeRQq5lnZ3VI/K4pRmKajXV3y/KOf4nP39GbRrXpMZwldB7lgteYhL4RF0eXAdnVJUYkQJBBXntkWZzqs1hjn4LRP6UdcKWYxR3g4jLy/veNwHHn+snokl0xWMm57f3fP/nBknCYOxyOusSiraBrNMDhcoxhWIoPQSuODx3uJ1dJGYRsr8Vw/FIT/HdePPgSd1bTO0BlTD5yEMoqmbVkpR9N0WGdBiQuLq8NTiiZFxf39O1CKfduy2nxWxeiXmKahGUdSSOQ4kcKejCL4wsM8UUoQJ34KQ9/SDRve7oS1FoLY6ShlOAmITwCnVgZrIUb52pzSWTv39DoRP3QdGAPyQJ+OvBPGwOP5SDmJyh8JNEorurYT6CpF+mGgbVvaxnF/d4dyltevX6MXL96RSSA3a8SSabNZs1qtuLq+5vrZc66fPccNKxL6cSOs0G6uJtVPxbHL4n9QFSZh/52gYqQyLymSS0I1tnbPYpFFzsJqVdVXECWgdsmwv8eESOcaBmPQ3jP/7mvefvkVn3/+BetsuF6veTk85+VPP2W1XWN8YDKaUWvef/E73k2RL/cL39/dsVsWnG3RxqGNJRWwTcvQrdCtbGjCCq3uMx/cMJnPhhTIKoLO4q5Tb33OUnEaY2mbhtY1csuywDAqS9WolAixb66vePHiuRj4zmMNj87VCNmISDlmDseZ3f7IsgRylopZV72TNtSOW6OxpMqqTMGLYUFITGMkxUwKGV2yJD58MMOVQ7CgiacuXck9TFkCUXWl/cckuXB+EhKTVtD2Mm9ZjiPRL+QYKClVY22wOmNKxOSIs805+DdTMwVTwSqJvNJFo3NC5UzyInxOu8xKKxqtyMYAUuXnouS9IJBtLMsHt8vUIpOaHSiHn6v5cqZqWQWadLpgdUG1hV5pFqdhCSi/4L0nz5mQ5XOy1mKNRXVtzfBTqFTQRaBoHzIpBubF43MmlozLnsZk1rZw3Tkh5PQWrw2+KKYpcZgjuzGyhCT2h8aSciE8KZ611nz62Wd0bcPXX/wN9zGglWI79Hz64gW//MlnfPLiuRiV950UsZXEkUpmqf++dz1u0/GX373hYc68mwszhqQ0MS7VurDgbEPf91xdXNA4h6lseq30OUkDZD/Qp4PwFCBd6l5d97pkNF2/4uL6JZfXH7E/pNPOhlLQDC3N0BFa8WmlJOY0UYonFFi1khGYvScdI6sYBL2xRp4Bax9HTHVuXCriEILnYbfn3d0dx+PEzcuPJCQbMY8IMXE47hnn+SxTKqql0Q5bD0LbOFzT11xRQfxijMJCzgkfI8b+bQqA//T1ow5BpaBvLa3TJD9xPEqExpw0yjSsV0N1yFBYp7m5vuR6e8F2e4GfZ+Y8sx6ueX93x2+/+JzPv/yaZ88u+V//F/85XdPQDTf85JOPIE8kf8/oE4lMamSoPR73fPGXv8OYDms73n/xW+7uBedvW5E9NE4Rk4SlhipahlLFpDJnyOcZ4uMVQsB7Lw+UepyFiYboiYD45K5/JtnocxzI6f9LiLjCl8J4dwc5c3N1xTyOJB+JSyQFqShb1zB0HU3r6LuWZy+uaapryZd/82t2D3cMqw3bGzkMbVuh5vM7eDycUxIa9dMrRek2i9XSoShx6LAqYVTBFiUH36wwrRBJikacxlMm7AOXSvNCG+i8uOtPni//4tfcv/6O17/7nN37Pe0Y+cOrFS9urvnFL/6Q8eqape/BObrLK/p+4N/+t/8v/vzzL/nXf/aXoAzKWLp+QwiFGBXr1YphGGhXa5rW0vQtTWPIRQI5z++4FG53D7hOoxtd3S8K5gRXF4XOYqC87gdySNW53mN6EbB7nygqo43i5UcvuLq5xjWWN2/vub+/Y394ECE4heUwM82e9w8Tf/arv+b9/QNzySjXsFr1aCczVq1hOk742bN7uKsWUPE8I1FFk3wR27qsIZUPOtwC7I8B13oRSHs5+NJ53cm8RQAHhQ8Cm8aoKUnQjOtnay4vB643N/zFX36OzokygCVS4kimx1hFqx2brpU5tZYDN+VEY8R1KMaFKSz4WAhB1nmximQgmUIyCXSUuQxCbjiNI5SGosfzulRKEiuG1Upy5LSuQa1yiDlrMCYTw8wyHQmm0BiJFupModea4WbguHYc5sjtlFhiYfJCXlNK0ziHdSI/YT6SqijeTxO5FHpnRIBuFdtO09uGVWPpnYR4L2ge5sQhBL7bTeznxH7JxKI5hXkvy8z8hKBljeVf/Mv/BdN45PLqgv/p3/0b8Av//A9+zn/xz/4Z/+RnP+ejl88pWtIyQgygCsPQ8/zmGqs1X/7uG9btwLrt+eXLT9lezJRppJ1HxnHE/81I8IGcMhfbFc9vLvjs4xf0vdy7gvogPMAUQzaPaRGyrqTQirEyuhSsLp9z8ewVr37+n9F2a7T7/oxqKaXoryUBIh4V2nYoU3DNSNt3ooNUDePDgd3+gbv37zFovrr7jk+ff8r15Q0/X0l6RKMbbt++I8VEyZkxjIzTxLev3/K7r77h7n6H2664vrnh5tkNwUfGlHh7/0BUBds5Ju9R1mKaRpCt7EkENustw7rj6voa17RY61iWhYf7B7799lvmcOQ4H37M0Sb388f+w1IrTte2582aCIUGEJeYk0NDTongF6ZxFJZh7cIKBm16oc3Omf3DkdwFVC/pEkZHrNVM9wdiyVhlISsMVuYjiAXLMnliyLRNg7PuLIBXFcLUSgubMz+K5nWVCBjzYZu8zIFpXDA6nBdDyaXqah6PQHj07CxVCKq1OVsXGXNyuU812iWRMxIVc5TUbIGvIilExuORUhLrskYbJcNupc/wmV8CTdNy2O9ZXV7Srzd0q7V8b/PoBZiq60YO8QkcKoVAiJGCUNq1UpQcJJPRQAwBMBRlpBMGKAWbMi7DpmvZGsPGavxbzfgw8s1X3zK9+Q6/u2U9L2z6AfvZJR99/IzN1RXcbHBDj25afNfz7e7A2+/e8adffM3v3rzHh0jTOZQ2hBgxrqOxDcN6xTCsWG02Et5sNfMyY7vh99ahKpIj2VhbKeqpzoQ6jG5QymHQJB8pLok9VwwoVbBGkawSuA+D6xwYxW6/Z3/YcTg+4P1MSkGciChMVdS7H0cO00yqps5GKVzr6r1XLGWSruu0CZ3T3wVyNpVEoYoYvCtgEbQKgDnNTGkU6K1IJ5hKOXt4qiTPny6QSxT9oFVkLdqsqAdaa2md4dnFirXTrJpGdJAli12ggBcYI92SOONIFBjILNIUTYgQi3CmYyUFWQPKSKYmJzcZSs01VHWmLlrTJ3frrPdEi5mB1pzzD1GKtuso1hCMYZwOTCEyLzONAVvd40NRZF3jkDQYW2g0WKPpWy2aNavPkTrBZLqaOmGtwZmCNYW+EQg7pcw+RWKGKcCDj4whc/CJOYoBvg+RlAVZEFvGD94W1jour2745S//McvuDuaRP/z4Iz56/ozLywuavkcsGOXz1bmIA856i9UG7wONMjTa8qnSNPPCcjzyzfGe4jTD9QXT7kiYPKuhZz30DH1brf20wND6BH+qGhz+eACeIsZOfqGm7i8Xly/YbG/oupWgAU/bAiXxZNZaGqfRpkGbgm2kuzLO0DQd3ntMa8CJOb0vgbf375iXhWG9ZrvZMgw9MUWxV5tG7o87xmXhsMgsvhk69vOIPhiKKxzniA8JNJIAojo2q4FhkJHGer1Cz4UlFlxjaVpLv+qrPE6LUUpJaCvrzf09usG/xyGY0MBqtaavLb5dsjDNkkHrk12awc8zx5y5T0HmbYiwGGUY1leUOFOwPNztyJ2lLI7p2NI2CmMy79+8I+TEKq5QOZBDojVWYjJiZh49OWRWwwatHUppUny8mdaKx2k+GUIr0fLkygw7vydgHGcO+/G8CASCq7mJT4yr69/UDlHmTk8PQa21mMhqhbWNtOcE9vsjh/sdD3f3wl7znuAXHu7umI4OXaDrO0zXUJJAWuPxyG73mpIzm+0FNy9ecnlzw/OPP6UZVjTD+jzvzDGSfCA/zc8qVD1UdZ+vLEUVZ0o9ZErWFFvITuMAU4AYaVJhlRU/u1rRNprGKR6+Urz/bs+f/rt/z6UOrErkpY88e/Gc5598Rv6Dj0l9h2862jKAbvB9z68+/4p/+6u/4r//078kBl+F0C1FGfbjzNX1hs32gtV6xXq1ZrvdQoU6d8cD/eqCpykWCrBoGu1YNT0xO9ngjZYZo3UUNKXAMgbKqgErtk2agnOa6ERQbzWY1pHI3L5/y939ew77+yqPmFmWGds4xjlxmCaOy8zoFygScmtJ9HRYpXFKCCIaIXdJ3maNRqpC92wyWUl13jVC4DjVqqUUxnykiYqYTzAXcgjWv6ck8UXlnC9b2XCJohRBbynW4azh1bMtJq347GrLm9t3HKaJsagTki8Hoha3UZ+TzLvq56gVjEHs/oISWzKlFK0Vob/R4hlKLqiatCQOIlle2w+xlgqvn/S35sTkrbd1GAYKEOKau9EzT4E8j/QWGgONM5L7V4wYq1NAF7oOukZxuTJnn9bgJU0gRl2Lc4EArZIAW6M0sy8cF8/DHJhD4WFO7H3E50KxDbFoclHMS5D5eYXGP3hXcjZzeXHNs+sbhhJh3PHzdc/HH71ge3WBa+UQ1LmaL5SCNZbry0uuNlvWw4CfZ+KysN1ecrFE9P7IeAehVWyW5xQ0Kh+43KzZrlasqjRCKXXO0jzFPcHfQoyp+5jSoG2DcT3PXn7GsLmmawdC+pAkCCKbcMbSWwv1EDQ2QtVO96uhmnh0tENLiRllNN+/f8Pb/A7rHB+/esUL85yUPNN85N3tO769fcsUAkVZ3KrjYhg4zEd88ezmHcdJ8lovtpf0XU/XNFxtL+g64Qhc765wo+YwZprW4VphnocgQbqH40FyIo2WMVTX8mOvH30IGhRWGxrb4KxQ2K0OUjkogzau4rsCxQUy0Smc7nDO0baOJWeUh/V64Ppixc9++pLBQWsV5IWHh4n9Ycd/9//5d6SU+fkffsrzmwu6xpJ9Ypo9xymhilQrWEcuSg6tJBIA7SzaNjLbIbP4UCvEmjzxlHJf4HAYeXg4aYAeOz6edIBwqqwE4z7h+7b+Ol2nsaK4xniiDzy8f4ufJ8I0cX25Ja97lnk+f800jaJXOxq2mw3aGC62l7RNT4yR4Bf297cs05FxPNCv16y3l6y2wqA0xnECpD64X9qiMcSQiLVbuhwGNIlcEg/zjEHRqpE371/TBs/P3FCd/Fumz3/N+7fvePjqG37z21+zf3jAv7vHt5bOOZ4Nl6TW8Tu78NH1K9qLZ2zW1zyUxPcPD/yf/+v/il//9iu+/Po1jRZIrGmt6BmBi+trLm5u2Gwv6PoO5xqykdmlMY6N2Uja+HH3AXRotMNaR+NaOtPXTqxWfUUkOaSCStDrlm234vnVNRfbNX3XcHs/UZAublkO+DBxd3fH3cMDu/2O/e5e5gwpcnF5wRwhFLh8fo3bDGKfNk2M44jRmq5pUG0rxC4UQz+w3VzVrLREKZFcovjcpkL00LQNOSZu2Z3XmM8PBISJGyvd/WQTRVFEv6BVwSqZdwpjueB0qay8A6p1rNdbnl9uYFloyPRWk5wlBjDIAvVKZp1ziDyME96L12NNXaaYjqK1HEApoy0MnaaxEVO7s5Ofp9XyWgpATr9v32csWWvIkcaI1MnqTPAz+5CxOrLqGtZDx88+uiCEjnFsSHXWs59mZi8H1u4YyTVpfvaK1immEOkbRefENYUiUV4ih5AuTmsDSrPbjxynhd1xAtOibIvr1mwuNqAtWWn2x4mHw8hhPEg3mBKNa8Sh5PygK/q2xRlF8hOfXF/QX6342dWWi6sbmtVKDL6LmPk3ocZdpcw8zYzzwt9894bD8cBxHMkBHsaFr+52fDfessseOkd7tcJ1llefveD5yytWQ4sxNaj61E0/LRLP0ojKCqVUjkbD6vI5q8uXXNx8RNP2wgK3jqbOzOWLFDobdDoFFif5HjFLWDiZ+RjJUSRsTduiW826WxF9YtyP/Lv/8G/54+mf0FhYD2syHce5lY4vBxLQtQ1t09K0GkhCcNOBRGReFF17Kc94K8+QDxPrTU/Ta7ZXPbGIs9Ob79+SUpXtLBGtHZeX11xdXeIX8/9/sbyY4yI06FgPk1J9JmtXpM8aNNmUjVY4K8ywvkv4nGn8QtsY2tbQthZnweqC94Hd7sD3b2+5vz9QlGIePWlboLa4MXmmeUZpW1mZBpJMTbRS1UdS1apToFnF4xyPH3R2IMSZcNY25bqmHg25oTwSYH6PMYp0i0V+z1F0lMHPAlHGwHG/OxvytrZ6dKoKJmURz6ak0dGQVyu0sWhjcI1DaUWM4ayBMoe9VP45i8jWtbi2h2LI8UMD7XIiAVXqnkIG+iWJTVRcPKoUTCnYeaSPgZVtKbt7xvvIcveO6d0t+29ec3j9LX6a6VMmBViMxV5fE3spbAIGimaOha/ubvnqzWt+88VXvLm9ZQleGK6VkaiMuI4Mw4pu6Gn6Dtc2MpPVlWiCxlhDSPzgaBcTgBgzKReMe2T1ChFIOhSDMDu7pqVru3MY9MllXhlDYywhTpQCh+MDD7t7HnYP7Pc7ClLILDEw+8xxntBW03QtkYxeZnFcCSJsD0YeOGVk9VtnsbYBArlUWApFUqUmdfxwFSKHJVFy286z6ypoUQqocP2pq1KnBBCFUpkUFmJcpLrXmlQUh3HhOEcmH5mCQpeMLorAcpZHHCbRPfqQZFGicW3VImqNLRptoLEae9I+5tNzkbHVkD7X50X9wLj4ROBSlHM2IIiEJZXINGusLvSNkZBZbbFqOEt8EoCOFBJdI2dsyvL+fFIcfTnr7pwSqDbnwjQn4QgUcE66JZ8yofrRWmuxztH2Hdo6ijbMXkwAYp2xy69M1umDLksKNYUmEf3MyhnWxrJei3ZXW4syRuKyOKtyCSlzu99z+/DA59++Znc8cJwmks8c5oXv7/c8+COTypiLAa0V7dByfbVhsx5EGqIe9+PHD7m+LvXI7ct1Bgiaph3oVxesL65pWjG5Viiy+kHxrJAwYTSURDnZByYpnFPJhOUoPy9VeZkykphTza+P8yiH+2HPZljhqo1Z07XYnIghC6ZvwDRC3Ck5i4l/KaS8EOOMD4bFT2c9cClZCoCmpXhBL0IISGasKAKMVrjGnbvHH3v9+E6w4s0hBKbKRGzbAWeb+sCf6LCFpma3rYeB9bCSTajpsd2RRQWsihiTiMUTsoRlLrsjv/vmLX/12y8JpaVvO4zpsabF2ZZ+teZ2N/OwP2LajqYYshYNXEac+nMWHpQkQyuapmFZTgnwJ1LJh5BBiEmCIs3JeO9DwgJQZz1ajLrrUF9YWkbovTmTE8RlZjwe+e6br6UqLpkSF07hmrlS9Zd5EqsyJBD0JOoWUXaW8FMj78F2Tc3ngzDNpMUz7Xbcvv4O6xyr9Zb1+gI/zeeOqQCL9ywh4JypBsGifYthIfgF9hMuBbYp8KoxrDcN28sVv/23/4FvfvVXvPvd57TGsOk6rqYFUxT9uuOrAu9bS/jnfwT7A+b2jnF34LhEfhe/5U/+5L/lb774ks+/fk1/ueH6xTVWd8QQGceZfr2h6QYurq9Zbzd0Q/+YxaeFXCVhugbxqkmP1S3wcDjiSwKjuTAaZQ2LF5g5xcDQrBmaju1qy/X2UkyANxuWZWQ+jOznCdvKsp/mt/gQeHv7HV98/SXv7+6JubBer9hu14z+wP1h5pvvbxk2F+hK5nDV4SRGz6KEmq1bDVnhl4itpPxCJJdAyoGQEjEVQhWbR/8Y9wXIPcpZCoFTppIpaCWdlbOpahJltqlQlFRwlZQSlwOHPegUeZgj48FzeHfHwzQyx4gvYnpeQiakhNLqzOYuiL5PHEUSK5PotMEqJebbRjE0DbbS1XNWtUxUGCeVyolj6JoPD8GcJXFC0hhAKbG5SzGQYxD2cZbPbNMqWme43q6wNeNznGbmOTDPnsPBs/jMYQzsA/gM+yUyh4jTkaZ6BucU2R0nYpJIs22fGRrNqjMM/cD19QbXbdCuxXQrjktkmgO7w479ODHO05Pc1Fps/KClMKZAmkn7d6ysZtt09Os1tu3QxkGRglO0ronFB97vdvwPv/4Nv/36a/6/f/6XHOeZ2XtiCIQQmL2XQt4azG7g5mrL9fWWP/jsJS+uL7BGPQb3alWL7yckq0qMOKWAZMBYy9WzT7h88RkXzz7GNa0UUUW8bVN6JAsqFNa2KGNJJ/vAlKF4lFMYpTgej7WXEOlLUYopeoo1mL6jW62Yl4U337/l5vIG1zY8f/GC934kW8P0/o6QPCpmOt2jtejPY0jkKCjb/pCYpj3zeKRre9q2Ywwj2ir6zYkgCAqJ30JZ2kbci6yT4u3vc/29OkEFxCh5giJOFVhUa41WVvDYxrDdDAyVDq0QeULXtvQp0bsOrRJaOQ7HxN08EpaFabfn4bCAXXHz4gV919B0A64dsI0j8YDPmSVnXDuIR6ltWGZPjKJByXVWUWp1qpXGWIctmRj9ma77wWLWYhxsTn6hShwMjJE5Q9e1OGNorT1/D+lwHdZoSWL2njkKmaJVsOl7KOKd17pLrJE8tGWe8MvCfr+rZKFTLpxAeikEKAVttMSRhMDhcBS/zlzo2g7XONq2ZZ6XWp2+pmk7+e90CsVFFm8UCyFdc7q0VaxVQ6M0g3rATA+Y27eUGNmFwJfHHX/97fd8d3tLDoGXOfFCFS4uhLk5fPyK61QoTcfFy09JL6ELkb98uOPzL37Lv/n1b/j+6zeMx4nN1UVle/bMh4DWlu3lDZcvXjCs11xeX5GTVJvjw4h1lqbtyNVpQitNKYZl8j/YgB5dgud5IRoFZBLi7BKJLHnhMB84Lj1uNjwc9syLwM5YRyyZw3RkSb18nU1MaeboZ6xrMI2i7Q3OZZzPKCPfnyKBuauhF9q/EvafT57ovbCPcyHOuQ7sI0pLlVu0ktw8Y4gp8sNecGgtKyewbn7q81qkcNPuRMhBNF8UMqU6JGV0LoRl5D4G3j7s2N+P3L27Zy4iVPYxk30m+cS8ePRpbTtBcFCFfhBJTywLU4ikLFT+hGGYrCR6WE1AzIzJCh2kK8wUtDIs8cPe3SqEEaqkYEypJrQURVGahPyKRYlDSon4GGR2SCanSIkZmzOXnSE1hm3n2PnEEjPzElFV99m6UxJ8x+WmA62xbS/htK1BlYSPmTmIFtDXGKPdFBiXyHGaWUIkFVBKCE/WyMjjQzZvYTrsaRuLno6srq9YdR3GOlSBFD0PD3ccRzFpP057bu/v+dXffM6f/tVv+ebtW97vDvja7cYgJuaCaEkcU5kCFy9aPrm84nqzom+tBIcbidsuFYlTRT1BFgTtOhH6XDvQ9htuXnzGsL2maTppA2KS/aV2U6elWCj4LHPMpDLFGDIiPWARxnRjmzqnrhrs2lG61QrTDDS5wWE47CbCkrBO47q2ku8KpIjrHG1jaZ0EBiz+KJaNSgiGZBlhzYvBOYuxAyYrUkkcDgeO00gqhfVmIKZCqsYW1p5GIz8cDv3d199DJygOESFnYRpVuFEqeKncnTN0XUvfdfSNwxqBAWJKNJ2lsY7GOGldlWWeM4edWFmNu4M4DJiO9eaKoWtoe/Ges40h5iy/SkY7h7ENjW2Fdk4gxqpL+YDtpB5JKxTOd+/JpStl2+hH5pqxmqZxtE3DZr3CWUNrLH6eJJcQYXppBQSpunSOWAqN1qz7npIFGhq67kwH3yuxk7PzQsgzIFqsE6srRTHyLdmwTDOLXxgPB6nWshgyd6lDoZhGCdgNPuAah/fhg06wpEIJdbOyimJA5UynFBtjWMeFMh+ID7f4/cQyjrx+/S3vpok77+lLAaPoFWw3K7rLS/qXL7nCoF2LXm0JWiCmzz//Lb/66iv+x7/4C7RXWO24vLgQ8wQMJMnrW20uubi6Ztis2V5eMO13zMfAMo3k1GCUFhZghQELErj5wRF4TsNWhOBJCawTJmDRhaQSvnhUVExhpvWWwzxWL1BPUYaQItFPLGEWhqQuhJzxKYk5txEI0JiCNgLf5Jps0lpL2zZi46dqinYK+JoAYbQWrVzUoCWeyKgiEVQ1DSFFPrBWUQpao2hsnW/Ln0q3VVNVTjZhMheUOjjrJyWByqTkCSFwnCf288TDOJGcJWnJ8kshkUJijh5TNCUpiRdTYltmGkPTNyzzQiqRJWYMFqUhzImkNDiYSefXqOszJ65MVBvEx8ucD0F59grlnDBQiiLJyKnq/+TvyAFVxOhBl3Qm4DjTYGvAblGFxmRcQUg6JdO6enBZA4n8Y1UAADTsSURBVE50qK5fsekbOmcpKTD5AJNnN4v5+XEMHKfA6JNoA5Ns7ELmEZ2js45kn3IJCn4eScVhQqisXCfShZKJIXO/27EbR3YHOQTfvL/lN7/7ms+/e82bu3umxVdpk/gln/YiVfdTXQqbruXZZsOqa2mMJqdYU3Oo6FCdCipheZbCmbeAUjTdQL++YL29wXUDRps6fpGfmXL+PTJNKkV01SqfNYi5zjMpGVd9kFEa7+N5du3aDt0aVG7I+4l58tLdZXBa8AoZnwmrt3FCThMSWULrXGHmR07GSc9rKjqUUq6ktaXeI1VTKipzunbt+W9pdv6u60cegoqPP37Fdr3i29ff1/mfJReHRvLRuq6lbSyrvmXV9XTOybwkeObgaTcCdykMq16iQI6HyHjMzHMh6QalC40qrDYN2+2Kj15d8+x6i9aFh7/5K44hsmRoqvg7FtmgtNN0pkUtCh0Cc6xJB5g6KC9QXc0/DJGE1hnaxj4K/K2tcNiG7XZL2zjxvPOR2TmBLvzMMk0s08T97TvpaHJmvd6wHlZcX13in8Acflk4HvbsdjvmaWa/3zGODwQ/E+aZpmno2g6LeK8WBQ/7PbNfmKZJsv+q3jHlSIgBpQ1N17G+uOTm2Q0pZf7k//nfiOEjcDwuuM6jlUW7icYkbpYdQz9w2Ti++upLzH7H9rDw1bffsMwTg4afU/hpKWycZXt9w9XHH9NcXYNrmUZoX95QNhu+ut/VyvY3/MVvf8NhmiGJcXjbtFjnOByOzPMDz28+4vL6mhcff0y/WWOcJUXRZ87LwuL9mQkJUskV8QeTCJsnaM+6G2jbBo1i9gtFJVZdj2nAGEPMgUTCl8C7yRCUpz8ONX5K4UPhYXfk9v4tF9cDrm2Yg2PJHVGt6FbPSBl2O8/FWhNDjdtRHusUQydhraXJTGEG46BV2Cgdm3OakoWckUskZyHGlJQqBd2hTRHI8wn6blSQIGIeN1tDnfGocs76Uyh0SZWBpcShSJ2CUkX43g6afjG0vaa9GMAaHu4PdSMrNNtLisqEuEiyijEM3cB6taZtO5rgiEvATx5/8OSU2GsFUyQ7zVHFullrkbRQ0VsD4/zhwLNvLa0VCvtpIxSNo6TKp2qKnXMh9hZn5D6GpT4/83QeB6gs8yGrNEYHjMqstRRB1tTElsrIdYNFGUcxsB897/3Mw35knAPHaWE/eYkqigWfIGZNKQ2liPuIzIU1rmnom/bD4rkUynGPUSvW1tK1kqSjtebhMLI7Tvz27oE5ZRYswQ7csee7w8L9HJhOXrmqoKqvp65ZiU3b4Jzj8mLg5z/5mD/8xacCDfOoGeUcg3TqedT5deWYQBtcN/Di1R9wcf2KbnVFTJFxPJJSkiml1rTO0rXNk8G78K9REsvU9FqMt1ODiVasE2eRQay3G6ZxlkiqkFhvt1jTEMvI7uiZpgPTGGi6QkdD8JCT4uLiktW6o+scMR1p24bLy5eMx6MwPadEN3Q0tuNifYNrGoE4cyaqLENhlShFfIy1NrjGkEKqeERimg6M04fRV3/X9aM7QXU2O612VlmhtT0TOSS3TrqVeV4oKdFae26ZQZ2trYQynMlJGGBC4FBnNtMyjRxV5r6zDBWySUWI6UqL1VJOkJRYg+WzjkcG+o3TlVZd2VKVQcqZ7PJ4aVUwWqrYrmvpupbr6ytW6xXD0ONrxRZSEDG8lpnOuAiNHiuuJ6oUfHVSWPxM9IEYxdk+nYI8l1lad8Tyx2hDOc0WKczTdK6ylDH0w5r15TWuFYZt07XCynWOGGWW2HYdNzfPCD5wzgYrMIWZPs30fU8fPd2yMN8fuN8MxK7hV19+TnOc+Sgp9iGigQsMdrXCrAeG9cDw7CM2n/wc/eIZURkOx5lvjhO3dw/8j9+945s3b/nmzfccJ09B03ct/TBgrGU8TqSssa5BNw5lLdoo5mWiLELtJxeatmFzsT1DwqcDJMlf/3AV0lrpGJdlwWcPJpOUfG9x6a1mB1HjCUzZs5+PWOVQaHxIktatLKgOrRoaq4TliGaJ0o34kNiuNxidaYwVwTGZxrjKHk2kLGQWdEJZeQiTimgrc2hTqowgCQKQk8KPCz6JccLTwyIliKm6dPDogHPquEC+lyxauckZCCcKf3USSknstGxrWW8H3KqhGMU0G0galRXDpqfojM8aZ5yYTZgWZRWRSDagGkOLyHZ0BlMrbIpobbWR/cDpx21YG/nvD64qKSo5kStdL5dSjS2SrHdtMG2HspZiFEVnsoVUNF7XGWLKBC/duCEztEpCfNtGtGGmmoLnIm4xu5lEYEkT05xYfOY4+se5bFLEbOoBWIinPaRkFEm6rCKMS6q7zgdvyy+Y3NI1TmalRhIlxsVz8JH24hqrDIMWD1Dbr/mn00IsoM033N3eVia5fpyHW4c1jq7tePXyBc9vLrnYrjilxKva9z/ao+lzN5hzRSusxbUDm5uP6TfX2HZFiPHMinfW1a+XmevJTerxCRP0zDqNsoJmaYQ1qrLGqEjJmeAX8Xy1ClMUjVEYXUiqGmkbISoWlDCOMeSiBYXJkVggpIClQVuJz3ImUoKvBY3DnRyGnKYphqI1ISvUDOTE4o+UYshZQ9aYYlHKEOJMCDM/9vrxBtpPxvhFSE8CaxorUUG1RY8hMJVM8hr6/nG4zFlyTgieQKHEUEM9c6X8Stc2jgdCWFAUVquBfuhIWfIKlRJj5VJvqhhB1zBYJQ9nqxtigRKfCEjFM+v3B9wVXjJaDHzXmzU3N9c0bYt1luM4EoInhkWgnJIZl5lxnpmXha5pzxX6cb9nnkce7m/JUYTx40HkF9KFQk4JVbIIplXNvqusuXkeqWpg8Q3tV9x89BHdakXb9zRtVzcfzXiUSqftOq6urpnH6UwggcLsR5Y0MljLel7oxz3Tm9+RtyvuhoY//eK39HNiMmtsW9gazSWW1aahWzW4m2uaZ5/QvfoF4eMXpJLZv37DX/zVX/PXX3/Df/M//JkM9VNi1fe0bcuwWtFX3dfx/S1Nt6LtB3TToJ1BaZimIyFKMOZ2u6Xve7phOM9rcxTKcwjCAv0htt84cdw/TAeiCWhdyCqQjcY42RhyoJr2RpYceJiONKbDKsvis2SNmRalRGDfOpn9aK1lzpQjukQ+Vg3GQG8bDpPo45y2xBKIIYozjIkUHc9yiKjS2RJMoVFJoaOBbAkxs1TCRvSPMFQpAgUGcUF7XLNVnyaIX66bdJ0VIoxMXY0QoDxKgYzCdo7NxQrdaZLKtJ2BqNBZs153FFsISCSVVgadxFc2Zi9fr5X8XS6omLFZSaqEojq9KIxROFWqVk3gvNE+MhRlr0hyAOZHAkYqpbJ8o+Q+Gtm4latzVl2EfFMs0Sh8CixZCHk5Z3QpZOvAWVS3RlmNMhCWhSUFjt6zn0eRVYyJcRLOQIjUot2BdmIZV06HZ64HXzofLFCjiDhx3h9vWFlmdF7TDR22dWCkMDvMC8eQWD97hW4aTNOgKFw+f8Fqe8H+eCClyP3t7bnblNGSxTohGfZdz2effMSLZ1dcbAaSX+p+K6MCOCXm6FrX5/PeZJqOdn3BzcufMqyvMc3A4pc69tH1EJQiLeXEB2d7AZVl3OCcAVvpT1k6VU0NIkiJeR5ZdSucMrhiaLXMphcV0UbQOeohWLKiYMkYlhhposekRMiBBoe2hkb3JB0Jk0ikjBF7PdfIIdjKfIIlKrQWJGFeDkiUlsHqjoJCG0nvCXHm92ro/8T1ow/B3eGIbK5eksyVOhvXOucgC9stF8kqCxT2hwOnQNum6zlMC+M4yYFSMrbOOE7aulMMTs6OJcL3044Yv6HvGmJwqNxhdSBULPr0sKkiUodc45SUsTJ4L+LskJ1DDqKMfgI3oaB3ik3vGFYrVquOtrECwcRAMIYwjhz2e96/fSMOOClVGzIRSacKe/plZjoe8GEhLEK9Tzkz+UAMnhA8Tp1mOJlhWOOaFud03UCKUNVjYvaBZx+9Yn1xxc3LT1BW5qhN19ZFn2iaNSAC4OAL+/0pUaOu5ttvccuei9+MUqUaRWkU/rAn3RX2yvIdnl9Pb/nnoeFn/YD97AXdzRXdxZbj6opf7Q/86X//3/Euwf145Le/+5LpGEixcH15w7hMjPPI1c01RmtSDLx9/5qUMn23phs2dKsNH//8p9X4V/FsdS2kIyebUM6Z/eFQk90j0ScJJSiKZYnM08zTlqlxPcopehXR/YBtFZvLXmZ3KhOjzFmzTigjsI7PYtSQtUI7Sw6KJWRu7/dsVz2roeVi1ePnFcX1kvqeIyyaTnd8+tErXr+9w4fEfByZlkmy6VQEl8U6rWiZfyqNPc8tjWg5XQPKElQiD8151nnk0RJu9JlmESJTqZloJzp/QWqjk4fs6T6rujGe/CJzljmPEDYFaisqoQxcXvbkRYFXuCzVf9domYOTMRSyzjKHtAqnDZ22uEahfCaOCe8g2cKwNrWgjKiYxMVHWVprmX4Qglwo5zOlnDpBYImJxXuy0mizYNwoVPyczwkeUEk0SZGVpVvLbMkoMXzwxnDIDS1yGM8Exqx48IXv72aOU2Y/iQTLGuhXDm1btOtYUpU1xkyuXdGJlGaKQQVDLgUfIsYm/A+N96Ont4bLm2do6wgpcbvbsWBRXc8wVDcTJTD1qhv46U9+zv/hv/w/8p//L/9X/F//L/8nvvrqd3z33Xc0ThiZGMv2YsNHz6/443/0M55drWmsqz/76XinskKVFIkpeZRpaLoVr372jxk212w2r8iId/PQ9+di6SRZSUlQtBjTB4dFCIkUBX7VFWAssXbhAaxtCNkzTxNWGfq25+rqCtO0AsmPkdh7ypIlQMFpMIWmM/TBEZQGHQjZY5zCWMF1BckRM/QYRTLnowedKMrKIZkhjA/M+wkfPD3Qtj1N51gPG5rG0fcWy8zQNk+nDX/n9aMPwRAkT0paZUfTtjVBXh5CTkPJnMUgv8hgP1f3jMUHQhWfpjpkxWpOWX/lPOQV9lOuN+p4mKrbjEYpR+s6op9J5TSQh6xUDZTM9XAU3pyqOHu2rtJmT1vK06dU3CQaJ3KLFDzjPmKsdLhhnojLRJgn5lG6GKUkSdoai8oRokelBVc3HBoxzI0RnNHk06ZEZZ5qS9O2NE0rdHddpBjQGmUSuercTukW1Fcda4Zdzulcl5YiFfU4Prr3lwLpOGJcy7OhQyWBo9Q4SUJ0KPxEWx5swy4jKdzG8l5rFm1xyvJumvlynPh6PPLd3YGHw4Hv399RosZgWa8LzmhhyGmEmq3qEB2BafvqG6lyIcdAyDIU11pXanbV0NUwWsXpIeVMTPigE1SganByg8H1GttKFmBBxOg5FsgnZrBFGy2mxWHBk+jbNZlCyInJLzStZWN7Mkk6u+Ax6lQxG6wzbPuG+8OeWCKTn/BJhMPoGq2VEyUrmdNaU62bpAI2ymFwpKKFuZxBWX3eIE/LMSbJQcvn56EeeCfdXZEDRPR2deNQnA8L0etWgsuJhGIzShjkWGPEjzcjhZcD3dSZVAGdZbPOKIqteX2IHg6rsFaTjZClUpH1VEoUUkotZlTOhPyhWL7UlV/UKYqsEJO4sYSYMUbs/UIQEhHlifWcElazcfL8dl1X358+yy6MUzXUuIBqK/Ik39c1mbW1tFbROEXbWtCOolt0qIzZOVbiGYg1u9jqaa1EcpVOHfaHh2DrLG3jsG0rz3olVhXbSv6hrqnrdQ1LBqLm5voZzlr+6I/+M0CzzJ5pmQUrUorNZuDqcsN23dNYI2QSbere+GT+x6kDBKUtTb+mXV+xvnhG128rU1UKElvJNPI15Ylu+veAMfmzE7Sa5A9Krs9VAuvEnCNVPoYydW2UXPV6mqZtKENCO00iMS4jRUW0LbStAe3JOWKUpZRESkGCdFOpVnDyOhc/kbHkYrHKEpZEWhIajdW2atANrTP0XVM5HbIf2B/C8n/H9aMPwSUEYs60Xc96u6Vt1zjXCL1a7tC5wgilkKugNyWh7M6LRF6klM4VrLWGnIRVlxMVWnUCZRbJMTgePeO00LUrjGrZDA0lPRBjkCSBCmNoJRtrLhFUBC1u66ZthT7dNuLi/0GETak+kQFnIYURP2cOh4PM4Go+WZxmSvSEZcZ7L+y2toHGorNH50SvEutBAw1p3bD4wOID2bdMpjBV2MgYQ9M0DKsB5ywxBgE4lKJ1svk6J/FGaZnx01EG/Kf3V829T/ZJKSXmZeHu9u4DRtRye6DZPucf//N/xvz9O+bbO+5/9TUXBbYFXjU9e9PxrikELa4if5oy1ifKceG3775hLIVRab55d8t8HLGlgTrDCPOIsYpt3xDiIgN05XDtGq0N680Fl1fXrDdb9ne3aC05e2ktf+9DwrlOkrNjQWEkIUEVigbnxBLNuw89ALOecc6xGgztxmKcJhPx3le2bEEpgzPN2Vs2psg0Hkmh8PKmxafIEiMP84gdLM87w9GP3O1vyWpiu94y9Ft009ANDcNlx9v9e8Y0cvdwJyYNxqJbRy6JEBMkYVBv24vKaBFYl2JQ2eKjdLl+CThTaZ2P+w4hCQoAnDdQa4rYo9WDTs6+J1Z9qGoPKIztE1s7LoVsEilr3ErMxktWLMrji2e11pgGaKqYu4DOmhghZUVx5uxwEskyMmh0ndcVjvNytiVUlYxSEFP2nQ8fbKy5aImHwqC0WMot/sjiI4tPKJ1ofCKGyLoTNyBnjcSfZSla16ue7WbNs5trrDVoXZgmsck67I9En0ghY3RHYwNd47i+ailAt+rpWylOQPIQl6TYz5nJR9R+wvtISmCiBi+2aUbXxPoqIwhPzCiUUmxXkvpi2wYfg4SLZ8A22K6v8pXyhM0slJZ+tabrB/7Vv/ovubl+jnOOX/3qV9WgW/HqxTU/+eQ566HFVEak1tWM4YkZCIh0oZBx/SXb6094/vHPWV8+E9JiFmP1E0/g1KCcQ8br+3iaJXjax8V6LoGuRVgsRB/JobodVWMS4wzWaVC5StVkjfarnqHtsK3B55n7+x0hjWiTWK0b0fVGYbPHqJknzZxAZYXSjcwTc+HhcEfjHK1r0NHhF48fI41p6NqWzWqgaRpc27BeNWgNhYA1tXj7kdePPgRTktiUpuuxVrrALJQ0qfLKY5eVK6OhaAW1U8xFqoecs8xMqhZP15w7XR1DtNb4XDVwWuAJidWRFl0pcEahEH1TTAmdpUNSKpFSEfigJJQy9K3DalivNyyTquyoehX47tuvUCpw3N9irVhAnYJ4Sx1+piyG1OiMcQaSEmp8WLjeDrS2E3q2bYkp8/5uRyjS0W3Wa5yTqkUM92UBHUcxXKbUUNBaQamaTHEy41ZFYKfH9y9TopOvYYhR4qLyh0SL5sVH3LmW/+o//jndGGGauD2MDCrTa4hWc0iZt0tkTIpsHU3niMsdUYl9WMpC1AjTIlE7qOrYb6osQWZQm80VxrUo07LbT8SYOM4zar8jpFjJFIaoFPM0C5HKiMZzzh7p6KXzmSYxfFZK471nPO7Om6oCusHRDJpAEgLSnBinUarVVEhBo5XB6ERjG6yzcggeZlLIrFaXKKXYbNbCjnOai7Xj2XXHeOx4czeyLIq9hqt1x2rteH7VM31yxeXG8v5+ZgmVXBEjuYiov6SC1YZW9QhfIbNEIU/No2faB8IikG+Te0x5NGdQULWkUkg6Zx6JaCejhyfxWSlL9lpBEaPMEQkRo2XDzHOkhEyZArQWowo51yzJLCMLiqIkBUXM5202xCguKxhzng81RQ44RRb5itEY1WDbigJpQXNi9dN1bv9B++6DFFY5Jbyqyd8BtG7pOsd6vWK16lmtVzgjPyfFSFhmSk5cblesuobN0KKSRFPFEmgbRdc2bFY901Giqnb3E6ooOgcvX9zQdgbXFsIiWt79uJBDJnhZJyUVtM6UItyEZa7ZpUFGO0orrLK/d1AopaoNYEdCiDUJBbZFNx2qac/GAKhS526qkoRklHN5fcM//Rf/gqtn16ANd7fvGfcPfPbqmo9fXlX3rcfDi9PYqDqoKAy2XdG0Pc8//SXr7Q2rCzkARRpWGRh1D3t68D39/YPOEqruLovdYvT1rzXz5PFzFC9ZV1ANNF2L64SYhBLyYQqJrmnoVx2mNYTgOS4HUg4onbCNQekWmxBSYxF8LsWIQuL6ShYW/HE+4KMhpoZ1c0HTGS7MhqSixKlFYVMvyaOsQKpKZUEPmn8IA20FSov4XKo/ff7gnpZ+Wgvsk+ufS+aaqRuq3MjTpv9B7lW9KWJSLRUOCrQV8k3JsogEvhRdX0Gjc6Yo+X45i75FukMZoJ+E/F3bkWPAmsdIlFIK93e3OKfxy0zXtdWaSIu7Soyi76pzxlK0HOZJYNOSE+pyjXFygLh2hQ+JspvIeDKSIk1t709wl0ClUUzJa2wPyAzxRFk/Vf+Uk51Vrseg/Eo5VWu0VA+N891AKYW7vGRxDb/+7mtWS0H7wP000WqJqylZM8bMvU+MSUI6hzngU8CnQjoeq9MMlJjQBWyFdLSWlI5Sck0XMWKfpAQiLuUE30qH5qq/qlJaumiESRZjql3g48MeQjibnAfv8d6f3xfIslNVXxTq3GA8zgI1F0WO1WZJR6lOUySVhI9eXH2SVJJ92+G0wipoFFyuB8bLDdMizF1FlniqkmgNXAwthozCMfnM4jO7w1ghKWEHaq2xqhVZBIWSCilIoopoGuU+/W1+FkoVtC6V4CWw+cmMVo4GkcfnItBpLlUScWKUFmE5W1Mktb7G5xShNAv5RVmUKZIOoHWFWsV2TkTmEV0yxjSAbKQ6enSJFILYFVpLa7tKXhBrqpwzOolswpr0hEQiGq4Qcr2nkiaTknTrkjrToLWjKCNOSaVQagIKpRrVG4F+U1gAObTatpXkFdvXMO7EeAiVg2C5vljT9xZnAwedmEoQNIYMOYmBvGgzRKqR5Gem+tmKHR88ZWM+vVzTYJwVFyhE5oGWhBdViYIV4ZRZHJzXOUDbtTx/8YK26/jZr3/Deui5f2e4vtqy3fRUrtP5a0rJkiiiVTUmcbT9hm615eL6JW2/wTXdIwu+/swTtP63XeXJ/56uU25pTplcpPvVygmK4SPFCgnbOcnbNBXVODNWK9plG0tWmVgiPi4SX1Ybn4KhYEUmU9dDTOkcjSjckkxMAW2KjIdcHSN1lpA9IUfGeZFnqkQO00GCsB10fY/6h+gEnWsku8k5QESKTWMqM7K62yupaEuqOHI6xaxo5mUhxoS1EnZqtEaVREmFXBLztEi1o6EQ5YzV0A0t1rYc7mdKipArIzAXiSWqEosTWq7OWLds0rkIeaTvV+iKdz9lUX79u2+4vb3FGM1qPdC24j0nvoGJtm2xTUPXr1BICOg0zlBEz/Ty+Y047qNZrdeYVGgPYvobYqJbDdC00DQwHgWOyIWhFdH7iR2bcybW9Wu1pmkkzqSEqRqNKpxrqnmjhiwLyChFcY7UuCdMIcX6Z58SJs9f//tv2b9/h59ntBVPTm00KUaJJNKtSF1SIT68F2jJJ5ijHLhK0VqLtYbGunp/MvvjJNCosZR3O1AiSdleXrNZr7Fdxyk5wLUdTdvRD6tzx+crfHIievxwozlDN090EgUYD54lKpbkhW6dCmlpzg/ko4GD5rA/oLQS13nTCPsOS2cbVm2HCxP6GEgPC7989RN+9uJj3j8cedhN7HYTeY6E3ZGH7wytNjTNhs2NI0RYQua7+EbkPrmgXUtRmojibncrmiwt8hpTDF1jKU4gfmM10T/dfAqxLCQsWll8SuSQmX05BRbJv6osxiVIWgLKSYROAafkZ4gPayOBvMrR2BWta+m7ntksLE1gu7nGOCcbtjQrkj+oCmhFv1pJ4oc27L7/lvm457i8Y92tcMOG5uojEpaYFPM4Cas0JgqiWzy/qwLLkiiIJvRkBPD0fh9GzxwS+3GibbTMxkui0cg4wjbMPvH+/gGdI85q1kNHDqqOXAp939D1CqVEk+vnwkVv6Bojfqm2pekUCkc7J7QOHG9H0pKYx8A8JZFVJIFsG6PR3SPHoZRSo8fOjxeudRhnKVo6GTHQkWxTbeyZjam1FDInecnpZIsp0vUD/bDmf/u/+1fc337P99/8lhdXLb1TkGOdJUrTIIGxnqZpME3HxfUrrl58wvrihvXFjezJ5eQYdVovT1bYeRaYz7/nIqECT29Y8JHQCLIhTlXipRp8JKVCo021dpQwZFkzyL6Coe1acozMfmbOgSV6gvI0nTCtlY6kVAvSqGro9ELJEWMy1kV5rktCuUI7WDYXA9dXK5xt0NrwcDwwzRNjMqQUiSlye3dP2zkuLtcY40j2B4kaf8f1ow/Bk2OLO6cVc4Y0jVaU6MXWydRqX8lpTkXzjBX4sm8buqapcoEiH1KQeY42MvS2RjwNM4XGOqxt0Ho+C2tPQ2SBiwqn8btRUIzCUciqAKlWZErCGd0p9eHJZlvqgB/DOB4Yx0e2qtbigKO15bA7nE2pQxSGbNM0fP/2WxEYNw372VNQ3O/2LGGWDiRFtDV0boVrm6oX9HU+WYg6C/yQMjlEXEkYZzBGQlBzFjp+ihm91Cwxbc6aqBAk5sQfDx925FhWq56f/uEfcbv5lnG/Y97vqhdgIWhh2VmgaS1ojZ8mMRXIQlNGnxyTxRtyTgGVpMLMRaOLhSLSFV3tpVabNe3QQ03YMMYICSJn0jRJZ1nNB6rf9Tlh/Ommk0v54D6dHtL93R7jFIGAcRqlrUQZSbmFs+68AVnb4axls9oIQUU5WtXRqlZs98iEYHj9/UiqqSOua9h0hka13Po75jHy9Td7TiyUlKs7hrY83B4FOivQdFmStY1BB42jw9GSTaboTLSenBMxR4rWH4QgF0RInGrTX4qiZF072zrzM5a2aem6TpAYtHR4lS05uJa2kWdrPu4I3rPMC5eX1zWTbeDB3EF+wAFOW1y7rtBCgXSKy1E43WIBlQuqqGpI3lCKIQSYdyO56r58te8rGbR1H1jzKqVohxV9P9DG+Eh2oZyhXqNPnUQh5yAWXRmsNsLoReLTUkhsVx1d27AaBloniRRGaYqWw3XoFc5orDKkuOBzwApwSNcYEg50JqvE6C1KLRymPY1xKAyroT0nwxSkO/Q1A/SHBvVK63oAyt6YSiHXZ+p0U7M8KGdy00kgX/+Lk6n/dntB4yxD39KoGVU8fnyAFM9RTto6umZgffGcbthy9exTVpsLmm74AblFnZEYeWTK+ffHw6/UfS9XuPTxa3V9lkoCo+TzSrnQdwbXSOZDSklsfirp6VSs5qzIWryPYw6QdJ1bVhgXyTHNSdAJsiLmzJJmlJKM1hh3ONtijKEfBtabNduLDd1aDAvmeWSJYzXCR54JrfGHGTSEGJmmhXn2/Njr730I5icFjdZaMgRthTKobbFWiI3nySewUo+NpjXiGwcQipjrxhSI0WMR/1E5BEXHYrUT2EOLRx1KuhO06JRirNh7qaGgSmEpFaI4GfdqMAZtDcaaH+yt1YVBaeZ5IoTAsoiLS9s2xNDJzU6QUpRFRKIfemLX8+bda6xtMKZhNy0obVl8qFWcYg6erm1p25ambQUGVUfSFMT9pchMQTwEZzKOtnPCGDWQcyAEIRXJDENE9jKrzMzzgtKaZXwqkYASC+2655Of/IKubdjf33L39TfEeSEv/gz3OBRNaynAcVzEtUZprO0FCrZiM5aywBPqPMuyQvooAoMaK478w3pFNwwkLRIaax3745EQM3HxaMHxyLVzOMFNOT/GXX1oCvyUDQfzYUZbYR73m14SG+qZoBS0rqkJCIq+HWibjuv1NUa5c8J7UxwOi1YtwRfevp3xy4xShVcfX9Ja6Rbv3z4wzZ7bO0+NPyDlxMXlBX3fs3sYBSLKim4F1tkqF9K40nFKW1ca6fRKYI4zSYm11tNLRL8GpURTSJGuIhVPTgFrHH2z4mp7RT9IhFTOFaJWmk030DpH6xzv3xrmaeKgRi43V6zWG/rViuQDy/GAQ1yXuqaTZzkXso4iCleiP1M5UbJEAAnLuiUXQ4yFcT9Raup6XMTvVqHR2v6gbFEMqw3rzaYmy58IHuLZqxRVDiIpENN4PM+hyymdsChCypAi9rIT1nG/xp6JJ8LpLCT6TgkZJBuSX8hZUbTDNg3OWjolBUlWmY3XpGxozEiyCmsUq82WpnG4phHT8xiY55ngg5jOP3lfJ5haZrSC4mTLoxykfq5JAdUVRlWmt6JUCZcgIcNqTT8MXFxe4Mdbwnyo4dsLJQYpKI3FNg3b61cM6ysunn2CqwSpck6MeGSPPj0YnzJCHw/Cqq/+ARxqKju/5GqwXqCoRNeJd+xueRDINefzz8t11CXjqCTm2zkIma8WtRrxRI1ewgYoBrIWeZn3aFP3yymw3Vpc09C0jmG9YrPd0PYN3nuWOOHDREgRpYSo07Qtx/BQeRSFZfEs/xCHYIiRxQeM8diuRWlFjlGMgakHV60sFFLdu+Yx6NEazdC2bFcrchaHcp8F91dUF4qqZTmJOZXWhMWTQqZxFg1EpfBekrK1g5wTxjxCFzoXQqqedkUMeM9Lt6ZcP31Qm1pRLvNCDLFCuBq/BJZ5Yc9eRKbOneeFSkMMCfDcvrslpowPEWW+FAOBpmXoe5qmoWmaM/TTNA5X3RHmMLNMC7d3t3XWKbhUVmDmhY2PoCWOJUfpzuZxFuPtmM9ziJASbd8R45POIhe+/J/+jK7fkJYdly+v+Oj6mhc//Rnvv/+e2zdvSd+9F+NhBdMijMx2uMBZITz4pZoXLxOxVC1RrdwVBpUMRjmsbsBZmvWKFx+9pFsNuKah7ftzTFXbdnS9wbqOtHhSjPXzFg/DaZrOHeCpoEkhiTOQf1zMWmv++Jf/hGHVU6AO2TVZVSG2koyzE81bhOD6nDSfYkKlaoO2LDStCIdLhv1eDBGO00hFsDjsZmF4ouv8QghXh92BefI401CMxqdImCNxycwm1PUrxcopkdupFkuD0x1FQaMXFN/WWZHm2fYTnl1fYJ3DmQFrGlrXk6rX42kttW1L04jrRyliLl1yRlUTTu9H8hRQodDrFqeE6TlOYrDeGOhsRuHx84FcpIJw1lV0IrD4BUgYgjAA9QCuJagGpSzbZiVMbmMwJZ3ZzaUUiDOnjdgYw7/4l/+S58+fi+m8FkfYnD3T8YFlOnLY38t7jJGpl1Bpv5yE2TAf9rI/aGjaDutafEziMUliPs51Qy8Y58SztWgOy0xOhbbVqCWgfAHTElKmlETXODZD5tlFT0hi5r3abM4df0gyW9R6zeI9jSucEyWQtJqUC9knFh8JRZGs/JlKYl1WSzxOqQ4lxifEFPNY6IkmCG1b+ouXtOtrih3w00QMntV6Tdf3rNYbVusLrG1Q2lUpQ01ZKbWooJwPwFMcXH4K66ZY/1uevRAfzV5lj+qxuhKpoqqSh146QhI6ZrSztJ2j7zY01pFmL4HGSaRmGEHmUhQINS+ZpXiUUqQgBbQuYFTEqkxSiq7ryLmwP0zia6KM5BUqS4gwjsvZJk0rhSqZEqGEBDrx2YtXwqLVljdv7whT/sHx/p++/n4zQVfnR/rEUKrocynnQe6pvS61ODm5XzRO/EVXwyC5Z0ERvCOlRja97M/UcK3kwxe3AWmvVSnVPNbV6lFuXkqGFMVaSVVWlNFCsVWlGspK6SFD1x8MuEWjJr9O2XTSlkdirBuzFY2SroSVmDLWtbRdh9IOtDBBRSQoxUFYFkqSsN9T9eWrsUDJWSj9OQrsmx8JP1pJ0kDwQSr0nESIXSDmiPcL01Q3mvo5qQXC8pRAUji8f4tvDsRwIKU9rrUopTk87Ksfo/idJs1ZJ6W1mDtnJXlqJZ0qvvOEvrLU5DM+MXeH1ZphtcI1LTkXQghiUlDjpFIIFCRLTBUpjGKKpOokkursoZRMqZKZFKIQS7x/ZIcquNhs2W7X1V5OHvQlBvkcqo2cOlFPKmyRY+LkwpyQwzXU9aKUISUR5nsvvqOnQ3Cpwn0qKUU2rVQp3vL6U6lGxNUbU7p1STNIWmOyJiVTmWuSCF4y6PSEHaoUm9UV2/VlHTH0GGVpbEO2DSVnOUy1Rhcl5tuqiDa2fnbCoUo1SUQ0i1ZJXFdKmVTEoqxtO6xrqpG4jA0eDSvkc9PGnD9vgwKb0bagsDL3arqKSCh0TazXtZsX5vbj/epbI6J8ldElyiw7TZAmVJ5wOmJNJquCaTXJWoI7xYcVZJ6iQMEcghQ9RTZCqyCiSeVE6NDEArFY5gQxZMY4o5QDJfc15oJPieO0MM6LSL8KoAwheTTi02vMieQh90+i1k5Pl4T1xiSdVEyZiMwoc+3u9OmZ0aVCwOoDBOoDmBLhMaDEuB1t6IaNdN8xslqvaduWbuixTmzuTmOg0/dQlTeY6/c9mUgXHtE46bjz+VCM+aR/fDJG0RqlCyh9hm2NdRQte6hzDtMYnGuxRoox7yXA+LR3ogTKLsjsM6WE955TLqDEPUk4tHSlTd0vCs7FmiBTzmSl4D2xQsci19QoJakaJSs0ItrXRuwJu7ajbf4BxPJDv2Y1bNC6fawC66EhcMhj5XFyNKA+HEop+r5nvV5zdbllnie8n2UYWoN3Uym1IJJuwxiD0oYQTxuM6LB041AIu4uiKTlKtIuPsnkVgUTOdrNJ5A4i1FUUrT/4YE5WU4BYZznZXJdlOQ+R27bj8vq6/vvCcZro11vW6w1K1ZlXlJuXU2YcR5IPJB8oIUrVlSRhwDpH9F6E1gps24jPaMjiwmMtq7bHTzNL3VzbrsM6RyiR/SJatdPsdOh7clhYpuWxAiyF/Ztv5LX5I6+/uCeXgFItznYY09ZZEIR8mldICkOsPgaoIBsCzaNBs7RYQu5wNV/Raa6eXdMPA841jFN11Um5GhoLVOt9wodEvxpAiYcolUB1Nk/Iifl4IMdADnKopPjUMUZxdXHB9dUV1lpCEHnI7nDANTKj7bpVZR0aSdrwniXOFV5W5BBIITAvs1TrSuZc47zg/czovXgfagVZV4Qh4ayphOiMX0KdDcssN5ZEiVULlmO1xDsfBdWGSjSLbdvJeRwed0SlFM8uXvDs4lIKh+oMAuDMyZLwJIlZiEtlHVKkWMkFY62wHGOE+myijeQERglHtabBrS9orCEkRQqcZ+ZaiRE1GEr1stVkDPE8UnBZPJdc26HJQmzLcmAWFDoXzA+Y6aYs6HQkxoXkR5nVTQdJYYgLBmm+tAEGsdcCS6hyjWUJlQtQeDiOzCHQdy2oVrR/qhF4WRV0tkIcAsZoGJcg8pniAIdSnlOKxXGamP3Mw+EoY0ytSAbaOrpwzp33rh/UzYAE5IaQatEXidqQla5m4BLmK9Z55YO1cLpK3e9Onq+nblFlQauG9VaaUmTvFNb4qcDIp4Xz5Ps9/V4C20sKydM5e51dVvanZKl+CMsLGz7XYqN6HDsHWsg/fd9jnKXpncxOUxaT/+wEsnWOnE9pGBDDTEryHCqF+KzWostVU4FTiHgpkLNwHv5/7V3ZchzHEczqnmtPyC8KP8j//0+OMEWGbCqCgIA9Z6avaj1UzewCBCAoJIZsozP4QBDc2Tmip+vIymRmMRKXqRxElyTwqS0INQwB0XvkxsBQg2W3BFnJxLebDdbr9dcP7QX8Dj9BoVhb08CaCoYMmqoWDUFjhHlJgOrEzItYikmErmmxXixws90icwQRo66/QwwrcQjOdyKK7IRFBmPRVBWQIxLEVXiz3WC72eD29guCHxGDB9oGtTVaZtFIp65EJSUxIkv6D1PJmZgKV7EqjGmEQKEkH5iJRl/Bdh0sQYShFy0mRZO6rvGPH37A99//Hed+QN8POBxPMg4BwI0rVEb8CFfdApzkZQ1VhanbBlXTgUjKsEZJPtvVCk1doWtqHA57xBBhrEFIwtCqm0acLdpWs2/JGv04IrrHER3liAokPalmgYwG0gG0EqhcjWZoPjL3ogADSy0ISqtX+xRTVyJT1LTYbG60LyKBRN/3Ipyui95p5CfZuUVVSQDh+v4SNLEQp2zOmnkHpXhrNjK1Ha7AOcK5Hvv9COfES9I5j0bZy9hmrDcr3Gw2WC4apMSIIeBwPOJ8PmO332km5xFPO8isHSEmD6aETF6EsVNCZTu57opEIk3HJjIkE7PLFkhiVjsMg9rTiLXOxJLkxOAImHotfTYvmX2Ij30SpxEPYVNcyFnMknHK2Iz8OyfJqKdgMRuJ+KkyqKsW1aKdK+zCLxJmsUEFQgsfRGu1qdRGiYCuNiJ4nLP0qRMQYgbZWloPxCDKMg+cnFokAdHq2BDTV2E3c8K/P37Aeb+BMRHWRHEPzBGUEyqSMr/JmPVPp/xGROYz6qZCDjJMf797gDEWTdvgoW5UfUiC0JQSEkPFzyP2hyOc9xhHD2NaGFMjsVpuZYaPHiknsPpCggiDGxFThPMOy8VSq1cLdF2HxWI5X1fOYvZ7bkYgMXrvkWwNWiaAAhILQWYWx7aq9UlGR18uVbSpZMqkI2SsSjjQagsRchKvUiSVhQQh0WVm+LKhTsLzwtaNHFVKTzZrTlmslDQoT8zYH/aPnpko9IhE4+l4QOKE1Wo9vydk5jvB+yzjS9rLTyHBZzm3rmvRth1CiqjrBuv1CsPQK6cizR60RJrt+SQEuarGdvs3EUKJEZEdmhrIaOB9kvtHVr7bZTiX0LYZKRKGUYJXBsNWQPMt5gR3uz0AA2s7NEqGObat+vBpjTYz+GrWZr6xRIiccDgdcO5P2O8fEGKQ2R8W2a8vt79gHD2cc2InUom0WAhCyrDWYNv3OJ7PuLu7RfAOMToZFE/yIpzEtH1klTFiBJbFLU37DOfdTAvOOctoBuQajC6ImMIcRVkDgAmVOWOyggkp43Q4o2sPOJ169P2I4+myCYYQUFsrL8IgyhopyuwYGYIZLOragcjAO69kIAvD4lN4JsLhsEeIklW4IEajPkQdHUhzr9UQwY0jTqfzo2Z4jD2yqcDJ6ctdSy5ZRx+uNsGJnALUSnYxmpUbEKL0RmBgU4XI4iEXkqjpcGaMfj89aC2n0Kxoo40KcQEISajmOc8NfYlUISXKEMDJaSlbVnWM7nKeOeOX+wecz2cM/aDMPdbRG+m39sOA9XkN54PKe8lCmzbB+91eBNFTgBYxkJKWO4nBNMomyAm20jlTQ5i81FO6sASrKqu6BmMYnJZ+ZAMNlQVlHeBP4hFpTVCWnHnkk8jM+PTpM/b7owwVq58iWdkE5Vx0U+WsIuwihQYteU1O6EaEaOUZs/bkM4mbh740nXfIkNlXC8kCm+oicj94OecYWayaDGBJbI9kTRgxoiYgkQVnErIgR9ze3c9VIGbGhw8/YrtdwpiEuhL1GUtqBowsnLS5gK2fA5A4I6mUm48JPkSMXlLXuqpR64vUWjvLmslnJMPphwEhRNH+NNLLlKpx1mwwIpNmaRc+yUz2W3Qd6roRNi5Z7B5283NnZvzzXz/iZn0HcMY5RLCtYG7uUdVSKTOYSovm0ubRtTG1ki4RXp7HpITzR+JZqTwJWZ9KWJp0Tqf+09z303KoElQmbVDWcv0kJjGpbMUk+rSf//PTpc/JjI8fP6NtCf14xPF4gCHCfudBlKQHm8Z5lCacDJAZx4c9UpAsqG06LBYduq7TwDbCeT/rLgtH0c7OOT4I+chYqZKtVqOIYMQAkMN6tcR6c8R4SjOvQtShIva7M9brAev1CeufLcho/Y8sPn388oQ1+zLoNfNBIspPfn7bUV86nhwEz53dW5uYb63zfnXQmbX15k+/+uV/9F78uchvfuB/Dr7ltb9+ITSz4PL05/FZ/cZz+UPP/xtiLrs9vaB89fcJv3UJz/3flz7/3O167r6+8XskwLrKcP/SdfLcxT/93VO8fL7X12WmzfPRjfpveie8DY+Y2MAlKHjDMrmaBrl6L770PU8/PP3i62O+5XjzMemFp5sfP688iVM/g9+1CRYUFBQUFPyv4bVN8DkFp4KCgoKCgneBVzPBgoKCgoKC/2eUTLCgoKCg4N2ibIIFBQUFBe8WZRMsKCgoKHi3KJtgQUFBQcG7RdkECwoKCgreLcomWFBQUFDwbvErT5Sr1iXnJDAAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training Images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"37f213b3\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4da71e67\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"c312d56e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(3, 1000)\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"majority_class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8895cbe3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- (To be fair, the classes in the test set are perfectly evenly distributed, so the majority class is an arbitrary choice in this case)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"50e6fb2b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.10 (10.00%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print('Accuracy when always predicting the majority class:')\\n\",\n    \"print(f'{baseline_acc:.2f} ({baseline_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d8390846\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8e8aafde\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"ba5d5ca3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.CIFAR10(root=self.data_path,\\n\",\n    \"                         download=True)\\n\",\n    \"\\n\",\n    \"        self.train_transform = torchvision.transforms.Compose([\\n\",\n    \"            # torchvision.transforms.Resize((70, 70)),\\n\",\n    \"            # torchvision.transforms.RandomCrop((64, 64)),\\n\",\n    \"            torchvision.transforms.ToTensor(),\\n\",\n    \"            # torchvision.transforms.Normalize(\\n\",\n    \"            #    (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"        ])\\n\",\n    \"\\n\",\n    \"        self.test_transform = torchvision.transforms.Compose([\\n\",\n    \"            # torchvision.transforms.Resize((70, 70)),        \\n\",\n    \"            # torchvision.transforms.CenterCrop((64, 64)),            \\n\",\n    \"            torchvision.transforms.ToTensor(),                \\n\",\n    \"            # torchvision.transforms.Normalize(\\n\",\n    \"            #    (0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"        ])\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        train = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                 train=True, \\n\",\n    \"                                 transform=self.train_transform,\\n\",\n    \"                                 download=False)\\n\",\n    \"\\n\",\n    \"        self.test = datasets.CIFAR10(root=self.data_path, \\n\",\n    \"                                     train=False, \\n\",\n    \"                                     transform=self.test_transform,\\n\",\n    \"                                     download=False)\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[45000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(dataset=self.train, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=True,\\n\",\n    \"                                  shuffle=True,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(dataset=self.valid, \\n\",\n    \"                                  batch_size=BATCH_SIZE, \\n\",\n    \"                                  drop_last=False,\\n\",\n    \"                                  shuffle=False,\\n\",\n    \"                                  num_workers=NUM_WORKERS)\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(dataset=self.test, \\n\",\n    \"                                 batch_size=BATCH_SIZE, \\n\",\n    \"                                 drop_last=False,\\n\",\n    \"                                 shuffle=False,\\n\",\n    \"                                 num_workers=NUM_WORKERS)\\n\",\n    \"        return test_loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4264e656\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"32764653\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"727e5eda\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e614d4ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. Here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"2b350171\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = PyTorchVGG19(num_classes=10)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(\\n\",\n    \"    pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode='max', monitor=\\\"valid_acc\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"19f6e3de\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"67242eaf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchVGG19 | 38.9 M\\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"38.9 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"38.9 M    Total params\\n\",\n      \"155.792   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e6a8b3606bba4c148eb8bdbdeb1ac739\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 15.25 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3dc36ea5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"893e5f30\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"33c60876\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 18,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='ACC')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e69f5f88\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"5c947f0d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_65/checkpoints/epoch=24-step=4374.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_65/checkpoints/epoch=24-step=4374.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cea53c9887ec47998a1237f5317d6f29\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.7494000196456909     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.7494000196456909    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.7494000196456909}]\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c33f957c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0fa1fbf4\",\n   \"metadata\": {},\n   \"source\": [\n    \"- You can use the `trainer.predict` method on a new `DataLoader` or `DataModule` to apply the model to new data.\\n\",\n    \"- Alternatively, you can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"ede892a2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_65/checkpoints/epoch=24-step=4374.ckpt\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"094b80cf\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(\\n\",\n    \"    path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"13f38a88\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that our PyTorch model, which is passed to the Lightning model requires input arguments. However, this is automatically being taken care of since we used `self.save_hyperparameters()` in our PyTorch model's `__init__` method.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"a19c8b68\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([3, 8, 8, 8, 6])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"\\n\",\n    \"all_true_labels = []\\n\",\n    \"all_predicted_labels = []\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, labels = batch\\n\",\n    \"    \\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    all_predicted_labels.append(predicted_labels)\\n\",\n    \"    all_true_labels.append(labels)\\n\",\n    \"    \\n\",\n    \"all_predicted_labels = torch.cat(all_predicted_labels)\\n\",\n    \"all_true_labels = torch.cat(all_true_labels)\\n\",\n    \"all_predicted_labels[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"29e5cc35\",\n   \"metadata\": {},\n   \"source\": [\n    \"Just as an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"7bc70633\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.7494 (74.94%)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"test_acc = torch.mean((all_predicted_labels == all_true_labels).float())\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"14089769\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5c49c01c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"83e96473\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Append the folder that contains the \\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"sys.path.append('../pytorch_ipynb')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"faa12b44\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from helper_data import UnNormalize\\n\",\n    \"from helper_plotting import show_examples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"b9d8d609\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"20:1: E122 continuation line missing indentation or outdented\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\\n\",\n    \"\\n\",\n    \"# We normalized each channel during training; here \\n\",\n    \"# we are reverting the normalization so that we \\n\",\n    \"# can plot them as images\\n\",\n    \"# unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    data_loader=test_dataloader,\\n\",\n    \"#    unnormalizer=unnormalizer,\\n\",\n    \"    class_dict=class_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"d0155a81\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"    with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"8c55e82e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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hEfEUkxOS2NF9EMpvW2SAi40Rkk8n5KVBE5onISVMBx2jt903uTodFpJ9ZWG4i8ptJd66IpDHTfbpgnEgHkxvUfhGZLCKWZwML2bplM9lz5LCo7vul0Dt8O2MDJxZ/zpnlg7n74DFrdxg+nn1aVmLnzA+Z9HlrMqVP/dS6TauX4sCJi4RZUfvq0qWL5MmTN+Zz7tx5uHjRvgTxrDh39iz79+8j0OQoZCuOdFGKD0e7PpnjqGMAcO6cyakrMIjTp06yfesWqgeXp36tauzZvctiHY9cuen9dj+CShbBzzs/6TNkpEr1WnbHB85voSXl3ASAUmobsAj4yFS7/zQwHfhYKVUKOERsF6cwpVRlYBKwEHgLKAF0EZEsIuIPdAWCMMp39xSRMqZ1vYApJt27wJsJBS8iPkBroKJSyhfDb+CpazZz16froaEWHRhz5s6ZRYtWlrXOMqVPTYPKJfBpPJxC9QaTNlVK2tTz56e/t1Ks6QiC2o/lyvW7jOoXuy69T6EcDH+7AX2//Muq2OIr1miLX+Kz5v79+7Rt1ZwxY7+121nKUS5KzxpHHoP79+/TsW1LRo4xnLoiIiK4fesWazdtY9iXo+nSoY3FhT1v377FqmVL2L7/OHv+Ocujhw/4e/ZMu+KLxtkJLSnnpngRkYxAJqXURtOk3wBzy+5Fpr+HgCNKqctKqf8B/5q2UwmYr5R6oJS6j+HoFH1hfl4ptdX0PiF3qGhqAP7ALpPzUw2gUNyFzF2fsmZ7yg40USIiIli0cD7NW7SyaPnqZT1ju0mtP0S5UgW4dvM+UVEKpRTTFoQQUDxfzDq5s2dk9ldd6fHFTM5cvGFVfLlz5+GCWWvk4sUL5MqVyyqNZ014eDhtWzWnddv2NGnazCGajnBRSghnuD458hiEh4fTsW0LWrVuRyOTU1eu3Llp2KQpIoJ/YFlcXFy4cf26RXpbNqwjb/4CZMmajRQpUlCvYRP27LTNECguTktoiTg3mafxuE5OlhLt0BRl9j76sxuJPx0d92cksZ8VAX4zc33yUkoNtjbYxFi/bg2ent7kzmOZe875K7coWzL/EzepwKIcP3OVnFnSxyzTuGpJjp6+AkDGdKmYN64ng75fxvaDZ62OLyAwkFOnTnL2zBnCwsL4a/YsXm9gvyuRs1BK0adnd7y8fXj3vfcdpusIF6WEcLTrkyOPgVKKvn164OXlQ18zp67XGzZm04b1AJw6eYLwsDCyZLXsPlquPHnZt3sHjx4+RCnFlo3rKeIgZzJnttAScm66KiI+JgMS89t694D0ACYTklvRDuZARwzzYEvZBDQRkTQikta0nc2meflEJNrwsi1Pu0OZsxZoISLZAUTkNRHJn8jyCdK1YztqVK3IyRPH8Sqcj99++RmAuXNm07K15e45u478x/y1B9g+4312z/oIFxfh5/nbGfFOQ3b9+RE7Z35I5YAi9P9mAQB9WlWicN4sfNKjVsyQjmyZLS/u6+bmxrjxE2n4eh18S/rQvGUrihUvnvSKSdCpQ1uqBpfnxPHjFC6Qh1+n/Wy3JsC2rVuZ+cfvbFy/jiB/X4L8fVmxfFnSKyZB+zYt8C9dnBbNGtnlotS5YzuqVanAyRPHKVooL7/98jMffPQJ69asoVQxT9atWcMHH31iV6yOPAYh27Yya+YMNm1cT6UgPyoF+bFqxTI6du7G2TP/Us6/FF07tePHqb9Y3BXhF1CW+o2aUbdqEDUr+BEVFUX7zj1YvmQhAcULsXdXCJ1bN6F989etjtdpJimJODdlBUZjuC4dBtIppbqISEUMr8z/YVjMpcfoI0uDcSnZVSl1K45DU1XT+wambZrPex/oZgpnqlLqWxEpACzDSHgVgJNAR1PSNV/3LBCglLouIq2BTzGSfzjwVhyD41j4+QeoTdt22nHk4idbpQ8drgnOKh/0cqHLB0FYRJTDNcF55YPyZHZP3q5PLwo6ob186ISWfBLaizQOTaPRaOxCJzSNRpNs0AlNo9EkG3RC02g0yQad0DQaTbLhxX9+4yUjIlJx836Yw3WddTey7sStSS9kJXO6l3W4JsDjcOfciUvp6pxHuZx18zR9Ksd/bZ1w4xSATGlSOEc4AXQLTaPRJBt0QtNoNMkGndA0Gk2yQSc0jUaTbNAJTaPRJBt0QtNoNMkGndCeEdMmT6RWJX9qVvTj50nfATBu9HDKlihEvapB1KsaxLrV9pmG2Ov2M6ubP9M6+DK1fWkmty0NQJdyefmrRwBT25dmavvSBBUwyub458vI5LalmdbBl8ltS1MmT0aLtnHn9m26d2xNRf8SVAooya4dIdy6eZOWjetRzrcYLRvX47aVrkfTJk+kVkU/alYoE3Nsv/5yMHWCA6hXpSwdmr/O1cuXrNKMjrVHpzZUCixJcNlS7N4ZwpFDB2lQqzLVKvjRqXVT7t29a5XmqZPHqRUcGPPyypeVn36cwNhRw/AvVjBm+tpVy5MWM+PC+fO8XqcGAb7FKetXkh9M7kzDhgyifKAvFYP8aNygDpcvWXcc4nMrO3hgP9UqV6BCWT8qVyjL7l3WF2N4o1c3CuTJQaCZ+9XQwZ8T5F+a8oFlaFTf+ljhFau2YSoftEQpVSLO9KnAN0mZpIhIF4yyQn0TWqaUr79asjb22K7j/xyhb89OLFq1mRQpU9KpVSNGjJnAgrmzSJM2Lb37vpeA2hOyZ0y6Fubly5e5cvkyZfz8uHfvHhWC/JkzdwE+xRL2njEfhzarmz+9Zx7gjlmFhC7l8vIoLJLZe2OfXEWypeXWw3BuPAijYJY0fNW0GC2n7gYSH4f2du9uBFWoRIfO3QgLC+PRw4eMHzuKTJlf4533+zPhm6+4c/sWnw8d+dS68Y1DO/7PEfr26Mii1VuMY9uyISO+/o6s2bKT3lR2+pfJ33PyxD98OXZivDElNA7tnT7dCapQkfadnsTauml9Bg0bRYVKlfnz91/579xZPh44ON71kxqHFhkZiX+xgixZvZnZM6eTNm1a+ryddEHG+MahXbl8mStXLuNbxvjfV64QyJ9z5pErd56Y8ts/fv8dx48d5dvvfnxq/YRKmW3ZvIl06dLRq3sXdu49CEDj1+vw1jv9qF2nHitXLOPbsV+zfPW6eNdPqEZatG7Pbp3Zte8QAHfv3o2J9YeJEzj2z1EmfD8p3vXTubvoahsJkZDjk6MMUU6dOEYZ/7KkNrkGBVUIZuVS+52T4uIMx6OEOBX6gBsPjAHEZ248JKWrCymSGKB67+5dtm/bQvtOXQFImTIlGTNlYsXSxbRu1xGA1u06snzJosRkYsdx4hhlAsyObUXj2KY3q6H/8OEDJNEixvHHGrJtM+06xo719KkTlK9o1B2tXK0GSxfb5v4EsGXjOvIXKESefDbVDI1FQu5MGeIeByv9IOJzKxORmJbp3Tt3Ygxe7NW1N1Z4NRPaU65P5o5PInJfRIaKyA6gvIh0FZETIrIRqGjLBj19irNz+xZu3bzBo4cPWb9mBZcuXQBg+s+TqFM5kA/f6c2d2/YZzJpji9uPUjCmWXEmty1NgxI5YqY39fXg5/a+9K9VhHTuT+f4KkWycCr0AeGRiTdJzp39lyxZsvLuGz2oUSmQ9/r25sGDB4SGXiNHTuNLkSOnB9evW2404+kd59iuXsmli8ax/Wr4IMqVLMyCubN4/9NBFmsasZ4hS9Zs9HuzJ7WCy/LB2314+OAB3j7FWblsMQCLF/wdsy1bWDjvL5o0f+Il8ctPk6hZ0Z/3+/bith3ngrk7E8DQLwbiUyQ/c2bNZMDnQ2zWjWbU1+MY+OnHeBfOz4BP+zN42Jd2a0YzeNAAvArnY/afMxn4xVCr138VE1pSrk9pgcNKqSDgNDAEI5HVIgHfUHPXp5s3nv4yFvX0ps87H9C+eQM6tWpEseKlcHN1o0PXnmzafZTlG3aQPUdOhg2yr/RyNLa6/fSdc4heMw/w8YKjNCntQancGVh48ArtftlDjz/2c+NBGG9WLhhrnQKvpaZXpfyMXXs6Sf2IiEgOHdhH5+69WbtlF2nSpOW7b76yev/MKeoVfWxfp1OrhhQrURI3kyNT/4FDCTl0miYt2vDb1KcvsxKNNTLCFGsvVm/eSeo0afhu3Bi+mTiZX6ZOonaVcjy4f5+UKVLaFHdYWBirli+hQZPmAHTq1ott+/5h1eZdZM+Rk6EDrXMijybanWmUyZ0JYNCQ4fxz6hyt2rRj8qTvbdI15+cpkxg1ZizHTp9j1FdjeatPT7s1oxk8dATHT/9H67btmPxj/F0EifEqJrSkXJ8igb9N74OADUqpUKVUGDA7PkFz16fXssTv+tSmQxeWrd/OX0vWkClzZgoULkK27DlwdXXFxcWFth27cWDvbrt3zh63n+hLyNuPwtly+gY+OdJx62E4Ucpwkll6+Co+OZ74EWRLl5JhDX0YufIkl+48TlI/V+7c5MqdB/9Ao4+tYZNmHDqwn2zZsnP1iuF6dPXKZbJmtc45q02HrixbH8JfS9aSKVNmChQqEmt+4xatWb54gVWauXLlxiNXHvwCjFgbNG7GoYP7KOrpzez5y1i1MYQmLVqRv+BTJmAWsX7NCkqW9iVbdqMlbH4utO/cjf17LPe5jCY8PJwOcdyZzGnZqi2LFsyzKV5zZs6YHqPftHlL9ux2fIXmVq3bsXC+9bG+igktKdenx0qpyETm28R1k3nsxQv/sWLJQho3axXzJQZYuXQhXt72Gcfb4/aTys2F1CZH9VRuLgTky8SZGw95zezh4kqFs3DmxkMA0rm7MrJxMX7aeo7Dl+9ZtI3sOXKSK3ceTp08DsDmDevw9PahTv2GzJ75OwCzZ/5O3dcbWhX7U8e2eSvOnD4VM3/18qVWO3Jnz5GTXHmexLpl43o8vXxithUVFcW3Y0bRqattrZMFc+fQpPkTcxzzc2H5koV4+VhnRKOU4q143JlOnToZ837Z0sV4etrnTA6Q0yMXWzYZnkUb16+jsIMs/U6dfBLr0iWL8LTBCepVrLaRT0TKK6W288T1KaFv0A5gvMkg+S7QEjhgy0b7dG3LrZs3SZEiBUO/+paMmTLzxRvdOHr4ICJCnrz5+XLsd7ZIxxDt9lOiREmC/H0BGDL8S+rWq5/kupnTpGBYQx8AXF2EtcdC2XnuNp/VKUqRbGlRCq7c/R9j1xqJomlpD3JnSkWnoDx0CjIs+D6cd5Tbj8IT3c6XY8bxZo/OhIWFkb9AQcb/MJWoqCh6dmnHzOm/kjtvXqb+9qdV+92nS5unjm3/d9/g31MncHFxIXfefHz5tfXHdsTocbzVswvhYWHkK1CQb3/4ib/+nMGvU407b/UbNqFNh85W6z56+JBNG9YyetyTy7/hX3zG0UMHjHMhX/5Y8ywh2p2peImSVAwybg4MGjKc33+dxsmTxnHImy8f306w7tK7a8d2bN68kRvXr+NVOB+fDfyC736YzMcfvkdERASpUqVK8E5kYnTp2I7NmzZw4/p1PAvlZcDng1m5YjknTxzHxcWFfPnyM36idbHCqzls4ynXJ9O0aMen+0qpdGbrdMVwfboM7AdcrR224QgsGbZhC7p8kC4fBAkP27Bf1znCCQ3beKVaaEqps8TfsV/VbJlYppVKqV+AX5wamEajcQivYh+aRqNJpuiEptFokg06oWk0mmSDTmgajSbZ8ErdFHgWuLoKmdLaNno8MZx1N3p2N8ffkew5a7/DNQGmtPZ1iu6V20kPCraFrOkdfx4ARDjh9mlSz+HaSqSzbvUmgG6haTSaZINOaBqNJtmgE5pGo0k26ISm0WiSDTqhaTSaZINOaBqNJtmgE9oz4sL58zSoU4NA3+IE+ZXkR5OJRTQTxo0lY2pXbly/btd2bt++TbvWLfEt4UOZksXYEbLdJp1oM5NKASUIDizJ7p0h9OrSjhqVAqhRKYCAkkWpUempZ4PjZUqbUoxvXpxxzYoztonxKG06d1eG1PPkx1YlGVLPk7QpjdJFbi7CO5ULML55cb5tVpwSHultjnfR/LlUDiqNRyZ39u/dk6TGFx++STW/QjSv9aTKb/+3utCqXkVa1atIvYolaFXPKFp8+9YNerR+nfI+Hoz8/AOLYozmpx8mUL18GWpU8OOtHh15/PjJsJFJ340jz2upuHnDuvPg8ePH1KxcjuAgP8oHlGLk8MEAHDqwn1pVK1C5nD/VKwVZXbssPpOUL4cNwbNQXiqU9aNCWT9WrlhmlWZC34WRw4fgXSgvlYL8qBTkxyordUGPQ7MIEakKhCmlttmq4ebmxvBRY2JMLKpUCKRajZp4+xTjwvnzrF+3mrx589kd60fv96NWnTrMnP0XYWFhPHz40CadgZ+8T/Wadfj599kxBiFTfp0ZM/+LAf2tqoY7cMlx7v3viflK89IeHLx0l78PXKF56Zw09/Vg+s4L1PY2iju++/cRMqZyY1BdTz5ccDTJonTxxZshY0amzZjDR/3esijGRi3b06ZzLwa+3ztm2lff/xrzfuywz0hn2md391S89eFATh0/yqnjiXrrxOLypYtMm/I967bvJ3Xq1PTp2p5F8+bQql0nLl04z+YNa8mdJ6/FetG4u7uzYNka0qVLR3h4OPVqVqZm7bqMHDaY/p9+Tq069Vi9YhmDB37C4hXxG5rER/uOnen9xlv06t4l1vS33u7Hu+9Zl8ijSei7APDm2/14x0Zd0C00S6mKUW7IZhIysQD4tP/7DB0x2u5SK3fv3mXLlk106dodMIw9MmXKZLXOvbt3Cdm6hXZxzEyiUUqxeP5cmrZonYBC0gTlz8S6EzcAWHfiBuXyG/p5M6XiwCWjYOSdxxE8CIukSLa0NsXr6eVDESsKO/oHVSRDpszxzlNKsWrpfOo2agFA6jRpKRNYnpTu1pd1ioiI4PHjR0RERPDo0cMYP4XBA/ozYMiXNp0HIkK6dEahmPDwcCLCIxARw9DknnE87969S86cuazSjc/MxF4S+y7Yyyud0ESkk8ks5YCI/C4iDUVkh4jsE5E1IpLDVEOtD/CeiOwXkWB7t2tuYrFsySJy5cpNyVKl7d6fM//+S9as2ejdoxvlAv14o3cPHjx4YH18Z/8lS9asvPtmD2pWCuR9k5lJNCHbtpA1W3YKFba8UumQ+p6MbVIspgWWMXUKbpmKQd56FE7G1EZl3DM3HxGUPxMuAtnTp6Rw1jRkTeLJi6TidQR7d24jS9bs5C9YJOmFE8EjV256932PoFJF8fMpQPoMGahSvRarli8hp0cuipUolbRIAkRGRlK5nD9eBTyoWr0GAYFBfPnVN3wx4GNKeBZg0Gf9GTR0hF3xRzPlx+8pF+DLG726c8tKH1Vz4hq6/DTpeyoE+vJWb9t0X9mEJiLFgQFAdaVUaeBdjOq15ZRSZYBZQH9TDbVJwDillK9SanM8WjEmKTdCE3csijaxGDnmG9zc3Ph69Eg+G2S/Ew8Yxh779+2lR+8+hOzaS9q0afn6q1HW65jMTLp0782aLbtIkzYtE8c9MTOZP3e2Va2zTxb9w/vzjzJ0xQnqF8tOsZzpElx2zfFQbjwIY2zT4vQol49jV+8TmcRjX0nF6whWLJob0zqzh9u3b7Fq+WK27zvGnqNnePTwIXNnzWDC2NF8+Jl1zlRxcXV1ZVPIHg6fOMfePbs4euQwv0ydzIjRYzl84izDR4/lnTfsNzTp0asPB/85ybade8mZ04PPPv7QJh3z70KGDBno3rMP+4+eZMuOveTI6cHAT6zXfWUTGlAdmKuUug6glLoJ5AFWisgh4CPAosLu5iYpWbIlbPARHh5ORzMTizP/nubcuTNUKluGkl6FuHjxApXLB3D1yhWbdih37jzkzpOHsibruqbNWrB//z6rdXLlzo1H7tgGIQcP7AeMy6VlixfQuFlLi/VuPjRaYnceRxBy9hae2dJx51E4mU2tssypU3DH1FqLUvBzyHnem3eEL1efIp27G5eTMGBJLF5HEBERwdoVi6jT0DrTmfjYsmEdefMVIEvWbKRIkYJ6DRoze+Z0zv93ltrBgZQr7cnlSxepW7Uc167adh5kzJSJisFVWLt6JX/+MZ2GjZsC0KRZC/bYYL4Sl+w5nhi6dOnWgz27bTN06RjH0MVct7ONuq9yQhOeNkD5DpiolCoJ9AYcVvdaKUXfOCYWxUuU5PR/Vzh0/F8OHf+X3LnzsGn7bnLkzGnTNnLmzEmePHk5cdww9li/bi0+Pj5W62TPkZPc5mYmG9fh6WXobNqwliKeXuTKncciLXc3F1KncIl5XyZPRs7desjOc7ep7pkFgOqeWdhx7jYAKV1dcHczli+dOwORUYrzSTw8nli8jmDHlvUULOxJDo/cdmvlypOXfbt38ujhQ5RSbNm0nnoNmnDgxHlCDpwg5MAJPHLlZsWGELLnsPw8uB4ayp3btwF49OgRG9evxdPLi5weudi62TA02bRhHYWt6CZIiCuXnxi6LF60gGLFrTd0iftdiKu7ZOECfIpZpwuv9l3OtcB8ERmnlLohIq8BGYHo3klz94t7gOW39OLB3MSikpmJRe26SRuYWMPYcRPo2rkD4WFhFChYiMlTp9mkM+Irw8wkPNwwM/n2+6kALPh7Dk2bW365mSl1Cj6tZfQ7uboIm07dYN+Fu5wKfcBHNYpQ0ysboffD+MpkvpIptRuD63kSpeDmwzDGbfjX5niXLV7AgP7vceN6KB1aNaZEydLMmr80QY1P3u7K7u1buH3rBrWDvHnjvc9o2qYTKxb/He/lZr2KJXhw7y7h4eGsX7WUH39fQGHPxJ2K/ALKUr9RU+pWK4ebqxvFS5WmfefuFu1jYly9cpk3e3UjMjKSqKgomjRvQZ16DciYMROffvQ+ERERuKdyZ5yVxiPxmaRs2bSRgwcNQ5d8+fMzYaJ1JikJfRfmzpnFITPdb7+z3nzllTJJiYuIdMa4tIwE9gHzgXEYSS0ECFRKVRURT2AuEAW8HV8/WjRl/APUxq2O9yl0VnmXu48ikl7ISnrN3u9wTdDlg6JJ4+74doizzi9nVQ/KmNpVm6TERSn1G/BbnMkL41nuBGD77SeNRvNMeJX70DQaTTJDJzSNRpNs0AlNo9EkG3RC02g0yQad0DQaTbLhlb7L6RQURDnhXrW4Oee3x8UJd+t/be/neFHA5935TtE9+m0Tp+g+Do9yiq4zhlq5yMtzfiVGgglNRO7xZCR9dFjK9F4ppewaaKrRaDSOJsGEppSyrLKeRqPRvCBY1M4UkUoi0tX0PquIFHRuWBqNRmM9SSY0EfkC+Bj41DQpJTDDmUFpNBqNLVjSQmsKNAIeACilLgH6clSj0bxwWJLQwpRxW0UBiEji9ZA1Go3mOWFJQpsjIpOBTCLSE1gD/OTcsJIfjx8/pkblclSK48oDMOXHiQT6FqN8QCkGDfjYZv1K5ctS1q80fqWLM2zIF3bF61+iKFXKlaFaxQBqVSkHwFdfDqWUVwGqVQygWsUA1qxcbnWM8TkTHT54gNrVKlIx0Je2LRpz9+7dJLUypE7B1D7l2Dy0NpuG1sa/0GsUy5ORJZ9UY/0XtZjetwLpUhn3vCr7ZGflwBqs/6IWKwfWoKJ3wkU44xIZGUnVCgG0bdEYgC8GfExQmRIEB5WhY5sWMTXILOXUyePUCg6MeXnly8pPPxquR9OmfE9wYAmqlfdl+KBPk1ByfqxxOXH8OOUCy8S8cmbNyMQJ39qlCfDd+HEE+JYgoExJOndsF8sFy1osKh8kIrWA2qaPq5RSq23e4nNERAYD95VSXztrG2X8AtT6LTuemq6U4sGDB7FceUaOGcfjR48Y+9VIZs9bjLu7O6HXrpEte/an1k9lsnlLiLj61atU4utvxhNUrlyi690zVYqNi3+JoqzauJ0sWbLGTPvqy6GkTZeOt955P1FNN9f4fycTOgaffNCPoV+OpmJwFWb89gvnzp1hwKChT61vPg5tQtcAQk5eZ+aWs6RwFVKndGPO+8EM+esg209cp23FAuTNmpavFh6hRN5MhN59zNU7j/HOlYE/+wVTpv+TumiJjUP74btx7N+7l3v37vLn3IWsX7ua4CrVcHNzY/DnRtIZPGxkvOsmNQ4tMjIS/2IFWbJ6M+fOnWHC2FFMn70Qd3d3rodeI2u2p88DgFQp4j++9sSaKkXi51d8sRcpmIeNm0PIlz+/Veuac+niRWpWC2bPgSOkTp2aju1aU7tuPTp26pLoemndXeItH2TpaLpDwGZgk+n9K4uI2DQYOa4rT7jJlWfa1Mn0+6A/7u7uAPEmM1v0I8LD7XaRcjQJOROdPHmcCpUqA1C1Rk0WL0x8AG26VG6U88zGzC1nDa1Ixd1H4RTOkZ7tJww/y41Hr9LAz6gwe/j8ba6ayngfu3QX9xQupLRgoPLFixdYtWI5HTp3i5lWrUYt3NyMUyAgMIjLFy9YcQRis2XjOvIXKESefPmZPm0Kb/X7KOY8SCiZPa9Y47J+3VoKFSpsVzKLJiIygkePDBeshw8f4uFhnTOVOZbc5ewB7ASaAS2AEBHplvhaLw4iMkBEjovIGsDLNK2wiKwQkT0isllEvE3Ts4nI3yKyy/SqaJo+WESmiMgqYLqtsURGRhJczh9PM1eeUydPsn3bFmpWKc/rdaqx146a75GRkQT5+5IvV3aq16xF2aCgpFdKABGhVZP61KwcxPRfpsZMnzblR6qU9+PdN3ty2wZXnviciXyKFWf50sUALJw3l0sXzieqkT9bWm7c+x/juwaw+vMajO3kT5qUrhy7eJc6pQ1LuIYBecj1Wuqn1m3gl5vD/90mLCLpUfwD+n/A4OEjcXGJ/2sy8/dfqVG7bpI6CbFw3l80ad4KgH9PnWTn9q00qFmJ5q/XZP/e3VZpOTvWuMz9axYtW7WxWydX7ty82+8DvIvkp3D+XGTImJGatWonvWICWNJC+wgoo5TqopTqDPhjDON44RERf6ANUAYjIQeaZk3BqDzrD3wI/GCaPh7D3SkQaA5MNZPzBxorpdrFs50Y16fr1xN2fXJ1dWVzyB6OmLnyREREcPv2bVZv2MbQEaPp2rGtzY+2uLq6smPPfk6dvcDuXTs5cviwTToAS1ZtYO3mnfz592Km/fQj27dupkuP3uw8cIz1Ww3fgy8G9LcpxrjORN/9OJWpk3+gWsWy3L9/jxQpE6/06ubiQsl8mfh1w7/UGraWh/+LoG89b977bTddqxVh5cAapEvl9lTS8sqVgYHNS/LRjL1Jxrly+VKyZsuGbxn/eOeP/Wokrq5utGz91OlgEWFhYaxavoQGTZoDEBkRwZ3bt1i8ejMDh46kT9d2Fp8Hzo41vtiXLVlM0+aWG+UkxK1bt1iyZBFHjv/LqbMXefjgAX/OtH1UmCUJ7QJGTf1o7gGJ/4S+OAQD85VSD5VSd4FFGMYnFYC/RGQ/MBnwMC1fE5homr4IyCAi0UNUFimlHsW3EXPXp6xZk+5wzpgpE5VMrjy5c+emYaMmiAj+AWVxcXHhxvXrduwyZMqUicpVqrJq1QqbNXKamv3ZsmWnfoPG7N2zi+zZn7jydOjcnX12tCbNnYk8vbyZt3gF67fupHnLNhQsWCjRdS/desjlW4/Yd+YmAEv2XqRUvkycunKPNt9ups7wtczfeZ5zoU+8OT0yp2bam+V5e9quWNMTYkfINlYsW4JvsSL07NKezRvX07t7JwD+/GM6q1YsZfK06TZf1q9fs4KSpX3Jlj2HEV/u3NRraJwHZfwDcXFx4eYNy84DZ8cal1UrllPa148cOXLYrbV+3RoKFChAtmyGC1ajJk3ZsX2bzXoJJjQReV9E3seor7/DdNn1BUat/VM2b/HZE/dnzgW4bfLYjH75mM0rbzY9t1IqOpnb5Vwb15Vnw/q1FPXyon7DxmzauB6AUydPEBYWRpasWRNRip/Q0FBum+mvW7sGL6/EDTsS4sGDB9w3uW0/ePCADevW4ONTnKtXnrjyLFu8EG8f61x5EnImCr12DYCoqCjGjv6SLt17J6oTevd/XLz1iMI5jP64YO/snLh8l6zpjf4nEXjvdR+mbzQMVjKkTsGMtyvy5bzD7Dp9w6JYBw0ZweETZ9l/9BQ//foHwVWqMfnn6axdvZIJ33zNH7PnkyZNGqv235wFc+fQxMxspk79RmzdtAGA06dOEBYWzmtZLDsPnB1rXP6aM4uWre2/3ATImzcfu3bs4KHJBWvD+nV4edvu2JVYB3d0y+S06RXNUzX3X2A2Ab+KyCiMfW2I0SI7IyItlVJ/ifGzVUopdQBYBfQFxgCIiK9Sar8jArkSx5WnafMW1K3XgLCwMPr26UH5gNKkTJmSH6dMs+mX9Mrly/Ts1tnQV1E0b9GK+q83sCnW0GtX6dLeuJyIjIigWcs2VK9Vhzd7duHIoQMgQr58+fl6/A9JKMUmIWeiSd9P4OcphhtRg0ZNaJ/EHS6AAX/u44ceZUnh5sK50Af0+3U3Lcvnp2u1wgAs23uRP7eeBaBb9cIUzJ6O9xr48F4D48vSZtxmrt/7n1XxA3z8wbv873//o3kjoz8qIDCIsROsOw6PHj5k04a1jB73fcy0Nh268EHfXlQvX4YUKVPy7Y9T7W5ROSLWuDx8+JB1a1cz4XvrHZniI7BsEE2aNadikD+ubm6U9i1Dtx69bNZL9q5PIjIA6AScw7h8Pgr8DfyIcamZApillBoqIlmB7wEfjAS4SSnVx5rhHgkN27CXpIZt2EpCwzbsIaFhG/aiywcZJDRswz5N55xfziKhYRtJDkEQkWxAfwwX8RjjXaVUdYdG6CSUUiOAEfHMeuqWj8lF/SnTSaXUYMdHptFoHI0lqf4P4BhQEBgCnAXs95PXaDQaB2NJQsuilPoZCFdKbVRKdQMSH36u0Wg0zwFLRr1Hd7JcFpHXgUtAHueFpNFoNLZhSUIbLiIZgQ+A74AMwHtOjUqj0WhsIMmEppRaYnp7B6jm3HA0Go3GdhIzSfmOpwelxqCUescpEb3kiICb64v1UHhipHDCEIvr98Mcrgnwz/imTtH16eec4SDHnBRvRGTyHmplD4m10Kx7Olaj0WieM4m5Pv32LAPRaDQae9HO6RqNJtmgE5pGo0k26ISm0WiSDZZUrPUUkbUictj0uZSIDHR+aBqNRmMdlrTQfsIwGQ4HUEodxKgCq7GCN3p1p2DenJT1KxUz7cthQ/AslJcKZf2oUNaPlSuW2azvSNenhByqRo0YQrEi+Qgu509wOX9WWRDvJ+/2JqhYfupXflIYYdSQz6hT0ZcGVcvyZpfW3L1zO2bepPFjqBFUgtoVSrN5veVePJGRkVQpH0Cb5o0AuHXzJk0b1CGglDdNG9SxuFx4htQpmNrb5CY1xHCTKp4nI0s/qcaaz2uy8rPqlCmQGYAUrsK3nQNY/0Ut1n5ekwqeSRf3fPz4MTWCTcfWvxQjhw0GYMG8uZT3L8VraVOwb49tAwz8ihehcpAvVSv4U7OyUX594fy5VAosTfYMKa0u6x0XZ7k+ff/deALKlCTAt4TdepYktDRKqZ1xpkXYtdUXBFPRyg+fxbbad+zM/EVPJ4C33u7Htp172bZzL3Xq1rdZ393dnRWr17Fz7wF27N7PqpUr2BESYrPWwmVr2LJjL5u272Ht6pXs2mlovdH3XTaH7GFzyB5qWxBvszYdmTZrQaxpFatUZ+nG3SzZsJMChYsyaYJRlenk8X9YumAuyzbt4ec/F/LFx/2IjIy0KOZJ30/A06yg5bdjR1OlanV2HzxGlarV+XbsaIt0hrcuzbojVwgetIoaQ1dz8vI9Pm9RirFL/qHmsDV8tegonzc3fpQ6BBuVdasNWU3rbzfzRctSJFXCzN3dnYXLTcc25Mmx9SlWnOl//kWFSsEWxZkQ85euYcO2PazZZJSw8vEpzq9/zKF8Rft0ATy9vAjZtY+QXfvYGrKb1GnS0KixfWPtjhw5zC/TprJp6w5Cdu9n+bKlnDp50mY9SxLadREpzBOj4RbA5cRX0cSlUnBlMmd+zWn6jnR9SsihyhbKlq9Exkyx9zu4as0YNyJf/0CuXLoIwNoVS3i9SQvc3d3Jm78A+QsW5qAFrYqLFy+wesUyOnZ54t2zfOli2rQ3ylC3ad+JZUsWJamTkJuUUor0Jo/P9KlTcOW2UYnd0yM9m48Z1Xav3/sfdx+G45s/c6LbiPfYInh5+1DU0yvJGK3F09uHIk7QdZTr0/Fj/1A2KIg0adLg5uZGcOXKLErC9SsxLElob2FUefUWkYtAP+ANm7f4nEnABcpXREJE5KCIzBeRzKbpgaZp20VkTHQ/oiOZ8uP3lAvw5Y1e3bllg4uSOY50fYrPoQrgp8k/ULFsGfr26WGT61Nc5s6cTpUahsvP1SuX8Mj9pO5BTo9cXLlyKUmNz/q/z+ARo2I5Hl27dpWcHh4mHQ9CQ68lqZM/q8lNqksAqwfWYGxHw01q0OwDfN6iFHtG1eeLFqX4cr5xGhy5cIe6vrlwdRHyZUlDqfyZyPVa0qWuIyMjCQ7yxzO/B1Vr1CCgrO3/J3NEhJZN6lEjuCzTpznXC9xRrk/FipVg6+bN3Lhxg4cPH7JyxXIuJuH6lRhJJjSl1L9KqZpANsBbKVVJKXXW5i0+RxJxgZoOfKyUKoXhOxrdAfUL0EcpVR5I8NonlutTaMKuT3Hp0asPB/85ybade8mZ04PPPrbv6teRrk/xOVR169GHfYdPsDlkDzly5mTgpx/ZFe8P40bj5uZGo+bGFyO+6slJtQxXLl9CtmzZE3Q8sgY3V5Ob1MZ/qTV8LQ/DIuhb15vOVQrxxZwD+H+yjC/mHOCbzsa2/tx6lku3HrFyQA2GtvZl9+kbREQmXaXW1dWVzTv2cOTkOfbuNo6tI1i6eiPrtuxi1rwlTPvpR7Zt2ewQ3bg40vXJ28eH9z/sT8P6tWnSsB4lS5bC1c0m61vAsrucg0RkEEa1jffMPr+MxOcClRbIpJTaaFrmN6CyiGQC0iuloi1oZiYkGsv1KVvSHcPRZM/xxEWpS7ce7NntmLqZjnB9isbcoco83s5d7Yt33uwZrF+9nLE//BKTtHJ65I5lhnvl8iVy5PBISAKAHdu3sXzpYkr7FKZHZ5PjUbdOZM+egyuXL5t0LpPNAuPep9yk9lykVP5MtKpQgKV7jcviRXsuUKaAcQkdGaX4Ys4Bag5bQ5cftpEhTUrOXLtv8TEwP7aOIJZTV8MmdrlyJYYjXZ8AOnftzrYde1i1diOZX3uNIkWK2qxlySXnA7NXJFAPKGDzFp8/lj7Z6/QnzKO/cACLFy2gWHHrXJTMcaTrU0IOVebxLlm0AB8b4920bhVTJn7DpOl/kdrMjahGnddZumAu//vf/zh/7ixn/z1FKb+nysbHYtDQLzly8hwH/jnN1N9MjkfTplO3fgNm/WF4Qs/6Yzr1Xm+YZFxPuUn5ZOfEpbtcuf0o5g5mJe/s/GtKWqlTupLG5PVQ2Sc7EZFRnLh8L35xE/EeWwf0cT3l1LV2Nd7FbD+fEsORrk8A10yuX+f/+49FC+bTsnVbm7UsKR801vyziHyN0bJ5GUnIBeqWiAQrpTYDHYGNSqlbInJPRMoppUKwc6hK147t2Lx5IzeuX8ercD4+G/gFWzZt5ODBA4gI+fLnZ8JE2510HOn6lJBDVe/unTlkFu+4CT8mqdWvd2d2btvErZs3qORbhHc/GsikCV8TFvY/urQy4vP1L8uwMd9R1LsY9Ro1o16wH25ubgweNQ5XV9vMO/p98DHdOrZhxvRfyJMnL7/MmG3RegP+3McP3U1uUtcNN6kVBy4xrLUvbi7C/yKi+Oj3PQBkTe/On+8GE6UUV24/4u1pSbeIrly5zJs9uxEZZTq2zVpQt34DlixcwMcfvMv166G0bt6IkqVK8/ei5Rbvb+i1q3Rp1wKAiIhImrVqQ41adVi6aAGfftSPG9dDadeiMcVLleavBbYPD3K06xNA+zYtuHnjBm4pUvDN+Ilkzpz4jZXEsNr1ydRhvlMpZXu78DmSgAvUGmASkAb4F+hqSmhBGOPwHgAbgMpKqYqJ6fv5B6hN2+KOcrEfZzkpPQ6zbFiENTirfFCWdIk7qtuKLh9ETEvzZcEe16dDPLlMc8W4OTDUseE9OxJxgYrPJ+GI6UYBIvIJuqSSRvNCY8ntBPPrlgjgqlIqWQystYDXReRTjON0DujyfMPRaDSJkWhCExEXYKlSqsQziueFQik1G7Cs80Wj0Tx3Eu2YUUpFAQdEJN8zikej0WhsxpJLTg/giIjsxOgcB0Ap1chpUWk0Go0NWJLQhjg9Co1Go3EAliS0+kqpj80niMhoYGMCy7/yuNj4IPfzwBkOVVmdNLzCxcU5x/XEhGZO0c1R3jnGaLd2TXS4ZlRU8nCSsmRwU614ptVzdCAajUZjL4n5cr4BvAkUEpGDZrPSA1udHZhGo9FYS2KXnDOB5cBI4BOz6feUUjedGpVGo9HYQGK+nHeAO4DtT4pqNBrNM0S7Pmk0mmSDTmgajSbZoBPac8KRTjfgWNen+ByqDh7YT7XKFahQ1o/KFcqye5f1FUUScpMCmPLjRAJ9i1E+oBSDBnycsEg8XDh/ngZ1ahDoW5wgv5L8OHFCrPkTxo0lY2pXbly/brfmyOFD8C6Ul0pBflQK8rPI+Qrg7fbV2DN3ALv/+ozfRnbBPaXR2/NGmyocmP85e+YOYMS7jQFoUy+AkFmfxLwe7JlAKc/cFsd+/vx56tSshm9JH/xKF2fihPEWr5sYL4Prk+21bl9CROQdDD+EvUqp9s8rDnOnm5QpU9K4QT3q1nudIkVtr8gU7fqULl06wsPDqV6lErXr1COoXHxFRBKnfcfO9H7jLXp17xIz7fPPPubTAZ9Tu049Vq5YxueffcLy1eusjnHhsjUxMdarWZmatevy+NEjli1ZxJYd+3B3dyf0WtL1/81xc3Nj+Kgx+Jbx4969e1SpEEi1GjXx9inGhfPnWb9uNXnzWvf0XkKaAG++3Y933vvAYq1c2TLyZtsqlGk+gsf/C2fG6G60rOPPf5dv0qBqSQJbjSQsPIJsmY3CkrOW72bWcqOwS/EiufhrXC8OnrhoVeyjvhpLGT8j9gpB/tSoWQufYsWsOAJPE+36BIYvQpGCeRzq+uSI78Kr1kJ7E2OgcEwyE5FnntQd7XQDjnV9is+hSkS4d/cuAHfv3MHDI/HS2JbEGO0mNW3qZPp90B93d3cAsmVPuly2OTk9PPAt4wdA+vTp8fL25pLJSerT/u8zdMRoq49FYpq24ObqSmr3FLi6upA6VUouh96hV8tgvv5lNWHhRvGa0FtPl+9uVdefOSv2WLUtDw8Pyvg9id3b28eu2OPjZXZ9ShaIyCSgELBIRO6IyBQRWQVMF5H8Jnf4g6a/+UzrFDa5Qe0SkaEiYnnB+ERwtNNNNI50fYrLqK/HMfDTj/EunJ8Bn/Zn8LAvbY4xrpvUqZMn2b5tCzWrlOf1OtXYa0ct/HPnznJw/34CAoNYtmQRuXLlpmSp0jbrxdUE+GnS91QI9OWt3pY5dV0KvcO309dyYvkwzqwewd37j1gbcowi+bNTsUxhNk3/kFVT38W/2NOtyBa1/ZizwvYyfOfOnmX//n0EOshZKpqX1vUpuaCU6gNcAqoB4wB/oLFSqh0wEZhuKub4BxDdCTMeGK+UCjStGy+xXJ+uJ+365Ginm2gc6foUl5+nTGLUmLEcO32OUV+N5a0+PW2OMa6bVEREBLdv32b1hm0MHTGarh3bxusAlRT379+nY9uWjBzzDW5ubnw9eiSfDbLvUWRzzQwZMtC9Zx/2Hz3Jlh17yZHTg4GfJO3UlSl9ahpULYlPgy8oVHsAaVOnpE39QNxcXcicIQ2VO33NZ+MWMOOrbrHWCyyRn4ePwzl62jYb3Pv379O2VXPGjP2WDBky2KQRHy+161MyZpFS6pHpfXmeuDr9DlQym/6X6b1lrk9ZLXN9cqTTTVwc6foUzcwZ02nUxHjmsWnzluzZbV+ZcXPHo9y5c9OwURNEBP+Asri4uFjVgQ/GJWzHti1o1bodjZo048y/pzl37gyVypahpFchLl68QOXyAVy9csVmTYjt1NXZQqeu6kHenL10g+u37hMREcWCdQcoV7ogF6/eZsHaAwDsPnKOqChFVlM/GkDLOv42t87Cw8Np26o5rdu2p0lTxz6r+rK7PiVXHiQyz+lP6jrS6QYc6/oUHzk9crFlk1GPYOP6dRS24aRLyE2qfsPGbNq4HoBTJ08QFhZGlqxZLdZVStG3Tw+8vHzo++57ABQvUZLT/13h0PF/OXT8X3LnzsOm7bvJkTOnzZoQ26lrycIF+FjgrHT+yk3KlixI6lQpAKhW1ovjZ66yeMNBqpb1BKBIvuykTOHGdVM/mojQrFYZ/lppXf9ZdOx9enbHy9uHd9973+r1k+Kldn16RdiG4er0O9Ae2GKaHgI0x6ha67j/II51ugHHuj7F51D13Q+T+fjD94iIiCBVqlQ2uf4k5CYVFhZG3z49KB9QmpQpU/LjlGlWdeKHbNvKrJkzKF6iJJWCjM7wQUOGU7tufatjTEpz7pxZsZyvvv0u6eOw6/A55q/Zx/aZHxMRGcWBYxf4+e+tKKWYPLg9u//6jLDwSHoM+j1mnUp+Rbh49TZnL96wOvZtW7cy84/fKVGiJEH+vgAMGf4ldevZfjyiSXauTy8zInIWCAD6AveVUl+bphcApgFZgVAM16f/RKQoMAPDo3Mp0EspleiAID//ALVlu+MNXp1VOscSp2/rNZ1zTjnrGDgLXT7Iedjs+pScUEoVML0dHGf6WaB6PKtcBMoppZSItEG7Pmk0LzSvVEKzAX9gohjXP7eBbokvrtFonic6oSWCyUndvkFMGo3mmfEq3+XUaDTJDJ3QNBpNskEnNI1Gk2zQfWhOIMoJQ2FccNawDSfE6qThFWERjh9iAjjpyEJoyISkF7KBzK+PdbjmraWWVw+xhshnPBxEt9A0Gk2yQSc0jUaTbNAJTaPRJBt0QtNoNMkGndA0Gk2yQSe0Z0R8xiPRjB83lvSpXLluZQ0wcxxpkpKQmcmoEUMoViQfweX8CS7nb7FBSDSONh4xx694ESoH+VK1gj81KxvVWUcO+4Iq5cpQtYI/LRvX48rlBGt0xkuZ4kUINmnWMGneunmT5o3qEujrQ/NGdbltQcVacxx5HhTNk5mQHzrGvK7O60vfpn6ULJSNDePasmtSJ+YOaUL6NCkBqO6Xn60TO7BrUie2TuxAldJ5rYrdsUY83SiQJweBZUrGTJv3918E+JYgfSpX9u6x7bHpZFNtw1QxY4lSqsTzjMPPP0Bt2vZ08cMtmzeRLl06enXvws69B2OmXzh/nr5v9OTE8eNs2r6LrAnUAXNzTfy3RynFgwcPYpmkfP3N+CRNUh6HRSapVa9mZUaOGcfa1StJmzYdb/dL/BZ/QsM2rly+zJUrl2MZj8ycM4/5f/9F2rTpkjQeSWzYhl/xIqzeGBKrjtq9u3dJb6rUOuXH7zhx7B++Hv/DU+smNGyjTPEirImjOXjgJ2TO/BrvftCf8WO/4vbtW3wxbGS867unePp/Zu95AJCt0binprm4CKf/6E2Vd/9g5sBGfPLTRrYcukCn2iUokDMDQ6dvo3Th7Fy79YDLNx9QLH8WFn/ZnMLtpwCWDduw5RxLaNhG9HHo2a0zu/YdAuDYP//g4uLCO3378OWoMfj5P1VMI4Z0CVTb0C00no1RSnzGIwCf9H+fYV9ab+IRF0eapCRkZmIvjjYeSYr0ZmWnHz546JB9WL50Ma3bdwSgdfuOLFuyyKr1nXUeVPPNx5nLt/nv2j2K5snMlkMXAFi37xxNKhlFJA+cvsblm0Zd06PnbuCe0o2UKVwt3oazjXi8fXzw9PKySS+a5JbQXEXkJxE5IiKrRCS1iPiajE4Oish8EckMICIbRORLEdkIvCsiLUXksIgcEJFNpmVcRWSMySTloIj0dmSwSx1k4hGNI01S4jMzAfhp8g9ULFuGvn16WH25ZY69xiNxERFaNqlHjeCyTJ/2U8z0EUM+p7R3Qf6e8ycfDxhstWaLJvWoHlyW30yaoaFXyZnTcLzKmdOD69ets9yLD0ecBy2rejNnwzHASFYNyhcGoFmwJ3mypX9q+aaVinLg9DXCwp9uoSeGM414HEFyS2hFge+VUsUxyv00B6YDH5sMUA4B5hf+mZRSVZRSY4FBQB2lVGmgkWl+d+COySQlEOgpIgXjbjSWSUpo0iYpYFT+/Hr0SAbYaeJhjiNNUuIzM+nWow/7Dp9gc8gecuTMycBPP7JJ2xHGI3FZunoj67bsYta8JUz76Ue2bdkMwIAvhnHg2Bmat2rLz1OevtxMSnP9ll3MjqPpSBxxHqRwc+H1coWZt+kEAL2/WUnvhr5sndiBdKlTEhYRO2n55M/C8O6V6Tt+tdXbcqYRjyNIbgntjFJqv+n9HqAwRtLaaJr2G1DZbPnZZu+3Ar+KSE8guh1eG+gkIvuBHUAWjKQZi1gmKdksM0k58+9pzp49Q4XAMhT3NEw8gstZZ+KREI40STE3M4llENLVMoOQuDjKeCQuOT1yAZAtW3bqN2zCvjhWeM1btWGJlX6PHnE09+7ZRbZsObhyxfAVuHLlMlmzWuchGhdHnAd1Aguy/9RVrt1+CMCJ8zdp+NnfVOw7gzkbjnHm8u2YZXNnTcfsQY3oMWY5Zy7fsTluZxjxOILkltD+Z/Y+EsiUxPIxRikmm7uBQF5gv4hkwegvflsp5Wt6FVRKrXJEoMVLlOTM+SscOfEvR04YJh6bQyw38YiLI01SEjIziWUQsmgBPsWTNggxx5HGI+Y8ePCA+/fuxbzfsHY13sWKc/rUyZhlVixbTBFPy/tnHjx4wL04mj7FilO3fgNm/2HU/p/9x+/Ue72hVbHGxRHnQSuzy02AbBlTAyACn7QL4qclxs2HjGndmTesKYN+2cL2o9bd8QXnG/E4guT+cPod4JaIBJuKNXYENsa3oIgUVkrtAHaISEOMxLYSeENE1imlwkXEE7iolErMMSpe4jMe6dy1u+17FgdHmqQkZGbSu3vnWAYh4yb8aJWuI41HzAm9dpUu7VoAEBERSbNWbahRqw5d2rfi9MkTuLgIefLm5+vx31ul2dlMs7lJs4xfAN07t2XG77+QJ09epk2fZVWsjj4PUru7Ud0vf6zLx1bVvOnd0BeAhVtPMX2VcVnYp5EvhXNl5pN25fiknXFnsuGncwm98+gp3fhw5DnWpWM7Nm/awI3r1/EslJcBnw8m82uv8eF773A9NJTmTRpQqpQvC5da1wJMtsM2RORDIB2wAJgEpAH+xTBAuSUiG4APlVK7TcvPw7icFGAt0M/0fjjQ0PQ+FGiilEqwrZ7QsA17SWrYhq3EN2zDXnS1DYP4hm04gviGbdjLy1ZtI6FhG8mmhWYyOilh9vlrs9lPDZRRSlWN8zk+N1YFfGZ6aTSaF5zk1oem0WheYXRC02g0yQad0DQaTbJBJzSNRpNs0AlNo9EkG5LNXc4Xhcgoxf3HEQ7XTZfKOf8qZwwtcJYvRpqUlj9IbQ0OeGb9meKMIRaZy77tcE2A0O3jnaKbELqFptFokg06oWk0mmSDTmgajSbZoBOaRqNJNuiEptFokg06oWk0mmSDTmjPkDu3b9OjUxsqBZYkuGwpdu8M4fDBA7xeM5ialQKpU7X8U4UJkyI+F6FDBw9QvUpFgvxL07JZI+7evWtX3N5FCxJYphRBAWWoWC7QZh1nOf3E5fvvxhNQpiQBviWYOOFbh2iCUX66XKAfzZrYVwPN2brnz5+nTs1q+Jb0wa90cSZOsG7oxNvtq7Hnr8/YPedTfvuyC+4pjSFDb7SuzIF5A9nz12eMeLcxANWDvNj6x0fsmv0pW//4iCqBnhZtIyH3q0k/TKRMSR8Cy5Rk4GcfWxU3vMIJTUTOishT1joi0khEPnHGNj//5AOq1azNll2HWLtlN0U9vRn2xae8//EA1mzZRf/PBjFskHWFPdp37Mz8RbFt3/q+0Yuhw75kx54DNGzUhPHffJ3A2pazfPU6duzex9YQ66vJRtO+YxcWLF4ea1qxYiWYOftvKgZXTmAt6zhy5DC/TJvKpq07CNm9n+XLlnLq5MmkV7SA778bj7e3j0O0nKnr5ubGqK/Gsv/QP2zcEsLkSd/zz9GjFq2bK1tG3mxThYodxhDQaiSuLkLLOv5UDihKg6qlCGw9Cv+WX/Lt9LUA3Lj9gBbvTiaw9Uh6DprBtGEdLdpOfOftpg3rWbp4ESG797Nr3yHeTcJdLD5e2YSWEEqpRUqpUY7WvXf3LiHbNtOuY1cAUqZMScZMmRCRmGqrd+/eJaeHh1W68bnnnDxxPCZBVK9Ri4UL5jlgD+zHWU4/5hw/9g9lg4JIkyYNbm5uBFeuzCIrS2/Hx4ULF1ixfBldujmuKKezdD08PCjj98Rdy9vbxyp3LTdXF1K7p8DV1YXUqVNyOfQOvVpU4utfVhMWbgwaD711H4ADxy9w+bpxBXD09GXcU6YgZYqkB4HHdy5M/WkS73/YH3d3dwCyZbe+vPkrkdBEJK2ILDU5Oh0WkdamWW+LyF4ROSQi3qZlu4jIRNP7X0VkkohsFpETImJbeU7g3NkzZMmajX5v9qRWcFk+eLsPDx88YOjIrxk66FP8ixdm6Oef8OmgYXbvr0/xEiw12avNnzeXixfO26UnIjSsX4cKQQH8PHWK3fE5k2LFSrB182Zu3LjBw4cPWbliud37D9D/g/cYPnI0Li6O/co4Szeac2fPsn//PgLLWubOdCn0Dt/+vpYTy4ZyZtVw7t57xNqQYxTJn52KfoXZ9NsHrPrpHfyL5Xtq3aY1fDlw/EJM0rOWUydPsm3rFqoFl6duzWo2eUu8EgkNqAtcUkqVNlW0ja7re10p5Qf8CCRkNVQAqAK8DkwSkVRxFzB3fbpxI37X64jICA4d2Efn7r1YvXknqdOk4btxY5j+8xSGjBjDniOnGfLlGD54236nvB8mT+WnST8QXD6Q+/fukSJlSrv01m7Ywvade1iweBlTfvyBLZs32R2js/D28eH9D/vTsH5tmjSsR8mSpXB1s++xsWVLl5Atezb8/PwdFKVzdaO5f/8+bVs1Z8zYb8lg5lGaGJnSp6ZB1VL4NBhMoToDSZvanTb1A3BzdSFz+tRU7jyWz75dyIzR3WKt51MoJ8PfaUTfEdaVJDcnIiKC27dvsW7TNoaPHE3n9m2wtqL2q5LQDgE1RWS0yV8guoR29LXYHozEFR9zlFJRSqmTGCW8n3KFMHd9ypIlfsfrXLly45ErD34BZQFo0LgZhw7uY86sGbzeqAkADZs0Z99e+zvGvby8Wbh0JZu376JF6zYUKlTYLr1cuQz3o+zZs9OwcRN273J8iXFH0rlrd7bt2MOqtRvJ/NprFCnylFGXVYRs28rSJYvxLlqQTh3asnH9Orp1tqyv6HnoguGu1bZVc1q3bU+TpvEVY46f6kFenL14g+u37xMREcWCdQcoV6oQF6/dZsG6AwDsPnKOqKgosmYyTIdzZ8/E7LE96THod85ciP8H3RJy585No8ZNERECAsvi4uLC9evW6b0SCU0pdQLwx0hsI0VkkGlWtEtUJAk/qB/3J8KmR6+z58hJrjx5OHXyOABbNq7H08uHHDk92L7FaPFs2bSegoWK2CIfi9BrhvltVFQUY0aOoFuPXjZrxXU/WrtmNcWKl0hirefLNdP+n//vPxYtmE/L1m3t0hs6YiSnzpzn2MkzTJ/xJ1WqVWfab7/bHaezdJVS9OnZHS9vH959732r1j1/5RZlSxYgdaoUAFQr68nxM1dYvP4gVU13MIvky0bKFG5cv32fjOlSM29CHwZ9t4jtB87YFXeDRo3ZuGE9ACdPniAsLIysWeNvICTEK1FtQ0RyATeVUjNE5D7QxYrVW4rIb0BBoBBw3NY4Rowex1s9uxAeFka+AgX59oefqFO/IZ9/8gGRERG4p0rFmPHWmeHG5yL04MEDpkwydBo1aUrHzl1tDZlrV6/SpqXxCx8REUGrNm2pXaeuTVrOcvqJS/s2Lbh54wZuKVLwzfiJZM6c2S69l41tW7cy84/fKVGiJEH+vgAMGf4ldevVT3LdXYfPMX/tfrb/8TERkZEcOH6Bn+dtQynF5MHt2T3nU8LCI+nxxQwA+rSuTOG8WfmkZ10+6WmcFw3f/D7mpkFCxHfeduzcjTd7daesXylSpkzJ5Km/IFaWQkk2rk+JISJ1gDFAFBAOvAHMBQKUUtdFJAD4WilVVUS6mKb3FZFfgVtAAJADeF8ptSSxbZUu469Wbtju8H1wVvkgVyc4NDmrfJCzqvy8bOWDrP2SW8LLVj4ofSrX5O36lBhKqZUYHpvmFDCbvxuoanr/K/Cr2XJblVLvodFoXnheiT40jUbzavBKtNBsRSnV5XnHoNFoLEe30DQaTbJBJzSNRpNs0AlNo9EkG3QfmoNxESGVE9yJHvwv0uGaABnTpHC8qJOGAjlreIWzRi45K94oJ4yLubrNOcMrslW2vgSQPegWmkajSTbohKbRaJINOqFpNJpkg05oGo0m2aATmkajSTbohKbRaJINOqE9Ix4/fkyN4HJUCvKjvH8pRg4bDMCCeXMp71+K19KmYJ8Nrkd3bt+me8fWVAooQXBgSXbvDGHR/LlUDiqNRyZ39u/dY1fc9joImfOsXJ+c4c504vhxygWWiXnlzJrRIY5SjnLU6tOrG/nz5CDA7NjevHmTBvVqU6qYJw3q1ebWrVtW6144f54GdWoQ6FucIL+S/DhxQqz5E8aNJWNqV25YUIjx7TbB7PnzA3bP/IDfhrWLcZMC6Ne+Co92jCFLxjQA5PPIzM2NXxLy+3uE/P4eEz62rEhlsktoIpJJRN50kFZVEUm0XJCluLu7s3D5Grbs2MumkD2sXb2SXTtD8ClWnOl//kWFSsE26Q785H2q16zDlt2HWbt1D0U9vfEuVpxpM+ZQrqJtmubY4yAUl2fh+gTOcWfy9PIiZNc+QnbtY2vIblKnSUOjxk0dou0IR60O8RzbsWNGUbV6dQ4ePUHV6tUZO8Z67x83NzeGjxrDrv1HWLNxGz9N/oFj/xj//wvnz7N+3Wry5n3aXyAuubJl4M3WlajYZTwB7cbi6uJCy1q+AOTJnpHqZYvy3+XYCfffizco13Ec5TqO453Rlhn9JLuEBmQCnkpoIuL40a5WICKkS2eULA4PDyc8PAJB8PL2oainba5H9+7eJWTrFtp1iu0k5enlQ5GijnFSstdByJxn4frkLHcmc9avW0uhQoXJlz+/07ZhLZWCK/NanGO7dPEi2nfoDED7Dp1Zsmih1bo5PTzwLfPk/+/l7R3z//+0//sMHTHa4vpssdykUqWIcYv66r1GDJi41Gr/gPhIjgltFFBYRPaLyC4RWS8iM4FDIlJARA5HLygiH4rIYNP7IiKyxuQMtVdEYhXiF5FAEdknIoVsDSwyMpLgIH8883tQtUYNAix04kmIc2f/JUvWrLz7Zg9qVgrk/b69efDggV2aiW/POgeh54GzXZQA5v41i5at2jhEy5mOWteuXcXDZIvo4eFBaOg1u/TOnTvLwf37CQgMYtmSReTKlZuSpUpbtO6l0Lt8+8dGTiwcwJmln3P3/mPW7jjB68HFuBR6h0MnLz+1ToFcr7F9ej9W/diHir4FLdpOckxonwCnlVK+wEdAWWCAUqpYEuv9AXyvlCoNVABijrCIVAAmAY2VUv/GXdHc9en69dAEN+Dq6srmHXs4cvIce3fv4uiRwwkuawkREZEcOrCPLt17s2bLLtKkTcvEcV/ZpZkQtjgIPWuc7aIEEBYWxrIli2navKVD9F4WR6379+/TsW1LRo75Bjc3N74ePZLPBg2xeP1M6VPToHJxfJqOpNDrw0ibOiXt6vnzcZcaDJ286qnlr1y/i2ejEZTv9C0fj1/Mr0PbkT6te5LbSY4JLS47lVKJujeISHogt1JqPoBS6rFS6qFptg8wBWiolPovvvXNXZ+yZs2WZEAZM2WiUnAV1q6OW0TXOnLlzo1H7thOUgcP7LdLMz5sdRB61jjTRSmaVSuWU9rXjxw5cjhEz5mOWtmz5+DyZeN3+fLly2TLZr1xLxj//45tW9CqdTsaNWnGmX9Pc+7cGSqVLUNJr0JcvHiByuUDuHrlSoIa1QOLcvbSTa7ffkBEZBQL1h+mU8MA8ud6jZ0z3uPY/E/JnT0j26f3I8dr6QkLj+TmXeMruO/YRf69cIOieZP+br0KCc38GiyC2Psc7bGZWCfAZeAxUMaeIK6HhnLn9m0AHj16xIb1a23uO4sme46c5M79xElq88Z1eHo5tjPcHgehZ42zXJTM+WvOLFq2dszlprMdteo3aMgfM34D4I8Zv/F6w0ZWayil6NunB15ePvR916hEX7xESU7/d4VDx//l0PF/yZ07D5u27yZHzpwJ6py/eouyJfKR2t3kJhVYhIXrD5O/3hC8m47Eu+lILl67Q/lO33L15j2yZkqLi8nvokCu1yiSNytnLt1IMt7kWG3jHpA+gXlXgewikgW4DzQAViil7orIBRFpopRaICLuQPRNhNtAd2CViDxQSm2wJagrVy7zZs9uREZFEhUVRdNmLahbvwFLFi7g4w/e5fr1UFo3b0TJUqX5e9HypAVNjPhqHG/26Ex4eBj5CxTk2++nsmzxAgb0f48b10Pp0KoxJUqWZtb8pbaEbZeDUFyeleuTs3j48CHr1q5mwveTHKLnSEetzmbHtmihvAz8fDAffPQJHdu1Zvov08iTNx8z/pxjtW7Itq3MmjmD4iVKUinIuDkwaMhwate17v+/68h55q87xPbp/YiIjOLAiYv8vCAkweUrlSnE571qExEZRWRkFG+P/ptbdx8luZ1k6fpkuglQCngEXFVKNTCb9w7wDnAGuAicVUoNFpGiwGQgK4YzVEsgH/ChUqqBiOQDlgPdlFI7Etp2Gb8AtX5rgrNt5n/hUQ7XBOeUD4p0ku2TEwyqgJevfJAz4o1w0v8sRxXnlA96vPPrV8f1SSnVLpF5E4AJ8Uw/CVSPM/lfYINp/n9AccdFqdFoHM2r0Iem0WheEXRC02g0yQad0DQaTbJBJzSNRpNs0AlNo9EkG5LlsI3niYiEAucsXDwrkHTdFet5mXRfplhfNt2XKVZrdfMrpZ56dEAntOeIiOyObyzNq6T7MsX6sum+TLE6Sldfcmo0mmSDTmgajSbZoBPa88WxBbBeTt2XKdaXTfdlitUhuroPTaPRJBt0C02j0SQbdELTaDTJBp3QNBpNskEnNM0zR0QqWjLNSk1XEZlhj4YmcUQkpYiUEpGSIpLSAXoF4plmuzkp+qbAc0FE8gNFlVJrRCQ14KaUumenpgDtgUJKqaGmgpQ5lVI2FakXke+ABE8OpdQ7tkUKIrJXKeWX1DQbdFdieD+E2aMTR/MQTx+HO8BuYLhSKum60M8YJ51fr2MYBZ3GKFlfEOitlLK8vPLTmnsx/l8XTZ+rABOVUiUTXzNhkmWBxxcZEekJ9AJeAwoDeTBOlBp2Sv8ARGEUqRyKUYr8b8DWX7xoG/OKQDFgtulzS8AmO3YRKY/hqJVNRMwNCjLwpOS5PZwFtorIIsy8JJRS39ihuRyIBGaaPkcbCtwFfgVssmcXkXsknCg/iM9dzEJdZ51fY4FqSqlTpu0UBpZiHB9b6Q0sEJGGgB/wJWB9bXczdEJ79ryFYa23A4xKuSJimx1PbIKUUn4iss+ke8ueywKl1G8AItIF40QON32eBDztO2YZKYF0GOedue/DXaCFrbGaccn0ciFhXwlrqaiUMr8cPiQiW5VSFUWkgx2632DEOhOjxdMGyAkcB6YBVW3Uddb5dS06mZn4F7DL6FMptctUEn8VhhFRLaVUwj6QFqAT2rPnf0qpsGi3aRFxI5FLOysIN7nDK5NuNowWm73kwkgON02f05mmWY1SaiOwUUR+VUpZ+gC/NfpDAEQkrVLKUY7L6UQkKNpHQkTKYhwDMFzEbKWuUsrcsXmKiISYugs+s0PXWefXERFZBswx6bUEdolIMwCl1DxLhURkcZyY0mC0Tn8WEZRS1ttTmdAJ7dmz0XTCphaRWsCbwGIH6E4A5mO4Wo3AaPEMdIDuKGCfiKw3fa4CDLZT86GIjMHwaIi2EkQpFdfTwSpMl7Q/YyScfCJSGqOf5007ZHsA00QkHUZL6i7QXUTSAiPt0I0SkVbAXNNn8xaqPQnIWedXKgzXtCqmz6EYl7UNMeK1OKEBXzsgnnjRNwWeMSLigmGLVxvjC7ISmKoc8I8QEW+MvhIB1iql/nFArOUwLi+iWxM7lFIJO8paprsKo0/uQ6AP0BkIVUrZZREkIjswEsMipVQZ07TDSim7zS5FJCPG9+W2vVomvULAeKC8adJ24D0MJzJ/pdQWG3Wddn45GhEpCFxWSj02fU4N5FBKnbVZ8wXcT40ViMhric1XSt1MbL4F+tuVUuWTXtIqzT1KKX8ROaiUKmWatlEpVSWpdZPQ3aGUChKRfWYJ7YBSqrQdmhmBL4DKpkkbgaFKqTv2xPosMJ0beZRSB+3Q6K+U+iqhu9523u3eDVSIvitt6vPdqpSyeeiGvuR8xpjGWw0G8mMcfwGUUqqQjZJ7ME40cxfI6M8KsFU3mlUi0hyY58Bf+XDT38um4QCXMO7G2ct5EakAKNOX4x3ArlYqRgf9YaCV6XNH4BegmT2iIpIH+A7jLrICtgDvKqUu2Km7AWiEcW7tB0JNPxa22t5HH7/diS5lG27mQ2xMfX/2jW9TSunXM3wBx4B6QHYgS/TreceVSLz3MG4uhGH0H90D7tqp2QDICJQA1mMk5YYOiDUr8AdGX881YIa9xxbYb8k0G3RXA10xEo8b0AVY7QDdfaa/PYAhpvcHn/d5lMgxaGT2uTFGV4nNmrqF9uy5o+wYjBgXEfFWSh0TkXgHpSql9tqjr5Ry1PAHc1oCW5RSh4Fqpkujr7Gz81opdR1jcLEjeSQilZSpT8vUwn7kAN1sSqlfzD7/KiL9HKDrJiIeGC3KAQ7QA0BEPDH6PAtgdmWn7LuR0wf4Q0QmYlxRnAc62aGnE9pzYL3pDt884H/RE+1IPO9jDKQcG888xdNu8Bbh5ERZSpl1riulbopIGTv0gJgv3Y8YHcslRKQURgtguB2yfYDppr40gFsYNzHs5bppHNufps9tAUc8dTAE40bAFmWM8yoEnHSA7l8YA3SnYgw0thul1GmgXPQdZGXn0wygbwo8c8yGP5ij7PylczgiMkUp1csUr/lJEt3nZ3O8InIAqKqUumX6/BqwUdnxyItJZyPwETBZ2XmXM86TDAKkNb1/gLH/9jx9gOnRtIkYdzkVsA2jD83m8XmmcYjvKKXG2RNbAtp7lFL+DtYcFN90pdRQWzV1C+0Zo5Sq5gxdEUmFMeaoEsYXZDMwSZluiVuLUqqX6W39eHR/tDPcscA2EZlr0mwFjLBTEyCNUmpn9KBSE7YOfo2+1PbCeHxsIUZi6wBssjlCYhLPl8qOAaTxoZSKFJFGgMMSmtld9MUi8ibGWEfzKwt77qKbD35OhdG3at9QI91Ce/aY7uzFHVRq86+SSXMORod9dMWJtkBmpVRLB+jexehsj9bNpJRqlfBaFukWw7gcjh4zd9QePZPmcqAv8JcyHgNrAXRXStWzQ3MV0Dz6ckhE0pv069oZq8MfpDfpjsC44TKb2M+z2tRFICJniP8uerSuvXfRzbfljjGGsI6tGrqF9owxPQuZBqiG0R/RArCpIkYcvFTs8VbrTZd2L6SuKYHZncTi8BZGXXpvEbkInMH+mwT5MO7wRhOG0TFuL2dx/IP0YDz8D0aBghhZbOxLVUoVBDA91bBCKXVXRD7HeJh8mD2BxkMa7BxmpBPas6eCUqqUaVDpEBEZi3WPjSTEPhEpp5QKARCRIGDrC6zrMOL0dy3DGArigpEommM8CG4rvwM7RWQ+RmJoCvxmq5iI/K6U6gi0xrg0dOSD9E7r0gAGKqXmiEgloBZGt8GPPHmCxGokdmkmVyAbsROx1eiE9uyJvuX/UERyYdzZKmirmNlJkQLoJCL/mWblw44WUAK6CmNAsKNbVvaSUH9XR+zs71JKjTBdygabJnVVSu2zQ9JfjHpl/2EMrHUoTnyyIfrO5usYfbMLRWSwnZoNzN5HAFeVUvY88K/70J41pub6dxjPXH6PkSSmKqU+t1Evf2Lzbb1r5ixdZ+Ks/i5HIka5nDcwfsQumc/CvidGovX/xniyIboV2REorZSy98mGJRjPmdYE/DF+mHcqGx8rMz1zetCWO9CJ6uqE9vwwdYKmcsCvZ7ReaZ60JDYrpRzRh/bSICLHML68/zN9dgcOKKW8n29kTyMiPyql3nCC7n6llG9S02zQTQPUBQ4po8aaB1BSKWVrbTxE5A/gU6XUf0kubCH6kvMZIaa6UQnMQ1lRTyoBjXeBnjzpj5thGkvm8MuaFxiH9nc5E2ckMxNOebJBKfUQs75epdRl4LKdsh4YddZ2EvvGiM3DWXQL7RkhIr8kMlsppbrZqX8QKK9MhQ3FqNe1XZmqWbwqmJ5siG6lbrKzv+ulQ0R8MZJ4rCcblB0VN5yFKZF9ZD4JGK1iF760Ct1Ce0Yopbo6eRNC7EdSIok9duiVwDTeyq7nV19y/gG+wvATyIRRCbYJ8MIlNIxqGxvNJ4hRE812Qfvi0ViLiGTBuAsVPfJ+C8ZdKHuf4/sF2GG63ALjJP7ZTk3Ny8dC4DZGUr/4fEOJHxF5A+Ppk0KmK4to0mPnkCB9yfmMEZHVGEMJokf0t8d4rrGmA7T9MBKl8ApebmkcV6HXmZiGlmTGKGH+idmse3Y+SqUT2rMmvod8RWS3UirAAdqZgbzELu/yKl9+vXKIyBTgO6XUoecdy/NAX3I+e9aLSBsM9xwwHn1aaq+oiAzDKBJ4miejr21+5EXzcmE2ENoN6Coi/2I8RB49vu2VuDmkW2jPGDEMZtPypAPflSe3rJVSKoONuscxxgU59GFnzcvByzgQ2hnoFtozRIy6NsUdOZDQjMMYd7XsMn/VvJy8KgkrKXQL7RnjjEJ5Jt0AjDtch4ldr8qhNbc0mhcZ3UJ79oSISKBSapeDdX8DRgOHcIxjukbz0qFbaM8YETkKeALnMPrOHNJpKw7wtdRoXnZ0QnvGJNR5a28fiIh8g3GpuQjHmK9oNC8dOqE9I0Qkg6naZ7xO53YPKHxJzFc0GmeiE9ozQkSWKKUamNVoj5mFA+pgaTQandCeC6ZWWlFim6RsTHgNizSdValUo3lp0AntGSMiPYB3gTzAfqAcsE0pVcNOXadUKtVoXiZ0QnvGmB5RCQRClFK+IuINDFFKtbZT1ymVSjWalwmX5x3AK8hjZTL/FRF3pdQxDHMPe3lkcuTBpO2QSqUazcuEHlj77LkgIpmABcBqEblFbLMMW+kDTDf1pYGpUqkDdDWalwZ9yfkcEZEqGKWSV9j7ULmIFFRKnRGRDACmISIFlVJnHBGrRvMyoBNaMkFE9iql/OJMc8pzoxrNi4q+5HzJMd1UKA5kjOMslQGzYSEazauATmgvP14YDtSZgIZm0+9h2NppNK8M+pIzmSAi5ZVS2593HBrN80QntGSCyffzqX+mvX6fGs3LhL7kTD4sMXufCsM13BHDQTSalwbdQkumiIgLsEZX29C8SugnBZIvRYF8zzsIjeZZoi85kwkmNylz+7qrQP/nF5FG8+zRCS2ZoJRKH09ZIt2foHml0AktmZBAWaLtaKNhzSuE7kNLPryLUZbonFKqGlAGCH2+IWk0zxad0JIPzipLpNG8NOhLzuSDs8oSaTQvDXocWjLEkWWJNJqXCZ3QNBpNskH3oWk0mmSDTmgajSbZoBOa5qVGRKqKyBLT+0Yi8kkiy2YSkTdt2MZgEfnQ0ulxlvlVRFpYsa0CInLY2hg1BjqhaV5IRMTV2nWUUouUUqMSWSQTYHVC07w86ISmeaaYWiDHROQ3ETkoInNFJI1p3lkRGSQiW4CWIlJbRLaLyF4R+UtE0pmWq2vS2AI0M9PuIiITTe9ziMh8ETlgelUARgGFRWS/iIwxLfeRiOwyxTLETGuAiBwXkTVYMJ5PRHqadA6IyN/R+2SipohsFpETItLAtLyriIwx23Zve4+tRic0zfPBC5iilCoF3CV2q+mxUqoSsAYYCNQ0mb/sBt4XkVTATxjlxoOBnAlsYwKwUSlVGvADjgCfAKeVUr5KqY9EpDbGs69lAV/AX0Qqi4g/0AbjaYtmGE9gJMU8pVSgaXv/AN3N5hUAqgCvA5NM+9AduKOUCjTp9xSRghZsR5MIemCt5nlwXim11fR+BvAO8LXp82zT33JAMWCriACkxHg21Rs4o5Q6CSAiM4Be8WyjOtAJQCkVCdwRkcxxlqlteu0zfU6HkeDSA/OVUg9N21hkwT6VEJHhGJe16YCVZvPmKKWigJMi8q9pH2oDpcz61zKatn3Cgm1pEkAnNM3zIO7gR/PPD0x/BVitlGprvqCI+Mazvq0IMFIpNTnONvrZsI1fgSZKqQMi0gWoajYvvv0V4G2llHniQ0QKWLldjRn6klPzPMgnIuVN79sCW+JZJgSoKCJFAEQkjYh4AseAgiJS2Gz9+FgLvGFa19VkwHwPo/UVzUqgm1nfXG4RyQ5sApqKSGoRSU9sN62ESA9cFpEUQPs481qKiIsp5kLAcdO23zAtj4h4ikhaC7ajSQSd0DTPg3+AziJyEHgN+DHuAkqpUKAL8KdpuRDA2/QAfi9gqemmwLkEtvEuUE1EDgF7gOJKqRsYl7CHRWSMUmoVMBPYblpuLpBeKbUX49J3P/A3sNmCffoc2AGsxki65hwHNgLLgT6mfZgKHAX2moZpTEZfMdmNfvRJ80wxXVItUUqVeN6xaJIfuoWm0WiSDbqFptFokg26habRaJINOqFpNJpkg05oGo0m2aATmkajSTbohKbRaJIN/wftG2x9MfdNawAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from helper_plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_confusion_matrix(\\n\",\n    \"    cmat_tensor.numpy(),\\n\",\n    \"    class_names=class_dict.values())\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"84b3b121\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Single-image usage\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"c3fbb1ae\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"49fc0838\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a2a9a626\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Assume we have a single image as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"d5e1a331\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"image = Image.open('data/cifar10_pngs/90_airplane.png')\\n\",\n    \"plt.imshow(image, cmap='Greys')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a9804a8c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that we have to use the same image transformation that we used earlier in the `DataModule`. \\n\",\n    \"- While we didn't apply any image augmentation, we could use the `to_tensor` function from the torchvision library; however, as a general template that provides flexibility for more complex transformation chains, let's use the `Compose` class for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"c1d0b315\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"transform = data_module.train_transform\\n\",\n    \"\\n\",\n    \"image_chw = transform(image)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ad19d939\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that `ToTensor` returns the image in the CHW format. CHW refers to the dimensions and stands for channel, height, and width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"3799087e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(image_chw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"df5cdcd6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- However, the PyTorch / PyTorch Lightning model expectes images in NCHW format, where N stands for the number of images (e.g., in a batch).\\n\",\n    \"- We can add the additional channel dimension via `unsqueeze` as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"59be99f2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch.Size([1, 3, 32, 32])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"image_nchw = image_chw.unsqueeze(0)\\n\",\n    \"print(image_nchw.shape)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"15b55256\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now that we have the image in the right format, we can feed it to our classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"id\": \"54262d1f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with torch.no_grad():  # since we don't need to backprop\\n\",\n    \"    logits = lightning_model(image_nchw)\\n\",\n    \"    probas = torch.softmax(logits, axis=1)\\n\",\n    \"    predicted_label = torch.argmax(probas)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"id\": \"406a04d1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"int_to_str = {\\n\",\n    \"    0: 'airplane',\\n\",\n    \"    1: 'automobile',\\n\",\n    \"    2: 'bird',\\n\",\n    \"    3: 'cat',\\n\",\n    \"    4: 'deer',\\n\",\n    \"    5: 'dog',\\n\",\n    \"    6: 'frog',\\n\",\n    \"    7: 'horse',\\n\",\n    \"    8: 'ship',\\n\",\n    \"    9: 'truck'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 37,\n   \"id\": \"9dd4af30\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Predicted label: airplane\\n\",\n      \"Class-membership probability 100.00%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(f'Predicted label: {int_to_str[predicted_label.item()]}')\\n\",\n    \"print(f'Class-membership probability {probas[0][predicted_label]*100:.2f}%')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/data-augmentation/autoaugment/helper_utilities.py",
    "content": "import lightning as L\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport torch\nimport torch.nn.functional as F\nimport torchmetrics\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.dataset import random_split\nfrom torchvision import datasets, transforms\n\n\nclass LightningModel(L.LightningModule):\n    def __init__(self, model, learning_rate):\n        super().__init__()\n\n        self.learning_rate = learning_rate\n        self.model = model\n\n        self.save_hyperparameters(ignore=[\"model\"])\n\n        self.train_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=10)\n        self.val_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=10)\n        self.test_acc = torchmetrics.Accuracy(task=\"multiclass\", num_classes=10)\n\n    def forward(self, x):\n        return self.model(x)\n\n    def _shared_step(self, batch):\n        features, true_labels = batch\n        logits = self(features)\n\n        loss = F.cross_entropy(logits, true_labels)\n        predicted_labels = torch.argmax(logits, dim=1)\n        return loss, true_labels, predicted_labels\n\n    def training_step(self, batch, batch_idx):\n        loss, true_labels, predicted_labels = self._shared_step(batch)\n\n        self.log(\"train_loss\", loss)\n        self.train_acc(predicted_labels, true_labels)\n        self.log(\n            \"train_acc\", self.train_acc, prog_bar=True, on_epoch=True, on_step=False\n        )\n        return loss\n\n    def validation_step(self, batch, batch_idx):\n        loss, true_labels, predicted_labels = self._shared_step(batch)\n\n        self.log(\"val_loss\", loss, prog_bar=True)\n        self.val_acc(predicted_labels, true_labels)\n        self.log(\"val_acc\", self.val_acc, prog_bar=True)\n\n    def test_step(self, batch, batch_idx):\n        loss, true_labels, predicted_labels = self._shared_step(batch)\n        self.test_acc(predicted_labels, true_labels)\n        self.log(\"test_acc\", self.test_acc)\n\n    def configure_optimizers(self):\n        optimizer = torch.optim.SGD(self.parameters(), lr=self.learning_rate)\n        return optimizer\n\n\nclass Cifar10DataModule(L.LightningDataModule):\n    def __init__(\n        self, data_path=\"./\", batch_size=64, num_workers=0, height_width=(32, 32),\n        train_transform=None, test_transform=None\n    ):\n        super().__init__()\n        self.batch_size = batch_size\n        self.data_path = data_path\n        self.num_workers = num_workers\n        self.height_width = height_width\n        self.train_transform = train_transform\n        self.test_transform = test_transform\n\n    def prepare_data(self):\n        datasets.CIFAR10(root=self.data_path, download=True)\n\n        if self.train_transform is None:\n            self.train_transform = transforms.Compose(\n                [\n                    transforms.Resize(self.height_width),\n                    transforms.ToTensor(),\n                ]\n            )\n\n        if self.test_transform is None:\n            self.test_transform = transforms.Compose(\n                [\n                    transforms.Resize(self.height_width),\n                    transforms.ToTensor(),\n                ]\n            )\n            return\n\n    def setup(self, stage=None):\n        train = datasets.CIFAR10(\n            root=self.data_path,\n            train=True,\n            transform=self.train_transform,\n            download=False,\n        )\n\n        self.test = datasets.CIFAR10(\n            root=self.data_path,\n            train=False,\n            transform=self.test_transform,\n            download=False,\n        )\n\n        self.train, self.valid = random_split(train, lengths=[45000, 5000])\n\n    def train_dataloader(self):\n        train_loader = DataLoader(\n            dataset=self.train,\n            batch_size=self.batch_size,\n            drop_last=True,\n            shuffle=True,\n            num_workers=self.num_workers,\n        )\n        return train_loader\n\n    def val_dataloader(self):\n        valid_loader = DataLoader(\n            dataset=self.valid,\n            batch_size=self.batch_size,\n            drop_last=False,\n            shuffle=False,\n            num_workers=self.num_workers,\n        )\n        return valid_loader\n\n    def test_dataloader(self):\n        test_loader = DataLoader(\n            dataset=self.test,\n            batch_size=self.batch_size,\n            drop_last=False,\n            shuffle=False,\n            num_workers=self.num_workers,\n        )\n        return test_loader\n\n\ndef plot_val_acc(\n    log_dir, acc_ylim=(0.5, 1.0), save_loss=None, save_acc=None):\n\n    metrics = pd.read_csv(f\"{log_dir}/metrics.csv\")\n\n    aggreg_metrics = []\n    agg_col = \"epoch\"\n\n    for i, dfg in metrics.groupby(agg_col):\n        agg = dict(dfg.mean())\n        agg[agg_col] = i\n        aggreg_metrics.append(agg)\n\n    df_metrics = pd.DataFrame(aggreg_metrics)\n    df_metrics[[\"val_acc\"]].plot(\n        grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n    )\n\n    plt.ylim(acc_ylim)\n    if save_acc is not None:\n        plt.savefig(save_acc)\n\n        \ndef plot_loss_and_acc(\n    log_dir, loss_ylim=(0.0, 0.9), acc_ylim=(0.3, 1.0), save_loss=None, save_acc=None\n):\n\n    metrics = pd.read_csv(f\"{log_dir}/metrics.csv\")\n\n    aggreg_metrics = []\n    agg_col = \"epoch\"\n    for i, dfg in metrics.groupby(agg_col):\n        agg = dict(dfg.mean())\n        agg[agg_col] = i\n        aggreg_metrics.append(agg)\n\n    df_metrics = pd.DataFrame(aggreg_metrics)\n    df_metrics[[\"train_loss\"]].plot(\n        grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"Loss\"\n    )\n\n    plt.ylim(loss_ylim)\n    if save_loss is not None:\n        plt.savefig(save_loss)\n\n    df_metrics[[\"train_acc\", \"val_acc\"]].plot(\n        grid=True, legend=True, xlabel=\"Epoch\", ylabel=\"ACC\"\n    )\n\n    plt.ylim(acc_ylim)\n    if save_acc is not None:\n        plt.savefig(save_acc)"
  },
  {
    "path": "pytorch-lightning_ipynb/data-augmentation/autoaugment/with-autoaugment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d8d2298c-29ea-4028-8d8f-004f1bfedeb4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# With AutoAugment\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6194920a-f622-4f3f-b17b-4cc817289226\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook trains a ResNet-18 model with the AutoAugment procedure on CIFAR-10. \\n\",\n    \"\\n\",\n    \"Compare the performance to the baseline [./without-augmentation.ipynb](./without-augmentation.ipynb).\\n\",\n    \"\\n\",\n    \"## References\\n\",\n    \"\\n\",\n    \"AutoAugment paper:\\n\",\n    \"- [AutoAugment: Learning Augmentation Policies from Data](https://arxiv.org/abs/1805.09501)\\n\",\n    \"\\n\",\n    \"PyTorch/torchvision documentation:\\n\",\n    \"- [transforms.AutoAugment](https://pytorch.org/vision/main/generated/torchvision.transforms.AutoAugment.html)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"4af58769\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch      : 1.13.1\\n\",\n      \"lightning  : 1.9.0\\n\",\n      \"torchvision: 0.14.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,lightning,torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"d4363916\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import time\\n\",\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torchmetrics\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"from helper_utilities import LightningModel,Cifar10DataModule,plot_loss_and_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"66486cc5-7c7e-497c-bfb9-fc5e918256f8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Global seed set to 123\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"train_transform = transforms.Compose(\\n\",\n    \"    [\\n\",\n    \"        transforms.Resize(32),\\n\",\n    \"        transforms.AutoAugment(transforms.autoaugment.AutoAugmentPolicy.CIFAR10),\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        \\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"L.seed_everything(123)\\n\",\n    \"dm = Cifar10DataModule(\\n\",\n    \"    height_width=(32, 32),\\n\",\n    \"    batch_size=256, \\n\",\n    \"    train_transform=train_transform, \\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"1cd186d4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"\\n\",\n      \"  | Name      | Type               | Params\\n\",\n      \"-------------------------------------------------\\n\",\n      \"0 | model     | ResNet             | 11.2 M\\n\",\n      \"1 | train_acc | MulticlassAccuracy | 0     \\n\",\n      \"2 | val_acc   | MulticlassAccuracy | 0     \\n\",\n      \"3 | test_acc  | MulticlassAccuracy | 0     \\n\",\n      \"-------------------------------------------------\\n\",\n      \"11.2 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"11.2 M    Total params\\n\",\n      \"44.727    Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"38166315c3fc4959ae473726d7b911e2\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": 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\"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6af1642855ac4731b3bc76892e4a27b0\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=500` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%capture --no-display\\n\",\n    \"\\n\",\n    \"pytorch_model = torchvision.models.resnet18(weights=None)\\n\",\n    \"pytorch_model.fc = torch.nn.Linear(512, 10)\\n\",\n    \"lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"start_1 = time.time()\\n\",\n    \"\\n\",\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=500,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[1],\\n\",\n    \"    logger=CSVLogger(save_dir=\\\"logs/\\\", name=\\\"baseline\\\"),\\n\",\n    \"    deterministic=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=dm)\\n\",\n    \"\\n\",\n    \"end_1 = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"011bd890\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training time: 51.74 min\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Training time: {(end_1-start_1)/60:.2f} min\\\")\\n\",\n    \"\\n\",\n    \"plot_loss_and_acc(\\n\",\n    \"    trainer.logger.log_dir, \\n\",\n    \"    acc_ylim=(0.3, 1.1), \\n\",\n    \"    save_loss=\\\"1_autoaugment-loss.pdf\\\", \\n\",\n    \"    save_acc=\\\"1_autoaugment-acc.pdf\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"8f38e1c7-10de-4184-9e99-2c30f89097a9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3af8fe5dabcf49a78dc0f3d49e4b269c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.7829999923706055     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.7829999923706055    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.7829999923706055}]\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=dm)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"2cc95e3c-b97b-43a1-a3a2-df15e3bfccba\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/data-augmentation/autoaugment/with-trivialaugment.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4b1589ad-eeff-4590-b235-c7882ff16ee3\",\n   \"metadata\": {},\n   \"source\": [\n    \"# With TrivialAugment\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"808b53e6-93b7-4cbc-bfe4-ce87ef3a95de\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook trains a ResNet-18 model with the AutoAugment procedure on CIFAR-10. \\n\",\n    \"\\n\",\n    \"Compare the performance to the baseline [./without-augmentation.ipynb](./without-augmentation.ipynb).\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"## References\\n\",\n    \"\\n\",\n    \"AutoAugment paper:\\n\",\n    \"- [TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation](https://arxiv.org/abs/2103.10158)\\n\",\n    \"\\n\",\n    \"PyTorch/torchvision documentation:\\n\",\n    \"- [transforms.TrivialAugmentWide](https://pytorch.org/vision/main/generated/torchvision.transforms.TrivialAugmentWide.html)\\n\",\n    \"\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"4af58769\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch      : 1.13.1\\n\",\n      \"lightning  : 1.9.0\\n\",\n      \"torchvision: 0.14.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,lightning,torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"d4363916\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import time\\n\",\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torchmetrics\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"from helper_utilities import LightningModel,Cifar10DataModule,plot_loss_and_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"66486cc5-7c7e-497c-bfb9-fc5e918256f8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Global seed set to 123\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"train_transform = transforms.Compose(\\n\",\n    \"    [\\n\",\n    \"        transforms.Resize(32),\\n\",\n    \"        transforms.TrivialAugmentWide(),\\n\",\n    \"        transforms.ToTensor(),\\n\",\n    \"        \\n\",\n    \"    ]\\n\",\n    \")\\n\",\n    \"L.seed_everything(123)\\n\",\n    \"dm = Cifar10DataModule(\\n\",\n    \"    height_width=(32, 32),\\n\",\n    \"    batch_size=256, \\n\",\n    \"    train_transform=train_transform, \\n\",\n    \"    num_workers=4\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"1cd186d4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"\\n\",\n      \"  | Name      | Type               | Params\\n\",\n      \"-------------------------------------------------\\n\",\n      \"0 | model     | ResNet             | 11.2 M\\n\",\n      \"1 | train_acc | MulticlassAccuracy | 0     \\n\",\n      \"2 | val_acc   | MulticlassAccuracy | 0     \\n\",\n      \"3 | test_acc  | MulticlassAccuracy | 0     \\n\",\n      \"-------------------------------------------------\\n\",\n      \"11.2 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"11.2 M    Total params\\n\",\n      \"44.727    Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"767433b9b76d4e15b4b7f6362044e6a7\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     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0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a5a387c9000147d8b26d3f76ab1e86d4\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=1000` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%capture --no-display\\n\",\n    \"\\n\",\n    \"pytorch_model = torchvision.models.resnet18(weights=None)\\n\",\n    \"pytorch_model.fc = torch.nn.Linear(512, 10)\\n\",\n    \"lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"start_1 = time.time()\\n\",\n    \"\\n\",\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=1000,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[1],\\n\",\n    \"    logger=CSVLogger(save_dir=\\\"logs/\\\", name=\\\"trivialaugment\\\"),\\n\",\n    \"    deterministic=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=dm)\\n\",\n    \"\\n\",\n    \"end_1 = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"011bd890\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training time: 98.82 min\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Training time: {(end_1-start_1)/60:.2f} min\\\")\\n\",\n    \"\\n\",\n    \"plot_loss_and_acc(\\n\",\n    \"    trainer.logger.log_dir, \\n\",\n    \"    acc_ylim=(0.3, 1.1), \\n\",\n    \"    save_loss=\\\"1_trivialaugment-loss.pdf\\\", \\n\",\n    \"    save_acc=\\\"1_trivialaugment-acc.pdf\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"8f38e1c7-10de-4184-9e99-2c30f89097a9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7cf7cbff91864a9a9137e882764ff5a1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.8167999982833862     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.8167999982833862    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.8167999982833862}]\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=dm)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/data-augmentation/autoaugment/without-augmentation.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b3cbc7c0-a6d9-4f1b-907a-b7eaa0ce6eab\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Without Data Augmentation\\n\",\n    \"\\n\",\n    \"This notebook is a baseline for the [./with-autoaugment.ipynb](./with-autoaugment.ipynb) and [./with-trivialaugment.ipynb](./with-trivialaugment.ipynb) notebooks. It's exactly the same code and model but without data augmentation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"4af58769\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch      : 1.13.1\\n\",\n      \"lightning  : 1.9.0\\n\",\n      \"torchvision: 0.14.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,lightning,torchvision\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"d4363916\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import time\\n\",\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torchmetrics\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"from helper_utilities import LightningModel,Cifar10DataModule,plot_loss_and_acc\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"1cd186d4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Global seed set to 123\\n\",\n      \"Using bfloat16 Automatic Mixed Precision (AMP)\\n\",\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"\\n\",\n      \"  | Name      | Type               | Params\\n\",\n      \"-------------------------------------------------\\n\",\n      \"0 | model     | ResNet             | 11.2 M\\n\",\n      \"1 | train_acc | MulticlassAccuracy | 0     \\n\",\n      \"2 | val_acc   | MulticlassAccuracy | 0     \\n\",\n      \"3 | test_acc  | MulticlassAccuracy | 0     \\n\",\n      \"-------------------------------------------------\\n\",\n      \"11.2 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"11.2 M    Total params\\n\",\n      \"22.363    Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e245ca29555e4a90979bac0e313ae5b5\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=40` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%%capture --no-display\\n\",\n    \"\\n\",\n    \"L.seed_everything(123)\\n\",\n    \"dm = Cifar10DataModule(height_width=(32, 32), batch_size=256, num_workers=4)\\n\",\n    \"\\n\",\n    \"pytorch_model = torchvision.models.resnet18(weights=None)\\n\",\n    \"pytorch_model.fc = torch.nn.Linear(512, 10)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"start_1 = time.time()\\n\",\n    \"\\n\",\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=40,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    precision=16,\\n\",\n    \"    devices=[0],\\n\",\n    \"    logger=CSVLogger(save_dir=\\\"logs/\\\", name=\\\"baseline\\\"),\\n\",\n    \"    deterministic=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=dm)\\n\",\n    \"\\n\",\n    \"end_1 = time.time()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"011bd890\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training time: 3.44 min\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 640x480 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"print(f\\\"Training time: {(end_1-start_1)/60:.2f} min\\\")\\n\",\n    \"\\n\",\n    \"plot_loss_and_acc(\\n\",\n    \"    trainer.logger.log_dir, \\n\",\n    \"    acc_ylim=(0.3, 1.1), \\n\",\n    \"    save_loss=\\\"1_baseline-loss.pdf\\\", \\n\",\n    \"    save_acc=\\\"1_baseline-acc.pdf\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"869ff966-fbac-4ed3-a3b9-6894a17b1ad9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c95938ffc923418eb9a4a70f9e99540f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_acc          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.6851000189781189     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_acc         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.6851000189781189    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.6851000189781189}]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=dm)\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.10.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/kfold/baseline-light-cnn-mnist.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Author: Sebastian Raschka\\n\",\n      \"\\n\",\n      \"Python implementation: CPython\\n\",\n      \"Python version       : 3.8.12\\n\",\n      \"IPython version      : 8.0.1\\n\",\n      \"\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.10\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch,pytorch_lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Baseline -- Convolutional Neural Network on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Settings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"RANDOM_SEED = 1\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_EPOCHS = 10\\n\",\n    \"BATCH_SIZE = 128\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"NUM_CLASSES = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#!pip install gitpython\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from git import Repo\\n\",\n    \"\\n\",\n    \"if not os.path.exists(\\\"mnist-pngs\\\"):\\n\",\n    \"    Repo.clone_from(\\\"https://github.com/rasbt/mnist-pngs\\\", \\\"mnist-pngs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"data_transforms = {\\n\",\n    \"    \\\"train\\\": transforms.Compose(\\n\",\n    \"        [\\n\",\n    \"            transforms.Resize(32),\\n\",\n    \"            transforms.RandomCrop((28, 28)),\\n\",\n    \"            transforms.ToTensor(),\\n\",\n    \"            # normalize images to [-1, 1] range\\n\",\n    \"            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n\",\n    \"        ]\\n\",\n    \"    ),\\n\",\n    \"    \\\"test\\\": transforms.Compose(\\n\",\n    \"        [\\n\",\n    \"            transforms.Resize(32),\\n\",\n    \"            transforms.CenterCrop((28, 28)),\\n\",\n    \"            transforms.ToTensor(),\\n\",\n    \"            # normalize images to [-1, 1] range\\n\",\n    \"            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n\",\n    \"        ]\\n\",\n    \"    ),\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torchvision.datasets import ImageFolder\\n\",\n    \"\\n\",\n    \"train_dset = ImageFolder(root=\\\"mnist-pngs/train\\\", transform=data_transforms[\\\"train\\\"])\\n\",\n    \"\\n\",\n    \"train_dset, valid_dset = random_split(train_dset, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"test_dset = ImageFolder(root=\\\"mnist-pngs/test\\\", transform=data_transforms[\\\"test\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    num_workers=4,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"valid_loader = DataLoader(\\n\",\n    \"    dataset=valid_dset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    num_workers=4,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    num_workers=4,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import torchvision.utils as vutils\\n\",\n    \"\\n\",\n    \"real_batch = next(iter(train_loader))\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(\\n\",\n    \"    np.transpose(\\n\",\n    \"        vutils.make_grid(real_batch[0][:64], padding=2, normalize=True), (1, 2, 0)\\n\",\n    \"    )\\n\",\n    \")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchCNN(torch.nn.Module):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=3,\\n\",\n    \"                out_channels=8,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1,\\n\",\n    \"            ),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=8,\\n\",\n    \"                out_channels=16,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1,\\n\",\n    \"            ),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.classifier = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Flatten(),\\n\",\n    \"            torch.nn.Linear(784, 128),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.Linear(128, num_classes),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = self.classifier(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\", self.valid_acc, on_epoch=True, on_step=False, prog_bar=True\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"torch.manual_seed(RANDOM_SEED)\\n\",\n    \"pytorch_model = PyTorchCNN(num_classes=NUM_CLASSES)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model=pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"),  # save top 1 model\\n\",\n    \"    pl.callbacks.progress.TQDMProgressBar(refresh_rate=50),\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type       | Params\\n\",\n      \"-----------------------------------------\\n\",\n      \"0 | model     | PyTorchCNN | 103 K \\n\",\n      \"1 | train_acc | Accuracy   | 0     \\n\",\n      \"2 | valid_acc | Accuracy   | 0     \\n\",\n      \"3 | test_acc  | Accuracy   | 0     \\n\",\n      \"-----------------------------------------\\n\",\n      \"103 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"103 K     Total params\\n\",\n      \"0.413     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6dd4d92ced0445a791f6979efbad3079\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 2.00 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(\\n\",\n    \"    model=lightning_model, train_dataloaders=train_loader, val_dataloaders=valid_loader\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='ACC'>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_0/checkpoints/epoch=8-step=3860.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_0/checkpoints/epoch=8-step=3860.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e6c58b4738ca47fea139718f03ec0a7f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.9926000237464905}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9926000237464905}]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, dataloaders=test_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy            : 1.22.2\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"pandas           : 1.2.5\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"pytorch_lightning: 1.5.10\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/kfold/kfold-light-cnn-mnist.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Author: Sebastian Raschka\\n\",\n      \"\\n\",\n      \"Python implementation: CPython\\n\",\n      \"Python version       : 3.8.12\\n\",\n      \"IPython version      : 8.0.1\\n\",\n      \"\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.10\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch,pytorch_lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The little caveat is that it currently only supports PyTorch Lightning < 1.6.0. So, if you have 1.6.0 or newer, you'd need to downgrade:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#!pip install pytorch-lightning==1.5.10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# K-Fold Cross-Validation Example -- Convolutional Neural Network on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook shows how to apply k-fold cross-validation to deep neural networks using PyTorch Lightning and [`pl_cross`](https://github.com/SkafteNicki/pl_cross).\\n\",\n    \"\\n\",\n    \"This notebook is based on a basic PyTorch Lightning notebook at [./baseline-cnn-mnist.ipynb](baseline-cnn-mnist.ipynb). The changes are annotated and/or highlighted.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Settings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"RANDOM_SEED = 1\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"NUM_EPOCHS = 10\\n\",\n    \"BATCH_SIZE = 128\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"NUM_CLASSES = 10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## NEW: Install `pl_cross` Library\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Uncomment the following line if you don't have Git Python installed yet.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#!pip install gitpython\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"from git import Repo\\n\",\n    \"\\n\",\n    \"if not os.path.exists(\\\"pl_cross\\\"):\\n\",\n    \"    Repo.clone_from(\\\"https://github.com/SkafteNicki/pl_cross.git\\\", \\\"pl_cross\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Uncomment the following line to install the cloned repo.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# !cd pl_cross; python setup.py installl; cd ..\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if not os.path.exists(\\\"mnist-pngs\\\"):\\n\",\n    \"    Repo.clone_from(\\\"https://github.com/rasbt/mnist-pngs\\\", \\\"mnist-pngs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"data_transforms = {\\n\",\n    \"    \\\"train\\\": transforms.Compose(\\n\",\n    \"        [\\n\",\n    \"            transforms.Resize(32),\\n\",\n    \"            transforms.RandomCrop((28, 28)),\\n\",\n    \"            transforms.ToTensor(),\\n\",\n    \"            # normalize images to [-1, 1] range\\n\",\n    \"            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n\",\n    \"        ]\\n\",\n    \"    ),\\n\",\n    \"    \\\"test\\\": transforms.Compose(\\n\",\n    \"        [\\n\",\n    \"            transforms.Resize(32),\\n\",\n    \"            transforms.CenterCrop((28, 28)),\\n\",\n    \"            transforms.ToTensor(),\\n\",\n    \"            # normalize images to [-1, 1] range\\n\",\n    \"            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\\n\",\n    \"        ]\\n\",\n    \"    ),\\n\",\n    \"}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"from torchvision.datasets import ImageFolder\\n\",\n    \"\\n\",\n    \"train_dset = ImageFolder(root=\\\"mnist-pngs/train\\\", transform=data_transforms[\\\"train\\\"])\\n\",\n    \"\\n\",\n    \"train_dset, valid_dset = random_split(train_dset, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"test_dset = ImageFolder(root=\\\"mnist-pngs/test\\\", transform=data_transforms[\\\"test\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    num_workers=4,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"valid_loader = DataLoader(\\n\",\n    \"    dataset=valid_dset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    num_workers=4,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    num_workers=4,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import torchvision.utils as vutils\\n\",\n    \"\\n\",\n    \"real_batch = next(iter(train_loader))\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(\\n\",\n    \"    np.transpose(\\n\",\n    \"        vutils.make_grid(real_batch[0][:64], padding=2, normalize=True), (1, 2, 0)\\n\",\n    \"    )\\n\",\n    \")\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The model setup is exactly as it is before, except that the current k-fold code won't be using `validation_step`. We can still leave it in the `LightningModule` as it doesn't hurt either.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class PyTorchCNN(torch.nn.Module):\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        self.features = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=3,\\n\",\n    \"                out_channels=8,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1,\\n\",\n    \"            ),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.Conv2d(\\n\",\n    \"                in_channels=8,\\n\",\n    \"                out_channels=16,\\n\",\n    \"                kernel_size=(3, 3),\\n\",\n    \"                stride=(1, 1),\\n\",\n    \"                padding=1,\\n\",\n    \"            ),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.classifier = torch.nn.Sequential(\\n\",\n    \"            torch.nn.Flatten(),\\n\",\n    \"            torch.nn.Linear(784, 128),\\n\",\n    \"            torch.nn.ReLU(),\\n\",\n    \"            torch.nn.Linear(128, num_classes),\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = self.classifier(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # To account for Dropout behavior during evaluation\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc.update(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\", self.valid_acc, on_epoch=True, on_step=False, prog_bar=True\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"NEW: Note that for logging, we need to import a modified version of the logger from the `pl_cross` library. Also, since the k-fold approach doesn't use `validation_step` we can't checkpoint based on teh validation set accuracy and have the `training_accuracy` instead.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# OLD:\\n\",\n    \"# from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"# NEW:\\n\",\n    \"from pl_cross.loggers import CSVLogger\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"\\n\",\n    \"torch.manual_seed(RANDOM_SEED)\\n\",\n    \"pytorch_model = PyTorchCNN(num_classes=NUM_CLASSES)\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model=pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# NEW: Kfold currently doesn't call model.validation_step()\\n\",\n    \"# so the valid_acc tracking does unfortunately not work, and\\n\",\n    \"# we have to change valid_acc to train_acc.\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(save_top_k=1, mode=\\\"max\\\", monitor=\\\"train_acc\\\"),  # save top 1 model\\n\",\n    \"    pl.callbacks.progress.TQDMProgressBar(refresh_rate=50),\\n\",\n    \"]\\n\",\n    \"\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-kfold-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"NEW: Instead of using `pl.Trainer`, we use the `Trainer` from the `pl_cross` library, which lets us specify the number of folds as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type       | Params\\n\",\n      \"-----------------------------------------\\n\",\n      \"0 | model     | PyTorchCNN | 103 K \\n\",\n      \"1 | train_acc | Accuracy   | 0     \\n\",\n      \"2 | valid_acc | Accuracy   | 0     \\n\",\n      \"3 | test_acc  | Accuracy   | 0     \\n\",\n      \"-----------------------------------------\\n\",\n      \"103 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"103 K     Total params\\n\",\n      \"0.413     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/data_loading.py:489: UserWarning: One of given dataloaders is None and it will be skipped.\\n\",\n      \"  rank_zero_warn(\\\"One of given dataloaders is None and it will be skipped.\\\")\\n\",\n      \"Starting fold 1/5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"37d6b65a74624395b7c7f411b2ff2a22\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"69a7132853284d84abdd36ab27edca28\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting fold 2/5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"04c49c19195f4c3b8bf5fbf08bc18470\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f604b42fcdd941f0a20183b5df7e21f8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting fold 3/5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"27ede6c5425e4bd598e382ddbb952690\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"9daac84bb50a4fef9f8334d786b77d68\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting fold 4/5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"2658c92102654509a4f3ddb082f3dc58\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e063d1e7c1a041c1a9202ad8a492e500\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Starting fold 5/5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"604af2de09964cb784bac35185390338\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"fd418f4689ef4bcebe45f96082222631\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'train_loss_mean': tensor(0.0157), 'train_loss_std': tensor(0.0082), 'train_loss_raw': tensor([0.0139, 0.0143, 0.0181, 0.0046, 0.0274]), 'train_acc_mean': tensor(0.9892), 'train_acc_std': tensor(0.0006), 'train_acc_raw': tensor([0.9883, 0.9892, 0.9890, 0.9896, 0.9898]), 'test_acc_mean': tensor(0.9850), 'test_acc_std': tensor(0.0023), 'test_acc_raw': tensor([0.9818, 0.9836, 0.9868, 0.9875, 0.9855])}\\n\",\n      \"Training took 6.85 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"# NEW:\\n\",\n    \"from pl_cross import Trainer\\n\",\n    \"\\n\",\n    \"trainer = Trainer(\\n\",\n    \"    num_folds=5,\\n\",\n    \"    shuffle=True,\\n\",\n    \"    # stratified=True,\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=100,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"\\n\",\n    \"# NEW\\n\",\n    \"kfold_results = trainer.cross_validate(\\n\",\n    \"    model=lightning_model,\\n\",\n    \"    train_dataloader=train_loader,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"NEW: We can now look at the different test-fold accuracies, which are returned by `trainer.cross_validate`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"{'train_loss_mean': tensor(0.0157), 'train_loss_std': tensor(0.0082), 'train_loss_raw': tensor([0.0139, 0.0143, 0.0181, 0.0046, 0.0274]), 'train_acc_mean': tensor(0.9892), 'train_acc_std': tensor(0.0006), 'train_acc_raw': tensor([0.9883, 0.9892, 0.9890, 0.9896, 0.9898]), 'test_acc_mean': tensor(0.9850), 'test_acc_std': tensor(0.0023), 'test_acc_raw': tensor([0.9818, 0.9836, 0.9868, 0.9875, 0.9855])}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print(kfold_results)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"NEW: We could consider the cross-validation accuracy for model selection and once we are happy with the hyperparameter settings, we could use those to train a new model using the regular trainer approach as in [./baseline-cnn-mnist.ipynb](baseline-cnn-mnist.ipynb) (except that we use the best hyperparameters from k-fold cross-validation). \\n\",\n    \"\\n\",\n    \"Alternatively, we can create an ensemble model from the *k* models as follows:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# ensemble_model = trainer.create_ensemble(model=lightning_model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# trainer.test(model=ensemble_model, dataloaders=test_loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Note: for some reason, the `trainer.create_ensemble` is complaining about the checkpoint files, it is potentially a bug. Will update once it is fixed or I know more :).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy            : 1.22.2\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.10\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/mlp/mlp-basic.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"cc648582\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"13354529\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"0f0b986d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"782b20a0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"441f19f3\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Multilayer Perceptron trained on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6a135998\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple multilayer perceptron [1][2]  trained on MNIST [3].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] https://en.wikipedia.org/wiki/Multilayer_perceptron\\n\",\n    \"- [2] L9.1 Multilayer Perceptron Architecture (24:24): https://www.youtube.com/watch?v=IUylp47hNA0\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/MNIST_database\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"274b586f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"294fccb3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"c53a8658\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"HIDDEN_UNITS = (128, 256)\\\\nBATCH_SIZE = 256\\\\nNUM_EPOCHS = 10\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"HIDDEN_UNITS = (128, 256)\\\\nBATCH_SIZE = 256\\\\nNUM_EPOCHS = 10\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"HIDDEN_UNITS = (128, 256)\\n\",\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 10\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"681a7147\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"43552cd7\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"036000c8\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"73206ea6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(torch.nn.Module):\\\\n    def __init__(self, input_size, hidden_units, num_classes):\\\\n        super().__init__()\\\\n\\\\n        # Initialize MLP layers\\\\n        all_layers = []\\\\n        for hidden_unit in hidden_units:\\\\n            layer = torch.nn.Linear(input_size, hidden_unit, bias=False)\\\\n            all_layers.append(layer)\\\\n            all_layers.append(torch.nn.ReLU())\\\\n            input_size = hidden_unit\\\\n\\\\n        output_layer = torch.nn.Linear(\\\\n            in_features=hidden_units[-1],\\\\n            out_features=num_classes)\\\\n\\\\n        all_layers.append(output_layer)\\\\n        self.layers = torch.nn.Sequential(*all_layers)\\\\n\\\\n    def forward(self, x):\\\\n        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\\\n        x = self.layers(x)\\\\n        return x  # x are the model's logits\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(torch.nn.Module):\\\\n    def __init__(self, input_size, hidden_units, num_classes):\\\\n        super().__init__()\\\\n\\\\n        # Initialize MLP layers\\\\n        all_layers = []\\\\n        for hidden_unit in hidden_units:\\\\n            layer = torch.nn.Linear(input_size, hidden_unit, bias=False)\\\\n            all_layers.append(layer)\\\\n            all_layers.append(torch.nn.ReLU())\\\\n            input_size = hidden_unit\\\\n\\\\n        output_layer = torch.nn.Linear(\\\\n            in_features=hidden_units[-1], out_features=num_classes\\\\n        )\\\\n\\\\n        all_layers.append(output_layer)\\\\n        self.layers = torch.nn.Sequential(*all_layers)\\\\n\\\\n    def forward(self, x):\\\\n        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\\\n        x = self.layers(x)\\\\n        return x  # x are the model's logits\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class PyTorchModel(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # Initialize MLP layers\\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit, bias=False)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        output_layer = torch.nn.Linear(\\n\",\n    \"            in_features=hidden_units[-1],\\n\",\n    \"            out_features=num_classes)\\n\",\n    \"\\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.layers = torch.nn.Sequential(*all_layers)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\n\",\n    \"        x = self.layers(x)\\n\",\n    \"        return x  # x are the model's logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"0456bb7d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"612f27ca\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8370dc4c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"398a98c9\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"e790d2fe\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 980),\\n\",\n       \" (1, 1135),\\n\",\n       \" (2, 1032),\\n\",\n       \" (3, 1010),\\n\",\n       \" (4, 982),\\n\",\n       \" (5, 892),\\n\",\n       \" (6, 958),\\n\",\n       \" (7, 1028),\\n\",\n       \" (8, 974),\\n\",\n       \" (9, 1009)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_mnist_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7614c2a1\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"06421076\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"281ba33f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 1\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.11 (11.35%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"de7f0c76\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"bf9a896e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1: E999 SyntaxError: invalid syntax\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"53db5ac7\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"45817f7f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"4a896220\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path, download=True)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"84e0c986\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"5bef4a20\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"torch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"torch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"98be3b90\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"70e8032c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"ab895891\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model = PyTorchModel(\\\\n    input_size=28*28,\\\\n    hidden_units=HIDDEN_UNITS,\\\\n    num_classes=10\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model = PyTorchModel(\\\\n    input_size=28 * 28, hidden_units=HIDDEN_UNITS, num_classes=10\\\\n)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(\\n\",\n    \"    input_size=28*28,\\n\",\n    \"    hidden_units=HIDDEN_UNITS,\\n\",\n    \"    num_classes=10\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"e1f1e0b2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bd23ba0b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"895c30e5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 135 K \\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"135 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"135 K     Total params\\n\",\n      \"0.543     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"632418f2dc854242b9ed931df6cb7fbd\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"3b3fa3d40a6b4363a52ee5dd1eeb65f8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 1.49 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=True,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=True,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9a51c407\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"37158f6b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"c45bdb30\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fdf88243\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"754de266\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_28/checkpoints/epoch=5-step=1283.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_28/checkpoints/epoch=5-step=1283.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"001b40c7001b4c0cab6065218773519f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.9732000231742859}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9732000231742859}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"58194874\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4628738b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"01989054\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_28/checkpoints/epoch=5-step=1283.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"af8beaa2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"22b5fe2a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"ae01649e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([1, 2, 3, 4, 5])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5dd0d4c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"95a755b1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9732 (97.32%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3ba5a7ab\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c4a4ae96\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"00a63e89\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"# In the case of MNIST, the class label mapping\\\\n# is relatively trivial\\\\nclass_dict = {0: 'digit 0',\\\\n              1: 'digit 1',\\\\n              2: 'digit 2',\\\\n              3: 'digit 3',\\\\n              4: 'digit 4',\\\\n              5: 'digit 5',\\\\n              6: 'digit 6',\\\\n              7: 'digit 7',\\\\n              8: 'digit 8',\\\\n              9: 'digit 9'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# In the case of MNIST, the class label mapping\\\\n# is relatively trivial\\\\nclass_dict = {\\\\n    0: \\\\\\\"digit 0\\\\\\\",\\\\n    1: \\\\\\\"digit 1\\\\\\\",\\\\n    2: \\\\\\\"digit 2\\\\\\\",\\\\n    3: \\\\\\\"digit 3\\\\\\\",\\\\n    4: \\\\\\\"digit 4\\\\\\\",\\\\n    5: \\\\\\\"digit 5\\\\\\\",\\\\n    6: \\\\\\\"digit 6\\\\\\\",\\\\n    7: \\\\\\\"digit 7\\\\\\\",\\\\n    8: \\\\\\\"digit 8\\\\\\\",\\\\n    9: \\\\\\\"digit 9\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# In the case of MNIST, the class label mapping\\n\",\n    \"# is relatively trivial\\n\",\n    \"class_dict = {0: 'digit 0',\\n\",\n    \"              1: 'digit 1',\\n\",\n    \"              2: 'digit 2',\\n\",\n    \"              3: 'digit 3',\\n\",\n    \"              4: 'digit 4',\\n\",\n    \"              5: 'digit 5',\\n\",\n    \"              6: 'digit 6',\\n\",\n    \"              7: 'digit 7',\\n\",\n    \"              8: 'digit 8',\\n\",\n    \"              9: 'digit 9'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"e85d1736\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f4c169e3\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"b4e18658\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"561c80ff\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"matplotlib       : 3.3.4\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/mlp/mlp-batchnorm.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"a26e9ac9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c94aeda6\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"d95a5e76\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"a3eefdb4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3903c912\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Multilayer Perceptron with BatchNorm trained on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"64c5c3ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple multilayer perceptron [1][2] with BatchNorm [3][4][5] trained on MNIST [6].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] https://en.wikipedia.org/wiki/Multilayer_perceptron\\n\",\n    \"- [2] L9.1 Multilayer Perceptron Architecture (24:24): https://www.youtube.com/watch?v=IUylp47hNA0\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/Batch_normalization\\n\",\n    \"- [4] L11.2 How BatchNorm Works (15:14): https://www.youtube.com/watch?v=34PDIFvvESc\\n\",\n    \"- [5] Batch normalization: Accelerating deep network training by reducing internal covariate shift, http://proceedings.mlr.press/v37/ioffe15.html\\n\",\n    \"- [6] https://en.wikipedia.org/wiki/MNIST_database\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"454310a9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"359a99b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"5e359318\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"HIDDEN_UNITS = (128, 256)\\\\nBATCH_SIZE = 256\\\\nNUM_EPOCHS = 10\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\\nDROPOUT_PROBA = 0.5\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"HIDDEN_UNITS = (128, 256)\\\\nBATCH_SIZE = 256\\\\nNUM_EPOCHS = 10\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\\nDROPOUT_PROBA = 0.5\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"HIDDEN_UNITS = (128, 256)\\n\",\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 10\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\\n\",\n    \"DROPOUT_PROBA = 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8358f61f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e8424235\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b958a33d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"2192b28a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(torch.nn.Module):\\\\n    def __init__(self, input_size, hidden_units, num_classes):\\\\n        super().__init__()\\\\n\\\\n        # Initialize MLP layers\\\\n        all_layers = []\\\\n        for hidden_unit in hidden_units:\\\\n            layer = torch.nn.Linear(input_size, hidden_unit, bias=False)\\\\n            all_layers.append(layer)\\\\n            all_layers.append(torch.nn.BatchNorm1d(hidden_unit))\\\\n            all_layers.append(torch.nn.ReLU())\\\\n            input_size = hidden_unit\\\\n\\\\n        output_layer = torch.nn.Linear(\\\\n            in_features=hidden_units[-1],\\\\n            out_features=num_classes)\\\\n\\\\n        all_layers.append(output_layer)\\\\n        self.layers = torch.nn.Sequential(*all_layers)\\\\n\\\\n    def forward(self, x):\\\\n        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\\\n        x = self.layers(x)\\\\n        return x  # x are the model's logits\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(torch.nn.Module):\\\\n    def __init__(self, input_size, hidden_units, num_classes):\\\\n        super().__init__()\\\\n\\\\n        # Initialize MLP layers\\\\n        all_layers = []\\\\n        for hidden_unit in hidden_units:\\\\n            layer = torch.nn.Linear(input_size, hidden_unit, bias=False)\\\\n            all_layers.append(layer)\\\\n            all_layers.append(torch.nn.BatchNorm1d(hidden_unit))\\\\n            all_layers.append(torch.nn.ReLU())\\\\n            input_size = hidden_unit\\\\n\\\\n        output_layer = torch.nn.Linear(\\\\n            in_features=hidden_units[-1], out_features=num_classes\\\\n        )\\\\n\\\\n        all_layers.append(output_layer)\\\\n        self.layers = torch.nn.Sequential(*all_layers)\\\\n\\\\n    def forward(self, x):\\\\n        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\\\n        x = self.layers(x)\\\\n        return x  # x are the model's logits\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class PyTorchModel(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # Initialize MLP layers\\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit, bias=False)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.BatchNorm1d(hidden_unit))\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        output_layer = torch.nn.Linear(\\n\",\n    \"            in_features=hidden_units[-1],\\n\",\n    \"            out_features=num_classes)\\n\",\n    \"\\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.layers = torch.nn.Sequential(*all_layers)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\n\",\n    \"        x = self.layers(x)\\n\",\n    \"        return x  # x are the model's logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"26f52f6c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1c38927e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4f9c27a8\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e8427abe\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"13239d0a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 980),\\n\",\n       \" (1, 1135),\\n\",\n       \" (2, 1032),\\n\",\n       \" (3, 1010),\\n\",\n       \" (4, 982),\\n\",\n       \" (5, 892),\\n\",\n       \" (6, 958),\\n\",\n       \" (7, 1028),\\n\",\n       \" (8, 974),\\n\",\n       \" (9, 1009)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_mnist_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d9eaff5\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"afcb616e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"8cead536\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 1\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.11 (11.35%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6a19bc75\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"4eb6fa59\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1: E999 SyntaxError: invalid syntax\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"86d6829f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d985135b\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"c4443e49\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path, download=True)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b529b097\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"82aa3f69\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"torch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"torch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"145890b8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1b02578f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"3bdf56f9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model = PyTorchModel(\\\\n    input_size=28*28,\\\\n    hidden_units=HIDDEN_UNITS,\\\\n    num_classes=10\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model = PyTorchModel(\\\\n    input_size=28 * 28, hidden_units=HIDDEN_UNITS, num_classes=10\\\\n)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(\\n\",\n    \"    input_size=28*28,\\n\",\n    \"    hidden_units=HIDDEN_UNITS,\\n\",\n    \"    num_classes=10\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"55b32795\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"23254d05\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"b92c3460\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 136 K \\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"136 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"136 K     Total params\\n\",\n      \"0.546     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"9d320e4597dc49cfafc51ccd8656904e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"af8467ef0148444f8682ec381c0854a8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 1.55 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=True,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=True,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"896acd5c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c6ed0d55\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"105d19fa\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0f56851c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"3d758dc5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_27/checkpoints/epoch=9-step=2139.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_27/checkpoints/epoch=9-step=2139.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"91a89ae0becc401f9a393fec58e86dd1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.9789000153541565}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9789000153541565}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3710fce6\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"845d2600\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"5fa39e10\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_27/checkpoints/epoch=9-step=2139.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"ff0a16e7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3e70777e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"b45e1a54\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([1, 2, 3, 4, 5])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4f61530a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"f09b25cb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9789 (97.89%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2c295fa7\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"292df05e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"5aa9198f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"# In the case of MNIST, the class label mapping\\\\n# is relatively trivial\\\\nclass_dict = {0: 'digit 0',\\\\n              1: 'digit 1',\\\\n              2: 'digit 2',\\\\n              3: 'digit 3',\\\\n              4: 'digit 4',\\\\n              5: 'digit 5',\\\\n              6: 'digit 6',\\\\n              7: 'digit 7',\\\\n              8: 'digit 8',\\\\n              9: 'digit 9'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# In the case of MNIST, the class label mapping\\\\n# is relatively trivial\\\\nclass_dict = {\\\\n    0: \\\\\\\"digit 0\\\\\\\",\\\\n    1: \\\\\\\"digit 1\\\\\\\",\\\\n    2: \\\\\\\"digit 2\\\\\\\",\\\\n    3: \\\\\\\"digit 3\\\\\\\",\\\\n    4: \\\\\\\"digit 4\\\\\\\",\\\\n    5: \\\\\\\"digit 5\\\\\\\",\\\\n    6: \\\\\\\"digit 6\\\\\\\",\\\\n    7: \\\\\\\"digit 7\\\\\\\",\\\\n    8: \\\\\\\"digit 8\\\\\\\",\\\\n    9: \\\\\\\"digit 9\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# In the case of MNIST, the class label mapping\\n\",\n    \"# is relatively trivial\\n\",\n    \"class_dict = {0: 'digit 0',\\n\",\n    \"              1: 'digit 1',\\n\",\n    \"              2: 'digit 2',\\n\",\n    \"              3: 'digit 3',\\n\",\n    \"              4: 'digit 4',\\n\",\n    \"              5: 'digit 5',\\n\",\n    \"              6: 'digit 6',\\n\",\n    \"              7: 'digit 7',\\n\",\n    \"              8: 'digit 8',\\n\",\n    \"              9: 'digit 9'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"16a6db06\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5b73177d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"50db45b8\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"2f25a950\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"matplotlib       : 3.3.4\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/mlp/mlp-dropout.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"d7bb5b4c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"07125f0f\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"864e1616\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"9d8a3c40\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 3;\\n\",\n       \"                var nbb_unformatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%load_ext nb_black\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"01859a43\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Multilayer Perceptron with Dropout trained on MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9315c0f1\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple multilayer perceptron [1][2] with Dropout [3][4][5] trained on MNIST [6].\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] https://en.wikipedia.org/wiki/Multilayer_perceptron\\n\",\n    \"- [2] L9.1 Multilayer Perceptron Architecture (24:24): https://www.youtube.com/watch?v=IUylp47hNA0\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/Dilution_(neural_networks)\\n\",\n    \"- [4] L10.5.1 The Main Concept Behind Dropout (11:07): https://www.youtube.com/watch?v=IHrZNBsgtwU\\n\",\n    \"- [5] Dropout: a simple way to prevent neural networks from overfitting, https://jmlr.org/papers/v15/srivastava14a.html\\n\",\n    \"- [6] https://en.wikipedia.org/wiki/MNIST_database\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2da793e7\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"41acc842\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"c4c24c79\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 4;\\n\",\n       \"                var nbb_unformatted_code = \\\"HIDDEN_UNITS = (128, 256)\\\\nBATCH_SIZE = 256\\\\nNUM_EPOCHS = 10\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\\nDROPOUT_PROBA = 0.5\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"HIDDEN_UNITS = (128, 256)\\\\nBATCH_SIZE = 256\\\\nNUM_EPOCHS = 10\\\\nLEARNING_RATE = 0.005\\\\nNUM_WORKERS = 4\\\\nDROPOUT_PROBA = 0.5\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"HIDDEN_UNITS = (128, 256)\\n\",\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 10\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\\n\",\n    \"DROPOUT_PROBA = 0.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a5a41296\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6d72e878\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2d657971\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"48a7733d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 5;\\n\",\n       \"                var nbb_unformatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(torch.nn.Module):\\\\n    def __init__(self, input_size, hidden_units, dropout_proba, num_classes):\\\\n        super().__init__()\\\\n        \\\\n        self.dropout_proba = dropout_proba\\\\n        # Initialize MLP layers\\\\n        all_layers = []\\\\n        for hidden_unit in hidden_units:\\\\n            layer = torch.nn.Linear(input_size, hidden_unit)\\\\n            all_layers.append(layer)\\\\n            all_layers.append(torch.nn.ReLU())\\\\n            all_layers.append(torch.nn.Dropout(dropout_proba))\\\\n            input_size = hidden_unit\\\\n\\\\n        output_layer = torch.nn.Linear(\\\\n            in_features=hidden_units[-1],\\\\n            out_features=num_classes)\\\\n\\\\n        all_layers.append(output_layer)\\\\n        self.layers = torch.nn.Sequential(*all_layers)\\\\n\\\\n    def forward(self, x):\\\\n        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\\\n        x = self.layers(x)\\\\n        return x  # x are the model's logits\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"import torch\\\\nimport torch.nn.functional as F\\\\n\\\\n\\\\n# Regular PyTorch Module\\\\nclass PyTorchModel(torch.nn.Module):\\\\n    def __init__(self, input_size, hidden_units, dropout_proba, num_classes):\\\\n        super().__init__()\\\\n\\\\n        self.dropout_proba = dropout_proba\\\\n        # Initialize MLP layers\\\\n        all_layers = []\\\\n        for hidden_unit in hidden_units:\\\\n            layer = torch.nn.Linear(input_size, hidden_unit)\\\\n            all_layers.append(layer)\\\\n            all_layers.append(torch.nn.ReLU())\\\\n            all_layers.append(torch.nn.Dropout(dropout_proba))\\\\n            input_size = hidden_unit\\\\n\\\\n        output_layer = torch.nn.Linear(\\\\n            in_features=hidden_units[-1], out_features=num_classes\\\\n        )\\\\n\\\\n        all_layers.append(output_layer)\\\\n        self.layers = torch.nn.Sequential(*all_layers)\\\\n\\\\n    def forward(self, x):\\\\n        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\\\n        x = self.layers(x)\\\\n        return x  # x are the model's logits\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class PyTorchModel(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, dropout_proba, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"        \\n\",\n    \"        self.dropout_proba = dropout_proba\\n\",\n    \"        # Initialize MLP layers\\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            all_layers.append(torch.nn.Dropout(dropout_proba))\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        output_layer = torch.nn.Linear(\\n\",\n    \"            in_features=hidden_units[-1],\\n\",\n    \"            out_features=num_classes)\\n\",\n    \"\\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.layers = torch.nn.Sequential(*all_layers)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = torch.flatten(x, start_dim=1)  # to make it work for image inputs\\n\",\n    \"        x = self.layers(x)\\n\",\n    \"        return x  # x are the model's logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"0ceb58d3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 6;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\\\nimport pytorch_lightning as pl\\\\nimport torchmetrics\\\\n\\\\n\\\\n# LightningModule that receives a PyTorch model as input\\\\nclass LightningModel(pl.LightningModule):\\\\n    def __init__(self, model, learning_rate):\\\\n        super().__init__()\\\\n\\\\n        self.learning_rate = learning_rate\\\\n        # The inherited PyTorch module\\\\n        self.model = model\\\\n        if hasattr(model, \\\\\\\"dropout_proba\\\\\\\"):\\\\n            self.dropout_proba = model.dropout_proba\\\\n\\\\n        # Save settings and hyperparameters to the log directory\\\\n        # but skip the model parameters\\\\n        self.save_hyperparameters(ignore=[\\\\\\\"model\\\\\\\"])\\\\n\\\\n        # Set up attributes for computing the accuracy\\\\n        self.train_acc = torchmetrics.Accuracy()\\\\n        self.valid_acc = torchmetrics.Accuracy()\\\\n        self.test_acc = torchmetrics.Accuracy()\\\\n\\\\n    # Defining the forward method is only necessary\\\\n    # if you want to use a Trainer's .predict() method (optional)\\\\n    def forward(self, x):\\\\n        return self.model(x)\\\\n\\\\n    # A common forward step to compute the loss and labels\\\\n    # this is used for training, validation, and testing below\\\\n    def _shared_step(self, batch):\\\\n        features, true_labels = batch\\\\n        logits = self(features)\\\\n        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\\\n        predicted_labels = torch.argmax(logits, dim=1)\\\\n\\\\n        return loss, true_labels, predicted_labels\\\\n\\\\n    def training_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"train_loss\\\\\\\", loss)\\\\n\\\\n        # Do another forward pass in .eval() mode to compute accuracy\\\\n        # while accountingfor Dropout, BatchNorm etc. behavior\\\\n        # during evaluation (inference)\\\\n        self.model.eval()\\\\n        with torch.no_grad():\\\\n            _, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.train_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"train_acc\\\\\\\", self.train_acc, on_epoch=True, on_step=False)\\\\n        self.model.train()\\\\n\\\\n        return loss  # this is passed to the optimzer for training\\\\n\\\\n    def validation_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.log(\\\\\\\"valid_loss\\\\\\\", loss)\\\\n        self.valid_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\n            \\\\\\\"valid_acc\\\\\\\",\\\\n            self.valid_acc,\\\\n            on_epoch=True,\\\\n            on_step=False,\\\\n            prog_bar=True,\\\\n        )\\\\n\\\\n    def test_step(self, batch, batch_idx):\\\\n        loss, true_labels, predicted_labels = self._shared_step(batch)\\\\n        self.test_acc(predicted_labels, true_labels)\\\\n        self.log(\\\\\\\"test_acc\\\\\\\", self.test_acc, on_epoch=True, on_step=False)\\\\n\\\\n    def configure_optimizers(self):\\\\n        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\\\n        return optimizer\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/lightningmodule_classifier_basic.py\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningModel(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"        if hasattr(model, \\\"dropout_proba\\\"):\\n\",\n    \"            self.dropout_proba = model.dropout_proba\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=[\\\"model\\\"])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the accuracy\\n\",\n    \"        self.train_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.valid_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    # Defining the forward method is only necessary\\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"\\n\",\n    \"        # Do another forward pass in .eval() mode to compute accuracy\\n\",\n    \"        # while accountingfor Dropout, BatchNorm etc. behavior\\n\",\n    \"        # during evaluation (inference)\\n\",\n    \"        self.model.eval()\\n\",\n    \"        with torch.no_grad():\\n\",\n    \"            _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.train_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_acc\\\", self.train_acc, on_epoch=True, on_step=False)\\n\",\n    \"        self.model.train()\\n\",\n    \"\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\n\",\n    \"            \\\"valid_acc\\\",\\n\",\n    \"            self.valid_acc,\\n\",\n    \"            on_epoch=True,\\n\",\n    \"            on_step=False,\\n\",\n    \"            prog_bar=True,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_acc(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_acc\\\", self.test_acc, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6c708125\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"70effd61\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5cd9d8d7\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"c006d71b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Training label distribution:\\n\",\n      \"\\n\",\n      \"Test label distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[(0, 980),\\n\",\n       \" (1, 1135),\\n\",\n       \" (2, 1032),\\n\",\n       \" (3, 1010),\\n\",\n       \" (4, 982),\\n\",\n       \" (5, 892),\\n\",\n       \" (6, 958),\\n\",\n       \" (7, 1028),\\n\",\n       \" (8, 974),\\n\",\n       \" (9, 1009)]\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 7;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/dataset_mnist_check.py\\\\nfrom collections import Counter\\\\nfrom torchvision import datasets\\\\nfrom torchvision import transforms\\\\nfrom torch.utils.data import DataLoader\\\\n\\\\n\\\\ntrain_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=True, transform=transforms.ToTensor(), download=True\\\\n)\\\\n\\\\ntrain_loader = DataLoader(\\\\n    dataset=train_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=True,\\\\n    shuffle=True,\\\\n)\\\\n\\\\ntest_dataset = datasets.MNIST(\\\\n    root=\\\\\\\"./data\\\\\\\", train=False, transform=transforms.ToTensor()\\\\n)\\\\n\\\\ntest_loader = DataLoader(\\\\n    dataset=test_dataset,\\\\n    batch_size=BATCH_SIZE,\\\\n    num_workers=NUM_WORKERS,\\\\n    drop_last=False,\\\\n    shuffle=False,\\\\n)\\\\n\\\\ntrain_counter = Counter()\\\\nfor images, labels in train_loader:\\\\n    train_counter.update(labels.tolist())\\\\n\\\\ntest_counter = Counter()\\\\nfor images, labels in test_loader:\\\\n    test_counter.update(labels.tolist())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTraining label distribution:\\\\\\\")\\\\nsorted(train_counter.items())\\\\n\\\\nprint(\\\\\\\"\\\\\\\\nTest label distribution:\\\\\\\")\\\\nsorted(test_counter.items())\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/dataset_mnist_check.py\\n\",\n    \"from collections import Counter\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=True, transform=transforms.ToTensor(), download=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=True,\\n\",\n    \"    shuffle=True,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(\\n\",\n    \"    root=\\\"./data\\\", train=False, transform=transforms.ToTensor()\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    num_workers=NUM_WORKERS,\\n\",\n    \"    drop_last=False,\\n\",\n    \"    shuffle=False,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"train_counter = Counter()\\n\",\n    \"for images, labels in train_loader:\\n\",\n    \"    train_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"test_counter = Counter()\\n\",\n    \"for images, labels in test_loader:\\n\",\n    \"    test_counter.update(labels.tolist())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTraining label distribution:\\\")\\n\",\n    \"sorted(train_counter.items())\\n\",\n    \"\\n\",\n    \"print(\\\"\\\\nTest label distribution:\\\")\\n\",\n    \"sorted(test_counter.items())\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"06f8b3ab\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"48152108\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"2964f21c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Majority class: 1\\n\",\n      \"Accuracy when always predicting the majority class:\\n\",\n      \"0.11 (11.35%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 8;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/performance_baseline.py\\\\nmajority_class = test_counter.most_common(1)[0]\\\\nprint(\\\\\\\"Majority class:\\\\\\\", majority_class[0])\\\\n\\\\nbaseline_acc = majority_class[1] / sum(test_counter.values())\\\\nprint(\\\\\\\"Accuracy when always predicting the majority class:\\\\\\\")\\\\nprint(f\\\\\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/performance_baseline.py\\n\",\n    \"majority_class = test_counter.most_common(1)[0]\\n\",\n    \"print(\\\"Majority class:\\\", majority_class[0])\\n\",\n    \"\\n\",\n    \"baseline_acc = majority_class[1] / sum(test_counter.values())\\n\",\n    \"print(\\\"Accuracy when always predicting the majority class:\\\")\\n\",\n    \"print(f\\\"{baseline_acc:.2f} ({baseline_acc*100:.2f}%)\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a65b93d9\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"208eba1a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1: E999 SyntaxError: invalid syntax\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 9;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:  \\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(np.transpose(torchvision.utils.make_grid(\\\\n    images[:64], \\\\n    padding=2,\\\\n    normalize=True),\\\\n    (1, 2, 0)))\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_dataset/plot_visual-check_basic.py\\\\n%matplotlib inline\\\\nimport matplotlib.pyplot as plt\\\\nimport numpy as np\\\\nimport torchvision\\\\n\\\\n\\\\nfor images, labels in train_loader:\\\\n    break\\\\n\\\\nplt.figure(figsize=(8, 8))\\\\nplt.axis(\\\\\\\"off\\\\\\\")\\\\nplt.title(\\\\\\\"Training images\\\\\\\")\\\\nplt.imshow(\\\\n    np.transpose(\\\\n        torchvision.utils.make_grid(images[:64], padding=2, normalize=True), (1, 2, 0)\\\\n    )\\\\n)\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_dataset/plot_visual-check_basic.py\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import torchvision\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(\\n\",\n    \"    images[:64], \\n\",\n    \"    padding=2,\\n\",\n    \"    normalize=True),\\n\",\n    \"    (1, 2, 0)))\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7c96452f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0c565e9f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"95064321\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 10;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\\\nfrom torch.utils.data.dataset import random_split\\\\n\\\\n\\\\nclass DataModule(pl.LightningDataModule):\\\\n    def __init__(self, data_path=\\\\\\\"./\\\\\\\"):\\\\n        super().__init__()\\\\n        self.data_path = data_path\\\\n\\\\n    def prepare_data(self):\\\\n        datasets.MNIST(root=self.data_path, download=True)\\\\n        return\\\\n\\\\n    def setup(self, stage=None):\\\\n        # Note transforms.ToTensor() scales input images\\\\n        # to 0-1 range\\\\n        train = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=True,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.test = datasets.MNIST(\\\\n            root=self.data_path,\\\\n            train=False,\\\\n            transform=transforms.ToTensor(),\\\\n            download=False,\\\\n        )\\\\n\\\\n        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\\\n\\\\n    def train_dataloader(self):\\\\n        train_loader = DataLoader(\\\\n            dataset=self.train,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=True,\\\\n            shuffle=True,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return train_loader\\\\n\\\\n    def val_dataloader(self):\\\\n        valid_loader = DataLoader(\\\\n            dataset=self.valid,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return valid_loader\\\\n\\\\n    def test_dataloader(self):\\\\n        test_loader = DataLoader(\\\\n            dataset=self.test,\\\\n            batch_size=BATCH_SIZE,\\\\n            drop_last=False,\\\\n            shuffle=False,\\\\n            num_workers=NUM_WORKERS,\\\\n        )\\\\n        return test_loader\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_mnist_basic.py\\n\",\n    \"from torch.utils.data.dataset import random_split\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path=\\\"./\\\"):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"\\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        datasets.MNIST(root=self.data_path, download=True)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        # Note transforms.ToTensor() scales input images\\n\",\n    \"        # to 0-1 range\\n\",\n    \"        train = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=True,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.test = datasets.MNIST(\\n\",\n    \"            root=self.data_path,\\n\",\n    \"            train=False,\\n\",\n    \"            transform=transforms.ToTensor(),\\n\",\n    \"            download=False,\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"        self.train, self.valid = random_split(train, lengths=[55000, 5000])\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        train_loader = DataLoader(\\n\",\n    \"            dataset=self.train,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=True,\\n\",\n    \"            shuffle=True,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return train_loader\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        valid_loader = DataLoader(\\n\",\n    \"            dataset=self.valid,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return valid_loader\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        test_loader = DataLoader(\\n\",\n    \"            dataset=self.test,\\n\",\n    \"            batch_size=BATCH_SIZE,\\n\",\n    \"            drop_last=False,\\n\",\n    \"            shuffle=False,\\n\",\n    \"            num_workers=NUM_WORKERS,\\n\",\n    \"        )\\n\",\n    \"        return test_loader\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"34182855\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"47bd719f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 11;\\n\",\n       \"                var nbb_unformatted_code = \\\"torch.manual_seed(1) \\\\ndata_module = DataModule(data_path='./data')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"torch.manual_seed(1)\\\\ndata_module = DataModule(data_path=\\\\\\\"./data\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d19674fd\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0f4e3ffd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"d555825a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 12;\\n\",\n       \"                var nbb_unformatted_code = \\\"pytorch_model = PyTorchModel(\\\\n    input_size=28*28,\\\\n    hidden_units=HIDDEN_UNITS,\\\\n    dropout_proba=0.5,\\\\n    num_classes=10\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"pytorch_model = PyTorchModel(\\\\n    input_size=28 * 28, hidden_units=HIDDEN_UNITS, dropout_proba=0.5, num_classes=10\\\\n)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(\\n\",\n    \"    input_size=28*28,\\n\",\n    \"    hidden_units=HIDDEN_UNITS,\\n\",\n    \"    dropout_proba=0.5,\\n\",\n    \"    num_classes=10\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"598f6194\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 13;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(\\\\n        save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\"\\\\n    )  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\\\nfrom pytorch_lightning.callbacks import ModelCheckpoint\\\\nfrom pytorch_lightning.loggers import CSVLogger\\\\n\\\\n\\\\nlightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\\\n\\\\ncallbacks = [\\\\n    ModelCheckpoint(save_top_k=1, mode=\\\\\\\"max\\\\\\\", monitor=\\\\\\\"valid_acc\\\\\\\")  # save top 1 model\\\\n]\\\\nlogger = CSVLogger(save_dir=\\\\\\\"logs/\\\\\\\", name=\\\\\\\"my-model\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_acc_basic.py\\n\",\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(pytorch_model, learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"valid_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"65b36418\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"1b4154cd\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type         | Params\\n\",\n      \"-------------------------------------------\\n\",\n      \"0 | model     | PyTorchModel | 136 K \\n\",\n      \"1 | train_acc | Accuracy     | 0     \\n\",\n      \"2 | valid_acc | Accuracy     | 0     \\n\",\n      \"3 | test_acc  | Accuracy     | 0     \\n\",\n      \"-------------------------------------------\\n\",\n      \"136 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"136 K     Total params\\n\",\n      \"0.544     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a7042cc7ac564deab2f9232bc13319d9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"9d57e2b0fc284593a90543130eaa3c04\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 1.47 min in total.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 14;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=True,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/trainer_nb_basic.py\\\\nimport time\\\\n\\\\n\\\\ntrainer = pl.Trainer(\\\\n    max_epochs=NUM_EPOCHS,\\\\n    callbacks=callbacks,\\\\n    progress_bar_refresh_rate=50,  # recommended for notebooks\\\\n    accelerator=\\\\\\\"auto\\\\\\\",  # Uses GPUs or TPUs if available\\\\n    devices=\\\\\\\"auto\\\\\\\",  # Uses all available GPUs/TPUs if applicable\\\\n    logger=logger,\\\\n    deterministic=True,\\\\n    log_every_n_steps=10,\\\\n)\\\\n\\\\nstart_time = time.time()\\\\ntrainer.fit(model=lightning_model, datamodule=data_module)\\\\n\\\\nruntime = (time.time() - start_time) / 60\\\\nprint(f\\\\\\\"Training took {runtime:.2f} min in total.\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/trainer_nb_basic.py\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time) / 60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7a1d6022\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"41053fb4\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"c3a243bc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 15;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\\\nimport pandas as pd\\\\nimport matplotlib.pyplot as plt\\\\n\\\\n\\\\nmetrics = pd.read_csv(f\\\\\\\"{trainer.logger.log_dir}/metrics.csv\\\\\\\")\\\\n\\\\naggreg_metrics = []\\\\nagg_col = \\\\\\\"epoch\\\\\\\"\\\\nfor i, dfg in metrics.groupby(agg_col):\\\\n    agg = dict(dfg.mean())\\\\n    agg[agg_col] = i\\\\n    aggreg_metrics.append(agg)\\\\n\\\\ndf_metrics = pd.DataFrame(aggreg_metrics)\\\\ndf_metrics[[\\\\\\\"train_loss\\\\\\\", \\\\\\\"valid_loss\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"Loss\\\\\\\"\\\\n)\\\\ndf_metrics[[\\\\\\\"train_acc\\\\\\\", \\\\\\\"valid_acc\\\\\\\"]].plot(\\\\n    grid=True, legend=True, xlabel=\\\\\\\"Epoch\\\\\\\", ylabel=\\\\\\\"ACC\\\\\\\"\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/logger_csv_plot_basic.py\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"Loss\\\"\\n\",\n    \")\\n\",\n    \"df_metrics[[\\\"train_acc\\\", \\\"valid_acc\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel=\\\"Epoch\\\", ylabel=\\\"ACC\\\"\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c5d6b9a5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"a12ebfdb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_26/checkpoints/epoch=4-step=1069.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_26/checkpoints/epoch=4-step=1069.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"284c0189c5194292bc96188a73332ec6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"--------------------------------------------------------------------------------\\n\",\n      \"DATALOADER:0 TEST RESULTS\\n\",\n      \"{'test_acc': 0.9678000211715698}\\n\",\n      \"--------------------------------------------------------------------------------\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_acc': 0.9678000211715698}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 16;\\n\",\n       \"                var nbb_unformatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path=\\\\\\\"best\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9ad02490\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a2c99c9f\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"888d7401\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"logs/my-model/version_26/checkpoints/epoch=4-step=1069.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 17;\\n\",\n       \"                var nbb_unformatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"path = trainer.checkpoint_callback.best_model_path\\\\nprint(path)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"a20a392d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 18;\\n\",\n       \"                var nbb_unformatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval();\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\\\nlightning_model.eval()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1ef39e66\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"1d52a91b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([1, 2, 3, 4, 5])\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 19;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/datamodule_testloader.py\\\\ntest_dataloader = data_module.test_dataloader()\\\\nacc = torchmetrics.Accuracy()\\\\n\\\\nfor batch in test_dataloader:\\\\n    features, true_labels = batch\\\\n\\\\n    with torch.no_grad():\\\\n        logits = lightning_model(features)\\\\n\\\\n    predicted_labels = torch.argmax(logits, dim=1)\\\\n    acc(predicted_labels, true_labels)\\\\n\\\\npredicted_labels[:5]\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/datamodule_testloader.py\\n\",\n    \"test_dataloader = data_module.test_dataloader()\\n\",\n    \"acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"for batch in test_dataloader:\\n\",\n    \"    features, true_labels = batch\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        logits = lightning_model(features)\\n\",\n    \"\\n\",\n    \"    predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"    acc(predicted_labels, true_labels)\\n\",\n    \"\\n\",\n    \"predicted_labels[:5]\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4135ecc0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"6f21a4dc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 0.9678 (96.78%)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 20;\\n\",\n       \"                var nbb_unformatted_code = \\\"test_acc = acc.compute()\\\\nprint(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"test_acc = acc.compute()\\\\nprint(f\\\\\\\"Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)\\\\\\\")\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e117f2b8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"936812ae\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"ca72a754\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 21;\\n\",\n       \"                var nbb_unformatted_code = \\\"# In the case of MNIST, the class label mapping\\\\n# is relatively trivial\\\\nclass_dict = {0: 'digit 0',\\\\n              1: 'digit 1',\\\\n              2: 'digit 2',\\\\n              3: 'digit 3',\\\\n              4: 'digit 4',\\\\n              5: 'digit 5',\\\\n              6: 'digit 6',\\\\n              7: 'digit 7',\\\\n              8: 'digit 8',\\\\n              9: 'digit 9'}\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# In the case of MNIST, the class label mapping\\\\n# is relatively trivial\\\\nclass_dict = {\\\\n    0: \\\\\\\"digit 0\\\\\\\",\\\\n    1: \\\\\\\"digit 1\\\\\\\",\\\\n    2: \\\\\\\"digit 2\\\\\\\",\\\\n    3: \\\\\\\"digit 3\\\\\\\",\\\\n    4: \\\\\\\"digit 4\\\\\\\",\\\\n    5: \\\\\\\"digit 5\\\\\\\",\\\\n    6: \\\\\\\"digit 6\\\\\\\",\\\\n    7: \\\\\\\"digit 7\\\\\\\",\\\\n    8: \\\\\\\"digit 8\\\\\\\",\\\\n    9: \\\\\\\"digit 9\\\\\\\",\\\\n}\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# In the case of MNIST, the class label mapping\\n\",\n    \"# is relatively trivial\\n\",\n    \"class_dict = {0: 'digit 0',\\n\",\n    \"              1: 'digit 1',\\n\",\n    \"              2: 'digit 2',\\n\",\n    \"              3: 'digit 3',\\n\",\n    \"              4: 'digit 4',\\n\",\n    \"              5: 'digit 5',\\n\",\n    \"              6: 'digit 6',\\n\",\n    \"              7: 'digit 7',\\n\",\n    \"              8: 'digit 8',\\n\",\n    \"              9: 'digit 9'}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"a68d8b92\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 22;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(\\\\n    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\\\n)\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_failurecases_basic.py\\\\n# Append the folder that contains the\\\\n# helper_data.py, helper_plotting.py, and helper_evaluate.py\\\\n# files so we can import from them\\\\n\\\\nimport sys\\\\n\\\\nsys.path.append(\\\\\\\"../../pytorch_ipynb\\\\\\\")\\\\n\\\\nfrom helper_plotting import show_examples\\\\n\\\\n\\\\nshow_examples(model=lightning_model, data_loader=test_dataloader, class_dict=class_dict)\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_failurecases_basic.py\\n\",\n    \"# Append the folder that contains the\\n\",\n    \"# helper_data.py, helper_plotting.py, and helper_evaluate.py\\n\",\n    \"# files so we can import from them\\n\",\n    \"\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.append(\\\"../../pytorch_ipynb\\\")\\n\",\n    \"\\n\",\n    \"from helper_plotting import show_examples\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"show_examples(\\n\",\n    \"    model=lightning_model, data_loader=test_dataloader, class_dict=class_dict\\n\",\n    \")\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4eb65020\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"3612be2a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 23;\\n\",\n       \"                var nbb_unformatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()  \\\\n    # normed colormaps highlight the off-diagonals \\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\\\nfrom torchmetrics import ConfusionMatrix\\\\nimport matplotlib\\\\nfrom mlxtend.plotting import plot_confusion_matrix\\\\n\\\\n\\\\ncmat = ConfusionMatrix(num_classes=len(class_dict))\\\\n\\\\nfor x, y in test_dataloader:\\\\n\\\\n    with torch.no_grad():\\\\n        pred = lightning_model(x)\\\\n    cmat(pred, y)\\\\n\\\\ncmat_tensor = cmat.compute()\\\\ncmat = cmat_tensor.numpy()\\\\n\\\\nfig, ax = plot_confusion_matrix(\\\\n    conf_mat=cmat,\\\\n    class_names=class_dict.values(),\\\\n    norm_colormap=matplotlib.colors.LogNorm()\\\\n    # normed colormaps highlight the off-diagonals\\\\n    # for high-accuracy models better\\\\n)\\\\n\\\\nplt.show()\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# %load ../code_lightningmodule/plot_confusion-matrix_basic.py\\n\",\n    \"from torchmetrics import ConfusionMatrix\\n\",\n    \"import matplotlib\\n\",\n    \"from mlxtend.plotting import plot_confusion_matrix\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"cmat = ConfusionMatrix(num_classes=len(class_dict))\\n\",\n    \"\\n\",\n    \"for x, y in test_dataloader:\\n\",\n    \"\\n\",\n    \"    with torch.no_grad():\\n\",\n    \"        pred = lightning_model(x)\\n\",\n    \"    cmat(pred, y)\\n\",\n    \"\\n\",\n    \"cmat_tensor = cmat.compute()\\n\",\n    \"cmat = cmat_tensor.numpy()\\n\",\n    \"\\n\",\n    \"fig, ax = plot_confusion_matrix(\\n\",\n    \"    conf_mat=cmat,\\n\",\n    \"    class_names=class_dict.values(),\\n\",\n    \"    norm_colormap=matplotlib.colors.LogNorm()  \\n\",\n    \"    # normed colormaps highlight the off-diagonals \\n\",\n    \"    # for high-accuracy models better\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"9ed5403b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"pytorch_lightning: 1.5.1\\n\",\n      \"torchvision      : 0.11.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"numpy            : 1.22.0\\n\",\n      \"sys              : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"pandas           : 1.4.1\\n\",\n      \"torch            : 1.10.1\\n\",\n      \"\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/javascript\": [\n       \"\\n\",\n       \"            setTimeout(function() {\\n\",\n       \"                var nbb_cell_id = 24;\\n\",\n       \"                var nbb_unformatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_formatted_code = \\\"%watermark --iversions\\\";\\n\",\n       \"                var nbb_cells = Jupyter.notebook.get_cells();\\n\",\n       \"                for (var i = 0; i < nbb_cells.length; ++i) {\\n\",\n       \"                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\\n\",\n       \"                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\\n\",\n       \"                             nbb_cells[i].set_text(nbb_formatted_code);\\n\",\n       \"                        }\\n\",\n       \"                        break;\\n\",\n       \"                    }\\n\",\n       \"                }\\n\",\n       \"            }, 500);\\n\",\n       \"            \"\n      ],\n      \"text/plain\": [\n       \"<IPython.core.display.Javascript object>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/mlp/template_classification_basic.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1e2086ae\",\n   \"metadata\": {},\n   \"source\": [\n    \"The three extensions below are optional, for more information, see\\n\",\n    \"- `watermark`:  https://github.com/rasbt/watermark\\n\",\n    \"- `pycodestyle_magic`: https://github.com/mattijn/pycodestyle_magic\\n\",\n    \"- `nb_black`: https://github.com/dnanhkhoa/nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"149f964e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"625d1d05\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703,E402,E999 --max_line_length=100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f52f4600\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext nb_black\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9dfafe15\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# TITLE\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"501948bd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- DESCRIPTION\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- ???\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b7e16245\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b3915fdd\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"7f540e31\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 10\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 4\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b472b8d9\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that using multiple workers can sometimes cause issues with too many open files in PyTorch for small datasets. If we have problems with the data loader later, try setting `NUM_WORKERS = 0` and reload the notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a85efca2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a Neural Network using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"041bfee2\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- In essence, `LightningModule` is a wrapper around a PyTorch module.\\n\",\n    \"- We start with defining our neural network model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"1cda97b5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# UNIQUE MODEL CODE\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"2d8c0e05\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/lightningmodule_classifier_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2961a362\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d86805b0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a6fa61b4\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"087f0762\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_dataset/dataset_???_check.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b8305a40\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d8c4d897\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's pretty helpful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- we want our model to be better than that!\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"993ad0d5\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_dataset/performance_baseline.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dab874ca\",\n   \"metadata\": {},\n   \"source\": [\n    \"## A quick visual check\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"3a6f0aa6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_dataset/plot_visual-check_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4f40449e\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"734560ae\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the following subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we will use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods, as we can see below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"f5c8cf5b\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/datamodule_???_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"25132aa4\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the dataset loading -- if we run our code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, let's initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"17132fed\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path='./data')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3ad8293d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"25c2b2fb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our model.\\n\",\n    \"- Also, we define a call back to obtain the model with the best validation set performance after training.\\n\",\n    \"- PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"c1acb2f1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"pytorch_model = PyTorchModel(\\n\",\n    \"    ???\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"0ca2ccdd\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/logger_csv_acc_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"64857a91\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"61f9f037\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/trainer_nb_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cb7a9818\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5556da91\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training ACC and validation ACC using pandas, which, in turn, uses matplotlib for plotting (PS: you may want to check out [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) later on, which take care of it for us):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"b424d39d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/logger_csv_plot_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ee93a525\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The `trainer` automatically saves the model with the best validation accuracy automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d02f1378\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b6cd101f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Predicting labels of new data\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ffed918e\",\n   \"metadata\": {},\n   \"source\": [\n    \"- We can use the `trainer.predict` method either on a new `DataLoader` (`trainer.predict(dataloaders=...)`) or `DataModule` (`trainer.predict(datamodule=...)`) to apply the model to new data.\\n\",\n    \"- Alternatively, we can also manually load the best model from a checkpoint as shown below:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"9e018ee1\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"path = trainer.checkpoint_callback.best_model_path\\n\",\n    \"print(path)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d2548be4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"lightning_model = LightningModel.load_from_checkpoint(path, model=pytorch_model)\\n\",\n    \"lightning_model.eval();\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"82eea81a\",\n   \"metadata\": {},\n   \"source\": [\n    \"- For simplicity, we reused our existing `pytorch_model` above. However, we could also reinitialize the `pytorch_model`, and the `.load_from_checkpoint` method would load the corresponding model weights for us from the checkpoint file.\\n\",\n    \"- Now, below is an example applying the model manually. Here, pretend that the `test_dataloader` is a new data loader.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"e5781814\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/datamodule_testloader.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ad3600a0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As an internal check, if the model was loaded correctly, the test accuracy below should be identical to the test accuracy we saw earlier in the previous section.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"c31bbca9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"test_acc = acc.compute()\\n\",\n    \"print(f'Test accuracy: {test_acc:.4f} ({test_acc*100:.2f}%)')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9e2265c7\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Inspecting Failure Cases\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4913044c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In practice, it is often informative to look at failure cases like wrong predictions for particular training instances as it can give us some insights into the model behavior and dataset.\\n\",\n    \"- Inspecting failure cases can sometimes reveal interesting patterns and even highlight dataset and labeling issues.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"d1b5c4a6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# In the case of ???, the class label mapping\\n\",\n    \"# ???\\n\",\n    \"class_dict = {???}\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"18ab0294\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/plot_failurecases_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"481b4f08\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In addition to inspecting failure cases visually, it is also informative to look at which classes the model confuses the most via a confusion matrix:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"a2cb8af8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load ../code_lightningmodule/plot_confusion-matrix_basic.py\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"8d48d1ef\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%watermark --iversions\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/ordinal/CORAL-light_cement.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"aebb3445\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"coral_pytorch    : 1.2.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib,coral_pytorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"504da038\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f6282332\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# CORAL MLP for ordinal regression and deep learning -- cement strength dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3675d863\",\n   \"metadata\": {},\n   \"source\": [\n    \"This tutorial explains how to train a deep neural network (here: multilayer perceptron) with the CORAL layer and loss function for ordinal regression. \\n\",\n    \"\\n\",\n    \"**CORAL reference:**\\n\",\n    \"\\n\",\n    \"- Wenzhi Cao, Vahid Mirjalili, and Sebastian Raschka (2020) \\n\",\n    \"[Rank Consistent Ordinal Regression for Neural Networks with Application to Age Estimation](https://www.sciencedirect.com/science/article/pii/S016786552030413X) \\n\",\n    \"Pattern Recognition Letters. 140, 325-331\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"17767cf8\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note:**\\n\",\n    \"    \\n\",\n    \"To keep the notation lean and minimal, this notebook only contains \\\"Squared-error reformulation\\\"-specific comments. For more comments on the PyTorch Lightning use, please see the cross-entropy baseline notebook [baseline-light_cement.ipynb](./baseline-light_cement.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"10219d24\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"c039fb28\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 32\\n\",\n    \"NUM_EPOCHS = 200\\n\",\n    \"LEARNING_RATE = 0.01\\n\",\n    \"NUM_WORKERS = 0\\n\",\n    \"\\n\",\n    \"DATA_BASEPATH = \\\".\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ec033e03\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Converting a regular classifier into a CORAL ordinal regression model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0dddd83f\",\n   \"metadata\": {},\n   \"source\": [\n    \"Changing a classifier to a CORAL model for ordinal regression is actually really simple and only requires a few changes:\\n\",\n    \"\\n\",\n    \"**1)**\\n\",\n    \"We replace the output layer  \\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"by a CORAL layer (available through `coral_pytorch`):\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"output_layer = CoralLayer(size_in=hidden_units[-1], num_classes=num_classes)`\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**2)**\\n\",\n    \"\\n\",\n    \"Convert the integer class labels into the extended binary label format using the `levels_from_labelbatch` provided via `coral_pytorch`:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"levels = levels_from_labelbatch(class_labels, \\n\",\n    \"                                num_classes=num_classes)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**3)** \\n\",\n    \"\\n\",\n    \"Swap the cross entropy loss from PyTorch,\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"with the CORAL loss (also provided via `coral_pytorch`):\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"loss = coral_loss(logits, levels)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**4)**\\n\",\n    \"\\n\",\n    \"In a regular classifier, we usually obtain the predicted class labels as follows:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Replace this with the following code to convert the predicted probabilities into the predicted labels:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"probas = torch.sigmoid(logits)\\n\",\n    \"predicted_labels = proba_to_label(probas)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1af9bd66\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a `MultiLayerPerceptron` using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"077b5287\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"- Given a multilayer perceptron classifier with cross-entropy loss, it is very easy to change this classifier into a ordinal regression model using CORAL as explained in the previous section. In the code example below, we use \\\"1) the `CoralLayer`\\\".\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"9e19e4de\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"from coral_pytorch.layers import CoralLayer\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class MultiLayerPerceptron(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # num_classes is used by the CORAL loss function\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        # Initialize MLP layers\\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        # CORAL: output layer -------------------------------------------\\n\",\n    \"        # Regular classifier would use the following output layer:\\n\",\n    \"        # output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"        \\n\",\n    \"        # We replace it by the CORAL layer:\\n\",\n    \"        output_layer = CoralLayer(size_in=hidden_units[-1],\\n\",\n    \"                                  num_classes=num_classes)\\n\",\n    \"        # ----------------------------------------------------------------\\n\",\n    \"        \\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.model = torch.nn.Sequential(*all_layers)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.model(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bbabc3d0\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In our `LightningModule` we use loggers to track mean absolute errors for both the training and validation set during training; this allows us to select the best model based on validation set performance later.\\n\",\n    \"- Note that we make changes 2) (`levels_from_labelbatch`), 3) (`coral_loss`), and 4) (`proba_to_label`) to implement a CORAL model instead of a regular classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"bfde200c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from coral_pytorch.losses import coral_loss\\n\",\n    \"from coral_pytorch.dataset import levels_from_labelbatch\\n\",\n    \"from coral_pytorch.dataset import proba_to_label\\n\",\n    \"\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningMLP(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        self.train_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.valid_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.test_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        \\n\",\n    \"        # Convert class labels for CORAL ------------------------\\n\",\n    \"        levels = levels_from_labelbatch(\\n\",\n    \"            true_labels, num_classes=self.model.num_classes)\\n\",\n    \"        # -------------------------------------------------------\\n\",\n    \"\\n\",\n    \"        logits = self(features)\\n\",\n    \"\\n\",\n    \"        # CORAL Loss --------------------------------------------\\n\",\n    \"        # A regular classifier uses:\\n\",\n    \"        # loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        loss = coral_loss(logits, levels.type_as(logits))\\n\",\n    \"        # -------------------------------------------------------\\n\",\n    \"\\n\",\n    \"        # CORAL Prediction to label -----------------------------\\n\",\n    \"        # A regular classifier uses:\\n\",\n    \"        # predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"        probas = torch.sigmoid(logits)\\n\",\n    \"        predicted_labels = proba_to_label(probas)\\n\",\n    \"        # -------------------------------------------------------\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        self.train_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_mae\\\", self.train_mae, on_epoch=True, on_step=False)\\n\",\n    \"        return loss\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_mae\\\", self.valid_mae,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_mae\\\", self.test_mae, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"71643d31\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Note: There Are No Changes Compared To The Baseline Below\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"147de9ed\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2ad9a35b\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"15574f7b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>response</th>\\n\",\n       \"      <th>V1</th>\\n\",\n       \"      <th>V2</th>\\n\",\n       \"      <th>V3</th>\\n\",\n       \"      <th>V4</th>\\n\",\n       \"      <th>V5</th>\\n\",\n       \"      <th>V6</th>\\n\",\n       \"      <th>V7</th>\\n\",\n       \"      <th>V8</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1040.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1055.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>270</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>365</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>198.6</td>\\n\",\n       \"      <td>132.4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>192.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>978.4</td>\\n\",\n       \"      <td>825.5</td>\\n\",\n       \"      <td>360</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   response     V1     V2   V3     V4   V5      V6     V7   V8\\n\",\n       \"0         4  540.0    0.0  0.0  162.0  2.5  1040.0  676.0   28\\n\",\n       \"1         4  540.0    0.0  0.0  162.0  2.5  1055.0  676.0   28\\n\",\n       \"2         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  270\\n\",\n       \"3         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  365\\n\",\n       \"4         2  198.6  132.4  0.0  192.0  0.0   978.4  825.5  360\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_df = pd.read_csv(\\\"https://raw.githubusercontent.com/gagolews/\\\"\\n\",\n    \"                      \\\"ordinal_regression_data/master/cement_strength.csv\\\")\\n\",\n    \"data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"\\n\",\n    \"data_labels = data_df[\\\"response\\\"]\\n\",\n    \"data_features = data_df.loc[:, [\\n\",\n    \"    \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"\\n\",\n    \"data_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"121ac20c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of features: 8\\n\",\n      \"Number of examples: 998\\n\",\n      \"Labels: [0 1 2 3 4]\\n\",\n      \"Label distribution: [196 310 244 152  96]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Number of features:', data_features.shape[1])\\n\",\n    \"print('Number of examples:', data_features.shape[0])\\n\",\n    \"print('Labels:', np.unique(data_labels.values))\\n\",\n    \"print('Label distribution:', np.bincount(data_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4d7e50c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"f46c09a4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline MAE: 1.03\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"avg_prediction = np.median(data_labels.values)  # median minimizes MAE\\n\",\n    \"baseline_mae = np.mean(np.abs(data_labels.values - avg_prediction))\\n\",\n    \"print(f'Baseline MAE: {baseline_mae:.2f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0c3b99ac\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating a `Dataset` class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"0a1f7264\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyDataset(Dataset):\\n\",\n    \"\\n\",\n    \"    def __init__(self, feature_array, label_array, dtype=np.float32):\\n\",\n    \"        self.features = feature_array.astype(dtype)\\n\",\n    \"        self.labels = label_array\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        inputs = self.features[index]\\n\",\n    \"        label = self.labels[index]\\n\",\n    \"        return inputs, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.features.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2b092ca9\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"b036894c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            'https://raw.githubusercontent.com/gagolews/'\\n\",\n    \"            'ordinal_regression_data/master/cement_strength.csv')\\n\",\n    \"        data_df.to_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'), index=None)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'))\\n\",\n    \"        data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"        self.data_labels = data_df[\\\"response\\\"]\\n\",\n    \"        self.data_features = data_df.loc[:, [\\n\",\n    \"            \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"        \\n\",\n    \"        # Split into\\n\",\n    \"        # 70% train, 10% validation, 20% testing\\n\",\n    \"        \\n\",\n    \"        X_temp, X_test, y_temp, y_test = train_test_split(\\n\",\n    \"            self.data_features.values,\\n\",\n    \"            self.data_labels.values,\\n\",\n    \"            test_size=0.2,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=self.data_labels.values)\\n\",\n    \"\\n\",\n    \"        X_train, X_valid, y_train, y_valid = train_test_split(\\n\",\n    \"            X_temp,\\n\",\n    \"            y_temp,\\n\",\n    \"            test_size=0.1,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=y_temp)\\n\",\n    \"        \\n\",\n    \"        # Standardize features\\n\",\n    \"        sc = StandardScaler()\\n\",\n    \"        X_train_std = sc.fit_transform(X_train)\\n\",\n    \"        X_valid_std = sc.transform(X_valid)\\n\",\n    \"        X_test_std = sc.transform(X_test)\\n\",\n    \"\\n\",\n    \"        self.train = MyDataset(X_train_std, y_train)\\n\",\n    \"        self.valid = MyDataset(X_valid_std, y_valid)\\n\",\n    \"        self.test = MyDataset(X_test_std, y_test)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        return DataLoader(self.train, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True)\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        return DataLoader(self.valid, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        return DataLoader(self.test, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"cdb18ceb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path=DATA_BASEPATH)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73e75df2\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"bfc57a43\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = MultiLayerPerceptron(\\n\",\n    \"    input_size=data_features.shape[1],\\n\",\n    \"    hidden_units=(24, 16),\\n\",\n    \"    num_classes=np.bincount(data_labels).shape[0])\\n\",\n    \"\\n\",\n    \"lightning_model = LightningMLP(\\n\",\n    \"    model=pytorch_model,\\n\",\n    \"    learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode=\\\"min\\\", monitor=\\\"valid_mae\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"mlp-coral-cement\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"caf327e0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type                 | Params\\n\",\n      \"---------------------------------------------------\\n\",\n      \"0 | model     | MultiLayerPerceptron | 636   \\n\",\n      \"1 | train_mae | MeanAbsoluteError    | 0     \\n\",\n      \"2 | valid_mae | MeanAbsoluteError    | 0     \\n\",\n      \"3 | test_mae  | MeanAbsoluteError    | 0     \\n\",\n      \"---------------------------------------------------\\n\",\n      \"636       Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"636       Total params\\n\",\n      \"0.003     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"6f2d5ded21124662b9d4ed2d4bb0cd1d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": 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\"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n 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{\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": 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\"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     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0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": 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\"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 0.60 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b031c637\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"efb086f5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='MAE'>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_mae\\\", \\\"valid_mae\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='MAE')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"c113e6b0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/mlp-coral-cement/version_0/checkpoints/epoch=114-step=2529.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/mlp-coral-cement/version_0/checkpoints/epoch=114-step=2529.ckpt\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"651ce18e66064ff996bf3f1a691d2959\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_mae          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">           0.25            </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_mae         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m          0.25           \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_mae': 0.25}]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/ordinal/CORN-light_cement.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"a5f4cdf9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"coral_pytorch    : 1.2.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib,coral_pytorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"e83c61eb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"705ad02a\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# CORN MLP for ordinal regression and deep learning -- cement strength dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0c030661\",\n   \"metadata\": {},\n   \"source\": [\n    \"This tutorial explains how to train a deep neural network (here: multilayer perceptron) with the CORN loss function for ordinal regression. \\n\",\n    \"\\n\",\n    \"**CORN reference:**\\n\",\n    \"\\n\",\n    \"- Xintong Shi, Wenzhi Cao, and Sebastian Raschka \\n\",\n    \"[Deep Neural Networks for Rank-Consistent Ordinal Regression Based On Conditional Probabilities](https://arxiv.org/abs/2111.08851).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a3dc673f\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note:**\\n\",\n    \"    \\n\",\n    \"To keep the notation lean and minimal, this notebook only contains \\\"Squared-error reformulation\\\"-specific comments. For more comments on the PyTorch Lightning use, please see the cross-entropy baseline notebook [baseline-light_cement.ipynb](./baseline-light_cement.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a1c21dd6\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"853b3b04\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 200\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 0\\n\",\n    \"\\n\",\n    \"DATA_BASEPATH = \\\".\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f24df342\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Converting a regular classifier into a CORN ordinal regression model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"302d4c8e\",\n   \"metadata\": {},\n   \"source\": [\n    \"Changing a classifier to a CORN model for ordinal regression is actually really simple and only requires a few changes:\\n\",\n    \"\\n\",\n    \"**1)**\\n\",\n    \"\\n\",\n    \"Consider the following output layer used by a neural network classifier:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"In CORN we reduce the number of classes by 1:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"output_layer = torch.nn.Linear(hidden_units[-1], num_classes-1)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**2)** \\n\",\n    \"\\n\",\n    \"We swap the cross entropy loss from PyTorch,\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"with the CORN loss (also provided via `coral_pytorch`):\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"loss = corn_loss(logits, true_labels,\\n\",\n    \"                 num_classes=num_classes)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Note that we pass `num_classes` instead of `num_classes-1` \\n\",\n    \"to the `corn_loss` as it takes care of the rest internally.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**3)**\\n\",\n    \"\\n\",\n    \"In a regular classifier, we usually obtain the predicted class labels as follows:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"In CORN, w replace this with the following code to convert the predicted probabilities into the predicted labels:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"predicted_labels = corn_label_from_logits(logits)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e062c5b6\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a `MultiLayerPerceptron` using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"3dc5a901\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class MultiLayerPerceptron(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # num_classes is used by the corn loss function\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        # Initialize MLP layers\\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        # CORN output layer -------------------------------------------\\n\",\n    \"        # Regular classifier would use num_classes instead of \\n\",\n    \"        # num_classes-1 below\\n\",\n    \"        output_layer = torch.nn.Linear(hidden_units[-1], num_classes-1)\\n\",\n    \"        # -------------------------------------------------------------\\n\",\n    \"        \\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.model = torch.nn.Sequential(*all_layers)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.model(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0af9e429\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In our `LightningModule` we use loggers to track mean absolute errors for both the training and validation set during training; this allows us to select the best model based on validation set performance later.\\n\",\n    \"- Given a multilayer perceptron classifier with cross-entropy loss, it is very easy to change this classifier into a ordinal regression model using CORN. In essence, it only requires three changes:\\n\",\n    \"    1. Instead of using `num_classes` in the output layer, use `num_classes-1` as shown above\\n\",\n    \"    2. Change the loss from   \\n\",\n    \"    `loss = torch.nn.functional.cross_entropy(logits, y)` to  \\n\",\n    \"    `loss = corn_loss(logits, y, num_classes=self.num_classes)`\\n\",\n    \"    3. To obtain the class/rank labels from the logits, change  \\n\",\n    \"    `predicted_labels = torch.argmax(logits, dim=1)` to  \\n\",\n    \"    `predicted_labels = corn_label_from_logits(logits)`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"734379e3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"#!pip install coral-pytorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"70caed83\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from coral_pytorch.losses import corn_loss\\n\",\n    \"from coral_pytorch.dataset import corn_label_from_logits\\n\",\n    \"\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningMLP(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        self.train_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.valid_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.test_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"\\n\",\n    \"        # Use CORN loss --------------------------------------\\n\",\n    \"        # A regular classifier uses:\\n\",\n    \"        # loss = torch.nn.functional.cross_entropy(logits, y)\\n\",\n    \"        loss = corn_loss(logits, true_labels,\\n\",\n    \"                         num_classes=self.model.num_classes)\\n\",\n    \"        # ----------------------------------------------------\\n\",\n    \"        \\n\",\n    \"        # CORN logits to labels ------------------------------\\n\",\n    \"        # A regular classifier uses:\\n\",\n    \"        # predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"        predicted_labels = corn_label_from_logits(logits)\\n\",\n    \"        # ----------------------------------------------------\\n\",\n    \"        \\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        self.train_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_mae\\\", self.train_mae, on_epoch=True, on_step=False)\\n\",\n    \"        return loss\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_mae\\\", self.valid_mae,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_mae\\\", self.test_mae, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"84ed8c2c\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Note: There Are No Changes Compared To The Baseline Below\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"23803478\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"94b55c58\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"74df9ef0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>response</th>\\n\",\n       \"      <th>V1</th>\\n\",\n       \"      <th>V2</th>\\n\",\n       \"      <th>V3</th>\\n\",\n       \"      <th>V4</th>\\n\",\n       \"      <th>V5</th>\\n\",\n       \"      <th>V6</th>\\n\",\n       \"      <th>V7</th>\\n\",\n       \"      <th>V8</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1040.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1055.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>270</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>365</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>198.6</td>\\n\",\n       \"      <td>132.4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>192.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>978.4</td>\\n\",\n       \"      <td>825.5</td>\\n\",\n       \"      <td>360</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   response     V1     V2   V3     V4   V5      V6     V7   V8\\n\",\n       \"0         4  540.0    0.0  0.0  162.0  2.5  1040.0  676.0   28\\n\",\n       \"1         4  540.0    0.0  0.0  162.0  2.5  1055.0  676.0   28\\n\",\n       \"2         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  270\\n\",\n       \"3         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  365\\n\",\n       \"4         2  198.6  132.4  0.0  192.0  0.0   978.4  825.5  360\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_df = pd.read_csv(\\\"https://raw.githubusercontent.com/gagolews/\\\"\\n\",\n    \"                      \\\"ordinal_regression_data/master/cement_strength.csv\\\")\\n\",\n    \"data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1\\n\",\n    \"\\n\",\n    \"data_labels = data_df[\\\"response\\\"]\\n\",\n    \"data_features = data_df.loc[:, [\\n\",\n    \"    \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"\\n\",\n    \"data_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"83b32cbb\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of features: 8\\n\",\n      \"Number of examples: 998\\n\",\n      \"Labels: [0 1 2 3 4]\\n\",\n      \"Label distribution: [196 310 244 152  96]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Number of features:', data_features.shape[1])\\n\",\n    \"print('Number of examples:', data_features.shape[0])\\n\",\n    \"print('Labels:', np.unique(data_labels.values))\\n\",\n    \"print('Label distribution:', np.bincount(data_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8e928da3\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"5262fcd7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline MAE: 1.03\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"avg_prediction = np.median(data_labels.values)  # median minimizes MAE\\n\",\n    \"baseline_mae = np.mean(np.abs(data_labels.values - avg_prediction))\\n\",\n    \"print(f'Baseline MAE: {baseline_mae:.2f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a1cb0a07\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating a `Dataset` class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"b36c978c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyDataset(Dataset):\\n\",\n    \"\\n\",\n    \"    def __init__(self, feature_array, label_array, dtype=np.float32):\\n\",\n    \"        self.features = feature_array.astype(dtype)\\n\",\n    \"        self.labels = label_array\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        inputs = self.features[index]\\n\",\n    \"        label = self.labels[index]\\n\",\n    \"        return inputs, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.features.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"719a608c\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"e6730811\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            'https://raw.githubusercontent.com/gagolews/'\\n\",\n    \"            'ordinal_regression_data/master/cement_strength.csv')\\n\",\n    \"        data_df.to_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'), index=None)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'))\\n\",\n    \"        data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"        self.data_labels = data_df[\\\"response\\\"]\\n\",\n    \"        self.data_features = data_df.loc[:, [\\n\",\n    \"            \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"        \\n\",\n    \"        # Split into\\n\",\n    \"        # 70% train, 10% validation, 20% testing\\n\",\n    \"        \\n\",\n    \"        X_temp, X_test, y_temp, y_test = train_test_split(\\n\",\n    \"            self.data_features.values,\\n\",\n    \"            self.data_labels.values,\\n\",\n    \"            test_size=0.2,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=self.data_labels.values)\\n\",\n    \"\\n\",\n    \"        X_train, X_valid, y_train, y_valid = train_test_split(\\n\",\n    \"            X_temp,\\n\",\n    \"            y_temp,\\n\",\n    \"            test_size=0.1,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=y_temp)\\n\",\n    \"        \\n\",\n    \"        # Standardize features\\n\",\n    \"        sc = StandardScaler()\\n\",\n    \"        X_train_std = sc.fit_transform(X_train)\\n\",\n    \"        X_valid_std = sc.transform(X_valid)\\n\",\n    \"        X_test_std = sc.transform(X_test)\\n\",\n    \"\\n\",\n    \"        self.train = MyDataset(X_train_std, y_train)\\n\",\n    \"        self.valid = MyDataset(X_valid_std, y_valid)\\n\",\n    \"        self.test = MyDataset(X_test_std, y_test)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        return DataLoader(self.train, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True)\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        return DataLoader(self.valid, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        return DataLoader(self.test, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"353aea97\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path=DATA_BASEPATH)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73b61d3c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"dfd42d86\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = MultiLayerPerceptron(\\n\",\n    \"    input_size=data_features.shape[1],\\n\",\n    \"    hidden_units=(40, 20),\\n\",\n    \"    num_classes=np.bincount(data_labels).shape[0])\\n\",\n    \"\\n\",\n    \"lightning_model = LightningMLP(\\n\",\n    \"    model=pytorch_model,\\n\",\n    \"    learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode=\\\"min\\\", monitor=\\\"valid_mae\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"mlp-corn-cement\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"35ddeed1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type                 | Params\\n\",\n      \"---------------------------------------------------\\n\",\n      \"0 | model     | MultiLayerPerceptron | 1.3 K \\n\",\n      \"1 | train_mae | MeanAbsoluteError    | 0     \\n\",\n      \"2 | valid_mae | MeanAbsoluteError    | 0     \\n\",\n      \"3 | test_mae  | MeanAbsoluteError    | 0     \\n\",\n      \"---------------------------------------------------\\n\",\n      \"1.3 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"1.3 K     Total params\\n\",\n      \"0.005     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1850: PossibleUserWarning: The number of training samples (5) is smaller than the logging interval Trainer(log_every_n_steps=10). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"13306318e6de400fa02aa1687d6bc6a0\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       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    \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": 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    \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 0.32 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4aabf616\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"cf02efe3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='MAE'>\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_mae\\\", \\\"valid_mae\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='MAE')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"4e41d562\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/mlp-corn-cement/version_3/checkpoints/epoch=194-step=974.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/mlp-corn-cement/version_3/checkpoints/epoch=194-step=974.ckpt\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b2791a16b2994d48aac25c9a1727c9db\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_mae          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">           0.25            </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_mae         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m          0.25           \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_mae': 0.25}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/ordinal/baseline-light_cement.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"66951a8c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"coral_pytorch    : 1.2.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib,coral_pytorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"60a1d929\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6dc0efd6\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Cross entropy baseline model for ordinal regression and deep learning -- cement strength dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"47f22067\",\n   \"metadata\": {},\n   \"source\": [\n    \"This is a regular cross entropy classifier as a baseline for comparison with ordinal regression methods.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6c528557\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2527be84\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Here, we specify some general hyperparameter values and general settings\\n\",\n    \"- Note that for small datatsets, it is not necessary and better not to use multiple workers as it can sometimes cause issues with too many open files in PyTorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"37d461ef\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 200\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 0\\n\",\n    \"\\n\",\n    \"DATA_BASEPATH = \\\".\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ddfe5704\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a `MultiLayerPerceptron` using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"829a6664\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we set up the main model architecture using the `LightningModule` from PyTorch Lightning.\\n\",\n    \"- We start with defining our `MultiLayerPerceptron` model in pure PyTorch, and then we use it in the `LightningModule` to get all the extra benefits that PyTorch Lightning provides.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"c03faadc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class MultiLayerPerceptron(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # num_classes is used by the corn loss function\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        # Initialize MLP layers\\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"        \\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.model = torch.nn.Sequential(*all_layers)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.model(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8ff4cacb\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In our `LightningModule` we use loggers to track mean absolute errors for both the training and validation set during training; this allows us to select the best model based on validation set performance later.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"0c14a021\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# LightningModule that receives a PyTorch model as input\\n\",\n    \"class LightningMLP(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        # The inherited PyTorch module\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        # Save settings and hyperparameters to the log directory\\n\",\n    \"        # but skip the model parameters\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        # Set up attributes for computing the MAE\\n\",\n    \"        self.train_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.valid_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.test_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        \\n\",\n    \"    # Defining the forward method is only necessary \\n\",\n    \"    # if you want to use a Trainer's .predict() method (optional)\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    # A common forward step to compute the loss and labels\\n\",\n    \"    # this is used for training, validation, and testing below\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"        loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        self.train_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_mae\\\", self.train_mae, on_epoch=True, on_step=False)\\n\",\n    \"        return loss  # this is passed to the optimzer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_mae\\\", self.valid_mae,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_mae\\\", self.test_mae, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6ec9d753\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5621120c\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In this section, we are going to set up our dataset.\\n\",\n    \"- We start by downloading and taking a look at the Cement dataset:\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"5311a22d\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"27c9ba77\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>response</th>\\n\",\n       \"      <th>V1</th>\\n\",\n       \"      <th>V2</th>\\n\",\n       \"      <th>V3</th>\\n\",\n       \"      <th>V4</th>\\n\",\n       \"      <th>V5</th>\\n\",\n       \"      <th>V6</th>\\n\",\n       \"      <th>V7</th>\\n\",\n       \"      <th>V8</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1040.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1055.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>270</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>365</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>198.6</td>\\n\",\n       \"      <td>132.4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>192.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>978.4</td>\\n\",\n       \"      <td>825.5</td>\\n\",\n       \"      <td>360</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   response     V1     V2   V3     V4   V5      V6     V7   V8\\n\",\n       \"0         4  540.0    0.0  0.0  162.0  2.5  1040.0  676.0   28\\n\",\n       \"1         4  540.0    0.0  0.0  162.0  2.5  1055.0  676.0   28\\n\",\n       \"2         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  270\\n\",\n       \"3         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  365\\n\",\n       \"4         2  198.6  132.4  0.0  192.0  0.0   978.4  825.5  360\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_df = pd.read_csv(\\\"https://raw.githubusercontent.com/gagolews/\\\"\\n\",\n    \"                      \\\"ordinal_regression_data/master/cement_strength.csv\\\")\\n\",\n    \"data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"\\n\",\n    \"data_labels = data_df[\\\"response\\\"]\\n\",\n    \"data_features = data_df.loc[:, [\\n\",\n    \"    \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"\\n\",\n    \"data_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"f9611a79\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of features: 8\\n\",\n      \"Number of examples: 998\\n\",\n      \"Labels: [0 1 2 3 4]\\n\",\n      \"Label distribution: [196 310 244 152  96]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Number of features:', data_features.shape[1])\\n\",\n    \"print('Number of examples:', data_features.shape[0])\\n\",\n    \"print('Labels:', np.unique(data_labels.values))\\n\",\n    \"print('Label distribution:', np.bincount(data_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"62085741\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Above, we can see that the dataset consists of 8 features, and there are 998 examples in total.\\n\",\n    \"- The labels are in range from 1 (weakest) to 5 (strongest), and we normalize them to start at zero (hence, the normalized labels are in the range 0 to 4). \\n\",\n    \"- Notice also that the dataset is quite imbalanced.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"293b9496\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"603b070d\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Especially for imbalanced datasets, it's quite useful to compute a performance baseline.\\n\",\n    \"- In classification contexts, a useful baseline is to compute the accuracy for a scenario where the model always predicts the majority class -- you want your model to be better than that!\\n\",\n    \"- Note that if you are intersted in a single number that minimized the dataset mean squared error (MSE), that's the mean; similary, the median is a number that minimzes the mean absolute error (MAE).\\n\",\n    \"- So, if we use the mean absolute error, $\\\\mathrm{MAE}=\\\\frac{1}{N} \\\\sum_{i=1}^{N}\\\\left|y_{i}-\\\\hat{y}_{i}\\\\right|$, to evaluate the model, it is useful to compute the MAE pretending the predicted label is always the median:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"a5625136\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline MAE: 1.03\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"avg_prediction = np.median(data_labels.values)  # median minimizes MAE\\n\",\n    \"baseline_mae = np.mean(np.abs(data_labels.values - avg_prediction))\\n\",\n    \"print(f'Baseline MAE: {baseline_mae:.2f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"260494f7\",\n   \"metadata\": {},\n   \"source\": [\n    \"- In other words, a model that would always predict the dataset median would achieve a MAE of 1.03. A model that has an MAE of > 1 is certainly a bad model.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f8155016\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating a `Dataset` class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"039d15f6\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, let us set up a data loading mechanism for our model.\\n\",\n    \"- Note that the Cement dataset is a relatively small dataset that fits into memory quite comfortably so this may seem like overkill. However, the following steps are useful as a template since you can use those for arbitrarily-sized datatsets.\\n\",\n    \"- First, we define a PyTorch `Dataset` class that returns the features (inputs) and labels:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"6cb989de\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyDataset(Dataset):\\n\",\n    \"\\n\",\n    \"    def __init__(self, feature_array, label_array, dtype=np.float32):\\n\",\n    \"        self.features = feature_array.astype(dtype)\\n\",\n    \"        self.labels = label_array\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        inputs = self.features[index]\\n\",\n    \"        label = self.labels[index]\\n\",\n    \"        return inputs, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.features.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"754c85d2\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3d6eb8dc\",\n   \"metadata\": {},\n   \"source\": [\n    \"- There are three main ways we can prepare the dataset for Lightning. We can\\n\",\n    \"  1. make the dataset part of the model;\\n\",\n    \"  2. set up the data loaders as usual and feed them to the fit method of a Lightning Trainer -- the Trainer is introduced in the next subsection;\\n\",\n    \"  3. create a LightningDataModule.\\n\",\n    \"- Here, we are going to use approach 3, which is the most organized approach. The `LightningDataModule` consists of several self-explanatory methods as we can see below:\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"91332326\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            'https://raw.githubusercontent.com/gagolews/'\\n\",\n    \"            'ordinal_regression_data/master/cement_strength.csv')\\n\",\n    \"        data_df.to_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'), index=None)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'))\\n\",\n    \"        data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"        self.data_labels = data_df[\\\"response\\\"]\\n\",\n    \"        self.data_features = data_df.loc[:, [\\n\",\n    \"            \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"        \\n\",\n    \"        # Split into\\n\",\n    \"        # 70% train, 10% validation, 20% testing\\n\",\n    \"        \\n\",\n    \"        X_temp, X_test, y_temp, y_test = train_test_split(\\n\",\n    \"            self.data_features.values,\\n\",\n    \"            self.data_labels.values,\\n\",\n    \"            test_size=0.2,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=self.data_labels.values)\\n\",\n    \"\\n\",\n    \"        X_train, X_valid, y_train, y_valid = train_test_split(\\n\",\n    \"            X_temp,\\n\",\n    \"            y_temp,\\n\",\n    \"            test_size=0.1,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=y_temp)\\n\",\n    \"        \\n\",\n    \"        # Standardize features\\n\",\n    \"        sc = StandardScaler()\\n\",\n    \"        X_train_std = sc.fit_transform(X_train)\\n\",\n    \"        X_valid_std = sc.transform(X_valid)\\n\",\n    \"        X_test_std = sc.transform(X_test)\\n\",\n    \"\\n\",\n    \"        self.train = MyDataset(X_train_std, y_train)\\n\",\n    \"        self.valid = MyDataset(X_valid_std, y_valid)\\n\",\n    \"        self.test = MyDataset(X_test_std, y_test)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        return DataLoader(self.train, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True)\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        return DataLoader(self.valid, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        return DataLoader(self.test, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ce22b8f5\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Note that the `prepare_data` method is usually used for steps that only need to be executed once, for example, downloading the dataset; the `setup` method defines the the dataset loading -- if you run your code in a distributed setting, this will be called on each node / GPU. \\n\",\n    \"- Next, lets initialize the `DataModule`; we use a random seed for reproducibility (so that the data set is shuffled the same way when we re-execute this code):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"4c1280d9\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path=DATA_BASEPATH)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f060cf94\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"8687bf17\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Next, we initialize our multilayer perceptron model (here, a 2-layer MLP with 24 units in the first hidden layer, and 16 units in the second hidden layer).\\n\",\n    \"- We wrap the model in our `LightningMLP` so that we can use PyTorch Lightning's powerful `Trainer` API.\\n\",\n    \"- Also, we define a callback so that we can obtain the model with the best validation set performance after training.\\n\",\n    \"- Note PyTorch Lightning offers [many advanced logging services](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) like Weights & Biases. However, here, we will keep things simple and use the `CSVLogger`:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"fc71caab\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = MultiLayerPerceptron(\\n\",\n    \"    input_size=data_features.shape[1],\\n\",\n    \"    hidden_units=(40, 20),\\n\",\n    \"    num_classes=np.bincount(data_labels).shape[0])\\n\",\n    \"\\n\",\n    \"lightning_model = LightningMLP(\\n\",\n    \"    model=pytorch_model,\\n\",\n    \"    learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode=\\\"min\\\", monitor=\\\"valid_mae\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"mlp-corn-cement\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"dd8cdbfe\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Now it's time to train our model:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"e531b3dc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type                 | Params\\n\",\n      \"---------------------------------------------------\\n\",\n      \"0 | model     | MultiLayerPerceptron | 1.3 K \\n\",\n      \"1 | train_mae | MeanAbsoluteError    | 0     \\n\",\n      \"2 | valid_mae | MeanAbsoluteError    | 0     \\n\",\n      \"3 | test_mae  | MeanAbsoluteError    | 0     \\n\",\n      \"---------------------------------------------------\\n\",\n      \"1.3 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"1.3 K     Total params\\n\",\n      \"0.005     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1850: PossibleUserWarning: The number of training samples (5) is smaller than the logging interval Trainer(log_every_n_steps=10). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"386ac3987ac84863a7845e29626e3f7b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 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\"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": 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\"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": 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    \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 0.29 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f66903b8\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"eb4bd0b8\",\n   \"metadata\": {},\n   \"source\": [\n    \"- After training, let's plot our training MAE and validation MAE using pandas, which, in turn, uses matplotlib for plotting (you may want to consider a [more advanced logger](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html) that does that for you):\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"1abca75f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='MAE'>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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UJw4Ly2X+8xCOnFEKIjsptVUNKqXeANCBaKZULPArYALTWs4AvgCuBnUAxMN1dsZwmMgWAXv7HyHf6UVpRRYBrRlIhhPA1bksEWuubz7BdA/e66/zNCokDoKtfIRDLgRMlpMR0vIYhIYTwBN8aWVzDYRJBrDoOQK5UDwkhfJhvJgI/OwRFEVF1FID9JyQRCCF8l28mAoCQBILK8rBalDQYCyF8mg8ngngszkPEhwZIiUAI4dN8NxE44uHkYbpGBEqJQAjh03w3EYTEg/Mw3cL8pUQghPBpvp0IdBW9HKUcLCihQh5SI4TwUT6cCMxEpyn2k1RrOFRQ6uWAhBDCO3w+EXS1mbmGpHpICOGrfDgRmEFlcZhBZdJgLITwVb6bCFyji8NlUJkQwsf5biKw2iA4BlvxYaIddikRCCF8lu8mAjA9hwoPkhgRKCUCIYTP8u1EEDcIcleTGG4n93ixt6MRQgiv8O1EkJIOJccY7r+PAydKqa523/ObhRCio/LxRHARAOeV/0B5VTX5zjIvBySEEJ7n24kgJB5iB5BcuAqAXGknEEL4IN9OBAAp6YTnr8VOufQcEkL4JEkEKRdhqSpjmGWn9BwSQvgkSQTxgwEY7H9ASgRCCJ8kiSAkAfxDOM9+WLqQCiF8klsTgVJqglJqm1Jqp1Lq4Ua2hymlPlVKrVdKbVJKTXdnPE0ECTF96an2c1BmIBVC+CC3JQKllBV4HpgIDABuVkoNaLDbvcBmrfV5QBrwd6WUv7tialJMXxIr93GoUBKBEML3uLNEMArYqbXepbUuBzKASQ320UCIUkoBDuAYUOnGmBoX3YeQyqNUFx+ntKLK46cXQghvUlq7ZzStUmoyMEFrfafr/TRgtNb6vnr7hADzgH5ACHCT1vrzRo41A5gBEBcXNyIjI6NNMTmdThwOx2nro/JXMzh7Jj8pe4ybxp5HXLBnm06aiqsj6KixSVytI3G1TmeMKz09PUtrndroRq21W17ADcDL9d5PA/7ZYJ/JwNOAAnoBu4HQ5o47YsQI3VZLlixpfMPRHK0fDdX//Yff6OU789t8/LZqMq4OoKPGJnG1jsTVOp0xLmCNbuK+6s6vvrlAt3rvE4EDDfaZDnzoinOnKxH0c2NMjQvvQbXVTi91gMPSTiCE8DHuTASrgd5KqWRXA/AUTDVQfXuBSwCUUnFAX2CXG2NqnMWKjupNb5UrPYeEED7HbYlAa10J3Ad8CWwB3tNab1JK3aWUusu12/8CFyilNgKLgYe01vnuiqk51viBDLDslRKBEMLn+DW3USkVqrUubGJbd6313uY+r7X+AviiwbpZ9ZYPAJe3PFw3ShhK3IZ3KTqaCwz0djRCCOExZyoRZNYsKKUWN9j2cXsH41VdhgEQenyTlwMRQgjPOlMiUPWWI5vZdu6LH0w1injnFm9HIoQQHnWmRKCbWG7s/bnN7uBYYBIpFTuorKr2djRCCOExzbYRALFKqQcx3/5rlnG9j3FrZF5QGDGQwcXfkucsIyEs0NvhCCGER5ypRPASZsSvo95yzfuX3Rua51XEDSVWnSD/4I/eDkUIITym2RKB1vrPTW1TSo1s/3C8y6/bCPgBqncthX6eH9cmhBDe0KpxBEqpAUqp/1FK7QBedFNMXhOcMoZ91THE7XzP26EIIYTHnDERKKV6KKUeVkqtB94C7gEu001NXnQOi3QE8E5VOvHHVkP+Tm+HI4QQHtFsIlBKLccMCLMBk7XWI4CTWus9HojN4/z9LMy3XUoVVsh6zdvhCCGER5ypRJCHaRyOo66XUOfqNtqAcsSxOXgkbF/g7VCEEMIjmk0EWutJwGBgLfBnpdRuIEIpNcoTwXlDlMOfXao7HP8RquUhNUKIzu+MbQRa6wKt9ata68uAMcCjwDNKqX1uj84LooLt5FTGQHUFFDacNVsIITqfVvUa0lof1lo/p7W+ABjrppi8Ksrhz/byKPPm+B6vxiKEEJ5wptlHGz4/oKFr2jGWDiHKYWdpSSTYgeO7IXmct0MSQgi3OtMUE+cD+4B3gJV0tonmGhHt8OeAjkJb/FBSIhBC+IAzJYJ44DLgZuAW4HPgHa11p52rOSrYThVWKhxd8ZdEIITwAWfqNVSltV6gtb4N01C8E8hUSv3SI9F5QZTDH4Ci4G5wbLeXoxFCCPc7U4kApZQduApTKkgCngM+dG9Y3hPtSgQn7F2JOPyVl6MRQgj3O1Nj8RvAIGA+8GetdbZHovKiqGA7AHl+CSSXHIPSAggI83JUQgjhPmcqEUwDioA+wK+Uqm0rVoDWWoe6MTavCAu0YbUoDlrizIrjeyDhPK/GJIQQ7nSmaahbNc6gM7BYFFHB/uyplkQghPANbr3RK6UmKKW2KaV2KqUebmKfNKXUOqXUJqXUN+6Mp6WiHHZ2VESbN9JgLITo5M7YWNxWSikr8Dym+2kusFopNU9rvbnePuHAC8AErfVepVSsu+JpjWiHP7nFlRAUJaOLhRCdnjtLBKOAnVrrXVrrciADmNRgn1uAD7XWewG01kfcGE+LJUYEsSvPiQ5PkkQghOj0lNbumVVaKTUZ803/Ttf7acBorfV99fZ5BvOsg4GY6a6f1Vq/2cixZgAzAOLi4kZkZGS0KSan04nD4TjjfktzK3g1u5xvurxAbPFOVo75d5vO195xeUNHjU3iah2Jq3U6Y1zp6elZTT5QTGvtlhdwA/ByvffTgH822OdfwAogGIgGdgB9mjvuiBEjdFstWbKkRfttO1Soezz0md78n99q/ViE1pUVbT5ne8blDR01NomrdSSu1umMcQFrdBP3VXdWDeUC3eq9TwQazuucCyzQWhdprfOBpYDXu+j0jHHgsPuxpTQSdBUU5no7JCGEcBt3JoLVQG+lVLJSyh+YAjSczfQTYJxSyk8pFQSMBra4MaYWsVoUQxLDWF3gGiYhPYeEEJ2Y23oNaa0rlVL3AV8CVuBVrfUmpdRdru2ztNZblFILgA1ANaYqqUOMXh7aLZxPl4aCP9JgLITo1NyWCAC01l8AXzRYN6vB+6eAp9wZR1uc1y2cWdXhVFv8sRyXEoEQovPyuZHDLTW8ewTVWCgI6CIlAiFEpyaJoAkxIXaSo4PJ1bGQtw3c1M1WCCG8TRJBM0YlRfJxyVDI2wrL/ubtcIQQwi0kETRjZHIkr5ReRGHv6+Drv8Ceb70dkhBCtDtJBM0YlRQJKD5LehiCo+H7570dkhBCtDtJBM3oFhlIXKidFXtLYNhPYfsCKNjv7bCEEKJdSSJohlKKMSlRfLM9j8IBt4CuhrWnTYUkhBDnNEkEZ3B3Wk9Ollbwt9Xl0PNikwiqKr0dlhBCtBtJBGfQLz6UaWN68J8VP7K/181w8gDsWNj4zs48OLAOCmRuIiHEuUMSQQvcf2kfNPDByUHgiIes107f6cA6eGYwzL4I/jkCSo57OkwhhGgTSQQtEBnsT9+4ENbkOmH4NNjxFWx4D7Z+bl6b50HGVPNEs8tnQmWpdDUVQpwz3DrXUGcyrHsEn204QPW107B8+wx8+PNTd/ALhDvmQ9wgyHwCcpZA///ySqxCCNEakghaaHj3cN5ZtZecikh6/+oHKDl26g4hXcARY5aTxsKuJZ4PUggh2kASQQsN7xEBwNq9x+k9sjuEd2t655R0M+bg+I8Q0cNDEQohRNtIImih5KhgwgJtrP3xBDeN7N78zilp5t8v/wDRvc3ywJ9AwhC3xiiEEG0hiaCFLBbFsO7hZO1tQW+gmL7QNdV0M92xEKoqTOPxnYvcH6gQQrSS9BpqhbG9otl5xMnu/KLmd1QKfr4Y/l+eeV0+E3JXw6EO8fA1IYQ4hSSCVrhycAIAn2840LoPDr0FrPbGxx8IIYSXSSJohS7hgaT2iOCzDQdb98GgSBh4LfwwB167Cta9febPlByHeb+EvO1tilUIIVpKEkErXT0kga2HTrJu34nWffDCB6D7aHAehk/uhZ2Lm98/6w0zr9E7N0FJK88lhBCtIImgla4cnECQv5Vrn/+O6a+toqq6hY+wjBsAt34Cv/gGYvqbkcjPDoVnh5K6+oFTp7eurjbVSJE94cQ++OwBN/wkQghhSCJopdjQAL58YDwzxqewZFsen29sZTWRfzDckgFDboTEkZCYSnDRj7Dmlbp9dmfC8T2Q9nsY9XMzjUV5cXv+GEIIUcutiUApNUEptU0ptVMp9XAz+41USlUppSa7M5720i0yiIcm9KNXrIN/Lt5BdUtLBTXCu8M1z8H1L8H1L3M0agT88B/TzbS6Cr59BgIjzRQVvS6FqnL4cblbfhYhhHBbIlBKWYHngYnAAOBmpdSAJvb7K/Clu2JxB6tF8cuLe7HjiJO3V+09q2MdTLjCtB1snAtf/y/s/gYu/hPYAqDHBabHkUxZIYRwE3cOKBsF7NRa7wJQSmUAk4DNDfb7JfABMNKNsbjF1UO68O7qffy/T7LRWjPt/KQ2Hedo1HAITYSP7zIrht8GqXeYZVugaWTeldkuMQshRENK61ZWa7T0wKaaZ4LW+k7X+2nAaK31ffX26Qq8DVwMvAJ8prWe28ixZgAzAOLi4kZkZGS0KSan04nD4WjTZ5tSXqV5fl0ZG/KqePT8AJLCrG2KK14fIbRwC1XWYI7EXoi22Gq3d/9xLim73+K7C96gwj8ce2k+torCU45RZo+mwj+06ZNoTVDxPizVlRQHJVJt9W9xbO19zdqDxNU6ElfrdMa40tPTs7TWqY1tc2eJQDWyrmHWeQZ4SGtdpVRju7s+pPVsYDZAamqqTktLa1NAmZmZtPWzzRlzYQUX/y2TefsD+OCaC2juZ2kqrtS0q2vf92+4w8EI+PdbXGhZD13GwDs/N89Prs/fAT/7yvROasznv4HVL5vl826B615scWzuuGZnS+JqHYmrdXwtLncmglyg/hSdiUDDIbmpQIbrxhkNXKmUqtRaf+zGuNpdaICN313Rj999sIF3Vu3jltFnmJSuteKHwLBp8O0/wBZknnmQVq/tvboSvvhvyLgZLv8LqAZNP4c2miQw4nYzJiH7A7jiL2agmxDC57kzEawGeiulkoH9wBTglvo7aK2Ta5aVUq9jqoY+dmNMbjN5RCLz1h/gkU+ySQgLIL1fbPsdXCm46u+Qtw2O7YIpc0zPo/oc8fDG1fDu1MaP0fMSuOof5hibPzajmy+4r/F9hRA+xW2JQGtdqZS6D9MbyAq8qrXepJS6y7V9lrvO7Q0Wi+LFnw7n5pdWcM+ctSx4YBw9ooLb7wR+drj9c6gohsDw07d3Hw0PbISTh07fpiwQOwAsVlN11G00rH7JHLO+5PFm5lQhhE9x6zTUWusvgC8arGs0AWitb3dnLJ4QEmDj5VtHctk/vuH3H25kzp2jW91e0Cw/f/NqMoB48zqT0XfB3OnwxW9PXR87EO7+zpRAhBA+Q55H0M7iwwL4/ZX9+cNHG3nl293cOS7F2yGdbtBPoGc6VFXWrcueCwseNtNldxvlvdiEEB4nU0y4wZSR3bi0fxwzP9/CrG9yvB1O4wIjzDOWa17Dfmp6Hq2RqbKF8DVSInADi0XxwtThPPjeOp6Yv5VRyZEM7x7h7bCaZw+BwTfA+ndAV9Wu7nf4MEQchvNugm0LzAjoEbed/vnqalj82OltFJE9YfxvTfuEEKJDkkTgJv5+Fv56/RCW5xzlucU7eH36OVDdMuYe2Ps97FtZuyqiqAA+yoSjO+Hbp6G6wnRhHXLDqZ/N+Rq+exZCu4LVNRhOV8OGd6GqDC55xHM/hxCiVSQRuFGw3Y87xyXz5IJtrNt3gqHdwr0dUvNi+sC9K09ZtXLxQsbnPA5Ln4TIFHDEwbz7YO0bkJgKlzxqGpezXoPgGPjVulMbtD+9H5b9HeIHw8Dr6tZXV8OiR+DAulNjCIqEiU9B2UlY+Ccod0K/q2DM3bD6FZNcRv389NhLTphBc87D5r3VBhc9BN3HtMeVEaJTkzYCN7v1/CQig/25/bVVzFn5I86yyjN/qAOptvqbcQtDpsDNGXDjW9D7cnOD/vZpWPY3KDwA2+bD0Kmn92qa+KTprvrxPac+sznzcVj+T9MdVlfXvbYvhPemwTtTYO9yKD5qGrE/uRc+fxC+/CMUH2sQZBV88DPY/EndcQ5vhnduNtN5CyGaJSUCN3PY/XjvF2P4w4fZ/PGjbP48bzPXDevKQxP7ERncsvl+vC60C/zk33Xvb3oLtIYPZ8DXf4FVL5l2hcbaDvzscOObMDsNMm6BGZmw51tTwhj6U5j0r1O7q2Z/AHPvAIufeZBPt9Hw5iQzTXdMP8jbCuvfoceejfDcr81nKsuhMBeufgZSp5t1R3PgpXT490VNj6BWFlMdNvJn7XCRhDh3SSLwgF6xIWTMGMOqPcf4fMNB3lm1ly83H2L6Bcncen4Pb4fXNkqZZyo4Yk11TMJ5puqoMSHxcNN/4LWJ8PaN5tt611QzWrrhmIVB15uH8ASGQ9JYs+7GN+H75031UMZUyHyC5LJC6DEWQhPMPokj65IAQFRPuOV988CfhvMy1TiaY6qTwntA70vP6nIIcS6TROAhFotiTEoUY1KimDqmO3+dv5WnF23n30tzGJug6DO0hC7hgd4Os3VsgWbOopZITIWrnzZVPI44U6qwBTS+7/Bpp74PjoZLHzXLqdPh47s5FnEekbd+AtZm/oS7jzavppQXwSuXw5zJ0MLZWM9kfHU1LGtQ42q1mevU+3KYc4Ppqjvm7nY5nxDtQRKBF/SLD+W16aPYeqiQf3+zi0/W7efrJ5dwzdAuzBifQt+4kPYdkdxRDPsp+AVA3EBT3dQWg2+A6ko2H41kbHNJoCX8g2Hq+5D1OlSWnd2xXHL37qV79wbzQO1bCZ89CFG9IH8bLPg9hHUzDx1y58R/Wpv2k6auU3VV27r1tvVznZHWdaXa+stN6aDXThKBF/WLD+Xpm4ZyYegxsivieHf1Pj5cu5/QAD9GJUfxi4tSGJnUyWYIHXyWTyO12mD4rVRmZrZLOIR2gfQ/tM+xgF2ZmXRvOE1waSG8fIlJAj95Cb57rm5ywFEz4Mqn2u38taqr4f3b4MhmuGPh6dvztptJCi/6HYy8s+XHXfsmfPUo3PGl6WXmy3K+hnenwX2r4dhu+M/1cN+q0yeErHE0B168EG5+x4zs70AkEXQA0YEWHps4kPsv6c2nGw6w9dBJFmQf4oZZ3zOiRwTTL0wirW8sDrv8us5JAaFw26eQvwOSx0HPi00Pp9zVsGo2hCVCylneGCJTwO4wJZu8bbDxfdgyzzSIz70dR+R1cND1paK60jT0Ow+bpDTiDig9AQHhYLFAyXGwh57+zXXvClOyqa4w05pf+SQUHYXgKLO95ETdhIhNLZ/rtIaifDMaf+di03tu5yLTI66yxDxbvGEicB4xbWnbvzT7rHjRJILiY3UlwuJjZrS/Uqcul5yAgDC3z/8ld5YOJCLYn1tdj7v8f1cN4P2sfcxeuov73v4Bfz8L1w7twkV9YjlYUMLALmGM6BGBn0VhsXTCaqTOpv6EgMHRpqfSiNvNTeWrdhhsF94dps83jekH15l1w35qGtQ/vovU3Ushq97+Fj9TGlk1G5Y/B988aW5OF/8/026SmGqqzWqSQeEB8+03vBtE94H1GeZm+PVMuOafpsrj8wdh6lyTUF69HNL/aKrD3r8Nrvs3DLnx7H9Ob1s3xyTD+9fXjYHJWQKHXV2jD/wA502p2z83C16+GKZ+UPfc8R0LTTJY8HsY9xvocT7MuRFG/8KMmXnzWhh6Mwy/HV6/0uxz0e/c+mNJIuigAv2t3Hp+EreM6k7Wj8eZt/4Ac7NyeW9N7in7WZQZq/CHK/vj7yfDQs4pFqvpTbX7G6iqaPtxSk+Ym9Pzo00D+IQnILo3JKeZ9oHo3mR/v4hBgwbVfSaqpylFZH8Aix41o8W3fmaeja2rIWex6VHV5wqz/9KnzJiP2+aZsR3bF5gkYAsy5wbzuZX/Nt9yqyth8f+YNiFdbW58Q1w9xqJ7144+t1SVQv5OiO7V9p+/vZQXQeFBiO6FparMVJ81rP7a+oUZKZ+zGA6uN+t2LDQlAzDJQWuTjLsMg+3zzfrv/wX7VkGfCaZksOBhsAWbcTgrgk036xUvmKo3q838m/0hVJaa7tljf103Yt8NJBF0cH5WC6NTohidEsVvLu/L/uMlxIXZWfvjcbYdcrLveDGvL9/DguxDhAfZuHpIAjPG95SkcK6wBdTdbM+GssDHd5tv9A17JCWmkh/jhP5pp38u9WemRHDbZ6ar7Yb3zPiNTR+a6p+smkkIlUlasf3NjS5+sCkF3PIuvHGNOX/Pi81nrP7mcahHNsHJwzD0LjP4cOGfzCDCQZPh+pehqpzz1j8C3+WYqrMeF5z9dWirynJ46zrYnwXTPmLwxpnwbbYpFfVydS2uqoQ9y8zy6leg/CQkjatblzTOfH7j+/Dhz+Hmd01pAepKA8OmmRv6vtXwsy9NFV3+DrhzEXz2a1OiuHOxSc67l5mpWRb/D2z9HAZe67YfXxLBOSQy2L92ENqEQQlMcH3Bu7R/HPPW7+dYUTl/W7idD9fu5660nlzWPw6LRbFy11EGdAklMSLIi9ELtxp6C/S6zFTXtEba703iCIqErsPhsv8xVVc9LjBJosrVmyooqq7uWylTDWW1m5Hkd39n1hcfNYmgqgzOv8dUIVWWmsSxYpZJAo54M+V5YDgU5RFWuM10J37vVjMliFKgrNDvavOz7F1hxnmEJpibZ0i8qZ4CqCgxN8tel5gSyK5M8yQ+tGnITUk3pa6di81Dl6z+5pt8jwtMSWbnYtO92B4C839nenc54uDNa4nQVWZ57h2miisi2dTVlxVCYCQcWGtiGPuASQTBMeZ3sGcZLPqz2fbt02a/oVPNZI4o00bU61IzANM/2CTgiiLTJvDTD83xgyJhyttQWmDOueY1k5AlEYjmTBgUz4RBpv55ydYjPPnlNn43dwNg/l9pDSF2P351SW925TvpFx/K1NHd8bNKqaFTaW0SANM4XNNgqZRJAjXLcQOa/pw9pG7ZP7ju3wGTTEKIH2zW1TwFL3W6qUKZvgC+/INJGMCeHjeRdPVv4dUrTn1Q0qrZJjG8f7upwrr8f00bRWgXMzo9KMqUgDZ9BGMfNA3f6+bA6LvNTXbVbPPtOyjSTIY4aLJJTJn/B32vNMlg4Z9Msuh3lbnRjn0QzrsZXpvAvshxdPvJYyau+a76+fghgIJxD5rP+gWY6rfu55snAHYZbvYrzIXQRNi3wrwf9lNTrVZaaG7s9dV/2JTVr+53YbHWLQ+/FZb+zbQnuYkkgk4mvV8saX1jWLn7GNn7CzhZWsnQ7uH8Y+F2/vLFFgJsFkor9vGfFT8yMjmS8b1juHxAnDQ4i/Yx+dXG11/xf3D5THOD+8ls895iZc+qDSTF9IEHN5uJBsFUr2TcYhqZI3vCiR/N+4gkM8352zea5LDpI4jpD9/+w3wupj+sfLFu+Ye36paz59Ytb/vCvGL6myqbXUtMaeriP5n4frONnGXf0S0yGe7fYL6Zf3y3KU10GQYDf2ISQfxgc/O+/XNAAdrU+1dXwJT/wOx0U/LomgrXj3bt0wajfm5KZzW9s9xAEkEnpFTdKOYaF/SMYldeEb1iHSzcdJjXvtvNZ+sP8PbKvaREBzNjfAoDuoRysKCUruGBpMQEE+Qvfx6ilZoaLFVT5VOz3LD0Ygs0L4C+E81khateMnX0u5fCd8+YOveD6+DTB0xD7YjbzX4ZU82352v+ZR7Bqixw/SvwyT3mJn7Tf0xp4/iPpk3jq0fh0AbTw2npU6Y65/qX62Kv3yhrCzCvya+YOa+G3ARhXaH3FXVToNT/mQdea7redhkGw6bWVZ+djUD3P8tE/qf7CLuflf4JoQBcNSSBq4YkUFWtmZ99kFnf5PDwhxtP+0yPqCCu7VHFRVpTUFJBWKCtc454Fh3PqJ/XTTceMa1u2pHoXqcPSvzp3LrlKXPqlq9/uW75mn/WLV/5ZN3yZX9uWTyBEfCLpXXvp77X+H7XvlC3POn5lh27A5BE4MOsFsXVQ7pw1eAEVu4+xonichLCAtl/ooScI07mZx/i2bXFvJfzNQcLSrl+eCIzrx1EoH/HGyIvhGg7tyYCpdQE4FnACrystX6iwfapwEOut07gbq31enfGJE5XU5VU4zzXA3R+Pj6FB15ZRGVgGJf0j2XOyr1sPljIi1OH0yMqCK2RtgUhOgG3JQKllBV4HrgMyAVWK6Xmaa0319ttN3CR1vq4UmoiMBtoZrpI4UkBNitT+tlJS0sF4JL+cTyQsY6Jzy7DoqCiSpMUHURKtIOBXUK5pH8c/RM66YR5QnRi7iwRjAJ2aq13ASilMoBJQG0i0Fovr7f/CiDRjfGIs5TeN5bPfjmWZxfvwGH3w+5nISeviO2HT/Ll5kP8/avtJEYEcuXgBG67IImu59q02kL4KKW1ds+BlZoMTNBa3+l6Pw0YrbW+r4n9fwv0q9m/wbYZwAyAuLi4ERkZGW2Kyel04nA42vRZd+qocUHLYyso06w7UsnaI1VszK9CAYkhFnqGWbiml41we/uOWeio10ziah2Jq3XOJq709PQsrXVqY9vcWSJorH6g0ayjlEoHfgaMbWy71no2ptqI1NRUndZwmt8WyszMpK2fdaeOGhe0LrZJrn9zjxczZ+VesvcX8O2uY6zOq2Bot3DsfhaSooJJTYpkfJ/os+qe2lGvmcTVOhJX67grLncmglygW733icCBhjsppYYALwMTtdZH3RiP8JDEiCAemtAPgF15Tv62cBuHCkrJO1nFsh35vPztbux+Fsb1jmZkUiQxIXZyj5cwqGsoF/eL83L0QvgedyaC1UBvpVQysB+YAtxSfwelVHfgQ2Ca1nq7G2MRXpIS4+CFqSNq31dUVbN69zEWbj7Moi2HWbTlyCn7X9QnhnG9o0mJCaZnjIPEiCCs0jNJCLdyWyLQWlcqpe4DvsR0H31Va71JKXWXa/ss4BEgCnjB1dOksqk6LNE52KwWLugVzQW9onnsmoEUlFSQd7KMuFA7b6/cy0vLdvHN9rza/f39LCRHBdMjKqh2RlU/iyKsvIJBzjKiHXZv/ShCdBpuHUegtf4C+KLBuln1lu8EWvGcPNHZhAXaCAs0Q/p/cVFPfnFRT44VlbMrz0lOnpNdeUXk5DnZc7SIymrTxFRUVsnhwnLe2fY114/oyozxPUmODvbmjyHEOU1GFosOx0y3HUlqE89r1lrzn8+WsLUyhvezcslYvY+L+8YyYVA8Vw5OIFge6SlEq8j/GHHOUUrRLcTCtLTBPHBpH177bjcf/bCfxVuPMPPzLVwxMA67n5X4sAB6xjjoFRtMSrRDRkEL0QRJBOKcFhNi53cT+vHfV/Rl7d7jzF66i8VbjlClNSeK6x7/GO2wM75PNH3iQrjmvC50kcFuQtSSRCA6BaUUI3pE8u9pddVJJ0sr2J1fxLZDJ8ncnsfS7fl8uHY/s77J4ZcX92bLwUKOF5UTGmjjltHdGdlEVZQQnZ0kAtFphQTYGJIYzpDEcG5INUNacvKc3DtnLf/72WbCg2wkRgSStfc4H/2wnz5xDlKTIjlSWEpMiJ2UaAdzs3KJCLbVjosoKKnA38/CyKRIbPKEN9FJSCIQPqVnjIOP772QXXlF9Ilz4Ge1UFJexdy1uXyx4SCfrjtAl/BAVuw6hrOskoFdQtl+2Ml1Lyw/5ThdwgIY1j2CfceLqazSOAL86BnjgMIK1i/awXc5+YQF2hjYJZS0vrGUV1bj72dhaLdwjhWVszvfyfDuETJBn+gQJBEInxNgszKgS2jt+0B/K9PG9GDamB6168oqq9h/vITk6GBOFFcwP/sQsSF2IoL9yTtZyuvL97BxfwE9ooKw+1k5XlzOguyDHC+uQG3bzuCuYRwrKmfxlsM8s2hH7XEv6hPD+twTnCiu4JJ+sfRPCMVZVsn4PtF0jwwm2G4lIUzaL4RnSSIQohF2PyspMWZyr4hgf24Z3f2U7RMGJTT6uU8XLuHCCy8kMtg8nvCos4zlOUcJCfAje38B//x6J0MSwxjbK4ZZ3+SQuT0Pm1Xx+vI9tcfoHWuqqGJD7Ow7Xozdz0JsSAD7T5TgZ1F0jwrC31o3uG50ShRdwgPZd6yYympNXKhdkoloFUkEQrSjEH9VmwQAohx2/uu8LgCk9Y3ljrHJBNqsKKWYMT4Fq0Wh0azefZxjxeXknSzj662HmZ99kBPFFcSHBlBeVc2xonJiQ+xUa02+s/yMcQxICGVw1zACbBZyj5cQVFFOYcQBDhWUcHG/WHrGONidX0RCWCCB/lb25BcRbPcjJkRGavsiSQRCeFD9GVfrP/JzbO/o2uWfjU0GqG1XaLhcXF5JlWuU9cnSSpZuz+NESQVJrmk4dh5xsmRrHou3Hqa0opr4sAByjlTwac4PADz15TZ6x4aw+WAhXcMDGdEjgnnrD6AUDO4aRp+4EEIDbGZ6j+ggUmIcxLomBuweGUS3yCAqqqqxKiVjMzoJSQRCdFA1N/6Gy/WTSUiAjSmjTq22urhfHDPG9zxl3acLl9Bz0AhCA/14+qsdbD5YyH9f0Zd56w7w+caDzBifQrC/H9/vymfp9jxKyqsoq6ymvKr6lONYFIzoEcGmA4WEBdq4/YIkhveIYO2Px/lq82HS+8USYLOyIPsgY3vFcNWQBOx+FrqGB2KxKE4UlxMWaEMpxfGicsqr3PM8FNE6kgiE8AEh/qq2gfzvN55Xu37G+BROFFfUVgndT+/abVXVmgMnStiZ5ySvsIwu4YF8uzOfzG1HmDS0K7vynDw+f2vt/inRwTz15bba5acXbefpRWZS4bhQO2GBNrYfdtIvPoQeUUEs3HwYNAzYtIxHrh7A6HrPzRaeJYlACB9ms1qabBewWhTdXFVBNcb2jubhif1q3+87VszOI05iQ+0M7BLGtkMnqaiqZlDXMHYcPkn2gQKKy6v4dkc+haUVTBiUwPyNB1m+8ygzxqVw+MA+1hyt4KbZK0iODiYlOpiUmGBCAmy15+gTF0L/hBC+23mUfGcZNquFpKggwoP8CfK3MrhrmNurqLTWnbqrryQCIUSbNUwUfeNDapd7x4XQO868nzq6rmvug5f1qV3OzDzM47eO443v97Ah9wS78opYtjOf8spTq6Sa0yfOQUSQPxtyCxjeI5zLB8QzqGsYH/2QS3llNTeP6k5VtWZXXhH7jhcTG2KnZ4yD7lFB5DtNA31VtSb3eDEAk0ckkp1fxa5vdzNxcDzPL9nJFxsP8cClvSkqq+KHvcf5yfCuWC0WNu4vIDE8kIhg/9oeXd0jg/CzKDYdKOT15XtYkH2IgV1M431xRRXLduShNUwZ2Y09R4spLKkgrW/saQk50GYlMSKQDfsLyD1ezPjeMbjr0cKSCIQQXhXob+Wui+raNLTWuNrCqayuJmvPcXYccXJBzyhSYhyUVFSxJ7+Ik6WV5B4v5s3vf6SgpIJrh3Vl1e6jPDpvEwB2PwtWi+K9Nbmtiufx+VtdjfGb+Z/PNgMm2TzyiTluZLC/qdZqgp9FERpo41hROQE2C1cMjGfrwZPMWbkXP4tiZHIkztJK/rZwO6EBfoQE2Jo9Xo0nF2zjsh5+pKe36sdpEUkEQogORSmF1VULY7VYax9kVMNh92NQ1zDXu6ja6UNq5OQ5Wb/vBOP7xGCzWPhqy2Eig230jHHQNTyQfGc5OXlO9h4rJirYn/iwACxK0SU8kCMnS3lvdS76xH5uvHQ0767ex9he0VzSP5Zvd+YTEeRP/4RQMrcdqZ1q5HBhKSdLKymrrGJPfjE5eU6OnCxjVFIklw2II6Jed+L69p8oITbEjp9FsTu/iOLyqlO2nyytZO+xInrFOugeGUzmtiMU5LrnQY6SCIQQnUrPGIeZ7sNl8ojEU7bHhwUQHxbAhY18NjLYn0f+awCZmUfonxDKY9cMrN02rndM7fIl/euerd0jqu6hSCN6tHziwq71ZsBNqRdvfef3rGtAvyG1G5nOnBYfvzVk1iwhhPBxkgiEEMLHSSIQQggfJ4lACCF8nFsTgVJqglJqm1Jqp1Lq4Ua2K6XUc67tG5RSw90ZjxBCiNO5LREopazA88BEYABws1JqQIPdJgK9Xa8ZwIvuikcIIUTj3FkiGAXs1Frv0lqXAxnApAb7TALe1MYKIFwp1fhE70IIIdxCuWvIslJqMjBBa32n6/00YLTW+r56+3wGPKG1/tb1fjHwkNZ6TYNjzcCUGIiLixuRkZHRppicTicOR+P9db2po8YFHTc2iat1JK7W6YxxpaenZ2mtUxvb5s4BZY3N0NQw67RkH7TWs4HZAEqpvPT09B/bGFM0kN/Gz7pTR40LOm5sElfrSFyt0xnj6tHUBncmglyg/tjvROBAG/Y5hdY6prntzVFKrWkqI3pTR40LOm5sElfrSFyt42txubONYDXQWymVrJTyB6YA8xrsMw+41dV7aAxQoLU+6MaYhBBCNOC2EoHWulIpdR/wJWAFXtVab1JK3eXaPgv4ArgS2AkUA9PdFY8QQojGuXXSOa31F5ibff11s+ota+Bed8bQwGwPnqs1Ompc0HFjk7haR+JqHZ+Ky229hoQQQpwbZIoJIYTwcZIIhBDCx/lMIjjTvEcejKObUmqJUmqLUmqTUup+1/rHlFL7lVLrXK8rvRDbHqXURtf517jWRSqlvlJK7XD9G+HhmPrWuybrlFKFSqkHvHG9lFKvKqWOKKWy661r8voopX7v+nvbppS6wsNxPaWU2uqaw+sjpVS4a32SUqqk3nWb1eSB3RNXk783L1+vd+vFtEcptc613pPXq6l7g/v/xrTWnf6F6bWUA6QA/sB6YICXYkkAhruWQ4DtmLmYHgN+6+XrtAeIbrDuSeBh1/LDwF+9/Hs8hBkY4/HrBYwHhgPZZ7o+rt/pesAOJLv+/qwejOtywM+1/Nd6cSXV388L16vR35u3r1eD7X8HHvHC9Wrq3uD2vzFfKRG0ZN4jj9BaH9Rar3UtnwS2AF29EUsLTQLecC2/AVzrvVC4BMjRWrd1ZPlZ0VovBY41WN3U9ZkEZGity7TWuzFdpEd5Ki6t9UKtdaXr7QrMYE2PauJ6NcWr16uGUkoBNwLvuOPczWnm3uD2vzFfSQRdgX313ufSAW6+SqkkYBiw0rXqPldR/lVPV8G4aGChUirLNb8TQJx2DfJz/RvrhbhqTOHU/6Devl7Q9PXpSH9zdwDz671PVkr9oJT6Rik1zgvxNPZ76yjXaxxwWGu9o946j1+vBvcGt/+N+UoiaNGcRp6klHIAHwAPaK0LMVNw9wSGAgcxxVNPu1BrPRwzPfi9SqnxXoihUcqMTr8GeN+1qiNcr+Z0iL85pdQfgUpgjmvVQaC71noY8CDwtlIq1IMhNfV76xDXC7iZU79sePx6NXJvaHLXRta16Zr5SiJo9ZxG7qSUsmF+0XO01h8CaK0Pa62rtNbVwEu4qVjcHK31Ade/R4CPXDEcVq6pwV3/HvF0XC4TgbVa68OuGL1+vVyauj5e/5tTSt0GXA1M1a5KZVc1wlHXchamXrmPp2Jq5vfWEa6XH/AT4N2adZ6+Xo3dG/DA35ivJIKWzHvkEa46yFeALVrrf9RbX/85DNcB2Q0/6+a4gpVSITXLmMbGbMx1us21223AJ56Mq55Tvql5+3rV09T1mQdMUUrZlVLJmIcvrfJUUEqpCcBDwDVa6+J662OUeWgUSqkUV1y7PBhXU783r14vl0uBrVrr3JoVnrxeTd0b8MTfmCdawzvCCzOn0XZMRv+jF+MYiym+bQDWuV5XAm8BG13r5wEJHo4rBdMDYT2wqeYaAVHAYmCH699IL1yzIOAoEFZvncevFyYRHQQqMN/Gftbc9QH+6Pp72wZM9HBcOzH1xzV/Y7Nc+17v+v2uB9YC/+XhuJr8vXnzernWvw7c1WBfT16vpu4Nbv8bkykmhBDCx/lK1ZAQQogmSCIQQggfJ4lACCF8nCQCIYTwcZIIhBDCx0kiEKIBpVSVOnXG03abrdY1m6W3xjwI0Si3PqpSiHNUidZ6qLeDEMJTpEQgRAu55qn/q1JqlevVy7W+h1JqsWsitcVKqe6u9XHKPAtgvet1getQVqXUS6455xcqpQK99kMJgSQCIRoT2KBq6KZ62wq11qOAfwHPuNb9C3hTaz0EM7nbc671zwHfaK3Pw8x/v8m1vjfwvNZ6IHACM3pVCK+RkcVCNKCUcmqtHY2s3wNcrLXe5Zoc7JDWOkoplY+ZKqHCtf6g1jpaKZUHJGqty+odIwn4Smvd2/X+IcCmtZ7pgR9NiEZJiUCI1tFNLDe1T2PK6i1XIW11wsskEQjROjfV+/d71/JyzIy2AFOBb13Li4G7AZRSVg/P+y9Ei8k3ESFOF6hcDy93WaC1rulCaldKrcR8ibrZte5XwKtKqf8G8oDprvX3A7OVUj/DfPO/GzPrpRAdirQRCNFCrjaCVK11vrdjEaI9SdWQEEL4OCkRCCGEj5MSgRBC+DhJBEII4eMkEQghhI+TRCCEED5OEoEQQvi4/w8O84tffDu1rgAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_mae\\\", \\\"valid_mae\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='MAE')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4d73be89\",\n   \"metadata\": {},\n   \"source\": [\n    \"- As we can see from the loss plot above, the model starts overfitting pretty quickly; however the validation set MAE keeps improving. Based on the MAE plot, we can see that the best model, based on the validation set MAE, may be around epoch 175.\\n\",\n    \"- The `trainer` saved this model automatically for us, we which we can load from the checkpoint via the `ckpt_path='best'` argument; below we use the `trainer` instance to evaluate the best model on the test set:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"d60a0ce3\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/mlp-corn-cement/version_2/checkpoints/epoch=155-step=779.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/mlp-corn-cement/version_2/checkpoints/epoch=155-step=779.ckpt\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"88b0f73bc1154f74898737527129654f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_mae          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.28999999165534973    </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_mae         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.28999999165534973   \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_mae': 0.28999999165534973}]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ec7d93a1\",\n   \"metadata\": {},\n   \"source\": [\n    \"- The MAE of our model is quite good, especially compared to the 1.03 MAE baseline earlier.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/ordinal/beckham2016-light_cement.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"b1739f6e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"coral_pytorch    : 1.2.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib,coral_pytorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"00079242\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d198b6a3\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Squared-error reformulation for ordinal regression and deep learning -- cement strength dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e0ffe6ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"Implementation of a method for ordinal regression by Beckham and Pal 2016.\\n\",\n    \"\\n\",\n    \"**Paper reference:**\\n\",\n    \"\\n\",\n    \"- Beckham, Christopher, and Christopher Pal. \\\"[A simple squared-error reformulation for ordinal classification](https://arxiv.org/abs/1612.00775).\\\" arXiv preprint arXiv:1612.00775 (2016).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"654568ec\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note:**\\n\",\n    \"    \\n\",\n    \"To keep the notation lean and minimal, this notebook only contains \\\"Squared-error reformulation\\\"-specific comments. For more comments on the PyTorch Lightning use, please see the cross-entropy baseline notebook [baseline-light_cement.ipynb](./baseline-light_cement.ipynb).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"13449160\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"1acc2499\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 200\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 0\\n\",\n    \"\\n\",\n    \"DATA_BASEPATH = \\\".\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1b7e4b44\",\n   \"metadata\": {\n    \"tags\": []\n   },\n   \"source\": [\n    \"## Converting a regular classifier into a *Reformulated Squared Error* ordinal regression model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4af15d92\",\n   \"metadata\": {},\n   \"source\": [\n    \"Changing a classifier to a Reformulated Squared Error model for ordinal regression is actually really simple and only requires a few changes:\\n\",\n    \"\\n\",\n    \"**1)**\\n\",\n    \"\\n\",\n    \"We add an additional parameter layer `a`:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"self.a = torch.nn.Parameter(torch.zeros(\\n\",\n    \"    num_classes).float().normal_(0.0, 0.1).view(-1, 1))\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**2)**\\n\",\n    \"\\n\",\n    \"We convert the logits (unnormalized outputs of the neural network) to \\\"predictions\\\" using the following function:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def beckham_logits_to_predictions(logits, model, num_classes):\\n\",\n    \"    probas = torch.softmax(logits, dim=1)\\n\",\n    \"    predictions = ((num_classes-1)\\n\",\n    \"                   * torch.sigmoid(probas.mm(model.a).view(-1)))\\n\",\n    \"    return predictions\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**3)** \\n\",\n    \"\\n\",\n    \"We swap the cross entropy loss from PyTorch,\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"with the squared error loss):\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def squared_error(targets, predictions):\\n\",\n    \"    return torch.mean((targets.float() - predictions)**2)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**4)**\\n\",\n    \"\\n\",\n    \"In a regular classifier, we usually obtain the predicted class labels as follows:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"In this method, we replace this with the following code to convert the predicted probabilities into the predicted labels:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def beckham_logits_to_labels(logits, model, num_classes):\\n\",\n    \"    predictions = beckham_logits_to_predictions(logits, model, num_classes)\\n\",\n    \"    return torch.round(predictions).float()\\n\",\n    \"\\n\",\n    \"predicted_labels = beckham_logits_to_labels(logits, model, model.num_classes)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"debfdfd1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a `MultiLayerPerceptron` using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"1922e731\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MultiLayerPerceptron(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"        \\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.model = torch.nn.Sequential(*all_layers)\\n\",\n    \"        \\n\",\n    \"        # -----------------------------------------------------\\n\",\n    \"        # Beckham 2016-specific parameter layer\\n\",\n    \"        self.a = torch.nn.Parameter(torch.zeros(\\n\",\n    \"            num_classes).float().normal_(0.0, 0.1).view(-1, 1))\\n\",\n    \"        # -----------------------------------------------------        \\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.model(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ce770cc2\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's define the following Squared Error Reformulation-specific functions:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"05434da4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def squared_error(targets, predictions):\\n\",\n    \"    return torch.mean((targets.float() - predictions)**2)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def beckham_logits_to_predictions(logits, model, num_classes):\\n\",\n    \"    probas = torch.softmax(logits, dim=1)\\n\",\n    \"    predictions = ((num_classes-1)\\n\",\n    \"                   * torch.sigmoid(probas.mm(model.a).view(-1)))\\n\",\n    \"    return predictions\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def beckham_logits_to_labels(logits, model, num_classes):\\n\",\n    \"    predictions = beckham_logits_to_predictions(logits, model, num_classes)\\n\",\n    \"    return torch.round(predictions).float()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6562578a\",\n   \"metadata\": {},\n   \"source\": [\n    \"And then, we will use them below in the `LightningModule`. They are only used in the `_shared_step` method as indicated:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"1381f777\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from coral_pytorch.losses import corn_loss\\n\",\n    \"from coral_pytorch.dataset import corn_label_from_logits\\n\",\n    \"\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningMLP(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        self.train_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.valid_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.test_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"\\n\",\n    \"        # Beckham 2016-specific functions-------------------------------### \\n\",\n    \"        predictions = beckham_logits_to_predictions(\\n\",\n    \"            logits, self.model, self.model.num_classes)\\n\",\n    \"        \\n\",\n    \"        loss = squared_error(predictions, true_labels)\\n\",\n    \"        \\n\",\n    \"        predicted_labels = beckham_logits_to_labels(\\n\",\n    \"            logits, self.model, self.model.num_classes)\\n\",\n    \"        # ---------------------------------------------------------------###   \\n\",\n    \"        \\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        self.train_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_mae\\\", self.train_mae, on_epoch=True, on_step=False)\\n\",\n    \"        return loss\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_mae\\\", self.valid_mae,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_mae\\\", self.test_mae, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0ae78a8e\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Note: There Are No Changes Compared To The Baseline Below\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"39ac693d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b070e95b\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"a26a6486\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>response</th>\\n\",\n       \"      <th>V1</th>\\n\",\n       \"      <th>V2</th>\\n\",\n       \"      <th>V3</th>\\n\",\n       \"      <th>V4</th>\\n\",\n       \"      <th>V5</th>\\n\",\n       \"      <th>V6</th>\\n\",\n       \"      <th>V7</th>\\n\",\n       \"      <th>V8</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1040.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1055.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>270</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>365</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>198.6</td>\\n\",\n       \"      <td>132.4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>192.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>978.4</td>\\n\",\n       \"      <td>825.5</td>\\n\",\n       \"      <td>360</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   response     V1     V2   V3     V4   V5      V6     V7   V8\\n\",\n       \"0         4  540.0    0.0  0.0  162.0  2.5  1040.0  676.0   28\\n\",\n       \"1         4  540.0    0.0  0.0  162.0  2.5  1055.0  676.0   28\\n\",\n       \"2         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  270\\n\",\n       \"3         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  365\\n\",\n       \"4         2  198.6  132.4  0.0  192.0  0.0   978.4  825.5  360\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_df = pd.read_csv(\\\"https://raw.githubusercontent.com/gagolews/\\\"\\n\",\n    \"                      \\\"ordinal_regression_data/master/cement_strength.csv\\\")\\n\",\n    \"data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1\\n\",\n    \"\\n\",\n    \"data_labels = data_df[\\\"response\\\"]\\n\",\n    \"data_features = data_df.loc[:, [\\n\",\n    \"    \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"\\n\",\n    \"data_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"b7044584\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of features: 8\\n\",\n      \"Number of examples: 998\\n\",\n      \"Labels: [0 1 2 3 4]\\n\",\n      \"Label distribution: [196 310 244 152  96]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Number of features:', data_features.shape[1])\\n\",\n    \"print('Number of examples:', data_features.shape[0])\\n\",\n    \"print('Labels:', np.unique(data_labels.values))\\n\",\n    \"print('Label distribution:', np.bincount(data_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a60df81f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"952a6c2d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline MAE: 1.03\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"avg_prediction = np.median(data_labels.values)  # median minimizes MAE\\n\",\n    \"baseline_mae = np.mean(np.abs(data_labels.values - avg_prediction))\\n\",\n    \"print(f'Baseline MAE: {baseline_mae:.2f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"14f82826\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating a `Dataset` class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"aac85ec8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyDataset(Dataset):\\n\",\n    \"\\n\",\n    \"    def __init__(self, feature_array, label_array, dtype=np.float32):\\n\",\n    \"        self.features = feature_array.astype(dtype)\\n\",\n    \"        self.labels = label_array\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        inputs = self.features[index]\\n\",\n    \"        label = self.labels[index]\\n\",\n    \"        return inputs, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.features.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"998d6e7a\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"64efaa4d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            'https://raw.githubusercontent.com/gagolews/'\\n\",\n    \"            'ordinal_regression_data/master/cement_strength.csv')\\n\",\n    \"        data_df.to_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'), index=None)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'))\\n\",\n    \"        data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"        self.data_labels = data_df[\\\"response\\\"]\\n\",\n    \"        self.data_features = data_df.loc[:, [\\n\",\n    \"            \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"        \\n\",\n    \"        # Split into\\n\",\n    \"        # 70% train, 10% validation, 20% testing\\n\",\n    \"        \\n\",\n    \"        X_temp, X_test, y_temp, y_test = train_test_split(\\n\",\n    \"            self.data_features.values,\\n\",\n    \"            self.data_labels.values,\\n\",\n    \"            test_size=0.2,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=self.data_labels.values)\\n\",\n    \"\\n\",\n    \"        X_train, X_valid, y_train, y_valid = train_test_split(\\n\",\n    \"            X_temp,\\n\",\n    \"            y_temp,\\n\",\n    \"            test_size=0.1,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=y_temp)\\n\",\n    \"        \\n\",\n    \"        # Standardize features\\n\",\n    \"        sc = StandardScaler()\\n\",\n    \"        X_train_std = sc.fit_transform(X_train)\\n\",\n    \"        X_valid_std = sc.transform(X_valid)\\n\",\n    \"        X_test_std = sc.transform(X_test)\\n\",\n    \"\\n\",\n    \"        self.train = MyDataset(X_train_std, y_train)\\n\",\n    \"        self.valid = MyDataset(X_valid_std, y_valid)\\n\",\n    \"        self.test = MyDataset(X_test_std, y_test)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        return DataLoader(self.train, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True)\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        return DataLoader(self.valid, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        return DataLoader(self.test, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"d909e6cc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path=DATA_BASEPATH)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6a448bc5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"8ee17411\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = MultiLayerPerceptron(\\n\",\n    \"    input_size=data_features.shape[1],\\n\",\n    \"    hidden_units=(40, 20),\\n\",\n    \"    num_classes=np.bincount(data_labels).shape[0])\\n\",\n    \"\\n\",\n    \"lightning_model = LightningMLP(\\n\",\n    \"    model=pytorch_model,\\n\",\n    \"    learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode=\\\"min\\\", monitor=\\\"valid_mae\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"mlp-beckham-cement\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"9e21b802\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type                 | Params\\n\",\n      \"---------------------------------------------------\\n\",\n      \"0 | model     | MultiLayerPerceptron | 1.3 K \\n\",\n      \"1 | train_mae | MeanAbsoluteError    | 0     \\n\",\n      \"2 | valid_mae | MeanAbsoluteError    | 0     \\n\",\n      \"3 | test_mae  | MeanAbsoluteError    | 0     \\n\",\n      \"---------------------------------------------------\\n\",\n      \"1.3 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"1.3 K     Total params\\n\",\n      \"0.005     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1850: PossibleUserWarning: The number of training samples (5) is smaller than the logging interval Trainer(log_every_n_steps=10). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1dab96b858854c048cacb51ec942ad8d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": 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\"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     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{},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 0.30 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73a8e075\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"d23d4a7d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='MAE'>\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_mae\\\", \\\"valid_mae\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='MAE')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"911969d1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/mlp-beckham-cement/version_2/checkpoints/epoch=198-step=994.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/mlp-beckham-cement/version_2/checkpoints/epoch=198-step=994.ckpt\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7f5a8ca959f641c59a458ecc2cf3a747\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_mae          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.3400000035762787     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_mae         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.3400000035762787    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_mae': 0.3400000035762787}]\"\n      ]\n     },\n     \"execution_count\": 16,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/ordinal/niu2016-light_cement.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"8aa08564\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch            : 1.10.1\\n\",\n      \"pytorch_lightning: 1.6.0.dev0\\n\",\n      \"torchmetrics     : 0.6.2\\n\",\n      \"matplotlib       : 3.3.4\\n\",\n      \"coral_pytorch    : 1.2.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,pytorch_lightning,torchmetrics,matplotlib,coral_pytorch\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"d8c56c2e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%load_ext pycodestyle_magic\\n\",\n    \"%flake8_on --ignore W291,W293,E703\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a2ea3b1f\",\n   \"metadata\": {},\n   \"source\": [\n    \"<a href=\\\"https://pytorch.org\\\"><img src=\\\"https://raw.githubusercontent.com/pytorch/pytorch/master/docs/source/_static/img/pytorch-logo-dark.svg\\\" width=\\\"90\\\"/></a> &nbsp; &nbsp;&nbsp;&nbsp;<a href=\\\"https://www.pytorchlightning.ai\\\"><img src=\\\"https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_static/images/logo.svg\\\" width=\\\"150\\\"/></a>\\n\",\n    \"\\n\",\n    \"# Binary extension MLP for ordinal regression and deep learning -- cement strength dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4b7fe9ae\",\n   \"metadata\": {},\n   \"source\": [\n    \"This tutorial explains how to train a deep neural network (here: multilayer perceptron) with the binary extension method by Niu at al. 2016 for ordinal regression. \\n\",\n    \"\\n\",\n    \"**Paper reference:**\\n\",\n    \"\\n\",\n    \"- Niu, Zhenxing, Mo Zhou, Le Wang, Xinbo Gao, and Gang Hua. \\\"[Ordinal regression with multiple output cnn for age estimation](https://openaccess.thecvf.com/content_cvpr_2016/papers/Niu_Ordinal_Regression_With_CVPR_2016_paper.pdf).\\\" In Proceedings of the IEEE conference on computer vision and pattern recognition.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"1a91c5cb\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note:**\\n\",\n    \"    \\n\",\n    \"To keep the notation lean and minimal, this notebook only contains \\\"Squared-error reformulation\\\"-specific comments. For more comments on the PyTorch Lightning use, please see the cross-entropy baseline notebook [baseline-light_cement.ipynb](./baseline-light_cement.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a8f10f9e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"558765e6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 32\\n\",\n    \"NUM_EPOCHS = 200\\n\",\n    \"LEARNING_RATE = 0.01\\n\",\n    \"NUM_WORKERS = 0\\n\",\n    \"\\n\",\n    \"DATA_BASEPATH = \\\".\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"455a429b\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Converting a regular classifier into a Niu et al. ordinal regression model\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e9607c53\",\n   \"metadata\": {},\n   \"source\": [\n    \"Changing a classifier to a binary extension model (as proposed by Niu et al.) for ordinal regression is actually really simple and only requires a few changes:\\n\",\n    \"\\n\",\n    \"**1)**\\n\",\n    \"We replace the output layer  \\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"by a CORAL layer (available through `coral_pytorch`):\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"output_layer = torch.nn.Linear(size_in=hidden_units[-1], num_classes=num_classes-1*2)`\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**2)**\\n\",\n    \"\\n\",\n    \"Convert the integer class labels into the extended binary label format using the `levels_from_labelbatch` provided via `coral_pytorch`:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"levels = levels_from_labelbatch(class_labels, \\n\",\n    \"                                num_classes=num_classes)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**3)** \\n\",\n    \"\\n\",\n    \"Swap the cross entropy loss from PyTorch,\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"with a new loss function:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"def niu_et_al_loss(logits, levels):\\n\",\n    \"    val = (-torch.sum((F.log_softmax(logits, dim=2)[:, :, 1]*levels\\n\",\n    \"                      + F.log_softmax(logits, dim=2)[:, :, 0]*(1-levels)), dim=1))\\n\",\n    \"    return torch.mean(val)\\n\",\n    \"\\n\",\n    \"loss = niu_et_al_loss(logits, levels)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"**4)**\\n\",\n    \"\\n\",\n    \"In a regular classifier, we usually obtain the predicted class labels as follows:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"Replace this with the following code to convert the predicted probabilities into the predicted labels:\\n\",\n    \"\\n\",\n    \"```python\\n\",\n    \"\\n\",\n    \"def niu_logits_to_labels(logits):\\n\",\n    \"    probas = F.softmax(logits, dim=2)[:, :, 1]\\n\",\n    \"    predict_levels = probas > 0.5\\n\",\n    \"    predicted_labels = torch.sum(predict_levels, dim=1)\\n\",\n    \"    return predicted_labels\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"predicted_labels = niu_logits_to_labels(logits)\\n\",\n    \"```\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4c569076\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a `MultiLayerPerceptron` using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a14c5681\",\n   \"metadata\": {},\n   \"source\": [\n    \"\\n\",\n    \"- Given a multilayer perceptron classifier with cross entropy loss, it is very easy to change this classifier into a ordinal regression model using the extended binary method as explained in the previous section. In the code example below, we just change the output layer:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"7c907a9c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Regular PyTorch Module\\n\",\n    \"class MultiLayerPerceptron(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        # num_classes is used by the CORAL loss function\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        # Initialize MLP layers\\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        # Modify output layer -------------------------------------------\\n\",\n    \"        # Regular classifier would use the following output layer:\\n\",\n    \"        # output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"        output_layer = torch.nn.Linear(hidden_units[-1], (num_classes-1)*2)\\n\",\n    \"        # ----------------------------------------------------------------\\n\",\n    \"        \\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.model = torch.nn.Sequential(*all_layers)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.model(x)\\n\",\n    \"        \\n\",\n    \"        # Reshape logits for Niu et al. loss\\n\",\n    \"        x = x.view(-1, (self.num_classes-1), 2)\\n\",\n    \"        # -------------------------------------------------------------------###        \\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"a5247f4d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from coral_pytorch.dataset import levels_from_labelbatch\\n\",\n    \"\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import pytorch_lightning as pl\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def niu_et_al_loss(logits, levels):\\n\",\n    \"    val = -torch.sum(\\n\",\n    \"        (\\n\",\n    \"            F.log_softmax(logits, dim=2)[:, :, 1] * levels\\n\",\n    \"            + F.log_softmax(logits, dim=2)[:, :, 0] * (1 - levels)\\n\",\n    \"        ),\\n\",\n    \"        dim=1,\\n\",\n    \"    )\\n\",\n    \"    return torch.mean(val)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def niu_logits_to_labels(logits):\\n\",\n    \"    probas = F.softmax(logits, dim=2)[:, :, 1]\\n\",\n    \"    predict_levels = probas > 0.5\\n\",\n    \"    predicted_labels = torch.sum(predict_levels, dim=1)\\n\",\n    \"    return predicted_labels\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningMLP(pl.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        self.train_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.valid_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.test_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"        \\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        \\n\",\n    \"        # Convert class labels to the extended binary labels  ---\\n\",\n    \"        levels = levels_from_labelbatch(\\n\",\n    \"            true_labels, num_classes=self.model.num_classes)\\n\",\n    \"        # -------------------------------------------------------\\n\",\n    \"\\n\",\n    \"        logits = self(features)\\n\",\n    \"\\n\",\n    \"        # Custom Loss --------------------------------------------\\n\",\n    \"        # A regular classifier uses:\\n\",\n    \"        # loss = torch.nn.functional.cross_entropy(logits, true_labels)\\n\",\n    \"        loss = niu_et_al_loss(logits, levels.type_as(logits))\\n\",\n    \"        # -------------------------------------------------------\\n\",\n    \"\\n\",\n    \"        # Prediction to label -----------------------------------\\n\",\n    \"        # A regular classifier uses:\\n\",\n    \"        # predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"        predicted_labels = niu_logits_to_labels(logits)\\n\",\n    \"        # -------------------------------------------------------\\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        self.train_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"train_mae\\\", self.train_mae, on_epoch=True, on_step=False)\\n\",\n    \"        return loss\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"valid_mae\\\", self.valid_mae,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        _, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_mae(predicted_labels, true_labels)\\n\",\n    \"        self.log(\\\"test_mae\\\", self.test_mae, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9083eeb8\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Note: There Are No Changes Compared To The Baseline Below\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f7959fab\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"cb6a5846\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"a4056461\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>response</th>\\n\",\n       \"      <th>V1</th>\\n\",\n       \"      <th>V2</th>\\n\",\n       \"      <th>V3</th>\\n\",\n       \"      <th>V4</th>\\n\",\n       \"      <th>V5</th>\\n\",\n       \"      <th>V6</th>\\n\",\n       \"      <th>V7</th>\\n\",\n       \"      <th>V8</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1040.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1055.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>270</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>365</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>198.6</td>\\n\",\n       \"      <td>132.4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>192.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>978.4</td>\\n\",\n       \"      <td>825.5</td>\\n\",\n       \"      <td>360</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   response     V1     V2   V3     V4   V5      V6     V7   V8\\n\",\n       \"0         4  540.0    0.0  0.0  162.0  2.5  1040.0  676.0   28\\n\",\n       \"1         4  540.0    0.0  0.0  162.0  2.5  1055.0  676.0   28\\n\",\n       \"2         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  270\\n\",\n       \"3         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  365\\n\",\n       \"4         2  198.6  132.4  0.0  192.0  0.0   978.4  825.5  360\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_df = pd.read_csv(\\\"https://raw.githubusercontent.com/gagolews/\\\"\\n\",\n    \"                      \\\"ordinal_regression_data/master/cement_strength.csv\\\")\\n\",\n    \"data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"\\n\",\n    \"data_labels = data_df[\\\"response\\\"]\\n\",\n    \"data_features = data_df.loc[:, [\\n\",\n    \"    \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"\\n\",\n    \"data_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"5bd821b7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of features: 8\\n\",\n      \"Number of examples: 998\\n\",\n      \"Labels: [0 1 2 3 4]\\n\",\n      \"Label distribution: [196 310 244 152  96]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Number of features:', data_features.shape[1])\\n\",\n    \"print('Number of examples:', data_features.shape[0])\\n\",\n    \"print('Labels:', np.unique(data_labels.values))\\n\",\n    \"print('Label distribution:', np.bincount(data_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"acc95a6e\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"13b69b54\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline MAE: 1.03\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"avg_prediction = np.median(data_labels.values)  # median minimizes MAE\\n\",\n    \"baseline_mae = np.mean(np.abs(data_labels.values - avg_prediction))\\n\",\n    \"print(f'Baseline MAE: {baseline_mae:.2f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"89e7ba49\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating a `Dataset` class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"98d88d3a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyDataset(Dataset):\\n\",\n    \"\\n\",\n    \"    def __init__(self, feature_array, label_array, dtype=np.float32):\\n\",\n    \"        self.features = feature_array.astype(dtype)\\n\",\n    \"        self.labels = label_array\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        inputs = self.features[index]\\n\",\n    \"        label = self.labels[index]\\n\",\n    \"        return inputs, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.features.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9da3ae6a\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"e002d9fe\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(pl.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            'https://raw.githubusercontent.com/gagolews/'\\n\",\n    \"            'ordinal_regression_data/master/cement_strength.csv')\\n\",\n    \"        data_df.to_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'), index=None)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'))\\n\",\n    \"        data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"        self.data_labels = data_df[\\\"response\\\"]\\n\",\n    \"        self.data_features = data_df.loc[:, [\\n\",\n    \"            \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"        \\n\",\n    \"        # Split into\\n\",\n    \"        # 70% train, 10% validation, 20% testing\\n\",\n    \"        \\n\",\n    \"        X_temp, X_test, y_temp, y_test = train_test_split(\\n\",\n    \"            self.data_features.values,\\n\",\n    \"            self.data_labels.values,\\n\",\n    \"            test_size=0.2,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=self.data_labels.values)\\n\",\n    \"\\n\",\n    \"        X_train, X_valid, y_train, y_valid = train_test_split(\\n\",\n    \"            X_temp,\\n\",\n    \"            y_temp,\\n\",\n    \"            test_size=0.1,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=y_temp)\\n\",\n    \"        \\n\",\n    \"        # Standardize features\\n\",\n    \"        sc = StandardScaler()\\n\",\n    \"        X_train_std = sc.fit_transform(X_train)\\n\",\n    \"        X_valid_std = sc.transform(X_valid)\\n\",\n    \"        X_test_std = sc.transform(X_test)\\n\",\n    \"\\n\",\n    \"        self.train = MyDataset(X_train_std, y_train)\\n\",\n    \"        self.valid = MyDataset(X_valid_std, y_valid)\\n\",\n    \"        self.test = MyDataset(X_test_std, y_test)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        return DataLoader(self.train, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True)\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        return DataLoader(self.valid, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        return DataLoader(self.test, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"e8530232\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path=DATA_BASEPATH)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"26a651dd\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"9e743c6f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = MultiLayerPerceptron(\\n\",\n    \"    input_size=data_features.shape[1],\\n\",\n    \"    hidden_units=(24, 16),\\n\",\n    \"    num_classes=np.bincount(data_labels).shape[0])\\n\",\n    \"\\n\",\n    \"lightning_model = LightningMLP(\\n\",\n    \"    model=pytorch_model,\\n\",\n    \"    learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode=\\\"min\\\", monitor=\\\"valid_mae\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"mlp-niu-cement\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"d87f7cad\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=50)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\\n\",\n      \"  rank_zero_deprecation(\\n\",\n      \"GPU available: True, used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"\\n\",\n      \"  | Name      | Type                 | Params\\n\",\n      \"---------------------------------------------------\\n\",\n      \"0 | model     | MultiLayerPerceptron | 752   \\n\",\n      \"1 | train_mae | MeanAbsoluteError    | 0     \\n\",\n      \"2 | valid_mae | MeanAbsoluteError    | 0     \\n\",\n      \"3 | test_mae  | MeanAbsoluteError    | 0     \\n\",\n      \"---------------------------------------------------\\n\",\n      \"752       Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"752       Total params\\n\",\n      \"0.003     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation sanity check: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5550d1f367fa46af99bd42aade855be3\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n 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{\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    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\"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validating: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 0.64 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = pl.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    progress_bar_refresh_rate=50,  # recommended for notebooks\\n\",\n    \"    accelerator=\\\"auto\\\",  # Uses GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"af30f94f\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"c6206c0b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='MAE'>\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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AI+KwiKHMsVymKQBAEwfcUgc2sWxzoZ5UxAkEQBDysCJRSk5VSu5RSaUqp++spk6yU2qSU2q6UWuJJeYBqiyBQFrAXBEEAPLhUpVLKCjwPXAhkAOuUUp9qrXe4lIkEXgAma60PKKU6ekqeavwCoLKUQH+JGhIEQQDPWgQjgTSt9V6tdTmwEJhWp8wNwEda6wMAWussD8pj8Asyg8UyRiAIggCA0lp7pmKlpmN6+rc59m8CRmmtZ7uUeQqwAQOAMOBprfUbbuqaBcwCiImJSVq4cGGzZCosLGT4oTeIzl7N9cFzOVpk5/Hxwc2qqyUpLCwkNDTU22K4pa3KJnI1DZGraZyNck2aNGmD1nqEu3Mecw0Bys2xulrHD0gCzgeCgFVKqdVa6921PqT1XGAuwIgRI3RycnKzBEpJSaFz156Qs5IunTuRtT+X5tbVkqSkpLQJOdzRVmUTuZqGyNU0fE0uTyqCDCDBZT8eOOymTLbWuggoUkotBYYAu/EUzjECm0UGiwVBEPDsGME6IFEp1V0p5Q/MAD6tU+YT4FyllJ9SKhgYBez0oEwmasheQZAflMri9YIgCJ6zCLTWlUqp2cDXgBWYr7XerpS6w3F+jtZ6p1LqK2ALYAfmaa23eUomwCgCIMxaRWmlKAJBEARPuobQWi8CFtU5NqfO/r+Af3lSjlo4FEGItZKKKk1FlR2b1ffm1QmCIDjxvRbQz6xSFmI11oCEkAqC4Ov4niKwBQEQosoBWcBeEATB9xSBwyIIslQCYhEIgiD4oCIwFkGQpQIQRSAIguCDisBYBMHKKALJNyQIgq/jg4rARA0FKqdrSMYIBEHwbXxPEdiciqAMEItAEATB9xSBwyIIwOEaktnFgiD4OD6oCMwYgb9DEZTJ7GJBEHwcH1QEJmooQJt5BGIRCILg6/igIjAWgQ3nhDJRBIIg+Da+pwgcM4v97Q6LQKKGBEHwcXxPEViNReBnN1FDYhEIguDr+J4isFjA6o+qKiPAzyKKQBAEn8f3FAHUWsBe5hEIguDr+KgicCxX6WcVi0AQBJ/HRxVBIFQ4LQIZLBYEwbfxTUVgC4TKUhkjEARBwMOKQCk1WSm1SymVppS63835ZKVUvlJqk+P1Z0/KU41fAFSWGYtAJpQJguDjeGzNYqWUFXgeuBDIANYppT7VWu+oU3SZ1vpST8nhFr9AqCwhNMCPgtLKVr20IAhCW8OTFsFIIE1rvVdrXQ4sBKZ58HqNxy8QKssID7JxoqTC29IIgiB4FaW19kzFSk0HJmutb3Ps3wSM0lrPdimTDHyIsRgOA7/TWm93U9csYBZATExM0sKFC5slU2FhIaGhoQza8ii2inx+Ffg3NmZW8sx5Ic2qr6VwytUWaauyiVxNQ+RqGmejXJMmTdqgtR7h9qTW2iMv4Bpgnsv+TcCzdcqEA6GO7alA6qnqTUpK0s1l8eLFZmPhjVo/N0o/sWinTvzjIm2325tdZ0tQLVcbpK3KJnI1DZGraZyNcgHrdT3tqiddQxlAgst+PKbX76qECrTWhY7tRYBNKRXtQZkMfiZqKDzIj/IqO2WVEkIqCILv4klFsA5IVEp1V0r5AzOAT10LKKU6KaWUY3ukQ54cD8pkcEwoCw+0AVAg4wSCIPgwHosa0lpXKqVmA18DVmC+1nq7UuoOx/k5wHTgTqVUJVACzHCYMJ7FkWIiPMihCEor6Bge6PHLCoIgtEU8pgig2t2zqM6xOS7bzwHPeVIGtzjmEYQHmq+fLxaBIAg+jG/OLPYLhIqSakVQUCJzCQRB8F0aVARKqfAGznVpeXFaCVsgoAn3N7sFpWIRCILgu5zKIkhxbiilvq9z7n8tLUyr4WfGAyJtJr2EDBYLguDLnEoRKJft9g2cO7NwKIJQq3EJSZoJQRB8mVMpAl3Ptrv9MweHIghUFQT4WcQiEATBpzlV1FBHpdRvMb1/5zaO/Q4elcyTOBQBFSaEVMYIBEHwZU6lCF4GwtxsA8zziEStgc2hCCpLCQ/0k6ghQRB8mgYVgdb6kfrOKaXOaXlxWokgx3DHiaOEB4XIPAJBEHyaJs0jUEr1V0o9qpRKBV70kEyeJ3YIKCscXEN4oLiGBEHwbU45s1gp1RW43vGqBLoCI7TW+zwrmgcJCIVOA+HgGiKCLmV/TpG3JRIEQfAap5pQthKTIsIGTNdaJwEnzmgl4CRhNBzaQGSghI8KguDbnMo1dAwzQBxDTZTQmRs26kqXUVBRTM+qfRSUVNAaue4EQRDaIg0qAq31NGAQsBF4RCmVDrRzpIw+s0kYBUDPsu1U2jUlFbKIvSAIvskpxwi01vnAfGC+UioGuA54SimVoLVOaPjTbZiIeAiPZ2T6HD7zj6Iwsw/BCb28LZUgCEKr06SoIa11ptb6Ga31WGC8h2RqPc7/MzkdRzHIsg+9+2tvSyMIguAVGrQIlFKfNnQeuLwFZWl9hlxHauAk/N4egfXQGuBub0skCILQ6pzKNTQGOAi8A6zhTE40Vw8dwgNZb+/NxKPrvS2KIAiCVziVa6gT8EdgIPA0cCGQrbVeorVecqrKlVKTlVK7lFJpSqn7Gyh3jlKqSik1vSnCtwSdI4JYb+9NcPEhKDjS2pcXBEHwOqeKGqrSWn+ltb4ZGA2kASlKqV+eqmKllBV4HpgC9AeuV0r1r6fcPzBrG7c64UF+bLf2MzsH13hDBEEQBK9yysFipVSAUuoq4C2ME/0Z4KNG1D0SSNNa79ValwMLgWluyv0S+BDIarTULYhSiryIfpQrf1EEgiD4JKcaLH4d4xb6EnhEa72tCXXHYcYXnGQAo+rUHwdcCZwHeC2JXcd24aSWJDJAFIEgCD6IamhGrVLKDjgT8bgWVIDWWje0pvE1wMVa69sc+zcBI7XWv3Qp8z7wpNZ6tVLqNeBzrfUHbuqaBcwCiImJSVq4cGEjv15tCgsLCQ0NPen4/G1lTDj2FrdavmD5+HewWwOaVX9zqU+utkBblU3kahoiV9M4G+WaNGnSBq31CLcntdYeeWEijr522X8AeKBOmXRgn+NViHEPXdFQvUlJSbq5LF682O3xp7/brWc+8KjWfwnXOn1Zs+tvLvXJ1RZoq7KJXE1D5GoaZ6NcwHpdT7vapAllTWQdkKiU6q6U8gdmALXmJWitu2utu2mtuwEfAHdprf/nQZncEhsRyEZ7otkR95AgCD7GKVNMNBetdaVSajYmGsgKzNdab1dK3eE4P8dT124qcZFB5BFGcXhPgg+IIhAEwbfwmCIA0FovwqSxdj3mVgForW/xpCwNERsZBEBm5FC6H/we7HaweNJYEgRBaDtIa4dxDQHsCegPpXmQk+pdgQRBEFoRUQRAoM1KVIg/2+lhDmQ2JUpWEAThzMajrqEzic6RQewocdyO/EPeFUYQBKEVEYvAQZ9OYaw+VIH2D4MCUQTCWUTeAdCy8JJQP6IIHFzQL4b8kgpKgjpBfoa3xRGEliE/A54ZRsesFd6WRGjDiCJwMKF3NAF+Fg7pKFEEwtnD/pVgrySw1CupvIQzBFEEDoL9/RjfK5odRWFoUQTC2YJjgqStosDLgghtGRksduGiATGkpkaiqrKhohRsgd4WSRCaxpJ/weoXwGqDa16DA05FcMK7cgltGrEIXLh4QCcK/GMAqMwTq0A4A9myEIKjQNvh2z9D1nZALAKhYUQRuBAZ7M+UcUkAfLZ0rZelEYQmUpQNOWkw7EYYMxsy1hmF4BeEX6VYBEL9iCKow5hhQwBY/+MGDnz4JyiUQTbhDOGgo/OSMBpGzAT/MFAW6D5BLAKhQWSMoC7hnQG4y/Y5cVuPsuLQQY6Me4zpSfFeFkwQTsHB1WCxQedhZnxr0gOQuR38Q7ClS/ioUD9iEdTFFgTB0cTpowAk5X7B4+8vY0tGnnflEoRTcWANdB5aE+Qw5m644gUIao+tshCqKr0qntB2EUXgjog48z7xfgIp547gH3jksx3OxXQgaydUVXhPPkFwkrXTvFeWweEfIWHUyWWCo8x7yfHWk0s4oxBF4I4O/aB9T5jwe+h1ITcGrGTD/uP0evBL7nnmbfSLYyHl796WUvB1Dv8IL4yGPYvhyGaoKoMuo08uF9zevJfktq58whmDjBG449L/QFU5WP0gYSShad/y98t78lN2FQPWvojys8O6l2H8ryEgzNvSCr5K3kHzvm8ZBEaabbcWgUMRFOe0iljCmYcoAnf4hwAhZjuqFwAzelTAwCgqf1zJmqq+jCr9CTa+CWPu8p6cgm/jbNgPrIGgSGjXHUI7nlwuyKkIxCIQ3COuoVMR7VjLODsV1r2CFTt/4m7SQ4aYGZx1xwq2vAePd4bHYmq/lvwLtIYF18L3f2397yG0HXZ8AvMnm+fhdHC6eg5tgAOr3buFoGaMoOAQPDPcPI/zLji9awtnFR5VBEqpyUqpXUqpNKXU/W7OT1NKbVFKbVJKrVdKjfekPM2ifU/znpMG6UtQCaPontif58sugfyDsP1/NWXtVbD4byYEddQval6dBsGKp2HnZ5D6Nax6DgqPeeXrCG2AvUvgwKrTH7x19vArS6A4GxJGui/ndA2lfgu5eyAiwUw2K5NJZoLBY4pAKWUFngemAP2B65VS/esU+x4YorUeCtwKzPOUPM3GP9j8cY5ugcObIGEUk/p05MPC/pS1SzQNvLNnt/MzOJ4O5z0EFz5a87rkP1B+Aj6aZXy5lWWwdq43v5XgTZxJDU93smJxLtiCa/YT6rEIbMFUWfwhfYnZH/ULhxyy7oZg8KRFMBJI01rv1VqXAwuBaa4FtNaFujomkxDgNG1lDxHVC3Z/A/YKSBjFxD4d0Fj4LORqyNzK0Zeugg9vg2//ZPy0/S6r/fnYwdAj2fTcxs6GPlPNYHN5sVe+juBlnAsfFdWjCNbMNc/Tot+bTsOJTJM3qKK0drniHPNsRnSBgAjo0Nd9fUpR6RdmAiCi+0DMQIcckk9LMHhysDgOOOiynwGcFNKglLoSeALoCFziriKl1CxgFkBMTAwpKSnNEqiwsLBZn+1VFkx8VRkAKw5UUnF0DQlhFv6Y1o9OtgF0ObqD4jwFSpHefQbHli47qY6wyKn0iMxhe1k/Imx2BpV8wcYv36Qgok+z5WoN2qpsZ7Jc43L2YQN2rE0ha7+99klt59xlD6GVH35VRezKsxFauJe4w1+ypSCc3KgR1UWHZe6jyhpIbvT5+FUWsW/p0nqvOcwaQgA5HLZ1Yf/Og4wBdq39gSMZ3o0XOZN/R2/gMbm01h55AdcA81z2bwKebaD8BOC7U9WblJSkm8vixYub98HVL2n9l3CtnxlefejlpXv02Ce+1/OX79Vd7/tcv74yvfH1Hdlq6tv20enJ1Qq0VdnOWLlKT5jf/i/hWq98/uTzeQfNubUva/3ieK2fGqz1X2PMse8erV326aFavz+zUXLl/ne8qWPjW1pXlmv9lwitv3+sUZ/1JGfs7+glTkcuYL2up131pGsoA0hw2Y8HDtdXWGu9FOiplIr2oEzNI9qEkLr6YH8+vjvL75vELWO7cU63djz7QxoZxxvp6nHOXJYFcHwP1/WwCzNPPp+dat6jEmHcPXB8n3EphsVWLzJTTXFuTWjoKaiwOea7JIwyaxWExcra3EI1nlQE64BEpVR3pZQ/MAP41LWAUqqXUko5tocD/kDbm/USMxCsAdDrvOpDSqnq1yOXD6SsoooZc1dzMLcRyiAwEvxDZbDOF3FV/kVuIsdy0sx7dG/of4WJWut3GfS73ISJOsOVqyqhNK8mNPQUFAfHQWRXiHJEwUXESUdEqMZjikBrXQnMBr4GdgLvaa23K6XuUErd4Sh2NbBNKbUJE2F0ncOEaVuEdoR7f4IBV7k93b9zOAtuG82J0srGKQOlIDxOBut8EWfjGxzlPmooO9V0EsI6mZnts1LgqnnQZRRUFMPRraZcaV5NPY1gf9fr4I7l5tkDx/MnHRHB4NF5BFrrRVrr3lrrnlrrxx3H5mit5zi2/6G1HqC1Hqq1HqO1Xu5JeU6L4PY1fyI3DIqPYMFtoygsM8rgQM4plEFTemRan/7kI6FtUHAIUBA7pB7X0G4TCeR81gLDTTZRp1vS6R5yzioObpxrSFv8TF1OIuLN8yfPlYDMLG5RBsYZZVBUXsl1c1exP6eo/sIR8cY1dHQrY1beUpNFsi5fPwiPRMJfO8D+lZ4Qu2UozoWnh8KuL4374oWxtSfb+SKr58Ab02ofyz9k/PPhnet3DTlns7sSEQfh8TWLzzgnkzVSEZxcXzxUlkraCQEQRdDiDIyL4O3bRlNaUcXM19ZRUFpPuurweBNHvuNTAsqPw/Kn3Jfb8amZmQyw+yuPyNwirH3ZTKZLX2oGOLO2m9mzvkzad+Z+uKYhyT9oGvWQjsY1ZHcJHy0vNuej3CgCgA69zb2FGougkYPFJxHuCFgQ96SAKAKP0L9zOC/+XxIHcoq57bX1PPnNLrYdyq9dyBk5tP0j877tg5NdRQWHIf8ADLneLDhyoE7USFuhogTWvmS2s1NrIl98fTAyJ9WsGXziSM2xgkOmEQ6NAV1VO81E7h7z7oxSq0u4izux2jXUuDGCk4hwrLjn67+RAIgi8Bije0Tx1ysGsuVQHs8tTuPal1axNt3FDHf+EXPSyG03zPhqV79YuxKnPzhhtAn7O/yjmWnaikQfWwmb3605UFkGX95vlJSTTQtMwxTZ1TR+OadQBDl74Js/mdxMZyuVZZB3wGw7o8O0NtsR8RDawRzbu9jMGta6RoFG93ZfZ4TDiqwsq0k4dzquIVfZBJ9GFIEHuX5kF3766xTWPHA+sRGB3DhvNde9tIqvtx81riEHmTETYODVsOE1KMmrqeDAGvALMikquow2C48c3tSq36F7+gJY4rIIz77lsOZF2OJQDvYqWPkcxCXBkBmm8cvcbs7VF5Wy8XVY+UxNqOTZSO5eYw1AzX0ozDRzAiK7GosA4It7Tb6q/IM198OZ6LAuzsa74LBRvH6BtXMNNYWQDhAYAZnbmvd54axCFEEr0DE8kIWzxjBzXHeOnSjjzrc28Gl6TbRGXlgflne8AcoLYcOrrE3PZfGuLLMYeVySmQDkXHCk7qQiT1KcS0hxBhzfD5Xlta/vdFP99LkZGxj7K+Pb1nbjGwczGFo3P47rZ7N3e1Z+b+L63fIdmVaqe/y9zBgB1ISBZqeaz0QkmESH7gh3mYhYfNyMDzQQydYgSkH8yNZ9noQ2iyiCVqJDWAB/nNqPz381nlHdo7jno91UBLSnKiiK326I5P++KGa5fRCFS57llpeXcdery6g6vIWquHNMBaEdTUK71vjj5u6F3PSaa+kq09iDyXsP5pzWpjfrTLTn9G0X54DNsbBPwSFjxTgtHefaulDTMDYD/7IcyD6FRVFyvCbuvrVxfjdbcI37xekyi0qsWUDGz7HQfE6a+UxUPeMDYJQEmHtanNP88QEnCaPg2E+ylvHpcmx3jas0Z0/NynFnEKIIWplgfz9euWUEXdoH82NFAt9WDKGwAp6eMZTv219PaEUOd0Vt5LEBh7FSxecFPao/m9V+BBVpi6E0v4ErtAAf/QLeuqp2uGp2qpnNmrEegtoZH/XGN8xs17GzwWKt3Yh1G+f43G545UJY8ZTZd66tC6flGuqVNg/ev6XhQsv+A69O9U6sfE4ahHU2StLpGspOM4ohPM64ZcJiYcxsCAg397e+0FEn4Z3Ne/5BYxW4W42sKXRxWpnrTq8eX+etq42LD2DhDfDJmbdqoSgCLxDs78e/pg/h/0ru5dfFM5k9LJBpQ+O4945Z5Ib15U7/RVxd8hFZtjj+uDmao/mlbD+cz127h2KrLOLIDy95TjitzZyG3L2wfj5Fwc5B7VTjT64ognNuN8e+/qPplQ690ewHhJnGDUzabTDhr1XlNfMknBZFdJ/TsgiCSo6YiKqGyN0LZQWeV5zuyE41FlJEfI1rKCfVpHiwWIxr5pcbYdKDRoHuW25cg/WFjoJxGQW1Nz3QrO0QP6L+so0hLgmU1bggheaRd9A8h/tXQlG2sbAOrjt55cI2jigCLzGye3sev3o4z9w4kj7trQCEBtpof+HvsObshsMb8Rv3Syq04oZ5q7l5/joygvqxjoH4r5/Dln1ZtUNSXXu9p9MDLsw0i+gAlBeS2364GdjMTq2ZzDTs/4xVUF4II2eBLajm884ebfeJ5v2nz827s9E/uMb0kruOqXGVNEPewNJs08A3FEVVnfe/lVeD09rR6Cc6ZpA7LYLU2g29f7BRCtGJcMyhKBuyCMAolt1fmbGY+lYkayz+ISYQYf8qKG9g8qNQP073aWke/Pim2a4sgSNbvCZScxBF4EWuGZHARQM61T444Aqz0EhwNO3H3cLjVwwkMshGn06hvPyzERSdczdR9hzmzn2GK19YwYb9ubDqeXg2yQzMrnoBnh5s3DjNwdlg970UgPyI/iacMTsV9qYYd0dkF+Nf9guqsQ6cRPcxCfo69IHgaNMjB0cWzXLzx+ky2tRZchw2vA7/7N60pTvLi7FVOpRV3Xw9y56El883201dCSx9Kfyz5+kvI1qYZZRUdKJxA5Xkmu+at999Q+96rDGKoKwAUBB/zunJCSY0+cBK+FtnMykQ4KWJZuynsbxzPSy88fRlORM5uAaUoxldPadm++AaeOeGxt2X4lz4d29I/c5zcp4C765KIZyM1QYzFhjT0hbENSMSuGZETTbvqk7XUv7j77m3WxbbjgXxizc3srLT5/jn7jFhmcueNOvXZm6FzsOafn1ntMtFj8GAK8k51g4CM024aGUpjP2lcWtc/DdjPYTUGbA8917of7n5HhHxRpbASNNj2vOD6Z0njKqJgPn6QWOB7F8OA65snIyuYalFWRDpku1852dmMLo4t8YScJfTxx37lht5myKLOzIcPvfOw2tmAu9bbnrx7lw/zmO2YKNoG8IZQhozwIwznC7jf23qXPG0cW8Mmg5HNoFfgEmDfSoObYRdi8z2kS3GwvAlDqyGbuMhcwcUHjXK+USm+S8e+8mUObLZ5JZqqI7CTNi3DBIvaB256yAWQVskdjDEJ7k9ZfXzw7/rSLoXb2Puz0ZQXl6KPrTBnPz6j6Yhgxo3TlPJSTM9/ciuMGg62mI1vdSKYuNPHnWnKRfVE7qOPfnz4bHQfYLZdjZag68z707TOWFUTYSR0w3VFHldJ6q59vbLi2pMcuf6vNB415DTGjrdGdwHV4PV3/z5nTPI96aYd3ezhp1WgHP8oCGcCvR03UJOwjqZwf7YIcad5YzEOvyj+9Dfuqx8xgx2+4fCymdbRqYzhbJCM27mnPAJ5r2LIxrLP8y8TnVfnGM0XpxXI4rgTCRhNGTtoHd4FX8fCwG6jAMx54O90vRCw+NqBmUbYtl/YNtHtY85QxhdGyRnj3XwdaahbyzORmuYwzze/ZXpxXboaxSN1R9CO5lelKu8q1+ErR+Y7Q2vm5cr9SmCQxtNqCvUNLxgelsZG2pm8B7ZDF/8zrjPMnfAZ782FphzzKLu4GnBEfjf3VBaUP93LSuE/91NUPERo9Q6DzNZQ533YOv75t1deGj7HoBqeKDYiVO51rdQfXOJTjShj06LsKrcWAZgBqcXXANvXAHbP675zPH9sOMTSLrFvLZ9aMo4XUyu7P7GbYMYeXwLfPdwi36VWpTkwSd3m9+wJUn5B7x5hbHyuoyqUcwJo2qUwoiZkHSz+Y/lHTCyfHyn2XY8L+TscZlXk2omaC76Axxt3Yl+4ho6E+kyCtBwaD1TIvYDcMOBaTzZwY+4kbcTn/r2qecbHNsN3z8KIdHQZ0rNgG9O6skupS6jzSIpE3/fNDkHXmUa5k6DzYBzYaaZxORUMmN/BZ0Gml78ymdM0rXibOMu6tDHzLZOecL8gfpeWuOGqusaclLdgCsXRaCMsti0ANa/AiNuNTO41883Fs3mdyD1Gxh6g/lTKquRp7zIDKYCLP8PbHoLBl4Jveox3Te8Bpveokf0Hjj+I4xyLLkR2cWsY5GfYb5rQNjJn7UFGTdM93NPfU+7TzD1JV546rJNIaqXsfrSlxo/t7bXjOek/A3SlxnFtrKgxm2W9q0pl3SLuVeZ28y8jfyDMLLO2NHaucb1NGZ2zSQ4u53E1Jeh+ICxNMNiWvY7gVFKP75lOhtJt7RMnXY7LP+vSe/Rewp0GWvGxo5sNtFyFcXGzTP2l0ahrpljxu5ComDz22adiQ79zDNVlm+sL2U1UW5HtzjydmmY+q+WkbcRiCI4E4lLMn/WA2tQ2buwhydwSb+RzFwVQ8m75fynawJXFnxkQttc/eeurHrW/CGLjpnGcMStNflxBl1bu2xgOFz7uvt6GqLLaPMC09stzKzpLQGc/yfzbgs2je2hDcbfrKtMuGnmtpqEbevmQfJ9Zjs/gzL/dgQoe22L4MAaY21UlNT45qMTTZnKkpoyTjfUd3+pyQe0/X/mD9x7Cuz+0lgX3c+FohzY+Gb1dd1SVQGrXwBloUO2I+Oq83tarHDNq6e+Vxc+cuoyYOYONKa+puJ0T6V+bSwUrc296pduev1jf2meuZXPGoXtH2zOh8aY8krBzz4xSnzdPPN511nPOakm9LjgcI27LO1bQood9//gGjO21JJUlJhGGE4rVPkkCjLM8zTh96bXD+Z+OH+XwHC49o2a8gOnmzEDW7C5h5sXOmaFW8yYFtQ8d1sclmMrz/gW19CZSECYWT5z1yLYvxJL19E8MKUfy/4wiVnn9uCVA2aiUeXS/8AuN6mrT2SahzHpFogdagYK171i3rW9/qRnp4PTN95l1MnnnNEv6142bqD2PQBtchiByb2z9iUj45HNRhEERJtGsTDLuCjWvWIa+IRRNY1acJTpkRdl1fi+0741uZDa9zRKwBZs3FNbFprzQ28w707rYt08hxJRJgzUXmWS8K17peb1zUPGSpn6b+zKhALXUnhnAs7fvOS4UdoJo0w00VcP1IwNJYw27kfnzPCDa4xLxLXBd7fOgWsCvhyXBnnFM5QGRJkos9Np+JzzXgCO7TIvMB2c4mzzG7vzv6d9f+qw2YPrTGcAjHvxRKZLqpBGuPIAxv3KdDKKs+HS/5qOQ+FRmPpvsDj64k73qfM5PLrNuI9aCVEEZyq9LjA95qJj1e6KqNAAHpjaj5unXUKmjsRv43x45zrTeLqyZo55GMfMNr2a4/vgi9/C4sfNn76eaKP07CITrtoITlpxtMsYE04a52YQPLg9xI0wPc+qMrjyJSPHtg/MIOTlz5qG5YvfwlvT4Xh6bUXw9R/NubJ86HV+TaMWEW96rLnpcMKRAmD7x4A2UU8RXeCc26DHxJq0zvHnQMwg01MrLzYKqPfkmsXe05fCx7PM9ZyvNXOM+ytpJkc7XWCUqzO76JlCaIwZ2ATTwPU63yiF3V+aeSPhsTV+8IOr4cRREw5bd6zC3ToHrgn4nI1o3kHYv5xDcZdA3PDTUwQf/Bw++aXZ/vA2M9O3ssx0JDoPM260uhZB+jIze/7HBfXXW1kOr11irGeABVfD94/UKJXGjOmAifDqe6n5Dwy/GQZfawbnk2aa/U6DoatjJn5xjvmf6CpjIbcSHnUNKaUmA08DVmCe1vrvdc7fCDjsfQqBO7XWdVotwS3n/xlG32kazDohnNeM6s5jRz7muzWb+D70Iawrn4Wr5wGwJ+MIPda9gup3mYlSieoJf0ivmQlpC3Qbllhl1/z89XXsPVbEbeO784fJffH3q+lHlFfaefr73Vw5LI7o0AAufmop917Uh2udoa+Dr4NB1xhXiTtmfmkaHuf1Ow00Cix+hElXcd8+43d99/+gCErjB0Gon/HnZ+824wlT/22USpEjcio83mTZdCZ26zjAzMhVFlPnrzaa+7fhVRMe61wreORt8Nk9JlVAcY4Zy/juYeP7dvY271xVO9dPUDuwWNjd+w46T5zYhB+yjaCUsdoO/2gUwaDp0PM881yEOJRacHujZA+sqcmQWtfycbp98jNqQiZdG2HXiYXA8XZDIbC9mQtTUVJ7cmJjqKo0v7/FanrumduM0vn4F2Z9h2teM73rnZ+bht3P33xu5TPm3Rni6Y7j6aZjkn/IdApKjptxDluwiZRqSoqPa9+ocZdd8aLZtljMM4s28ge1N3NORt4OKX8396hH6zxLHrMIlFJWzIL0U4D+wPVKqf51iqUDE7XWg4G/AnM9Jc9Zh1LmQawbx+/glxcNIj8onv9ZLsK+9SPmffAps1/+mg9efBhVlo8e+ysAjp0o4+X1eRTY2pvBOhclcMJldbVPNx9i77EixveKZt7ydK6Zs7LWuszPLU7j+cV7uPf9LcxdupfMgjK+3nYUgA83ZLA3u6h+JQDmD+p6fddwPICgSNOrcqzWVhbQwWTwzN1rTO7uE2py8ztN9oi4mnTPAMNvMu8xA4x7zWozf0bnNZxrBQ+eYere/rGxVLqOrZkhnJNqGoGO/Yy8zpezgVGWhr9nW8bZw3W+BzueCdcIsoRRpoFKX2oS5tWNj3cmxnNd56A62V4vl8isNWALoSikm6nTXgF7l5jJfK6vouyamedVlTUrulVVmuN5+81nK0tNAIC2G7m2fwztukG/y83zoKuM5VtWaBRZ6je1ZSsvNtdzDZl1Kq2irJqghOPpRhm4rivdGCxWM0h80rbL8+J8bhMvNM/XvuW174UHc2Z50iIYCaRprfcCKKUWAtOAHc4CWmvXRXhXA/EILUJEsI2HLx/As/+7iMv1p9y2zdEI2mCNvS+5efEkRZRy/cur2XOsiDdW72NSn44cyC3m71cNZnfmCW55dS3TetoYMaaSZ75Po19sOG/cOpKvtx/lDx9u4cL/LmHGOQnEtwvm+cVp9OgQwuaDeWzJyEMpWJuey77sIu59fzOD4iL45O5xWCyN/PN0GWMiTbq4uB6UgrH3wEe3URoYYywCHH8OVxdFdB9AmYagutemzCD4Nw+Zul3p2K8mrBWMVTJqFvzwmPHvKmVcHru+PHlx+bOJjn0B1fAYUZcxZj7I+leMO8OpAJ0ER5uwYFfXUHaaca3FDq0ZqD+wGuKTzDyVhFHmuu9c5/6aY2bDBY/Ai2PNAP6Uf8G8840bzzV6ao1jZu/5f4GvHzCfs1hrFNuuRcb9WVVuevXdzjVRTmUn4L8DzGzw8Hj4tSNjrVNJFB6rHZSQtb1mbkxL0qGvsV46OdYfWT8f/u0Sbvxnz60vrU7y5bZUxUpNByZrrW9z7N8EjNJaz66n/O+Avs7ydc7NAmYBxMTEJC1cuLBZMhUWFhIaGtqsz3oST8sVeXwzwcWmh2bXmofSB7KjLBq7Ns3otX38WbS3ghMVmio7jOxk5WiRZv8JO3YNgVYorYJfDw9gaEfTd8gpsfO/tApWHK7ErqFDkOIvY4L4z4ZS0vPtXNbTxqd7KhjZycraoya2/+cD/Tk33tY4oXUVUTkbyIk6p3ajq+1E5azngH9vehato++u56jwC2HFuLdqpvdj4tNPhPUi7EQaQzf/iZLAGNaMnktE3g6Kg+Oo8K/t/gorSKXcvx1lgdEAKHsF7XM3khNlBkPjMj4jMW0eldZAsqNH81O/37gVuy09Y9uzqzhwws7F3fwoLio6pVzWymLCTqSR167+2cGWqnI6Zi3FYi8nL3IwxSEn991Grf4FBeGJ7Oz/OwCGbfwDdos/eZED6bZvISvHvs7Ylbewv+t0tnWYRmhoKO1z1hNYenIqkOjsNUTk72Rvj5tJTJuLXfmRmng7fXa/SElgRw7FXUqvPfMpt4XjX1FAYUh31o94kujsteREjURbrFgrizh3+Q1UWQIAzd4eN1MY2oOI/G30SF/AtgH3M3D738ltN4T2xzezbsTTZBJNUsZ8Yo9+T7ktgt2972Lg9ieq5UrvdiP7u117kryng39ZLgFl2ZwI741/2XGis1dT3dEBDneeTGFRcbOfr0mTJm3QWrvNVOhJi8Bdl8mt1lFKTQJ+Dox3d15rPReH22jEiBE6OTm5WQKlpKTQ3M96Es/LVbvuB/bn8ujnOxnQOZwbRnZhYFwEj9g1VVrz1He7eX6xWTv3iasG8cOGnVhCo7gzuRdDEyJr1XM1xn1UUlFFZJA//n4WzhldSmpmIb07hfLp49+z9mgVg+IisFkV76UWctw/kk7hgXQIC+Cm0V1ZmnqMOUv28sKNw4kODagj9/n1fJ/zyElJoW/fCbDrOWzdx5E86Tz33znrJ9j8J4LiBznucTLucXfcpbe58wSkzcOvqpROA8bTaYL7etrKM5ZfUsFv/51CblE5fpGdOD+SFpTrooZP70skqKqCmORkxxKsmTDwatp1Gwf73mFcWAZgp9u517Evw6/h3yVzB7w4hsQ98yA8HsuJw/RJNdl3g0qz6GU9DEHt8E+8CLa8S2i/80iedD4nPTubYrAWZsKIW0m89N/m2I5PIH0BAys2g7LQ/tpn4aUJnNPJTkphKLE2E7XjX1HAwC7tYTtmImTefrqfcxHdB9Qjc4tRO81Jbzz3fHlSEWQArkHs8cDhuoWUUoOBecAUrXWOB+URHCR1bc8nd4+rdcxiUVhQ3JXci/fXZxDkb2V6UjyxxXtJTq4/3XFYoI2wwJpefkx4IDHhZrGVnh1C2HOsiEsHx3LRgE7886ufWLLrGAWlFVRUaZbuPsba9FyKyqt4acke7r2oDz8eyGNMz0YuuOIcxGwoVNPpGjrdkFhnNAw0PlrEizzzfSrHi8u5YmhnFqw5wLIwCwEJ2YzrFe35i4fHwf4VZr2KjW+awfroxJr7tvRfVCfNy/ix4bpi+kPiRcanP/EPZo3n7R+bwIOt70PqtyagIGGkGfDvMtp9PVGO+SRjZtc+BqbujgOMSyakg2MwvIcZI7D6O9KoOzzafS+F1c83PnT0DMGTimAdkKiU6g4cAmYAN7gWUEp1AT4CbtJan8XrFp45hAT48b+7x2G1KGzW04slGNMzij3Hipg6KJaE9sG8+H81oaMvL93L44t2Eh0awKgeUby5ej+bDuaxbt9x3r59FGN7NqLBihkAw3/WsL82qJ2ZDDVw+ml9l+pBUGjzjUBmQSmvr9zHdSMSeOKqQUzs04G/fbqF215fz/qHLiAkwMPzSCPizMSxbx4yCQcTL4LeF5t7OOhaM7Ewbnjjk+Zd8LBR6IOvM+MStmC4+HET4ltZahr0ftNMnH9iPdbKyNuh90UmSs6JM7WHrjLzW5RyDIavxhZ6iYng6TIGDqwyc0+Co8zcm8oSxzjU2YPHngitdaVSajbwNSZ8dL7WertS6g7H+TnAn4Eo4AVl/MCV9fmwhNajc2QTQ/jqYfakRM5N7EBC+5PX4L19Qg96dAiha1QwVouFC/6zhA37j+PvZ+HzLUcapwj8Aswcg4ZQymRSPV1Cos3Ep6pyRwNyemituf2N9VwyOJYrh7VsjMSSXceotGtuGdcNpRRXDosnK30XT6wt5YefsrhsSGeOF5Xzq4U/cuOorkwe2OnUlTaFiHjTuJbmw/99XDuB4tVu8hCdipgBMO15sx3dC654wWzHJRnLI7qXmbdx5Yv11zHgipOP2QLNhEPX+RBdRsNPnxORv93sdx1bowgiu0CH3mZS2FmGR7sGWutFwKI6x+a4bN8GnDQ4LJwddIoIpFNE/Y3M+f1qQjv/NX0w7UL8+XjjIb7adpRHLx+A32laJC2KUmapSF3V9Fh3NxzKK+G7nVmsTc9lYu+OtA/xd1tud+YJLAp6dXSTo6gelqVl0yEsgD4xNZ9JbGehY1gAX2w5wpSBnfjlOz+yPC2bnUcKGNcrqpZ777QJdyi2ruPqzaLbIiSMNIrgdFx10YkOReCSNA7ocuBDs991nEntXlF0+kuDtmEk15DQJrhquGk8yivtfLr5MKv25nBuYhubndtlTIvNEXCuLldQWsljn+9gxsguDIwLJ9i/5i9ZXmnn+rmrySkqJ7lPBzqEBhAbGcTE3tEkdW3vtl67XbMiLZuJvTugXKKtLEoxdVAs76w9UK0Efj6+O68sT+e5H9J4YGq/FvlegGOeRoTx6XuSPlNNiGXc8ObX0e1cMzs/sovZjx0CoZ0IP5FqxjpclwM9ixVBG+pyCQJM7N2BEH8rC9ce9LYoJ3PlizDtuRapaktGPn4WxfUju/DRj4e49qVVPPzp9lplluw+Rk5ROZcMjmXvsSKWp2Xz3A+pXP3iKlakZbutd+fRAnKLyhnvZlD4ksGxlFXa+WZHJg9M6cufLu3P9KR4Xl2xj+NF5S3yvQAzRnD//pp1qz1Fwki4/4Cx1JrL+F/DL5bWhCj7BcBvtrFs/Dtwz2YzjmFzZKF1nZx4liEWgdCmCLRZ+fn47jzzQxr9F6eRU1hOXLsgfj6+u7dFa1G2HsqnT6cwHr9iIFcNj+O1lfv4ZNNhHpzan4hg46b5aGMG0aH+PH3d0Go3WX5xBZOeTOGNVfvcRgAtTzUKYnziyeeSurTj9xf3YVT39ozoZiyKW8d154MNGXy+5TA3jenWcl/wTJ5wZ7VR5RdsZp6DGX84XlQTpXYWIopAaHPcc0FvthzK519f76o+Fh7oR0lFFV9uPcqeY4Ukx9qZMEE3fqZyG0JrzdZD+Uwe0AmLRXFOt/YE2ax8seUIH/2YwUUDOnEgp5jvd2Zx05iutcZKIoJtXJMUz7zl6WQWlFaH6oJJF/Layn30jw2vddyJxaK4e1LthXH6dw6nb6cwPtx4qGUVwdlEaIxJTyEWgSC0HlaL4pnrh/HeuoNc0C+G33+wmd9/YJag7NspjJ4dQnlvdw4r/rmYYH8r/WLDuXJYHJP6nuzDLSmv4vMthxnXK7rFoqFOl4zjJeQVVzAoviZ8cmBcBEPiI/jPN7v56+c7sDumXl41PO6kz18/sgsvLd3LwrUHuecCM1BaUWXn7gUbOV5czss/a1rg3dXD43l80U72HCukZ4e2MSu6TeG0BM60jLJNQBSB0CYJD7Rx27kmTPOFG5N4fnEaFw/oxJieUWiteXTBdxyzRFJWaWflnhw+33KY12aO5Eh+CWvScxnYOYKDx4v5bPNhsgvL6RwRyMJZY+gSdXIoq6fILSrn2Iky+nSqHfGz1TFQPCiudhz9Lyb25L4Pt3DbuT0Y2zOKdsH+DOh8cqx9t+gQzu/bkecWp9K/czgX9o/hs82HWbsvl/9cO4SBcY2Mz3cwbWhn/v7VT1z1wkou7B/DE1cNOu05JGcVTktALAJB8B4dwgJ4+PIB1ftKKSbG20hONtEiRWWVXP3iSm59bR2Vdk1YgB8fbTxEoM3CuJ7RXDI4lkc/38EN81bz/b0T8bNYyC4sc+s+aSkO5hZz/curySwoZeGs0bWifL7dkUmQzXqSgpg6KJapgxq3JvR/rhvKz+av5a4FG3ht5kheXpZOYsdQrhx2sgVxKjqGB/LGrSN5f/1BPtiQQf/YcG49y8ZkTgtntFDI2Rs1JIpAOOMJCfBj7k0juHPBBi4d3JlfTOhB5olSokICqtdMaBfsz8zX1vH19kxSM0/w0tK9fPebiR6xEA7mFjNj7mpOlFYQEx7IHW9t5PZzu5PQLpjencL4ZNMhbju3BwF+zQ9FjQiy8ebPR3L1Cyv5+evrKK2w84+rB9UKGW0K43pFM7ZnFDlF5Tz13W6uGBZX79wGn2PAVWZdBgkfFYS2TZeoYL741bncmdwTi0URGxFUa+Gcib070KV9MC8v3csry9Mpr7Tz3OKaBVN2Hikgr/j0QyiPFtmZMXc1hWWVvH37aObfcg52u+Zvi37izgUbmf7iSgL8rMyacPqzk8MDbbz8sxH4Wy1Eh/ozbWjTrQFXlFL86dL+FJVXccdbG8gqKD31h3yBDr3N+tpnciTUKRCLQPAJLBbFjJEJ/POrXSgF5/ftyIcbD9G3Uzjf7cxk5Z4cLh/SmWeud79M56morLLzyGc7eHtNCWFBNhbcNqraV7/6j+dTWlHFO2sP8M+vdvGLiT3cZFptHt2iQ/j47nFUVmkCbac/2a13TBhPXjOEBz7ayiXPLufb30wgMlgsg7MdsQgEn+GapAT8/SxMG9KZJ64ehM2qePTzHaRlFdIvNpzFu7KorLJjt7tfo8Nu1yevxezgq+1HeXP1fsbF+fHlPefWGrC1WS2EBdqYNaEn6x68gHsvbNmEZT07hJ403nA6XDEsjndmjebYiTIWrDnQYvUKbRexCASfoUNYAJ//cjydI4MIDfDj09njUUCvjqF8ue0ody3YyPr9x/nX17uICQ/g2euHY3XMU6iya/5v3hrySip4ZsZQEmNqN7wLVh8gvl0Qtwwwbqn6aHeG+N2HJkRybmI0r6/cx+3n9qjlZhPOPkQRCD5Fb5cG3HV7fGI0VoviL59sZ1fmCQB6RO/mdxeb3vu8ZXtZtTeH0AA/pjy9jLBAP6JDAxjdI4pxvaJZtTeH31/cB4vK4GzhtnN7cPP8tXy6+TDTk2QV2bMZUQSCgBl4TerajrXpufSOCWVYQjueW5xGTHgA3aJDePLb3Vw8IIa/ThvIKyvSKS6r4kBuMR9syODN1fvxsyiuGRHPjg1njyKYkBhNv9hwnvpuN5cOjm2RMQihbSKKQBAcTOrTkbXpufz6gt5c0C+GnKJy/vTJdpSCXh1CeeyKQXQIC+CBKTWZOvOKy3l95X7Cg/zoGBbIDi/K39IopfjTJf24Yd4aXlmeflJ6CsE9Wmt+OnqCfrHh3hal0YgiEAQHN43pSufIwOocQC/cOJw//W8bATYLD0zpR5D/yT3iyGD/6jQPZyNje0VzUf8YnvshjQA/CzeO6ur2Pgg1fLH1CLPf/pG3bxvF2NZYGrQFkBEgQXAQGuDHtKFx1Yns/P0s/GP6YB6dNtCnG79Hpw1kaEIkj32xk+lzVrbIfIuzmTdX7Qfgy21HvSxJ4/GoIlBKTVZK7VJKpSml7ndzvq9SapVSqkwp9TtPyiIIQvPoFBHIO7NGM/emJFKzCrnh5TUUl1d6W6w2SVrWCdak5+LvZ+HbHZn1hhu3NTymCJRSVuB5YArQH7heKdW/TrFc4FfAvz0lhyAILcNFAzrx4o3D2XGkgNdX7q8+vnhXFivrWSjnTMZu19z62jo+/rHxAQBvrzmIzaq498LeHC0oZXlaNq+uSCe3JRf+8QCetAhGAmla671a63JgITDNtYDWOktrvQ6o8KAcgiC0EOf3i2Fi7w7MXbqHo/mlPPS/rcx8dR03zV/LZ5sPAyb19/c7MymvtHtZ2tNj66F8fvgpi8e/+ImS8io27D/Oe+sPVi/+UxetNV9uO8J5fTtyzYgELApunr+WRz7bwdSnl7Fhf24rf4PGozxluiilpgOTHQvUo5S6CRiltZ7tpuzDQKHW2q1loJSaBcwCiImJSVq4cGGzZCosLCQ0tO3lW2+rckHblU3kahotKdfevCoeXV2KVUGVhou7+bEv387u43a6R1jILtEUlGsu7ubHtJ7+fLmvgnB/RbAfHDhhZ0ysH90irNVy2YJC+MuKEqb0sDEx3tYiMp4uhYWFfH3Yn8/3VqCBgdFWtmdX4Wwt7xoSQFKMlewSTUyI6U9nFtm5b1kJP+vvz3ldbDy1oZSMQjtX9rLxyZ4KCss1j48Pol1g8/vfp/M7Tpo0aYPW2u1iFZ6MGnKXoalZWkdrPReYCzBixAidnJzcLIFSUlJo7mc9SVuVC9qubCJX02hJuZKB/WobmQVl3D2pF4PiIygqq2Tu0r2sSMumayc/AvysfL3jKHuLA0nNqjH4lYIfDlZx96ReXNg/Br1rI8fCenG0eAvfZFh58PoJtVZkA8gqKGXVXpMLypldtbSiCpvVUj3zu6VJSUlhV5FiVI8wrBbFirQcJvTuwGPTBvLLdzbyTmox6/KDWbfvOE9dN5QrhsWxcO0BYCs/mzyGXh1DGXeuHatSWCyK648VMvWZZXx2NIxXbh7R7Cyxnnq+PKkIMoAEl/144LAHrycIQivxyLSBtfZDAvz4zYW9+c2FvQGzRsS2p/LZl1PE3JuS6N85nKKyKjqEBXD/h1t46rtUnvoulandbRyuPECwv5VDeSUs2naU9sH+HCssJcjmR3y7IO5+eyP7c4oBmDY0jv05Rdzw8hpCA/x4/sZh9OrYcnmWnGQV29mdWcKfL+3PpL4d+XTTYX4xsQeBNiv/vmYIlzy7nO2HC+jbKYz7PtxCr46hrNqbQ4ewAHp2MIvduy7u06NDKH+4uC+Pfr6Dcx7/ngv6deSOiT3pFh3S4rI3B08qgnVAolKqO3AImAHc4MHrCYLQRggJ8OPdX4whv7iC/p1rT6ya+7MRHM4r4envUnl3/UEgj4cu6ceCNQf4zbubqKqT9C8swI8eHUL4+5c/ER5k44EPt1JWWUVpRRWXPbuCR6cNIL+kgldX7OOlm5IYGBfB4bySepcmXZuey+7ME9w4qgvF5VUs3pXFtkMFTBnYiSEJkQCkHDRRURf2jyGhfXCtuSKJMWF8eMdYIoNtBPlbufzZ5cx6Yz3lVXbG9Iyut7d/y9hutAuxkbLrGB//eIiF6w5itSiSurbjzZ+PPK31KU4XjykCrXWlUmo28DVgBeZrrbcrpe5wnJ+jlOoErAfCAbtS6tdAf611gafkEgShdYiLDCKunsa4c2QQj105kE17DpFRpLgmKYHOkUG8tHQvPxvdlaSu7cgtLmfzwTzG9Yomr7iCa19axcxX1xHrCGdtH+zPPQs3Va9nbbUoHvlsO1MGmhXp7pvcl4m9O/DAR1soKK1kaEIkv72wN7e/sZ78kgo2H8xjTXouB3KNtTFv2V7umtSLjmEBLEqv4Orh8SS0d79wket60y/dNILpc1ZSVmlndI/2bsuDSYV+5bB4rhwWT9aJUj7ccIjMglJeW7mPp75L5b7JfZt7q08bj84s1lovAhbVOTbHZfsoxmUkCIKPYbNa+P05gQxIGk1EsO2kpTq7EcLwLu2q9/9yWX9HTqeE6rxHb902ivnL0wkNNE3ZAx9tZd2+44QF+vHPr3/ihZQ0gv2tDEtox/82HeKrbUexa820oZ15f0MGnSMCef3WkQzsHM6DH2/jme/NYkXdwy08fmVt91d9DIqP4J/TB/Pwp9uZ2LtxC9x3DAvkzuSeABSXV/LSkj3Etwvi2hEJJ60X/fmWw6zak8ODl/RzV1WLICkmBEHwGn6WhtN2uzJz3MnrKFstitsdq71V2TUL1x2ktLyKt24bxS2vriWvuIJ3bh9Nl6hg3lt/kPs/3MJDl/Rn5rhuTBvameFd2lUvvDPnpiQyjhfz44E8dOauJiXZmzY0rtZgdlN46NL+pGUV8uDH23hlWTpPzxhGxvFiVuzJ5mh+Gd/tzAQgNauQW3t6JspTFIEgCGcFVovi3VmjsVoUNquFj+4ai9ZUN+jXjkhgysBOhAWaENXz+sacVEd8u2Di2wWTkrK7yddvbiRQeKCND+8cyw8/ZfHgx9u47LnlAIQF+hFos3Jnck/6xIRx7/ubCa60MvmCZl2mQUQRCIJw1uDai3c3+OpUAm0NpRTn94thWJd2PPN9KkMTIrlsSOda4bERQTaKDmz3yPVFEQiCILQR2of48/DlA9yem9S3IylHPZPoXLKPCoIg+DiiCARBEHwcUQSCIAg+jigCQRAEH0cUgSAIgo8jikAQBMHHEUUgCILg44giEARB8HE8tkKZp1BKHQP2n7Kge6KBtri4aluVC9qubCJX0xC5msbZKFdXrbXbrHhnnCI4HZRS6+tbqs2btFW5oO3KJnI1DZGrafiaXOIaEgRB8HFEEQiCIPg4vqYI5npbgHpoq3JB25VN5GoaIlfT8Cm5fGqMQBAEQTgZX7MIBEEQhDqIIhAEQfBxfEYRKKUmK6V2KaXSlFL3e1GOBKXUYqXUTqXUdqXUPY7jDyulDimlNjleU70g2z6l1FbH9dc7jrVXSn2rlEp1vLc7VT0tLFMfl3uySSlVoJT6tTful1JqvlIqSym1zeVYvfdHKfWA43nbpZS6uJXl+pdS6iel1Bal1MdKqUjH8W5KqRKX+zanleWq93fz8v1610WmfUqpTY7jrXm/6msbPP+Maa3P+hdgBfYAPQB/YDPQ30uyxALDHdthwG6gP/Aw8Dsv36d9QHSdY/8E7nds3w/8w8u/41GgqzfuFzABGA5sO9X9cfymm4EAoLvj+bO2olwXAX6O7X+4yNXNtZwX7pfb383b96vO+SeBP3vhftXXNnj8GfMVi2AkkKa13qu1LgcWAtO8IYjW+ojWeqNj+wSwE4jzhiyNZBrwumP7deAK74nC+cAerXVzZ5afFlrrpUBuncP13Z9pwEKtdZnWOh1IwzyHrSKX1vobrXWlY3c1EO+JazdVrgbw6v1yoswK9NcC73ji2g3RQNvg8WfMVxRBHHDQZT+DNtD4KqW6AcOANY5Dsx2m/PzWdsE40MA3SqkNSqlZjmMxWusjYB5UoKMX5HIyg9p/UG/fL6j//rSlZ+5W4EuX/e5KqR+VUkuUUud6QR53v1tbuV/nApla61SXY61+v+q0DR5/xnxFESg3x7waN6uUCgU+BH6ttS4AXgR6AkOBIxjztLUZp7UeDkwB7lZKTfCCDG5RSvkDlwPvOw61hfvVEG3imVNKPQhUAgsch44AXbTWw4DfAm8rpcJbUaT6frc2cb+A66nd2Wj1++Wmbai3qJtjzbpnvqIIMoAEl/144LCXZEEpZcP80Au01h8BaK0ztdZVWms78DIeMosbQmt92PGeBXzskCFTKRXrkDsWyGptuRxMATZqrTMdMnr9fjmo7/54/ZlTSt0MXArcqB1OZYcbIcexvQHjV+7dWjI18Lu1hfvlB1wFvOs81tr3y13bQCs8Y76iCNYBiUqp7o6e5QzgU28I4vBBvgLs1Fr/x+V4rEuxK4FtdT/rYblClFJhzm3MYOM2zH262VHsZuCT1pTLhVo9NW/fLxfquz+fAjOUUgFKqe5AIrC2tYRSSk0G7gMu11oXuxzvoJSyOrZ7OOTa24py1fe7efV+ObgA+ElrneE80Jr3q762gdZ4xlpjNLwtvICpmFH4PcCDXpRjPMZ82wJscrymAm8CWx3HPwViW1muHpgIhM3Aduc9AqKA74FUx3t7L9yzYCAHiHA51ur3C6OIjgAVmN7Yzxu6P8CDjudtFzClleVKw/iPnc/YHEfZqx2/72ZgI3BZK8tV7+/mzfvlOP4acEedsq15v+prGzz+jEmKCUEQBB/HV1xDgiAIQj2IIhAEQfBxRBEIgiD4OKIIBEEQfBxRBIIgCD6OKAJBqINSqkrVznjaYtlqHdksvTXnQRDc4udtAQShDVKitR7qbSEEobUQi0AQGokjT/0/lFJrHa9ejuNdlVLfOxKpfa+U6uI4HqPMWgCbHa+xjqqsSqmXHTnnv1FKBXntSwkCoggEwR1BdVxD17mcK9BajwSeA55yHHsOeENrPRiT3O0Zx/FngCVa6yGY/PfbHccTgee11gOAPMzsVUHwGjKzWBDqoJQq1FqHujm+DzhPa73XkRzsqNY6SimVjUmVUOE4fkRrHa2UOgbEa63LXOroBnyrtU507N8H2LTWj7XCVxMEt4hFIAhNQ9ezXV8Zd5S5bFchY3WClxFFIAhN4zqX91WO7ZWYjLYANwLLHdvfA3cCKKWsrZz3XxAajfREBOFkgpRj8XIHX2mtnSGkAUqpNZhO1PWOY78C5iulfg8cA2Y6jt8DzFVK/RzT878Tk/VSENoUMkYgCI3EMUYwQmud7W1ZBKElEdeQIAiCjyMWgSAIgo8jFoEgCIKPI4pAEATBxxFFIAiC4OOIIhAEQfBxRBEIgiD4OP8PXZQrHsFs3G0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_mae\\\", \\\"valid_mae\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='MAE')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"f2bccedd\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/mlp-niu-cement/version_8/checkpoints/epoch=178-step=3937.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at logs/mlp-niu-cement/version_8/checkpoints/epoch=178-step=3937.ckpt\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:258: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a4faab93962f4ed9b3f7110191ea5eae\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_mae          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.3199999928474426     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_mae         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.3199999928474426    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_mae': 0.3199999928474426}]\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/ordinal/polat2022-light_cement.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"b1739f6e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       : 1.12.1\\n\",\n      \"lightning   : 2022.9.8\\n\",\n      \"torchmetrics: 0.9.3\\n\",\n      \"matplotlib  : 3.5.2\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -p torch,lightning,torchmetrics,matplotlib\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d198b6a3\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Class distance weighted cross-entropy loss for ordinal regression and deep learning -- cement strength dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e0ffe6ee\",\n   \"metadata\": {},\n   \"source\": [\n    \"Implementation of a method for ordinal regression by Polat et al 2022 [1].\\n\",\n    \"\\n\",\n    \"**Paper reference:**\\n\",\n    \"\\n\",\n    \"- [1] G Polat, I Ergenc, HT Kani, YO Alahdab, O Atug, A Temizel. \\\"[Class Distance Weighted Cross-Entropy Loss for Ulcerative Colitis Severity Estimation](https://arxiv.org/abs/2202.05167).\\\" arXiv preprint arXiv:1612.00775 (2022).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"654568ec\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Note:**\\n\",\n    \"    \\n\",\n    \"To keep the notation lean and minimal, this notebook only contains \\\"Class Distance Weighted Cross-Entropy Loss \\\"-specific comments. For more comments on the PyTorch Lightning use, please see the cross-entropy baseline notebook [baseline-light_cement.ipynb](./baseline-light_cement.ipynb).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"13449160\",\n   \"metadata\": {},\n   \"source\": [\n    \"## General settings and hyperparameters\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"1acc2499\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 200\\n\",\n    \"LEARNING_RATE = 0.005\\n\",\n    \"NUM_WORKERS = 0\\n\",\n    \"\\n\",\n    \"DATA_BASEPATH = \\\".\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fa5d9470-1ffa-499e-8ef4-ba4c97bb8f3c\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 2 - Implementing the class distance weighted cross-entropy loss\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"f6179955-6513-4be7-9384-87a8b056b858\",\n   \"metadata\": {},\n   \"source\": [\n    \"According to the paper, the loss is described as follows:\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{C D WC E}=-\\\\sum_{i=0}^{N-1} \\\\log (1-\\\\hat{y}) \\\\times|i-c|^{\\\\text {power }}$$\\n\",\n    \"\\n\",\n    \"where\\n\",\n    \"\\n\",\n    \"- $N$: the number of class labels\\n\",\n    \"- $\\\\hat{y}$: the predicted scores\\n\",\n    \"- $c$: ground-truth class\\n\",\n    \"- power: a hyperparameter term that determines the strength of the cost coefficient\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"55306c8a-d3e1-4555-bd48-47f85fd384c2\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([[0.3792, 0.3104, 0.3104],\\n\",\n       \"        [0.3072, 0.4147, 0.2780],\\n\",\n       \"        [0.4263, 0.2248, 0.3490],\\n\",\n       \"        [0.2668, 0.2978, 0.4354]])\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"targets = torch.tensor([0, 2, 1, 2])\\n\",\n    \"\\n\",\n    \"logits = torch.tensor( [[-0.3,  -0.5, -0.5], # each row is 1 training example\\n\",\n    \"                        [-0.4,  -0.1, -0.5],\\n\",\n    \"                        [-0.3,  -0.94, -0.5],\\n\",\n    \"                        [-0.99, -0.88, -0.5]])\\n\",\n    \"\\n\",\n    \"probas = F.softmax(logits, dim=1)\\n\",\n    \"probas\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"a8628712-7e04-46b9-a06e-64221e7fe313\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([12.2654, 12.2824,  0.9848, 10.2828])\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def cdw_ce_loss_naive1(probas, targets, power=5):\\n\",\n    \"    \\n\",\n    \"    loss = torch.zeros(probas.shape[0])\\n\",\n    \"    for example in range(probas.shape[0]):\\n\",\n    \"        for i in range(probas.shape[1]):\\n\",\n    \"            loss[example] += -torch.log(1-probas[example, i]) * torch.abs(i - targets[example])**power\\n\",\n    \"    \\n\",\n    \"    return loss\\n\",\n    \"        \\n\",\n    \"cdw_ce_loss_naive1(probas, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"4dfa0d27-972d-4239-8a88-3864858131f0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"147 µs ± 2.64 µs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit cdw_ce_loss_naive1(probas, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"2ed813da-4787-4d8e-898a-e12e832a25f0\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([12.2654, 12.2824,  0.9848, 10.2828])\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def cdw_ce_loss_naive2(probas, targets, power=5):\\n\",\n    \"    \\n\",\n    \"    loss = 0.\\n\",\n    \"    for i in range(probas.shape[1]):\\n\",\n    \"        loss += (-torch.log(1-probas[:, i]) * torch.abs(i - targets)**power)\\n\",\n    \"        \\n\",\n    \"    return loss\\n\",\n    \"        \\n\",\n    \"cdw_ce_loss_naive2(probas, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"893da285-61ed-4be9-9abd-f78abb37cfcc\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"33.6 µs ± 82.6 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit cdw_ce_loss_naive2(probas, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"217c1285-8535-402c-aa75-1cc91abe758c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor([12.2654, 12.2824,  0.9848, 10.2828])\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def cdw_ce_loss_naive3(probas, targets, power=5):\\n\",\n    \"    \\n\",\n    \"    labels = torch.arange(probas.shape[1]).repeat(probas.shape[0], 1)\\n\",\n    \"    loss = (-torch.log(1-probas) * torch.abs(labels - targets.reshape(probas.shape[0], 1))**power).sum(dim=1)\\n\",\n    \"        \\n\",\n    \"    return loss\\n\",\n    \"        \\n\",\n    \"cdw_ce_loss_naive3(probas, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"218b4093-e98d-4b2a-990d-992a9af5a9ef\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"14.6 µs ± 61.4 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%timeit cdw_ce_loss_naive3(probas, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"3da4f855-1463-4953-9a7c-e77ee6cc78df\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor(8.9538)\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"def cdw_ce_loss(logits, targets, power=5, reduction=\\\"mean\\\"):\\n\",\n    \"    \\n\",\n    \"    probas = torch.softmax(logits, dim=1)\\n\",\n    \"    labels = torch.arange(probas.shape[1]).repeat(probas.shape[0], 1).type_as(logits)\\n\",\n    \"    loss = (-torch.log(1-probas) * torch.abs(labels - targets.type_as(labels).\\\\\\n\",\n    \"                                             reshape(probas.shape[0], 1))**power).sum(dim=1)\\n\",\n    \"        \\n\",\n    \"    if reduction == \\\"none\\\":\\n\",\n    \"        return loss\\n\",\n    \"    elif reduction == \\\"sum\\\":\\n\",\n    \"        return loss.sum()\\n\",\n    \"    elif reduction == \\\"mean\\\":\\n\",\n    \"        return loss.mean()    \\n\",\n    \"    else:\\n\",\n    \"        raise ValueError(\\\"reduction must be 'none', 'sum', or 'mean'\\\")    \\n\",\n    \"\\n\",\n    \"cdw_ce_loss(logits, targets)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"debfdfd1\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Implementing a `MultiLayerPerceptron` using PyTorch Lightning's `LightningModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"1922e731\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MultiLayerPerceptron(torch.nn.Module):\\n\",\n    \"    def __init__(self, input_size, hidden_units, num_classes):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        all_layers = []\\n\",\n    \"        for hidden_unit in hidden_units:\\n\",\n    \"            layer = torch.nn.Linear(input_size, hidden_unit)\\n\",\n    \"            all_layers.append(layer)\\n\",\n    \"            all_layers.append(torch.nn.ReLU())\\n\",\n    \"            input_size = hidden_unit\\n\",\n    \"\\n\",\n    \"        output_layer = torch.nn.Linear(hidden_units[-1], num_classes)\\n\",\n    \"        \\n\",\n    \"        all_layers.append(output_layer)\\n\",\n    \"        self.model = torch.nn.Sequential(*all_layers) \\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.model(x)\\n\",\n    \"        return x\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6562578a\",\n   \"metadata\": {},\n   \"source\": [\n    \"And then, we will use them below in the `LightningModule`. They are only used in the `_shared_step` method as indicated:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"1381f777\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningMLP(L.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.save_hyperparameters(ignore=['model'])\\n\",\n    \"\\n\",\n    \"        self.train_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.valid_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        self.test_mae = torchmetrics.MeanAbsoluteError()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        return self.model(x)\\n\",\n    \"\\n\",\n    \"    def _shared_step(self, batch):\\n\",\n    \"        features, true_labels = batch\\n\",\n    \"        logits = self(features)\\n\",\n    \"\\n\",\n    \"        loss = cdw_ce_loss(logits, true_labels)\\n\",\n    \"        predicted_labels = torch.argmax(logits, dim=1)\\n\",\n    \"        \\n\",\n    \"        return loss, true_labels, predicted_labels\\n\",\n    \"\\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"train_loss\\\", loss)\\n\",\n    \"        self.train_mae(predicted_labels.float(), true_labels.float())\\n\",\n    \"        self.log(\\\"train_mae\\\", self.train_mae, on_epoch=True, on_step=False)\\n\",\n    \"        return loss\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.log(\\\"valid_loss\\\", loss)\\n\",\n    \"        self.valid_mae(predicted_labels.float(), true_labels.float())\\n\",\n    \"        self.log(\\\"valid_mae\\\", self.valid_mae,\\n\",\n    \"                 on_epoch=True, on_step=False, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        loss, true_labels, predicted_labels = self._shared_step(batch)\\n\",\n    \"        self.test_mae(predicted_labels.float(), true_labels.float())\\n\",\n    \"        self.log(\\\"test_mae\\\", self.test_mae, on_epoch=True, on_step=False)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0ae78a8e\",\n   \"metadata\": {},\n   \"source\": [\n    \"---\\n\",\n    \"\\n\",\n    \"# Note: There Are No Changes Compared To The Baseline Below\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"39ac693d\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Setting up the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"b070e95b\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Inspecting the dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"a26a6486\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>response</th>\\n\",\n       \"      <th>V1</th>\\n\",\n       \"      <th>V2</th>\\n\",\n       \"      <th>V3</th>\\n\",\n       \"      <th>V4</th>\\n\",\n       \"      <th>V5</th>\\n\",\n       \"      <th>V6</th>\\n\",\n       \"      <th>V7</th>\\n\",\n       \"      <th>V8</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1040.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>540.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>162.0</td>\\n\",\n       \"      <td>2.5</td>\\n\",\n       \"      <td>1055.0</td>\\n\",\n       \"      <td>676.0</td>\\n\",\n       \"      <td>28</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>270</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>332.5</td>\\n\",\n       \"      <td>142.5</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>228.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>932.0</td>\\n\",\n       \"      <td>594.0</td>\\n\",\n       \"      <td>365</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>198.6</td>\\n\",\n       \"      <td>132.4</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>192.0</td>\\n\",\n       \"      <td>0.0</td>\\n\",\n       \"      <td>978.4</td>\\n\",\n       \"      <td>825.5</td>\\n\",\n       \"      <td>360</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   response     V1     V2   V3     V4   V5      V6     V7   V8\\n\",\n       \"0         4  540.0    0.0  0.0  162.0  2.5  1040.0  676.0   28\\n\",\n       \"1         4  540.0    0.0  0.0  162.0  2.5  1055.0  676.0   28\\n\",\n       \"2         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  270\\n\",\n       \"3         2  332.5  142.5  0.0  228.0  0.0   932.0  594.0  365\\n\",\n       \"4         2  198.6  132.4  0.0  192.0  0.0   978.4  825.5  360\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"data_df = pd.read_csv(\\\"https://raw.githubusercontent.com/gagolews/\\\"\\n\",\n    \"                      \\\"ordinal_regression_data/master/cement_strength.csv\\\")\\n\",\n    \"data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1\\n\",\n    \"\\n\",\n    \"data_labels = data_df[\\\"response\\\"]\\n\",\n    \"data_features = data_df.loc[:, [\\n\",\n    \"    \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"\\n\",\n    \"data_df.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"b7044584\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Number of features: 8\\n\",\n      \"Number of examples: 998\\n\",\n      \"Labels: [0 1 2 3 4]\\n\",\n      \"Label distribution: [196 310 244 152  96]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Number of features:', data_features.shape[1])\\n\",\n    \"print('Number of examples:', data_features.shape[0])\\n\",\n    \"print('Labels:', np.unique(data_labels.values))\\n\",\n    \"print('Label distribution:', np.bincount(data_labels))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a60df81f\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Performance baseline\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"952a6c2d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Baseline MAE: 1.03\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"avg_prediction = np.median(data_labels.values)  # median minimizes MAE\\n\",\n    \"baseline_mae = np.mean(np.abs(data_labels.values - avg_prediction))\\n\",\n    \"print(f'Baseline MAE: {baseline_mae:.2f}')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"14f82826\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Creating a `Dataset` class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"aac85ec8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyDataset(Dataset):\\n\",\n    \"\\n\",\n    \"    def __init__(self, feature_array, label_array, dtype=np.float32):\\n\",\n    \"        self.features = feature_array.astype(dtype)\\n\",\n    \"        self.labels = label_array\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        inputs = self.features[index]\\n\",\n    \"        label = self.labels[index]\\n\",\n    \"        return inputs, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.features.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"998d6e7a\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setting up a `DataModule`\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"64efaa4d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/sebastianraschka/miniforge3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.1\\n\",\n      \"  warnings.warn(f\\\"A NumPy version >={np_minversion} and <{np_maxversion}\\\"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"from sklearn.preprocessing import StandardScaler\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DataModule(L.LightningDataModule):\\n\",\n    \"    def __init__(self, data_path='./'):\\n\",\n    \"        super().__init__()\\n\",\n    \"        self.data_path = data_path\\n\",\n    \"        \\n\",\n    \"    def prepare_data(self):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            'https://raw.githubusercontent.com/gagolews/'\\n\",\n    \"            'ordinal_regression_data/master/cement_strength.csv')\\n\",\n    \"        data_df.to_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'), index=None)\\n\",\n    \"        return\\n\",\n    \"\\n\",\n    \"    def setup(self, stage=None):\\n\",\n    \"        data_df = pd.read_csv(\\n\",\n    \"            os.path.join(self.data_path, 'cement_strength.csv'))\\n\",\n    \"        data_df[\\\"response\\\"] = data_df[\\\"response\\\"]-1  # labels should start at 0\\n\",\n    \"        self.data_labels = data_df[\\\"response\\\"]\\n\",\n    \"        self.data_features = data_df.loc[:, [\\n\",\n    \"            \\\"V1\\\", \\\"V2\\\", \\\"V3\\\", \\\"V4\\\", \\\"V5\\\", \\\"V6\\\", \\\"V7\\\", \\\"V8\\\"]]\\n\",\n    \"        \\n\",\n    \"        # Split into\\n\",\n    \"        # 70% train, 10% validation, 20% testing\\n\",\n    \"        \\n\",\n    \"        X_temp, X_test, y_temp, y_test = train_test_split(\\n\",\n    \"            self.data_features.values,\\n\",\n    \"            self.data_labels.values,\\n\",\n    \"            test_size=0.2,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=self.data_labels.values)\\n\",\n    \"\\n\",\n    \"        X_train, X_valid, y_train, y_valid = train_test_split(\\n\",\n    \"            X_temp,\\n\",\n    \"            y_temp,\\n\",\n    \"            test_size=0.1,\\n\",\n    \"            random_state=1,\\n\",\n    \"            stratify=y_temp)\\n\",\n    \"        \\n\",\n    \"        # Standardize features\\n\",\n    \"        sc = StandardScaler()\\n\",\n    \"        X_train_std = sc.fit_transform(X_train)\\n\",\n    \"        X_valid_std = sc.transform(X_valid)\\n\",\n    \"        X_test_std = sc.transform(X_test)\\n\",\n    \"\\n\",\n    \"        self.train = MyDataset(X_train_std, y_train)\\n\",\n    \"        self.valid = MyDataset(X_valid_std, y_valid)\\n\",\n    \"        self.test = MyDataset(X_test_std, y_test)\\n\",\n    \"\\n\",\n    \"    def train_dataloader(self):\\n\",\n    \"        return DataLoader(self.train, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS,\\n\",\n    \"                          drop_last=True)\\n\",\n    \"\\n\",\n    \"    def val_dataloader(self):\\n\",\n    \"        return DataLoader(self.valid, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\\n\",\n    \"\\n\",\n    \"    def test_dataloader(self):\\n\",\n    \"        return DataLoader(self.test, batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=NUM_WORKERS)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"d909e6cc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(1) \\n\",\n    \"data_module = DataModule(data_path=DATA_BASEPATH)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6a448bc5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the model using the PyTorch Lightning Trainer class\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"8ee17411\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from pytorch_lightning.callbacks import ModelCheckpoint\\n\",\n    \"from pytorch_lightning.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"pytorch_model = MultiLayerPerceptron(\\n\",\n    \"    input_size=data_features.shape[1],\\n\",\n    \"    hidden_units=(40, 20),\\n\",\n    \"    num_classes=np.bincount(data_labels).shape[0])\\n\",\n    \"\\n\",\n    \"lightning_model = LightningMLP(\\n\",\n    \"    model=pytorch_model,\\n\",\n    \"    learning_rate=LEARNING_RATE)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [ModelCheckpoint(\\n\",\n    \"    save_top_k=1, mode=\\\"min\\\", monitor=\\\"valid_mae\\\")]  # save top 1 model \\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"mlp-beckham-cement\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"9e21b802\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (mps), used: False\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"/Users/sebastianraschka/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1791: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"\\n\",\n      \"  | Name      | Type                 | Params\\n\",\n      \"---------------------------------------------------\\n\",\n      \"0 | model     | MultiLayerPerceptron | 1.3 K \\n\",\n      \"1 | train_mae | MeanAbsoluteError    | 0     \\n\",\n      \"2 | valid_mae | MeanAbsoluteError    | 0     \\n\",\n      \"3 | test_mae  | MeanAbsoluteError    | 0     \\n\",\n      \"---------------------------------------------------\\n\",\n      \"1.3 K     Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"1.3 K     Total params\\n\",\n      \"0.005     Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/sebastianraschka/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:219: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/Users/sebastianraschka/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:219: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/Users/sebastianraschka/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:1894: PossibleUserWarning: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=10). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"025ef56594a54bc7a45de8b2cd99b869\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       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    \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": 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\"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=200` reached.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training took 0.13 min in total.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=NUM_EPOCHS,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"cpu\\\",  # Uses Apple Silicon, GPUs or TPUs if available\\n\",\n    \"    devices=\\\"auto\\\",  # Uses all available GPUs/TPUs if applicable\\n\",\n    \"    logger=logger,\\n\",\n    \"    deterministic=True,\\n\",\n    \"    log_every_n_steps=10)\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"trainer.fit(model=lightning_model, datamodule=data_module)\\n\",\n    \"\\n\",\n    \"runtime = (time.time() - start_time)/60\\n\",\n    \"print(f\\\"Training took {runtime:.2f} min in total.\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"73a8e075\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"d23d4a7d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<AxesSubplot:xlabel='Epoch', ylabel='MAE'>\"\n      ]\n     },\n     \"execution_count\": 21,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"metrics = pd.read_csv(f\\\"{trainer.logger.log_dir}/metrics.csv\\\")\\n\",\n    \"\\n\",\n    \"aggreg_metrics = []\\n\",\n    \"agg_col = \\\"epoch\\\"\\n\",\n    \"for i, dfg in metrics.groupby(agg_col):\\n\",\n    \"    agg = dict(dfg.mean())\\n\",\n    \"    agg[agg_col] = i\\n\",\n    \"    aggreg_metrics.append(agg)\\n\",\n    \"\\n\",\n    \"df_metrics = pd.DataFrame(aggreg_metrics)\\n\",\n    \"df_metrics[[\\\"train_loss\\\", \\\"valid_loss\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='Loss')\\n\",\n    \"df_metrics[[\\\"train_mae\\\", \\\"valid_mae\\\"]].plot(\\n\",\n    \"    grid=True, legend=True, xlabel='Epoch', ylabel='MAE')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"911969d1\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/mlp-beckham-cement/version_16/checkpoints/epoch=178-step=895.ckpt\\n\",\n      \"Loaded model weights from checkpoint at logs/mlp-beckham-cement/version_16/checkpoints/epoch=178-step=895.ckpt\\n\",\n      \"/Users/sebastianraschka/miniforge3/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:219: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"8de3728789984b59be6db2942c0e39f9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         test_mae          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.5199999809265137     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        test_mae         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.5199999809265137    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'test_mae': 0.5199999809265137}]\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(model=lightning_model, datamodule=data_module, ckpt_path='best')\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/transformer/distilbert-finetune-last-layers.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c5d72f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Finetuning a DistilBERT Classifier in Lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"18a56df9-d19c-4dfa-afab-097e4a57f5a1\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here, we are finetuning the output layers of a pretrained transformer:\\n\",\n    \"\\n\",\n    \"![](figures/finetuning-i.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"6fd9cda8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"92ea5612\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"fe7191cf-62ed-4793-8358-bee70b233d05\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"033b75c5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\\n\",\n      \"  if not hasattr(tensorboard, \\\"__version__\\\") or LooseVersion(\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       : 1.12.1\\n\",\n      \"transformers: 4.23.1\\n\",\n      \"datasets    : 2.6.1\\n\",\n      \"lightning   : 2022.10.20\\n\",\n      \"\\n\",\n      \"conda environment: dl-fundamentals\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark --conda -p torch,transformers,datasets,lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4cfd724d\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1 Loading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fd06d930\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60fe0b76\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1a) Load from `datasets` Hub\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"447e24bb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datasets import list_datasets, load_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"2baf2f16\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# list_datasets()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"6310d5bf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found cached dataset imdb (/home/raschka/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"70c772b75480491d8590da7e1e6cab0a\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    unsupervised: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 50000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_data = load_dataset(\\\"imdb\\\")\\n\",\n    \"print(imdb_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"552bbb2e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'text': \\\"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\\\",\\n\",\n       \" 'label': 0}\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imdb_data[\\\"train\\\"][99]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40bdb9c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1b) Load from local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9103ec2d\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\\n\",\n    \"\\n\",\n    \"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\\n\",\n    \"\\n\",\n    \"    tar -zxf aclImdb_v1.tar.gz\\n\",\n    \"\\n\",\n    \"B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac508bb8\",\n   \"metadata\": {},\n   \"source\": [\n    \"C) Use the following code to download and unzip the dataset via Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"241ecc96\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Download the movie reviews**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"02aeade4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import time\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"source = \\\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\"\\n\",\n    \"target = \\\"aclImdb_v1.tar.gz\\\"\\n\",\n    \"\\n\",\n    \"if os.path.exists(target):\\n\",\n    \"    os.remove(target)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def reporthook(count, block_size, total_size):\\n\",\n    \"    global start_time\\n\",\n    \"    if count == 0:\\n\",\n    \"        start_time = time.time()\\n\",\n    \"        return\\n\",\n    \"    duration = time.time() - start_time\\n\",\n    \"    progress_size = int(count * block_size)\\n\",\n    \"    speed = progress_size / (1024.0**2 * duration)\\n\",\n    \"    percent = count * block_size * 100.0 / total_size\\n\",\n    \"\\n\",\n    \"    sys.stdout.write(\\n\",\n    \"        f\\\"\\\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \\\"\\n\",\n    \"        f\\\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\\\"\\n\",\n    \"    )\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if not os.path.isdir(\\\"aclImdb\\\") and not os.path.isfile(\\\"aclImdb_v1.tar.gz\\\"):\\n\",\n    \"    urllib.request.urlretrieve(source, target, reporthook)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"2a867dcc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if not os.path.isdir(\\\"aclImdb\\\"):\\n\",\n    \"\\n\",\n    \"    with tarfile.open(target, \\\"r:gz\\\") as tar:\\n\",\n    \"        tar.extractall()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9318d4d0\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Convert them to a pandas DataFrame and save them as CSV**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"464e587c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████| 50000/50000 [00:54<00:00, 914.11it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from packaging import version\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"\\n\",\n    \"# change the `basepath` to the directory of the\\n\",\n    \"# unzipped movie dataset\\n\",\n    \"\\n\",\n    \"basepath = \\\"aclImdb\\\"\\n\",\n    \"\\n\",\n    \"labels = {\\\"pos\\\": 1, \\\"neg\\\": 0}\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"\\n\",\n    \"with tqdm(total=50000) as pbar:\\n\",\n    \"    for s in (\\\"test\\\", \\\"train\\\"):\\n\",\n    \"        for l in (\\\"pos\\\", \\\"neg\\\"):\\n\",\n    \"            path = os.path.join(basepath, s, l)\\n\",\n    \"            for file in sorted(os.listdir(path)):\\n\",\n    \"                with open(os.path.join(path, file), \\\"r\\\", encoding=\\\"utf-8\\\") as infile:\\n\",\n    \"                    txt = infile.read()\\n\",\n    \"\\n\",\n    \"                if version.parse(pd.__version__) >= version.parse(\\\"1.3.2\\\"):\\n\",\n    \"                    x = pd.DataFrame(\\n\",\n    \"                        [[txt, labels[l]]], columns=[\\\"review\\\", \\\"sentiment\\\"]\\n\",\n    \"                    )\\n\",\n    \"                    df = pd.concat([df, x], ignore_index=False)\\n\",\n    \"\\n\",\n    \"                else:\\n\",\n    \"                    df = df.append([[txt, labels[l]]], ignore_index=True)\\n\",\n    \"                pbar.update()\\n\",\n    \"df.columns = [\\\"text\\\", \\\"label\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"02649593\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.random.seed(0)\\n\",\n    \"df = df.reindex(np.random.permutation(df.index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"59ca0386\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Basic datasets analysis and sanity checks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"c2db547a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([25000, 25000])\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Class distribution:\\\")\\n\",\n    \"np.bincount(df[\\\"label\\\"].values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"a007e612\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 173.0, 2470)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"text_len = df[\\\"text\\\"].apply(lambda x: len(x.split()))\\n\",\n    \"text_len.min(), text_len.median(), text_len.max() \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"00f4b04d\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Split data into training, validation, and test sets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"ff703901\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df_shuffled = df.sample(frac=1, random_state=1).reset_index()\\n\",\n    \"\\n\",\n    \"df_train = df_shuffled.iloc[:35_000]\\n\",\n    \"df_val = df_shuffled.iloc[35_000:40_000]\\n\",\n    \"df_test = df_shuffled.iloc[40_000:]\\n\",\n    \"\\n\",\n    \"df_train.to_csv(\\\"train.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_val.to_csv(\\\"validation.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_test.to_csv(\\\"test.csv\\\", index=False, encoding=\\\"utf-8\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"846d83b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2 Tokenization and Numericalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2bd5f770\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Load the dataset via `load_dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"a1aa66c7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration default-b388e5fa43c4de1a\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading and preparing dataset csv/default to /home/raschka/.cache/huggingface/datasets/csv/default-b388e5fa43c4de1a/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"701fc998eec243eb8f1fe3ad1181f3be\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"bfefa8ed13f44ca394b44a8ecc0b8392\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"0 tables [00:00, ? tables/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/train.csv'>\\n\",\n      \"  self.handle.detach()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"0 tables [00:00, ? tables/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/validation.csv'>\\n\",\n      \"  self.handle.detach()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"0 tables [00:00, ? tables/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset csv downloaded and prepared to /home/raschka/.cache/huggingface/datasets/csv/default-b388e5fa43c4de1a/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/test.csv'>\\n\",\n      \"  self.handle.detach()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"33ff4316fd5e492496d6751af7373330\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 35000\\n\",\n      \"    })\\n\",\n      \"    validation: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 5000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 10000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_dataset = load_dataset(\\n\",\n    \"    \\\"csv\\\",\\n\",\n    \"    data_files={\\n\",\n    \"        \\\"train\\\": \\\"train.csv\\\",\\n\",\n    \"        \\\"validation\\\": \\\"validation.csv\\\",\\n\",\n    \"        \\\"test\\\": \\\"test.csv\\\",\\n\",\n    \"    },\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(imdb_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"029adc8f-cdfe-4386-9552-a1120f49adee\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tokenize the dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"5ea762ba\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Tokenizer input max length: 512\\n\",\n      \"Tokenizer vocabulary size: 30522\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoTokenizer\\n\",\n    \"\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"distilbert-base-uncased\\\")\\n\",\n    \"print(\\\"Tokenizer input max length:\\\", tokenizer.model_max_length)\\n\",\n    \"print(\\\"Tokenizer vocabulary size:\\\", tokenizer.vocab_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"8432c15c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tokenize_text(batch):\\n\",\n    \"    return tokenizer(batch[\\\"text\\\"], truncation=True, padding=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"0bb392cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"fba13816cb154594aae6ea9562743a43\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"010c518cd2e84d158031017dfe5c34ff\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7edb2a0be5844ed899ad895b806439b7\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"6d4103c3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"del imdb_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"89ef894c-978f-47f2-9d61-cb6a9f38e745\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"imdb_tokenized.set_format(\\\"torch\\\", columns=[\\\"input_ids\\\", \\\"attention_mask\\\", \\\"label\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"0ea67091-aeb7-46c1-871f-638ce58d8a0e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"TOKENIZERS_PARALLELISM\\\"] = \\\"false\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4c4f8cd8-e641-45fb-9893-70677631917a\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3 Set Up DataLoaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"0807b068-7d8f-4055-a26a-177e07dea4c7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader, Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class IMDBDataset(Dataset):\\n\",\n    \"    def __init__(self, dataset_dict, partition_key=\\\"train\\\"):\\n\",\n    \"        self.partition = dataset_dict[partition_key]\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        return self.partition[index]\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.partition.num_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"90cb08f3-ef77-4351-8b19-42d99dd24f98\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"train\\\")\\n\",\n    \"val_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"validation\\\")\\n\",\n    \"test_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"test\\\")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    shuffle=True, \\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"val_loader = DataLoader(\\n\",\n    \"    dataset=val_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    num_workers=4\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6c977b07-2559-4ff7-85c4-d62785e81558\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4 Initializing DistilBERT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"10b1e449-7e00-4965-9883-f97374503996\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias']\\n\",\n      \"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n      \"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\",\n      \"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.weight', 'classifier.bias', 'pre_classifier.bias']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoModelForSequenceClassification\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(\\n\",\n    \"    \\\"distilbert-base-uncased\\\", num_labels=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"e9854e27-fc4f-418e-996f-a8ef04a295ac\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Freeze all layers**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"c14e61f3-3279-4725-ad7a-3c77f85aa39d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for param in model.parameters():\\n\",\n    \"    param.requires_grad = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"a6d3e532-3684-4dbf-a8ce-60b6c6675a00\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Unfreeze last layer**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"1bca22b5-8a48-4006-929f-66734a745a50\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DistilBertForSequenceClassification(\\n\",\n       \"  (distilbert): DistilBertModel(\\n\",\n       \"    (embeddings): Embeddings(\\n\",\n       \"      (word_embeddings): Embedding(30522, 768, padding_idx=0)\\n\",\n       \"      (position_embeddings): Embedding(512, 768)\\n\",\n       \"      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"      (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"    )\\n\",\n       \"    (transformer): Transformer(\\n\",\n       \"      (layer): ModuleList(\\n\",\n       \"        (0): TransformerBlock(\\n\",\n       \"          (attention): MultiHeadSelfAttention(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (q_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (k_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (v_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (out_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"          )\\n\",\n       \"          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"          (ffn): FFN(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (lin1): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n       \"            (lin2): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n       \"            (activation): GELUActivation()\\n\",\n       \"          )\\n\",\n       \"          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"        )\\n\",\n       \"        (1): TransformerBlock(\\n\",\n       \"          (attention): MultiHeadSelfAttention(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (q_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (k_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (v_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (out_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"          )\\n\",\n       \"          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"          (ffn): FFN(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (lin1): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n       \"            (lin2): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n       \"            (activation): GELUActivation()\\n\",\n       \"          )\\n\",\n       \"          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"        )\\n\",\n       \"        (2): TransformerBlock(\\n\",\n       \"          (attention): MultiHeadSelfAttention(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (q_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (k_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (v_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (out_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"          )\\n\",\n       \"          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"          (ffn): FFN(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (lin1): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n       \"            (lin2): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n       \"            (activation): GELUActivation()\\n\",\n       \"          )\\n\",\n       \"          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"        )\\n\",\n       \"        (3): TransformerBlock(\\n\",\n       \"          (attention): MultiHeadSelfAttention(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (q_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (k_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (v_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (out_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"          )\\n\",\n       \"          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"          (ffn): FFN(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (lin1): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n       \"            (lin2): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n       \"            (activation): GELUActivation()\\n\",\n       \"          )\\n\",\n       \"          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"        )\\n\",\n       \"        (4): TransformerBlock(\\n\",\n       \"          (attention): MultiHeadSelfAttention(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (q_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (k_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (v_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (out_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"          )\\n\",\n       \"          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"          (ffn): FFN(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (lin1): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n       \"            (lin2): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n       \"            (activation): GELUActivation()\\n\",\n       \"          )\\n\",\n       \"          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"        )\\n\",\n       \"        (5): TransformerBlock(\\n\",\n       \"          (attention): MultiHeadSelfAttention(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (q_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (k_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (v_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"            (out_lin): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"          )\\n\",\n       \"          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"          (ffn): FFN(\\n\",\n       \"            (dropout): Dropout(p=0.1, inplace=False)\\n\",\n       \"            (lin1): Linear(in_features=768, out_features=3072, bias=True)\\n\",\n       \"            (lin2): Linear(in_features=3072, out_features=768, bias=True)\\n\",\n       \"            (activation): GELUActivation()\\n\",\n       \"          )\\n\",\n       \"          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\\n\",\n       \"        )\\n\",\n       \"      )\\n\",\n       \"    )\\n\",\n       \"  )\\n\",\n       \"  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\\n\",\n       \"  (classifier): Linear(in_features=768, out_features=2, bias=True)\\n\",\n       \"  (dropout): Dropout(p=0.2, inplace=False)\\n\",\n       \")\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"4f852fc6-3b00-4831-a973-e03b539a6cd0\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"for param in model.pre_classifier.parameters():\\n\",\n    \"    param.requires_grad = True\\n\",\n    \"\\n\",\n    \"for param in model.classifier.parameters():\\n\",\n    \"    param.requires_grad = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"83971bc4-143d-4d82-b5cb-8774f9112f53\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 5 Finetuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"730b9a87-709e-4f3d-9c9f-475ccbb56908\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Wrap in LightningModule for Training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"9f2c474d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/torchmetrics/utilities/prints.py:36: DeprecationWarning: From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification metric. Moving forward we recommend using these versions. This base metric will still work as it did prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required and the general order of arguments may change, such that this metric will just function as an single entrypoint to calling the three specialized versions.\\n\",\n      \"  warnings.warn(*args, **kwargs)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import torch\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningModel(L.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate=5e-5):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.val_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    def forward(self, input_ids, attention_mask, labels):\\n\",\n    \"        return self.model(input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        \\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"train_loss\\\", outputs[\\\"loss\\\"])\\n\",\n    \"        return outputs[\\\"loss\\\"]  # this is passed to the optimizer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"val_loss\\\", outputs[\\\"loss\\\"], prog_bar=True)\\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.val_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"val_acc\\\", self.val_acc, prog_bar=True)\\n\",\n    \"        \\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.test_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"accuracy\\\", self.test_acc, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"e6dab813-e1fc-47cd-87a1-5eb8070699c6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from lightning.pytorch.callbacks import ModelCheckpoint\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"val_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"492aa043-02da-459e-a266-091b34254ac6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"\\n\",\n      \"  | Name     | Type                                | Params\\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"0 | model    | DistilBertForSequenceClassification | 67.0 M\\n\",\n      \"1 | val_acc  | Accuracy                            | 0     \\n\",\n      \"2 | test_acc | Accuracy                            | 0     \\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"592 K     Trainable params\\n\",\n      \"66.4 M    Non-trainable params\\n\",\n      \"67.0 M    Total params\\n\",\n      \"267.820   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"fd6c15df9d69447d9a07183168aaaebf\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=3` reached.\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=3,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[0],\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"trainer.fit(model=lightning_model,\\n\",\n    \"            train_dataloaders=train_loader,\\n\",\n    \"            val_dataloaders=val_loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"d795778a-70d2-4b04-96fb-598eccbcd1be\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:489: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4db6c89372a1429abb9b51f38ee3c194\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.8672000169754028     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.8672000169754028    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.8672000169754028}]\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=train_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"id\": \"10ca0af1-106e-4ef7-9793-478d580af827\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5a48602c7ba74884bd0943f597476010\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.8723999857902527     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.8723999857902527    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.8723999857902527}]\"\n      ]\n     },\n     \"execution_count\": 33,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=val_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"id\": \"eeb92de4-d483-4627-b9f3-f0bba0cddd9c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_2/checkpoints/epoch=1-step=5834.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"439ec322301a459499a0e88471191765\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.8640999794006348     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.8640999794006348    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa859dc2dc0>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7fa797a59760>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.8640999794006348}]\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=test_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"ce4d2f10-4761-4e92-af94-70f7af5fccf0\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/transformer/distilbert-finetune-scheduler.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"14ec0fd7\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Finetuning a DistilBERT Classifier in Lightning with `finetuning-scheduler`\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"56f0ce0a\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](figures/finetuning-ii.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9c70f0d1-fae7-4f33-a119-63ab47739f27\",\n   \"metadata\": {},\n   \"source\": [\n    \"Here, we are using the [finetuning-scheduler](https://github.com/speediedan/finetuning-scheduler) callback for freezing and unfreezing layers during finetuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"7133d6ae\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"32c47e21\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"5832dbd2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install pytorch_lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"58c34189\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install finetuning-scheduler\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"8f06f3c4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"2022-11-16 19:25:15.509473: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch               : 1.12.0\\n\",\n      \"transformers        : 4.24.0\\n\",\n      \"datasets            : 2.7.0\\n\",\n      \"pytorch_lightning   : 1.8.1\\n\",\n      \"finetuning_scheduler: 0.3.1\\n\",\n      \"\\n\",\n      \"conda environment: base\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark --conda -p torch,transformers,datasets,pytorch_lightning,finetuning_scheduler\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"c4cab474\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1 Loading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7f099ed3\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7070196e\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1a) Load from `datasets` Hub\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"c58a5bee\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datasets import list_datasets, load_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"ba04345f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# list_datasets()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"f99da96b\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found cached dataset imdb (/home/jovyan/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"180fa99b0aa24d84942f8cd6d0165a2e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    unsupervised: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 50000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_data = load_dataset(\\\"imdb\\\")\\n\",\n    \"print(imdb_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"7c80ddb7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'text': \\\"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\\\",\\n\",\n       \" 'label': 0}\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imdb_data[\\\"train\\\"][99]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"90af222a\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1b) Load from local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"51b3b63d\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\\n\",\n    \"\\n\",\n    \"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\\n\",\n    \"\\n\",\n    \"    tar -zxf aclImdb_v1.tar.gz\\n\",\n    \"\\n\",\n    \"B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"6c409d5e\",\n   \"metadata\": {},\n   \"source\": [\n    \"C) Use the following code to download and unzip the dataset via Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7220fff4\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Download the movie reviews**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"c9fd3127\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import time\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"source = \\\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\"\\n\",\n    \"target = \\\"aclImdb_v1.tar.gz\\\"\\n\",\n    \"\\n\",\n    \"if os.path.exists(target):\\n\",\n    \"    os.remove(target)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def reporthook(count, block_size, total_size):\\n\",\n    \"    global start_time\\n\",\n    \"    if count == 0:\\n\",\n    \"        start_time = time.time()\\n\",\n    \"        return\\n\",\n    \"    duration = time.time() - start_time\\n\",\n    \"    progress_size = int(count * block_size)\\n\",\n    \"    speed = progress_size / (1024.0**2 * duration)\\n\",\n    \"    percent = count * block_size * 100.0 / total_size\\n\",\n    \"\\n\",\n    \"    sys.stdout.write(\\n\",\n    \"        f\\\"\\\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \\\"\\n\",\n    \"        f\\\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\\\"\\n\",\n    \"    )\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if not os.path.isdir(\\\"aclImdb\\\") and not os.path.isfile(\\\"aclImdb_v1.tar.gz\\\"):\\n\",\n    \"    urllib.request.urlretrieve(source, target, reporthook)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"8a140908\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if not os.path.isdir(\\\"aclImdb\\\"):\\n\",\n    \"\\n\",\n    \"    with tarfile.open(target, \\\"r:gz\\\") as tar:\\n\",\n    \"        tar.extractall()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"20917799\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Convert them to a pandas DataFrame and save them as CSV**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"6491b911\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|██████████| 50000/50000 [01:21<00:00, 614.13it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from packaging import version\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"\\n\",\n    \"# change the `basepath` to the directory of the\\n\",\n    \"# unzipped movie dataset\\n\",\n    \"\\n\",\n    \"basepath = \\\"aclImdb\\\"\\n\",\n    \"\\n\",\n    \"labels = {\\\"pos\\\": 1, \\\"neg\\\": 0}\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"\\n\",\n    \"with tqdm(total=50000) as pbar:\\n\",\n    \"    for s in (\\\"test\\\", \\\"train\\\"):\\n\",\n    \"        for l in (\\\"pos\\\", \\\"neg\\\"):\\n\",\n    \"            path = os.path.join(basepath, s, l)\\n\",\n    \"            for file in sorted(os.listdir(path)):\\n\",\n    \"                with open(os.path.join(path, file), \\\"r\\\", encoding=\\\"utf-8\\\") as infile:\\n\",\n    \"                    txt = infile.read()\\n\",\n    \"\\n\",\n    \"                if version.parse(pd.__version__) >= version.parse(\\\"1.3.2\\\"):\\n\",\n    \"                    x = pd.DataFrame(\\n\",\n    \"                        [[txt, labels[l]]], columns=[\\\"review\\\", \\\"sentiment\\\"]\\n\",\n    \"                    )\\n\",\n    \"                    df = pd.concat([df, x], ignore_index=False)\\n\",\n    \"\\n\",\n    \"                else:\\n\",\n    \"                    df = df.append([[txt, labels[l]]], ignore_index=True)\\n\",\n    \"                pbar.update()\\n\",\n    \"df.columns = [\\\"text\\\", \\\"label\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"890e08eb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.random.seed(0)\\n\",\n    \"df = df.reindex(np.random.permutation(df.index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"949b9362\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Basic datasets analysis and sanity checks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"7dcf9d12\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([25000, 25000])\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Class distribution:\\\")\\n\",\n    \"np.bincount(df[\\\"label\\\"].values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"537aea79\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 173.0, 2470)\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"text_len = df[\\\"text\\\"].apply(lambda x: len(x.split()))\\n\",\n    \"text_len.min(), text_len.median(), text_len.max() \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"934f4554\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Split data into training, validation, and test sets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"4d2fa677\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df_shuffled = df.sample(frac=1, random_state=1).reset_index()\\n\",\n    \"\\n\",\n    \"df_train = df_shuffled.iloc[:35_000]\\n\",\n    \"df_val = df_shuffled.iloc[35_000:40_000]\\n\",\n    \"df_test = df_shuffled.iloc[40_000:]\\n\",\n    \"\\n\",\n    \"df_train.to_csv(\\\"train.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_val.to_csv(\\\"validation.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_test.to_csv(\\\"test.csv\\\", index=False, encoding=\\\"utf-8\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4e87ad72\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2 Tokenization and Numericalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d448a60b\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Load the dataset via `load_dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"122ea695\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration default-d2824d6740ca7436\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading and preparing dataset csv/default to /home/jovyan/.cache/huggingface/datasets/csv/default-d2824d6740ca7436/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"02424be626814a81bff3c02499c3c8af\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"   \"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"62c435a3f0bc474db33c1f9e10147bec\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files #2:   0%|          | 0/1 [00:00<?, ?obj/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"238d65110d484f1bb9c09b6a643d511d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files #0:   0%|          | 0/1 [00:00<?, ?obj/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"b62f93c0ebcd4b3e8aa1d7c38e66fbc6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files #1:   0%|          | 0/1 [00:00<?, ?obj/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating train split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating validation split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating test split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset csv downloaded and prepared to /home/jovyan/.cache/huggingface/datasets/csv/default-d2824d6740ca7436/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d4761a06ace04f6a98a4506c8493e543\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 35000\\n\",\n      \"    })\\n\",\n      \"    validation: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 5000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 10000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_dataset = load_dataset(\\n\",\n    \"    \\\"csv\\\",\\n\",\n    \"    data_files={\\n\",\n    \"        \\\"train\\\": \\\"train.csv\\\",\\n\",\n    \"        \\\"validation\\\": \\\"validation.csv\\\",\\n\",\n    \"        \\\"test\\\": \\\"test.csv\\\",\\n\",\n    \"    },\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(imdb_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"d39a5ea8\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tokenize the dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"d30070ba\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Tokenizer input max length: 512\\n\",\n      \"Tokenizer vocabulary size: 30522\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoTokenizer\\n\",\n    \"\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"distilbert-base-uncased\\\")\\n\",\n    \"print(\\\"Tokenizer input max length:\\\", tokenizer.model_max_length)\\n\",\n    \"print(\\\"Tokenizer vocabulary size:\\\", tokenizer.vocab_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"a9a96a04\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tokenize_text(batch):\\n\",\n    \"    return tokenizer(batch[\\\"text\\\"], truncation=True, padding=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"32792e13\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cb7b69bebfb34ac7b4236c66f0b0ba5b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a623f934deaf495a851dca62796e728b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c492a8c311934347aa09e0f6a90fe1ba\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"26b53ecc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"del imdb_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"ef74024d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"imdb_tokenized.set_format(\\\"torch\\\", columns=[\\\"input_ids\\\", \\\"attention_mask\\\", \\\"label\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"1952d15f\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"TOKENIZERS_PARALLELISM\\\"] = \\\"false\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"7679c6ea\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3 Set Up DataLoaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"22d94c8c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader, Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class IMDBDataset(Dataset):\\n\",\n    \"    def __init__(self, dataset_dict, partition_key=\\\"train\\\"):\\n\",\n    \"        self.partition = dataset_dict[partition_key]\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        return self.partition[index]\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.partition.num_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"40bbe57a\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"train\\\")\\n\",\n    \"val_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"validation\\\")\\n\",\n    \"test_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"test\\\")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    shuffle=True, \\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"val_loader = DataLoader(\\n\",\n    \"    dataset=val_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    num_workers=4\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0d21316a\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4 Initializing DistilBERT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"de14af0c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias']\\n\",\n      \"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n      \"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\",\n      \"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.bias', 'classifier.weight', 'pre_classifier.bias']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoModelForSequenceClassification\\n\",\n    \"\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(\\n\",\n    \"    \\\"distilbert-base-uncased\\\", num_labels=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3409fb84\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5 Finetuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"82bf5e5c\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Wrap in LightningModule for Training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"b4f7f003\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pytorch_lightning as L\\n\",\n    \"import torch\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningModel(L.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate=5e-5):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.val_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    def forward(self, input_ids, attention_mask, labels):\\n\",\n    \"        return self.model(input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        \\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"train_loss\\\", outputs[\\\"loss\\\"])\\n\",\n    \"        return outputs[\\\"loss\\\"]  # this is passed to the optimizer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"val_loss\\\", outputs[\\\"loss\\\"], prog_bar=True)\\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.val_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"val_acc\\\", self.val_acc, prog_bar=True)\\n\",\n    \"        \\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.test_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"accuracy\\\", self.test_acc, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"2ee664a2\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from finetuning_scheduler import FinetuningScheduler\\n\",\n    \"\\n\",\n    \"callbacks = [FinetuningScheduler()]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"698b7a7d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/finetuning_scheduler/fts_supporters.py:1482: UserWarning: FinetuningScheduler currently depends upon an FTSEarlyStopping callback unless configured in epoch_transitions_only mode. Adding an FTSEarlyStopping callback with default configuration.\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/finetuning_scheduler/fts_supporters.py:1496: UserWarning: FinetuningScheduler currently depends upon a fine-tuning schedule capable ModelCheckpoint callback such as FTSCheckpoint. Substituting current ModelCheckpoint for FTSCheckpoint\\n\",\n      \"  rank_zero_warn(\\n\",\n      \"No fine-tuning schedule provided, max_depth set to -1 so iteratively thawing entire model\\n\",\n      \"fine-tuning schedule dumped to /home/jovyan/lightning_logs/version_1/LightningModel_ft_schedule.yaml.\\n\",\n      \"Generated default fine-tuning schedule '/home/jovyan/lightning_logs/version_1/LightningModel_ft_schedule.yaml' for iterative fine-tuning\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/finetuning_scheduler/fts_supporters.py:1377: UserWarning: FinetuningScheduler configured the provided model to have 2 trainable parameters in phase 0 (the initial training phase) but the optimizer has subsequently been initialized with 102 additional parameters that do not require a gradient. If non-intentional, this state is commonly caused by failing to filter out parameters that do not require a gradient when initializing the optimizer (e.g., `parameters = list(filter(lambda x: x.requires_grad, self.parameters()))` If you intended to initialize the optimizer with parameters that do not require a gradient you may want to ensure they are not included in the 104 parameters that the FinetuningScheduler is currently configured to thaw (sum of all phases) to avoid triggering a  parameter collision and training failure in pytorch during a future fine-tuning phase.\\n\",\n      \"  rank_zero_warn(warn_msg)\\n\",\n      \"\\n\",\n      \"  | Name     | Type                                | Params\\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"0 | model    | DistilBertForSequenceClassification | 67.0 M\\n\",\n      \"1 | val_acc  | Accuracy                            | 0     \\n\",\n      \"2 | test_acc | Accuracy                            | 0     \\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"1.5 K     Trainable params\\n\",\n      \"67.0 M    Non-trainable params\\n\",\n      \"67.0 M    Total params\\n\",\n      \"267.820   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"d5097d74e5c74211b4a2456aa4d0f3d7\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 0, global step 2917: 'val_loss' reached 0.64435 (best 0.64435), saving model to '/home/jovyan/lightning_logs/version_1/checkpoints/epoch=0-step=2917.ckpt' as top 1\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 1, global step 5834: 'val_loss' reached 0.60712 (best 0.60712), saving model to '/home/jovyan/lightning_logs/version_1/checkpoints/epoch=1-step=5834.ckpt' as top 1\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch 2, global step 8751: 'val_loss' reached 0.57646 (best 0.57646), saving model to '/home/jovyan/lightning_logs/version_1/checkpoints/epoch=2-step=8751.ckpt' as top 1\\n\",\n      \"`Trainer.fit` stopped: `max_epochs=3` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=3,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[0],\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"trainer.fit(model=lightning_model,\\n\",\n    \"            train_dataloaders=train_loader,\\n\",\n    \"            val_dataloaders=val_loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"a8eefa9f\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"fine-tuning schedule dumped to /home/jovyan/lightning_logs/version_1/LightningModel_ft_schedule.yaml.\\n\",\n      \"Restoring states from the checkpoint path at /home/jovyan/lightning_logs/version_1/checkpoints/epoch=2-step=8751.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at /home/jovyan/lightning_logs/version_1/checkpoints/epoch=2-step=8751.ckpt\\n\",\n      \"/home/jovyan/conda/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:488: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a68b6e55608e4e3285adb8cd21ba6c66\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.7920571565628052     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.7920571565628052    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.7920571565628052}]\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=train_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"a52d01f4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"fine-tuning schedule dumped to /home/jovyan/lightning_logs/version_1/LightningModel_ft_schedule.yaml.\\n\",\n      \"Restoring states from the checkpoint path at /home/jovyan/lightning_logs/version_1/checkpoints/epoch=2-step=8751.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at /home/jovyan/lightning_logs/version_1/checkpoints/epoch=2-step=8751.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7e646d0759c54fa0a54cfec976b7f3c6\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.7865999937057495     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.7865999937057495    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.7865999937057495}]\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=val_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"id\": \"0106f6f9\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"fine-tuning schedule dumped to /home/jovyan/lightning_logs/version_1/LightningModel_ft_schedule.yaml.\\n\",\n      \"Restoring states from the checkpoint path at /home/jovyan/lightning_logs/version_1/checkpoints/epoch=2-step=8751.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\\n\",\n      \"Loaded model weights from checkpoint at /home/jovyan/lightning_logs/version_1/checkpoints/epoch=2-step=8751.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"fd91b70174b0412e815143ea9349817d\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.7882999777793884     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.7882999777793884    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.7882999777793884}]\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=test_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"id\": \"6e33b9ea\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii-amp/distilbert-finetuning-ii-2.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c5d72f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Finetuning a DistilBERT Classifier in Lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0873756d-262a-4525-b809-4e0b3a1e63dd\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](figures/finetuning-ii.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"6fd9cda8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"92ea5612\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"fe7191cf-62ed-4793-8358-bee70b233d05\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4cfd724d\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1 Loading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fd06d930\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60fe0b76\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1a) Load from `datasets` Hub\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"447e24bb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datasets import list_datasets, load_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"2baf2f16\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# list_datasets()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"6310d5bf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found cached dataset imdb (/home/sebastian/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f27a60f14965423ba6be9cfaf0c887df\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    unsupervised: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 50000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_data = load_dataset(\\\"imdb\\\")\\n\",\n    \"print(imdb_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"552bbb2e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'text': \\\"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\\\",\\n\",\n       \" 'label': 0}\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imdb_data[\\\"train\\\"][99]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40bdb9c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1b) Load from local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9103ec2d\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\\n\",\n    \"\\n\",\n    \"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\\n\",\n    \"\\n\",\n    \"    tar -zxf aclImdb_v1.tar.gz\\n\",\n    \"\\n\",\n    \"B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac508bb8\",\n   \"metadata\": {},\n   \"source\": [\n    \"C) Use the following code to download and unzip the dataset via Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"241ecc96\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Download the movie reviews**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"02aeade4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import time\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"source = \\\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\"\\n\",\n    \"target = \\\"aclImdb_v1.tar.gz\\\"\\n\",\n    \"\\n\",\n    \"if os.path.exists(target):\\n\",\n    \"    os.remove(target)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def reporthook(count, block_size, total_size):\\n\",\n    \"    global start_time\\n\",\n    \"    if count == 0:\\n\",\n    \"        start_time = time.time()\\n\",\n    \"        return\\n\",\n    \"    duration = time.time() - start_time\\n\",\n    \"    progress_size = int(count * block_size)\\n\",\n    \"    speed = progress_size / (1024.0**2 * duration)\\n\",\n    \"    percent = count * block_size * 100.0 / total_size\\n\",\n    \"\\n\",\n    \"    sys.stdout.write(\\n\",\n    \"        f\\\"\\\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \\\"\\n\",\n    \"        f\\\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\\\"\\n\",\n    \"    )\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if not os.path.isdir(\\\"aclImdb\\\") and not os.path.isfile(\\\"aclImdb_v1.tar.gz\\\"):\\n\",\n    \"    urllib.request.urlretrieve(source, target, reporthook)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"2a867dcc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if not os.path.isdir(\\\"aclImdb\\\"):\\n\",\n    \"\\n\",\n    \"    with tarfile.open(target, \\\"r:gz\\\") as tar:\\n\",\n    \"        tar.extractall()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9318d4d0\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Convert them to a pandas DataFrame and save them as CSV**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"464e587c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████| 50000/50000 [00:27<00:00, 1805.38it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from packaging import version\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"\\n\",\n    \"# change the `basepath` to the directory of the\\n\",\n    \"# unzipped movie dataset\\n\",\n    \"\\n\",\n    \"basepath = \\\"aclImdb\\\"\\n\",\n    \"\\n\",\n    \"labels = {\\\"pos\\\": 1, \\\"neg\\\": 0}\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"\\n\",\n    \"with tqdm(total=50000) as pbar:\\n\",\n    \"    for s in (\\\"test\\\", \\\"train\\\"):\\n\",\n    \"        for l in (\\\"pos\\\", \\\"neg\\\"):\\n\",\n    \"            path = os.path.join(basepath, s, l)\\n\",\n    \"            for file in sorted(os.listdir(path)):\\n\",\n    \"                with open(os.path.join(path, file), \\\"r\\\", encoding=\\\"utf-8\\\") as infile:\\n\",\n    \"                    txt = infile.read()\\n\",\n    \"\\n\",\n    \"                if version.parse(pd.__version__) >= version.parse(\\\"1.3.2\\\"):\\n\",\n    \"                    x = pd.DataFrame(\\n\",\n    \"                        [[txt, labels[l]]], columns=[\\\"review\\\", \\\"sentiment\\\"]\\n\",\n    \"                    )\\n\",\n    \"                    df = pd.concat([df, x], ignore_index=False)\\n\",\n    \"\\n\",\n    \"                else:\\n\",\n    \"                    df = df.append([[txt, labels[l]]], ignore_index=True)\\n\",\n    \"                pbar.update()\\n\",\n    \"df.columns = [\\\"text\\\", \\\"label\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"02649593\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.random.seed(0)\\n\",\n    \"df = df.reindex(np.random.permutation(df.index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"59ca0386\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Basic datasets analysis and sanity checks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"c2db547a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([25000, 25000])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Class distribution:\\\")\\n\",\n    \"np.bincount(df[\\\"label\\\"].values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"a007e612\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 173.0, 2470)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"text_len = df[\\\"text\\\"].apply(lambda x: len(x.split()))\\n\",\n    \"text_len.min(), text_len.median(), text_len.max() \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"00f4b04d\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Split data into training, validation, and test sets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"ff703901\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df_shuffled = df.sample(frac=1, random_state=1).reset_index()\\n\",\n    \"\\n\",\n    \"df_train = df_shuffled.iloc[:35_000]\\n\",\n    \"df_val = df_shuffled.iloc[35_000:40_000]\\n\",\n    \"df_test = df_shuffled.iloc[40_000:]\\n\",\n    \"\\n\",\n    \"df_train.to_csv(\\\"train.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_val.to_csv(\\\"validation.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_test.to_csv(\\\"test.csv\\\", index=False, encoding=\\\"utf-8\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"846d83b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2 Tokenization and Numericalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2bd5f770\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Load the dataset via `load_dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"a1aa66c7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration default-08e4c4dd6e79c2ca\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading and preparing dataset csv/default to /home/sebastian/.cache/huggingface/datasets/csv/default-08e4c4dd6e79c2ca/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1969f12e5dfc4410af7f0b58ba1f3396\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1be27a221e8e47788fec9424d3536274\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating train split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating validation split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating test split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset csv downloaded and prepared to /home/sebastian/.cache/huggingface/datasets/csv/default-08e4c4dd6e79c2ca/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"629bbec1ef1749c298c9f1763649e637\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 35000\\n\",\n      \"    })\\n\",\n      \"    validation: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 5000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 10000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_dataset = load_dataset(\\n\",\n    \"    \\\"csv\\\",\\n\",\n    \"    data_files={\\n\",\n    \"        \\\"train\\\": \\\"train.csv\\\",\\n\",\n    \"        \\\"validation\\\": \\\"validation.csv\\\",\\n\",\n    \"        \\\"test\\\": \\\"test.csv\\\",\\n\",\n    \"    },\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(imdb_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"029adc8f-cdfe-4386-9552-a1120f49adee\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tokenize the dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"5ea762ba\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Tokenizer input max length: 512\\n\",\n      \"Tokenizer vocabulary size: 30522\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoTokenizer\\n\",\n    \"\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"distilbert-base-uncased\\\")\\n\",\n    \"print(\\\"Tokenizer input max length:\\\", tokenizer.model_max_length)\\n\",\n    \"print(\\\"Tokenizer vocabulary size:\\\", tokenizer.vocab_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"8432c15c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tokenize_text(batch):\\n\",\n    \"    return tokenizer(batch[\\\"text\\\"], truncation=True, padding=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"0bb392cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"653e98ee4ab74596a0d50335260b8c47\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7295559572cf456ebec133d13e5e58d9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"24afe06b079745719e7accb84dac22cb\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"6d4103c3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"del imdb_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"89ef894c-978f-47f2-9d61-cb6a9f38e745\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"imdb_tokenized.set_format(\\\"torch\\\", columns=[\\\"input_ids\\\", \\\"attention_mask\\\", \\\"label\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"0ea67091-aeb7-46c1-871f-638ce58d8a0e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"TOKENIZERS_PARALLELISM\\\"] = \\\"false\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4c4f8cd8-e641-45fb-9893-70677631917a\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3 Set Up DataLoaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"0807b068-7d8f-4055-a26a-177e07dea4c7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader, Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class IMDBDataset(Dataset):\\n\",\n    \"    def __init__(self, dataset_dict, partition_key=\\\"train\\\"):\\n\",\n    \"        self.partition = dataset_dict[partition_key]\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        return self.partition[index]\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.partition.num_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"90cb08f3-ef77-4351-8b19-42d99dd24f98\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"train\\\")\\n\",\n    \"val_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"validation\\\")\\n\",\n    \"test_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"test\\\")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    shuffle=True, \\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"val_loader = DataLoader(\\n\",\n    \"    dataset=val_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    num_workers=4\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"78e774ab-45a0-4c48-ad61-a3d0e1927ef4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4 Initializing DistilBERT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"dc28ddbe-1a96-4c24-9f5c-40ffdca4a572\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight', 'vocab_transform.bias']\\n\",\n      \"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n      \"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\",\n      \"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'pre_classifier.bias', 'pre_classifier.weight', 'classifier.weight']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoModelForSequenceClassification\\n\",\n    \"\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(\\n\",\n    \"    \\\"distilbert-base-uncased\\\", num_labels=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"def1cf25-0a7d-4bb2-9419-b7a8fe1c1eab\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5 Finetuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"534f7a59-2c86-4895-ad7c-2cdd675b003a\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Wrap in LightningModule for Training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"9f2c474d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import torch\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningModel(L.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate=5e-5):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.val_acc = torchmetrics.Accuracy(task=\\\"multiclass\\\", num_classes=2)\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy(task=\\\"multiclass\\\", num_classes=2)\\n\",\n    \"\\n\",\n    \"    def forward(self, input_ids, attention_mask, labels):\\n\",\n    \"        return self.model(input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        \\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"train_loss\\\", outputs[\\\"loss\\\"])\\n\",\n    \"        return outputs[\\\"loss\\\"]  # this is passed to the optimizer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"val_loss\\\", outputs[\\\"loss\\\"], prog_bar=True)\\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.val_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"val_acc\\\", self.val_acc, prog_bar=True)\\n\",\n    \"        \\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.test_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"accuracy\\\", self.test_acc, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"e6dab813-e1fc-47cd-87a1-5eb8070699c6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from lightning.pytorch.callbacks import ModelCheckpoint\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"val_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"492aa043-02da-459e-a266-091b34254ac6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"\\n\",\n      \"  | Name     | Type                                | Params\\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"0 | model    | DistilBertForSequenceClassification | 67.0 M\\n\",\n      \"1 | val_acc  | MulticlassAccuracy                  | 0     \\n\",\n      \"2 | test_acc | MulticlassAccuracy                  | 0     \\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"67.0 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"67.0 M    Total params\\n\",\n      \"267.820   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"fb6ed097acc144b3b80df060656afa97\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=3` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=3,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[2],\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \"    deterministic=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start = time.time()\\n\",\n    \"trainer.fit(model=lightning_model,\\n\",\n    \"            train_dataloaders=train_loader,\\n\",\n    \"            val_dataloaders=val_loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"b6116cbc-f2c5-43c0-bb76-737be4164752\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Time elapsed 19.71 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"end = time.time()\\n\",\n    \"elapsed = end-start\\n\",\n    \"print(f\\\"Time elapsed {elapsed/60:.2f} min\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"eeb92de4-d483-4627-b9f3-f0bba0cddd9c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"Restoring states from the checkpoint path at logs/my-model/version_1/checkpoints/epoch=2-step=1641.ckpt\\n\",\n      \"Lightning automatically upgraded your loaded checkpoint from v2.0.0dev to v2.0.0dev. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file logs/my-model/version_1/checkpoints/epoch=2-step=1641.ckpt`\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"Loaded model weights from the checkpoint at logs/my-model/version_1/checkpoints/epoch=2-step=1641.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4df92dfd6b524a718fe9656341eba727\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9327999949455261     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9327999949455261    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.9327999949455261}]\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=test_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.15\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii-amp/distilbert-finetuning-ii-amp16.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c5d72f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Finetuning a DistilBERT Classifier in Lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0873756d-262a-4525-b809-4e0b3a1e63dd\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](figures/finetuning-ii.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"6fd9cda8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"92ea5612\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"fe7191cf-62ed-4793-8358-bee70b233d05\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4cfd724d\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1 Loading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fd06d930\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60fe0b76\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1a) Load from `datasets` Hub\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"447e24bb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datasets import list_datasets, load_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"2baf2f16\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# list_datasets()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"6310d5bf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found cached dataset imdb (/home/sebastian/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"525863ad76484082b0c0569c648c602e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    unsupervised: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 50000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_data = load_dataset(\\\"imdb\\\")\\n\",\n    \"print(imdb_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"552bbb2e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'text': \\\"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\\\",\\n\",\n       \" 'label': 0}\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imdb_data[\\\"train\\\"][99]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40bdb9c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1b) Load from local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9103ec2d\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\\n\",\n    \"\\n\",\n    \"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\\n\",\n    \"\\n\",\n    \"    tar -zxf aclImdb_v1.tar.gz\\n\",\n    \"\\n\",\n    \"B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac508bb8\",\n   \"metadata\": {},\n   \"source\": [\n    \"C) Use the following code to download and unzip the dataset via Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"241ecc96\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Download the movie reviews**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"02aeade4\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import time\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"source = \\\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\"\\n\",\n    \"target = \\\"aclImdb_v1.tar.gz\\\"\\n\",\n    \"\\n\",\n    \"if os.path.exists(target):\\n\",\n    \"    os.remove(target)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def reporthook(count, block_size, total_size):\\n\",\n    \"    global start_time\\n\",\n    \"    if count == 0:\\n\",\n    \"        start_time = time.time()\\n\",\n    \"        return\\n\",\n    \"    duration = time.time() - start_time\\n\",\n    \"    progress_size = int(count * block_size)\\n\",\n    \"    speed = progress_size / (1024.0**2 * duration)\\n\",\n    \"    percent = count * block_size * 100.0 / total_size\\n\",\n    \"\\n\",\n    \"    sys.stdout.write(\\n\",\n    \"        f\\\"\\\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \\\"\\n\",\n    \"        f\\\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\\\"\\n\",\n    \"    )\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if not os.path.isdir(\\\"aclImdb\\\") and not os.path.isfile(\\\"aclImdb_v1.tar.gz\\\"):\\n\",\n    \"    urllib.request.urlretrieve(source, target, reporthook)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"2a867dcc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if not os.path.isdir(\\\"aclImdb\\\"):\\n\",\n    \"\\n\",\n    \"    with tarfile.open(target, \\\"r:gz\\\") as tar:\\n\",\n    \"        tar.extractall()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9318d4d0\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Convert them to a pandas DataFrame and save them as CSV**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"464e587c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████| 50000/50000 [00:27<00:00, 1804.33it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from packaging import version\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"\\n\",\n    \"# change the `basepath` to the directory of the\\n\",\n    \"# unzipped movie dataset\\n\",\n    \"\\n\",\n    \"basepath = \\\"aclImdb\\\"\\n\",\n    \"\\n\",\n    \"labels = {\\\"pos\\\": 1, \\\"neg\\\": 0}\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"\\n\",\n    \"with tqdm(total=50000) as pbar:\\n\",\n    \"    for s in (\\\"test\\\", \\\"train\\\"):\\n\",\n    \"        for l in (\\\"pos\\\", \\\"neg\\\"):\\n\",\n    \"            path = os.path.join(basepath, s, l)\\n\",\n    \"            for file in sorted(os.listdir(path)):\\n\",\n    \"                with open(os.path.join(path, file), \\\"r\\\", encoding=\\\"utf-8\\\") as infile:\\n\",\n    \"                    txt = infile.read()\\n\",\n    \"\\n\",\n    \"                if version.parse(pd.__version__) >= version.parse(\\\"1.3.2\\\"):\\n\",\n    \"                    x = pd.DataFrame(\\n\",\n    \"                        [[txt, labels[l]]], columns=[\\\"review\\\", \\\"sentiment\\\"]\\n\",\n    \"                    )\\n\",\n    \"                    df = pd.concat([df, x], ignore_index=False)\\n\",\n    \"\\n\",\n    \"                else:\\n\",\n    \"                    df = df.append([[txt, labels[l]]], ignore_index=True)\\n\",\n    \"                pbar.update()\\n\",\n    \"df.columns = [\\\"text\\\", \\\"label\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"02649593\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.random.seed(0)\\n\",\n    \"df = df.reindex(np.random.permutation(df.index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"59ca0386\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Basic datasets analysis and sanity checks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"c2db547a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([25000, 25000])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Class distribution:\\\")\\n\",\n    \"np.bincount(df[\\\"label\\\"].values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"a007e612\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 173.0, 2470)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"text_len = df[\\\"text\\\"].apply(lambda x: len(x.split()))\\n\",\n    \"text_len.min(), text_len.median(), text_len.max() \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"00f4b04d\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Split data into training, validation, and test sets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"ff703901\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df_shuffled = df.sample(frac=1, random_state=1).reset_index()\\n\",\n    \"\\n\",\n    \"df_train = df_shuffled.iloc[:35_000]\\n\",\n    \"df_val = df_shuffled.iloc[35_000:40_000]\\n\",\n    \"df_test = df_shuffled.iloc[40_000:]\\n\",\n    \"\\n\",\n    \"df_train.to_csv(\\\"train.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_val.to_csv(\\\"validation.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_test.to_csv(\\\"test.csv\\\", index=False, encoding=\\\"utf-8\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"846d83b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2 Tokenization and Numericalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2bd5f770\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Load the dataset via `load_dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"a1aa66c7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration default-1d250764c9d66b6e\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading and preparing dataset csv/default to /home/sebastian/.cache/huggingface/datasets/csv/default-1d250764c9d66b6e/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ee33d5e9109e45d5ad6583928fc67a9b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"acfc164436394caaa48e4109ed77bda4\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating train split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating validation split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating test split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset csv downloaded and prepared to /home/sebastian/.cache/huggingface/datasets/csv/default-1d250764c9d66b6e/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e78afceb516846689ed48ed52473708f\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 35000\\n\",\n      \"    })\\n\",\n      \"    validation: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 5000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 10000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_dataset = load_dataset(\\n\",\n    \"    \\\"csv\\\",\\n\",\n    \"    data_files={\\n\",\n    \"        \\\"train\\\": \\\"train.csv\\\",\\n\",\n    \"        \\\"validation\\\": \\\"validation.csv\\\",\\n\",\n    \"        \\\"test\\\": \\\"test.csv\\\",\\n\",\n    \"    },\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(imdb_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"029adc8f-cdfe-4386-9552-a1120f49adee\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tokenize the dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"5ea762ba\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Tokenizer input max length: 512\\n\",\n      \"Tokenizer vocabulary size: 30522\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoTokenizer\\n\",\n    \"\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"distilbert-base-uncased\\\")\\n\",\n    \"print(\\\"Tokenizer input max length:\\\", tokenizer.model_max_length)\\n\",\n    \"print(\\\"Tokenizer vocabulary size:\\\", tokenizer.vocab_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"8432c15c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tokenize_text(batch):\\n\",\n    \"    return tokenizer(batch[\\\"text\\\"], truncation=True, padding=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"0bb392cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e6043434420a47b49f8a40ee726baca8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"10735abb6ee4404783608ae19d235ba1\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cfa7b96225a84a36ac4697219a8b21a4\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"6d4103c3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"del imdb_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"89ef894c-978f-47f2-9d61-cb6a9f38e745\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"imdb_tokenized.set_format(\\\"torch\\\", columns=[\\\"input_ids\\\", \\\"attention_mask\\\", \\\"label\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"0ea67091-aeb7-46c1-871f-638ce58d8a0e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"TOKENIZERS_PARALLELISM\\\"] = \\\"false\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4c4f8cd8-e641-45fb-9893-70677631917a\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3 Set Up DataLoaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"0807b068-7d8f-4055-a26a-177e07dea4c7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader, Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class IMDBDataset(Dataset):\\n\",\n    \"    def __init__(self, dataset_dict, partition_key=\\\"train\\\"):\\n\",\n    \"        self.partition = dataset_dict[partition_key]\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        return self.partition[index]\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.partition.num_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"90cb08f3-ef77-4351-8b19-42d99dd24f98\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"train\\\")\\n\",\n    \"val_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"validation\\\")\\n\",\n    \"test_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"test\\\")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    shuffle=True, \\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"val_loader = DataLoader(\\n\",\n    \"    dataset=val_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    num_workers=4\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"78e774ab-45a0-4c48-ad61-a3d0e1927ef4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4 Initializing DistilBERT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"dc28ddbe-1a96-4c24-9f5c-40ffdca4a572\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_transform.weight']\\n\",\n      \"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n      \"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\",\n      \"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.bias', 'classifier.weight', 'pre_classifier.bias']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoModelForSequenceClassification\\n\",\n    \"\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(\\n\",\n    \"    \\\"distilbert-base-uncased\\\", num_labels=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"def1cf25-0a7d-4bb2-9419-b7a8fe1c1eab\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5 Finetuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"534f7a59-2c86-4895-ad7c-2cdd675b003a\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Wrap in LightningModule for Training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"9f2c474d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import torch\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningModel(L.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate=5e-5):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.val_acc = torchmetrics.Accuracy(task=\\\"multiclass\\\", num_classes=2)\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy(task=\\\"multiclass\\\", num_classes=2)\\n\",\n    \"\\n\",\n    \"    def forward(self, input_ids, attention_mask, labels):\\n\",\n    \"        return self.model(input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        \\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"train_loss\\\", outputs[\\\"loss\\\"])\\n\",\n    \"        return outputs[\\\"loss\\\"]  # this is passed to the optimizer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"val_loss\\\", outputs[\\\"loss\\\"], prog_bar=True)\\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.val_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"val_acc\\\", self.val_acc, prog_bar=True)\\n\",\n    \"        \\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.test_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"accuracy\\\", self.test_acc, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"e6dab813-e1fc-47cd-87a1-5eb8070699c6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from lightning.pytorch.callbacks import ModelCheckpoint\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"val_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"492aa043-02da-459e-a266-091b34254ac6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using bfloat16 Automatic Mixed Precision (AMP)\\n\",\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"\\n\",\n      \"  | Name     | Type                                | Params\\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"0 | model    | DistilBertForSequenceClassification | 67.0 M\\n\",\n      \"1 | val_acc  | MulticlassAccuracy                  | 0     \\n\",\n      \"2 | test_acc | MulticlassAccuracy                  | 0     \\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"67.0 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"67.0 M    Total params\\n\",\n      \"133.910   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"4859afa9fd6e4a39b1b70c46dc964652\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=3` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=3,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    precision=16,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[0],\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \"    deterministic=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start = time.time()\\n\",\n    \"trainer.fit(model=lightning_model,\\n\",\n    \"            train_dataloaders=train_loader,\\n\",\n    \"            val_dataloaders=val_loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"b6116cbc-f2c5-43c0-bb76-737be4164752\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Time elapsed 5.53 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"end = time.time()\\n\",\n    \"elapsed = end-start\\n\",\n    \"print(f\\\"Time elapsed {elapsed/60:.2f} min\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"eeb92de4-d483-4627-b9f3-f0bba0cddd9c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"Restoring states from the checkpoint path at logs/my-model/version_11/checkpoints/epoch=2-step=1641.ckpt\\n\",\n      \"Lightning automatically upgraded your loaded checkpoint from v2.0.0dev to v2.0.0dev. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file logs/my-model/version_11/checkpoints/epoch=2-step=1641.ckpt`\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"Loaded model weights from the checkpoint at logs/my-model/version_11/checkpoints/epoch=2-step=1641.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c3e51db1b0a3481c83c3e0dd36befbef\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9283000230789185     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9283000230789185    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.9283000230789185}]\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=test_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.15\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii-amp/distilbert-finetuning-ii-ampb16.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c5d72f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Finetuning a DistilBERT Classifier in Lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0873756d-262a-4525-b809-4e0b3a1e63dd\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](figures/finetuning-ii.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"6fd9cda8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"92ea5612\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"fe7191cf-62ed-4793-8358-bee70b233d05\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4cfd724d\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1 Loading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fd06d930\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60fe0b76\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1a) Load from `datasets` Hub\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"447e24bb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datasets import list_datasets, load_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"2baf2f16\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# list_datasets()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"6310d5bf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found cached dataset imdb (/home/sebastian/.cache/huggingface/datasets/imdb/plain_text/1.0.0/d613c88cf8fa3bab83b4ded3713f1f74830d1100e171db75bbddb80b3345c9c0)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1b7cb09fb60242bc802cd825c40aec72\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    unsupervised: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 50000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_data = load_dataset(\\\"imdb\\\")\\n\",\n    \"print(imdb_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"552bbb2e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'text': \\\"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\\\",\\n\",\n       \" 'label': 0}\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imdb_data[\\\"train\\\"][99]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40bdb9c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1b) Load from local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9103ec2d\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\\n\",\n    \"\\n\",\n    \"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\\n\",\n    \"\\n\",\n    \"    tar -zxf aclImdb_v1.tar.gz\\n\",\n    \"\\n\",\n    \"B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac508bb8\",\n   \"metadata\": {},\n   \"source\": [\n    \"C) Use the following code to download and unzip the dataset via Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"241ecc96\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Download the movie reviews**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"02aeade4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100% | 80.23 MB | 2.64 MB/s | 30.34 sec elapsed\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import time\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"source = \\\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\"\\n\",\n    \"target = \\\"aclImdb_v1.tar.gz\\\"\\n\",\n    \"\\n\",\n    \"if os.path.exists(target):\\n\",\n    \"    os.remove(target)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def reporthook(count, block_size, total_size):\\n\",\n    \"    global start_time\\n\",\n    \"    if count == 0:\\n\",\n    \"        start_time = time.time()\\n\",\n    \"        return\\n\",\n    \"    duration = time.time() - start_time\\n\",\n    \"    progress_size = int(count * block_size)\\n\",\n    \"    speed = progress_size / (1024.0**2 * duration)\\n\",\n    \"    percent = count * block_size * 100.0 / total_size\\n\",\n    \"\\n\",\n    \"    sys.stdout.write(\\n\",\n    \"        f\\\"\\\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \\\"\\n\",\n    \"        f\\\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\\\"\\n\",\n    \"    )\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if not os.path.isdir(\\\"aclImdb\\\") and not os.path.isfile(\\\"aclImdb_v1.tar.gz\\\"):\\n\",\n    \"    urllib.request.urlretrieve(source, target, reporthook)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"2a867dcc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if not os.path.isdir(\\\"aclImdb\\\"):\\n\",\n    \"\\n\",\n    \"    with tarfile.open(target, \\\"r:gz\\\") as tar:\\n\",\n    \"        tar.extractall()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9318d4d0\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Convert them to a pandas DataFrame and save them as CSV**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"464e587c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████████████████████████████████| 50000/50000 [00:27<00:00, 1809.68it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from packaging import version\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"\\n\",\n    \"# change the `basepath` to the directory of the\\n\",\n    \"# unzipped movie dataset\\n\",\n    \"\\n\",\n    \"basepath = \\\"aclImdb\\\"\\n\",\n    \"\\n\",\n    \"labels = {\\\"pos\\\": 1, \\\"neg\\\": 0}\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"\\n\",\n    \"with tqdm(total=50000) as pbar:\\n\",\n    \"    for s in (\\\"test\\\", \\\"train\\\"):\\n\",\n    \"        for l in (\\\"pos\\\", \\\"neg\\\"):\\n\",\n    \"            path = os.path.join(basepath, s, l)\\n\",\n    \"            for file in sorted(os.listdir(path)):\\n\",\n    \"                with open(os.path.join(path, file), \\\"r\\\", encoding=\\\"utf-8\\\") as infile:\\n\",\n    \"                    txt = infile.read()\\n\",\n    \"\\n\",\n    \"                if version.parse(pd.__version__) >= version.parse(\\\"1.3.2\\\"):\\n\",\n    \"                    x = pd.DataFrame(\\n\",\n    \"                        [[txt, labels[l]]], columns=[\\\"review\\\", \\\"sentiment\\\"]\\n\",\n    \"                    )\\n\",\n    \"                    df = pd.concat([df, x], ignore_index=False)\\n\",\n    \"\\n\",\n    \"                else:\\n\",\n    \"                    df = df.append([[txt, labels[l]]], ignore_index=True)\\n\",\n    \"                pbar.update()\\n\",\n    \"df.columns = [\\\"text\\\", \\\"label\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"02649593\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.random.seed(0)\\n\",\n    \"df = df.reindex(np.random.permutation(df.index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"59ca0386\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Basic datasets analysis and sanity checks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"c2db547a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([25000, 25000])\"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Class distribution:\\\")\\n\",\n    \"np.bincount(df[\\\"label\\\"].values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"a007e612\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 173.0, 2470)\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"text_len = df[\\\"text\\\"].apply(lambda x: len(x.split()))\\n\",\n    \"text_len.min(), text_len.median(), text_len.max() \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"00f4b04d\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Split data into training, validation, and test sets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"ff703901\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df_shuffled = df.sample(frac=1, random_state=1).reset_index()\\n\",\n    \"\\n\",\n    \"df_train = df_shuffled.iloc[:35_000]\\n\",\n    \"df_val = df_shuffled.iloc[35_000:40_000]\\n\",\n    \"df_test = df_shuffled.iloc[40_000:]\\n\",\n    \"\\n\",\n    \"df_train.to_csv(\\\"train.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_val.to_csv(\\\"validation.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_test.to_csv(\\\"test.csv\\\", index=False, encoding=\\\"utf-8\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"846d83b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2 Tokenization and Numericalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2bd5f770\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Load the dataset via `load_dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"a1aa66c7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration default-2352009af2284f6d\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading and preparing dataset csv/default to /home/sebastian/.cache/huggingface/datasets/csv/default-2352009af2284f6d/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"acea2a6e77cd4cde9e9fd5d112a35f69\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c1c067de4083435fafd7627871abc92c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating train split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating validation split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Generating test split: 0 examples [00:00, ? examples/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset csv downloaded and prepared to /home/sebastian/.cache/huggingface/datasets/csv/default-2352009af2284f6d/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/sebastian/miniforge3/envs/lightning2/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:776: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a6bf5b53e94c47e4b40c8647b78fb44c\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 35000\\n\",\n      \"    })\\n\",\n      \"    validation: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 5000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 10000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_dataset = load_dataset(\\n\",\n    \"    \\\"csv\\\",\\n\",\n    \"    data_files={\\n\",\n    \"        \\\"train\\\": \\\"train.csv\\\",\\n\",\n    \"        \\\"validation\\\": \\\"validation.csv\\\",\\n\",\n    \"        \\\"test\\\": \\\"test.csv\\\",\\n\",\n    \"    },\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(imdb_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"029adc8f-cdfe-4386-9552-a1120f49adee\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tokenize the dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"5ea762ba\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Tokenizer input max length: 512\\n\",\n      \"Tokenizer vocabulary size: 30522\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoTokenizer\\n\",\n    \"\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"distilbert-base-uncased\\\")\\n\",\n    \"print(\\\"Tokenizer input max length:\\\", tokenizer.model_max_length)\\n\",\n    \"print(\\\"Tokenizer vocabulary size:\\\", tokenizer.vocab_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"8432c15c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tokenize_text(batch):\\n\",\n    \"    return tokenizer(batch[\\\"text\\\"], truncation=True, padding=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"0bb392cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"ed0b8624e0ea4400ab0967bba8998e4b\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c8d4c4c04a6b45bea53800338047c761\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"94972c84d1504fab964c388114017010\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"6d4103c3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"del imdb_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"89ef894c-978f-47f2-9d61-cb6a9f38e745\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"imdb_tokenized.set_format(\\\"torch\\\", columns=[\\\"input_ids\\\", \\\"attention_mask\\\", \\\"label\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"0ea67091-aeb7-46c1-871f-638ce58d8a0e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"TOKENIZERS_PARALLELISM\\\"] = \\\"false\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4c4f8cd8-e641-45fb-9893-70677631917a\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3 Set Up DataLoaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"0807b068-7d8f-4055-a26a-177e07dea4c7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader, Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class IMDBDataset(Dataset):\\n\",\n    \"    def __init__(self, dataset_dict, partition_key=\\\"train\\\"):\\n\",\n    \"        self.partition = dataset_dict[partition_key]\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        return self.partition[index]\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.partition.num_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"90cb08f3-ef77-4351-8b19-42d99dd24f98\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"train\\\")\\n\",\n    \"val_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"validation\\\")\\n\",\n    \"test_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"test\\\")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    shuffle=True, \\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"val_loader = DataLoader(\\n\",\n    \"    dataset=val_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=64,\\n\",\n    \"    num_workers=4\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"78e774ab-45a0-4c48-ad61-a3d0e1927ef4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4 Initializing DistilBERT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"dc28ddbe-1a96-4c24-9f5c-40ffdca4a572\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_transform.weight']\\n\",\n      \"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n      \"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\",\n      \"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'pre_classifier.weight', 'classifier.weight', 'pre_classifier.bias']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoModelForSequenceClassification\\n\",\n    \"\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(\\n\",\n    \"    \\\"distilbert-base-uncased\\\", num_labels=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"def1cf25-0a7d-4bb2-9419-b7a8fe1c1eab\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5 Finetuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"534f7a59-2c86-4895-ad7c-2cdd675b003a\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Wrap in LightningModule for Training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"9f2c474d\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import torch\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningModel(L.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate=5e-5):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.val_acc = torchmetrics.Accuracy(task=\\\"multiclass\\\", num_classes=2)\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy(task=\\\"multiclass\\\", num_classes=2)\\n\",\n    \"\\n\",\n    \"    def forward(self, input_ids, attention_mask, labels):\\n\",\n    \"        return self.model(input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        \\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"train_loss\\\", outputs[\\\"loss\\\"])\\n\",\n    \"        return outputs[\\\"loss\\\"]  # this is passed to the optimizer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"val_loss\\\", outputs[\\\"loss\\\"], prog_bar=True)\\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.val_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"val_acc\\\", self.val_acc, prog_bar=True)\\n\",\n    \"        \\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.test_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"accuracy\\\", self.test_acc, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"e6dab813-e1fc-47cd-87a1-5eb8070699c6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from lightning.pytorch.callbacks import ModelCheckpoint\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"val_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"492aa043-02da-459e-a266-091b34254ac6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using bfloat16 Automatic Mixed Precision (AMP)\\n\",\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"Missing logger folder: logs/my-model\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"\\n\",\n      \"  | Name     | Type                                | Params\\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"0 | model    | DistilBertForSequenceClassification | 67.0 M\\n\",\n      \"1 | val_acc  | MulticlassAccuracy                  | 0     \\n\",\n      \"2 | test_acc | MulticlassAccuracy                  | 0     \\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"67.0 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"67.0 M    Total params\\n\",\n      \"133.910   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"26fa7b1ba00849a597fdd5e03b7872bf\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=3` reached.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"\\n\",\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=3,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    precision=\\\"bf16\\\",\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[1],\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \"    deterministic=True\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"start = time.time()\\n\",\n    \"trainer.fit(model=lightning_model,\\n\",\n    \"            train_dataloaders=train_loader,\\n\",\n    \"            val_dataloaders=val_loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"b6116cbc-f2c5-43c0-bb76-737be4164752\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Time elapsed 6.12 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"end = time.time()\\n\",\n    \"elapsed = end-start\\n\",\n    \"print(f\\\"Time elapsed {elapsed/60:.2f} min\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"eeb92de4-d483-4627-b9f3-f0bba0cddd9c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"You are using a CUDA device ('NVIDIA A100-SXM4-40GB') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\\n\",\n      \"Restoring states from the checkpoint path at logs/my-model/version_0/checkpoints/epoch=2-step=1641.ckpt\\n\",\n      \"Lightning automatically upgraded your loaded checkpoint from v2.0.0dev to v2.0.0dev. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file logs/my-model/version_0/checkpoints/epoch=2-step=1641.ckpt`\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3,4,5,6,7]\\n\",\n      \"Loaded model weights from the checkpoint at logs/my-model/version_0/checkpoints/epoch=2-step=1641.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"cbeb91cb6ee34932bb268f9fab708fdd\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9277999997138977     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9277999997138977    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.9277999997138977}]\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=test_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.15\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch-lightning_ipynb/transformer/distilbert-finetuning-ii.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"3c5d72f4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Finetuning a DistilBERT Classifier in Lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"0873756d-262a-4525-b809-4e0b3a1e63dd\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](figures/finetuning-ii.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"6fd9cda8\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install transformers\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"92ea5612\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install datasets\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"fe7191cf-62ed-4793-8358-bee70b233d05\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# pip install lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"033b75c5\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/torch/utils/tensorboard/__init__.py:4: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\\n\",\n      \"  if not hasattr(tensorboard, \\\"__version__\\\") or LooseVersion(\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       : 1.12.1\\n\",\n      \"transformers: 4.23.1\\n\",\n      \"datasets    : 2.6.1\\n\",\n      \"lightning   : 2022.10.20\\n\",\n      \"\\n\",\n      \"conda environment: dl-fundamentals\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark --conda -p torch,transformers,datasets,lightning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4cfd724d\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 1 Loading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"fd06d930\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review dataset consists of 50k movie reviews with sentiment label (0: negative, 1: positive).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"60fe0b76\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1a) Load from `datasets` Hub\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"447e24bb\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from datasets import list_datasets, load_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"2baf2f16\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# list_datasets()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"id\": \"6310d5bf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Found cached dataset imdb (/home/raschka/.cache/huggingface/datasets/imdb/plain_text/1.0.0/2fdd8b9bcadd6e7055e742a706876ba43f19faee861df134affd7a3f60fc38a1)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"c20d69bcd01447158c2a2595a9c1fc05\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 25000\\n\",\n      \"    })\\n\",\n      \"    unsupervised: Dataset({\\n\",\n      \"        features: ['text', 'label'],\\n\",\n      \"        num_rows: 50000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_data = load_dataset(\\\"imdb\\\")\\n\",\n    \"print(imdb_data)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"id\": \"552bbb2e\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"{'text': \\\"This film is terrible. You don't really need to read this review further. If you are planning on watching it, suffice to say - don't (unless you are studying how not to make a good movie).<br /><br />The acting is horrendous... serious amateur hour. Throughout the movie I thought that it was interesting that they found someone who speaks and looks like Michael Madsen, only to find out that it is actually him! A new low even for him!!<br /><br />The plot is terrible. People who claim that it is original or good have probably never seen a decent movie before. Even by the standard of Hollywood action flicks, this is a terrible movie.<br /><br />Don't watch it!!! Go for a jog instead - at least you won't feel like killing yourself.\\\",\\n\",\n       \" 'label': 0}\"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"imdb_data[\\\"train\\\"][99]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"40bdb9c5\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 1b) Load from local directory\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9103ec2d\",\n   \"metadata\": {},\n   \"source\": [\n    \"The IMDB movie review set can be downloaded from http://ai.stanford.edu/~amaas/data/sentiment/. After downloading the dataset, decompress the files.\\n\",\n    \"\\n\",\n    \"A) If you are working with Linux or MacOS X, open a new terminal window cd into the download directory and execute\\n\",\n    \"\\n\",\n    \"    tar -zxf aclImdb_v1.tar.gz\\n\",\n    \"\\n\",\n    \"B) If you are working with Windows, download an archiver such as 7Zip to extract the files from the download archive.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"ac508bb8\",\n   \"metadata\": {},\n   \"source\": [\n    \"C) Use the following code to download and unzip the dataset via Python\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"241ecc96\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Download the movie reviews**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"id\": \"02aeade4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100% | 80.23 MB | 10.58 MB/s | 7.58 sec elapsed\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"import tarfile\\n\",\n    \"import time\\n\",\n    \"import urllib.request\\n\",\n    \"\\n\",\n    \"source = \\\"http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz\\\"\\n\",\n    \"target = \\\"aclImdb_v1.tar.gz\\\"\\n\",\n    \"\\n\",\n    \"if os.path.exists(target):\\n\",\n    \"    os.remove(target)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def reporthook(count, block_size, total_size):\\n\",\n    \"    global start_time\\n\",\n    \"    if count == 0:\\n\",\n    \"        start_time = time.time()\\n\",\n    \"        return\\n\",\n    \"    duration = time.time() - start_time\\n\",\n    \"    progress_size = int(count * block_size)\\n\",\n    \"    speed = progress_size / (1024.0**2 * duration)\\n\",\n    \"    percent = count * block_size * 100.0 / total_size\\n\",\n    \"\\n\",\n    \"    sys.stdout.write(\\n\",\n    \"        f\\\"\\\\r{int(percent)}% | {progress_size / (1024.**2):.2f} MB \\\"\\n\",\n    \"        f\\\"| {speed:.2f} MB/s | {duration:.2f} sec elapsed\\\"\\n\",\n    \"    )\\n\",\n    \"    sys.stdout.flush()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if not os.path.isdir(\\\"aclImdb\\\") and not os.path.isfile(\\\"aclImdb_v1.tar.gz\\\"):\\n\",\n    \"    urllib.request.urlretrieve(source, target, reporthook)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"id\": \"2a867dcc\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"if not os.path.isdir(\\\"aclImdb\\\"):\\n\",\n    \"\\n\",\n    \"    with tarfile.open(target, \\\"r:gz\\\") as tar:\\n\",\n    \"        tar.extractall()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"9318d4d0\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Convert them to a pandas DataFrame and save them as CSV**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"id\": \"464e587c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"100%|███████████████████████████████████████████████████████| 50000/50000 [00:56<00:00, 884.75it/s]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import os\\n\",\n    \"import sys\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from packaging import version\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"\\n\",\n    \"# change the `basepath` to the directory of the\\n\",\n    \"# unzipped movie dataset\\n\",\n    \"\\n\",\n    \"basepath = \\\"aclImdb\\\"\\n\",\n    \"\\n\",\n    \"labels = {\\\"pos\\\": 1, \\\"neg\\\": 0}\\n\",\n    \"\\n\",\n    \"df = pd.DataFrame()\\n\",\n    \"\\n\",\n    \"with tqdm(total=50000) as pbar:\\n\",\n    \"    for s in (\\\"test\\\", \\\"train\\\"):\\n\",\n    \"        for l in (\\\"pos\\\", \\\"neg\\\"):\\n\",\n    \"            path = os.path.join(basepath, s, l)\\n\",\n    \"            for file in sorted(os.listdir(path)):\\n\",\n    \"                with open(os.path.join(path, file), \\\"r\\\", encoding=\\\"utf-8\\\") as infile:\\n\",\n    \"                    txt = infile.read()\\n\",\n    \"\\n\",\n    \"                if version.parse(pd.__version__) >= version.parse(\\\"1.3.2\\\"):\\n\",\n    \"                    x = pd.DataFrame(\\n\",\n    \"                        [[txt, labels[l]]], columns=[\\\"review\\\", \\\"sentiment\\\"]\\n\",\n    \"                    )\\n\",\n    \"                    df = pd.concat([df, x], ignore_index=False)\\n\",\n    \"\\n\",\n    \"                else:\\n\",\n    \"                    df = df.append([[txt, labels[l]]], ignore_index=True)\\n\",\n    \"                pbar.update()\\n\",\n    \"df.columns = [\\\"text\\\", \\\"label\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"id\": \"02649593\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"np.random.seed(0)\\n\",\n    \"df = df.reindex(np.random.permutation(df.index))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"59ca0386\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Basic datasets analysis and sanity checks**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"id\": \"c2db547a\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class distribution:\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([25000, 25000])\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"print(\\\"Class distribution:\\\")\\n\",\n    \"np.bincount(df[\\\"label\\\"].values)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"id\": \"a007e612\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"(4, 173.0, 2470)\"\n      ]\n     },\n     \"execution_count\": 14,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"text_len = df[\\\"text\\\"].apply(lambda x: len(x.split()))\\n\",\n    \"text_len.min(), text_len.median(), text_len.max() \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"00f4b04d\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Split data into training, validation, and test sets**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"id\": \"ff703901\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df_shuffled = df.sample(frac=1, random_state=1).reset_index()\\n\",\n    \"\\n\",\n    \"df_train = df_shuffled.iloc[:35_000]\\n\",\n    \"df_val = df_shuffled.iloc[35_000:40_000]\\n\",\n    \"df_test = df_shuffled.iloc[40_000:]\\n\",\n    \"\\n\",\n    \"df_train.to_csv(\\\"train.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_val.to_csv(\\\"validation.csv\\\", index=False, encoding=\\\"utf-8\\\")\\n\",\n    \"df_test.to_csv(\\\"test.csv\\\", index=False, encoding=\\\"utf-8\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"846d83b1\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 2 Tokenization and Numericalization\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"2bd5f770\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Load the dataset via `load_dataset`**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"id\": \"a1aa66c7\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using custom data configuration default-a8c53805fb54dee6\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading and preparing dataset csv/default to /home/raschka/.cache/huggingface/datasets/csv/default-a8c53805fb54dee6/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"abc99760170048c5baec843d247f02b3\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f3366bd87980412a927980b8c1459a14\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"0 tables [00:00, ? tables/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/train.csv'>\\n\",\n      \"  self.handle.detach()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"0 tables [00:00, ? tables/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/validation.csv'>\\n\",\n      \"  self.handle.detach()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"0 tables [00:00, ? tables/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py:714: FutureWarning: the 'mangle_dupe_cols' keyword is deprecated and will be removed in a future version. Please take steps to stop the use of 'mangle_dupe_cols'\\n\",\n      \"  return pd.read_csv(xopen(filepath_or_buffer, \\\"rb\\\", use_auth_token=use_auth_token), **kwargs)\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Dataset csv downloaded and prepared to /home/raschka/.cache/huggingface/datasets/csv/default-a8c53805fb54dee6/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pandas/io/common.py:122: ResourceWarning: unclosed file <_io.BufferedReader name='/home/raschka/scratch/deeplearning-models/pytorch-lightning_ipynb/transformer/test.csv'>\\n\",\n      \"  self.handle.detach()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"0eb3cbd7a6564e4087f7ed51548b2c5e\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/3 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"DatasetDict({\\n\",\n      \"    train: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 35000\\n\",\n      \"    })\\n\",\n      \"    validation: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 5000\\n\",\n      \"    })\\n\",\n      \"    test: Dataset({\\n\",\n      \"        features: ['index', 'text', 'label'],\\n\",\n      \"        num_rows: 10000\\n\",\n      \"    })\\n\",\n      \"})\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"imdb_dataset = load_dataset(\\n\",\n    \"    \\\"csv\\\",\\n\",\n    \"    data_files={\\n\",\n    \"        \\\"train\\\": \\\"train.csv\\\",\\n\",\n    \"        \\\"validation\\\": \\\"validation.csv\\\",\\n\",\n    \"        \\\"test\\\": \\\"test.csv\\\",\\n\",\n    \"    },\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"print(imdb_dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"029adc8f-cdfe-4386-9552-a1120f49adee\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Tokenize the dataset**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"id\": \"5ea762ba\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Tokenizer input max length: 512\\n\",\n      \"Tokenizer vocabulary size: 30522\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoTokenizer\\n\",\n    \"\\n\",\n    \"tokenizer = AutoTokenizer.from_pretrained(\\\"distilbert-base-uncased\\\")\\n\",\n    \"print(\\\"Tokenizer input max length:\\\", tokenizer.model_max_length)\\n\",\n    \"print(\\\"Tokenizer vocabulary size:\\\", tokenizer.vocab_size)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"id\": \"8432c15c\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def tokenize_text(batch):\\n\",\n    \"    return tokenizer(batch[\\\"text\\\"], truncation=True, padding=True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"id\": \"0bb392cf\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"5a0279afe1ba4885931e7581c8ad93d8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"f2524872c83447dba6abdda279d57c89\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a9cb2a88785b4603a6025e687faa1664\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/1 [00:00<?, ?ba/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"imdb_tokenized = imdb_dataset.map(tokenize_text, batched=True, batch_size=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"id\": \"6d4103c3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"del imdb_dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"id\": \"89ef894c-978f-47f2-9d61-cb6a9f38e745\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"imdb_tokenized.set_format(\\\"torch\\\", columns=[\\\"input_ids\\\", \\\"attention_mask\\\", \\\"label\\\"])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"id\": \"0ea67091-aeb7-46c1-871f-638ce58d8a0e\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"os.environ[\\\"TOKENIZERS_PARALLELISM\\\"] = \\\"false\\\"\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4c4f8cd8-e641-45fb-9893-70677631917a\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 3 Set Up DataLoaders\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"id\": \"0807b068-7d8f-4055-a26a-177e07dea4c7\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torch.utils.data import DataLoader, Dataset\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class IMDBDataset(Dataset):\\n\",\n    \"    def __init__(self, dataset_dict, partition_key=\\\"train\\\"):\\n\",\n    \"        self.partition = dataset_dict[partition_key]\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        return self.partition[index]\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.partition.num_rows\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"id\": \"90cb08f3-ef77-4351-8b19-42d99dd24f98\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"train\\\")\\n\",\n    \"val_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"validation\\\")\\n\",\n    \"test_dataset = IMDBDataset(imdb_tokenized, partition_key=\\\"test\\\")\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(\\n\",\n    \"    dataset=train_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    shuffle=True, \\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"val_loader = DataLoader(\\n\",\n    \"    dataset=val_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    num_workers=4\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(\\n\",\n    \"    dataset=test_dataset,\\n\",\n    \"    batch_size=12,\\n\",\n    \"    num_workers=4\\n\",\n    \")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"78e774ab-45a0-4c48-ad61-a3d0e1927ef4\",\n   \"metadata\": {},\n   \"source\": [\n    \"# 4 Initializing DistilBERT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"id\": \"dc28ddbe-1a96-4c24-9f5c-40ffdca4a572\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_transform.weight', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_layer_norm.bias', 'vocab_projector.bias']\\n\",\n      \"- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\\n\",\n      \"- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\\n\",\n      \"Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.bias', 'pre_classifier.bias', 'classifier.weight']\\n\",\n      \"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from transformers import AutoModelForSequenceClassification\\n\",\n    \"\\n\",\n    \"model = AutoModelForSequenceClassification.from_pretrained(\\n\",\n    \"    \\\"distilbert-base-uncased\\\", num_labels=2)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"def1cf25-0a7d-4bb2-9419-b7a8fe1c1eab\",\n   \"metadata\": {},\n   \"source\": [\n    \"## 5 Finetuning\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"534f7a59-2c86-4895-ad7c-2cdd675b003a\",\n   \"metadata\": {},\n   \"source\": [\n    \"**Wrap in LightningModule for Training**\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"id\": \"9f2c474d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/torchmetrics/utilities/prints.py:36: DeprecationWarning: From v0.10 an `'Binary*'`, `'Multiclass*', `'Multilabel*'` version now exist of each classification metric. Moving forward we recommend using these versions. This base metric will still work as it did prior to v0.10 until v0.11. From v0.11 the `task` argument introduced in this metric will be required and the general order of arguments may change, such that this metric will just function as an single entrypoint to calling the three specialized versions.\\n\",\n      \"  warnings.warn(*args, **kwargs)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import lightning as L\\n\",\n    \"import torch\\n\",\n    \"import torchmetrics\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class LightningModel(L.LightningModule):\\n\",\n    \"    def __init__(self, model, learning_rate=5e-5):\\n\",\n    \"        super().__init__()\\n\",\n    \"\\n\",\n    \"        self.learning_rate = learning_rate\\n\",\n    \"        self.model = model\\n\",\n    \"\\n\",\n    \"        self.val_acc = torchmetrics.Accuracy()\\n\",\n    \"        self.test_acc = torchmetrics.Accuracy()\\n\",\n    \"\\n\",\n    \"    def forward(self, input_ids, attention_mask, labels):\\n\",\n    \"        return self.model(input_ids, attention_mask=attention_mask, labels=labels)\\n\",\n    \"        \\n\",\n    \"    def training_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"train_loss\\\", outputs[\\\"loss\\\"])\\n\",\n    \"        return outputs[\\\"loss\\\"]  # this is passed to the optimizer for training\\n\",\n    \"\\n\",\n    \"    def validation_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        self.log(\\\"val_loss\\\", outputs[\\\"loss\\\"], prog_bar=True)\\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.val_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"val_acc\\\", self.val_acc, prog_bar=True)\\n\",\n    \"        \\n\",\n    \"    def test_step(self, batch, batch_idx):\\n\",\n    \"        outputs = self(batch[\\\"input_ids\\\"], attention_mask=batch[\\\"attention_mask\\\"],\\n\",\n    \"                       labels=batch[\\\"label\\\"])        \\n\",\n    \"        \\n\",\n    \"        logits = outputs[\\\"logits\\\"]\\n\",\n    \"        predicted_labels = torch.argmax(logits, 1)\\n\",\n    \"        self.test_acc(predicted_labels, batch[\\\"label\\\"])\\n\",\n    \"        self.log(\\\"accuracy\\\", self.test_acc, prog_bar=True)\\n\",\n    \"\\n\",\n    \"    def configure_optimizers(self):\\n\",\n    \"        optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\\n\",\n    \"        return optimizer\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"lightning_model = LightningModel(model)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"id\": \"e6dab813-e1fc-47cd-87a1-5eb8070699c6\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from lightning.pytorch.callbacks import ModelCheckpoint\\n\",\n    \"from lightning.pytorch.loggers import CSVLogger\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"callbacks = [\\n\",\n    \"    ModelCheckpoint(\\n\",\n    \"        save_top_k=1, mode=\\\"max\\\", monitor=\\\"val_acc\\\"\\n\",\n    \"    )  # save top 1 model\\n\",\n    \"]\\n\",\n    \"logger = CSVLogger(save_dir=\\\"logs/\\\", name=\\\"my-model\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"id\": \"492aa043-02da-459e-a266-091b34254ac6\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GPU available: True (cuda), used: True\\n\",\n      \"TPU available: False, using: 0 TPU cores\\n\",\n      \"IPU available: False, using: 0 IPUs\\n\",\n      \"HPU available: False, using: 0 HPUs\\n\",\n      \"Missing logger folder: logs/my-model\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"\\n\",\n      \"  | Name     | Type                                | Params\\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"0 | model    | DistilBertForSequenceClassification | 67.0 M\\n\",\n      \"1 | val_acc  | Accuracy                            | 0     \\n\",\n      \"2 | test_acc | Accuracy                            | 0     \\n\",\n      \"-----------------------------------------------------------------\\n\",\n      \"67.0 M    Trainable params\\n\",\n      \"0         Non-trainable params\\n\",\n      \"67.0 M    Total params\\n\",\n      \"267.820   Total estimated model params size (MB)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Sanity Checking: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7845f11c734d4e75adb62c1745de4221\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Training: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Validation: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"`Trainer.fit` stopped: `max_epochs=3` reached.\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"trainer = L.Trainer(\\n\",\n    \"    max_epochs=3,\\n\",\n    \"    callbacks=callbacks,\\n\",\n    \"    accelerator=\\\"gpu\\\",\\n\",\n    \"    devices=[2],\\n\",\n    \"    logger=logger,\\n\",\n    \"    log_every_n_steps=10,\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"trainer.fit(model=lightning_model,\\n\",\n    \"            train_dataloaders=train_loader,\\n\",\n    \"            val_dataloaders=val_loader)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"id\": \"d795778a-70d2-4b04-96fb-598eccbcd1be\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_0/checkpoints/epoch=0-step=2917.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_0/checkpoints/epoch=0-step=2917.ckpt\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:489: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\\n\",\n      \"  rank_zero_warn(\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"339b5923cc2548718600de6ba9c55e60\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9646571278572083     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9646571278572083    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.9646571278572083}]\"\n      ]\n     },\n     \"execution_count\": 29,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=train_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"id\": \"10ca0af1-106e-4ef7-9793-478d580af827\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_0/checkpoints/epoch=0-step=2917.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_0/checkpoints/epoch=0-step=2917.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"08afa00bb1f94fc7a223661f3edb03e4\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9314000010490417     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9314000010490417    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.9314000010490417}]\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=val_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"id\": \"eeb92de4-d483-4627-b9f3-f0bba0cddd9c\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Restoring states from the checkpoint path at logs/my-model/version_0/checkpoints/epoch=0-step=2917.ckpt\\n\",\n      \"LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1,2,3]\\n\",\n      \"Loaded model weights from checkpoint at logs/my-model/version_0/checkpoints/epoch=0-step=2917.ckpt\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"8fde6b80518446b0a73a7803d9ab4630\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"Testing: 0it [00:00, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<pre style=\\\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\\\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃<span style=\\\"font-weight: bold\\\">        Test metric        </span>┃<span style=\\\"font-weight: bold\\\">       DataLoader 0        </span>┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│<span style=\\\"color: #008080; text-decoration-color: #008080\\\">         accuracy          </span>│<span style=\\\"color: #800080; text-decoration-color: #800080\\\">    0.9261999726295471     </span>│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\",\n       \"</pre>\\n\"\n      ],\n      \"text/plain\": [\n       \"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\\n\",\n       \"┃\\u001b[1m \\u001b[0m\\u001b[1m       Test metric       \\u001b[0m\\u001b[1m \\u001b[0m┃\\u001b[1m \\u001b[0m\\u001b[1m      DataLoader 0       \\u001b[0m\\u001b[1m \\u001b[0m┃\\n\",\n       \"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\\n\",\n       \"│\\u001b[36m \\u001b[0m\\u001b[36m        accuracy         \\u001b[0m\\u001b[36m \\u001b[0m│\\u001b[35m \\u001b[0m\\u001b[35m   0.9261999726295471    \\u001b[0m\\u001b[35m \\u001b[0m│\\n\",\n       \"└───────────────────────────┴───────────────────────────┘\\n\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5ebd6bfb20>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\",\n      \"/home/raschka/miniforge3/envs/dl-fundamentals/lib/python3.9/multiprocessing/popen_fork.py:66: ResourceWarning: Unclosed socket <zmq.Socket(zmq.PUSH) at 0x7f5dfb35b340>\\n\",\n      \"  self.pid = os.fork()\\n\",\n      \"ResourceWarning: Enable tracemalloc to get the object allocation traceback\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[{'accuracy': 0.9261999726295471}]\"\n      ]\n     },\n     \"execution_count\": 31,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"trainer.test(lightning_model, dataloaders=test_loader, ckpt_path=\\\"best\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.13\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-basic-with-rf.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.7.3\\n\",\n      \"IPython 7.9.0\\n\",\n      \"\\n\",\n      \"torch 1.3.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Autoencoder (MNIST) + Scikit-Learn Random Forest Classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple, single-hidden-layer, fully-connected autoencoder that compresses 768-pixel MNIST images into 32-pixel vectors (32-times smaller representations). A random forest classifier is then trained for predicting the class labels based on that 32-pixel compressed space.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:0\\n\",\n      \"Image batch dimensions: torch.Size([256, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 123\\n\",\n    \"learning_rate = 0.005\\n\",\n    \"num_epochs = 5\\n\",\n    \"batch_size = 256\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_features = 784\\n\",\n    \"num_hidden_1 = 32\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"class Autoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_features):\\n\",\n    \"        super(Autoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        \\n\",\n    \"        self.linear_1 = torch.nn.Linear(num_features, num_hidden_1)\\n\",\n    \"        # The following to lones are not necessary, \\n\",\n    \"        # but used here to demonstrate how to access the weights\\n\",\n    \"        # and use a different weight initialization.\\n\",\n    \"        # By default, PyTorch uses Xavier/Glorot initialization, which\\n\",\n    \"        # should usually be preferred.\\n\",\n    \"        self.linear_1.weight.detach().normal_(0.0, 0.1)\\n\",\n    \"        self.linear_1.bias.detach().zero_()\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        self.linear_2 = torch.nn.Linear(num_hidden_1, num_features)\\n\",\n    \"        self.linear_1.weight.detach().normal_(0.0, 0.1)\\n\",\n    \"        self.linear_1.bias.detach().zero_()\\n\",\n    \"        \\n\",\n    \"    def encoder(self, x):\\n\",\n    \"        encoded = self.linear_1(x)\\n\",\n    \"        encoded = F.leaky_relu(encoded)\\n\",\n    \"        return encoded\\n\",\n    \"    \\n\",\n    \"    def decoder(self, encoded_x):\\n\",\n    \"        logits = self.linear_2(encoded_x)\\n\",\n    \"        decoded = torch.sigmoid(logits)\\n\",\n    \"        return decoded\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        encoded = self.encoder(x)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        decoded = self.decoder(encoded)\\n\",\n    \"        \\n\",\n    \"        return decoded\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = Autoencoder(num_features=num_features)\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/005 | Batch 000/235 | Cost: 0.7100\\n\",\n      \"Epoch: 001/005 | Batch 050/235 | Cost: 0.2026\\n\",\n      \"Epoch: 001/005 | Batch 100/235 | Cost: 0.1635\\n\",\n      \"Epoch: 001/005 | Batch 150/235 | Cost: 0.1349\\n\",\n      \"Epoch: 001/005 | Batch 200/235 | Cost: 0.1301\\n\",\n      \"Time elapsed: 0.11 min\\n\",\n      \"Epoch: 002/005 | Batch 000/235 | Cost: 0.1238\\n\",\n      \"Epoch: 002/005 | Batch 050/235 | Cost: 0.1131\\n\",\n      \"Epoch: 002/005 | Batch 100/235 | Cost: 0.1099\\n\",\n      \"Epoch: 002/005 | Batch 150/235 | Cost: 0.1062\\n\",\n      \"Epoch: 002/005 | Batch 200/235 | Cost: 0.1035\\n\",\n      \"Time elapsed: 0.22 min\\n\",\n      \"Epoch: 003/005 | Batch 000/235 | Cost: 0.1009\\n\",\n      \"Epoch: 003/005 | Batch 050/235 | Cost: 0.0977\\n\",\n      \"Epoch: 003/005 | Batch 100/235 | Cost: 0.0983\\n\",\n      \"Epoch: 003/005 | Batch 150/235 | Cost: 0.0975\\n\",\n      \"Epoch: 003/005 | Batch 200/235 | Cost: 0.0937\\n\",\n      \"Time elapsed: 0.33 min\\n\",\n      \"Epoch: 004/005 | Batch 000/235 | Cost: 0.0946\\n\",\n      \"Epoch: 004/005 | Batch 050/235 | Cost: 0.0961\\n\",\n      \"Epoch: 004/005 | Batch 100/235 | Cost: 0.0960\\n\",\n      \"Epoch: 004/005 | Batch 150/235 | Cost: 0.0972\\n\",\n      \"Epoch: 004/005 | Batch 200/235 | Cost: 0.0899\\n\",\n      \"Time elapsed: 0.43 min\\n\",\n      \"Epoch: 005/005 | Batch 000/235 | Cost: 0.0948\\n\",\n      \"Epoch: 005/005 | Batch 050/235 | Cost: 0.0927\\n\",\n      \"Epoch: 005/005 | Batch 100/235 | Cost: 0.0932\\n\",\n      \"Epoch: 005/005 | Batch 150/235 | Cost: 0.0938\\n\",\n      \"Epoch: 005/005 | Batch 200/235 | Cost: 0.0935\\n\",\n      \"Time elapsed: 0.54 min\\n\",\n      \"Total Training Time: 0.54 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = features.view(-1, 28*28).to(device)\\n\",\n    \"            \\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        decoded = model(features)\\n\",\n    \"        cost = F.binary_cross_entropy(decoded, features)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Training Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Image batch dimensions: torch.Size([15, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([15])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=15, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\\n\",\n    \"    \\n\",\n    \"# =============================================================\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Image batch dimensions: torch.Size([15, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([15])\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=15, \\n\",\n    \"                         shuffle=True)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in test_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\\n\",\n    \"    \\n\",\n    \"# =============================================================\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Scikit-learn Classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### On Original MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=60000, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                          batch_size=10000, \\n\",\n    \"                          shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Image batch dimensions: torch.Size([60000, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([60000])\\n\",\n      \"Image batch dimensions: torch.Size([10000, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([10000])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"X_train = np.array(images.reshape(60000, 28*28))\\n\",\n    \"y_train = np.array(labels)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for images, labels in test_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"X_test = np.array(images.reshape(10000, 28*28))\\n\",\n    \"y_test = np.array(labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train Accuracy: 100.0%\\n\",\n      \"Test Accuracy: 97.09%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"rf = RandomForestClassifier(n_estimators=500, n_jobs=-1).fit(X_train, y_train)\\n\",\n    \"print(f'Train Accuracy: {rf.score(X_train, y_train)*100}%')\\n\",\n    \"print(f'Test Accuracy: {rf.score(X_test, y_test)*100}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Using PCA\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.decomposition import PCA\\n\",\n    \"\\n\",\n    \"pca = PCA(n_components=32)  # same size as autoencoder latent space\\n\",\n    \"X_train_pca = pca.fit_transform(X_train)\\n\",\n    \"X_test_pca = pca.transform(X_test)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train Accuracy: 100.0%\\n\",\n      \"Test Accuracy: 95.7%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"rf = RandomForestClassifier(n_estimators=500, n_jobs=-1).fit(X_train_pca, y_train)\\n\",\n    \"print(f'Train Accuracy: {rf.score(X_train_pca, y_train)*100}%')\\n\",\n    \"print(f'Test Accuracy: {rf.score(X_test_pca, y_test)*100}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Compressed MNIST\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=1000, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                          batch_size=1000, \\n\",\n    \"                          shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_train_compr = np.ones((60000, num_hidden_1))\\n\",\n    \"y_train = np.ones(60000)\\n\",\n    \"\\n\",\n    \"start_idx = 0\\n\",\n    \"\\n\",\n    \"for idx, (images, labels) in enumerate(train_loader): \\n\",\n    \"    features = images.view(-1, 28*28).to(device)\\n\",\n    \"    decoded = model.encoder(features)\\n\",\n    \"    X_train_compr[start_idx:start_idx+1000] = decoded.to(torch.device('cpu')).detach().numpy()\\n\",\n    \"    y_train[start_idx:start_idx+1000] = labels\\n\",\n    \"    start_idx += 1000\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"X_test_compr = np.ones((10000, num_hidden_1))\\n\",\n    \"y_test = np.ones(10000)\\n\",\n    \"\\n\",\n    \"start_idx = 0\\n\",\n    \"\\n\",\n    \"for idx, (images, labels) in enumerate(test_loader): \\n\",\n    \"    features = images.view(-1, 28*28).to(device)\\n\",\n    \"    decoded = model.encoder(features)\\n\",\n    \"    X_test_compr[start_idx:start_idx+1000] = decoded.to(torch.device('cpu')).detach().numpy()\\n\",\n    \"    y_test[start_idx:start_idx+1000] = labels\\n\",\n    \"    start_idx += 1000\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train Accuracy: 100.0%\\n\",\n      \"Test Accuracy: 95.23%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"rf = RandomForestClassifier(n_estimators=500, n_jobs=-1).fit(X_train_compr, y_train)\\n\",\n    \"print(f'Train Accuracy: {rf.score(X_train_compr, y_train)*100}%')\\n\",\n    \"print(f'Test Accuracy: {rf.score(X_test_compr, y_test)*100}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       1.3.0\\n\",\n      \"torchvision 0.4.1a0+d94043a\\n\",\n      \"numpy       1.17.2\\n\",\n      \"matplotlib  3.1.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-cnn-cvae_no-out-concat.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Convolutional Conditional Variational Autoencoder\\n\",\n    \"\\n\",\n    \"## (without labels in reconstruction loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple convolutional conditional variational autoencoder that compresses 768-pixel MNIST images down to a 50-pixel latent vector representation.\\n\",\n    \"\\n\",\n    \"This implementation DOES NOT concatenate the inputs with the class labels when computing the reconstruction loss in contrast to how it is commonly done in non-convolutional conditional variational autoencoders. Not considering class-labels in the reconstruction loss leads to substantially better results compared to the implementation that does concatenate the labels with the inputs to compute the reconstruction loss. For reference, see the implementation [./autoencoder-cnn-cvae.ipynb](./autoencoder-cnn-cvae.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:1\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:1\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 0\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"num_epochs = 50\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_classes = 10\\n\",\n    \"num_features = 784\\n\",\n    \"num_latent = 50\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def to_onehot(labels, num_classes, device):\\n\",\n    \"\\n\",\n    \"    labels_onehot = torch.zeros(labels.size()[0], num_classes).to(device)\\n\",\n    \"    labels_onehot.scatter_(1, labels.view(-1, 1), 1)\\n\",\n    \"\\n\",\n    \"    return labels_onehot\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConditionalVariationalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_features, num_latent, num_classes):\\n\",\n    \"        super(ConditionalVariationalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        ###############\\n\",\n    \"        # ENCODER\\n\",\n    \"        ##############\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"\\n\",\n    \"        self.enc_conv_1 = torch.nn.Conv2d(in_channels=1+self.num_classes,\\n\",\n    \"                                          out_channels=16,\\n\",\n    \"                                          kernel_size=(6, 6),\\n\",\n    \"                                          stride=(2, 2),\\n\",\n    \"                                          padding=0)\\n\",\n    \"\\n\",\n    \"        self.enc_conv_2 = torch.nn.Conv2d(in_channels=16,\\n\",\n    \"                                          out_channels=32,\\n\",\n    \"                                          kernel_size=(4, 4),\\n\",\n    \"                                          stride=(2, 2),\\n\",\n    \"                                          padding=0)                 \\n\",\n    \"        \\n\",\n    \"        self.enc_conv_3 = torch.nn.Conv2d(in_channels=32,\\n\",\n    \"                                          out_channels=64,\\n\",\n    \"                                          kernel_size=(2, 2),\\n\",\n    \"                                          stride=(2, 2),\\n\",\n    \"                                          padding=0)                     \\n\",\n    \"        \\n\",\n    \"        self.z_mean = torch.nn.Linear(64*2*2, num_latent)\\n\",\n    \"        # in the original paper (Kingma & Welling 2015, we use\\n\",\n    \"        # have a z_mean and z_var, but the problem is that\\n\",\n    \"        # the z_var can be negative, which would cause issues\\n\",\n    \"        # in the log later. Hence we assume that latent vector\\n\",\n    \"        # has a z_mean and z_log_var component, and when we need\\n\",\n    \"        # the regular variance or std_dev, we simply use \\n\",\n    \"        # an exponential function\\n\",\n    \"        self.z_log_var = torch.nn.Linear(64*2*2, num_latent)\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        ###############\\n\",\n    \"        # DECODER\\n\",\n    \"        ##############\\n\",\n    \"        \\n\",\n    \"        self.dec_linear_1 = torch.nn.Linear(num_latent+self.num_classes, 64*2*2)\\n\",\n    \"               \\n\",\n    \"        self.dec_deconv_1 = torch.nn.ConvTranspose2d(in_channels=64,\\n\",\n    \"                                                     out_channels=32,\\n\",\n    \"                                                     kernel_size=(2, 2),\\n\",\n    \"                                                     stride=(2, 2),\\n\",\n    \"                                                     padding=0)\\n\",\n    \"                                 \\n\",\n    \"        self.dec_deconv_2 = torch.nn.ConvTranspose2d(in_channels=32,\\n\",\n    \"                                                     out_channels=16,\\n\",\n    \"                                                     kernel_size=(4, 4),\\n\",\n    \"                                                     stride=(3, 3),\\n\",\n    \"                                                     padding=1)\\n\",\n    \"        \\n\",\n    \"        self.dec_deconv_3 = torch.nn.ConvTranspose2d(in_channels=16,\\n\",\n    \"                                                     out_channels=1,\\n\",\n    \"                                                     kernel_size=(6, 6),\\n\",\n    \"                                                     stride=(3, 3),\\n\",\n    \"                                                     padding=4)        \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    def reparameterize(self, z_mu, z_log_var):\\n\",\n    \"        # Sample epsilon from standard normal distribution\\n\",\n    \"        eps = torch.randn(z_mu.size(0), z_mu.size(1)).to(device)\\n\",\n    \"        # note that log(x^2) = 2*log(x); hence divide by 2 to get std_dev\\n\",\n    \"        # i.e., std_dev = exp(log(std_dev^2)/2) = exp(log(var)/2)\\n\",\n    \"        z = z_mu + eps * torch.exp(z_log_var/2.) \\n\",\n    \"        return z\\n\",\n    \"        \\n\",\n    \"    def encoder(self, features, targets):\\n\",\n    \"        \\n\",\n    \"        ### Add condition\\n\",\n    \"        onehot_targets = to_onehot(targets, self.num_classes, device)\\n\",\n    \"        onehot_targets = onehot_targets.view(-1, self.num_classes, 1, 1)\\n\",\n    \"        \\n\",\n    \"        ones = torch.ones(features.size()[0], \\n\",\n    \"                          self.num_classes,\\n\",\n    \"                          features.size()[2], \\n\",\n    \"                          features.size()[3], \\n\",\n    \"                          dtype=features.dtype).to(device)\\n\",\n    \"        ones = ones * onehot_targets\\n\",\n    \"        x = torch.cat((features, ones), dim=1)\\n\",\n    \"        \\n\",\n    \"        x = self.enc_conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('conv1 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.enc_conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('conv2 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.enc_conv_3(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('conv3 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        z_mean = self.z_mean(x.view(-1, 64*2*2))\\n\",\n    \"        z_log_var = self.z_log_var(x.view(-1, 64*2*2))\\n\",\n    \"        encoded = self.reparameterize(z_mean, z_log_var)\\n\",\n    \"        return z_mean, z_log_var, encoded\\n\",\n    \"    \\n\",\n    \"    def decoder(self, encoded, targets):\\n\",\n    \"        ### Add condition\\n\",\n    \"        onehot_targets = to_onehot(targets, self.num_classes, device)\\n\",\n    \"        encoded = torch.cat((encoded, onehot_targets), dim=1)        \\n\",\n    \"        \\n\",\n    \"        x = self.dec_linear_1(encoded)\\n\",\n    \"        x = x.view(-1, 64, 2, 2)\\n\",\n    \"        \\n\",\n    \"        x = self.dec_deconv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('deconv1 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.dec_deconv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('deconv2 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.dec_deconv_3(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('deconv1 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        decoded = torch.sigmoid(x)\\n\",\n    \"        return decoded\\n\",\n    \"\\n\",\n    \"    def forward(self, features, targets):\\n\",\n    \"        \\n\",\n    \"        z_mean, z_log_var, encoded = self.encoder(features, targets)\\n\",\n    \"        decoded = self.decoder(encoded, targets)\\n\",\n    \"        \\n\",\n    \"        return z_mean, z_log_var, encoded, decoded\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConditionalVariationalAutoencoder(num_features,\\n\",\n    \"                                          num_latent,\\n\",\n    \"                                          num_classes)\\n\",\n    \"model = model.to(device)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### COST AND OPTIMIZER\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/050 | Batch 000/469 | Cost: 69516.4531\\n\",\n      \"Epoch: 001/050 | Batch 050/469 | Cost: 65598.3438\\n\",\n      \"Epoch: 001/050 | Batch 100/469 | Cost: 38653.3398\\n\",\n      \"Epoch: 001/050 | Batch 150/469 | Cost: 31647.0234\\n\",\n      \"Epoch: 001/050 | Batch 200/469 | Cost: 29562.4902\\n\",\n      \"Epoch: 001/050 | Batch 250/469 | Cost: 27448.7969\\n\",\n      \"Epoch: 001/050 | Batch 300/469 | Cost: 27149.4609\\n\",\n      \"Epoch: 001/050 | Batch 350/469 | Cost: 25922.6641\\n\",\n      \"Epoch: 001/050 | Batch 400/469 | Cost: 25652.2539\\n\",\n      \"Epoch: 001/050 | Batch 450/469 | Cost: 25503.6504\\n\",\n      \"Time elapsed: 0.15 min\\n\",\n      \"Epoch: 002/050 | Batch 000/469 | Cost: 25003.4570\\n\",\n      \"Epoch: 002/050 | Batch 050/469 | Cost: 24463.9062\\n\",\n      \"Epoch: 002/050 | 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\"Epoch: 003/050 | Batch 350/469 | Cost: 20510.7891\\n\",\n      \"Epoch: 003/050 | Batch 400/469 | Cost: 21242.7695\\n\",\n      \"Epoch: 003/050 | Batch 450/469 | Cost: 20785.4922\\n\",\n      \"Time elapsed: 0.44 min\\n\",\n      \"Epoch: 004/050 | Batch 000/469 | Cost: 20328.3145\\n\",\n      \"Epoch: 004/050 | Batch 050/469 | Cost: 20273.4453\\n\",\n      \"Epoch: 004/050 | Batch 100/469 | Cost: 20539.0879\\n\",\n      \"Epoch: 004/050 | Batch 150/469 | Cost: 20137.0156\\n\",\n      \"Epoch: 004/050 | Batch 200/469 | Cost: 19641.2148\\n\",\n      \"Epoch: 004/050 | Batch 250/469 | Cost: 20138.8418\\n\",\n      \"Epoch: 004/050 | Batch 300/469 | Cost: 18882.3086\\n\",\n      \"Epoch: 004/050 | Batch 350/469 | Cost: 19263.8516\\n\",\n      \"Epoch: 004/050 | Batch 400/469 | Cost: 19991.0703\\n\",\n      \"Epoch: 004/050 | Batch 450/469 | Cost: 18806.1582\\n\",\n      \"Time elapsed: 0.58 min\\n\",\n      \"Epoch: 005/050 | Batch 000/469 | Cost: 19555.3555\\n\",\n      \"Epoch: 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   \"Epoch: 006/050 | Batch 300/469 | Cost: 17752.4570\\n\",\n      \"Epoch: 006/050 | Batch 350/469 | Cost: 17146.1660\\n\",\n      \"Epoch: 006/050 | Batch 400/469 | Cost: 17564.4121\\n\",\n      \"Epoch: 006/050 | Batch 450/469 | Cost: 16982.0527\\n\",\n      \"Time elapsed: 0.88 min\\n\",\n      \"Epoch: 007/050 | Batch 000/469 | Cost: 17229.5156\\n\",\n      \"Epoch: 007/050 | Batch 050/469 | Cost: 17952.2988\\n\",\n      \"Epoch: 007/050 | Batch 100/469 | Cost: 17679.9102\\n\",\n      \"Epoch: 007/050 | Batch 150/469 | Cost: 16431.1602\\n\",\n      \"Epoch: 007/050 | Batch 200/469 | Cost: 16707.6699\\n\",\n      \"Epoch: 007/050 | Batch 250/469 | Cost: 16626.2344\\n\",\n      \"Epoch: 007/050 | Batch 300/469 | Cost: 17091.8594\\n\",\n      \"Epoch: 007/050 | Batch 350/469 | Cost: 16410.7461\\n\",\n      \"Epoch: 007/050 | Batch 400/469 | Cost: 16464.0039\\n\",\n      \"Epoch: 007/050 | Batch 450/469 | Cost: 17185.2910\\n\",\n      \"Time elapsed: 1.03 min\\n\",\n      \"Epoch: 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   \"Epoch: 035/050 | Batch 300/469 | Cost: 13733.6855\\n\",\n      \"Epoch: 035/050 | Batch 350/469 | Cost: 14387.2490\\n\",\n      \"Epoch: 035/050 | Batch 400/469 | Cost: 14289.1094\\n\",\n      \"Epoch: 035/050 | Batch 450/469 | Cost: 13157.4883\\n\",\n      \"Time elapsed: 5.11 min\\n\",\n      \"Epoch: 036/050 | Batch 000/469 | Cost: 13923.4131\\n\",\n      \"Epoch: 036/050 | Batch 050/469 | Cost: 13152.2998\\n\",\n      \"Epoch: 036/050 | Batch 100/469 | Cost: 13996.1729\\n\",\n      \"Epoch: 036/050 | Batch 150/469 | Cost: 13884.8965\\n\",\n      \"Epoch: 036/050 | Batch 200/469 | Cost: 13887.7607\\n\",\n      \"Epoch: 036/050 | Batch 250/469 | Cost: 13652.5996\\n\",\n      \"Epoch: 036/050 | Batch 300/469 | Cost: 13951.4346\\n\",\n      \"Epoch: 036/050 | Batch 350/469 | Cost: 13787.7617\\n\",\n      \"Epoch: 036/050 | Batch 400/469 | Cost: 14097.5078\\n\",\n      \"Epoch: 036/050 | Batch 450/469 | Cost: 13684.4854\\n\",\n      \"Time elapsed: 5.26 min\\n\",\n      \"Epoch: 037/050 | Batch 000/469 | Cost: 14580.7109\\n\",\n      \"Epoch: 037/050 | Batch 050/469 | Cost: 13706.5557\\n\",\n      \"Epoch: 037/050 | Batch 100/469 | Cost: 14079.7070\\n\",\n      \"Epoch: 037/050 | Batch 150/469 | Cost: 14231.3975\\n\",\n      \"Epoch: 037/050 | Batch 200/469 | Cost: 13724.7275\\n\",\n      \"Epoch: 037/050 | Batch 250/469 | Cost: 14127.0488\\n\",\n      \"Epoch: 037/050 | Batch 300/469 | Cost: 14432.3828\\n\",\n      \"Epoch: 037/050 | Batch 350/469 | Cost: 13770.9668\\n\",\n      \"Epoch: 037/050 | Batch 400/469 | Cost: 14457.6172\\n\",\n      \"Epoch: 037/050 | Batch 450/469 | Cost: 13425.8623\\n\",\n      \"Time elapsed: 5.41 min\\n\",\n      \"Epoch: 038/050 | Batch 000/469 | Cost: 13763.5371\\n\",\n      \"Epoch: 038/050 | Batch 050/469 | Cost: 13891.8945\\n\",\n      \"Epoch: 038/050 | Batch 100/469 | Cost: 13626.1357\\n\",\n      \"Epoch: 038/050 | Batch 150/469 | Cost: 14679.0449\\n\",\n      \"Epoch: 038/050 | Batch 200/469 | Cost: 13221.4004\\n\",\n      \"Epoch: 038/050 | Batch 250/469 | Cost: 13140.2148\\n\",\n      \"Epoch: 038/050 | Batch 300/469 | Cost: 13809.6084\\n\",\n      \"Epoch: 038/050 | Batch 350/469 | Cost: 13575.6592\\n\",\n      \"Epoch: 038/050 | Batch 400/469 | Cost: 14249.9180\\n\",\n      \"Epoch: 038/050 | Batch 450/469 | Cost: 14097.8291\\n\",\n      \"Time elapsed: 5.56 min\\n\",\n      \"Epoch: 039/050 | Batch 000/469 | Cost: 14015.1768\\n\",\n      \"Epoch: 039/050 | Batch 050/469 | Cost: 13973.9795\\n\",\n      \"Epoch: 039/050 | Batch 100/469 | Cost: 13633.8730\\n\",\n      \"Epoch: 039/050 | Batch 150/469 | Cost: 14055.6895\\n\",\n      \"Epoch: 039/050 | Batch 200/469 | Cost: 13871.2949\\n\",\n      \"Epoch: 039/050 | Batch 250/469 | Cost: 13746.9258\\n\",\n      \"Epoch: 039/050 | Batch 300/469 | Cost: 13203.3242\\n\",\n      \"Epoch: 039/050 | Batch 350/469 | Cost: 13911.6846\\n\",\n      \"Epoch: 039/050 | Batch 400/469 | Cost: 14241.5703\\n\",\n      \"Epoch: 039/050 | Batch 450/469 | Cost: 13677.2559\\n\",\n      \"Time elapsed: 5.70 min\\n\",\n      \"Epoch: 040/050 | Batch 000/469 | Cost: 14490.0547\\n\",\n      \"Epoch: 040/050 | Batch 050/469 | Cost: 13689.6680\\n\",\n      \"Epoch: 040/050 | Batch 100/469 | Cost: 14046.6895\\n\",\n      \"Epoch: 040/050 | Batch 150/469 | Cost: 13632.8125\\n\",\n      \"Epoch: 040/050 | Batch 200/469 | Cost: 13456.0918\\n\",\n      \"Epoch: 040/050 | Batch 250/469 | Cost: 13832.4795\\n\",\n      \"Epoch: 040/050 | Batch 300/469 | Cost: 13813.2939\\n\",\n      \"Epoch: 040/050 | Batch 350/469 | Cost: 13484.2520\\n\",\n      \"Epoch: 040/050 | Batch 400/469 | Cost: 13600.7803\\n\",\n      \"Epoch: 040/050 | Batch 450/469 | Cost: 13492.7578\\n\",\n      \"Time elapsed: 5.85 min\\n\",\n      \"Epoch: 041/050 | Batch 000/469 | Cost: 13993.5547\\n\",\n      \"Epoch: 041/050 | Batch 050/469 | Cost: 13833.7031\\n\",\n      \"Epoch: 041/050 | Batch 100/469 | Cost: 13798.5264\\n\",\n      \"Epoch: 041/050 | Batch 150/469 | Cost: 14379.4717\\n\",\n      \"Epoch: 041/050 | Batch 200/469 | Cost: 13919.1445\\n\",\n      \"Epoch: 041/050 | Batch 250/469 | Cost: 13361.4160\\n\",\n      \"Epoch: 041/050 | Batch 300/469 | Cost: 14154.9043\\n\",\n      \"Epoch: 041/050 | Batch 350/469 | Cost: 13858.2715\\n\",\n      \"Epoch: 041/050 | Batch 400/469 | Cost: 14078.7451\\n\",\n      \"Epoch: 041/050 | Batch 450/469 | Cost: 13970.0488\\n\",\n      \"Time elapsed: 6.00 min\\n\",\n      \"Epoch: 042/050 | Batch 000/469 | Cost: 14093.0371\\n\",\n      \"Epoch: 042/050 | Batch 050/469 | Cost: 14073.4688\\n\",\n      \"Epoch: 042/050 | Batch 100/469 | Cost: 13645.2754\\n\",\n      \"Epoch: 042/050 | Batch 150/469 | Cost: 13464.0029\\n\",\n      \"Epoch: 042/050 | Batch 200/469 | Cost: 13615.8643\\n\",\n      \"Epoch: 042/050 | Batch 250/469 | Cost: 13301.9805\\n\",\n      \"Epoch: 042/050 | Batch 300/469 | Cost: 13605.0020\\n\",\n      \"Epoch: 042/050 | Batch 350/469 | Cost: 14035.0498\\n\",\n      \"Epoch: 042/050 | Batch 400/469 | Cost: 13637.4297\\n\",\n      \"Epoch: 042/050 | Batch 450/469 | Cost: 14165.7686\\n\",\n      \"Time elapsed: 6.15 min\\n\",\n      \"Epoch: 043/050 | Batch 000/469 | Cost: 13715.1055\\n\",\n      \"Epoch: 043/050 | Batch 050/469 | Cost: 14122.5898\\n\",\n      \"Epoch: 043/050 | Batch 100/469 | Cost: 14184.3633\\n\",\n      \"Epoch: 043/050 | Batch 150/469 | Cost: 13745.1133\\n\",\n      \"Epoch: 043/050 | Batch 200/469 | Cost: 13448.2559\\n\",\n      \"Epoch: 043/050 | Batch 250/469 | Cost: 13323.3438\\n\",\n      \"Epoch: 043/050 | Batch 300/469 | Cost: 13835.5723\\n\",\n      \"Epoch: 043/050 | Batch 350/469 | Cost: 13462.5098\\n\",\n      \"Epoch: 043/050 | Batch 400/469 | Cost: 14195.2227\\n\",\n      \"Epoch: 043/050 | Batch 450/469 | Cost: 13253.4600\\n\",\n      \"Time elapsed: 6.30 min\\n\",\n      \"Epoch: 044/050 | Batch 000/469 | Cost: 14028.9277\\n\",\n      \"Epoch: 044/050 | Batch 050/469 | Cost: 13369.4111\\n\",\n      \"Epoch: 044/050 | Batch 100/469 | Cost: 13645.9971\\n\",\n      \"Epoch: 044/050 | Batch 150/469 | Cost: 13864.8613\\n\",\n      \"Epoch: 044/050 | Batch 200/469 | Cost: 13508.7471\\n\",\n      \"Epoch: 044/050 | Batch 250/469 | Cost: 14534.7754\\n\",\n      \"Epoch: 044/050 | Batch 300/469 | Cost: 13565.7900\\n\",\n      \"Epoch: 044/050 | Batch 350/469 | Cost: 13719.3438\\n\",\n      \"Epoch: 044/050 | Batch 400/469 | Cost: 13678.1367\\n\",\n      \"Epoch: 044/050 | Batch 450/469 | Cost: 14057.3779\\n\",\n      \"Time elapsed: 6.44 min\\n\",\n      \"Epoch: 045/050 | Batch 000/469 | Cost: 13414.4121\\n\",\n      \"Epoch: 045/050 | Batch 050/469 | Cost: 13531.8555\\n\",\n      \"Epoch: 045/050 | Batch 100/469 | Cost: 13470.2266\\n\",\n      \"Epoch: 045/050 | Batch 150/469 | Cost: 13866.7627\\n\",\n      \"Epoch: 045/050 | Batch 200/469 | Cost: 13438.2832\\n\",\n      \"Epoch: 045/050 | Batch 250/469 | Cost: 14194.3691\\n\",\n      \"Epoch: 045/050 | Batch 300/469 | Cost: 14172.3320\\n\",\n      \"Epoch: 045/050 | Batch 350/469 | Cost: 13798.1680\\n\",\n      \"Epoch: 045/050 | Batch 400/469 | Cost: 13684.1064\\n\",\n      \"Epoch: 045/050 | Batch 450/469 | Cost: 13255.7441\\n\",\n      \"Time elapsed: 6.59 min\\n\",\n      \"Epoch: 046/050 | Batch 000/469 | Cost: 13833.5371\\n\",\n      \"Epoch: 046/050 | Batch 050/469 | Cost: 13982.0898\\n\",\n      \"Epoch: 046/050 | Batch 100/469 | Cost: 13699.0674\\n\",\n      \"Epoch: 046/050 | Batch 150/469 | Cost: 13579.7803\\n\",\n      \"Epoch: 046/050 | Batch 200/469 | Cost: 13611.3682\\n\",\n      \"Epoch: 046/050 | Batch 250/469 | Cost: 14532.4092\\n\",\n      \"Epoch: 046/050 | Batch 300/469 | Cost: 13690.0381\\n\",\n      \"Epoch: 046/050 | Batch 350/469 | Cost: 13886.2227\\n\",\n      \"Epoch: 046/050 | Batch 400/469 | Cost: 13716.4883\\n\",\n      \"Epoch: 046/050 | Batch 450/469 | Cost: 13887.5723\\n\",\n      \"Time elapsed: 6.74 min\\n\",\n      \"Epoch: 047/050 | Batch 000/469 | Cost: 13460.0312\\n\",\n      \"Epoch: 047/050 | Batch 050/469 | Cost: 13862.8320\\n\",\n      \"Epoch: 047/050 | Batch 100/469 | Cost: 13045.7754\\n\",\n      \"Epoch: 047/050 | Batch 150/469 | Cost: 13520.7910\\n\",\n      \"Epoch: 047/050 | Batch 200/469 | Cost: 13966.8848\\n\",\n      \"Epoch: 047/050 | Batch 250/469 | Cost: 14337.5615\\n\",\n      \"Epoch: 047/050 | Batch 300/469 | Cost: 13835.9805\\n\",\n      \"Epoch: 047/050 | Batch 350/469 | Cost: 13705.6699\\n\",\n      \"Epoch: 047/050 | Batch 400/469 | Cost: 14085.0215\\n\",\n      \"Epoch: 047/050 | Batch 450/469 | Cost: 13708.9961\\n\",\n      \"Time elapsed: 6.89 min\\n\",\n      \"Epoch: 048/050 | Batch 000/469 | Cost: 13683.8477\\n\",\n      \"Epoch: 048/050 | Batch 050/469 | Cost: 14290.2441\\n\",\n      \"Epoch: 048/050 | Batch 100/469 | Cost: 13824.9033\\n\",\n      \"Epoch: 048/050 | Batch 150/469 | Cost: 13902.4424\\n\",\n      \"Epoch: 048/050 | Batch 200/469 | Cost: 13742.8066\\n\",\n      \"Epoch: 048/050 | Batch 250/469 | Cost: 13804.6270\\n\",\n      \"Epoch: 048/050 | Batch 300/469 | Cost: 14011.4414\\n\",\n      \"Epoch: 048/050 | Batch 350/469 | Cost: 13902.3428\\n\",\n      \"Epoch: 048/050 | Batch 400/469 | Cost: 13671.2607\\n\",\n      \"Epoch: 048/050 | Batch 450/469 | Cost: 13533.4326\\n\",\n      \"Time elapsed: 7.03 min\\n\",\n      \"Epoch: 049/050 | Batch 000/469 | Cost: 13808.8584\\n\",\n      \"Epoch: 049/050 | Batch 050/469 | Cost: 14385.1328\\n\",\n      \"Epoch: 049/050 | Batch 100/469 | Cost: 13595.7334\\n\",\n      \"Epoch: 049/050 | Batch 150/469 | Cost: 13449.9658\\n\",\n      \"Epoch: 049/050 | Batch 200/469 | Cost: 13782.0635\\n\",\n      \"Epoch: 049/050 | Batch 250/469 | Cost: 13681.0293\\n\",\n      \"Epoch: 049/050 | Batch 300/469 | Cost: 14259.2988\\n\",\n      \"Epoch: 049/050 | Batch 350/469 | Cost: 13350.5176\\n\",\n      \"Epoch: 049/050 | Batch 400/469 | Cost: 12788.5156\\n\",\n      \"Epoch: 049/050 | Batch 450/469 | Cost: 13642.1787\\n\",\n      \"Time elapsed: 7.18 min\\n\",\n      \"Epoch: 050/050 | Batch 000/469 | Cost: 13596.1172\\n\",\n      \"Epoch: 050/050 | Batch 050/469 | Cost: 13988.5371\\n\",\n      \"Epoch: 050/050 | Batch 100/469 | Cost: 14061.5742\\n\",\n      \"Epoch: 050/050 | Batch 150/469 | Cost: 13996.9111\\n\",\n      \"Epoch: 050/050 | Batch 200/469 | Cost: 13628.2070\\n\",\n      \"Epoch: 050/050 | Batch 250/469 | Cost: 13667.3203\\n\",\n      \"Epoch: 050/050 | Batch 300/469 | Cost: 13978.5820\\n\",\n      \"Epoch: 050/050 | Batch 350/469 | Cost: 13589.2910\\n\",\n      \"Epoch: 050/050 | Batch 400/469 | Cost: 13307.0566\\n\",\n      \"Epoch: 050/050 | Batch 450/469 | Cost: 13997.9141\\n\",\n      \"Time elapsed: 7.33 min\\n\",\n      \"Total Training Time: 7.33 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        features = features.to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        z_mean, z_log_var, encoded, decoded = model(features, targets)\\n\",\n    \"\\n\",\n    \"        # cost = reconstruction loss + Kullback-Leibler divergence\\n\",\n    \"        kl_divergence = (0.5 * (z_mean**2 + \\n\",\n    \"                                torch.exp(z_log_var) - z_log_var - 1)).sum()\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        ### Add condition\\n\",\n    \"        # Disabled for reconstruction loss as it gives poor results\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        onehot_targets = to_onehot(targets, num_classes, device)\\n\",\n    \"        onehot_targets = onehot_targets.view(-1, num_classes, 1, 1)\\n\",\n    \"        \\n\",\n    \"        ones = torch.ones(features.size()[0], \\n\",\n    \"                          num_classes,\\n\",\n    \"                          features.size()[2], \\n\",\n    \"                          features.size()[3], \\n\",\n    \"                          dtype=features.dtype).to(device)\\n\",\n    \"        ones = ones * onehot_targets\\n\",\n    \"        x_con = torch.cat((features, ones), dim=1)\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"        \\n\",\n    \"        ### Compute loss\\n\",\n    \"        #pixelwise_bce = F.binary_cross_entropy(decoded, x_con, reduction='sum')\\n\",\n    \"        pixelwise_bce = F.binary_cross_entropy(decoded, features, reduction='sum')\\n\",\n    \"        cost = kl_divergence + pixelwise_bce\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        cost.backward()\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Reconstruction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images, 0]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        cpu_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(cpu_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### New random-conditional images\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 0\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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59unn7mmM0r2aS+fPn23yj+1PzaVFRkRUE1rzarl27GsVl9ft5eXlmm+7hjRs32vVMF4qu6Ho3a9bM5kupT+q7fv362c+Sx1j1QsB6f1FRkeUeS41dtmyZbUZIF5orlHv6wQcf2Hx55ZVXAoln+7Bhw2oU+y0pKanSb5DYSDB06FDbBKJcSFemHMdxHMdxsoSsVKbkBWgXxsSJE221qeKVRx11FBAX5dxaboy8QK285UV26tTJSihUfy0TaOv8008/bXkJ8iSktBx77LHmUUhtadWqlV0TXS/lpnz++eeWT6X3bd68OW1KTTLKp7n33nttR6LaqT465phjLOatInLdu3e3/tHvyRPs1auX2aKdYWVlZTW2rDc0uu46muKyyy4zj//UU08FErH8goIC66/atC8nJ8e8Zn1G586dbSekFLl0K1NTp07lZz/7WZWfyVMcN26cKadSMDp27Gj9qPxFcc0119jvqW8rKyszrhI/9dRTNs8ItenQQw+1fpQ6s2nTJmu/+ie5/IOQYjpy5Ejr24ZSptTGk046yZRgtSknJ8fsUY6NjvUJIdRQ0po3b25evuZalb7o3bu35QkqD6Vfv34ZO8MueQefdnCrb2RDq1at7Pd0v+bm5tYYd5q7Fi5caHmCuu+effZZTjrppIY0pQZSplQ4dN26dabo6qtKrey66661GlsaJ7vvvrupj1JVBw4cyPe///16tGD7SNnXTrzNmzebiij19LDDDgPi8gbVcw5btGhh95nmHZ0TumnTJiuroL/TrVs3Ww+kSlYuprQ9XIdQAhxwwAFAIlynC7a9iVevaxIoKSmxBYZu+EyGGBQyue2222xhpQEiyXPJkiWWdJecKF/ddkmWyQmRSpwcMGCAhRbSgdquAzgvueQSS9hUX+j6H3PMMRx88MFAon+31q/JiystyHTNunTpYnVS0nXO4rx584DEluuysjLbQq226CHVrl27HV4kaJGiheOBBx5okrWS07V4aehke/Xn0qVLLTyrv/nTn/4UiGV0LRJ0TyWXRtCkpWvSunVrWwBrUVxaWpqxc8+SE+mr26i25+Tk2D2bnEhfHb2/srLSXj/rrLOAODxRXxP4tkhe9KhitDYHlJeXW7gjuV8hkXSe/BlQc9GnxVRubq4tGFUHqGfPnnaIfLpR+Gbq1Kk2N1S/75YtW2aL3mQbq6dCaLG8dOlSeyZpM0Hr1q2tj9OF5gOFyzdv3myLO815CvPV9oxL2V9QUGCbIlRaID8/3xad6TozU2VglAwPWMkbLab0nNtaqZ+cnBzbAKHXNQ42bdpk86dqTC5ZsoTTTz8dSH3jhIf5HMdxHMdxUiArlSlV7NU21I4dO9rWXXlOtT1HSCvr5KKQ8vSry9npRF6wihYWFxfX8IzkNQ4bNsxW2fLakz1FfVby+6XcaFv0qaeeaq+no2SAvBtJrJs3bzYbJNtKQh46dKiFNLfWF7oO8ibLysqsoJ5sHz58uIXD0lHBt7y83BQKhWf79etn3r+k6FRUsuSEYYjHiNQ8XUPZ39A2S8nQ2YkQj8vkv33QQQdtNZSs9uueVQi6Q4cO5v0nV9vOFPrbGzZssPuseoXwOXPmWKjgmGOOqfEZyWfc6TOl1ik5v6ioKG1zTps2bcyj//rXvw7EGxqksOirzoTcVtpEddTnGn+QGK+fffZZ2kvN6LpL4ayoqKjRdxqbEyZMMEVXCmplZaV9hpQMlVp5/fXXbZzq2gwcODDt1d+39rzSNZc9dVVX1q9fbxtd1Hdr165Na3mZ5LNKNd/k5+dbH1XfGLG9tul+To5O6bn46KOPAvE9OWjQICARTairCufKlOM4juM4TgpkpTIlD0GlEbbm7SarMVtboep1ebxSgD777DPLD6i+ck0n8ijUvuREVa28te2zb9++tkVVCk5ywUe1X3atXLnSPk9Jpx9++CEHHnhglc9vKJYtW2ZFNXWERcuWLS3P4rTTTgMSidU9evTYqtKYrBRA4mytBx980H4mBWHUqFEW808HX3zxhXnz6ofRo0db4cJU87aiKLJ8iFmzZgGx2ic1RKUX0nWOlhI2P/74Y8vJUyK6VOPtecXqY92bZ599tm13/sMf/gDEJR8yVfRR9+JLL71km1KkoikX5/LLL69SMgBiL1pzltTK5LNBzzzzTCBOXof05Z8I5ahdfPHFAFxwwQU18omUq9i2bdtazYeaa0IINv51DX7+85/zgx/8ANh6XktDoPlU7YJEuQAVzlVbTjjhBFOVkvNqlNMp1Vt5ibNnz7b5VPNv7969075xSXnCUlJmzZplY0mq547eO8r7mjlzpuUTJZNOZSq5pI/GZ15ent1vmvOTNxR81VjV/Sm1cunSpfYZutdnzZplzyMl4Ne1CGtWLqaUnKlQxieffGJVoPUAlqzZuXPnGg+U5ENy9cBTAuGLL75osqHO1qpeeyMd6IGiHRr5+fk2SJRsrlpC3bp1s59t7eGpAa+dcMuWLashw0+ZMsXCag39AH7jjTfsYFD1Q4sWLWqEVzUZbdiwoUpVcIjDD5JkVVdF12r27Nl2rbTgPOigg9J6QOfKlSutBpF21PXv39+k6FRZu3at7SBSqLSystLGbH39ne2hB6QSNqMosvO/dF5dbSfw6sncCqNAYnJbuXKlLbrThSbw6dOnA/GmCdWL0sNKtaVCCDXqulVWVtp41KGssue8886zc+8ysZsWEjao2vn5559vh4ar7/Sgru3iRxW3CwsLLcE9eTet7l0tPhoaOY1PPPEEEM/pP/zhD4HEYl/Vvlu0aFFjrggh2GJC97UcwjVr1lhf/+QnPwHizSDpPhBYiddyqF555RWrz1fnBcCW+bZHjx7W9xovBQUFaV9M6Xmlr6WlpXbqiU670NxXWFhoznRyO6unFey3335AbJf6WPNaRUWF1S9MDlnXqf0pvdtxHMdxHGcnJyuVKa34VTPjvffeszBd8ooV4qQ1rTL1tbi42Fab+iqPZfHixSZ3a8WaidPOJWOKZs2aWXKxZEepUQMGDKiV9J5cC6Z62KywsDBtdn755ZcWFkr2ErR5QEqOFME1a9ZUqTMEcThJ3q0SfqdMmQLE0rSu1U033QQkwhTpYs2aNTWqzHfu3LnWGyO2hWToefPmmd0vvvgiAJdeeqlVrE6XV6z2SCpv3769hU12NAFXYVvZ9cwzz1h/6xqmKyyUjK6vwpZSOSCx4UUKTnIdLNkTRRG33norkFBiNT5HjhxpfZZpVFG/Q4cOdl8q0Te59ENt0Pt32WUXq/l33333AfGcnO7q4KryLhV7t912s7BqsiIFVW1MHpMKESqsrmhIeXm5VXXXM0PXMp1obkku6aP0j7rOO7oWrVu3tvtZ9+eaNWvS/mzU31OkYsOGDVVUa0jU+urWrVuNjQ5bU6iSTwqRjXotPz/fPj/VuduVKcdxHMdxnBTY7lIshNADuBfYFYiAO6IouiWE0Al4EOgJLAK+G0XRmvpolBIHFSNOrk6rVani85s3b7ZVprb5rlixwrwMFY1UUuGYMWMs3p+uvJOtUX17Z15enhWu1BZmeUPb8xblBSovbPny5XadlKdx2mmnpS3xNVklkqcOiZwnKYgLFiwA4rwK2aAtyK1bt7aE0N///vdAou/z8vJMHZH3me5NBK1bt66htKxevdrUNHlDX+XZRVFk16J6kv3HH39slatHjBgBxOUftM0+Xeg+0jlnGzdutP7dUa9V41N5RcuWLbN+u+aaa4BEEmi6eOyxx7jooouARPmUyspKy0HR+Eo+L1NbuOXRTpkyxRQeKRb33HMPkB3n1AnNORdccIGpvJdddhmA5RftKFEUmdKsTRfr169Pa2X+0tJSHnzwQSDxXBg/frz1YfW5trKy0vpQ89Nzzz1nJzRonEr16N+/v0ULVPIkkyj3beLEiabgVD/PtbZo/ikrK7NNFlK7evfunVZlKoRg5Vb0LJw2bZolyauivUoYlJSU1KjMn0xyiQeI86elQuv3W7RoYRGgVAvp1uYJVA78JIqivYFhwI9CCHsDVwLPR1HUF3h+y/8dx3Ecx3F2KrarTEVRtBxYvuX7L0MI7wNFwPHAoVt+7R7gReCK+miUPArtMMnPzzfPXeqEzifq0KGDrUCTz8XSeWkqva8TsYcNG2Yr+0xSvdBmFEU1FDMpH9uK5ep1beHVyv3GG2+0z5UyNXTo0Hq3YVu0adPGtupqF1hpaanl3fzud78DEmpMz549bYeYrsujjz5qxTDl9cumCRMmcO655wKZKWsBsY3HHXcckNj1s3jxYh566CEgsctwr732AmLvWO2XF7l27Vobz++99x6QOI/y3XffNU9J5/xpt2Y6Sc6fgDiXQbvwtNv2q/K3oiiyvEWpNTr3rqKiwnKNTjzxRCB9+Yvqi3feeccUU9nRrFkz886F8qhatGhhhXB1nFAURbYDcfLkyUDCs85G2rRpY7mGmhd3FF2/t99+23KMFBno0qVLWo8EWr9+vSm6yYrTa6+9BiQUR/VRTk6OKeAqDD1+/PgaR6dcf/31QKwEqeRJNqCxmZ+fb8qiIjDa7V5bdG3eeustm4+F5q50opIv3/72t4H4eBmp/a+++iqQuLdatWpl+XDJZY70veYd7eh//fXXLQ9Otg4bNoxRo0bVS9t3KOMqhNAT+AbwGrDrloUWwAriMGC9oMGs5M+ysjKbzFSlVeimgMQ5dO+88449lLWleeTIkUBCIsw0sjE5yVFhA03uumk6duxoCyrd8OvWrbOkeknbCpVUVFTYgkwhsnSGT/bbbz9rywsvvADEdYT0YK5+LlZxcbE9mJJRH8r2q666CojrEykUnCn22GMPqyythdNdd91lCyXVJ1I/Dxo0yJKsJVsvWLCgyoGekAgdDRkyxMasShCkmiBZF6qXASgvL7dJKvkAZognd/VZstOjxbNsFVOmTLGyAelOPFd4Z/LkyTVOBmjfvr19r0WuEps3btxYoxr21VdfXaNWTSY2tdSWZs2a2QL9+OOPB3b8+uu6zJgxg/nz51d5TSH4dFFcXGzPB6UL3HbbbVZaRvOj5s6Kigquu+46IHEv5ubm2iJfzvzo0aOB1MM/9Y0W/aNHj7aEatU3U6297c0Vmm8ee+wxIF6o6B7/xS9+AaSvrEUyGofatHHAAQcwbdo0IDGnqO8uv/zyGqUU2rVrZ4snlUHQ/LN+/Xq7L3XW76RJk+qtkn2t3foQQhvgUeDiKIrWJb8WxbNRtI33nRtCmBNCmKOYbFOjqdvY1O0Dt7Gp0NRtbOr2gdvYVNgZbEymVq5uCKEZ8ULq/iiKHtvy489DCIVRFC0PIRQC/9vae6MougO4A2DIkCFbXXBtC3m+BQUF5l1IUpa60bVrVwtz6XfatGljlZW1Um9IRaouNkrBUEG5adOmWVEyeUayoW3btuYdffDBB0CcxFtd1ZBnUVBQwLhx4wA46aSTqryWDvtycnKs2roScg8//HDuv/9+AB544AEgoQ6Ul5ebZyE7QwiWbH3CCScAsScCDVNkdUdtbN68OaeccoreC8RjTJW85UXJE5o3b57Jz/pZu3btrF9UBkQKx+DBg03xqq9ij3UZpwrd6JoXFxczfvx4ADuHUBW2W7VqZd6/knmLi4vtZ0pQVv+PGDGi3sNBtbVRifWlpaU1SqusXbvWNj9ICdf9mpOTY+Ei3WMXX3xx2s5pS2U+TUZjsLYFV3VtFDqTWjB58mQbI1IVUk2j2FEb161bV2V8QhzikToutSr57FLZ/fOf/xyIw0p63uizGjKFoD76sVOnThx77LEAPPzww0DipILu3bvb/aa5NTnJXGfTKXz/7LPP2nNHEZFU1eJUbFT47oYbbrD0BkVZFLm57LLLrI80j7Rv397WAVIdFR1o374955xzDhBvwoBEKlF9sN3REuK77i7g/SiKfpf00hPA97d8/33g8XprleM4juM4TiOhNsrUgcBY4D8hhLlbfjYB+A3wUAjhbGAx8N36bpxypsaMGWPHk2hVKoXmvffeM49fR8yMGzfOvOZ0H01RW6ofabPrrrta+QblzfzoRz8CYo9eXpVW28loda6EyUMOOcQSwDOVoC3PV8pUUVGReRiXXHIJkDgvsayszDxKeVbdunWzREOsYRILAAAGqUlEQVR5jJmyZVvIux07diwQ50DJU1S8PvlYEik0Sqjs0qWLjXHlgMk7TPVsv/pCCaHKo7jiiitsQ8Bdd90FJHI4oiiqUawxLy+PSZMmAYl8oiOOOAJI/xl1yai8wy677GIKqe6x8vLyKgU5oWrenlTEo48+GshModF0INvXr19vidozZswAEsVyNVcB1s/KR0kXnTt3tkR6qfkhhCr5UJBQnK666ipTzqWqtm3bNqvz3LaFSlAMHz4cSByF07FjR8tl1Nz60Ucf2TXRPSwlPTc319RkbazJJJrr+/TpY/Or8lAnTpwIxBGZ6ue3btq0yebZ6rm2++yzj12nhlCSa7Ob7yVgW6NseP02pyqapMaOHWs7h7SYSh40qv2hB9HYsWOz5mG0Laon2k2YMMHq3WjA6+YuLy+vUf22oKDAKjYrhKmE6Gyc3HNzc20A66se1Mn1lvQ13edepYKut+qVQGKSTqZ6onNjQPed6iVNmDDBwicKHyTvpFFtOMnoAwYMsMk5Ewn020Jzxd13321n8t15551AHKLVZK5Q8xlnnAHEdYayeadefaL0gccff5yXX34ZiK8XVHXqdD+PGTMGSP+9W1RUxMknnwwknJKFCxdaaFa70rSY79+/f0YX8g2Bxqkc1qeffto2ZMlJX7t2rYVkNRfpOtx88802H2fTtWnWrJk50xJGNBfNnTvXNoMkn0Wr31e/aw5r6IT67HL1HcdxHMdxGhnZ4yp+BX379rUQniRLhcByc3NtxZltYaDaoHDDmWeeabV2qqszIQRT2pLPIMomTz8VQgiNSq2pK43ZxkGDBgFxyFWbGhQ20Ths3769hT63dlZWNnLIIYeYmiHlIicnx+oKScGS+pht2+QbEs05w4cPN5VH40Defps2bUwJ0LVKNyEES5rWppCKiopGMwbrA9moeoJdunSxDSy6DnfccYepydokoBIZffr0yfpogO49RXOGDh1aQ+0vKyvLWGSm8a0+HMdxHMdxsohGI20ojqucjKZGbm5uxjw7x6ktLVu2rJIb1hSQIpXucwGzHSkahYWFnHXWWRluzY6R7SpLQyGFpmfPnpZHpZzGk08+2fq0+nmFjZGtRTQymS/sypTjOI7jOE4KNBplynEcx3Gc2iHVpqnk1mY7rkw5juM4juOkgC+mHMdxHMdxUiAkn1fU4H8shC+ADcDKtP3RurMLVdu5exRFBdt7UwjhS+DDBmtV/bLDNjbyPoSmb2Ntx+nOYKPfi9mD34vbYCexsUnfi5DmxRRACGFOFEVD0vpH60Bd29lY7IOmb2Mq7XQbs4emPk6h6dvo47Th3ptOmvo4hbq31cN8juM4juM4KeCLKcdxHMdxnBTIxGLqjgz8zbpQ13Y2Fvug6duYSjvdxuyhqY9TaPo2+jhtuPemk6Y+TqGObU17zpTjOI7jOE5TwsN8juM4juM4KZC2xVQIYUQI4cMQwoIQwpXp+rvbI4TQI4QwPYTw3xDCeyGEi7b8/BchhM9CCHO3/BtVi89yGzNEfdmYrfZB07fRx6nbWO1zmrR9W97jNmaI+rQRgCiKGvwfkAt8DPQCmgPzgL3T8bdr0bZCYN8t37cF5gN7A78ALnUbdx4bs9m+ncFGH6du485in9vYdGzUv3QpU98EFkRRtDCKolLgb8DxafrbX0kURcujKHpry/dfAu8DRXX4KLcxg9STjVlrHzR9G32c7hBN3cambh+4jRmlHm0E0hfmKwKWJv3/U1JodEMRQugJfAN4bcuPLgghvBNC+FMIoeN23u42Zgkp2Ngo7IOmb6OP053exqZuH7iNWUOKNgKegG6EENoAjwIXR1G0DrgN6A0MApYDN2WwefWC2+g2Ngaaun3gNtIEbGzq9oHbyA7YmK7F1GdAj6T/d9/ys6wghNCM+GLeH0XRYwBRFH0eRVFFFEWVwFRiufKrcBszTD3YmNX2QdO30cep27iFpm4fuI0Zp55sBNK3mHoD6BtC2COE0BwYAzyRpr/9lYQQAnAX8H4URb9L+nlh0q+dALy7nY9yGzNIPdmYtfZB07fRx6nhNjZ9+8BtzCj1aGPMjmas1/UfMIo4W/5j4Kp0/d1atOsgIALeAeZu+TcK+Avwny0/fwIodBubvo3Zat/OYKOPU7dxZ7LPbWw6NkZR5BXQHcdxHMdxUsET0B3HcRzHcVLAF1OO4ziO4zgp4Ispx3Ecx3GcFPDFlOM4juM4Tgr4YspxHMdxHCcFfDHlOI7jOI6TAr6YchzHcRzHSQFfTDmO4ziO46TA/wOuvplHH+NmBQAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 1\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 2\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 3\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 4\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 6\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 7\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 9\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(10):\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### RANDOM SAMPLE\\n\",\n    \"    ##########################    \\n\",\n    \"    \\n\",\n    \"    labels = torch.tensor([i]*10).to(device)\\n\",\n    \"    n_images = labels.size()[0]\\n\",\n    \"    rand_features = torch.randn(n_images, num_latent).to(device)\\n\",\n    \"    new_images = model.decoder(rand_features, labels)\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### VISUALIZATION\\n\",\n    \"    ##########################\\n\",\n    \"\\n\",\n    \"    image_width = 28\\n\",\n    \"\\n\",\n    \"    fig, axes = plt.subplots(nrows=1, ncols=n_images, figsize=(10, 2.5), sharey=True)\\n\",\n    \"    decoded_images = new_images[:n_images, 0]\\n\",\n    \"\\n\",\n    \"    print('Class Label %d' % i)\\n\",\n    \"\\n\",\n    \"    for ax, img in zip(axes, decoded_images):\\n\",\n    \"        cpu_img = img.detach().to(torch.device('cpu'))\\n\",\n    \"        ax.imshow(cpu_img.view((image_width, image_width)), cmap='binary')\\n\",\n    \"        \\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-conv-nneighbor-celeba.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"11xi8CRmVA1d\"\n   },\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 119\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 2889,\n     \"status\": \"ok\",\n     \"timestamp\": 1525034072517,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"WuXDfh6UVA1g\",\n    \"outputId\": \"80c92b95-76aa-444e-9aa3-9bcf5d000a6e\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Cii2luqnVA1s\"\n   },\n   \"source\": [\n    \"- Runs on CPU (not recommended here) or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"EYAtjwgyVA1t\"\n   },\n   \"source\": [\n    \"# Model Zoo -- Convolutional Autoencoder with Nearest-neighbor Interpolation (Trained on CelebA)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Ke9a_LDUVA1v\"\n   },\n   \"source\": [\n    \"A convolutional autoencoder using nearest neighbor upscaling layers that compresses 128*128*3=49,152 pixel face images to an intermediate 1000-pixel representation (~50x reduction!). \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"1iZAQwueVA1x\"\n   },\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"CO_0yUH6VA1z\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from PIL import Image\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"from torch.utils.data import Dataset\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"uhGdecraVA1-\"\n   },\n   \"source\": [\n    \"## Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"sxaXYk5cVA2A\"\n   },\n   \"source\": [\n    \"### Downloading the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"T05ELUTiVA2B\"\n   },\n   \"source\": [\n    \"Note that the ~200,000 CelebA face image dataset is relatively large (~1.3 Gb). The download link provided below was provided by the author on the official CelebA website at http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html. \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"1) Download and unzip the file `img_align_celeba.zip`, which contains the images in jpeg format.\\n\",\n    \"\\n\",\n    \"2) Download the `list_eval_partition.txt` file, which contains  training/validation/test partitioning info\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Preparing the Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Partition</th>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>Filename</th>\\n\",\n       \"      <th></th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>000001.jpg</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>000002.jpg</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>000003.jpg</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>000004.jpg</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>000005.jpg</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            Partition\\n\",\n       \"Filename             \\n\",\n       \"000001.jpg          0\\n\",\n       \"000002.jpg          0\\n\",\n       \"000003.jpg          0\\n\",\n       \"000004.jpg          0\\n\",\n       \"000005.jpg          0\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"df2 = pd.read_csv('list_eval_partition.txt', sep=\\\"\\\\s+\\\", skiprows=0, header=None)\\n\",\n    \"df2.columns = ['Filename', 'Partition']\\n\",\n    \"df2 = df2.set_index('Filename')\\n\",\n    \"\\n\",\n    \"df2.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"df2.loc[df2['Partition'] == 0].to_csv('celeba-train.csv')\\n\",\n    \"df2.loc[df2['Partition'] == 1].to_csv('celeba-valid.csv')\\n\",\n    \"df2.loc[df2['Partition'] == 2].to_csv('celeba-test.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(218, 178, 3)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"img = Image.open('img_align_celeba/000001.jpg')\\n\",\n    \"print(np.asarray(img, dtype=np.uint8).shape)\\n\",\n    \"plt.imshow(img);\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Z1zF9mPwVA2P\"\n   },\n   \"source\": [\n    \"### Create a Custom Data Loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"fV2cRUDx7mqz\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class CelebaDataset(Dataset):\\n\",\n    \"    \\\"\\\"\\\"Custom Dataset for loading CelebA face images\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, csv_path, img_dir, transform=None):\\n\",\n    \"    \\n\",\n    \"        df = pd.read_csv(csv_path, index_col=0)\\n\",\n    \"        self.img_dir = img_dir\\n\",\n    \"        self.csv_path = csv_path\\n\",\n    \"        self.img_names = df.index.values\\n\",\n    \"        self.transform = transform\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        img = Image.open(os.path.join(self.img_dir,\\n\",\n    \"                                      self.img_names[index]))\\n\",\n    \"        \\n\",\n    \"        if self.transform is not None:\\n\",\n    \"            img = self.transform(img)\\n\",\n    \"        \\n\",\n    \"        return img\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.img_names.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Note that transforms.ToTensor()\\n\",\n    \"# already divides pixels by 255. internally\\n\",\n    \"\\n\",\n    \"custom_transform = transforms.Compose([transforms.CenterCrop((178, 178)),\\n\",\n    \"                                       transforms.Resize((128, 128)),\\n\",\n    \"                                       #transforms.Grayscale(),                                       \\n\",\n    \"                                       #transforms.Lambda(lambda x: x/255.),\\n\",\n    \"                                       transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"train_dataset = CelebaDataset(csv_path='celeba-gender-train.csv',\\n\",\n    \"                              img_dir='img_align_celeba/',\\n\",\n    \"                              transform=custom_transform)\\n\",\n    \"\\n\",\n    \"BATCH_SIZE=128\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset,\\n\",\n    \"                          batch_size=BATCH_SIZE,\\n\",\n    \"                          shuffle=True,\\n\",\n    \"                          num_workers=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"wHRUkZFFVA2T\"\n   },\n   \"source\": [\n    \"## Settings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 34\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 330,\n     \"status\": \"ok\",\n     \"timestamp\": 1525034084237,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"KthquBjBVA2V\",\n    \"outputId\": \"4014037d-f8b6-4dcc-e6ec-9db4e4cd38fd\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:2\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 123\\n\",\n    \"learning_rate = 1e-4\\n\",\n    \"num_epochs = 20\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"pnSfNaJrVA2Z\"\n   },\n   \"source\": [\n    \"### Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"aKDo_CM9-5eL\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class AutoEncoder(nn.Module):\\n\",\n    \"    def __init__(self, in_channels, dec_channels, latent_size):\\n\",\n    \"        super(AutoEncoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        self.in_channels = in_channels\\n\",\n    \"        self.dec_channels = dec_channels\\n\",\n    \"        self.latent_size = latent_size\\n\",\n    \"\\n\",\n    \"        ###############\\n\",\n    \"        # ENCODER\\n\",\n    \"        ##############\\n\",\n    \"        self.e_conv_1 = nn.Conv2d(in_channels, dec_channels, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(2, 2), padding=1)\\n\",\n    \"        self.e_bn_1 = nn.BatchNorm2d(dec_channels)\\n\",\n    \"\\n\",\n    \"        self.e_conv_2 = nn.Conv2d(dec_channels, dec_channels*2, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(2, 2), padding=1)\\n\",\n    \"        self.e_bn_2 = nn.BatchNorm2d(dec_channels*2)\\n\",\n    \"\\n\",\n    \"        self.e_conv_3 = nn.Conv2d(dec_channels*2, dec_channels*4, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(2, 2), padding=1)\\n\",\n    \"        self.e_bn_3 = nn.BatchNorm2d(dec_channels*4)\\n\",\n    \"\\n\",\n    \"        self.e_conv_4 = nn.Conv2d(dec_channels*4, dec_channels*8, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(2, 2), padding=1)\\n\",\n    \"        self.e_bn_4 = nn.BatchNorm2d(dec_channels*8)\\n\",\n    \"\\n\",\n    \"        self.e_conv_5 = nn.Conv2d(dec_channels*8, dec_channels*16, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(2, 2), padding=1)\\n\",\n    \"        self.e_bn_5 = nn.BatchNorm2d(dec_channels*16)\\n\",\n    \"       \\n\",\n    \"        self.e_fc_1 = nn.Linear(dec_channels*16*4*4, latent_size)\\n\",\n    \"\\n\",\n    \"        ###############\\n\",\n    \"        # DECODER\\n\",\n    \"        ##############\\n\",\n    \"        \\n\",\n    \"        self.d_fc_1 = nn.Linear(latent_size, dec_channels*16*4*4)\\n\",\n    \"\\n\",\n    \"        self.d_conv_1 = nn.Conv2d(dec_channels*16, dec_channels*8, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(1, 1), padding=0)\\n\",\n    \"        self.d_bn_1 = nn.BatchNorm2d(dec_channels*8)\\n\",\n    \"\\n\",\n    \"        self.d_conv_2 = nn.Conv2d(dec_channels*8, dec_channels*4, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(1, 1), padding=0)\\n\",\n    \"        self.d_bn_2 = nn.BatchNorm2d(dec_channels*4)\\n\",\n    \"\\n\",\n    \"        self.d_conv_3 = nn.Conv2d(dec_channels*4, dec_channels*2, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(1, 1), padding=0)\\n\",\n    \"        self.d_bn_3 = nn.BatchNorm2d(dec_channels*2)\\n\",\n    \"\\n\",\n    \"        self.d_conv_4 = nn.Conv2d(dec_channels*2, dec_channels, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(1, 1), padding=0)\\n\",\n    \"        self.d_bn_4 = nn.BatchNorm2d(dec_channels)\\n\",\n    \"        \\n\",\n    \"        self.d_conv_5 = nn.Conv2d(dec_channels, in_channels, \\n\",\n    \"                                  kernel_size=(4, 4), stride=(1, 1), padding=0)\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        # Reinitialize weights using He initialization\\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, torch.nn.Conv2d):\\n\",\n    \"                nn.init.kaiming_normal_(m.weight.detach())\\n\",\n    \"                m.bias.detach().zero_()\\n\",\n    \"            elif isinstance(m, torch.nn.ConvTranspose2d):\\n\",\n    \"                nn.init.kaiming_normal_(m.weight.detach())\\n\",\n    \"                m.bias.detach().zero_()\\n\",\n    \"            elif isinstance(m, torch.nn.Linear):\\n\",\n    \"                nn.init.kaiming_normal_(m.weight.detach())\\n\",\n    \"                m.bias.detach().zero_()\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    def encode(self, x):\\n\",\n    \"        \\n\",\n    \"        #h1\\n\",\n    \"        x = self.e_conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True)\\n\",\n    \"        x = self.e_bn_1(x)\\n\",\n    \"        \\n\",\n    \"        #h2\\n\",\n    \"        x = self.e_conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True)    \\n\",\n    \"        x = self.e_bn_2(x)     \\n\",\n    \"\\n\",\n    \"        #h3\\n\",\n    \"        x = self.e_conv_3(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True) \\n\",\n    \"        x = self.e_bn_3(x)\\n\",\n    \"        \\n\",\n    \"        #h4\\n\",\n    \"        x = self.e_conv_4(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True) \\n\",\n    \"        x = self.e_bn_4(x)\\n\",\n    \"        \\n\",\n    \"        #h5\\n\",\n    \"        x = self.e_conv_5(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True) \\n\",\n    \"        x = self.e_bn_5(x)        \\n\",\n    \"        \\n\",\n    \"        #fc\\n\",\n    \"        x = x.view(-1, self.dec_channels*16*4*4)\\n\",\n    \"        x = self.e_fc_1(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    def decode(self, x):\\n\",\n    \"        \\n\",\n    \"        # h1\\n\",\n    \"        #x = x.view(-1, self.latent_size, 1, 1)\\n\",\n    \"        x = self.d_fc_1(x)\\n\",\n    \"        \\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True)  \\n\",\n    \"        x = x.view(-1, self.dec_channels*16, 4, 4) \\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"        # h2\\n\",\n    \"        x = F.interpolate(x, scale_factor=2)\\n\",\n    \"        x = F.pad(x, pad=(2, 1, 2, 1), mode='replicate')\\n\",\n    \"        x = self.d_conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True)\\n\",\n    \"        x = self.d_bn_1(x)\\n\",\n    \"        \\n\",\n    \"        # h3\\n\",\n    \"        x = F.interpolate(x, scale_factor=2)\\n\",\n    \"        x = F.pad(x, pad=(2, 1, 2, 1), mode='replicate')\\n\",\n    \"        x = self.d_conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True)\\n\",\n    \"        x = self.d_bn_2(x)\\n\",\n    \"        \\n\",\n    \"        # h4\\n\",\n    \"        x = F.interpolate(x, scale_factor=2)\\n\",\n    \"        x = F.pad(x, pad=(2, 1, 2, 1), mode='replicate')\\n\",\n    \"        x = self.d_conv_3(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True)\\n\",\n    \"        x = self.d_bn_3(x)  \\n\",\n    \"\\n\",\n    \"        # h5\\n\",\n    \"        x = F.interpolate(x, scale_factor=2)\\n\",\n    \"        x = F.pad(x, pad=(2, 1, 2, 1), mode='replicate')\\n\",\n    \"        x = self.d_conv_4(x)\\n\",\n    \"        x = F.leaky_relu(x, negative_slope=0.2, inplace=True)\\n\",\n    \"        x = self.d_bn_4(x)\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        # out\\n\",\n    \"        x = F.interpolate(x, scale_factor=2)\\n\",\n    \"        x = F.pad(x, pad=(2, 1, 2, 1), mode='replicate')\\n\",\n    \"        x = self.d_conv_5(x)\\n\",\n    \"        x = torch.sigmoid(x)\\n\",\n    \"        \\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        z = self.encode(x)\\n\",\n    \"        decoded = self.decode(z)\\n\",\n    \"        return z, decoded\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"PfAT3P8_VA2e\"\n   },\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 1224\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 10453399,\n     \"status\": \"ok\",\n     \"timestamp\": 1525044538453,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"rbZ8ploO_JW2\",\n    \"outputId\": \"42e24455-31a2-425b-fea4-599e7cc144d3\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Load autoencoder_i_20_cuda:2.pt\\n\",\n      \"Total Training Time: 0.00 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### TRAINING\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"epoch_start = 1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = AutoEncoder(in_channels=3, dec_channels=32, latent_size=1000)\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"################## Load previous\\n\",\n    \"# the code saves the autoencoder\\n\",\n    \"# after each epoch so that in case\\n\",\n    \"# the training process gets interrupted,\\n\",\n    \"# we will not have to start training it\\n\",\n    \"# from scratch\\n\",\n    \"files = os.listdir()\\n\",\n    \"\\n\",\n    \"for f in files:\\n\",\n    \"    if f.startswith('autoencoder_i_') and f.endswith('.pt'):\\n\",\n    \"        print('Load', f)\\n\",\n    \"        epoch_start = int(f.split('_')[-2]) + 1\\n\",\n    \"        model.load_state_dict(torch.load(f))\\n\",\n    \"        break\\n\",\n    \"##################\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(epoch_start, num_epochs+1):\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    for batch_idx, features in enumerate(train_loader):\\n\",\n    \"\\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = features.to(device)\\n\",\n    \"        \\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        latent_vector, decoded = model(features)\\n\",\n    \"        cost = F.mse_loss(decoded, features)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 500:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' \\n\",\n    \"                   %(epoch, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"\\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"        \\n\",\n    \"    # Save model\\n\",\n    \"    if os.path.isfile('autoencoder_i_%d_%s.pt' % (epoch-1, device)):\\n\",\n    \"        os.remove('autoencoder_i_%d_%s.pt' % (epoch-1, device))\\n\",\n    \"    torch.save(model.state_dict(), 'autoencoder_i_%d_%s.pt' % (epoch, device))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"OBO9L5FnVA2h\"\n   },\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 323\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 3782,\n     \"status\": \"ok\",\n     \"timestamp\": 1525044542253,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"UpJLf9FnVqSw\",\n    \"outputId\": \"121c6c55-6171-4b1b-c6ea-5a199abb4bc5\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x360 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"model = AutoEncoder(in_channels=3, dec_channels=32, latent_size=1000)\\n\",\n    \"model = model.to(device)\\n\",\n    \"model.load_state_dict(torch.load('autoencoder_i_20_%s.pt' % device))\\n\",\n    \"model.eval()\\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"\\n\",\n    \"for batch_idx, features in enumerate(train_loader):\\n\",\n    \"    features = features.to(device)\\n\",\n    \"    logits, decoded = model(features)\\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 5\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(18, 5))\\n\",\n    \"orig_images = features.detach().cpu().numpy()[:n_images]\\n\",\n    \"orig_images = np.moveaxis(orig_images, 1, -1)\\n\",\n    \"\\n\",\n    \"decoded_images = decoded.detach().cpu().numpy()[:n_images]\\n\",\n    \"decoded_images = np.moveaxis(decoded_images, 1, -1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        ax[i].axis('off')\\n\",\n    \"        ax[i].imshow(img[i])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"pandas      0.23.4\\n\",\n      \"PIL.Image   5.3.0\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"default_view\": {},\n   \"name\": \"autoencoder-conv-2.ipynb\",\n   \"provenance\": [],\n   \"version\": \"0.3.2\",\n   \"views\": {}\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-conv-nneighbor-quickdraw-1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"11xi8CRmVA1d\"\n   },\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 119\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 2889,\n     \"status\": \"ok\",\n     \"timestamp\": 1525034072517,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"WuXDfh6UVA1g\",\n    \"outputId\": \"80c92b95-76aa-444e-9aa3-9bcf5d000a6e\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Cii2luqnVA1s\"\n   },\n   \"source\": [\n    \"- Runs on CPU (not recommended here) or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"EYAtjwgyVA1t\"\n   },\n   \"source\": [\n    \"# Model Zoo -- Convolutional Autoencoder with Nearest-neighbor Interpolation (Trained on 10 categories of the Quickdraw dataset)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Ke9a_LDUVA1v\"\n   },\n   \"source\": [\n    \"A convolutional autoencoder using nearest neighbor upscaling layers that compresses 768-pixel Quickdraw images down to a 7x7x8 (392 pixel) representation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"1iZAQwueVA1x\"\n   },\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"CO_0yUH6VA1z\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"from PIL import Image\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"\\n\",\n    \"from torch.utils.data import Dataset\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"uhGdecraVA1-\"\n   },\n   \"source\": [\n    \"## Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook is based on Google's Quickdraw dataset (https://quickdraw.withgoogle.com). In particular we will be working with an arbitrary subset of 10 categories in png format:\\n\",\n    \"\\n\",\n    \"    label_dict = {\\n\",\n    \"             \\\"lollipop\\\": 0,\\n\",\n    \"             \\\"binoculars\\\": 1,\\n\",\n    \"             \\\"mouse\\\": 2,\\n\",\n    \"             \\\"basket\\\": 3,\\n\",\n    \"             \\\"penguin\\\": 4,\\n\",\n    \"             \\\"washing machine\\\": 5,\\n\",\n    \"             \\\"canoe\\\": 6,\\n\",\n    \"             \\\"eyeglasses\\\": 7,\\n\",\n    \"             \\\"beach\\\": 8,\\n\",\n    \"             \\\"screwdriver\\\": 9,\\n\",\n    \"    }\\n\",\n    \"    \\n\",\n    \"(The class labels 0-9 can be ignored in this notebook). \\n\",\n    \"\\n\",\n    \"For more details on obtaining and preparing the dataset, please see the\\n\",\n    \"\\n\",\n    \"- [custom-data-loader-quickdraw.ipynb](custom-data-loader-quickdraw.ipynb)\\n\",\n    \"\\n\",\n    \"notebook.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"(28, 28)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"df = pd.read_csv('quickdraw_png_set1_train.csv', index_col=0)\\n\",\n    \"df.head()\\n\",\n    \"\\n\",\n    \"main_dir = 'quickdraw-png_set1/'\\n\",\n    \"\\n\",\n    \"img = Image.open(os.path.join(main_dir, df.index[99]))\\n\",\n    \"img = np.asarray(img, dtype=np.uint8)\\n\",\n    \"print(img.shape)\\n\",\n    \"plt.imshow(np.array(img), cmap='binary')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"Z1zF9mPwVA2P\"\n   },\n   \"source\": [\n    \"### Create a Custom Data Loader\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"fV2cRUDx7mqz\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"class QuickdrawDataset(Dataset):\\n\",\n    \"    \\\"\\\"\\\"Custom Dataset for loading Quickdraw images\\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, txt_path, img_dir, transform=None):\\n\",\n    \"    \\n\",\n    \"        df = pd.read_csv(txt_path, sep=\\\",\\\", index_col=0)\\n\",\n    \"        self.img_dir = img_dir\\n\",\n    \"        self.txt_path = txt_path\\n\",\n    \"        self.img_names = df.index.values\\n\",\n    \"        self.y = df['Label'].values\\n\",\n    \"        self.transform = transform\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        img = Image.open(os.path.join(self.img_dir,\\n\",\n    \"                                      self.img_names[index]))\\n\",\n    \"        \\n\",\n    \"        if self.transform is not None:\\n\",\n    \"            img = self.transform(img)\\n\",\n    \"        \\n\",\n    \"        label = self.y[index]\\n\",\n    \"        return img, label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.y.shape[0]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# Note that transforms.ToTensor()\\n\",\n    \"# already divides pixels by 255. internally\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"BATCH_SIZE = 128\\n\",\n    \"\\n\",\n    \"custom_transform = transforms.Compose([#transforms.Lambda(lambda x: x/255.),\\n\",\n    \"                                       transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"train_dataset = QuickdrawDataset(txt_path='quickdraw_png_set1_train.csv',\\n\",\n    \"                                 img_dir='quickdraw-png_set1/',\\n\",\n    \"                                 transform=custom_transform)\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset,\\n\",\n    \"                          batch_size=BATCH_SIZE,\\n\",\n    \"                          shuffle=True,\\n\",\n    \"                          num_workers=4) \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 1 | Batch index: 0 | Batch size: 128\\n\",\n      \"Epoch: 2 | Batch index: 0 | Batch size: 128\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"device = torch.device(\\\"cuda:3\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"torch.manual_seed(0)\\n\",\n    \"\\n\",\n    \"num_epochs = 2\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"\\n\",\n    \"    for batch_idx, (x, y) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        print('Epoch:', epoch+1, end='')\\n\",\n    \"        print(' | Batch index:', batch_idx, end='')\\n\",\n    \"        print(' | Batch size:', y.size()[0])\\n\",\n    \"        \\n\",\n    \"        x = x.to(device)\\n\",\n    \"        y = y.to(device)\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"wHRUkZFFVA2T\"\n   },\n   \"source\": [\n    \"## Settings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 34\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 330,\n     \"status\": \"ok\",\n     \"timestamp\": 1525034084237,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"KthquBjBVA2V\",\n    \"outputId\": \"4014037d-f8b6-4dcc-e6ec-9db4e4cd38fd\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:3\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:3\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 123\\n\",\n    \"learning_rate = 0.0005\\n\",\n    \"num_epochs = 50\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"pnSfNaJrVA2Z\"\n   },\n   \"source\": [\n    \"### Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"aKDo_CM9-5eL\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Autoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(Autoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        \\n\",\n    \"        # 28x28x1 => 28x28x4\\n\",\n    \"        self.conv_1 = torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)\\n\",\n    \"        # 28x28x4 => 14x14x4                              \\n\",\n    \"        self.pool_1 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(14-1) - 28 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)                                       \\n\",\n    \"        # 14x14x4 => 14x14x8\\n\",\n    \"        self.conv_2 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=8,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(14-1) - 14 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)                 \\n\",\n    \"        # 14x14x8 => 7x7x8                             \\n\",\n    \"        self.pool_2 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(7-1) - 14 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"                                         \\n\",\n    \"        # 7x7x8 => 14x14x8                          \\n\",\n    \"        \\n\",\n    \"        ## interpolation\\n\",\n    \"        \\n\",\n    \"        # 14x14x8 => 14x14x8\\n\",\n    \"        self.conv_3 = torch.nn.Conv2d(in_channels=8,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(14-1) - 14 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)\\n\",\n    \"        # 14x14x4 => 28x28x4                            \\n\",\n    \"\\n\",\n    \"        ## interpolation\\n\",\n    \"        \\n\",\n    \"        # 28x28x4 => 28x28x1\\n\",\n    \"        self.conv_4 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=1,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        x = self.conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_1(x)\\n\",\n    \"        x = self.conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_2(x)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        x = F.interpolate(x, scale_factor=2, mode='nearest')\\n\",\n    \"        x = self.conv_3(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = F.interpolate(x, scale_factor=2, mode='nearest')\\n\",\n    \"        x = self.conv_4(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = torch.sigmoid(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = Autoencoder()\\n\",\n    \"model = model.to(device)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### COST AND OPTIMIZER\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"cost_fn = torch.nn.BCELoss() # torch.nn.MSELoss()\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"PfAT3P8_VA2e\"\n   },\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 1224\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 10453399,\n     \"status\": \"ok\",\n     \"timestamp\": 1525044538453,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"rbZ8ploO_JW2\",\n    \"outputId\": \"42e24455-31a2-425b-fea4-599e7cc144d3\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/050 | Batch 0000/8290 | Cost: 0.2212\\n\",\n      \"Epoch: 001/050 | Batch 0500/8290 | Cost: 0.0968\\n\",\n      \"Epoch: 001/050 | Batch 1000/8290 | Cost: 0.0681\\n\",\n      \"Epoch: 001/050 | Batch 1500/8290 | Cost: 0.0658\\n\",\n      \"Epoch: 001/050 | Batch 2000/8290 | Cost: 0.0531\\n\",\n      \"Epoch: 001/050 | Batch 2500/8290 | Cost: 0.0411\\n\",\n      \"Epoch: 001/050 | Batch 3000/8290 | Cost: 0.0417\\n\",\n      \"Epoch: 001/050 | Batch 3500/8290 | Cost: 0.0363\\n\",\n      \"Epoch: 001/050 | Batch 4000/8290 | Cost: 0.0338\\n\",\n      \"Epoch: 001/050 | Batch 4500/8290 | Cost: 0.0352\\n\",\n      \"Epoch: 001/050 | Batch 5000/8290 | Cost: 0.0363\\n\",\n      \"Epoch: 001/050 | Batch 5500/8290 | Cost: 0.0334\\n\",\n      \"Epoch: 001/050 | Batch 6000/8290 | Cost: 0.0346\\n\",\n      \"Epoch: 001/050 | Batch 6500/8290 | Cost: 0.0299\\n\",\n      \"Epoch: 001/050 | Batch 7000/8290 | Cost: 0.0320\\n\",\n      \"Epoch: 001/050 | Batch 7500/8290 | Cost: 0.0288\\n\",\n      \"Epoch: 001/050 | Batch 8000/8290 | Cost: 0.0288\\n\",\n      \"Time elapsed: 1.08 min\\n\",\n      \"Epoch: 002/050 | Batch 0000/8290 | Cost: 0.0308\\n\",\n      \"Epoch: 002/050 | Batch 0500/8290 | Cost: 0.0291\\n\",\n      \"Epoch: 002/050 | Batch 1000/8290 | Cost: 0.0292\\n\",\n      \"Epoch: 002/050 | Batch 1500/8290 | Cost: 0.0291\\n\",\n      \"Epoch: 002/050 | Batch 2000/8290 | Cost: 0.0259\\n\",\n      \"Epoch: 002/050 | Batch 2500/8290 | Cost: 0.0274\\n\",\n      \"Epoch: 002/050 | Batch 3000/8290 | Cost: 0.0257\\n\",\n      \"Epoch: 002/050 | Batch 3500/8290 | Cost: 0.0262\\n\",\n      \"Epoch: 002/050 | Batch 4000/8290 | Cost: 0.0261\\n\",\n      \"Epoch: 002/050 | Batch 4500/8290 | Cost: 0.0281\\n\",\n      \"Epoch: 002/050 | Batch 5000/8290 | Cost: 0.0238\\n\",\n      \"Epoch: 002/050 | Batch 5500/8290 | Cost: 0.0236\\n\",\n      \"Epoch: 002/050 | Batch 6000/8290 | Cost: 0.0234\\n\",\n      \"Epoch: 002/050 | Batch 6500/8290 | Cost: 0.0219\\n\",\n      \"Epoch: 002/050 | Batch 7000/8290 | Cost: 0.0225\\n\",\n      \"Epoch: 002/050 | Batch 7500/8290 | Cost: 0.0237\\n\",\n      \"Epoch: 002/050 | Batch 8000/8290 | Cost: 0.0237\\n\",\n      \"Time elapsed: 2.01 min\\n\",\n      \"Epoch: 003/050 | Batch 0000/8290 | Cost: 0.0263\\n\",\n      \"Epoch: 003/050 | Batch 0500/8290 | Cost: 0.0231\\n\",\n      \"Epoch: 003/050 | Batch 1000/8290 | Cost: 0.0231\\n\",\n      \"Epoch: 003/050 | Batch 1500/8290 | Cost: 0.0238\\n\",\n      \"Epoch: 003/050 | Batch 2000/8290 | Cost: 0.0235\\n\",\n      \"Epoch: 003/050 | Batch 2500/8290 | Cost: 0.0218\\n\",\n      \"Epoch: 003/050 | Batch 3000/8290 | Cost: 0.0214\\n\",\n      \"Epoch: 003/050 | Batch 3500/8290 | Cost: 0.0223\\n\",\n      \"Epoch: 003/050 | Batch 4000/8290 | Cost: 0.0230\\n\",\n      \"Epoch: 003/050 | Batch 4500/8290 | Cost: 0.0231\\n\",\n      \"Epoch: 003/050 | Batch 5000/8290 | Cost: 0.0222\\n\",\n      \"Epoch: 003/050 | Batch 5500/8290 | Cost: 0.0240\\n\",\n      \"Epoch: 003/050 | Batch 6000/8290 | Cost: 0.0224\\n\",\n      \"Epoch: 003/050 | Batch 6500/8290 | Cost: 0.0221\\n\",\n      \"Epoch: 003/050 | Batch 7000/8290 | Cost: 0.0225\\n\",\n      \"Epoch: 003/050 | Batch 7500/8290 | Cost: 0.0223\\n\",\n      \"Epoch: 003/050 | Batch 8000/8290 | Cost: 0.0210\\n\",\n      \"Time elapsed: 2.95 min\\n\",\n      \"Epoch: 004/050 | Batch 0000/8290 | Cost: 0.0215\\n\",\n      \"Epoch: 004/050 | Batch 0500/8290 | Cost: 0.0225\\n\",\n      \"Epoch: 004/050 | Batch 1000/8290 | Cost: 0.0235\\n\",\n      \"Epoch: 004/050 | Batch 1500/8290 | Cost: 0.0230\\n\",\n      \"Epoch: 004/050 | Batch 2000/8290 | Cost: 0.0218\\n\",\n      \"Epoch: 004/050 | Batch 2500/8290 | Cost: 0.0228\\n\",\n      \"Epoch: 004/050 | Batch 3000/8290 | Cost: 0.0223\\n\",\n      \"Epoch: 004/050 | Batch 3500/8290 | Cost: 0.0221\\n\",\n      \"Epoch: 004/050 | Batch 4000/8290 | Cost: 0.0225\\n\",\n      \"Epoch: 004/050 | Batch 4500/8290 | Cost: 0.0212\\n\",\n      \"Epoch: 004/050 | Batch 5000/8290 | Cost: 0.0219\\n\",\n      \"Epoch: 004/050 | Batch 5500/8290 | Cost: 0.0216\\n\",\n      \"Epoch: 004/050 | Batch 6000/8290 | Cost: 0.0229\\n\",\n      \"Epoch: 004/050 | Batch 6500/8290 | Cost: 0.0221\\n\",\n      \"Epoch: 004/050 | Batch 7000/8290 | Cost: 0.0237\\n\",\n      \"Epoch: 004/050 | Batch 7500/8290 | Cost: 0.0195\\n\",\n      \"Epoch: 004/050 | Batch 8000/8290 | Cost: 0.0195\\n\",\n      \"Time elapsed: 3.88 min\\n\",\n      \"Epoch: 005/050 | Batch 0000/8290 | Cost: 0.0219\\n\",\n      \"Epoch: 005/050 | Batch 0500/8290 | Cost: 0.0214\\n\",\n      \"Epoch: 005/050 | Batch 1000/8290 | Cost: 0.0217\\n\",\n      \"Epoch: 005/050 | Batch 1500/8290 | Cost: 0.0210\\n\",\n      \"Epoch: 005/050 | Batch 2000/8290 | Cost: 0.0220\\n\",\n      \"Epoch: 005/050 | Batch 2500/8290 | Cost: 0.0215\\n\",\n      \"Epoch: 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0.0218\\n\",\n      \"Epoch: 007/050 | Batch 1500/8290 | Cost: 0.0216\\n\",\n      \"Epoch: 007/050 | Batch 2000/8290 | Cost: 0.0218\\n\",\n      \"Epoch: 007/050 | Batch 2500/8290 | Cost: 0.0203\\n\",\n      \"Epoch: 007/050 | Batch 3000/8290 | Cost: 0.0196\\n\",\n      \"Epoch: 007/050 | Batch 3500/8290 | Cost: 0.0203\\n\",\n      \"Epoch: 007/050 | Batch 4000/8290 | Cost: 0.0202\\n\",\n      \"Epoch: 007/050 | Batch 4500/8290 | Cost: 0.0219\\n\",\n      \"Epoch: 007/050 | Batch 5000/8290 | Cost: 0.0190\\n\",\n      \"Epoch: 007/050 | Batch 5500/8290 | Cost: 0.0204\\n\",\n      \"Epoch: 007/050 | Batch 6000/8290 | Cost: 0.0206\\n\",\n      \"Epoch: 007/050 | Batch 6500/8290 | Cost: 0.0201\\n\",\n      \"Epoch: 007/050 | Batch 7000/8290 | Cost: 0.0210\\n\",\n      \"Epoch: 007/050 | Batch 7500/8290 | Cost: 0.0213\\n\",\n      \"Epoch: 007/050 | Batch 8000/8290 | Cost: 0.0215\\n\",\n      \"Time elapsed: 6.69 min\\n\",\n      \"Epoch: 008/050 | Batch 0000/8290 | Cost: 0.0218\\n\",\n      \"Epoch: 008/050 | Batch 0500/8290 | Cost: 0.0229\\n\",\n      \"Epoch: 008/050 | Batch 1000/8290 | Cost: 0.0194\\n\",\n      \"Epoch: 008/050 | Batch 1500/8290 | Cost: 0.0220\\n\",\n      \"Epoch: 008/050 | Batch 2000/8290 | Cost: 0.0208\\n\",\n      \"Epoch: 008/050 | Batch 2500/8290 | Cost: 0.0209\\n\",\n      \"Epoch: 008/050 | Batch 3000/8290 | Cost: 0.0197\\n\",\n      \"Epoch: 008/050 | Batch 3500/8290 | Cost: 0.0218\\n\",\n      \"Epoch: 008/050 | Batch 4000/8290 | Cost: 0.0214\\n\",\n      \"Epoch: 008/050 | Batch 4500/8290 | Cost: 0.0209\\n\",\n      \"Epoch: 008/050 | Batch 5000/8290 | Cost: 0.0202\\n\",\n      \"Epoch: 008/050 | Batch 5500/8290 | Cost: 0.0202\\n\",\n      \"Epoch: 008/050 | Batch 6000/8290 | Cost: 0.0215\\n\",\n      \"Epoch: 008/050 | Batch 6500/8290 | Cost: 0.0213\\n\",\n      \"Epoch: 008/050 | Batch 7000/8290 | Cost: 0.0215\\n\",\n      \"Epoch: 008/050 | Batch 7500/8290 | Cost: 0.0201\\n\",\n      \"Epoch: 008/050 | Batch 8000/8290 | Cost: 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4000/8290 | Cost: 0.0176\\n\",\n      \"Epoch: 039/050 | Batch 4500/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 039/050 | Batch 5000/8290 | Cost: 0.0189\\n\",\n      \"Epoch: 039/050 | Batch 5500/8290 | Cost: 0.0176\\n\",\n      \"Epoch: 039/050 | Batch 6000/8290 | Cost: 0.0183\\n\",\n      \"Epoch: 039/050 | Batch 6500/8290 | Cost: 0.0191\\n\",\n      \"Epoch: 039/050 | Batch 7000/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 039/050 | Batch 7500/8290 | Cost: 0.0179\\n\",\n      \"Epoch: 039/050 | Batch 8000/8290 | Cost: 0.0168\\n\",\n      \"Time elapsed: 36.60 min\\n\",\n      \"Epoch: 040/050 | Batch 0000/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 040/050 | Batch 0500/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 040/050 | Batch 1000/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 040/050 | Batch 1500/8290 | Cost: 0.0194\\n\",\n      \"Epoch: 040/050 | Batch 2000/8290 | Cost: 0.0176\\n\",\n      \"Epoch: 040/050 | Batch 2500/8290 | Cost: 0.0197\\n\",\n      \"Epoch: 040/050 | Batch 3000/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 040/050 | Batch 3500/8290 | Cost: 0.0170\\n\",\n      \"Epoch: 040/050 | Batch 4000/8290 | Cost: 0.0182\\n\",\n      \"Epoch: 040/050 | Batch 4500/8290 | Cost: 0.0175\\n\",\n      \"Epoch: 040/050 | Batch 5000/8290 | Cost: 0.0194\\n\",\n      \"Epoch: 040/050 | Batch 5500/8290 | Cost: 0.0166\\n\",\n      \"Epoch: 040/050 | Batch 6000/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 040/050 | Batch 6500/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 040/050 | Batch 7000/8290 | Cost: 0.0179\\n\",\n      \"Epoch: 040/050 | Batch 7500/8290 | Cost: 0.0208\\n\",\n      \"Epoch: 040/050 | Batch 8000/8290 | Cost: 0.0184\\n\",\n      \"Time elapsed: 37.53 min\\n\",\n      \"Epoch: 041/050 | Batch 0000/8290 | Cost: 0.0196\\n\",\n      \"Epoch: 041/050 | Batch 0500/8290 | Cost: 0.0179\\n\",\n      \"Epoch: 041/050 | Batch 1000/8290 | Cost: 0.0183\\n\",\n      \"Epoch: 041/050 | Batch 1500/8290 | Cost: 0.0169\\n\",\n      \"Epoch: 041/050 | Batch 2000/8290 | Cost: 0.0189\\n\",\n      \"Epoch: 041/050 | Batch 2500/8290 | Cost: 0.0187\\n\",\n      \"Epoch: 041/050 | Batch 3000/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 041/050 | Batch 3500/8290 | Cost: 0.0182\\n\",\n      \"Epoch: 041/050 | Batch 4000/8290 | Cost: 0.0173\\n\",\n      \"Epoch: 041/050 | Batch 4500/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 041/050 | Batch 5000/8290 | Cost: 0.0168\\n\",\n      \"Epoch: 041/050 | Batch 5500/8290 | Cost: 0.0196\\n\",\n      \"Epoch: 041/050 | Batch 6000/8290 | Cost: 0.0179\\n\",\n      \"Epoch: 041/050 | Batch 6500/8290 | Cost: 0.0171\\n\",\n      \"Epoch: 041/050 | Batch 7000/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 041/050 | Batch 7500/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 041/050 | Batch 8000/8290 | Cost: 0.0187\\n\",\n      \"Time elapsed: 38.47 min\\n\",\n      \"Epoch: 042/050 | Batch 0000/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 042/050 | Batch 0500/8290 | Cost: 0.0175\\n\",\n      \"Epoch: 042/050 | Batch 1000/8290 | Cost: 0.0188\\n\",\n      \"Epoch: 042/050 | Batch 1500/8290 | Cost: 0.0196\\n\",\n      \"Epoch: 042/050 | Batch 2000/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 042/050 | Batch 2500/8290 | Cost: 0.0189\\n\",\n      \"Epoch: 042/050 | Batch 3000/8290 | Cost: 0.0193\\n\",\n      \"Epoch: 042/050 | Batch 3500/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 042/050 | Batch 4000/8290 | Cost: 0.0179\\n\",\n      \"Epoch: 042/050 | Batch 4500/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 042/050 | Batch 5000/8290 | Cost: 0.0170\\n\",\n      \"Epoch: 042/050 | Batch 5500/8290 | Cost: 0.0187\\n\",\n      \"Epoch: 042/050 | Batch 6000/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 042/050 | Batch 6500/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 042/050 | Batch 7000/8290 | Cost: 0.0156\\n\",\n      \"Epoch: 042/050 | Batch 7500/8290 | Cost: 0.0183\\n\",\n      \"Epoch: 042/050 | Batch 8000/8290 | Cost: 0.0186\\n\",\n      \"Time elapsed: 39.41 min\\n\",\n      \"Epoch: 043/050 | Batch 0000/8290 | Cost: 0.0179\\n\",\n      \"Epoch: 043/050 | Batch 0500/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 043/050 | Batch 1000/8290 | Cost: 0.0189\\n\",\n      \"Epoch: 043/050 | Batch 1500/8290 | Cost: 0.0182\\n\",\n      \"Epoch: 043/050 | Batch 2000/8290 | Cost: 0.0167\\n\",\n      \"Epoch: 043/050 | Batch 2500/8290 | Cost: 0.0178\\n\",\n      \"Epoch: 043/050 | Batch 3000/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 043/050 | Batch 3500/8290 | Cost: 0.0175\\n\",\n      \"Epoch: 043/050 | Batch 4000/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 043/050 | Batch 4500/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 043/050 | Batch 5000/8290 | Cost: 0.0174\\n\",\n      \"Epoch: 043/050 | Batch 5500/8290 | Cost: 0.0200\\n\",\n      \"Epoch: 043/050 | Batch 6000/8290 | Cost: 0.0168\\n\",\n      \"Epoch: 043/050 | Batch 6500/8290 | Cost: 0.0186\\n\",\n      \"Epoch: 043/050 | Batch 7000/8290 | Cost: 0.0186\\n\",\n      \"Epoch: 043/050 | Batch 7500/8290 | Cost: 0.0202\\n\",\n      \"Epoch: 043/050 | Batch 8000/8290 | Cost: 0.0189\\n\",\n      \"Time elapsed: 40.35 min\\n\",\n      \"Epoch: 044/050 | Batch 0000/8290 | Cost: 0.0166\\n\",\n      \"Epoch: 044/050 | Batch 0500/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 044/050 | Batch 1000/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 044/050 | Batch 1500/8290 | Cost: 0.0186\\n\",\n      \"Epoch: 044/050 | Batch 2000/8290 | Cost: 0.0168\\n\",\n      \"Epoch: 044/050 | Batch 2500/8290 | Cost: 0.0166\\n\",\n      \"Epoch: 044/050 | Batch 3000/8290 | Cost: 0.0178\\n\",\n      \"Epoch: 044/050 | Batch 3500/8290 | Cost: 0.0176\\n\",\n      \"Epoch: 044/050 | Batch 4000/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 044/050 | Batch 4500/8290 | Cost: 0.0175\\n\",\n      \"Epoch: 044/050 | Batch 5000/8290 | Cost: 0.0186\\n\",\n      \"Epoch: 044/050 | Batch 5500/8290 | Cost: 0.0162\\n\",\n      \"Epoch: 044/050 | Batch 6000/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 044/050 | Batch 6500/8290 | Cost: 0.0183\\n\",\n      \"Epoch: 044/050 | Batch 7000/8290 | Cost: 0.0190\\n\",\n      \"Epoch: 044/050 | Batch 7500/8290 | Cost: 0.0175\\n\",\n      \"Epoch: 044/050 | Batch 8000/8290 | Cost: 0.0174\\n\",\n      \"Time elapsed: 41.30 min\\n\",\n      \"Epoch: 045/050 | Batch 0000/8290 | Cost: 0.0166\\n\",\n      \"Epoch: 045/050 | Batch 0500/8290 | Cost: 0.0186\\n\",\n      \"Epoch: 045/050 | Batch 1000/8290 | Cost: 0.0186\\n\",\n      \"Epoch: 045/050 | Batch 1500/8290 | Cost: 0.0176\\n\",\n      \"Epoch: 045/050 | Batch 2000/8290 | Cost: 0.0182\\n\",\n      \"Epoch: 045/050 | Batch 2500/8290 | Cost: 0.0188\\n\",\n      \"Epoch: 045/050 | Batch 3000/8290 | Cost: 0.0171\\n\",\n      \"Epoch: 045/050 | Batch 3500/8290 | Cost: 0.0171\\n\",\n      \"Epoch: 045/050 | Batch 4000/8290 | Cost: 0.0187\\n\",\n      \"Epoch: 045/050 | Batch 4500/8290 | Cost: 0.0172\\n\",\n      \"Epoch: 045/050 | Batch 5000/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 045/050 | Batch 5500/8290 | Cost: 0.0183\\n\",\n      \"Epoch: 045/050 | Batch 6000/8290 | Cost: 0.0183\\n\",\n      \"Epoch: 045/050 | Batch 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Cost: 0.0190\\n\",\n      \"Epoch: 046/050 | Batch 6000/8290 | Cost: 0.0171\\n\",\n      \"Epoch: 046/050 | Batch 6500/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 046/050 | Batch 7000/8290 | Cost: 0.0167\\n\",\n      \"Epoch: 046/050 | Batch 7500/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 046/050 | Batch 8000/8290 | Cost: 0.0188\\n\",\n      \"Time elapsed: 43.18 min\\n\",\n      \"Epoch: 047/050 | Batch 0000/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 047/050 | Batch 0500/8290 | Cost: 0.0167\\n\",\n      \"Epoch: 047/050 | Batch 1000/8290 | Cost: 0.0169\\n\",\n      \"Epoch: 047/050 | Batch 1500/8290 | Cost: 0.0182\\n\",\n      \"Epoch: 047/050 | Batch 2000/8290 | Cost: 0.0191\\n\",\n      \"Epoch: 047/050 | Batch 2500/8290 | Cost: 0.0172\\n\",\n      \"Epoch: 047/050 | Batch 3000/8290 | Cost: 0.0169\\n\",\n      \"Epoch: 047/050 | Batch 3500/8290 | Cost: 0.0183\\n\",\n      \"Epoch: 047/050 | Batch 4000/8290 | Cost: 0.0173\\n\",\n      \"Epoch: 047/050 | Batch 4500/8290 | Cost: 0.0163\\n\",\n      \"Epoch: 047/050 | Batch 5000/8290 | Cost: 0.0165\\n\",\n      \"Epoch: 047/050 | Batch 5500/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 047/050 | Batch 6000/8290 | Cost: 0.0163\\n\",\n      \"Epoch: 047/050 | Batch 6500/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 047/050 | Batch 7000/8290 | Cost: 0.0184\\n\",\n      \"Epoch: 047/050 | Batch 7500/8290 | Cost: 0.0181\\n\",\n      \"Epoch: 047/050 | Batch 8000/8290 | Cost: 0.0180\\n\",\n      \"Time elapsed: 44.11 min\\n\",\n      \"Epoch: 048/050 | Batch 0000/8290 | Cost: 0.0173\\n\",\n      \"Epoch: 048/050 | Batch 0500/8290 | Cost: 0.0166\\n\",\n      \"Epoch: 048/050 | Batch 1000/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 048/050 | Batch 1500/8290 | Cost: 0.0174\\n\",\n      \"Epoch: 048/050 | Batch 2000/8290 | Cost: 0.0188\\n\",\n      \"Epoch: 048/050 | Batch 2500/8290 | Cost: 0.0192\\n\",\n      \"Epoch: 048/050 | Batch 3000/8290 | Cost: 0.0170\\n\",\n      \"Epoch: 048/050 | Batch 3500/8290 | Cost: 0.0170\\n\",\n      \"Epoch: 048/050 | Batch 4000/8290 | Cost: 0.0194\\n\",\n      \"Epoch: 048/050 | Batch 4500/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 048/050 | Batch 5000/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 048/050 | Batch 5500/8290 | Cost: 0.0187\\n\",\n      \"Epoch: 048/050 | Batch 6000/8290 | Cost: 0.0168\\n\",\n      \"Epoch: 048/050 | Batch 6500/8290 | Cost: 0.0199\\n\",\n      \"Epoch: 048/050 | Batch 7000/8290 | Cost: 0.0174\\n\",\n      \"Epoch: 048/050 | Batch 7500/8290 | Cost: 0.0189\\n\",\n      \"Epoch: 048/050 | Batch 8000/8290 | Cost: 0.0175\\n\",\n      \"Time elapsed: 45.03 min\\n\",\n      \"Epoch: 049/050 | Batch 0000/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 049/050 | Batch 0500/8290 | Cost: 0.0185\\n\",\n      \"Epoch: 049/050 | Batch 1000/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 049/050 | Batch 1500/8290 | Cost: 0.0173\\n\",\n      \"Epoch: 049/050 | Batch 2000/8290 | Cost: 0.0163\\n\",\n      \"Epoch: 049/050 | Batch 2500/8290 | Cost: 0.0191\\n\",\n      \"Epoch: 049/050 | Batch 3000/8290 | Cost: 0.0177\\n\",\n      \"Epoch: 049/050 | Batch 3500/8290 | Cost: 0.0161\\n\",\n      \"Epoch: 049/050 | Batch 4000/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 049/050 | Batch 4500/8290 | Cost: 0.0179\\n\",\n      \"Epoch: 049/050 | Batch 5000/8290 | Cost: 0.0173\\n\",\n      \"Epoch: 049/050 | Batch 5500/8290 | Cost: 0.0190\\n\",\n      \"Epoch: 049/050 | Batch 6000/8290 | Cost: 0.0165\\n\",\n      \"Epoch: 049/050 | Batch 6500/8290 | Cost: 0.0186\\n\",\n      \"Epoch: 049/050 | Batch 7000/8290 | Cost: 0.0161\\n\",\n      \"Epoch: 049/050 | Batch 7500/8290 | Cost: 0.0173\\n\",\n      \"Epoch: 049/050 | Batch 8000/8290 | Cost: 0.0178\\n\",\n      \"Time elapsed: 45.96 min\\n\",\n      \"Epoch: 050/050 | Batch 0000/8290 | Cost: 0.0171\\n\",\n      \"Epoch: 050/050 | Batch 0500/8290 | Cost: 0.0168\\n\",\n      \"Epoch: 050/050 | Batch 1000/8290 | Cost: 0.0178\\n\",\n      \"Epoch: 050/050 | Batch 1500/8290 | Cost: 0.0169\\n\",\n      \"Epoch: 050/050 | Batch 2000/8290 | Cost: 0.0172\\n\",\n      \"Epoch: 050/050 | Batch 2500/8290 | Cost: 0.0169\\n\",\n      \"Epoch: 050/050 | Batch 3000/8290 | Cost: 0.0168\\n\",\n      \"Epoch: 050/050 | Batch 3500/8290 | Cost: 0.0155\\n\",\n      \"Epoch: 050/050 | Batch 4000/8290 | Cost: 0.0180\\n\",\n      \"Epoch: 050/050 | Batch 4500/8290 | Cost: 0.0187\\n\",\n      \"Epoch: 050/050 | Batch 5000/8290 | Cost: 0.0189\\n\",\n      \"Epoch: 050/050 | Batch 5500/8290 | Cost: 0.0182\\n\",\n      \"Epoch: 050/050 | Batch 6000/8290 | Cost: 0.0193\\n\",\n      \"Epoch: 050/050 | Batch 6500/8290 | Cost: 0.0189\\n\",\n      \"Epoch: 050/050 | Batch 7000/8290 | Cost: 0.0176\\n\",\n      \"Epoch: 050/050 | Batch 7500/8290 | Cost: 0.0174\\n\",\n      \"Epoch: 050/050 | Batch 8000/8290 | Cost: 0.0180\\n\",\n      \"Time elapsed: 46.89 min\\n\",\n      \"Total Training Time: 46.89 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### TRAINING\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"epoch_start = 1\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = Autoencoder()\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"################## Load previous\\n\",\n    \"# the code saves the autoencoder\\n\",\n    \"# after each epoch so that in case\\n\",\n    \"# the training process gets interrupted,\\n\",\n    \"# we will not have to start training it\\n\",\n    \"# from scratch\\n\",\n    \"files = os.listdir()\\n\",\n    \"\\n\",\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(epoch_start, num_epochs+1):\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"    for batch_idx, (x, y) in enumerate(train_loader):\\n\",\n    \"\\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = x.to(device)\\n\",\n    \"        \\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        decoded = model(features)\\n\",\n    \"        cost = F.mse_loss(decoded, features)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 500:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %04d/%04d | Cost: %.4f' \\n\",\n    \"                   %(epoch, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"        \\n\",\n    \"# Save model\\n\",\n    \"if os.path.isfile('autoencoder_quickdraw-1_i_%d_%s.pt' % (epoch-1, device)):\\n\",\n    \"    os.remove('autoencoder_quickdraw-1_i_%d_%s.pt' % (epoch-1, device))\\n\",\n    \"torch.save(model.state_dict(), 'autoencoder_quickdraw-1_i_%d_%s.pt' % (epoch, device))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"OBO9L5FnVA2h\"\n   },\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 323\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 3782,\n     \"status\": \"ok\",\n     \"timestamp\": 1525044542253,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"UpJLf9FnVqSw\",\n    \"outputId\": \"121c6c55-6171-4b1b-c6ea-5a199abb4bc5\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1296x360 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"model = Autoencoder()\\n\",\n    \"model = model.to(device)\\n\",\n    \"model.load_state_dict(torch.load('autoencoder_quickdraw-1_i_%d_%s.pt' % (num_epochs, device)))\\n\",\n    \"model.eval()\\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"\\n\",\n    \"for batch_idx, (x, y) in enumerate(train_loader):\\n\",\n    \"    features = x.to(device)\\n\",\n    \"    decoded = model(features)\\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 5\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(18, 5))\\n\",\n    \"orig_images = features.detach().cpu().numpy()[:n_images]\\n\",\n    \"orig_images = np.moveaxis(orig_images, 1, -1)\\n\",\n    \"\\n\",\n    \"decoded_images = decoded.detach().cpu().numpy()[:n_images]\\n\",\n    \"decoded_images = np.moveaxis(decoded_images, 1, -1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        ax[i].axis('off')\\n\",\n    \"        ax[i].imshow(img[i].reshape(28, 28), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"pandas      0.23.4\\n\",\n      \"PIL.Image   5.3.0\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"default_view\": {},\n   \"name\": \"autoencoder-conv-2.ipynb\",\n   \"provenance\": [],\n   \"version\": \"0.3.2\",\n   \"views\": {}\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-conv-nneighbor.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Convolutional Autoencoder with Nearest-neighbor Interpolation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A convolutional autoencoder using nearest neighbor upscaling layers that compresses 768-pixel MNIST images down to a 7x7x8 (392 pixel) representation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:0\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 123\\n\",\n    \"learning_rate = 0.05\\n\",\n    \"num_epochs = 10\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConvolutionalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvolutionalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        \\n\",\n    \"        # 28x28x1 => 28x28x4\\n\",\n    \"        self.conv_1 = torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)\\n\",\n    \"        # 28x28x4 => 14x14x4\\n\",\n    \"        self.pool_1 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(14-1) - 28 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)                                       \\n\",\n    \"        # 14x14x4 => 14x14x8\\n\",\n    \"        self.conv_2 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=8,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(14-1) - 14 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)                 \\n\",\n    \"        # 14x14x8 => 7x7x8                             \\n\",\n    \"        self.pool_2 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(7-1) - 14 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"                                         \\n\",\n    \"        # 7x7x8 => 14x14x8               \\n\",\n    \"        # Unpool\\n\",\n    \"\\n\",\n    \"        # 14x14x8 => 14x14x8\\n\",\n    \"        self.conv_3 = torch.nn.Conv2d(in_channels=8,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(14-1) - 14 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)\\n\",\n    \"        # 14x14x4 => 28x28x4                            \\n\",\n    \"        # Unpool\\n\",\n    \"        \\n\",\n    \"        # 28x28x4 => 28x28x1\\n\",\n    \"        self.conv_4 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=1,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        x = self.conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_1(x)\\n\",\n    \"        x = self.conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_2(x)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        x = F.interpolate(x, scale_factor=2, mode='nearest')\\n\",\n    \"        x = self.conv_3(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = F.interpolate(x, scale_factor=2, mode='nearest')\\n\",\n    \"        x = self.conv_4(x)\\n\",\n    \"        logits = F.leaky_relu(x)\\n\",\n    \"        probas = torch.sigmoid(logits)\\n\",\n    \"        return logits, probas\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConvolutionalAutoencoder()\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/010 | Batch 000/469 | Cost: 0.7008\\n\",\n      \"Epoch: 001/010 | Batch 050/469 | Cost: 0.1782\\n\",\n      \"Epoch: 001/010 | Batch 100/469 | Cost: 0.1394\\n\",\n      \"Epoch: 001/010 | Batch 150/469 | Cost: 0.1240\\n\",\n      \"Epoch: 001/010 | Batch 200/469 | Cost: 0.1173\\n\",\n      \"Epoch: 001/010 | Batch 250/469 | Cost: 0.1105\\n\",\n      \"Epoch: 001/010 | Batch 300/469 | Cost: 0.1045\\n\",\n      \"Epoch: 001/010 | Batch 350/469 | Cost: 0.1060\\n\",\n      \"Epoch: 001/010 | Batch 400/469 | Cost: 0.1051\\n\",\n      \"Epoch: 001/010 | Batch 450/469 | Cost: 0.1002\\n\",\n      \"Time elapsed: 0.12 min\\n\",\n      \"Epoch: 002/010 | Batch 000/469 | Cost: 0.0998\\n\",\n      \"Epoch: 002/010 | Batch 050/469 | Cost: 0.1023\\n\",\n      \"Epoch: 002/010 | Batch 100/469 | Cost: 0.1018\\n\",\n      \"Epoch: 002/010 | Batch 150/469 | Cost: 0.0967\\n\",\n      \"Epoch: 002/010 | Batch 200/469 | Cost: 0.0982\\n\",\n      \"Epoch: 002/010 | Batch 250/469 | Cost: 0.0985\\n\",\n      \"Epoch: 002/010 | Batch 300/469 | Cost: 0.0961\\n\",\n      \"Epoch: 002/010 | Batch 350/469 | Cost: 0.0998\\n\",\n      \"Epoch: 002/010 | Batch 400/469 | Cost: 0.0975\\n\",\n      \"Epoch: 002/010 | Batch 450/469 | Cost: 0.0967\\n\",\n      \"Time elapsed: 0.24 min\\n\",\n      \"Epoch: 003/010 | Batch 000/469 | Cost: 0.0953\\n\",\n      \"Epoch: 003/010 | Batch 050/469 | Cost: 0.0921\\n\",\n      \"Epoch: 003/010 | Batch 100/469 | Cost: 0.0955\\n\",\n      \"Epoch: 003/010 | Batch 150/469 | Cost: 0.0960\\n\",\n      \"Epoch: 003/010 | Batch 200/469 | Cost: 0.0959\\n\",\n      \"Epoch: 003/010 | Batch 250/469 | Cost: 0.0929\\n\",\n      \"Epoch: 003/010 | Batch 300/469 | Cost: 0.0952\\n\",\n      \"Epoch: 003/010 | Batch 350/469 | Cost: 0.0935\\n\",\n      \"Epoch: 003/010 | Batch 400/469 | Cost: 0.0939\\n\",\n      \"Epoch: 003/010 | Batch 450/469 | Cost: 0.0946\\n\",\n      \"Time elapsed: 0.36 min\\n\",\n      \"Epoch: 004/010 | Batch 000/469 | Cost: 0.0931\\n\",\n      \"Epoch: 004/010 | Batch 050/469 | Cost: 0.0965\\n\",\n      \"Epoch: 004/010 | Batch 100/469 | Cost: 0.0900\\n\",\n      \"Epoch: 004/010 | Batch 150/469 | Cost: 0.0883\\n\",\n      \"Epoch: 004/010 | Batch 200/469 | Cost: 0.0957\\n\",\n      \"Epoch: 004/010 | Batch 250/469 | Cost: 0.0956\\n\",\n      \"Epoch: 004/010 | Batch 300/469 | Cost: 0.0885\\n\",\n      \"Epoch: 004/010 | Batch 350/469 | Cost: 0.0901\\n\",\n      \"Epoch: 004/010 | Batch 400/469 | Cost: 0.0943\\n\",\n      \"Epoch: 004/010 | Batch 450/469 | Cost: 0.0933\\n\",\n      \"Time elapsed: 0.48 min\\n\",\n      \"Epoch: 005/010 | Batch 000/469 | Cost: 0.0885\\n\",\n      \"Epoch: 005/010 | Batch 050/469 | Cost: 0.0953\\n\",\n      \"Epoch: 005/010 | Batch 100/469 | Cost: 0.0896\\n\",\n      \"Epoch: 005/010 | Batch 150/469 | Cost: 0.0944\\n\",\n      \"Epoch: 005/010 | Batch 200/469 | Cost: 0.0935\\n\",\n      \"Epoch: 005/010 | Batch 250/469 | Cost: 0.0914\\n\",\n      \"Epoch: 005/010 | Batch 300/469 | Cost: 0.0898\\n\",\n      \"Epoch: 005/010 | Batch 350/469 | Cost: 0.0883\\n\",\n      \"Epoch: 005/010 | Batch 400/469 | Cost: 0.0906\\n\",\n      \"Epoch: 005/010 | Batch 450/469 | Cost: 0.0924\\n\",\n      \"Time elapsed: 0.60 min\\n\",\n      \"Epoch: 006/010 | Batch 000/469 | Cost: 0.0862\\n\",\n      \"Epoch: 006/010 | Batch 050/469 | Cost: 0.0881\\n\",\n      \"Epoch: 006/010 | Batch 100/469 | Cost: 0.0879\\n\",\n      \"Epoch: 006/010 | Batch 150/469 | Cost: 0.0847\\n\",\n      \"Epoch: 006/010 | Batch 200/469 | Cost: 0.0922\\n\",\n      \"Epoch: 006/010 | Batch 250/469 | Cost: 0.0871\\n\",\n      \"Epoch: 006/010 | Batch 300/469 | Cost: 0.0893\\n\",\n      \"Epoch: 006/010 | Batch 350/469 | Cost: 0.0900\\n\",\n      \"Epoch: 006/010 | Batch 400/469 | Cost: 0.0843\\n\",\n      \"Epoch: 006/010 | Batch 450/469 | Cost: 0.0856\\n\",\n      \"Time elapsed: 0.72 min\\n\",\n      \"Epoch: 007/010 | Batch 000/469 | Cost: 0.0898\\n\",\n      \"Epoch: 007/010 | Batch 050/469 | Cost: 0.0840\\n\",\n      \"Epoch: 007/010 | Batch 100/469 | Cost: 0.0898\\n\",\n      \"Epoch: 007/010 | Batch 150/469 | Cost: 0.0928\\n\",\n      \"Epoch: 007/010 | Batch 200/469 | Cost: 0.0920\\n\",\n      \"Epoch: 007/010 | Batch 250/469 | Cost: 0.0864\\n\",\n      \"Epoch: 007/010 | Batch 300/469 | Cost: 0.0860\\n\",\n      \"Epoch: 007/010 | Batch 350/469 | Cost: 0.0839\\n\",\n      \"Epoch: 007/010 | Batch 400/469 | Cost: 0.0856\\n\",\n      \"Epoch: 007/010 | Batch 450/469 | Cost: 0.0843\\n\",\n      \"Time elapsed: 0.84 min\\n\",\n      \"Epoch: 008/010 | Batch 000/469 | Cost: 0.0817\\n\",\n      \"Epoch: 008/010 | Batch 050/469 | Cost: 0.0858\\n\",\n      \"Epoch: 008/010 | Batch 100/469 | Cost: 0.0894\\n\",\n      \"Epoch: 008/010 | Batch 150/469 | Cost: 0.0897\\n\",\n      \"Epoch: 008/010 | Batch 200/469 | Cost: 0.0815\\n\",\n      \"Epoch: 008/010 | Batch 250/469 | Cost: 0.0892\\n\",\n      \"Epoch: 008/010 | Batch 300/469 | Cost: 0.0859\\n\",\n      \"Epoch: 008/010 | Batch 350/469 | Cost: 0.0883\\n\",\n      \"Epoch: 008/010 | Batch 400/469 | Cost: 0.0834\\n\",\n      \"Epoch: 008/010 | Batch 450/469 | Cost: 0.0940\\n\",\n      \"Time elapsed: 0.95 min\\n\",\n      \"Epoch: 009/010 | Batch 000/469 | Cost: 0.0888\\n\",\n      \"Epoch: 009/010 | Batch 050/469 | Cost: 0.0856\\n\",\n      \"Epoch: 009/010 | Batch 100/469 | Cost: 0.0881\\n\",\n      \"Epoch: 009/010 | Batch 150/469 | Cost: 0.0853\\n\",\n      \"Epoch: 009/010 | Batch 200/469 | Cost: 0.0868\\n\",\n      \"Epoch: 009/010 | Batch 250/469 | Cost: 0.0795\\n\",\n      \"Epoch: 009/010 | Batch 300/469 | Cost: 0.0868\\n\",\n      \"Epoch: 009/010 | Batch 350/469 | Cost: 0.0899\\n\",\n      \"Epoch: 009/010 | Batch 400/469 | Cost: 0.0882\\n\",\n      \"Epoch: 009/010 | Batch 450/469 | Cost: 0.0950\\n\",\n      \"Time elapsed: 1.07 min\\n\",\n      \"Epoch: 010/010 | Batch 000/469 | Cost: 0.0854\\n\",\n      \"Epoch: 010/010 | Batch 050/469 | Cost: 0.0839\\n\",\n      \"Epoch: 010/010 | Batch 100/469 | Cost: 0.0873\\n\",\n      \"Epoch: 010/010 | Batch 150/469 | Cost: 0.0871\\n\",\n      \"Epoch: 010/010 | Batch 200/469 | Cost: 0.0851\\n\",\n      \"Epoch: 010/010 | Batch 250/469 | Cost: 0.0831\\n\",\n      \"Epoch: 010/010 | Batch 300/469 | Cost: 0.0843\\n\",\n      \"Epoch: 010/010 | Batch 350/469 | Cost: 0.0860\\n\",\n      \"Epoch: 010/010 | Batch 400/469 | Cost: 0.0840\\n\",\n      \"Epoch: 010/010 | Batch 450/469 | Cost: 0.0818\\n\",\n      \"Time elapsed: 1.19 min\\n\",\n      \"Total Training Time: 1.19 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = features.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        logits, decoded = model(features)\\n\",\n    \"        cost = F.binary_cross_entropy_with_logits(logits, features)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-conv-var.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Convolutional Variational Autoencoder\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple convolutional variational autoencoder that compresses 768-pixel MNIST images down to a 15-pixel latent vector representation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:1\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:1\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 0\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"num_epochs = 50\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_features = 784\\n\",\n    \"num_latent = 15\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"## ### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"class ConvVariationalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_features, num_latent):\\n\",\n    \"        super(ConvVariationalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        ###############\\n\",\n    \"        # ENCODER\\n\",\n    \"        ##############\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"\\n\",\n    \"        self.enc_conv_1 = torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                                          out_channels=16,\\n\",\n    \"                                          kernel_size=(6, 6),\\n\",\n    \"                                          stride=(2, 2),\\n\",\n    \"                                          padding=0)\\n\",\n    \"\\n\",\n    \"        self.enc_conv_2 = torch.nn.Conv2d(in_channels=16,\\n\",\n    \"                                          out_channels=32,\\n\",\n    \"                                          kernel_size=(4, 4),\\n\",\n    \"                                          stride=(2, 2),\\n\",\n    \"                                          padding=0)                 \\n\",\n    \"        \\n\",\n    \"        self.enc_conv_3 = torch.nn.Conv2d(in_channels=32,\\n\",\n    \"                                          out_channels=64,\\n\",\n    \"                                          kernel_size=(2, 2),\\n\",\n    \"                                          stride=(2, 2),\\n\",\n    \"                                          padding=0)                     \\n\",\n    \"        \\n\",\n    \"        self.z_mean = torch.nn.Linear(64*2*2, num_latent)\\n\",\n    \"        # in the original paper (Kingma & Welling 2015, we use\\n\",\n    \"        # have a z_mean and z_var, but the problem is that\\n\",\n    \"        # the z_var can be negative, which would cause issues\\n\",\n    \"        # in the log later. Hence we assume that latent vector\\n\",\n    \"        # has a z_mean and z_log_var component, and when we need\\n\",\n    \"        # the regular variance or std_dev, we simply use \\n\",\n    \"        # an exponential function\\n\",\n    \"        self.z_log_var = torch.nn.Linear(64*2*2, num_latent)\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        ###############\\n\",\n    \"        # DECODER\\n\",\n    \"        ##############\\n\",\n    \"        \\n\",\n    \"        self.dec_linear_1 = torch.nn.Linear(num_latent, 64*2*2)\\n\",\n    \"               \\n\",\n    \"        self.dec_deconv_1 = torch.nn.ConvTranspose2d(in_channels=64,\\n\",\n    \"                                                     out_channels=32,\\n\",\n    \"                                                     kernel_size=(2, 2),\\n\",\n    \"                                                     stride=(2, 2),\\n\",\n    \"                                                     padding=0)\\n\",\n    \"                                 \\n\",\n    \"        self.dec_deconv_2 = torch.nn.ConvTranspose2d(in_channels=32,\\n\",\n    \"                                                     out_channels=16,\\n\",\n    \"                                                     kernel_size=(4, 4),\\n\",\n    \"                                                     stride=(3, 3),\\n\",\n    \"                                                     padding=1)\\n\",\n    \"        \\n\",\n    \"        self.dec_deconv_3 = torch.nn.ConvTranspose2d(in_channels=16,\\n\",\n    \"                                                     out_channels=1,\\n\",\n    \"                                                     kernel_size=(6, 6),\\n\",\n    \"                                                     stride=(3, 3),\\n\",\n    \"                                                     padding=4)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"    def reparameterize(self, z_mu, z_log_var):\\n\",\n    \"        # Sample epsilon from standard normal distribution\\n\",\n    \"        eps = torch.randn(z_mu.size(0), z_mu.size(1)).to(device)\\n\",\n    \"        # note that log(x^2) = 2*log(x); hence divide by 2 to get std_dev\\n\",\n    \"        # i.e., std_dev = exp(log(std_dev^2)/2) = exp(log(var)/2)\\n\",\n    \"        z = z_mu + eps * torch.exp(z_log_var/2.) \\n\",\n    \"        return z\\n\",\n    \"        \\n\",\n    \"    def encoder(self, features):\\n\",\n    \"        x = self.enc_conv_1(features)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('conv1 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.enc_conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('conv2 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.enc_conv_3(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('conv3 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        z_mean = self.z_mean(x.view(-1, 64*2*2))\\n\",\n    \"        z_log_var = self.z_log_var(x.view(-1, 64*2*2))\\n\",\n    \"        encoded = self.reparameterize(z_mean, z_log_var)\\n\",\n    \"        \\n\",\n    \"        return z_mean, z_log_var, encoded\\n\",\n    \"    \\n\",\n    \"    def decoder(self, encoded):\\n\",\n    \"        x = self.dec_linear_1(encoded)\\n\",\n    \"        x = x.view(-1, 64, 2, 2)\\n\",\n    \"        \\n\",\n    \"        x = self.dec_deconv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('deconv1 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.dec_deconv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('deconv2 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        x = self.dec_deconv_3(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        #print('deconv1 out:', x.size())\\n\",\n    \"        \\n\",\n    \"        decoded = torch.sigmoid(x)\\n\",\n    \"        return decoded\\n\",\n    \"\\n\",\n    \"    def forward(self, features):\\n\",\n    \"        \\n\",\n    \"        z_mean, z_log_var, encoded = self.encoder(features)\\n\",\n    \"        decoded = self.decoder(encoded)\\n\",\n    \"        \\n\",\n    \"        return z_mean, z_log_var, encoded, decoded\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConvVariationalAutoencoder(num_features,\\n\",\n    \"                                   num_latent)\\n\",\n    \"model = model.to(device)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### COST AND OPTIMIZER\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/050 | Batch 000/469 | Cost: 70508.8125\\n\",\n      \"Epoch: 001/050 | Batch 050/469 | Cost: 69360.6953\\n\",\n      \"Epoch: 001/050 | Batch 100/469 | Cost: 41214.9258\\n\",\n      \"Epoch: 001/050 | Batch 150/469 | Cost: 34571.9688\\n\",\n      \"Epoch: 001/050 | Batch 200/469 | Cost: 28148.7500\\n\",\n      \"Epoch: 001/050 | Batch 250/469 | Cost: 29157.5918\\n\",\n      \"Epoch: 001/050 | Batch 300/469 | Cost: 27405.8359\\n\",\n      \"Epoch: 001/050 | Batch 350/469 | Cost: 26417.7324\\n\",\n      \"Epoch: 001/050 | Batch 400/469 | Cost: 25685.3320\\n\",\n      \"Epoch: 001/050 | Batch 450/469 | Cost: 25538.5293\\n\",\n      \"Time elapsed: 0.14 min\\n\",\n      \"Epoch: 002/050 | Batch 000/469 | Cost: 25301.1914\\n\",\n      \"Epoch: 002/050 | Batch 050/469 | Cost: 24180.0254\\n\",\n      \"Epoch: 002/050 | Batch 100/469 | Cost: 24124.1309\\n\",\n      \"Epoch: 002/050 | Batch 150/469 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\"Epoch: 016/050 | Batch 450/469 | Cost: 15753.2812\\n\",\n      \"Time elapsed: 2.27 min\\n\",\n      \"Epoch: 017/050 | Batch 000/469 | Cost: 15676.1680\\n\",\n      \"Epoch: 017/050 | Batch 050/469 | Cost: 15123.8203\\n\",\n      \"Epoch: 017/050 | Batch 100/469 | Cost: 15131.5918\\n\",\n      \"Epoch: 017/050 | Batch 150/469 | Cost: 14856.3496\\n\",\n      \"Epoch: 017/050 | Batch 200/469 | Cost: 15176.2002\\n\",\n      \"Epoch: 017/050 | Batch 250/469 | Cost: 14768.6816\\n\",\n      \"Epoch: 017/050 | Batch 300/469 | Cost: 14871.7480\\n\",\n      \"Epoch: 017/050 | Batch 350/469 | Cost: 14418.3633\\n\",\n      \"Epoch: 017/050 | Batch 400/469 | Cost: 15398.9326\\n\",\n      \"Epoch: 017/050 | Batch 450/469 | Cost: 14675.2832\\n\",\n      \"Time elapsed: 2.40 min\\n\",\n      \"Epoch: 018/050 | Batch 000/469 | Cost: 15558.1592\\n\",\n      \"Epoch: 018/050 | Batch 050/469 | Cost: 14836.9766\\n\",\n      \"Epoch: 018/050 | Batch 100/469 | Cost: 14535.8203\\n\",\n      \"Epoch: 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| Cost: 14357.0439\\n\",\n      \"Epoch: 037/050 | Batch 100/469 | Cost: 13578.9121\\n\",\n      \"Epoch: 037/050 | Batch 150/469 | Cost: 14657.7266\\n\",\n      \"Epoch: 037/050 | Batch 200/469 | Cost: 14293.0732\\n\",\n      \"Epoch: 037/050 | Batch 250/469 | Cost: 13609.5859\\n\",\n      \"Epoch: 037/050 | Batch 300/469 | Cost: 13738.5283\\n\",\n      \"Epoch: 037/050 | Batch 350/469 | Cost: 14079.2803\\n\",\n      \"Epoch: 037/050 | Batch 400/469 | Cost: 14029.6797\\n\",\n      \"Epoch: 037/050 | Batch 450/469 | Cost: 14522.6406\\n\",\n      \"Time elapsed: 5.18 min\\n\",\n      \"Epoch: 038/050 | Batch 000/469 | Cost: 14005.6035\\n\",\n      \"Epoch: 038/050 | Batch 050/469 | Cost: 13756.8330\\n\",\n      \"Epoch: 038/050 | Batch 100/469 | Cost: 15247.8760\\n\",\n      \"Epoch: 038/050 | Batch 150/469 | Cost: 14034.3789\\n\",\n      \"Epoch: 038/050 | Batch 200/469 | Cost: 14204.7061\\n\",\n      \"Epoch: 038/050 | Batch 250/469 | Cost: 14023.4863\\n\",\n      \"Epoch: 038/050 | Batch 300/469 | Cost: 13636.5508\\n\",\n      \"Epoch: 038/050 | Batch 350/469 | Cost: 14509.3711\\n\",\n      \"Epoch: 038/050 | Batch 400/469 | Cost: 14496.3965\\n\",\n      \"Epoch: 038/050 | Batch 450/469 | Cost: 14460.8896\\n\",\n      \"Time elapsed: 5.32 min\\n\",\n      \"Epoch: 039/050 | Batch 000/469 | Cost: 14317.6602\\n\",\n      \"Epoch: 039/050 | Batch 050/469 | Cost: 14440.6855\\n\",\n      \"Epoch: 039/050 | Batch 100/469 | Cost: 13772.3691\\n\",\n      \"Epoch: 039/050 | Batch 150/469 | Cost: 14023.2480\\n\",\n      \"Epoch: 039/050 | Batch 200/469 | Cost: 14576.5449\\n\",\n      \"Epoch: 039/050 | Batch 250/469 | Cost: 14164.7266\\n\",\n      \"Epoch: 039/050 | Batch 300/469 | Cost: 13657.8369\\n\",\n      \"Epoch: 039/050 | Batch 350/469 | Cost: 14456.4014\\n\",\n      \"Epoch: 039/050 | Batch 400/469 | Cost: 14202.3047\\n\",\n      \"Epoch: 039/050 | Batch 450/469 | Cost: 14564.9531\\n\",\n      \"Time elapsed: 5.45 min\\n\",\n      \"Epoch: 040/050 | Batch 000/469 | Cost: 14392.9277\\n\",\n      \"Epoch: 040/050 | Batch 050/469 | Cost: 13708.4375\\n\",\n      \"Epoch: 040/050 | Batch 100/469 | Cost: 14689.3535\\n\",\n      \"Epoch: 040/050 | Batch 150/469 | Cost: 13887.5840\\n\",\n      \"Epoch: 040/050 | Batch 200/469 | Cost: 14047.1543\\n\",\n      \"Epoch: 040/050 | Batch 250/469 | Cost: 14142.5859\\n\",\n      \"Epoch: 040/050 | Batch 300/469 | Cost: 14016.5820\\n\",\n      \"Epoch: 040/050 | Batch 350/469 | Cost: 14962.1387\\n\",\n      \"Epoch: 040/050 | Batch 400/469 | Cost: 14433.1416\\n\",\n      \"Epoch: 040/050 | Batch 450/469 | Cost: 14622.0762\\n\",\n      \"Time elapsed: 5.60 min\\n\",\n      \"Epoch: 041/050 | Batch 000/469 | Cost: 15024.6074\\n\",\n      \"Epoch: 041/050 | Batch 050/469 | Cost: 14015.1895\\n\",\n      \"Epoch: 041/050 | Batch 100/469 | Cost: 14236.3535\\n\",\n      \"Epoch: 041/050 | Batch 150/469 | Cost: 13553.2012\\n\",\n      \"Epoch: 041/050 | Batch 200/469 | Cost: 14393.0205\\n\",\n      \"Epoch: 041/050 | Batch 250/469 | Cost: 14220.9316\\n\",\n      \"Epoch: 041/050 | Batch 300/469 | Cost: 13906.4434\\n\",\n      \"Epoch: 041/050 | Batch 350/469 | Cost: 13650.9873\\n\",\n      \"Epoch: 041/050 | Batch 400/469 | Cost: 14031.2979\\n\",\n      \"Epoch: 041/050 | Batch 450/469 | Cost: 14202.7402\\n\",\n      \"Time elapsed: 5.74 min\\n\",\n      \"Epoch: 042/050 | Batch 000/469 | Cost: 13856.5684\\n\",\n      \"Epoch: 042/050 | Batch 050/469 | Cost: 14359.9023\\n\",\n      \"Epoch: 042/050 | Batch 100/469 | Cost: 14294.4902\\n\",\n      \"Epoch: 042/050 | Batch 150/469 | Cost: 14577.5811\\n\",\n      \"Epoch: 042/050 | Batch 200/469 | Cost: 14028.5820\\n\",\n      \"Epoch: 042/050 | Batch 250/469 | Cost: 13892.3926\\n\",\n      \"Epoch: 042/050 | Batch 300/469 | Cost: 13972.0322\\n\",\n      \"Epoch: 042/050 | Batch 350/469 | Cost: 14635.3506\\n\",\n      \"Epoch: 042/050 | Batch 400/469 | Cost: 13453.1562\\n\",\n      \"Epoch: 042/050 | Batch 450/469 | Cost: 14930.7197\\n\",\n      \"Time elapsed: 5.88 min\\n\",\n      \"Epoch: 043/050 | Batch 000/469 | Cost: 14080.6318\\n\",\n      \"Epoch: 043/050 | Batch 050/469 | Cost: 14356.2100\\n\",\n      \"Epoch: 043/050 | Batch 100/469 | Cost: 14747.7344\\n\",\n      \"Epoch: 043/050 | Batch 150/469 | Cost: 14025.0693\\n\",\n      \"Epoch: 043/050 | Batch 200/469 | Cost: 14294.0615\\n\",\n      \"Epoch: 043/050 | Batch 250/469 | Cost: 14147.0391\\n\",\n      \"Epoch: 043/050 | Batch 300/469 | Cost: 14254.3008\\n\",\n      \"Epoch: 043/050 | Batch 350/469 | Cost: 13503.6582\\n\",\n      \"Epoch: 043/050 | Batch 400/469 | Cost: 14689.1816\\n\",\n      \"Epoch: 043/050 | Batch 450/469 | Cost: 14308.2051\\n\",\n      \"Time elapsed: 6.02 min\\n\",\n      \"Epoch: 044/050 | Batch 000/469 | Cost: 13875.5928\\n\",\n      \"Epoch: 044/050 | Batch 050/469 | Cost: 14699.4229\\n\",\n      \"Epoch: 044/050 | Batch 100/469 | Cost: 14394.9424\\n\",\n      \"Epoch: 044/050 | Batch 150/469 | Cost: 14657.7197\\n\",\n      \"Epoch: 044/050 | Batch 200/469 | Cost: 14011.2949\\n\",\n      \"Epoch: 044/050 | Batch 250/469 | Cost: 13314.2246\\n\",\n      \"Epoch: 044/050 | Batch 300/469 | Cost: 14493.9434\\n\",\n      \"Epoch: 044/050 | Batch 350/469 | Cost: 13947.0000\\n\",\n      \"Epoch: 044/050 | Batch 400/469 | Cost: 14538.6055\\n\",\n      \"Epoch: 044/050 | Batch 450/469 | Cost: 13822.2129\\n\",\n      \"Time elapsed: 6.16 min\\n\",\n      \"Epoch: 045/050 | Batch 000/469 | Cost: 14430.2080\\n\",\n      \"Epoch: 045/050 | Batch 050/469 | Cost: 13560.6621\\n\",\n      \"Epoch: 045/050 | Batch 100/469 | Cost: 14101.0293\\n\",\n      \"Epoch: 045/050 | Batch 150/469 | Cost: 13972.5605\\n\",\n      \"Epoch: 045/050 | Batch 200/469 | Cost: 13934.4883\\n\",\n      \"Epoch: 045/050 | Batch 250/469 | Cost: 14146.7676\\n\",\n      \"Epoch: 045/050 | Batch 300/469 | Cost: 14229.7588\\n\",\n      \"Epoch: 045/050 | Batch 350/469 | Cost: 14473.1758\\n\",\n      \"Epoch: 045/050 | Batch 400/469 | Cost: 14182.4443\\n\",\n      \"Epoch: 045/050 | Batch 450/469 | Cost: 13847.8311\\n\",\n      \"Time elapsed: 6.30 min\\n\",\n      \"Epoch: 046/050 | Batch 000/469 | Cost: 13579.7725\\n\",\n      \"Epoch: 046/050 | Batch 050/469 | Cost: 14197.9629\\n\",\n      \"Epoch: 046/050 | Batch 100/469 | Cost: 14378.0156\\n\",\n      \"Epoch: 046/050 | Batch 150/469 | Cost: 13889.5391\\n\",\n      \"Epoch: 046/050 | Batch 200/469 | Cost: 14234.4473\\n\",\n      \"Epoch: 046/050 | Batch 250/469 | Cost: 14565.4922\\n\",\n      \"Epoch: 046/050 | Batch 300/469 | Cost: 14121.4434\\n\",\n      \"Epoch: 046/050 | Batch 350/469 | Cost: 13544.7070\\n\",\n      \"Epoch: 046/050 | Batch 400/469 | Cost: 13669.2461\\n\",\n      \"Epoch: 046/050 | Batch 450/469 | Cost: 14321.6992\\n\",\n      \"Time elapsed: 6.45 min\\n\",\n      \"Epoch: 047/050 | Batch 000/469 | Cost: 14563.6592\\n\",\n      \"Epoch: 047/050 | Batch 050/469 | Cost: 14157.8525\\n\",\n      \"Epoch: 047/050 | Batch 100/469 | Cost: 14169.9375\\n\",\n      \"Epoch: 047/050 | Batch 150/469 | Cost: 14047.9561\\n\",\n      \"Epoch: 047/050 | Batch 200/469 | Cost: 14237.7090\\n\",\n      \"Epoch: 047/050 | Batch 250/469 | Cost: 14265.3633\\n\",\n      \"Epoch: 047/050 | Batch 300/469 | Cost: 14120.1963\\n\",\n      \"Epoch: 047/050 | Batch 350/469 | Cost: 13613.9072\\n\",\n      \"Epoch: 047/050 | Batch 400/469 | Cost: 13844.0146\\n\",\n      \"Epoch: 047/050 | Batch 450/469 | Cost: 13815.9531\\n\",\n      \"Time elapsed: 6.59 min\\n\",\n      \"Epoch: 048/050 | Batch 000/469 | Cost: 14768.5332\\n\",\n      \"Epoch: 048/050 | Batch 050/469 | Cost: 13807.6055\\n\",\n      \"Epoch: 048/050 | Batch 100/469 | Cost: 14027.3555\\n\",\n      \"Epoch: 048/050 | Batch 150/469 | Cost: 14198.5234\\n\",\n      \"Epoch: 048/050 | Batch 200/469 | Cost: 14043.7871\\n\",\n      \"Epoch: 048/050 | Batch 250/469 | Cost: 14150.2158\\n\",\n      \"Epoch: 048/050 | Batch 300/469 | Cost: 14136.1113\\n\",\n      \"Epoch: 048/050 | Batch 350/469 | Cost: 13921.3516\\n\",\n      \"Epoch: 048/050 | Batch 400/469 | Cost: 14452.8145\\n\",\n      \"Epoch: 048/050 | Batch 450/469 | Cost: 13998.9541\\n\",\n      \"Time elapsed: 6.73 min\\n\",\n      \"Epoch: 049/050 | Batch 000/469 | Cost: 14730.7822\\n\",\n      \"Epoch: 049/050 | Batch 050/469 | Cost: 14744.3809\\n\",\n      \"Epoch: 049/050 | Batch 100/469 | Cost: 14377.9961\\n\",\n      \"Epoch: 049/050 | Batch 150/469 | Cost: 13894.9863\\n\",\n      \"Epoch: 049/050 | Batch 200/469 | Cost: 14319.2900\\n\",\n      \"Epoch: 049/050 | Batch 250/469 | Cost: 14335.9785\\n\",\n      \"Epoch: 049/050 | Batch 300/469 | Cost: 14045.4326\\n\",\n      \"Epoch: 049/050 | Batch 350/469 | Cost: 14342.3359\\n\",\n      \"Epoch: 049/050 | Batch 400/469 | Cost: 13990.9199\\n\",\n      \"Epoch: 049/050 | Batch 450/469 | Cost: 13979.7559\\n\",\n      \"Time elapsed: 6.87 min\\n\",\n      \"Epoch: 050/050 | Batch 000/469 | Cost: 13741.7539\\n\",\n      \"Epoch: 050/050 | Batch 050/469 | Cost: 14258.0557\\n\",\n      \"Epoch: 050/050 | Batch 100/469 | Cost: 14187.6738\\n\",\n      \"Epoch: 050/050 | Batch 150/469 | Cost: 14332.6895\\n\",\n      \"Epoch: 050/050 | Batch 200/469 | Cost: 14304.8984\\n\",\n      \"Epoch: 050/050 | Batch 250/469 | Cost: 13983.0000\\n\",\n      \"Epoch: 050/050 | Batch 300/469 | Cost: 14277.3750\\n\",\n      \"Epoch: 050/050 | Batch 350/469 | Cost: 13838.4023\\n\",\n      \"Epoch: 050/050 | Batch 400/469 | Cost: 13978.5732\\n\",\n      \"Epoch: 050/050 | Batch 450/469 | Cost: 13924.4717\\n\",\n      \"Time elapsed: 7.01 min\\n\",\n      \"Total Training Time: 7.01 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = features.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        z_mean, z_log_var, encoded, decoded = model(features)\\n\",\n    \"\\n\",\n    \"        # cost = reconstruction loss + Kullback-Leibler divergence\\n\",\n    \"        kl_divergence = (0.5 * (z_mean**2 + \\n\",\n    \"                                torch.exp(z_log_var) - z_log_var - 1)).sum()\\n\",\n    \"        pixelwise_bce = F.binary_cross_entropy(decoded, features, reduction='sum')\\n\",\n    \"        cost = kl_divergence + pixelwise_bce\\n\",\n    \"        \\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Reconstruction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        ax[i].imshow(img[i].detach().to(torch.device('cpu')).reshape((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Generate new images\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAlMAAABSCAYAAABwglFkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJztnXl0nVX19z8nYyfSgc4TtTMpQycsKBasIKXgQoSFoOBQEFnyA4QXEFSWiigvIE4LcYFWCq+wfqgIgjLIUFEZW6Qy1dgUQumYlmZomzZJ733eP26/+94kpdNNbp57uz9rdaW5uck9+znnOc8+373PPiGKIhzHcRzHcZz9o6i7G+A4juM4jpPPuDPlOI7jOI6TBe5MOY7jOI7jZIE7U47jOI7jOFngzpTjOI7jOE4WuDPlOI7jOI6TBe5MOY7jOI7jZEFWzlQIYW4IoSqEUB1CuKazGuU4juM4jpMvhP0t2hlCKAb+C5wIrAIWA+dEUfRW5zXPcRzHcRwn3mSjTH0YqI6i6O0oilqA/wVO65xmOY7jOI7j5AclWfzuCOC9jO9XAbPavymEcCFwIUDv3r1nTJ48OYuP7D5qamrYuHFj2NXPCt3GQrEP4JVXXtkYRdGg9q8Xio0H8jiFwrexUOwDvxdxG/OC3dnYhiiK9usfcCbw64zvzwNu293vzJgxI8pXdrZ9j9el0G3MZ/uiKIqAJVEB2+jj9MCxMZ/tiyK/FyO3MS/YWxuzUaZWA6Myvh+58zXHcRzHcWJAtDMvesuWLezYsQOA/v37d2eTCpJscqYWAxNCCB8KIZQBZwMPd06zHMdxHMdx8oP9VqaiKNoRQvgf4AmgGPhNFEVvdlrL9kBTUxMAK1asYPXqlCC2detWADZt2kTv3r0BGDx4MABz5swBoKjIS2s58UWryKamJlpaWgAoLy8HoKysjJKSbMRkx3EOFNauXQvAokWLAHjttdcYPXo0AF/5ylcAKC0t7Z7GdSHJZBJIKXGQtrG8vLxLn/9ZzcxRFD0KPNpJbXEcx3Ecx8k78m6ZK2/73nvvBWD58uW89NJLAFRVVdn75IGfe+65ACxduhSAr3/963m3uo+iiBD2vJnAyT+2b98OpMfuyy+/DMC6detMVZ0yZQoAkyZNom/fvkB6tRX3ceFj13FyzyuvvMJjjz0GwE9+8hMABg0axOmnn96dzepympqa+M1vfgPAX/7yFwCOOuooAEaMGMHcuXOBtH/QmXOTx7wcx3Ecx3GyIG8kmk2bNgHwxBNPAHDfffcBUF9fz7Zt2wDo0aOHvb+2thaAG2+8EYBTTz0VgOrqaiZOnAjEL39Ksd7GxkYgraa98sorNDc3AzBmzBggtRvjk5/8JADFxcU5bum+E0WR2aXcNuW9bdmyhYMPPhjAlBflDkH6umgMvP/++/ZaXV0dkFp1DBs2DIABAwYA8b8uzc3N/PrXvwbS10JfQwh2nURFRYXZpOsTJ9WnqamJN99MpU1u3LgRgFWrVnHSSScB6dWgk3/ofhMad3EafwCJRALAdq1Bep7P/Nq+3fmuoMreP/zhD0Dq2fHGG28AcOSRRwIwcOBAxo8fD5B30Zk9oflw8eLF/OIXvwDSY7a+vh6AadOmMWLECABGjUoVIujMPs+LK9rS0sIjjzwCwOWXXw5gzkXfvn0tHKKLV1ZWZhdw/fr1ACZ5Tp06lbPOOguAcePGAd07Iejmb2xstDZWV1cD8NBDDwGwZs0ae7Dqprn88svtd0855RQgfhMbpNv7zjvvmCOsB+3rr78OwBFHHNGh7Znfq18znUshB/q8885DReE+8YlPAGmnKm7Inptuuoknn3wSSF0fSIelR48ezciRI4H0ZFhaWhqrBYBClM8//7x9lR16raGhwfr70ksvBbDNIXGh/aRbXV1t408/03wzZcoUa78c2+bmZrNRDv9zzz0HpPpMTuTJJ58MxPM+zUQPJm3sefXVV82u1tZWAMaOHQukNvgMHDgQSIWRoHsWMZofNe62bt3aYeGhth988MGUlZUB2CYPSJcL0PNkyJAhQPwW3ZnIpgcffBBIz5FRFHHYYYfZ/yElMMiZiPsY3FfefvttAG644Qbeey9VS7xfv35Auo9Xr15t/oDmrl69enVaG+I7ShzHcRzHcfKAvFCm/vSnP/HTn/4USIdB5FGOHj3aksoyve1///vfAPznP/8B0onrt9xyi4WD1q1bB8Cxxx7b1SZ0QCtdqTOPPfaYqTj33HMPkF7lNjU1mb3yqG+77TZLQv7oRz8KxLMQm8Jwv/rVr8wuof4qLy/vEHpNJpOWlF1TUwOkwnsAmzdvNlVO1+POO+/k5ptvBuCggw7qKnM6hRtuuAGA22+/3VbUUp/Ul+PGjeNDH/oQAD179uyGVn4wWg0rpHD//fcDqVWx+k99u23bNv7xj38AadUtbsqU7Fm+fDkAP/vZz3j00dQmZY0zrW579+7NlVdeCaTvt379+tnYvPXWWwF49913gZRKc8cddwBYOkJnroY7m2QyydNPPw2k1Y4///nPFhbSdnOF40eNGmXzr5SQk046KafqVBRFpiq++OKLQMoOPQMOP/xwID3uqqqqbN7V1y1btjB9+nQAjj76aCCtbMydO5fhw4cD8QqPRVFkfaRxJ+Vp8uTJpr4ptBdFkSVjFworVqwAsGhTdXW1pYxUVFQA6fv7/ffft1QTPX9dmXIcx3Ecx4kJ8XGzd0FmPpFyMbTi1cpo9OjRu8y3mTp1KpBe8f/3v/8F4NFHH7WV5SWXXALAzJkz2ySv5wIlyGsFrER5wHIQVKwR0oqNVIrKykr7/+bNm4H0SioO8XDF6bVCX7hwYYd2Kpl+1KhRu1zxnXjiiW2+l3J34403miKlz+nbt6+tQONaiE7j+cc//jGQUhzPPPNMAPs6c+ZMAIYOHRqLftwVUoelHK5atQqAkSNHcsIJJ3R4vxQfqTdK/owLGnvKUTvnnHNsC/nKlSvbvDeRSHDXXXcBmJqaTCYtR0rKjcblQQcdZHNQ3BTGXfHOO+/wwgsvAOncxOHDh/O1r30NSM9DmWreM888A6RzphKJRM7zpqT6KXempqbGIg6aF3Q/NTc3m9KkzRGtra1cf/31QFqtqqysBFLjQ/mYUq9y/bzYFXV1dbaB5V//+heQnjNLS0utzVJhysrKYtHuzmLdunVcdtllAJZsP3jwYFOm1MfKZ9yxY4dFArpCHY+1M5UZKlAiqBwMXbApU6ZwyCGHAGmJs7y83B6oqtujie/xxx+3G++Pf/wjkHqQSaLOBclk0pI7dRO0tLTYpC55VpN7RUWFhRSUQDdv3jyOO+44IPXgjRvtk3o3b95sr2miVYJnnz59bCJWv44dO9augx5M2ik2f/58FixYAKTDiKWlpeakxRWFexoaGoDUZojvf//7QDqhN64OVCZyahUu1waIoqIiu+80dt99990OYfS47UTUeNT8MXz4cHN8lQogJ6mqqsr6TBP4ihUrLAwoJ0NOx4033mhhlrjYuyv0kFm0aJFtfDnvvPOAVBqE7FLoRKH3tWvX2rXRvJrrhO0oimxxKsd1+vTpTJo0CcC+agFeXFxs/aXNPsuXL2f+/PlA+rmgBfgvf/lL5s2bB6TmKkg5Wt0d8luzZg1vvfUWkH4GqH3l5eXmMGgRu23btja7pPMV2XPttdfaBh45iUcddZQ5z1rsKcUnkUjY3NV+d2pn4GE+x3Ecx3GcLIi1MiXvsaWlxSRyedZSNQYOHGgrD22FHzRoUJuEUUgnpPfo0cO8U0n4NTU19jdyESIKIdj2289+9rMAnHHGGbalU4qMUNIcpMoIQEoNkNqmlbVWvq2trW1CpJBaqSjZTisqXYfm5uYuC0EoHBdCsBWrEsQlmffp04cJEyYA6VDB2LFjrS80DtTuzEr3avdhhx0W6zDKHXfcwcUXXwykx9iTTz5pId18QuNT40f9OnXqVOsj9ePs2bNNNZDsHmeFBlLtax/607wzYsQIHnjggTbvf/nlly2UIttUnmPKlCmmiMQR2aWyLAsXLuTCCy8E0pshBg0aZPOo+lyhk23bttlcphB1rkN8IQRT/xSCrqioaFOTL7NdURTZ80HjtK6uzlQqJWm/+uqrAGzYsMHqGqp/E4mEKSDdpVBlzvMXXXRRm/aNGzfOTk7QdXj11VdtnErByifap0ncfffddu0VnTriiCNsTCsqorFaUlJikY+uUE9dmXIcx3Ecx8mCWCtTitOXlJRYrpRWGR/+8IeBlDIhr1Q/y1SXlEcjr3bu3Ln89a9/BdKrmJqaGvNitVLpSkIItkqfMWMGkFrx6bOVsKs2V1VVmXKmxHrZDB1PyV6xYoWpbsopmjp1qr1Pf1dfhw4daquXzkJ/W9e1tLTUVgxSyPR9SUmJ9ZkKHPbs2bND4cTMAnuyS8yaNSuWJRGkylx99dX2mlRSrZLyDfVD+9V95hjSvdja2mrqWxxLd+wtmRW/dZ8pd6OmpsZ+rqRfJeJrm31c0RyoOWf27Nl8/OMfB9Lqf+/evW0uVr6f3r906VIrRqqE7e5QHjXfSHEZOHCg5dFontF7ksmkKeaan1avXm0qh8ax5ti7777bxvrjjz9uP9NGiu5Sl0eMGME111wDpHPepEZNmTLF1JfMop0qt5OPc8+yZcuAdBmIKIosGvWZz3ymw/vV/8rzKykpYdasWUDH6E9n4MqU4ziO4zhOFsRamVKss6WlxXIXVCBOXvfMmTMtnq+cmRBCG9UDsAKI8+fPN/VDp0r//Oc/tzi58hu6wnPNRF6zcoUSiYSpVVo1LFq0CEjtRGy/w6++vt48bqk02tK8evVqW0GKHTt2dMgpkpIzY8aMTl9N6vNV0iLzPCzF7Z999lkgtTNRqoXalBnT1qpYO6sWLlxoK0utho8//vgu77N9Qavghx9+GEjlrqm4ZRyOMcoG5eFJYdT9V1tba7uKNCZra2tth1V3737qDBKJhK2Gv/e97wGpFbKugXIgv/CFLwDxt1nlD6SyTZ8+vUPRw0QiYfOpdrjpvu7du7f1eXcVY00kEra7UHNFU1MTGzZsANK5tFK4GxoaLPdL7x8+fLgpcTqSJXMHueYz7Qyrqamx/+vvd8exM5rTdU/q2bZ27Vprj56jGzZssDlUz518QP0gZUp9NnLkSC644AIgnd+3cuVKU1tVikXq4wUXXGA7druCWN/pktOTyaRJyRo8cn6GDx9u0m7mw0n/1ySg35s4caINKG0B3rx5s91AmhByVSpB7SwpKTFHQvKykiozK0u/9tprQKpWjwaLyixIim7vSEGqmromdknBCkF0hRMiWzKTx9s7vQpxTpo0aZfbx+WQrFmzBkhX2t6wYYP93W9/+9tA+lrFBZ0R9tWvfhVIhZvVr/m8Pbm5udkWIRp/mQdYK6yp8dqnTx8bl3qAxbUO2O7ILM+hOlMqBVBWVmZlBE477TQgfb5bXFFtMFUMV2hr5MiR1j96aLW2ttpJEk899RSQHt9z5szh+OOPB3K/OJADuGTJEnOmNMZ69uxpzqDGohLQi4uL7Wfq1969e9s8qLD13/72N/ss/V3NSb17926zeO8O+vTpY4tQVQL/+9//DqSeIXL25Pg+/PDDnHrqqUD8ypPsDs0f6r8f/vCHQGoBLQdY6TzTp0+32o1C/TR06NAuTePxMJ/jOI7jOE4WxFKZktesFcUhhxxiYR2tHpT817dv391K6fK89Xu9evUyWVqrySeeeMK2wR566KGdasu+IM+7feJcCMHkeJUFeOGFF2w1ovfp68SJEzsUcauvr7fVpxImu3LLtq63bJo/f77JzVLGlKw7depU22CQufVYiocStrV9u7m52YpcSq6OS2VfJYKqWKwUtOOPP96SP6W4lpaWxvpE+l3x3nvvWT8uXLgQaHtPtk/wPOiggzrYqHB2PilUUnuXLFli4XSN1crKSpuPFIaPc7+2tLTYeYkKgShkG0KwuVb3X21tLYsXLwbgt7/9LZC2c8qUKTnfWCClQhuJ3nnnHWufFKpTTjmlQwK25qKBAwfaBgE9O1pbW21c632ak7Zu3WoqncohjB071uzuLnWnpKTEbFNZHbVl1apVtklAatX48ePzsiSCbFK0Q303YcKEDpsMIN2nGr+yecaMGV2aChLfO95xHMdxHCcPiKUypZWHVvDDhg2zFb9yEZQkubcF4uTdFhUVdcjd2Lhxo+U/KHmtO2PK8p7lWZeWllpJBB11UFdXZwmwuia6Xq2trXbMgFbR/fr1szwwHQPSlbZpVXjFFVcAqfOulOOg1Z0UsszkdJGZVKrz/ZSPU1xczOc+9zkgnYAeF5RPdMMNNwDp/IsjjzzSjh+RCjBs2LAOfadVVVFRUSzzbt566y0eeeQRIN0v6us1a9ZY4UO9dv7551s/6n7TvTVs2LA250/GEbU18xxC5c1ozPbq1cvuqTgXjpUtr7/+up0/p3lPeaSNjY0dziN87733LP9G7zv33HOBlOKa6zlSbVA777//fntNilkikTBVeNq0aUB6rhgwYICp8urLpqYmUx/1rFEe7bZt20z1nzNnDgDHHHOMJZ53F83Nzdbml156CUiXrBg7dqwpbcpNPfroo2M9PveE+lFqVObcoTFYXFxsERg9P6UaaxNaVxE7ZyqKIjuvTjd/a2urXSxJdnKi9lZO199qamqywzAVdikpKbEbUzsGVME4l6EItVETgxwiSN/0n/rUp4BUpVdNHGqjEoKrqqos2VkOTEtLiyXr5XLXmxyHo446qk2tng8is8KyQmXf+MY32rxn8uTJtlsq19WWd0dmvbJvfvObQNpxHTJkiMnUcpIaGxstoVeOon7/5JNP3ucFQy6orKy0JFaNN42/0tJSm7g0kT3yyCPmxGvcaTdnr169dlkbLk7I2VB9oXvuucf6Q47vhAkTbLGj8R5HNIc88MADtqlDIVctbFavXm3zojaMLF26lFtuuQVI359yTHJ5Hqbar/lbi+5Vq1bZ3Ja5S031snRqhPoms+adHK5kMmk7FVXhXht7WltbOeaYY4D0wchxOIi8Z8+e5lBo/sisv6dnmO67adOm2dyjfsxMkdH1VZi3vLy8Q/J+rkkmk+bctj9xYVfXv6WlxRateo5+6UtfArq+6ruH+RzHcRzHcbIgdsrU9u3brT6IVr7btm2zFb5e00qqV69eHUJymXWmtPKQQrNu3Trz4pWY19jYaN54+23BuVwxy25Jtpkn0EtmVo2iqVOnmtIhWzO3ameWXNB7pHp0R3Ls7j4z86wsydRvvPEG1157LZBegaovPv/5z5sqFye2bNliKziFMiU5DxgwwFZWmTV7MtVHSKsBlZWVsVKkxNixY622i6oOq2+3b9/Oc889B7StOv273/0OSKsGutcyt+FL4eju1X57lNirmmgtLS12L0oVGDlypCkjcWt/JlJa1q1b16E2Uub5npp3FLK97bbbbB7RKl+bR3I5RjWmpK5oHI0ePdpUJ6kP48ePt81ECgtlns0nG/U8WbZsmYXFlMyuvz906FA+/elPA+n7Og73ZnFxsYWulCYgZs6caWNR78mcU9qf3VpbW2sbfRTSnTNnjp28kWt71dcrV640pVSoj6MosvdJ7X7jjTdsvhEa612dUuDKlOM4juM4ThbsUZkKIYwC7gGGABFwZxRFPwshDADuB8YANcBZURTVfdDf2VuWLFnCVVddBaS901NPPdXivplJn5DyyLVqkvdcWlpqKoDUGHndy5Yts7+rcgiJRKJDQclcVS5WWxoaGnjwwQeBdJFKUV5ebjlQSiYcMGCAreoVU1ZSZaaallnhWCUJpNLJY++u1bRWh+vXr7d8BZV7uOyyy0xpE9/61rcAuOSSS2KVYyPlrLa21lY/Kt2girshBFMBlJe3fv16K0Q6f/58AL785S8DXb+K2l+KioosL0pftcptbW21MfjPf/4TSI013bt33303kM63aWho4NhjjwXSBWS7q4p2e7RiV2FfqTqZ51gqPyWEEOtcKanzSjrv379/h1Ik2niTWfBX/VpXV2fjWXPI9OnTc9DytmSWNoD0+Js4caIli0u1Gjp0qPWPlAzdp4lEwp4Lb775JpB6PmhDT/syPD/4wQ8s8TxOpQVCCGbv9ddfD6SjE8lk0hK2P/KRjwCpTVt6rrXPy33hhRdYsGABkC4ZtG7dupyXCtJY1X23YsUKm+tlq/qloqLC+lT357nnnsv69esBbG7RqQRdzd4oUzuA/xNFUSVwNHBxCKESuAZ4OoqiCcDTO793HMdxHMc5oNij/BJF0Vpg7c7/bw4hLANGAKcBx+98293A34Bv7OJP7BVSaJ5++mkrfy8PdNGiRaZcqCigchn69etnMVWtKCoqKiyGrNLyUkHWrl3bpmgipFYxygXQGT+5Uj6kUtx///22clRcPrMN7bfyNjc3mwcu73zJkiVAasu6ivJJterXr5/ldXTXeWFqu3ZO6micuro6U+O++93vAqn+UjulSOmE9LipNsoxqK6uNoWi/c61RCJhu5C0zf66667jO9/5DpDOP+quE+izQYrBtm3b7D7NPIdROVa6x5WT0tDQYP0uJTkOJJNJ7rvvPiBd6kLzR0VFRYcczSFDhsSmaOyu0LVVflBdXZ3totR41dxZXFxs84l28A0ZMsTuwbPOOgvonrxLXW/dI9pB2djYaMfdiKqqKlPZ9HzQ7yeTSZsrn3nmGfuqPta8c+uttwKpMghxLFMC6RxaHemj5+KWLVtsF63sSSaTNvcqH07HAj300EP2PNEOz/Hjx+f8WaH54IknngDg3nvvtX7R0WFSjQcPHmwKm54NmzdvtlziH/3oR/a+XLBPVyqEMAaYBrwEDNnpaAGsIxUG3G8k761Zs6ZNFWxIheokQ2u7vByi7du37/asMzkkmuwmT55sMqY+Z/bs2Sbd66DIrg596UZXmGfBggUWYtSDOFNiVfhL8nRJSYlNEvpbkkZra2vtfUrcP//8881RVHX0XIb3amtrre90o8jRXbNmjTkd6suysjIrL3DOOecA8XOiNGaVNN/Q0GAPJU1Sek9TU5OFHfSwmj9/PqeccgqQPo8xH5HNy5cvt8lN9924cePs4ae+lYM/ZswYCxtpAowDW7dutXtLZ9jJ+WhpaTGnWHPKkCFDYl3xXGieqK+vt4eukuk1JkMINk4vv/xyINWXmjPjEF7XtVYSfI8ePcyx0nl6b775pjlKmgsVAquvr7cFkJ4rO3bssBDzlVdeCaQfwvqcOCJnR2fVqq2NjY220Ur3Z2trqzkrWsSr5MfKlSvNIdHmnjFjxuR8XGu+1LOioqLCXlMajJ7RyWTSNobIxmHDhnHzzTcD2AHruWKvr1QIoQ/wAPD1KIoaM38WpWbJXXo0IYQLQwhLQghLtFut0Ch0GwvdPnAbC4VCt7HQ7QO3sVA4EGzMZK+UqRBCKSlH6t4oiv648+X1IYRhURStDSEMA2p39btRFN0J3Akwc+bMD5SQtGoYPny4ecNSpjZt2mQ/b7+9HNLeub4efvjhViF79uzZqcbulDCLi4tN/lQCdllZWVYe+N7amInKH/z+978HUjaq/UoKVMikpKTEpE2tikMItprU72mlPGbMGFtdSd2aMWOGrbT2VZHaH/uEVhVPPfWU2dr+VG/JuJAOZ15xxRUWHpLs3JXsj40akwpX3n777aaeSY2RAtDc3Gxh1jPPPBNIKW4TJ07sLBP2SDb9uDs0Tp999lkLYZ544olAKqFcqo4U0cwCl51dQLYzbHz77bft3lIfq+2bNm1qs2UeUvdbrlTe/bFPYVilSLz88su2+UYV+pWsPGjQIJsfVezy0EMPzamKvbc2KkRZWVlpW/jV9sWLF5uqqAe5inEmEokOlcBHjRplm0B0LfTs6ArbO/te1Nyvr/3797d59cUXXwRSYd677roLSIfKtBnp0ksvtZChin1mWw4hGxtVKqW6urrDvaiTFxKJhD23VUZnwoQJnH322Z3S/n1ljx5ESI2kBcCyKIp+nPGjh4Ev7vz/F4E/dX7zHMdxHMdx4s3eKFMfBc4DXg8hLN352jeB/wv8LoRwPvAucFZWDclQlXR8iOKhiUTCPGjFc7W6PeSQQyxOrJ8NHjzYVh5xLKKXTCZtFaCEzueff97i2VotSjkrLi62Fb+Um/r6etsafOGFFwLpWPKsWbNsS76uQ3cVmZOCWFtba9vl26uLvXr1MtXs4osvBuCiiy6KzTb5D0IqVKbip5wh5QUpF+yqq66y/LXTTjsNiFee0P4g1VFJre+//74pcrL/2GOPtTHbvrxJXNm+fbvl2whtvW5oaDC7tWkl19vH9xXd+1Jtzj77bMtVU99obFZWVtrc1F0bVfaH9mUTTjrpJMuHkjJ18sknAymVXMqH7t0hQ4ZYccv9VfDjRElJCePHjwfS8+zChQvtGaP8WZV8mDVrViyORNIzXHNkVVWV5fApUiV7jjnmGDvmR7mnyh3rDvZmN98/gQ8aVZ/orIZInpw3b57J0Uo8LioqsoeSbvTMiTkfkj8zKSoqspCPkuQqKytNztROCxFC6LCLprW11a6TJhBNfrk8e29P6MFTVlZmE5gcY03k8+bN44wzzgDguOOOA+JTb2h3aAJXZe/rrrvOHEVN5ApR9u/fnxNOOKEbWtn1aPPGpk2bzEFU3aiDDz44FtWi94W+fftaYracQn1NJpMW/lEdmzjdb7tDD8kTTjjB6kRpsSV7862vPoiioiKrCdW+NlRlZaXdp1oQFRUV5d1zZE/oeaAQ6MUXX2w13vQcleM4bty4WPS9+kCh1ptuusmei0qZyDzUWMJLLDZGdHcDHMdxHMdx8pnY6bg9e/aM5blrnY1WS6effjqQUprkgQt56WVlZbFYNewPWh2NHz+e888/H2ibcA4pufZjH/sYEL/yB7tDKyQphLNmzepQg6iQkY2ZZUXU33GuCP5BqO/q6+uZOXMmkK7dpjE7c+ZMUxgVVs8X1F89evTIyaaOuFJeXp5X80y26DkyevRovvjFL+7h3fFAEQpI35f6GlcFMZ6tchzHcRzHyRNip0wdqJSWlsYi7tvZaDV84oknmvqkVb7yovJVddsVB4IiJWRrnM4rywbXvJV+AAADoUlEQVTZM27cOEvevfrqq4H0GJ00aZIpkQeSuuE43YXuy7jPra5MOY7jOI7jZIErU05OCCHYzqH2BfMcJ04MGjTIchm1Gs7c9RX3FbLjOLnHnSnHOUCJosgdgw8gH0pzOI4THzzM5ziO4ziOkwUh84y7Lv+wEDYAW4GNOfvQ/Wcgbdt5SBRFg/b0SyGEzUBVl7Wqc9lnG/O8D6HwbdzbcXog2Oj3Ynzwe/EDOEBsLOh7EXLsTAGEEJZEUTQzpx+6H+xvO/PFPih8G7Npp9sYHwp9nELh2+jjtOt+N5cU+jiF/W+rh/kcx3Ecx3GywJ0px3Ecx3GcLOgOZ+rObvjM/WF/25kv9kHh25hNO93G+FDo4xQK30Yfp133u7mk0Mcp7Gdbc54z5TiO4ziOU0h4mM9xHMdxHCcLcuZMhRDmhhCqQgjVIYRrcvW5eyKEMCqEsCiE8FYI4c0QwmU7X/9uCGF1CGHpzn/z9uJvuY3dRGfZGFf7oPBt9HHqNrb7OwVt387fcRu7ic60EUhVQe7qf0AxsAIYC5QB/wYqc/HZe9G2YcD0nf8/CPgvUAl8F7jSbTxwbIyzfQeCjT5O3cYDxT63sXBs1L9cKVMfBqqjKHo7iqIW4H+B03L02bsliqK1URT9a+f/NwPLgBH78afcxm6kk2yMrX1Q+Db6ON0nCt3GQrcP3MZupRNtBHIX5hsBvJfx/SqyaHRXEUIYA0wDXtr50v+EEF4LIfwmhNB/D7/uNsaELGzMC/ug8G30cXrA21jo9oHbGBuytBHwBHQjhNAHeAD4ehRFjcAvgXHAVGAtcGs3Nq9TcBvdxnyg0O0Dt5ECsLHQ7QO3kX2wMVfO1GpgVMb3I3e+FgtCCKWkLua9URT9ESCKovVRFCWiKEoCvyIlV+4Ot7Gb6QQbY20fFL6NPk7dxp0Uun3gNnY7nWQjkDtnajEwIYTwoRBCGXA28HCOPnu3hBACsABYFkXRjzNeH5bxttOBN/bwp9zGbqSTbIytfVD4Nvo4NdzGwrcP3MZupRNtTLGvGev7+w+YRypbfgXwrVx97l6061ggAl4Dlu78Nw/4f8DrO19/GBjmNha+jXG170Cw0cep23gg2ec2Fo6NURR5BXTHcRzHcZxs8AR0x3Ecx3GcLHBnynEcx3EcJwvcmXIcx3Ecx8kCd6Ycx3Ecx3GywJ0px3Ecx3GcLHBnynEcx3EcJwvcmXIcx3Ecx8kCd6Ycx3Ecx3Gy4P8Dj3RLiA+iYOEAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(10):\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### RANDOM SAMPLE\\n\",\n    \"    ##########################    \\n\",\n    \"    \\n\",\n    \"    n_images = 10\\n\",\n    \"    rand_features = torch.randn(n_images, num_latent).to(device)\\n\",\n    \"    new_images = model.decoder(rand_features)\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### VISUALIZATION\\n\",\n    \"    ##########################\\n\",\n    \"\\n\",\n    \"    image_width = 28\\n\",\n    \"\\n\",\n    \"    fig, axes = plt.subplots(nrows=1, ncols=n_images, figsize=(10, 2.5), sharey=True)\\n\",\n    \"    decoded_images = new_images[:n_images]\\n\",\n    \"\\n\",\n    \"    for ax, img in zip(axes, decoded_images):\\n\",\n    \"        ax.imshow(img.detach().to(torch.device('cpu')).reshape((image_width, image_width)), cmap='binary')\\n\",\n    \"        \\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-cvae.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Conditional Variational Autoencoder\\n\",\n    \"\\n\",\n    \"## (with labels in reconstruction loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple conditional variational autoencoder that compresses 768-pixel MNIST images down to a 35-pixel latent vector representation.\\n\",\n    \"\\n\",\n    \"This implementation concatenates the inputs with the class labels when computing the reconstruction loss as it is commonly done in non-convolutional conditional variational autoencoders. This leads to sightly better results compared to the implementation that does NOT concatenate the labels with the inputs to compute the reconstruction loss. For reference, see the implementation [./autoencoder-cvae_no-out-concat.ipynb](./autoencoder-cvae_no-out-concat.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:1\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:1\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 0\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"num_epochs = 50\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_classes = 10\\n\",\n    \"num_features = 784\\n\",\n    \"num_hidden_1 = 500\\n\",\n    \"num_latent = 35\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def to_onehot(labels, num_classes, device):\\n\",\n    \"\\n\",\n    \"    labels_onehot = torch.zeros(labels.size()[0], num_classes).to(device)\\n\",\n    \"    labels_onehot.scatter_(1, labels.view(-1, 1), 1)\\n\",\n    \"\\n\",\n    \"    return labels_onehot\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConditionalVariationalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_features, num_hidden_1, num_latent, num_classes):\\n\",\n    \"        super(ConditionalVariationalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        self.hidden_1 = torch.nn.Linear(num_features+num_classes, num_hidden_1)\\n\",\n    \"        self.z_mean = torch.nn.Linear(num_hidden_1, num_latent)\\n\",\n    \"        # in the original paper (Kingma & Welling 2015, we use\\n\",\n    \"        # have a z_mean and z_var, but the problem is that\\n\",\n    \"        # the z_var can be negative, which would cause issues\\n\",\n    \"        # in the log later. Hence we assume that latent vector\\n\",\n    \"        # has a z_mean and z_log_var component, and when we need\\n\",\n    \"        # the regular variance or std_dev, we simply use \\n\",\n    \"        # an exponential function\\n\",\n    \"        self.z_log_var = torch.nn.Linear(num_hidden_1, num_latent)\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        self.linear_3 = torch.nn.Linear(num_latent+num_classes, num_hidden_1)\\n\",\n    \"        self.linear_4 = torch.nn.Linear(num_hidden_1, num_features+num_classes)\\n\",\n    \"\\n\",\n    \"    def reparameterize(self, z_mu, z_log_var):\\n\",\n    \"        # Sample epsilon from standard normal distribution\\n\",\n    \"        eps = torch.randn(z_mu.size(0), z_mu.size(1)).to(device)\\n\",\n    \"        # note that log(x^2) = 2*log(x); hence divide by 2 to get std_dev\\n\",\n    \"        # i.e., std_dev = exp(log(std_dev^2)/2) = exp(log(var)/2)\\n\",\n    \"        z = z_mu + eps * torch.exp(z_log_var/2.) \\n\",\n    \"        return z\\n\",\n    \"        \\n\",\n    \"    def encoder(self, features, targets):\\n\",\n    \"        ### Add condition\\n\",\n    \"        onehot_targets = to_onehot(targets, self.num_classes, device)\\n\",\n    \"        x = torch.cat((features, onehot_targets), dim=1)\\n\",\n    \"\\n\",\n    \"        ### ENCODER\\n\",\n    \"        x = self.hidden_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        z_mean = self.z_mean(x)\\n\",\n    \"        z_log_var = self.z_log_var(x)\\n\",\n    \"        encoded = self.reparameterize(z_mean, z_log_var)\\n\",\n    \"        return z_mean, z_log_var, encoded\\n\",\n    \"    \\n\",\n    \"    def decoder(self, encoded, targets):\\n\",\n    \"        ### Add condition\\n\",\n    \"        onehot_targets = to_onehot(targets, self.num_classes, device)\\n\",\n    \"        encoded = torch.cat((encoded, onehot_targets), dim=1)        \\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        x = self.linear_3(encoded)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.linear_4(x)\\n\",\n    \"        decoded = torch.sigmoid(x)\\n\",\n    \"        return decoded\\n\",\n    \"\\n\",\n    \"    def forward(self, features, targets):\\n\",\n    \"        \\n\",\n    \"        z_mean, z_log_var, encoded = self.encoder(features, targets)\\n\",\n    \"        decoded = self.decoder(encoded, targets)\\n\",\n    \"        \\n\",\n    \"        return z_mean, z_log_var, encoded, decoded\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConditionalVariationalAutoencoder(num_features,\\n\",\n    \"                                          num_hidden_1,\\n\",\n    \"                                          num_latent,\\n\",\n    \"                                          num_classes)\\n\",\n    \"model = model.to(device)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### COST AND OPTIMIZER\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/050 | Batch 000/003 | Cost: 71038.5859\\n\",\n      \"Epoch: 001/050 | Batch 050/003 | Cost: 27687.3066\\n\",\n      \"Epoch: 001/050 | Batch 100/003 | Cost: 22471.4355\\n\",\n      \"Epoch: 001/050 | Batch 150/003 | Cost: 20541.7598\\n\",\n      \"Epoch: 001/050 | Batch 200/003 | Cost: 19670.0254\\n\",\n      \"Epoch: 001/050 | Batch 250/003 | Cost: 18063.1172\\n\",\n      \"Epoch: 001/050 | Batch 300/003 | Cost: 18197.8105\\n\",\n      \"Epoch: 001/050 | Batch 350/003 | Cost: 17140.0234\\n\",\n      \"Epoch: 001/050 | Batch 400/003 | Cost: 17088.2832\\n\",\n      \"Epoch: 001/050 | Batch 450/003 | Cost: 16489.5078\\n\",\n      \"Time elapsed: 0.12 min\\n\",\n      \"Epoch: 002/050 | Batch 000/003 | Cost: 16543.6680\\n\",\n      \"Epoch: 002/050 | Batch 050/003 | Cost: 15903.9375\\n\",\n      \"Epoch: 002/050 | Batch 100/003 | Cost: 15624.4824\\n\",\n      \"Epoch: 002/050 | Batch 150/003 | Cost: 15635.6016\\n\",\n      \"Epoch: 002/050 | Batch 200/003 | Cost: 15245.4121\\n\",\n      \"Epoch: 002/050 | Batch 250/003 | Cost: 15592.2266\\n\",\n      \"Epoch: 002/050 | Batch 300/003 | Cost: 14563.4980\\n\",\n      \"Epoch: 002/050 | Batch 350/003 | Cost: 15090.5811\\n\",\n      \"Epoch: 002/050 | Batch 400/003 | Cost: 14832.8682\\n\",\n      \"Epoch: 002/050 | Batch 450/003 | Cost: 15250.2480\\n\",\n      \"Time elapsed: 0.24 min\\n\",\n      \"Epoch: 003/050 | Batch 000/003 | Cost: 14894.0859\\n\",\n      \"Epoch: 003/050 | Batch 050/003 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\"Epoch: 011/050 | Batch 450/003 | Cost: 12833.6787\\n\",\n      \"Time elapsed: 1.33 min\\n\",\n      \"Epoch: 012/050 | Batch 000/003 | Cost: 13168.7441\\n\",\n      \"Epoch: 012/050 | Batch 050/003 | Cost: 12641.6289\\n\",\n      \"Epoch: 012/050 | Batch 100/003 | Cost: 12435.3809\\n\",\n      \"Epoch: 012/050 | Batch 150/003 | Cost: 12635.2422\\n\",\n      \"Epoch: 012/050 | Batch 200/003 | Cost: 12657.0459\\n\",\n      \"Epoch: 012/050 | Batch 250/003 | Cost: 12965.5098\\n\",\n      \"Epoch: 012/050 | Batch 300/003 | Cost: 13022.1289\\n\",\n      \"Epoch: 012/050 | Batch 350/003 | Cost: 13023.3535\\n\",\n      \"Epoch: 012/050 | Batch 400/003 | Cost: 12723.5039\\n\",\n      \"Epoch: 012/050 | Batch 450/003 | Cost: 12593.6680\\n\",\n      \"Time elapsed: 1.45 min\\n\",\n      \"Epoch: 013/050 | Batch 000/003 | Cost: 13062.8193\\n\",\n      \"Epoch: 013/050 | Batch 050/003 | Cost: 12942.4668\\n\",\n      \"Epoch: 013/050 | Batch 100/003 | Cost: 13116.7461\\n\",\n      \"Epoch: 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   \"Epoch: 014/050 | Batch 400/003 | Cost: 12984.9512\\n\",\n      \"Epoch: 014/050 | Batch 450/003 | Cost: 12996.2832\\n\",\n      \"Time elapsed: 1.69 min\\n\",\n      \"Epoch: 015/050 | Batch 000/003 | Cost: 12615.2100\\n\",\n      \"Epoch: 015/050 | Batch 050/003 | Cost: 13215.7402\\n\",\n      \"Epoch: 015/050 | Batch 100/003 | Cost: 12532.8447\\n\",\n      \"Epoch: 015/050 | Batch 150/003 | Cost: 12863.5928\\n\",\n      \"Epoch: 015/050 | Batch 200/003 | Cost: 12683.9209\\n\",\n      \"Epoch: 015/050 | Batch 250/003 | Cost: 12564.1309\\n\",\n      \"Epoch: 015/050 | Batch 300/003 | Cost: 12535.2500\\n\",\n      \"Epoch: 015/050 | Batch 350/003 | Cost: 12853.1445\\n\",\n      \"Epoch: 015/050 | Batch 400/003 | Cost: 12600.9902\\n\",\n      \"Epoch: 015/050 | Batch 450/003 | Cost: 12867.0781\\n\",\n      \"Time elapsed: 1.81 min\\n\",\n      \"Epoch: 016/050 | Batch 000/003 | Cost: 12429.7402\\n\",\n      \"Epoch: 016/050 | Batch 050/003 | Cost: 13151.1377\\n\",\n      \"Epoch: 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\"Epoch: 040/050 | Batch 450/003 | Cost: 12191.8916\\n\",\n      \"Time elapsed: 4.80 min\\n\",\n      \"Epoch: 041/050 | Batch 000/003 | Cost: 12098.7090\\n\",\n      \"Epoch: 041/050 | Batch 050/003 | Cost: 12562.2539\\n\",\n      \"Epoch: 041/050 | Batch 100/003 | Cost: 12609.0088\\n\",\n      \"Epoch: 041/050 | Batch 150/003 | Cost: 12270.9854\\n\",\n      \"Epoch: 041/050 | Batch 200/003 | Cost: 12757.7578\\n\",\n      \"Epoch: 041/050 | Batch 250/003 | Cost: 12277.8584\\n\",\n      \"Epoch: 041/050 | Batch 300/003 | Cost: 12219.4121\\n\",\n      \"Epoch: 041/050 | Batch 350/003 | Cost: 12215.5977\\n\",\n      \"Epoch: 041/050 | Batch 400/003 | Cost: 12476.9668\\n\",\n      \"Epoch: 041/050 | Batch 450/003 | Cost: 12220.5449\\n\",\n      \"Time elapsed: 4.92 min\\n\",\n      \"Epoch: 042/050 | Batch 000/003 | Cost: 12391.3916\\n\",\n      \"Epoch: 042/050 | Batch 050/003 | Cost: 12526.2002\\n\",\n      \"Epoch: 042/050 | Batch 100/003 | Cost: 12929.2305\\n\",\n      \"Epoch: 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   \"Epoch: 043/050 | Batch 400/003 | Cost: 13071.2891\\n\",\n      \"Epoch: 043/050 | Batch 450/003 | Cost: 12398.8311\\n\",\n      \"Time elapsed: 5.16 min\\n\",\n      \"Epoch: 044/050 | Batch 000/003 | Cost: 12913.1631\\n\",\n      \"Epoch: 044/050 | Batch 050/003 | Cost: 12169.1523\\n\",\n      \"Epoch: 044/050 | Batch 100/003 | Cost: 11856.3672\\n\",\n      \"Epoch: 044/050 | Batch 150/003 | Cost: 12280.6045\\n\",\n      \"Epoch: 044/050 | Batch 200/003 | Cost: 12343.7998\\n\",\n      \"Epoch: 044/050 | Batch 250/003 | Cost: 12746.3164\\n\",\n      \"Epoch: 044/050 | Batch 300/003 | Cost: 12279.5156\\n\",\n      \"Epoch: 044/050 | Batch 350/003 | Cost: 12548.7598\\n\",\n      \"Epoch: 044/050 | Batch 400/003 | Cost: 12430.6104\\n\",\n      \"Epoch: 044/050 | Batch 450/003 | Cost: 12775.3105\\n\",\n      \"Time elapsed: 5.28 min\\n\",\n      \"Epoch: 045/050 | Batch 000/003 | Cost: 12245.9482\\n\",\n      \"Epoch: 045/050 | Batch 050/003 | Cost: 12547.1270\\n\",\n      \"Epoch: 045/050 | Batch 100/003 | Cost: 12214.5732\\n\",\n      \"Epoch: 045/050 | Batch 150/003 | Cost: 12484.7715\\n\",\n      \"Epoch: 045/050 | Batch 200/003 | Cost: 12552.6123\\n\",\n      \"Epoch: 045/050 | Batch 250/003 | Cost: 12510.6064\\n\",\n      \"Epoch: 045/050 | Batch 300/003 | Cost: 12465.5566\\n\",\n      \"Epoch: 045/050 | Batch 350/003 | Cost: 12111.4629\\n\",\n      \"Epoch: 045/050 | Batch 400/003 | Cost: 12435.2578\\n\",\n      \"Epoch: 045/050 | Batch 450/003 | Cost: 12433.7461\\n\",\n      \"Time elapsed: 5.40 min\\n\",\n      \"Epoch: 046/050 | Batch 000/003 | Cost: 12598.7734\\n\",\n      \"Epoch: 046/050 | Batch 050/003 | Cost: 12568.2207\\n\",\n      \"Epoch: 046/050 | Batch 100/003 | Cost: 12853.6328\\n\",\n      \"Epoch: 046/050 | Batch 150/003 | Cost: 12230.1533\\n\",\n      \"Epoch: 046/050 | Batch 200/003 | Cost: 12326.4727\\n\",\n      \"Epoch: 046/050 | Batch 250/003 | Cost: 12770.4814\\n\",\n      \"Epoch: 046/050 | Batch 300/003 | Cost: 12082.1006\\n\",\n      \"Epoch: 046/050 | Batch 350/003 | Cost: 11948.0430\\n\",\n      \"Epoch: 046/050 | Batch 400/003 | Cost: 12462.7773\\n\",\n      \"Epoch: 046/050 | Batch 450/003 | Cost: 12145.4609\\n\",\n      \"Time elapsed: 5.52 min\\n\",\n      \"Epoch: 047/050 | Batch 000/003 | Cost: 12146.9893\\n\",\n      \"Epoch: 047/050 | Batch 050/003 | Cost: 11917.3779\\n\",\n      \"Epoch: 047/050 | Batch 100/003 | Cost: 12135.3457\\n\",\n      \"Epoch: 047/050 | Batch 150/003 | Cost: 11984.8691\\n\",\n      \"Epoch: 047/050 | Batch 200/003 | Cost: 12437.4248\\n\",\n      \"Epoch: 047/050 | Batch 250/003 | Cost: 12444.1416\\n\",\n      \"Epoch: 047/050 | Batch 300/003 | Cost: 12078.9043\\n\",\n      \"Epoch: 047/050 | Batch 350/003 | Cost: 12138.3818\\n\",\n      \"Epoch: 047/050 | Batch 400/003 | Cost: 12556.3086\\n\",\n      \"Epoch: 047/050 | Batch 450/003 | Cost: 12726.3828\\n\",\n      \"Time elapsed: 5.64 min\\n\",\n      \"Epoch: 048/050 | Batch 000/003 | Cost: 12927.1035\\n\",\n      \"Epoch: 048/050 | Batch 050/003 | Cost: 12747.8994\\n\",\n      \"Epoch: 048/050 | Batch 100/003 | Cost: 12002.6406\\n\",\n      \"Epoch: 048/050 | Batch 150/003 | Cost: 12093.2988\\n\",\n      \"Epoch: 048/050 | Batch 200/003 | Cost: 11541.1982\\n\",\n      \"Epoch: 048/050 | Batch 250/003 | Cost: 12530.8398\\n\",\n      \"Epoch: 048/050 | Batch 300/003 | Cost: 12200.4463\\n\",\n      \"Epoch: 048/050 | Batch 350/003 | Cost: 12818.4082\\n\",\n      \"Epoch: 048/050 | Batch 400/003 | Cost: 12760.9844\\n\",\n      \"Epoch: 048/050 | Batch 450/003 | Cost: 12186.3496\\n\",\n      \"Time elapsed: 5.76 min\\n\",\n      \"Epoch: 049/050 | Batch 000/003 | Cost: 12394.8848\\n\",\n      \"Epoch: 049/050 | Batch 050/003 | Cost: 12406.0801\\n\",\n      \"Epoch: 049/050 | Batch 100/003 | Cost: 12390.3223\\n\",\n      \"Epoch: 049/050 | Batch 150/003 | Cost: 12788.7949\\n\",\n      \"Epoch: 049/050 | Batch 200/003 | Cost: 12015.3936\\n\",\n      \"Epoch: 049/050 | Batch 250/003 | Cost: 12259.9609\\n\",\n      \"Epoch: 049/050 | Batch 300/003 | Cost: 12216.9688\\n\",\n      \"Epoch: 049/050 | Batch 350/003 | Cost: 12795.6113\\n\",\n      \"Epoch: 049/050 | Batch 400/003 | Cost: 11885.8955\\n\",\n      \"Epoch: 049/050 | Batch 450/003 | Cost: 12445.2891\\n\",\n      \"Time elapsed: 5.88 min\\n\",\n      \"Epoch: 050/050 | Batch 000/003 | Cost: 12782.9785\\n\",\n      \"Epoch: 050/050 | Batch 050/003 | Cost: 12291.9814\\n\",\n      \"Epoch: 050/050 | Batch 100/003 | Cost: 12721.1641\\n\",\n      \"Epoch: 050/050 | Batch 150/003 | Cost: 12482.5762\\n\",\n      \"Epoch: 050/050 | Batch 200/003 | Cost: 12581.8125\\n\",\n      \"Epoch: 050/050 | Batch 250/003 | Cost: 12422.5527\\n\",\n      \"Epoch: 050/050 | Batch 300/003 | Cost: 12384.3047\\n\",\n      \"Epoch: 050/050 | Batch 350/003 | Cost: 12031.4541\\n\",\n      \"Epoch: 050/050 | Batch 400/003 | Cost: 12278.7617\\n\",\n      \"Epoch: 050/050 | Batch 450/003 | Cost: 12190.8232\\n\",\n      \"Time elapsed: 6.00 min\\n\",\n      \"Total Training Time: 6.00 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        features = features.view(-1, 28*28).to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        z_mean, z_log_var, encoded, decoded = model(features, targets)\\n\",\n    \"\\n\",\n    \"        # cost = reconstruction loss + Kullback-Leibler divergence\\n\",\n    \"        kl_divergence = (0.5 * (z_mean**2 + \\n\",\n    \"                                torch.exp(z_log_var) - z_log_var - 1)).sum()\\n\",\n    \"        \\n\",\n    \"        # add condition\\n\",\n    \"        x_con = torch.cat((features, to_onehot(targets, num_classes, device)), dim=1)\\n\",\n    \"        \\n\",\n    \"        pixelwise_bce = F.binary_cross_entropy(decoded, x_con, reduction='sum')\\n\",\n    \"        cost = kl_divergence + pixelwise_bce\\n\",\n    \"        \\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader)//batch_size, cost))\\n\",\n    \"        \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Reconstruction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images][:, :-num_classes]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### New random-conditional images\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 0\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 1\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 2\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 3\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 4\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 6\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 7\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 9\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(10):\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### RANDOM SAMPLE\\n\",\n    \"    ##########################    \\n\",\n    \"    \\n\",\n    \"    labels = torch.tensor([i]*10).to(device)\\n\",\n    \"    n_images = labels.size()[0]\\n\",\n    \"    rand_features = torch.randn(n_images, num_latent).to(device)\\n\",\n    \"    new_images = model.decoder(rand_features, labels)\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### VISUALIZATION\\n\",\n    \"    ##########################\\n\",\n    \"\\n\",\n    \"    image_width = 28\\n\",\n    \"\\n\",\n    \"    fig, axes = plt.subplots(nrows=1, ncols=n_images, figsize=(10, 2.5), sharey=True)\\n\",\n    \"    decoded_images = new_images[:n_images][:, :-num_classes]\\n\",\n    \"\\n\",\n    \"    print('Class Label %d' % i)\\n\",\n    \"\\n\",\n    \"    for ax, img in zip(axes, decoded_images):\\n\",\n    \"        curr_img = img.detach().to(torch.device('cpu'))\\n\",\n    \"        ax.imshow(curr_img.view((image_width, image_width)), cmap='binary')\\n\",\n    \"        \\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-cvae_no-out-concat.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.7.1\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Conditional Variational Autoencoder \\n\",\n    \"\\n\",\n    \"## (without labels in reconstruction loss)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A simple conditional variational autoencoder that compresses 768-pixel MNIST images down to a 35-pixel latent vector representation.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"This implementation DOES NOT concatenate the inputs with the class labels when computing the reconstruction loss in contrast to how it is commonly done in non-convolutional conditional variational autoencoders. Not considering class-labels in the reconstruction loss leads to slightly worse results compared to the implementation that does concatenate the labels with the inputs to compute the reconstruction loss. For reference, see the implementation [./autoencoder-cvae.ipynb](./autoencoder-cvae.ipynb)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:1\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:1\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 0\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"num_epochs = 50\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_classes = 10\\n\",\n    \"num_features = 784\\n\",\n    \"num_hidden_1 = 500\\n\",\n    \"num_latent = 35\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def to_onehot(labels, num_classes, device):\\n\",\n    \"\\n\",\n    \"    labels_onehot = torch.zeros(labels.size()[0], num_classes).to(device)\\n\",\n    \"    labels_onehot.scatter_(1, labels.view(-1, 1), 1)\\n\",\n    \"\\n\",\n    \"    return labels_onehot\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConditionalVariationalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_features, num_hidden_1, num_latent, num_classes):\\n\",\n    \"        super(ConditionalVariationalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        self.hidden_1 = torch.nn.Linear(num_features+num_classes, num_hidden_1)\\n\",\n    \"        self.z_mean = torch.nn.Linear(num_hidden_1, num_latent)\\n\",\n    \"        # in the original paper (Kingma & Welling 2015, we use\\n\",\n    \"        # have a z_mean and z_var, but the problem is that\\n\",\n    \"        # the z_var can be negative, which would cause issues\\n\",\n    \"        # in the log later. Hence we assume that latent vector\\n\",\n    \"        # has a z_mean and z_log_var component, and when we need\\n\",\n    \"        # the regular variance or std_dev, we simply use \\n\",\n    \"        # an exponential function\\n\",\n    \"        self.z_log_var = torch.nn.Linear(num_hidden_1, num_latent)\\n\",\n    \"        \\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        self.linear_3 = torch.nn.Linear(num_latent+num_classes, num_hidden_1)\\n\",\n    \"        # don't output labels in resulting image as it yields worse results\\n\",\n    \"        #self.linear_4 = torch.nn.Linear(num_hidden_1, num_features+num_classes)\\n\",\n    \"        self.linear_4 = torch.nn.Linear(num_hidden_1, num_features)\\n\",\n    \"\\n\",\n    \"    def reparameterize(self, z_mu, z_log_var):\\n\",\n    \"        # Sample epsilon from standard normal distribution\\n\",\n    \"        eps = torch.randn(z_mu.size(0), z_mu.size(1)).to(device)\\n\",\n    \"        # note that log(x^2) = 2*log(x); hence divide by 2 to get std_dev\\n\",\n    \"        # i.e., std_dev = exp(log(std_dev^2)/2) = exp(log(var)/2)\\n\",\n    \"        z = z_mu + eps * torch.exp(z_log_var/2.) \\n\",\n    \"        return z\\n\",\n    \"        \\n\",\n    \"    def encoder(self, features, targets):\\n\",\n    \"        ### Add condition\\n\",\n    \"        onehot_targets = to_onehot(targets, self.num_classes, device)\\n\",\n    \"        x = torch.cat((features, onehot_targets), dim=1)\\n\",\n    \"\\n\",\n    \"        ### ENCODER\\n\",\n    \"        x = self.hidden_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        z_mean = self.z_mean(x)\\n\",\n    \"        z_log_var = self.z_log_var(x)\\n\",\n    \"        encoded = self.reparameterize(z_mean, z_log_var)\\n\",\n    \"        return z_mean, z_log_var, encoded\\n\",\n    \"    \\n\",\n    \"    def decoder(self, encoded, targets):\\n\",\n    \"        ### Add condition\\n\",\n    \"        onehot_targets = to_onehot(targets, self.num_classes, device)\\n\",\n    \"        encoded = torch.cat((encoded, onehot_targets), dim=1)        \\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        x = self.linear_3(encoded)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.linear_4(x)\\n\",\n    \"        decoded = torch.sigmoid(x)\\n\",\n    \"        return decoded\\n\",\n    \"\\n\",\n    \"    def forward(self, features, targets):\\n\",\n    \"        \\n\",\n    \"        z_mean, z_log_var, encoded = self.encoder(features, targets)\\n\",\n    \"        decoded = self.decoder(encoded, targets)\\n\",\n    \"        \\n\",\n    \"        return z_mean, z_log_var, encoded, decoded\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConditionalVariationalAutoencoder(num_features,\\n\",\n    \"                                          num_hidden_1,\\n\",\n    \"                                          num_latent,\\n\",\n    \"                                          num_classes)\\n\",\n    \"model = model.to(device)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### COST AND OPTIMIZER\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/050 | Batch 000/469 | Cost: 70192.2188\\n\",\n      \"Epoch: 001/050 | Batch 050/469 | Cost: 25749.1211\\n\",\n      \"Epoch: 001/050 | Batch 100/469 | Cost: 21997.0703\\n\",\n      \"Epoch: 001/050 | Batch 150/469 | Cost: 19867.7832\\n\",\n      \"Epoch: 001/050 | Batch 200/469 | Cost: 19859.0391\\n\",\n      \"Epoch: 001/050 | Batch 250/469 | Cost: 18637.0820\\n\",\n      \"Epoch: 001/050 | Batch 300/469 | Cost: 17863.7227\\n\",\n      \"Epoch: 001/050 | Batch 350/469 | Cost: 16946.2188\\n\",\n      \"Epoch: 001/050 | Batch 400/469 | Cost: 16602.0566\\n\",\n      \"Epoch: 001/050 | Batch 450/469 | Cost: 16563.0742\\n\",\n      \"Epoch: 002/050 | Batch 000/469 | Cost: 15998.6934\\n\",\n      \"Epoch: 002/050 | Batch 050/469 | Cost: 15980.9590\\n\",\n      \"Epoch: 002/050 | Batch 100/469 | Cost: 15842.0508\\n\",\n      \"Epoch: 002/050 | Batch 150/469 | Cost: 15789.3096\\n\",\n      \"Epoch: 002/050 | Batch 200/469 | Cost: 15471.0068\\n\",\n      \"Epoch: 002/050 | Batch 250/469 | Cost: 15047.9609\\n\",\n      \"Epoch: 002/050 | Batch 300/469 | Cost: 14812.4609\\n\",\n      \"Epoch: 002/050 | Batch 350/469 | Cost: 15064.4570\\n\",\n      \"Epoch: 002/050 | Batch 400/469 | Cost: 14684.2832\\n\",\n      \"Epoch: 002/050 | Batch 450/469 | Cost: 14621.3662\\n\",\n      \"Epoch: 003/050 | Batch 000/469 | Cost: 14662.3740\\n\",\n      \"Epoch: 003/050 | Batch 050/469 | Cost: 14373.9258\\n\",\n      \"Epoch: 003/050 | Batch 100/469 | Cost: 14580.2539\\n\",\n      \"Epoch: 003/050 | Batch 150/469 | Cost: 14757.9639\\n\",\n      \"Epoch: 003/050 | Batch 200/469 | Cost: 14678.1953\\n\",\n      \"Epoch: 003/050 | Batch 250/469 | Cost: 14471.2031\\n\",\n      \"Epoch: 003/050 | Batch 300/469 | Cost: 14082.8926\\n\",\n      \"Epoch: 003/050 | Batch 350/469 | Cost: 14371.0566\\n\",\n      \"Epoch: 003/050 | Batch 400/469 | Cost: 13371.3496\\n\",\n      \"Epoch: 003/050 | Batch 450/469 | Cost: 14689.0518\\n\",\n      \"Epoch: 004/050 | Batch 000/469 | Cost: 13579.7764\\n\",\n      \"Epoch: 004/050 | Batch 050/469 | Cost: 14012.3291\\n\",\n      \"Epoch: 004/050 | Batch 100/469 | Cost: 13688.7236\\n\",\n      \"Epoch: 004/050 | Batch 150/469 | Cost: 13726.9590\\n\",\n      \"Epoch: 004/050 | Batch 200/469 | Cost: 13938.3027\\n\",\n      \"Epoch: 004/050 | Batch 250/469 | Cost: 14040.9287\\n\",\n      \"Epoch: 004/050 | Batch 300/469 | Cost: 13934.2998\\n\",\n      \"Epoch: 004/050 | Batch 350/469 | Cost: 13701.2197\\n\",\n      \"Epoch: 004/050 | Batch 400/469 | Cost: 13695.0098\\n\",\n      \"Epoch: 004/050 | Batch 450/469 | Cost: 13170.8828\\n\",\n      \"Epoch: 005/050 | Batch 000/469 | Cost: 13500.4805\\n\",\n      \"Epoch: 005/050 | Batch 050/469 | Cost: 13655.4971\\n\",\n      \"Epoch: 005/050 | Batch 100/469 | Cost: 13458.8867\\n\",\n      \"Epoch: 005/050 | Batch 150/469 | Cost: 13754.9385\\n\",\n      \"Epoch: 005/050 | Batch 200/469 | Cost: 13421.4209\\n\",\n      \"Epoch: 005/050 | Batch 250/469 | Cost: 13213.7803\\n\",\n      \"Epoch: 005/050 | Batch 300/469 | Cost: 12693.9590\\n\",\n      \"Epoch: 005/050 | Batch 350/469 | Cost: 13030.9766\\n\",\n      \"Epoch: 005/050 | Batch 400/469 | Cost: 13811.0107\\n\",\n      \"Epoch: 005/050 | Batch 450/469 | Cost: 14092.8613\\n\",\n      \"Epoch: 006/050 | Batch 000/469 | Cost: 13308.8340\\n\",\n      \"Epoch: 006/050 | Batch 050/469 | Cost: 13082.6172\\n\",\n      \"Epoch: 006/050 | Batch 100/469 | Cost: 13904.7197\\n\",\n      \"Epoch: 006/050 | Batch 150/469 | Cost: 13171.1230\\n\",\n      \"Epoch: 006/050 | Batch 200/469 | Cost: 13504.8125\\n\",\n      \"Epoch: 006/050 | Batch 250/469 | Cost: 13535.9785\\n\",\n      \"Epoch: 006/050 | Batch 300/469 | Cost: 13284.6123\\n\",\n      \"Epoch: 006/050 | Batch 350/469 | Cost: 13442.9844\\n\",\n      \"Epoch: 006/050 | Batch 400/469 | Cost: 13444.6738\\n\",\n      \"Epoch: 006/050 | Batch 450/469 | Cost: 13511.4160\\n\",\n      \"Epoch: 007/050 | Batch 000/469 | Cost: 13908.5977\\n\",\n      \"Epoch: 007/050 | Batch 050/469 | Cost: 13451.8223\\n\",\n      \"Epoch: 007/050 | Batch 100/469 | Cost: 13165.7402\\n\",\n      \"Epoch: 007/050 | Batch 150/469 | Cost: 13328.9355\\n\",\n      \"Epoch: 007/050 | Batch 200/469 | Cost: 12998.3633\\n\",\n      \"Epoch: 007/050 | Batch 250/469 | Cost: 13683.5605\\n\",\n      \"Epoch: 007/050 | Batch 300/469 | Cost: 13152.0830\\n\",\n      \"Epoch: 007/050 | Batch 350/469 | Cost: 13329.2920\\n\",\n      \"Epoch: 007/050 | Batch 400/469 | Cost: 13330.4443\\n\",\n      \"Epoch: 007/050 | Batch 450/469 | Cost: 13523.7051\\n\",\n      \"Epoch: 008/050 | Batch 000/469 | Cost: 13326.9102\\n\",\n      \"Epoch: 008/050 | Batch 050/469 | Cost: 12950.0498\\n\",\n      \"Epoch: 008/050 | Batch 100/469 | Cost: 13583.9219\\n\",\n      \"Epoch: 008/050 | Batch 150/469 | Cost: 12776.9805\\n\",\n      \"Epoch: 008/050 | Batch 200/469 | Cost: 12999.6387\\n\",\n      \"Epoch: 008/050 | Batch 250/469 | Cost: 13324.9883\\n\",\n      \"Epoch: 008/050 | Batch 300/469 | Cost: 13418.3408\\n\",\n      \"Epoch: 008/050 | Batch 350/469 | Cost: 13043.9551\\n\",\n      \"Epoch: 008/050 | Batch 400/469 | Cost: 13222.0293\\n\",\n      \"Epoch: 008/050 | Batch 450/469 | Cost: 12615.9102\\n\",\n      \"Epoch: 009/050 | Batch 000/469 | Cost: 13419.1953\\n\",\n      \"Epoch: 009/050 | Batch 050/469 | Cost: 13427.7383\\n\",\n      \"Epoch: 009/050 | Batch 100/469 | Cost: 12853.0498\\n\",\n      \"Epoch: 009/050 | Batch 150/469 | Cost: 13082.6865\\n\",\n      \"Epoch: 009/050 | Batch 200/469 | Cost: 12926.8877\\n\",\n      \"Epoch: 009/050 | Batch 250/469 | Cost: 13223.6982\\n\",\n      \"Epoch: 009/050 | Batch 300/469 | Cost: 12966.2803\\n\",\n      \"Epoch: 009/050 | Batch 350/469 | Cost: 12672.2607\\n\",\n      \"Epoch: 009/050 | Batch 400/469 | Cost: 13285.1992\\n\",\n      \"Epoch: 009/050 | Batch 450/469 | Cost: 12638.7812\\n\",\n      \"Epoch: 010/050 | Batch 000/469 | Cost: 13139.2168\\n\",\n      \"Epoch: 010/050 | Batch 050/469 | Cost: 12674.6816\\n\",\n      \"Epoch: 010/050 | Batch 100/469 | Cost: 13080.3828\\n\",\n      \"Epoch: 010/050 | Batch 150/469 | Cost: 12448.9199\\n\",\n      \"Epoch: 010/050 | Batch 200/469 | Cost: 12761.3613\\n\",\n      \"Epoch: 010/050 | Batch 250/469 | Cost: 13139.7520\\n\",\n      \"Epoch: 010/050 | Batch 300/469 | Cost: 12969.9932\\n\",\n      \"Epoch: 010/050 | Batch 350/469 | Cost: 12518.5615\\n\",\n      \"Epoch: 010/050 | Batch 400/469 | Cost: 13042.4551\\n\",\n      \"Epoch: 010/050 | Batch 450/469 | Cost: 13296.8926\\n\",\n      \"Epoch: 011/050 | Batch 000/469 | Cost: 13233.5322\\n\",\n      \"Epoch: 011/050 | Batch 050/469 | Cost: 13085.0918\\n\",\n      \"Epoch: 011/050 | Batch 100/469 | Cost: 12664.7422\\n\",\n      \"Epoch: 011/050 | Batch 150/469 | Cost: 13344.7686\\n\",\n      \"Epoch: 011/050 | Batch 200/469 | Cost: 12498.5938\\n\",\n      \"Epoch: 011/050 | Batch 250/469 | Cost: 13314.7920\\n\",\n      \"Epoch: 011/050 | Batch 300/469 | Cost: 13175.9463\\n\",\n      \"Epoch: 011/050 | Batch 350/469 | Cost: 13034.4180\\n\",\n      \"Epoch: 011/050 | Batch 400/469 | Cost: 12425.1221\\n\",\n      \"Epoch: 011/050 | Batch 450/469 | Cost: 12548.4668\\n\",\n      \"Epoch: 012/050 | Batch 000/469 | Cost: 12779.4053\\n\",\n      \"Epoch: 012/050 | Batch 050/469 | Cost: 13129.1328\\n\",\n      \"Epoch: 012/050 | Batch 100/469 | Cost: 12274.7422\\n\",\n      \"Epoch: 012/050 | Batch 150/469 | Cost: 13289.4688\\n\",\n      \"Epoch: 012/050 | Batch 200/469 | Cost: 13256.5312\\n\",\n      \"Epoch: 012/050 | Batch 250/469 | Cost: 12437.4629\\n\",\n      \"Epoch: 012/050 | Batch 300/469 | Cost: 12500.7627\\n\",\n      \"Epoch: 012/050 | Batch 350/469 | Cost: 13362.0430\\n\",\n      \"Epoch: 012/050 | Batch 400/469 | Cost: 13271.1768\\n\",\n      \"Epoch: 012/050 | Batch 450/469 | Cost: 13070.1992\\n\",\n      \"Epoch: 013/050 | Batch 000/469 | Cost: 12979.0723\\n\",\n      \"Epoch: 013/050 | Batch 050/469 | Cost: 12714.0527\\n\",\n      \"Epoch: 013/050 | Batch 100/469 | Cost: 12925.5879\\n\",\n      \"Epoch: 013/050 | Batch 150/469 | Cost: 13068.3555\\n\",\n      \"Epoch: 013/050 | Batch 200/469 | Cost: 12462.0791\\n\",\n      \"Epoch: 013/050 | Batch 250/469 | Cost: 12443.1250\\n\",\n      \"Epoch: 013/050 | Batch 300/469 | Cost: 12773.1631\\n\",\n      \"Epoch: 013/050 | Batch 350/469 | Cost: 12435.6836\\n\",\n      \"Epoch: 013/050 | Batch 400/469 | Cost: 12659.2441\\n\",\n      \"Epoch: 013/050 | Batch 450/469 | Cost: 12680.4297\\n\",\n      \"Epoch: 014/050 | Batch 000/469 | Cost: 12963.3291\\n\",\n      \"Epoch: 014/050 | Batch 050/469 | Cost: 12406.1680\\n\",\n      \"Epoch: 014/050 | Batch 100/469 | Cost: 13342.7998\\n\",\n      \"Epoch: 014/050 | Batch 150/469 | Cost: 13050.4004\\n\",\n      \"Epoch: 014/050 | Batch 200/469 | Cost: 12695.7129\\n\",\n      \"Epoch: 014/050 | Batch 250/469 | Cost: 12899.9678\\n\",\n      \"Epoch: 014/050 | Batch 300/469 | Cost: 12568.9746\\n\",\n      \"Epoch: 014/050 | Batch 350/469 | Cost: 12800.3164\\n\",\n      \"Epoch: 014/050 | Batch 400/469 | Cost: 12908.6758\\n\",\n      \"Epoch: 014/050 | Batch 450/469 | Cost: 13055.2197\\n\",\n      \"Epoch: 015/050 | Batch 000/469 | Cost: 12697.0527\\n\",\n      \"Epoch: 015/050 | Batch 050/469 | Cost: 13206.3340\\n\",\n      \"Epoch: 015/050 | Batch 100/469 | Cost: 12505.6865\\n\",\n      \"Epoch: 015/050 | Batch 150/469 | Cost: 12765.6504\\n\",\n      \"Epoch: 015/050 | Batch 200/469 | Cost: 12692.5625\\n\",\n      \"Epoch: 015/050 | Batch 250/469 | Cost: 12564.1904\\n\",\n      \"Epoch: 015/050 | Batch 300/469 | Cost: 12480.1055\\n\",\n      \"Epoch: 015/050 | Batch 350/469 | Cost: 12703.9590\\n\",\n      \"Epoch: 015/050 | Batch 400/469 | Cost: 12782.6943\\n\",\n      \"Epoch: 015/050 | Batch 450/469 | Cost: 12501.6982\\n\",\n      \"Epoch: 016/050 | Batch 000/469 | Cost: 12316.8369\\n\",\n      \"Epoch: 016/050 | Batch 050/469 | Cost: 12879.1367\\n\",\n      \"Epoch: 016/050 | Batch 100/469 | Cost: 12799.4814\\n\",\n      \"Epoch: 016/050 | Batch 150/469 | Cost: 13116.8818\\n\",\n      \"Epoch: 016/050 | Batch 200/469 | Cost: 12788.3652\\n\",\n      \"Epoch: 016/050 | Batch 250/469 | Cost: 12618.8379\\n\",\n      \"Epoch: 016/050 | Batch 300/469 | Cost: 13378.3730\\n\",\n      \"Epoch: 016/050 | Batch 350/469 | Cost: 12751.9121\\n\",\n      \"Epoch: 016/050 | Batch 400/469 | Cost: 12654.6123\\n\",\n      \"Epoch: 016/050 | Batch 450/469 | Cost: 12693.1211\\n\",\n      \"Epoch: 017/050 | Batch 000/469 | Cost: 13261.9746\\n\",\n      \"Epoch: 017/050 | Batch 050/469 | Cost: 13040.6025\\n\",\n      \"Epoch: 017/050 | Batch 100/469 | Cost: 12892.2832\\n\",\n      \"Epoch: 017/050 | Batch 150/469 | Cost: 12776.0957\\n\",\n      \"Epoch: 017/050 | Batch 200/469 | Cost: 12676.0645\\n\",\n      \"Epoch: 017/050 | Batch 250/469 | Cost: 13100.1250\\n\",\n      \"Epoch: 017/050 | Batch 300/469 | Cost: 12229.5742\\n\",\n      \"Epoch: 017/050 | Batch 350/469 | Cost: 12896.2207\\n\",\n      \"Epoch: 017/050 | Batch 400/469 | Cost: 12986.7246\\n\",\n      \"Epoch: 017/050 | Batch 450/469 | Cost: 12528.6777\\n\",\n      \"Epoch: 018/050 | Batch 000/469 | Cost: 12395.8604\\n\",\n      \"Epoch: 018/050 | Batch 050/469 | Cost: 12674.4678\\n\",\n      \"Epoch: 018/050 | Batch 100/469 | Cost: 12528.5469\\n\",\n      \"Epoch: 018/050 | Batch 150/469 | Cost: 13454.7070\\n\",\n      \"Epoch: 018/050 | Batch 200/469 | Cost: 12878.5322\\n\",\n      \"Epoch: 018/050 | Batch 250/469 | Cost: 12682.8457\\n\",\n      \"Epoch: 018/050 | Batch 300/469 | Cost: 12604.6943\\n\",\n      \"Epoch: 018/050 | Batch 350/469 | Cost: 13185.8828\\n\",\n      \"Epoch: 018/050 | Batch 400/469 | Cost: 12933.7246\\n\",\n      \"Epoch: 018/050 | Batch 450/469 | Cost: 12973.7314\\n\",\n      \"Epoch: 019/050 | Batch 000/469 | Cost: 12347.1924\\n\",\n      \"Epoch: 019/050 | Batch 050/469 | Cost: 12655.2314\\n\",\n      \"Epoch: 019/050 | Batch 100/469 | Cost: 12840.0889\\n\",\n      \"Epoch: 019/050 | Batch 150/469 | Cost: 12790.1152\\n\",\n      \"Epoch: 019/050 | Batch 200/469 | Cost: 12546.3301\\n\",\n      \"Epoch: 019/050 | Batch 250/469 | Cost: 12630.3662\\n\",\n      \"Epoch: 019/050 | Batch 300/469 | Cost: 12877.1553\\n\",\n      \"Epoch: 019/050 | Batch 350/469 | Cost: 12754.5049\\n\",\n      \"Epoch: 019/050 | Batch 400/469 | Cost: 12562.9287\\n\",\n      \"Epoch: 019/050 | Batch 450/469 | Cost: 12670.7939\\n\",\n      \"Epoch: 020/050 | Batch 000/469 | Cost: 13078.5391\\n\",\n      \"Epoch: 020/050 | Batch 050/469 | Cost: 13251.5137\\n\",\n      \"Epoch: 020/050 | Batch 100/469 | Cost: 12222.6816\\n\",\n      \"Epoch: 020/050 | Batch 150/469 | Cost: 13020.2549\\n\",\n      \"Epoch: 020/050 | Batch 200/469 | Cost: 12660.7695\\n\",\n      \"Epoch: 020/050 | Batch 250/469 | Cost: 12797.1309\\n\",\n      \"Epoch: 020/050 | Batch 300/469 | Cost: 12559.7441\\n\",\n      \"Epoch: 020/050 | Batch 350/469 | Cost: 12983.9473\\n\",\n      \"Epoch: 020/050 | Batch 400/469 | Cost: 12665.8516\\n\",\n      \"Epoch: 020/050 | Batch 450/469 | Cost: 12557.4512\\n\",\n      \"Epoch: 021/050 | Batch 000/469 | Cost: 12259.2539\\n\",\n      \"Epoch: 021/050 | Batch 050/469 | Cost: 12225.6787\\n\",\n      \"Epoch: 021/050 | Batch 100/469 | Cost: 13265.6328\\n\",\n      \"Epoch: 021/050 | Batch 150/469 | Cost: 12958.9795\\n\",\n      \"Epoch: 021/050 | Batch 200/469 | Cost: 13201.1504\\n\",\n      \"Epoch: 021/050 | Batch 250/469 | Cost: 12173.3027\\n\",\n      \"Epoch: 021/050 | Batch 300/469 | Cost: 11880.8125\\n\",\n      \"Epoch: 021/050 | Batch 350/469 | Cost: 12684.7500\\n\",\n      \"Epoch: 021/050 | Batch 400/469 | Cost: 12973.6250\\n\",\n      \"Epoch: 021/050 | Batch 450/469 | Cost: 12326.9854\\n\",\n      \"Epoch: 022/050 | Batch 000/469 | Cost: 12506.0596\\n\",\n      \"Epoch: 022/050 | Batch 050/469 | Cost: 12992.8047\\n\",\n      \"Epoch: 022/050 | Batch 100/469 | Cost: 12908.5557\\n\",\n      \"Epoch: 022/050 | Batch 150/469 | Cost: 12658.6768\\n\",\n      \"Epoch: 022/050 | Batch 200/469 | Cost: 13097.6426\\n\",\n      \"Epoch: 022/050 | Batch 250/469 | Cost: 12514.5166\\n\",\n      \"Epoch: 022/050 | Batch 300/469 | Cost: 13067.9795\\n\",\n      \"Epoch: 022/050 | Batch 350/469 | Cost: 13335.4814\\n\",\n      \"Epoch: 022/050 | Batch 400/469 | Cost: 12482.6094\\n\",\n      \"Epoch: 022/050 | Batch 450/469 | Cost: 12887.1328\\n\",\n      \"Epoch: 023/050 | Batch 000/469 | Cost: 12895.0732\\n\",\n      \"Epoch: 023/050 | Batch 050/469 | Cost: 12596.9219\\n\",\n      \"Epoch: 023/050 | Batch 100/469 | Cost: 12961.1699\\n\",\n      \"Epoch: 023/050 | Batch 150/469 | Cost: 12497.6240\\n\",\n      \"Epoch: 023/050 | Batch 200/469 | Cost: 12390.3174\\n\",\n      \"Epoch: 023/050 | Batch 250/469 | Cost: 12916.2070\\n\",\n      \"Epoch: 023/050 | Batch 300/469 | Cost: 12608.6494\\n\",\n      \"Epoch: 023/050 | Batch 350/469 | Cost: 12270.3037\\n\",\n      \"Epoch: 023/050 | Batch 400/469 | Cost: 12774.8906\\n\",\n      \"Epoch: 023/050 | Batch 450/469 | Cost: 12438.0068\\n\",\n      \"Epoch: 024/050 | Batch 000/469 | Cost: 12060.3467\\n\",\n      \"Epoch: 024/050 | Batch 050/469 | Cost: 12482.3770\\n\",\n      \"Epoch: 024/050 | Batch 100/469 | Cost: 12389.7715\\n\",\n      \"Epoch: 024/050 | Batch 150/469 | Cost: 13020.0859\\n\",\n      \"Epoch: 024/050 | Batch 200/469 | Cost: 12233.1670\\n\",\n      \"Epoch: 024/050 | Batch 250/469 | Cost: 12507.4473\\n\",\n      \"Epoch: 024/050 | Batch 300/469 | Cost: 12403.1035\\n\",\n      \"Epoch: 024/050 | Batch 350/469 | Cost: 12475.9551\\n\",\n      \"Epoch: 024/050 | Batch 400/469 | Cost: 12369.6104\\n\",\n      \"Epoch: 024/050 | Batch 450/469 | Cost: 12104.8066\\n\",\n      \"Epoch: 025/050 | Batch 000/469 | Cost: 12380.4355\\n\",\n      \"Epoch: 025/050 | Batch 050/469 | Cost: 12826.3662\\n\",\n      \"Epoch: 025/050 | Batch 100/469 | Cost: 12431.5898\\n\",\n      \"Epoch: 025/050 | Batch 150/469 | Cost: 12982.6113\\n\",\n      \"Epoch: 025/050 | Batch 200/469 | Cost: 12823.1465\\n\",\n      \"Epoch: 025/050 | Batch 250/469 | Cost: 12800.5156\\n\",\n      \"Epoch: 025/050 | Batch 300/469 | Cost: 13140.7812\\n\",\n      \"Epoch: 025/050 | Batch 350/469 | Cost: 12483.5723\\n\",\n      \"Epoch: 025/050 | Batch 400/469 | Cost: 12694.3594\\n\",\n      \"Epoch: 025/050 | Batch 450/469 | Cost: 12767.1543\\n\",\n      \"Epoch: 026/050 | Batch 000/469 | Cost: 11855.4678\\n\",\n      \"Epoch: 026/050 | Batch 050/469 | Cost: 12363.9590\\n\",\n      \"Epoch: 026/050 | Batch 100/469 | Cost: 13079.2793\\n\",\n      \"Epoch: 026/050 | Batch 150/469 | Cost: 12977.3594\\n\",\n      \"Epoch: 026/050 | Batch 200/469 | Cost: 12642.0938\\n\",\n      \"Epoch: 026/050 | Batch 250/469 | Cost: 12530.8447\\n\",\n      \"Epoch: 026/050 | Batch 300/469 | Cost: 12514.3311\\n\",\n      \"Epoch: 026/050 | Batch 350/469 | Cost: 12100.2314\\n\",\n      \"Epoch: 026/050 | Batch 400/469 | Cost: 12814.0479\\n\",\n      \"Epoch: 026/050 | Batch 450/469 | Cost: 12364.0166\\n\",\n      \"Epoch: 027/050 | Batch 000/469 | Cost: 12499.8721\\n\",\n      \"Epoch: 027/050 | Batch 050/469 | Cost: 12678.9111\\n\",\n      \"Epoch: 027/050 | Batch 100/469 | Cost: 12261.5918\\n\",\n      \"Epoch: 027/050 | Batch 150/469 | Cost: 12901.1641\\n\",\n      \"Epoch: 027/050 | Batch 200/469 | Cost: 12548.0469\\n\",\n      \"Epoch: 027/050 | Batch 250/469 | Cost: 12211.9111\\n\",\n      \"Epoch: 027/050 | Batch 300/469 | Cost: 13003.7646\\n\",\n      \"Epoch: 027/050 | Batch 350/469 | Cost: 12214.5781\\n\",\n      \"Epoch: 027/050 | Batch 400/469 | Cost: 12604.0361\\n\",\n      \"Epoch: 027/050 | Batch 450/469 | Cost: 12504.3213\\n\",\n      \"Epoch: 028/050 | Batch 000/469 | Cost: 12680.8613\\n\",\n      \"Epoch: 028/050 | Batch 050/469 | Cost: 13018.3525\\n\",\n      \"Epoch: 028/050 | Batch 100/469 | Cost: 13040.8760\\n\",\n      \"Epoch: 028/050 | Batch 150/469 | Cost: 12745.8643\\n\",\n      \"Epoch: 028/050 | Batch 200/469 | Cost: 12417.4248\\n\",\n      \"Epoch: 028/050 | Batch 250/469 | Cost: 12684.0645\\n\",\n      \"Epoch: 028/050 | Batch 300/469 | Cost: 12119.3633\\n\",\n      \"Epoch: 028/050 | Batch 350/469 | Cost: 12281.8008\\n\",\n      \"Epoch: 028/050 | Batch 400/469 | Cost: 12434.8438\\n\",\n      \"Epoch: 028/050 | Batch 450/469 | Cost: 12379.5928\\n\",\n      \"Epoch: 029/050 | Batch 000/469 | Cost: 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\"Epoch: 030/050 | Batch 300/469 | Cost: 12891.4102\\n\",\n      \"Epoch: 030/050 | Batch 350/469 | Cost: 12546.6768\\n\",\n      \"Epoch: 030/050 | Batch 400/469 | Cost: 12653.6172\\n\",\n      \"Epoch: 030/050 | Batch 450/469 | Cost: 12576.2285\\n\",\n      \"Epoch: 031/050 | Batch 000/469 | Cost: 12499.4316\\n\",\n      \"Epoch: 031/050 | Batch 050/469 | Cost: 12517.8770\\n\",\n      \"Epoch: 031/050 | Batch 100/469 | Cost: 12340.2480\\n\",\n      \"Epoch: 031/050 | Batch 150/469 | Cost: 12368.0469\\n\",\n      \"Epoch: 031/050 | Batch 200/469 | Cost: 12331.4121\\n\",\n      \"Epoch: 031/050 | Batch 250/469 | Cost: 12736.1953\\n\",\n      \"Epoch: 031/050 | Batch 300/469 | Cost: 12985.6914\\n\",\n      \"Epoch: 031/050 | Batch 350/469 | Cost: 12383.8086\\n\",\n      \"Epoch: 031/050 | Batch 400/469 | Cost: 12270.4277\\n\",\n      \"Epoch: 031/050 | Batch 450/469 | Cost: 12418.8633\\n\",\n      \"Epoch: 032/050 | Batch 000/469 | Cost: 12244.7559\\n\",\n      \"Epoch: 032/050 | Batch 050/469 | Cost: 12531.9453\\n\",\n      \"Epoch: 032/050 | Batch 100/469 | Cost: 12477.5752\\n\",\n      \"Epoch: 032/050 | Batch 150/469 | Cost: 12838.6650\\n\",\n      \"Epoch: 032/050 | Batch 200/469 | Cost: 12590.4707\\n\",\n      \"Epoch: 032/050 | Batch 250/469 | Cost: 12658.5674\\n\",\n      \"Epoch: 032/050 | Batch 300/469 | Cost: 12619.9316\\n\",\n      \"Epoch: 032/050 | Batch 350/469 | Cost: 12790.5488\\n\",\n      \"Epoch: 032/050 | Batch 400/469 | Cost: 12336.5918\\n\",\n      \"Epoch: 032/050 | Batch 450/469 | Cost: 11956.5361\\n\",\n      \"Epoch: 033/050 | Batch 000/469 | Cost: 12257.5645\\n\",\n      \"Epoch: 033/050 | Batch 050/469 | Cost: 12238.9277\\n\",\n      \"Epoch: 033/050 | Batch 100/469 | Cost: 12166.1533\\n\",\n      \"Epoch: 033/050 | Batch 150/469 | Cost: 12442.1953\\n\",\n      \"Epoch: 033/050 | Batch 200/469 | Cost: 12383.0957\\n\",\n      \"Epoch: 033/050 | Batch 250/469 | Cost: 12242.8730\\n\",\n      \"Epoch: 033/050 | Batch 300/469 | Cost: 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\"Epoch: 035/050 | Batch 100/469 | Cost: 12402.5156\\n\",\n      \"Epoch: 035/050 | Batch 150/469 | Cost: 12334.3750\\n\",\n      \"Epoch: 035/050 | Batch 200/469 | Cost: 12532.5967\\n\",\n      \"Epoch: 035/050 | Batch 250/469 | Cost: 12294.9727\\n\",\n      \"Epoch: 035/050 | Batch 300/469 | Cost: 12221.8359\\n\",\n      \"Epoch: 035/050 | Batch 350/469 | Cost: 12979.2939\\n\",\n      \"Epoch: 035/050 | Batch 400/469 | Cost: 12789.4639\\n\",\n      \"Epoch: 035/050 | Batch 450/469 | Cost: 12396.4160\\n\",\n      \"Epoch: 036/050 | Batch 000/469 | Cost: 12536.0049\\n\",\n      \"Epoch: 036/050 | Batch 050/469 | Cost: 12159.3613\\n\",\n      \"Epoch: 036/050 | Batch 100/469 | Cost: 12361.6260\\n\",\n      \"Epoch: 036/050 | Batch 150/469 | Cost: 12638.1709\\n\",\n      \"Epoch: 036/050 | Batch 200/469 | Cost: 12634.9355\\n\",\n      \"Epoch: 036/050 | Batch 250/469 | Cost: 12643.7432\\n\",\n      \"Epoch: 036/050 | Batch 300/469 | Cost: 12563.5137\\n\",\n      \"Epoch: 036/050 | Batch 350/469 | Cost: 12375.0566\\n\",\n      \"Epoch: 036/050 | Batch 400/469 | Cost: 12551.1367\\n\",\n      \"Epoch: 036/050 | Batch 450/469 | Cost: 12317.5762\\n\",\n      \"Epoch: 037/050 | Batch 000/469 | Cost: 12063.4453\\n\",\n      \"Epoch: 037/050 | Batch 050/469 | Cost: 11987.3984\\n\",\n      \"Epoch: 037/050 | Batch 100/469 | Cost: 12577.7441\\n\",\n      \"Epoch: 037/050 | Batch 150/469 | Cost: 12403.6309\\n\",\n      \"Epoch: 037/050 | Batch 200/469 | Cost: 12922.1729\\n\",\n      \"Epoch: 037/050 | Batch 250/469 | Cost: 12302.4805\\n\",\n      \"Epoch: 037/050 | Batch 300/469 | Cost: 12353.8057\\n\",\n      \"Epoch: 037/050 | Batch 350/469 | Cost: 12627.0859\\n\",\n      \"Epoch: 037/050 | Batch 400/469 | Cost: 12517.3809\\n\",\n      \"Epoch: 037/050 | Batch 450/469 | Cost: 11899.2090\\n\",\n      \"Epoch: 038/050 | Batch 000/469 | Cost: 11766.3467\\n\",\n      \"Epoch: 038/050 | Batch 050/469 | Cost: 12509.6875\\n\",\n      \"Epoch: 038/050 | Batch 100/469 | Cost: 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\"Epoch: 039/050 | Batch 400/469 | Cost: 12270.1885\\n\",\n      \"Epoch: 039/050 | Batch 450/469 | Cost: 12623.2764\\n\",\n      \"Epoch: 040/050 | Batch 000/469 | Cost: 12347.0195\\n\",\n      \"Epoch: 040/050 | Batch 050/469 | Cost: 12172.0439\\n\",\n      \"Epoch: 040/050 | Batch 100/469 | Cost: 12112.8770\\n\",\n      \"Epoch: 040/050 | Batch 150/469 | Cost: 12661.4824\\n\",\n      \"Epoch: 040/050 | Batch 200/469 | Cost: 12516.9434\\n\",\n      \"Epoch: 040/050 | Batch 250/469 | Cost: 11665.0059\\n\",\n      \"Epoch: 040/050 | Batch 300/469 | Cost: 12424.7168\\n\",\n      \"Epoch: 040/050 | Batch 350/469 | Cost: 12546.3516\\n\",\n      \"Epoch: 040/050 | Batch 400/469 | Cost: 12085.0430\\n\",\n      \"Epoch: 040/050 | Batch 450/469 | Cost: 12052.1777\\n\",\n      \"Epoch: 041/050 | Batch 000/469 | Cost: 12553.8594\\n\",\n      \"Epoch: 041/050 | Batch 050/469 | Cost: 12719.8916\\n\",\n      \"Epoch: 041/050 | Batch 100/469 | Cost: 12318.2598\\n\",\n      \"Epoch: 041/050 | Batch 150/469 | Cost: 12868.4424\\n\",\n      \"Epoch: 041/050 | Batch 200/469 | Cost: 12110.4648\\n\",\n      \"Epoch: 041/050 | Batch 250/469 | Cost: 12877.4014\\n\",\n      \"Epoch: 041/050 | Batch 300/469 | Cost: 12044.2422\\n\",\n      \"Epoch: 041/050 | Batch 350/469 | Cost: 12094.7090\\n\",\n      \"Epoch: 041/050 | Batch 400/469 | Cost: 12124.3301\\n\",\n      \"Epoch: 041/050 | Batch 450/469 | Cost: 12671.7217\\n\",\n      \"Epoch: 042/050 | Batch 000/469 | Cost: 12054.0957\\n\",\n      \"Epoch: 042/050 | Batch 050/469 | Cost: 12345.2227\\n\",\n      \"Epoch: 042/050 | Batch 100/469 | Cost: 12810.0957\\n\",\n      \"Epoch: 042/050 | Batch 150/469 | Cost: 11998.7207\\n\",\n      \"Epoch: 042/050 | Batch 200/469 | Cost: 12693.5879\\n\",\n      \"Epoch: 042/050 | Batch 250/469 | Cost: 11996.5615\\n\",\n      \"Epoch: 042/050 | Batch 300/469 | Cost: 12084.2832\\n\",\n      \"Epoch: 042/050 | Batch 350/469 | Cost: 12159.6025\\n\",\n      \"Epoch: 042/050 | Batch 400/469 | Cost: 12514.6943\\n\",\n      \"Epoch: 042/050 | Batch 450/469 | Cost: 12273.8809\\n\",\n      \"Epoch: 043/050 | Batch 000/469 | Cost: 12472.9395\\n\",\n      \"Epoch: 043/050 | Batch 050/469 | Cost: 12462.2734\\n\",\n      \"Epoch: 043/050 | Batch 100/469 | Cost: 12303.0898\\n\",\n      \"Epoch: 043/050 | Batch 150/469 | Cost: 12641.2676\\n\",\n      \"Epoch: 043/050 | Batch 200/469 | Cost: 11870.0820\\n\",\n      \"Epoch: 043/050 | Batch 250/469 | Cost: 12087.6504\\n\",\n      \"Epoch: 043/050 | Batch 300/469 | Cost: 12615.6992\\n\",\n      \"Epoch: 043/050 | Batch 350/469 | Cost: 12327.5391\\n\",\n      \"Epoch: 043/050 | Batch 400/469 | Cost: 12761.4795\\n\",\n      \"Epoch: 043/050 | Batch 450/469 | Cost: 12429.0576\\n\",\n      \"Epoch: 044/050 | Batch 000/469 | Cost: 12172.6055\\n\",\n      \"Epoch: 044/050 | Batch 050/469 | Cost: 12338.0742\\n\",\n      \"Epoch: 044/050 | Batch 100/469 | Cost: 12473.4297\\n\",\n      \"Epoch: 044/050 | Batch 150/469 | Cost: 12260.2695\\n\",\n      \"Epoch: 044/050 | Batch 200/469 | Cost: 12475.7871\\n\",\n      \"Epoch: 044/050 | Batch 250/469 | Cost: 12570.5645\\n\",\n      \"Epoch: 044/050 | Batch 300/469 | Cost: 12297.6982\\n\",\n      \"Epoch: 044/050 | Batch 350/469 | Cost: 12525.9111\\n\",\n      \"Epoch: 044/050 | Batch 400/469 | Cost: 12596.0791\\n\",\n      \"Epoch: 044/050 | Batch 450/469 | Cost: 11957.3623\\n\",\n      \"Epoch: 045/050 | Batch 000/469 | Cost: 12849.4238\\n\",\n      \"Epoch: 045/050 | Batch 050/469 | Cost: 12080.3203\\n\",\n      \"Epoch: 045/050 | Batch 100/469 | Cost: 12260.8994\\n\",\n      \"Epoch: 045/050 | Batch 150/469 | Cost: 12638.3770\\n\",\n      \"Epoch: 045/050 | Batch 200/469 | Cost: 12635.9248\\n\",\n      \"Epoch: 045/050 | Batch 250/469 | Cost: 12265.3184\\n\",\n      \"Epoch: 045/050 | Batch 300/469 | Cost: 12359.3242\\n\",\n      \"Epoch: 045/050 | Batch 350/469 | Cost: 12409.3135\\n\",\n      \"Epoch: 045/050 | Batch 400/469 | Cost: 12485.5879\\n\",\n      \"Epoch: 045/050 | Batch 450/469 | Cost: 12399.2988\\n\",\n      \"Epoch: 046/050 | Batch 000/469 | Cost: 12027.0762\\n\",\n      \"Epoch: 046/050 | Batch 050/469 | Cost: 12070.3789\\n\",\n      \"Epoch: 046/050 | Batch 100/469 | Cost: 12531.2441\\n\",\n      \"Epoch: 046/050 | Batch 150/469 | Cost: 12265.9395\\n\",\n      \"Epoch: 046/050 | Batch 200/469 | Cost: 12452.6680\\n\",\n      \"Epoch: 046/050 | Batch 250/469 | Cost: 13118.8711\\n\",\n      \"Epoch: 046/050 | Batch 300/469 | Cost: 12208.3818\\n\",\n      \"Epoch: 046/050 | Batch 350/469 | Cost: 12624.9814\\n\",\n      \"Epoch: 046/050 | Batch 400/469 | Cost: 12488.5791\\n\",\n      \"Epoch: 046/050 | Batch 450/469 | Cost: 12633.9775\\n\",\n      \"Epoch: 047/050 | Batch 000/469 | Cost: 12152.1914\\n\",\n      \"Epoch: 047/050 | Batch 050/469 | Cost: 12525.3857\\n\",\n      \"Epoch: 047/050 | Batch 100/469 | Cost: 12195.7227\\n\",\n      \"Epoch: 047/050 | Batch 150/469 | Cost: 12642.2949\\n\",\n      \"Epoch: 047/050 | Batch 200/469 | Cost: 12667.8174\\n\",\n      \"Epoch: 047/050 | Batch 250/469 | Cost: 12729.5176\\n\",\n      \"Epoch: 047/050 | Batch 300/469 | Cost: 12052.5898\\n\",\n      \"Epoch: 047/050 | Batch 350/469 | Cost: 12097.2480\\n\",\n      \"Epoch: 047/050 | Batch 400/469 | Cost: 12530.8574\\n\",\n      \"Epoch: 047/050 | Batch 450/469 | Cost: 12496.5098\\n\",\n      \"Epoch: 048/050 | Batch 000/469 | Cost: 12613.0137\\n\",\n      \"Epoch: 048/050 | Batch 050/469 | Cost: 12692.5273\\n\",\n      \"Epoch: 048/050 | Batch 100/469 | Cost: 12363.4863\\n\",\n      \"Epoch: 048/050 | Batch 150/469 | Cost: 11625.2861\\n\",\n      \"Epoch: 048/050 | Batch 200/469 | Cost: 12005.9697\\n\",\n      \"Epoch: 048/050 | Batch 250/469 | Cost: 12227.3750\\n\",\n      \"Epoch: 048/050 | Batch 300/469 | Cost: 12684.3359\\n\",\n      \"Epoch: 048/050 | Batch 350/469 | Cost: 12430.2783\\n\",\n      \"Epoch: 048/050 | Batch 400/469 | Cost: 12213.2578\\n\",\n      \"Epoch: 048/050 | Batch 450/469 | Cost: 13208.1133\\n\",\n      \"Epoch: 049/050 | Batch 000/469 | Cost: 12118.3057\\n\",\n      \"Epoch: 049/050 | Batch 050/469 | Cost: 12340.2715\\n\",\n      \"Epoch: 049/050 | Batch 100/469 | Cost: 12029.6094\\n\",\n      \"Epoch: 049/050 | Batch 150/469 | Cost: 12366.4453\\n\",\n      \"Epoch: 049/050 | Batch 200/469 | Cost: 12537.2998\\n\",\n      \"Epoch: 049/050 | Batch 250/469 | Cost: 12324.0312\\n\",\n      \"Epoch: 049/050 | Batch 300/469 | Cost: 12378.3457\\n\",\n      \"Epoch: 049/050 | Batch 350/469 | Cost: 12218.6914\\n\",\n      \"Epoch: 049/050 | Batch 400/469 | Cost: 12550.4785\\n\",\n      \"Epoch: 049/050 | Batch 450/469 | Cost: 12444.4463\\n\",\n      \"Epoch: 050/050 | Batch 000/469 | Cost: 12246.0020\\n\",\n      \"Epoch: 050/050 | Batch 050/469 | Cost: 12554.1836\\n\",\n      \"Epoch: 050/050 | Batch 100/469 | Cost: 12373.2930\\n\",\n      \"Epoch: 050/050 | Batch 150/469 | Cost: 12895.8096\\n\",\n      \"Epoch: 050/050 | Batch 200/469 | Cost: 12233.0605\\n\",\n      \"Epoch: 050/050 | Batch 250/469 | Cost: 12621.2920\\n\",\n      \"Epoch: 050/050 | Batch 300/469 | Cost: 12492.7812\\n\",\n      \"Epoch: 050/050 | Batch 350/469 | Cost: 12525.1934\\n\",\n      \"Epoch: 050/050 | Batch 400/469 | Cost: 13032.4062\\n\",\n      \"Epoch: 050/050 | Batch 450/469 | Cost: 12618.7773\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        features = features.view(-1, 28*28).to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        z_mean, z_log_var, encoded, decoded = model(features, targets)\\n\",\n    \"\\n\",\n    \"        # cost = reconstruction loss + Kullback-Leibler divergence\\n\",\n    \"        kl_divergence = (0.5 * (z_mean**2 + \\n\",\n    \"                                torch.exp(z_log_var) - z_log_var - 1)).sum()\\n\",\n    \"        \\n\",\n    \"        ### Add condition\\n\",\n    \"        # Disabled for reconstruction loss as it gives poor results\\n\",\n    \"        # x_con = torch.cat((features, to_onehot(targets, num_classes, device)), dim=1)\\n\",\n    \"        \\n\",\n    \"        ### Compute loss\\n\",\n    \"        # pixelwise_bce = F.binary_cross_entropy(decoded, x_con, reduction='sum')\\n\",\n    \"        pixelwise_bce = F.binary_cross_entropy(decoded, features, reduction='sum')\\n\",\n    \"        cost = kl_divergence + pixelwise_bce\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        cost.backward()\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Reconstruction\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### New random-conditional images\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 0\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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5DIdqxY8fy2GOPAVGl9MbiypTjOI7jOE4WbBfKVCqVsgqvyq2Io5Wokn4HDBiw1fhnGIbmUSuHoaCgwLwRJfgmRXwLtZIcMz3+kpISSziWJ1tYWFhj+/bhhx8OpOPBUqaUG5BkUm/cs4t7Tsqd6dOnDxCpbKlUyrwHeSZt27Y1+xX7l+eYn59vic7Kv6qoqDBvS58pbzpXqpw+R0VWr7nmmhoFRqWWXnfddVZgVDbW5gGr33bo0ME8fvWH8vJy8zyT9vw1Vu6++24rVaH2URLvNddcY+0RzzvJvFb9v6ioyBKxVYF7xYoV3HHHHUA6jyX+ObkgDEOz56abbgLSuTWnnnpqNTsy/wYiO+JKrBQCqbAjRoyw69fnzJ492/JsRo0aVe3vcoHKiMTLIKifKV9EpSk6d+5c7fw5SNup79WfpbxBtCHhqquuAtJ5SEkqqLXNN3PmzDFFUH03ftqE5grlccbVxUyqqqpMDZJ6s27dOrsX2pyQayVc7//CCy8A6Vxa2SsFSZsBjjjiCGvHeAkJXZsUPG0yuPrqq61IsEp5TJ482XKTcxnZiCvWmWWNKisrLUdKCpWKPk+fPt3mQ53/edppp5l6KjVR0ZGvvvrKcqYU2frggw94/PHHAfjlL38JND53ypUpx3Ecx3GcLNgulKmFCxfaijvz3KguXbrw9NNPA9F27Lo8g8rKStsBobhpKpWqsbJPmpUrV9Y4f0+KzDHHHGMrb21bDYLAXqccB6k1bdq0sS2z8obHjh2b6Dlo8jDkAfXs2bNGQUe1U2VlpXkDUtCKi4trKFK1eVhx1ULesF6fy901YRha3oj6jDxgiHJRzjvvPADOOuusBiksGzZssGJz8SN25EkmhT5b+TAff/yxtYN2ot1www1AuvCf7nl9vPMgCEyllNKxYsUKO4pFHmUulan169fz8MMPA+m8EUh74frM2lTE2pQ2KUvq9+q7YRha7pd2ns6YMcPUokWLFgGRQtTUpFIp24EWR3mLZ555JhApvIWFhdaG8fEWPx4Iop24BQUF1k46LmjGjBk2XyWloKqfKuf1008/tTM9paqoTTZs2MDMmTOB6DiZQw89tFbFUe+t91KOX15eHvvttx8Q5X7mukyJbJQyFS9vofwgnTdbVFRU571XGyvH77jjjrP8I+22fffdd/nxj38MJLPbNC8vr0a+K0T5morcaOwUFBRYjqYU3g4dOtRoPz0Xd9llF8uL0m7UV155xfLGFDnQ2GgoLXoxpUa96aabTGZVJ9ANGjNmjIWy6uo8apB58+bZ4kvJaF27djWJT1uAk0ILomnTptl2bE0IetCMHDnS7I0P2MwJXGzevNnCCJqsDzroII4++uga75Er4gMEqicGamBqEVhcXGyv16K2sLDQ7FO71nYWV/xr5iGZuZzIFy1aZOeaaaKNf6YWPaozVVxcXK/r0n146qmnalSn3mOPPSxxNikyk5c3bdpkfVFnk+22225A4yZctbHeY8aMGRby1fjX5zUlus9z5861BFR9Xp8+fTjrrLOAaHFf30VibUnMmlO0wJg4caKFcPVQz9ViCqLE9zhqq8GDBwNR/bp27drVOT+ovfR3HTt2tDBfvK6TbM0lCnHl5eXVSHmYNWuWPTi1SUVtuWzZMqsLdsghhwDVHa/MB/rmzZvNsZH9++yzjy2Sk6rLpEWrFoCpVMr6mQ5+jzvb9UGvW716tbWf5tmddtop8aromWUQNm7caM6Vnttq65NOOslKHtUWVs68B/n5+ea0qabWRx99ZPOs1gONXUx5mM9xHMdxHCcLWrQyNXv2bAA70w0iL0+qwAUXXFBr8mb8TLj4e91///22BVrVtseMGcNPfvITIPmK0ioF8OSTT5pXpRW1pNtBgwbVel2Z6oxCE/369TMvRoXl7r77bitAmAtPP5NMD6NDhw7mtcsr1NcwDGtI8W3atKlhc23qQPz7JBQpeUW33XabeYhx5BlKHo8rG/W5Lt23xx9/vEbx0eOPP97uTxKEYWghonfffRdI32OVHVGBzqbwXuMhlcywbi7QvX3ppZdMvRXnn38+AwcOBOqfGJ4Z1o4nb8seFdDt16+fKVI6hUHhtqYmCAIbN7WdWSovfFv3PLPvah4uKSmpcf+WLFmSSHhPn7F582ZTFZXC0alTJytZoHC02nL16tU290j9D4LAxnZckYJ0yFCFSZWisM8++9gGido2KeQCRSw0p1dUVNj1qPhoQ8eM1KgpU6bYPVR/GTx4cCK2qR0rKiosjUe2Ll682Krtq60074wcObLBGx1kj+7XwIEDTZFSZKixha5dmXIcx3Ecx8mCFqlMSaHROVBlZWW2Ah87diwQxTzjnrpUkPiZPVOnTgWwoznWrVtnq3d5FqNGjUrs6Aoh70ceqs6+gmj7sbYr1zdxXHb16dPHcjBUNv/DDz+0z9LW0Vx6j5l5B23atDEvQl6h2i4vL6/OoohbSwzNfH0S3rC25b700kvmyepzO3bsyPnnnw9ESoNsra/HqNwMeUlQPZk9yZIIqVTKtkvHz/5Sv1SeTTZorOu+5ufnmyrUFO+/NdSHnn/+efOGla939tlnN7hUgTz8utTT+FZ65RnlWmkMgsDym9SW8fIw+trQzRqyqTZlQPNwrokX6JR6IaV/zz33tDIyalddc79+/Uw51LgsLy+vUSxZquxbb71lP1Mu5NChQ22OTWpMar5RP8rLy6tRqqKh1/L+++8D8Prrr9v9lHozbNiwROebyspK26ShjVNlZWWWD6bct0w1tTFofPft29cUuWzLB7W4xVQYhnZekA5jzM/Pt2Q/1aUpKSkBqu8A0CCO79DRIkqTdkFBgSVC67DDpJPOIZrMFX6srKy0jqtOo7pMDSU/P99s1AD85ptvLKQgiTPXu90gqkFTWlpqC4vM3XbxOl9xdO2ZB1dCNAkmHZZV7Zqvv/7aJmQtdo455hhLOG/sYcs6q04PZ4gW/aqpkhTr16+3yU0TTrdu3axfZjvRplIpq9Gl8ExeXp4daN3YRND6oLniq6++sn4ou7Qxor6kUqlqydBQfcGf6Qzsv//+VuVdCf65rFGk+nOyM5VK2ZjSRpWGnvWo/hAP8WWmJ+QajZEvvvjCnEaFbAYPHmwOWuamgPz8/BqbWjZu3GgPcJ2lqN2JlZWVFvrRQn/IkCF11ovLBZpntOgLgsCS7Bsaalf7//a3vwWiGk4QLT4HDRqU3QXXE/X91atXWx0vOVe9evWyRXFtyfUNHTcap3r/BQsW2EJUuzIbi4f5HMdxHMdxsqDFKVObNm3itttuAyKpb+edd7awnpLQ4tvmtcpWDZD777+fBx98EIiUEa3qd999dwsf6r2SVjcgWklLSo5L41qBy8ZUKtVgFUlb6OWVVVVVWcJ9Ep5UvNowpOV3SdKZdWxqq5KdSqUsAVQJl2rDtm3b5lS1qAvVJwnD0K5f4aiSkhK75szk8W2hvq4SBBDdH6lVSffTsrIyO0MuXlU+rpo1BvWNDz/80KqOS+no0aMHZ5xxBpDbfqr2qaystHGm/llRUVEvj1fvUVFRYd9rLoorVPpdXBnR61TnR6/JhVosz15q6fLly+3zpeBffvnlQO1hu9pQORCFpSGaa7TRJdfEVQZVtNb97N+/v/WpzPAVVFekIK1QfPbZZ0D0HFHEYsOGDaYKS70oKipKLPFcZIaEq6qqbBOMbK1v+6lchjZjQdQ/Nd8kVZdQY+2LL76wtBzNO4MHD7ZSKXouxmuKxc/kg7pDf1VVVdbGEydOBNLPFq0tGqpIZ+LKlOM4juM4ThZsU5kKgqAv8BiwC5AC7g/D8P8FQdAV+BvQH5gPnBmG4ZqtvU99mTZtGtOnTweiRMBBgwZZ3F+rZXl+qVTKVuc67frtt9+2v9VKdcSIEUC6RIDyiZrrZPM4UiTi5QHkXTWWjRs3WrK5PLBu3bpZIl8SCoc+Q17F119/bQqOvLy6vKgwDK0NFc+XQrW188NyiTwg5RHEc/Wkfj777LNWxV5FYNXv4jkN8eR82fiLX/wCqO7pKx9CW7uTpqKiwhLP1Y/qygWqz/tBVA7g4osvrqEcjx07NpGT6jUvDBkyxMaK8pfeeustazf1WfXn+PZttV28EnXm6/U3ELXtkiVL7LSGE088Echt/qLmE20cuPHGG2soM8rd3NbZpFIFlDtYm+1JlF6BqE++++67toFH6tsnn3xiSrxep35aVlZmhSCVu7Zy5Ur727giBWnFWe+hdsple20N9dl4dX3Nr2+++SaAlS3Z2piUgnXNNdcA1U9t0HNRynBS6N7OmjXLzgWM59pmnlmrvppKpex8VxX9TaVSNZ4J+rtPP/3Uzo/UM+WEE06ws0WzVRrr81StBC4Pw3AQcChwSRAEewNXAy+FYTgAeGnL/x3HcRzHcXYotqlMhWG4FFi65fvSIAg+A3oDo4CjtrzsUWA6cFW2FzR9+nTbeacV5sEHH2xeT+ap1ytWrOD0008Hot0X8TL7OtFbMVLtAmxu5NUr9yc/P9+8WZ2vVN8dYXoveR2PPPKIld6XB3XeeedlvfWzIeiatdpfunSpeSDaEVPXMQzx887UllLxKioqrD9Iocq1p6h7LC/mlltuMW84rlBMmzYNiE6Xv++++4B0m8pDUjtVVlZy9913A9hZivFjeHTmXdJHOoi4cqjxFoahHYWSedRK/HXxr3q98qN0Btj69etNkZKqPGbMmEQUY13zBRdcwPjx44FoR+Gll15qO2qHDRsGRFvPKyoq7PqUP9OuXTvry/qZ7l0qlTL1QCrQ5s2bTWlPYuebrlc7t9asWWO5efrd9ddfD6T7tXau1VYeQuqdXh9XKnVeX1LHq2g+KS0ttevSvZ42bZqpZ7oevWb58uU1SlP06tXLnhVSaKTadO/e3fpp/PxTkcudmHH0mbrOWbNmWZ9SnpN2/paUlNS4nvLycsuNkyIXP3PxzjvvBHJfriMT2dW9e3ebS9WOgM2Rimio3Q855BBrI0UE1qxZY7vGpZpqx+prr71m85KKdJ966qlNtiZo0CwdBEF/YBjwLtBjy0KLMAyXBkHQvSku6O2337abpYfIkiVL7Gw+JYnpsMOxY8fajYyz9957A1FIIZc1axqDOrpCOJMnT7aOra25Cgu0a9eu1sWCHs56qCuZ9IknnrAFqe7DqFGjEn0oazGliWzTpk2W2KmvOlx1a+geaUGmgbBmzRqT4nOZuBtH9mgyHTVqFPfeey9QfVtxvPYNwO233w6kZWgNfIVd5s6da/K8kmnF0KFD+dGPfpQTW+pLx44dLdylulcrVqyws+y0YFAb5+XlWWKy6v5MmTLFJm71Z7Vr9+7dzcZx48YBuT3UOI6uYfjw4VxyySVA1Fbz5s2zBFg9kNXuxcXFlqiuTR677LKLLSQUGlKItry83OYsje9dd93VwhJJLTwgGke33nqrnWf6hz/8AYjsPO200ywhd8yYMUB6zlXb6WBZlcyA6F7qQZ1U+oT6yvDhwy0UpOtcvHixPUwzD2lu3769/a3acNddd+XYY48FojBf/LxQPaA1dsMwrBH6yzWag6688kog7YBp7lEiuc6qu/HGGy0lQWPyqquusgTvzA0yRx55pIWck0Z29e/f3zYvKNVn7dq19qzXglYbxzZt2mTtrRIda9eutbZVeF1jbI899rAzN1V7rXfv3k3WX+udPBMEQUdgEnBpGIbrGvB3FwVBMCMIghnxA2FbE63dxtZuH7iNrYXWbmNrtw/cxtbCjmBjnHpJFUEQFJBeSP0lDMN/bvnx8iAIem5RpXoCK2r72zAM7wfuBzjwwANrHg4VvQ5Ih0wyz9WbNGmSeR5KNtfXeAKkVph77bWXJbIl4enW18Y4ulZ5Aw8++KAlReosQr3m9NNPt0Q7eUELFy7knnvuAaJz0+SllJWV2WnoCjMNGDCg0SvwxtinNpR60bdvX0sclGqhcEKPHj1qJP9VVFSYFK+qxgrz7bTTTqYONJXaVl8b5UVde+21HH/88UCkCM6bN8+uR5WS5RW9/PLLZofCCMuWLbMwr/qpZPoHHnigwVW4t0VD27GgoMDOGJSn+NVXX1lpkWeffRaI1OLNmzdXSxzVz9RnZatsHDdunCmzSbej6Ny5sxVMT9iAAAAIOklEQVRalVpz8803m6IdD7tC9bIJas9UKmXquELzas/OnTvb/ZGS3L9/f3tdQ5NeGzMWMykoKLBK/VLg1KYLFy60sMqrr75qf6MxG1dhhcK9RxxxRGMupwb1tVH3eNSoURaGVQRj6tSpFmqXQqE5dN999zVVXH2yd+/ell6hn8VLt2SqT/HSLZnFQZvSxtpQ5fXx48dz3XXXAVEI+Z133gHSzxWFrzQm165dW+N8Rs1FEydObPJSDw21cejQoYwePRqInhszZ840RV/Xp/v95ZdfWpFWpRJUVFTYs2HIkCFA+kQDSIdH9czR+zflZqxtvlOQfgI/BHwWhuGtsV89DZy75ftzgclNdlWO4ziO4zjbCfVxBw8HfgJ8EgTBx1t+Ng64Cfh7EATnAwuArPZTxpWa+NZHSHvwSuytDXm1SiQdP3584gXVGouS6saNG8cVV1wBRDFuJZG/+uqr1c64g7RKk5lnoy3lZ5xxht0LxfiTLgOReXbXbrvtZgVKlRAoDj74YMtdEAsWLLDXK/9L3uTAgQObzS7Rvn17O+NQx/NUVVXZ9cRPtAc49thjrV2lxsSPEFK7Kl+juZLO4wRBYAnS6ps33HCD5cvEzw/U64XGX3FxseWiXHzxxQCmmrZr167Zy5MEQWC5GEoKnzRpkilN8aKGkM7TkKcvD7hHjx6WxyE1QN5zvE/EN0tk9pOkUftok4MUgdGjR5uio3PbNm7cWEPREEVFRabiJd1n4+eraf7QnHjmmWfWOIYqrjhlJo0HQVDjXMXayoDomRR/j6SL6er6fvWrX9mcoiiF5srS0tJqSdyZf6tx/e9//xsgkXIk26KoqMjmCs2R69atM2VKqqjaaf78+aae6nddunSxvDHlX8VzOnOZ31af3XxvAFsb8cc07eWkJ23t9tFkvbWBDOkG0M4Uhcyao6J5Y9G1nnzyyRbGUoKvqm0vW7bM7oEm5LZt29rOIT3UlRw6ePDgJg8RNZTMw13jtaEUMlAo9o9//KOFPSRXl5eX2wNKA19hhP79+7eIxYao65xAbXxoqhBI0qgfKSl5v/32s52xakdtKIBo19vRRx8NpCX2zPPDmnsBtTV0XW3btrVz+vS1tjmotp/VZlvmz+qaz5oL1WR67rnnbFyqnV988UWbm9THtctx4sSJFnZqLtq0aWMPSc0xTbHhqLbDqmV/VVVVjTP/kqagoMAWw6oNdeGFFwLpne0Kb8Y3fMgpUpg3yQ0Q9UH3VBs+unTpUuOMWo2fww47zBLK46cMZLZVUuuB7WfV4TiO4ziO0wJpOe79FoqLi+1sPoWqlHwOUdhKoZWHH3642c5pa0rat29vW7RVqVj1iEpLS23FHv+q8ETmWXctCV1vz549TYpX4q5O7l66dKklEMaTB1WxXYmE2s6a1JlRToTacfDgwUyYMKGZryZ56qM4ZfNeLYWioiKGDx8OYF/jJFVTqaUTV8Oa+zogqlz/3nvvAelUEdXVUlmH4uLiFqXo15fMvtZS+17Le/o6juM4juNsR7S4ZWp+fj6nnHIKgH0Nw7DFrkabknjORvxrtmf1NSfxKvY6vV7erZJFgyCooarFY9+O47QMfEy2bKQg9+rVy3JqnWRwZcpxHMdxHCcLWpwyVRvuDbUuMo+JcRzHcZztGVemHMdxHMdxssAXU47jOI7jOFkQJFlALgiClcAGYFViH9p4dqb6de4ahmG3bf1REASlwOc5u6qmpcE2budtCK3fxvr20x3BRh+LLQcfi1thB7GxVY9FSHgxBRAEwYwwDA9M9EMbQWOvc3uxD1q/jdlcp9vYcmjt/RRav43eT3P3t0nS2vspNP5aPcznOI7jOI6TBb6YchzHcRzHyYLmWEzd3wyf2Rgae53bi33Q+m3M5jrdxpZDa++n0Ppt9H6au79NktbeT6GR15p4zpTjOI7jOE5rwsN8juM4juM4WZDYYioIguODIPg8CII5QRBcndTnbosgCPoGQfBKEASfBUEwOwiCX235+XVBECwOguDjLf9OrMd7uY3NRFPZ2FLtg9Zvo/dTtzHjfVq1fVv+xm1sJprSRiB96Gyu/wFtgLnAt4C2wExg7yQ+ux7X1hPYf8v3nYAvgL2B64Ar3MYdx8aWbN+OYKP3U7dxR7HPbWw9NupfUsrUwcCcMAznhWFYDvwVGJXQZ9dJGIZLwzD8cMv3pcBnQO9GvJXb2Iw0kY0t1j5o/TZ6P20Qrd3G1m4fuI3NShPaCCQX5usNLIz9fxFZXHSuCIKgPzAMeHfLj8YEQTArCIKHgyDoso0/dxtbCFnYuF3YB63fRu+nO7yNrd0+cBtbDFnaCCS3mApq+VmL2kYYBEFHYBJwaRiG64B7gN2B/YClwIRtvUUtP3MbEyZLG1u8fdD6bfR+6jbS+u0Dt7FF0AQ2AsktphYBfWP/7wMsSeizt0kQBAWkb+ZfwjD8J0AYhsvDMKwKwzAFPEBarqwLt7GZaQIbW7R90Ppt9H7qNm6htdsHbmOz00Q2Asktpt4HBgRBsFsQBG2Bs4CnE/rsOgmCIAAeAj4Lw/DW2M97xl72feA/23grt7EZaSIbW6x90Ppt9H5quI2t3z5wG5uVJrQxTUMz1hv7DziRdLb8XGB8Up9bj+s6grTsOAv4eMu/E4E/AZ9s+fnTQE+3sfXb2FLt2xFs9H7qNu5I9rmNrcfGMAy9ArrjOI7jOE42eAV0x3Ecx3GcLPDFlOM4juM4Thb4YspxHMdxHCcLfDHlOI7jOI6TBb6YchzHcRzHyQJfTDmO4ziO42SBL6Ycx3Ecx3GywBdTjuM4juM4WfD/AZ2AXEUYt3AXAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 1\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 2\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 3\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 4\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 5\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 6\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 7\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 8\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class Label 9\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x180 with 10 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"for i in range(10):\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### RANDOM SAMPLE\\n\",\n    \"    ##########################    \\n\",\n    \"    \\n\",\n    \"    labels = torch.tensor([i]*10).to(device)\\n\",\n    \"    n_images = labels.size()[0]\\n\",\n    \"    rand_features = torch.randn(n_images, num_latent).to(device)\\n\",\n    \"    new_images = model.decoder(rand_features, labels)\\n\",\n    \"\\n\",\n    \"    ##########################\\n\",\n    \"    ### VISUALIZATION\\n\",\n    \"    ##########################\\n\",\n    \"\\n\",\n    \"    image_width = 28\\n\",\n    \"\\n\",\n    \"    fig, axes = plt.subplots(nrows=1, ncols=n_images, figsize=(10, 2.5), sharey=True)\\n\",\n    \"    decoded_images = new_images[:n_images]\\n\",\n    \"\\n\",\n    \"    print('Class Label %d' % i)\\n\",\n    \"\\n\",\n    \"    for ax, img in zip(axes, decoded_images):\\n\",\n    \"        curr_img = img.detach().to(torch.device('cpu'))\\n\",\n    \"        ax.imshow(curr_img.view((image_width, image_width)), cmap='binary')\\n\",\n    \"        \\n\",\n    \"    plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-deconv-jaccard.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.7.3\\n\",\n      \"IPython 7.9.0\\n\",\n      \"\\n\",\n      \"torch 1.3.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Convolutional Autoencoder with Deconvolutions and Continuous Jaccard Distance\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A convolutional autoencoder using deconvolutional layers that compresses 768-pixel MNIST images down to a 7x7x8 (392 pixel) representation.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"This convolutional VAE uses a continuous Jaccard distance. I.e., given 2 vectors, $x$ and $y$:\\n\",\n    \"\\n\",\n    \"$$J(x, y)=1-\\\\frac{\\\\sum_{i} \\\\min \\\\left(x_{i}, y_{i}\\\\right)}{\\\\sum_{i} \\\\max \\\\left(x_{i}, y_{i}\\\\right)}$$\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Reference:\\n\",\n    \"    \\n\",\n    \"-    [1] https://en.wikipedia.org/wiki/Jaccard_index\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"tensor(0.6275)\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import torch\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def continuous_jaccard(x, y):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    Implementation of the continuous version of the\\n\",\n    \"    Jaccard distance:\\n\",\n    \"    1 - [sum_i min(x_i, y_i)] / [sum_i max(x_i, y_i)]\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    c = torch.cat((x.view(-1).unsqueeze(1), y.view(-1).unsqueeze(1)), dim=1)\\n\",\n    \"\\n\",\n    \"    numerator = torch.sum(torch.min(c, dim=1)[0])\\n\",\n    \"    denominator = torch.sum(torch.max(c, dim=1)[0])\\n\",\n    \"\\n\",\n    \"    return 1. - numerator/denominator\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Example\\n\",\n    \"\\n\",\n    \"x = torch.tensor([7, 2, 3, 4, 5, 6]).float()\\n\",\n    \"y = torch.tensor([1, 8, 9, 10, 11, 4]).float()\\n\",\n    \"\\n\",\n    \"continuous_jaccard(x, y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Additional Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"0it [00:00, ?it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:0\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"9920512it [00:02, 3410868.60it/s]                             \\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"32768it [00:00, 280881.47it/s]                           \\n\",\n      \"0it [00:00, ?it/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\\n\",\n      \"Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"1654784it [00:00, 1928783.37it/s]                            \\n\",\n      \"8192it [00:00, 113077.53it/s]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\\n\",\n      \"Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\\n\",\n      \"Extracting data/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/raw\\n\",\n      \"Processing...\\n\",\n      \"Done!\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 456\\n\",\n    \"learning_rate = 0.005\\n\",\n    \"num_epochs = 10\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConvolutionalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvolutionalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        \\n\",\n    \"        # 28x28x1 => 28x28x4\\n\",\n    \"        self.conv_1 = torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"                                      padding=1) \\n\",\n    \"        # 28x28x4 => 14x14x4                              \\n\",\n    \"        self.pool_1 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(14-1) - 28 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)                                       \\n\",\n    \"        # 14x14x4 => 14x14x8\\n\",\n    \"        self.conv_2 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=8,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(14-1) - 14 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)                 \\n\",\n    \"        # 14x14x8 => 7x7x8                             \\n\",\n    \"        self.pool_2 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(7-1) - 14 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"                                         \\n\",\n    \"        # 7x7x8 => 15x15x4                          \\n\",\n    \"        self.deconv_1 = torch.nn.ConvTranspose2d(in_channels=8,\\n\",\n    \"                                                 out_channels=4,\\n\",\n    \"                                                 kernel_size=(3, 3),\\n\",\n    \"                                                 stride=(2, 2),\\n\",\n    \"                                                 padding=0)\\n\",\n    \"        \\n\",\n    \"        # 15x15x4  => 31x31x1                           \\n\",\n    \"        self.deconv_2 = torch.nn.ConvTranspose2d(in_channels=4,\\n\",\n    \"                                                 out_channels=1,\\n\",\n    \"                                                 kernel_size=(3, 3),\\n\",\n    \"                                                 stride=(2, 2),\\n\",\n    \"                                                 padding=0)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        x = self.conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_1(x)\\n\",\n    \"        x = self.conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_2(x)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        x = self.deconv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.deconv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        logits = x[:, :, 2:30, 2:30]\\n\",\n    \"        probas = torch.sigmoid(logits)\\n\",\n    \"        return logits, probas\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConvolutionalAutoencoder()\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/010 | Batch 000/468 | Cost: 0.8663\\n\",\n      \"Epoch: 001/010 | Batch 050/468 | Cost: 0.8086\\n\",\n      \"Epoch: 001/010 | Batch 100/468 | Cost: 0.7729\\n\",\n      \"Epoch: 001/010 | Batch 150/468 | Cost: 0.7322\\n\",\n      \"Epoch: 001/010 | Batch 200/468 | Cost: 0.3983\\n\",\n      \"Epoch: 001/010 | Batch 250/468 | Cost: 0.2963\\n\",\n      \"Epoch: 001/010 | Batch 300/468 | Cost: 0.2927\\n\",\n      \"Epoch: 001/010 | Batch 350/468 | Cost: 0.2783\\n\",\n      \"Epoch: 001/010 | Batch 400/468 | Cost: 0.2780\\n\",\n      \"Epoch: 001/010 | Batch 450/468 | Cost: 0.2609\\n\",\n      \"Time elapsed: 0.14 min\\n\",\n      \"Epoch: 002/010 | Batch 000/468 | Cost: 0.2694\\n\",\n      \"Epoch: 002/010 | Batch 050/468 | Cost: 0.2671\\n\",\n      \"Epoch: 002/010 | Batch 100/468 | Cost: 0.2444\\n\",\n      \"Epoch: 002/010 | Batch 150/468 | Cost: 0.2378\\n\",\n      \"Epoch: 002/010 | Batch 200/468 | Cost: 0.2540\\n\",\n      \"Epoch: 002/010 | Batch 250/468 | Cost: 0.2515\\n\",\n      \"Epoch: 002/010 | Batch 300/468 | Cost: 0.2393\\n\",\n      \"Epoch: 002/010 | Batch 350/468 | Cost: 0.2528\\n\",\n      \"Epoch: 002/010 | Batch 400/468 | Cost: 0.2283\\n\",\n      \"Epoch: 002/010 | Batch 450/468 | Cost: 0.2420\\n\",\n      \"Time elapsed: 0.27 min\\n\",\n      \"Epoch: 003/010 | Batch 000/468 | Cost: 0.2317\\n\",\n      \"Epoch: 003/010 | Batch 050/468 | Cost: 0.2274\\n\",\n      \"Epoch: 003/010 | Batch 100/468 | Cost: 0.2489\\n\",\n      \"Epoch: 003/010 | Batch 150/468 | Cost: 0.2246\\n\",\n      \"Epoch: 003/010 | Batch 200/468 | Cost: 0.2178\\n\",\n      \"Epoch: 003/010 | Batch 250/468 | Cost: 0.2200\\n\",\n      \"Epoch: 003/010 | Batch 300/468 | Cost: 0.2200\\n\",\n      \"Epoch: 003/010 | Batch 350/468 | Cost: 0.2309\\n\",\n      \"Epoch: 003/010 | Batch 400/468 | Cost: 0.2215\\n\",\n      \"Epoch: 003/010 | Batch 450/468 | Cost: 0.2218\\n\",\n      \"Time elapsed: 0.40 min\\n\",\n      \"Epoch: 004/010 | Batch 000/468 | Cost: 0.2124\\n\",\n      \"Epoch: 004/010 | Batch 050/468 | Cost: 0.2191\\n\",\n      \"Epoch: 004/010 | Batch 100/468 | Cost: 0.2121\\n\",\n      \"Epoch: 004/010 | Batch 150/468 | Cost: 0.2184\\n\",\n      \"Epoch: 004/010 | Batch 200/468 | Cost: 0.2118\\n\",\n      \"Epoch: 004/010 | Batch 250/468 | Cost: 0.2090\\n\",\n      \"Epoch: 004/010 | Batch 300/468 | Cost: 0.2114\\n\",\n      \"Epoch: 004/010 | Batch 350/468 | Cost: 0.2150\\n\",\n      \"Epoch: 004/010 | Batch 400/468 | Cost: 0.2218\\n\",\n      \"Epoch: 004/010 | Batch 450/468 | Cost: 0.2015\\n\",\n      \"Time elapsed: 0.53 min\\n\",\n      \"Epoch: 005/010 | Batch 000/468 | Cost: 0.1985\\n\",\n      \"Epoch: 005/010 | Batch 050/468 | Cost: 0.2053\\n\",\n      \"Epoch: 005/010 | Batch 100/468 | Cost: 0.2067\\n\",\n      \"Epoch: 005/010 | Batch 150/468 | Cost: 0.2003\\n\",\n      \"Epoch: 005/010 | Batch 200/468 | Cost: 0.2004\\n\",\n      \"Epoch: 005/010 | Batch 250/468 | Cost: 0.2076\\n\",\n      \"Epoch: 005/010 | Batch 300/468 | Cost: 0.2006\\n\",\n      \"Epoch: 005/010 | Batch 350/468 | Cost: 0.2162\\n\",\n      \"Epoch: 005/010 | Batch 400/468 | Cost: 0.2137\\n\",\n      \"Epoch: 005/010 | Batch 450/468 | Cost: 0.2077\\n\",\n      \"Time elapsed: 0.67 min\\n\",\n      \"Epoch: 006/010 | Batch 000/468 | Cost: 0.1986\\n\",\n      \"Epoch: 006/010 | Batch 050/468 | Cost: 0.2048\\n\",\n      \"Epoch: 006/010 | Batch 100/468 | Cost: 0.2063\\n\",\n      \"Epoch: 006/010 | Batch 150/468 | Cost: 0.2069\\n\",\n      \"Epoch: 006/010 | Batch 200/468 | Cost: 0.2092\\n\",\n      \"Epoch: 006/010 | Batch 250/468 | Cost: 0.1947\\n\",\n      \"Epoch: 006/010 | Batch 300/468 | Cost: 0.2006\\n\",\n      \"Epoch: 006/010 | Batch 350/468 | Cost: 0.1927\\n\",\n      \"Epoch: 006/010 | Batch 400/468 | Cost: 0.2018\\n\",\n      \"Epoch: 006/010 | Batch 450/468 | Cost: 0.1964\\n\",\n      \"Time elapsed: 0.79 min\\n\",\n      \"Epoch: 007/010 | Batch 000/468 | Cost: 0.1809\\n\",\n      \"Epoch: 007/010 | Batch 050/468 | Cost: 0.1996\\n\",\n      \"Epoch: 007/010 | Batch 100/468 | Cost: 0.1942\\n\",\n      \"Epoch: 007/010 | Batch 150/468 | Cost: 0.1909\\n\",\n      \"Epoch: 007/010 | Batch 200/468 | Cost: 0.1894\\n\",\n      \"Epoch: 007/010 | Batch 250/468 | Cost: 0.1937\\n\",\n      \"Epoch: 007/010 | Batch 300/468 | Cost: 0.1956\\n\",\n      \"Epoch: 007/010 | Batch 350/468 | Cost: 0.1938\\n\",\n      \"Epoch: 007/010 | Batch 400/468 | Cost: 0.1963\\n\",\n      \"Epoch: 007/010 | Batch 450/468 | Cost: 0.2060\\n\",\n      \"Time elapsed: 0.92 min\\n\",\n      \"Epoch: 008/010 | Batch 000/468 | Cost: 0.1947\\n\",\n      \"Epoch: 008/010 | Batch 050/468 | Cost: 0.2044\\n\",\n      \"Epoch: 008/010 | Batch 100/468 | Cost: 0.1811\\n\",\n      \"Epoch: 008/010 | Batch 150/468 | Cost: 0.1980\\n\",\n      \"Epoch: 008/010 | Batch 200/468 | Cost: 0.1794\\n\",\n      \"Epoch: 008/010 | Batch 250/468 | Cost: 0.2008\\n\",\n      \"Epoch: 008/010 | Batch 300/468 | Cost: 0.1949\\n\",\n      \"Epoch: 008/010 | Batch 350/468 | Cost: 0.1843\\n\",\n      \"Epoch: 008/010 | Batch 400/468 | Cost: 0.1942\\n\",\n      \"Epoch: 008/010 | Batch 450/468 | Cost: 0.1932\\n\",\n      \"Time elapsed: 1.05 min\\n\",\n      \"Epoch: 009/010 | Batch 000/468 | Cost: 0.1901\\n\",\n      \"Epoch: 009/010 | Batch 050/468 | Cost: 0.1894\\n\",\n      \"Epoch: 009/010 | Batch 100/468 | Cost: 0.1976\\n\",\n      \"Epoch: 009/010 | Batch 150/468 | Cost: 0.1935\\n\",\n      \"Epoch: 009/010 | Batch 200/468 | Cost: 0.1949\\n\",\n      \"Epoch: 009/010 | Batch 250/468 | Cost: 0.1921\\n\",\n      \"Epoch: 009/010 | Batch 300/468 | Cost: 0.1917\\n\",\n      \"Epoch: 009/010 | Batch 350/468 | Cost: 0.1900\\n\",\n      \"Epoch: 009/010 | Batch 400/468 | Cost: 0.1913\\n\",\n      \"Epoch: 009/010 | Batch 450/468 | Cost: 0.1815\\n\",\n      \"Time elapsed: 1.19 min\\n\",\n      \"Epoch: 010/010 | Batch 000/468 | Cost: 0.1845\\n\",\n      \"Epoch: 010/010 | Batch 050/468 | Cost: 0.1910\\n\",\n      \"Epoch: 010/010 | Batch 100/468 | Cost: 0.1929\\n\",\n      \"Epoch: 010/010 | Batch 150/468 | Cost: 0.1919\\n\",\n      \"Epoch: 010/010 | Batch 200/468 | Cost: 0.1822\\n\",\n      \"Epoch: 010/010 | Batch 250/468 | Cost: 0.1974\\n\",\n      \"Epoch: 010/010 | Batch 300/468 | Cost: 0.1919\\n\",\n      \"Epoch: 010/010 | Batch 350/468 | Cost: 0.1750\\n\",\n      \"Epoch: 010/010 | Batch 400/468 | Cost: 0.1879\\n\",\n      \"Epoch: 010/010 | Batch 450/468 | Cost: 0.1785\\n\",\n      \"Time elapsed: 1.32 min\\n\",\n      \"Total Training Time: 1.32 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = features.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        logits, decoded = model(features)\\n\",\n    \"        #cost = F.binary_cross_entropy_with_logits(logits, features)\\n\",\n    \"        cost = continuous_jaccard(features, decoded)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_dataset)//batch_size, cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       1.3.0\\n\",\n      \"matplotlib  3.1.0\\n\",\n      \"torchvision 0.4.1a0+d94043a\\n\",\n      \"numpy       1.17.2\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-deconv-nopool.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Convolutional Autoencoder with Deconvolutions (without pooling operations)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A convolutional autoencoder using deconvolutional layers that compresses 768-pixel MNIST images down to a 7x7x8 (392 pixel) representation without using pooling operations but increasing the stride in convolutional layers.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:0\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 456\\n\",\n    \"learning_rate = 0.005\\n\",\n    \"num_epochs = 10\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConvolutionalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvolutionalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        \\n\",\n    \"        # 28x28x1 => 14x14x4  \\n\",\n    \"        self.conv_1 = torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(2, 2),\\n\",\n    \"                                      # floor((2(14-1) - 28 + 3) / 2) = 0\\n\",\n    \"                                      padding=0)\\n\",\n    \"        \\n\",\n    \"        # 14x14x4 => 7x7x8\\n\",\n    \"        self.conv_2 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=8,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(2, 2),\\n\",\n    \"                                      # ceil((2(7-1) - 14 + 3) / 2) = 1\\n\",\n    \"                                      padding=1)                 \\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"                                         \\n\",\n    \"        # 7x7x8 => 15x15x4                          \\n\",\n    \"        self.deconv_1 = torch.nn.ConvTranspose2d(in_channels=8,\\n\",\n    \"                                                 out_channels=4,\\n\",\n    \"                                                 kernel_size=(3, 3),\\n\",\n    \"                                                 stride=(2, 2),\\n\",\n    \"                                                 padding=0)\\n\",\n    \"        \\n\",\n    \"        # 15x15x4  => 29x29x1                           \\n\",\n    \"        self.deconv_2 = torch.nn.ConvTranspose2d(in_channels=4,\\n\",\n    \"                                                 out_channels=1,\\n\",\n    \"                                                 kernel_size=(3, 3),\\n\",\n    \"                                                 stride=(2, 2),\\n\",\n    \"                                                 padding=1)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        x = self.conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"          \\n\",\n    \"        ### DECODER\\n\",\n    \"        x = self.deconv_1(x)  \\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.deconv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = x[:, :, :-1, :-1]\\n\",\n    \"        x = torch.sigmoid(x)\\n\",\n    \"        return x\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConvolutionalAutoencoder()\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/010 | Batch 000/469 | Cost: 0.7184\\n\",\n      \"Epoch: 001/010 | Batch 050/469 | Cost: 0.6903\\n\",\n      \"Epoch: 001/010 | Batch 100/469 | Cost: 0.6569\\n\",\n      \"Epoch: 001/010 | Batch 150/469 | Cost: 0.5834\\n\",\n      \"Epoch: 001/010 | Batch 200/469 | Cost: 0.2208\\n\",\n      \"Epoch: 001/010 | Batch 250/469 | Cost: 0.1800\\n\",\n      \"Epoch: 001/010 | Batch 300/469 | Cost: 0.1723\\n\",\n      \"Epoch: 001/010 | Batch 350/469 | Cost: 0.1573\\n\",\n      \"Epoch: 001/010 | Batch 400/469 | Cost: 0.1450\\n\",\n      \"Epoch: 001/010 | Batch 450/469 | Cost: 0.1451\\n\",\n      \"Time elapsed: 0.12 min\\n\",\n      \"Epoch: 002/010 | Batch 000/469 | Cost: 0.1384\\n\",\n      \"Epoch: 002/010 | Batch 050/469 | Cost: 0.1396\\n\",\n      \"Epoch: 002/010 | Batch 100/469 | Cost: 0.1325\\n\",\n      \"Epoch: 002/010 | Batch 150/469 | Cost: 0.1306\\n\",\n      \"Epoch: 002/010 | Batch 200/469 | Cost: 0.1284\\n\",\n      \"Epoch: 002/010 | Batch 250/469 | Cost: 0.1256\\n\",\n      \"Epoch: 002/010 | Batch 300/469 | Cost: 0.1235\\n\",\n      \"Epoch: 002/010 | Batch 350/469 | Cost: 0.1256\\n\",\n      \"Epoch: 002/010 | Batch 400/469 | Cost: 0.1251\\n\",\n      \"Epoch: 002/010 | Batch 450/469 | Cost: 0.1195\\n\",\n      \"Time elapsed: 0.24 min\\n\",\n      \"Epoch: 003/010 | Batch 000/469 | Cost: 0.1189\\n\",\n      \"Epoch: 003/010 | Batch 050/469 | Cost: 0.1209\\n\",\n      \"Epoch: 003/010 | Batch 100/469 | Cost: 0.1197\\n\",\n      \"Epoch: 003/010 | Batch 150/469 | Cost: 0.1147\\n\",\n      \"Epoch: 003/010 | Batch 200/469 | Cost: 0.1152\\n\",\n      \"Epoch: 003/010 | Batch 250/469 | Cost: 0.1142\\n\",\n      \"Epoch: 003/010 | Batch 300/469 | Cost: 0.1146\\n\",\n      \"Epoch: 003/010 | Batch 350/469 | Cost: 0.1182\\n\",\n      \"Epoch: 003/010 | Batch 400/469 | Cost: 0.1150\\n\",\n      \"Epoch: 003/010 | Batch 450/469 | Cost: 0.1099\\n\",\n      \"Time elapsed: 0.36 min\\n\",\n      \"Epoch: 004/010 | Batch 000/469 | Cost: 0.1165\\n\",\n      \"Epoch: 004/010 | Batch 050/469 | Cost: 0.1159\\n\",\n      \"Epoch: 004/010 | Batch 100/469 | Cost: 0.1092\\n\",\n      \"Epoch: 004/010 | Batch 150/469 | Cost: 0.1112\\n\",\n      \"Epoch: 004/010 | Batch 200/469 | Cost: 0.1147\\n\",\n      \"Epoch: 004/010 | Batch 250/469 | Cost: 0.1148\\n\",\n      \"Epoch: 004/010 | Batch 300/469 | Cost: 0.1136\\n\",\n      \"Epoch: 004/010 | Batch 350/469 | Cost: 0.1141\\n\",\n      \"Epoch: 004/010 | Batch 400/469 | Cost: 0.1098\\n\",\n      \"Epoch: 004/010 | Batch 450/469 | Cost: 0.1160\\n\",\n      \"Time elapsed: 0.48 min\\n\",\n      \"Epoch: 005/010 | Batch 000/469 | Cost: 0.1103\\n\",\n      \"Epoch: 005/010 | Batch 050/469 | Cost: 0.1097\\n\",\n      \"Epoch: 005/010 | Batch 100/469 | Cost: 0.1102\\n\",\n      \"Epoch: 005/010 | Batch 150/469 | Cost: 0.1116\\n\",\n      \"Epoch: 005/010 | Batch 200/469 | Cost: 0.1120\\n\",\n      \"Epoch: 005/010 | Batch 250/469 | Cost: 0.1110\\n\",\n      \"Epoch: 005/010 | Batch 300/469 | Cost: 0.1137\\n\",\n      \"Epoch: 005/010 | Batch 350/469 | Cost: 0.1110\\n\",\n      \"Epoch: 005/010 | Batch 400/469 | Cost: 0.1087\\n\",\n      \"Epoch: 005/010 | Batch 450/469 | Cost: 0.1150\\n\",\n      \"Time elapsed: 0.60 min\\n\",\n      \"Epoch: 006/010 | Batch 000/469 | Cost: 0.1111\\n\",\n      \"Epoch: 006/010 | Batch 050/469 | Cost: 0.1038\\n\",\n      \"Epoch: 006/010 | Batch 100/469 | Cost: 0.1088\\n\",\n      \"Epoch: 006/010 | Batch 150/469 | Cost: 0.1112\\n\",\n      \"Epoch: 006/010 | Batch 200/469 | Cost: 0.1080\\n\",\n      \"Epoch: 006/010 | Batch 250/469 | Cost: 0.1091\\n\",\n      \"Epoch: 006/010 | Batch 300/469 | Cost: 0.1073\\n\",\n      \"Epoch: 006/010 | Batch 350/469 | Cost: 0.1089\\n\",\n      \"Epoch: 006/010 | Batch 400/469 | Cost: 0.1058\\n\",\n      \"Epoch: 006/010 | Batch 450/469 | Cost: 0.1121\\n\",\n      \"Time elapsed: 0.71 min\\n\",\n      \"Epoch: 007/010 | Batch 000/469 | Cost: 0.1064\\n\",\n      \"Epoch: 007/010 | Batch 050/469 | Cost: 0.1049\\n\",\n      \"Epoch: 007/010 | Batch 100/469 | Cost: 0.1067\\n\",\n      \"Epoch: 007/010 | Batch 150/469 | Cost: 0.1061\\n\",\n      \"Epoch: 007/010 | Batch 200/469 | Cost: 0.1078\\n\",\n      \"Epoch: 007/010 | Batch 250/469 | Cost: 0.1010\\n\",\n      \"Epoch: 007/010 | Batch 300/469 | Cost: 0.1028\\n\",\n      \"Epoch: 007/010 | Batch 350/469 | Cost: 0.1076\\n\",\n      \"Epoch: 007/010 | Batch 400/469 | Cost: 0.1061\\n\",\n      \"Epoch: 007/010 | Batch 450/469 | Cost: 0.1049\\n\",\n      \"Time elapsed: 0.83 min\\n\",\n      \"Epoch: 008/010 | Batch 000/469 | Cost: 0.1087\\n\",\n      \"Epoch: 008/010 | Batch 050/469 | Cost: 0.1069\\n\",\n      \"Epoch: 008/010 | Batch 100/469 | Cost: 0.1079\\n\",\n      \"Epoch: 008/010 | Batch 150/469 | Cost: 0.1028\\n\",\n      \"Epoch: 008/010 | Batch 200/469 | Cost: 0.1044\\n\",\n      \"Epoch: 008/010 | Batch 250/469 | Cost: 0.1024\\n\",\n      \"Epoch: 008/010 | Batch 300/469 | Cost: 0.1015\\n\",\n      \"Epoch: 008/010 | Batch 350/469 | Cost: 0.1013\\n\",\n      \"Epoch: 008/010 | Batch 400/469 | Cost: 0.1052\\n\",\n      \"Epoch: 008/010 | Batch 450/469 | Cost: 0.1024\\n\",\n      \"Time elapsed: 0.95 min\\n\",\n      \"Epoch: 009/010 | Batch 000/469 | Cost: 0.1059\\n\",\n      \"Epoch: 009/010 | Batch 050/469 | Cost: 0.1008\\n\",\n      \"Epoch: 009/010 | Batch 100/469 | Cost: 0.1059\\n\",\n      \"Epoch: 009/010 | Batch 150/469 | Cost: 0.1046\\n\",\n      \"Epoch: 009/010 | Batch 200/469 | Cost: 0.1038\\n\",\n      \"Epoch: 009/010 | Batch 250/469 | Cost: 0.1039\\n\",\n      \"Epoch: 009/010 | Batch 300/469 | Cost: 0.0989\\n\",\n      \"Epoch: 009/010 | Batch 350/469 | Cost: 0.1043\\n\",\n      \"Epoch: 009/010 | Batch 400/469 | Cost: 0.1048\\n\",\n      \"Epoch: 009/010 | Batch 450/469 | Cost: 0.1047\\n\",\n      \"Time elapsed: 1.06 min\\n\",\n      \"Epoch: 010/010 | Batch 000/469 | Cost: 0.1054\\n\",\n      \"Epoch: 010/010 | Batch 050/469 | Cost: 0.1027\\n\",\n      \"Epoch: 010/010 | Batch 100/469 | Cost: 0.1027\\n\",\n      \"Epoch: 010/010 | Batch 150/469 | Cost: 0.1005\\n\",\n      \"Epoch: 010/010 | Batch 200/469 | Cost: 0.1035\\n\",\n      \"Epoch: 010/010 | Batch 250/469 | Cost: 0.1006\\n\",\n      \"Epoch: 010/010 | Batch 300/469 | Cost: 0.1039\\n\",\n      \"Epoch: 010/010 | Batch 350/469 | Cost: 0.0974\\n\",\n      \"Epoch: 010/010 | Batch 400/469 | Cost: 0.1034\\n\",\n      \"Epoch: 010/010 | Batch 450/469 | Cost: 0.1026\\n\",\n      \"Time elapsed: 1.18 min\\n\",\n      \"Total Training Time: 1.18 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = features.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        decoded = model(features)\\n\",\n    \"        cost = F.binary_cross_entropy(decoded, features)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/autoencoder/ae-deconv.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.7.1\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Convolutional Autoencoder with Deconvolutions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"A convolutional autoencoder using deconvolutional layers that compresses 768-pixel MNIST images down to a 7x7x8 (392 pixel) representation.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Device: cuda:0\\n\",\n      \"Image batch dimensions: torch.Size([128, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"print('Device:', device)\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 456\\n\",\n    \"learning_rate = 0.005\\n\",\n    \"num_epochs = 10\\n\",\n    \"batch_size = 128\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConvolutionalAutoencoder(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self):\\n\",\n    \"        super(ConvolutionalAutoencoder, self).__init__()\\n\",\n    \"        \\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        \\n\",\n    \"        # 28x28x1 => 28x28x4\\n\",\n    \"        self.conv_1 = torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"                                      padding=1) \\n\",\n    \"        # 28x28x4 => 14x14x4                              \\n\",\n    \"        self.pool_1 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(14-1) - 28 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)                                       \\n\",\n    \"        # 14x14x4 => 14x14x8\\n\",\n    \"        self.conv_2 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=8,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      # (1(14-1) - 14 + 3) / 2 = 1\\n\",\n    \"                                      padding=1)                 \\n\",\n    \"        # 14x14x8 => 7x7x8                             \\n\",\n    \"        self.pool_2 = torch.nn.MaxPool2d(kernel_size=(2, 2),\\n\",\n    \"                                         stride=(2, 2),\\n\",\n    \"                                         # (2(7-1) - 14 + 2) / 2 = 0\\n\",\n    \"                                         padding=0)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"                                         \\n\",\n    \"        # 7x7x8 => 15x15x4                          \\n\",\n    \"        self.deconv_1 = torch.nn.ConvTranspose2d(in_channels=8,\\n\",\n    \"                                                 out_channels=4,\\n\",\n    \"                                                 kernel_size=(3, 3),\\n\",\n    \"                                                 stride=(2, 2),\\n\",\n    \"                                                 padding=0)\\n\",\n    \"        \\n\",\n    \"        # 15x15x4  => 31x31x1                           \\n\",\n    \"        self.deconv_2 = torch.nn.ConvTranspose2d(in_channels=4,\\n\",\n    \"                                                 out_channels=1,\\n\",\n    \"                                                 kernel_size=(3, 3),\\n\",\n    \"                                                 stride=(2, 2),\\n\",\n    \"                                                 padding=0)\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        \\n\",\n    \"        ### ENCODER\\n\",\n    \"        x = self.conv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_1(x)\\n\",\n    \"        x = self.conv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.pool_2(x)\\n\",\n    \"        \\n\",\n    \"        ### DECODER\\n\",\n    \"        x = self.deconv_1(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        x = self.deconv_2(x)\\n\",\n    \"        x = F.leaky_relu(x)\\n\",\n    \"        logits = x[:, :, 2:30, 2:30]\\n\",\n    \"        probas = torch.sigmoid(logits)\\n\",\n    \"        return logits, probas\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConvolutionalAutoencoder()\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/010 | Batch 000/468 | Cost: 0.7191\\n\",\n      \"Epoch: 001/010 | Batch 050/468 | Cost: 0.6911\\n\",\n      \"Epoch: 001/010 | Batch 100/468 | Cost: 0.5994\\n\",\n      \"Epoch: 001/010 | Batch 150/468 | Cost: 0.3594\\n\",\n      \"Epoch: 001/010 | Batch 200/468 | Cost: 0.3035\\n\",\n      \"Epoch: 001/010 | Batch 250/468 | Cost: 0.2480\\n\",\n      \"Epoch: 001/010 | Batch 300/468 | Cost: 0.2085\\n\",\n      \"Epoch: 001/010 | Batch 350/468 | Cost: 0.1780\\n\",\n      \"Epoch: 001/010 | Batch 400/468 | Cost: 0.1586\\n\",\n      \"Epoch: 001/010 | Batch 450/468 | Cost: 0.1532\\n\",\n      \"Epoch: 002/010 | Batch 000/468 | Cost: 0.1442\\n\",\n      \"Epoch: 002/010 | Batch 050/468 | Cost: 0.1366\\n\",\n      \"Epoch: 002/010 | Batch 100/468 | Cost: 0.1362\\n\",\n      \"Epoch: 002/010 | Batch 150/468 | Cost: 0.1293\\n\",\n      \"Epoch: 002/010 | Batch 200/468 | Cost: 0.1286\\n\",\n      \"Epoch: 002/010 | Batch 250/468 | Cost: 0.1244\\n\",\n      \"Epoch: 002/010 | Batch 300/468 | Cost: 0.1226\\n\",\n      \"Epoch: 002/010 | Batch 350/468 | Cost: 0.1240\\n\",\n      \"Epoch: 002/010 | Batch 400/468 | Cost: 0.1239\\n\",\n      \"Epoch: 002/010 | Batch 450/468 | Cost: 0.1193\\n\",\n      \"Epoch: 003/010 | Batch 000/468 | Cost: 0.1196\\n\",\n      \"Epoch: 003/010 | Batch 050/468 | Cost: 0.1197\\n\",\n      \"Epoch: 003/010 | Batch 100/468 | Cost: 0.1217\\n\",\n      \"Epoch: 003/010 | Batch 150/468 | Cost: 0.1167\\n\",\n      \"Epoch: 003/010 | Batch 200/468 | Cost: 0.1115\\n\",\n      \"Epoch: 003/010 | Batch 250/468 | Cost: 0.1144\\n\",\n      \"Epoch: 003/010 | Batch 300/468 | Cost: 0.1092\\n\",\n      \"Epoch: 003/010 | Batch 350/468 | Cost: 0.1164\\n\",\n      \"Epoch: 003/010 | Batch 400/468 | Cost: 0.1141\\n\",\n      \"Epoch: 003/010 | Batch 450/468 | Cost: 0.1071\\n\",\n      \"Epoch: 004/010 | Batch 000/468 | Cost: 0.1121\\n\",\n      \"Epoch: 004/010 | Batch 050/468 | Cost: 0.1130\\n\",\n      \"Epoch: 004/010 | Batch 100/468 | Cost: 0.1043\\n\",\n      \"Epoch: 004/010 | Batch 150/468 | Cost: 0.1098\\n\",\n      \"Epoch: 004/010 | Batch 200/468 | Cost: 0.1104\\n\",\n      \"Epoch: 004/010 | Batch 250/468 | Cost: 0.1095\\n\",\n      \"Epoch: 004/010 | Batch 300/468 | Cost: 0.1105\\n\",\n      \"Epoch: 004/010 | Batch 350/468 | Cost: 0.1088\\n\",\n      \"Epoch: 004/010 | Batch 400/468 | Cost: 0.1040\\n\",\n      \"Epoch: 004/010 | Batch 450/468 | Cost: 0.1098\\n\",\n      \"Epoch: 005/010 | Batch 000/468 | Cost: 0.1045\\n\",\n      \"Epoch: 005/010 | Batch 050/468 | Cost: 0.1030\\n\",\n      \"Epoch: 005/010 | Batch 100/468 | Cost: 0.1029\\n\",\n      \"Epoch: 005/010 | Batch 150/468 | Cost: 0.1063\\n\",\n      \"Epoch: 005/010 | Batch 200/468 | Cost: 0.1056\\n\",\n      \"Epoch: 005/010 | Batch 250/468 | Cost: 0.1046\\n\",\n      \"Epoch: 005/010 | Batch 300/468 | Cost: 0.1074\\n\",\n      \"Epoch: 005/010 | Batch 350/468 | Cost: 0.1062\\n\",\n      \"Epoch: 005/010 | Batch 400/468 | Cost: 0.1029\\n\",\n      \"Epoch: 005/010 | Batch 450/468 | Cost: 0.1074\\n\",\n      \"Epoch: 006/010 | Batch 000/468 | Cost: 0.1051\\n\",\n      \"Epoch: 006/010 | Batch 050/468 | Cost: 0.0983\\n\",\n      \"Epoch: 006/010 | Batch 100/468 | Cost: 0.1031\\n\",\n      \"Epoch: 006/010 | Batch 150/468 | Cost: 0.1060\\n\",\n      \"Epoch: 006/010 | Batch 200/468 | Cost: 0.1044\\n\",\n      \"Epoch: 006/010 | Batch 250/468 | Cost: 0.1013\\n\",\n      \"Epoch: 006/010 | Batch 300/468 | Cost: 0.0992\\n\",\n      \"Epoch: 006/010 | Batch 350/468 | Cost: 0.1010\\n\",\n      \"Epoch: 006/010 | Batch 400/468 | Cost: 0.1020\\n\",\n      \"Epoch: 006/010 | Batch 450/468 | Cost: 0.1047\\n\",\n      \"Epoch: 007/010 | Batch 000/468 | Cost: 0.0979\\n\",\n      \"Epoch: 007/010 | Batch 050/468 | Cost: 0.0978\\n\",\n      \"Epoch: 007/010 | Batch 100/468 | Cost: 0.1001\\n\",\n      \"Epoch: 007/010 | Batch 150/468 | Cost: 0.1023\\n\",\n      \"Epoch: 007/010 | Batch 200/468 | Cost: 0.1008\\n\",\n      \"Epoch: 007/010 | Batch 250/468 | Cost: 0.0943\\n\",\n      \"Epoch: 007/010 | Batch 300/468 | Cost: 0.0968\\n\",\n      \"Epoch: 007/010 | Batch 350/468 | Cost: 0.1017\\n\",\n      \"Epoch: 007/010 | Batch 400/468 | Cost: 0.0988\\n\",\n      \"Epoch: 007/010 | Batch 450/468 | Cost: 0.0992\\n\",\n      \"Epoch: 008/010 | Batch 000/468 | Cost: 0.1015\\n\",\n      \"Epoch: 008/010 | Batch 050/468 | Cost: 0.0995\\n\",\n      \"Epoch: 008/010 | Batch 100/468 | Cost: 0.0988\\n\",\n      \"Epoch: 008/010 | Batch 150/468 | Cost: 0.0980\\n\",\n      \"Epoch: 008/010 | Batch 200/468 | Cost: 0.0986\\n\",\n      \"Epoch: 008/010 | Batch 250/468 | Cost: 0.0958\\n\",\n      \"Epoch: 008/010 | Batch 300/468 | Cost: 0.0958\\n\",\n      \"Epoch: 008/010 | Batch 350/468 | Cost: 0.0928\\n\",\n      \"Epoch: 008/010 | Batch 400/468 | Cost: 0.0986\\n\",\n      \"Epoch: 008/010 | Batch 450/468 | Cost: 0.0972\\n\",\n      \"Epoch: 009/010 | Batch 000/468 | Cost: 0.0985\\n\",\n      \"Epoch: 009/010 | Batch 050/468 | Cost: 0.0958\\n\",\n      \"Epoch: 009/010 | Batch 100/468 | Cost: 0.1002\\n\",\n      \"Epoch: 009/010 | Batch 150/468 | Cost: 0.0980\\n\",\n      \"Epoch: 009/010 | Batch 200/468 | Cost: 0.0973\\n\",\n      \"Epoch: 009/010 | Batch 250/468 | Cost: 0.0966\\n\",\n      \"Epoch: 009/010 | Batch 300/468 | Cost: 0.0948\\n\",\n      \"Epoch: 009/010 | Batch 350/468 | Cost: 0.0983\\n\",\n      \"Epoch: 009/010 | Batch 400/468 | Cost: 0.0986\\n\",\n      \"Epoch: 009/010 | Batch 450/468 | Cost: 0.0976\\n\",\n      \"Epoch: 010/010 | Batch 000/468 | Cost: 0.0975\\n\",\n      \"Epoch: 010/010 | Batch 050/468 | Cost: 0.0971\\n\",\n      \"Epoch: 010/010 | Batch 100/468 | Cost: 0.0976\\n\",\n      \"Epoch: 010/010 | Batch 150/468 | Cost: 0.0953\\n\",\n      \"Epoch: 010/010 | Batch 200/468 | Cost: 0.0982\\n\",\n      \"Epoch: 010/010 | Batch 250/468 | Cost: 0.0964\\n\",\n      \"Epoch: 010/010 | Batch 300/468 | Cost: 0.1003\\n\",\n      \"Epoch: 010/010 | Batch 350/468 | Cost: 0.0914\\n\",\n      \"Epoch: 010/010 | Batch 400/468 | Cost: 0.0971\\n\",\n      \"Epoch: 010/010 | Batch 450/468 | Cost: 0.0959\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        # don't need labels, only the images (features)\\n\",\n    \"        features = features.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        logits, decoded = model(features)\\n\",\n    \"        cost = F.binary_cross_entropy_with_logits(logits, features)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_dataset)//batch_size, cost))\\n\",\n    \"            \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 1440x180 with 30 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### VISUALIZATION\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"n_images = 15\\n\",\n    \"image_width = 28\\n\",\n    \"\\n\",\n    \"fig, axes = plt.subplots(nrows=2, ncols=n_images, \\n\",\n    \"                         sharex=True, sharey=True, figsize=(20, 2.5))\\n\",\n    \"orig_images = features[:n_images]\\n\",\n    \"decoded_images = decoded[:n_images]\\n\",\n    \"\\n\",\n    \"for i in range(n_images):\\n\",\n    \"    for ax, img in zip(axes, [orig_images, decoded_images]):\\n\",\n    \"        curr_img = img[i].detach().to(torch.device('cpu'))\\n\",\n    \"        ax[i].imshow(curr_img.view((image_width, image_width)), cmap='binary')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch_ipynb/basic-ml/perceptron.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Rosenblatt Perceptron\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Implementation of the classic Perceptron by Frank Rosenblatt for binary classification (here: 0/1 class labels).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import torch\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Preparing a toy dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class label counts: [50 50]\\n\",\n      \"X.shape: (100, 2)\\n\",\n      \"y.shape: (100,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"data = np.genfromtxt('../data/perceptron_toydata.txt', delimiter='\\\\t')\\n\",\n    \"X, y = data[:, :2], data[:, 2]\\n\",\n    \"y = y.astype(np.int)\\n\",\n    \"\\n\",\n    \"print('Class label counts:', np.bincount(y))\\n\",\n    \"print('X.shape:', X.shape)\\n\",\n    \"print('y.shape:', y.shape)\\n\",\n    \"\\n\",\n    \"# Shuffling & train/test split\\n\",\n    \"shuffle_idx = np.arange(y.shape[0])\\n\",\n    \"shuffle_rng = np.random.RandomState(123)\\n\",\n    \"shuffle_rng.shuffle(shuffle_idx)\\n\",\n    \"X, y = X[shuffle_idx], y[shuffle_idx]\\n\",\n    \"\\n\",\n    \"X_train, X_test = X[shuffle_idx[:70]], X[shuffle_idx[70:]]\\n\",\n    \"y_train, y_test = y[shuffle_idx[:70]], y[shuffle_idx[70:]]\\n\",\n    \"\\n\",\n    \"# Normalize (mean zero, unit variance)\\n\",\n    \"mu, sigma = X_train.mean(axis=0), X_train.std(axis=0)\\n\",\n    \"X_train = (X_train - mu) / sigma\\n\",\n    \"X_test = (X_test - mu) / sigma\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.scatter(X_train[y_train==0, 0], X_train[y_train==0, 1], label='class 0', marker='o')\\n\",\n    \"plt.scatter(X_train[y_train==1, 0], X_train[y_train==1, 1], label='class 1', marker='s')\\n\",\n    \"plt.xlabel('feature 1')\\n\",\n    \"plt.ylabel('feature 2')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Defining the Perceptron model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def custom_where(cond, x_1, x_2):\\n\",\n    \"    return (cond * x_1) + ((1-cond) * x_2)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class Perceptron():\\n\",\n    \"    def __init__(self, num_features):\\n\",\n    \"        self.num_features = num_features\\n\",\n    \"        self.weights = torch.zeros(num_features, 1, \\n\",\n    \"                                   dtype=torch.float32, device=device)\\n\",\n    \"        self.bias = torch.zeros(1, dtype=torch.float32, device=device)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        linear = torch.add(torch.mm(x, self.weights), self.bias)\\n\",\n    \"        predictions = custom_where(linear > 0., 1, 0).float()\\n\",\n    \"        return predictions\\n\",\n    \"        \\n\",\n    \"    def backward(self, x, y):  \\n\",\n    \"        predictions = self.forward(x)\\n\",\n    \"        errors = y - predictions\\n\",\n    \"        return errors\\n\",\n    \"        \\n\",\n    \"    def train(self, x, y, epochs):\\n\",\n    \"        for e in range(epochs):\\n\",\n    \"            \\n\",\n    \"            for i in range(y.size()[0]):\\n\",\n    \"                # use view because backward expects a matrix (i.e., 2D tensor)\\n\",\n    \"                errors = self.backward(x[i].view(1, self.num_features), y[i]).view(-1)\\n\",\n    \"                self.weights += (errors * x[i]).view(self.num_features, 1)\\n\",\n    \"                self.bias += errors\\n\",\n    \"                \\n\",\n    \"    def evaluate(self, x, y):\\n\",\n    \"        predictions = self.forward(x).view(-1)\\n\",\n    \"        accuracy = torch.sum(predictions == y).float() / y.size()[0]\\n\",\n    \"        return accuracy\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training the Perceptron\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Model parameters:\\n\",\n      \"  Weights: tensor([[1.2734],\\n\",\n      \"        [1.3464]], device='cuda:0')\\n\",\n      \"  Bias: tensor([-1.], device='cuda:0')\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"ppn = Perceptron(num_features=2)\\n\",\n    \"\\n\",\n    \"X_train_tensor = torch.tensor(X_train, dtype=torch.float32, device=device)\\n\",\n    \"y_train_tensor = torch.tensor(y_train, dtype=torch.float32, device=device)\\n\",\n    \"\\n\",\n    \"ppn.train(X_train_tensor, y_train_tensor, epochs=5)\\n\",\n    \"\\n\",\n    \"print('Model parameters:')\\n\",\n    \"print('  Weights: %s' % ppn.weights)\\n\",\n    \"print('  Bias: %s' % ppn.bias)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluating the model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test set accuracy: 93.33%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X_test_tensor = torch.tensor(X_test, dtype=torch.float32, device=device)\\n\",\n    \"y_test_tensor = torch.tensor(y_test, dtype=torch.float32, device=device)\\n\",\n    \"\\n\",\n    \"test_acc = ppn.evaluate(X_test_tensor, y_test_tensor)\\n\",\n    \"print('Test set accuracy: %.2f%%' % (test_acc*100))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 504x216 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### 2D Decision Boundary\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"w, b = ppn.weights, ppn.bias\\n\",\n    \"\\n\",\n    \"x_min = -2\\n\",\n    \"y_min = ( (-(w[0] * x_min) - b[0]) \\n\",\n    \"          / w[1] )\\n\",\n    \"\\n\",\n    \"x_max = 2\\n\",\n    \"y_max = ( (-(w[0] * x_max) - b[0]) \\n\",\n    \"          / w[1] )\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"fig, ax = plt.subplots(1, 2, sharex=True, figsize=(7, 3))\\n\",\n    \"\\n\",\n    \"ax[0].plot([x_min, x_max], [y_min, y_max])\\n\",\n    \"ax[1].plot([x_min, x_max], [y_min, y_max])\\n\",\n    \"\\n\",\n    \"ax[0].scatter(X_train[y_train==0, 0], X_train[y_train==0, 1], label='class 0', marker='o')\\n\",\n    \"ax[0].scatter(X_train[y_train==1, 0], X_train[y_train==1, 1], label='class 1', marker='s')\\n\",\n    \"\\n\",\n    \"ax[1].scatter(X_test[y_test==0, 0], X_test[y_test==0, 1], label='class 0', marker='o')\\n\",\n    \"ax[1].scatter(X_test[y_test==1, 0], X_test[y_test==1, 1], label='class 1', marker='s')\\n\",\n    \"\\n\",\n    \"ax[1].legend(loc='upper left')\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/basic-ml/softmax-regression-mlxtend-1.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.7.5\\n\",\n      \"IPython 7.10.2\\n\",\n      \"\\n\",\n      \"torch 1.3.1\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Softmax Regression with MLxtend's plot_decision_regions on Iris\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Implementation of softmax regression (multinomial logistic regression) and demonstration for how to use PyTorch models with MLxtend's [`plot_decision_regions`](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_decision_regions/) function\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch\\n\",\n    \"import numpy as np\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Settings and Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 0\\n\",\n    \"learning_rate = 0.05\\n\",\n    \"num_epochs = 10\\n\",\n    \"batch_size = 8\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_features = 2\\n\",\n    \"num_classes = 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Class label counts: [50 50 50]\\n\",\n      \"X.shape: (150, 2)\\n\",\n      \"y.shape: (150,)\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"data = np.genfromtxt('../data/iris.data', delimiter=',', dtype=str)\\n\",\n    \"X, y = data[:, [2, 3]], data[:, 4]\\n\",\n    \"X = X.astype(float)\\n\",\n    \"\\n\",\n    \"d = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}\\n\",\n    \"y = np.array([d[x] for x in y])\\n\",\n    \"y = y.astype(np.int)\\n\",\n    \"\\n\",\n    \"print('Class label counts:', np.bincount(y))\\n\",\n    \"print('X.shape:', X.shape)\\n\",\n    \"print('y.shape:', y.shape)\\n\",\n    \"\\n\",\n    \"# Shuffling & train/test split\\n\",\n    \"shuffle_idx = np.arange(y.shape[0])\\n\",\n    \"shuffle_rng = np.random.RandomState(123)\\n\",\n    \"shuffle_rng.shuffle(shuffle_idx)\\n\",\n    \"X, y = X[shuffle_idx], y[shuffle_idx]\\n\",\n    \"\\n\",\n    \"X_train, X_test = X[shuffle_idx[:70]], X[shuffle_idx[70:]]\\n\",\n    \"y_train, y_test = y[shuffle_idx[:70]], y[shuffle_idx[70:]]\\n\",\n    \"\\n\",\n    \"# Normalize (mean zero, unit variance)\\n\",\n    \"mu, sigma = X_train.mean(axis=0), X_train.std(axis=0)\\n\",\n    \"X_train = (X_train - mu) / sigma\\n\",\n    \"X_test = (X_test - mu) / sigma\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### Data Loaders\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"from torch.utils.data import Dataset\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class MyDataset(Dataset):\\n\",\n    \"\\n\",\n    \"    def __init__(self, X, y):\\n\",\n    \"    \\n\",\n    \"        self.X = torch.tensor(X, dtype=torch.float32)\\n\",\n    \"        self.y = torch.tensor(y, dtype=torch.int64)\\n\",\n    \"\\n\",\n    \"    def __getitem__(self, index):\\n\",\n    \"        training_example, training_label = self.X[index], self.y[index]\\n\",\n    \"        return training_example, training_label\\n\",\n    \"\\n\",\n    \"    def __len__(self):\\n\",\n    \"        return self.y.shape[0]\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"train_dataset = MyDataset(X[:100], y[:100])\\n\",\n    \"test_dataset = MyDataset(X[100:], y[100:])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset,\\n\",\n    \"                          batch_size=batch_size,\\n\",\n    \"                          shuffle=True, # want to shuffle the dataset\\n\",\n    \"                          num_workers=4) # number processes/CPUs to use\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset,\\n\",\n    \"                         batch_size=batch_size,\\n\",\n    \"                         shuffle=False,\\n\",\n    \"                         num_workers=4)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"class SoftmaxRegression(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_features, num_classes):\\n\",\n    \"        super(SoftmaxRegression, self).__init__()\\n\",\n    \"        self.linear = torch.nn.Linear(num_features, num_classes)\\n\",\n    \"        \\n\",\n    \"        self.linear.weight.detach().zero_()\\n\",\n    \"        self.linear.bias.detach().zero_()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        logits = self.linear(x)\\n\",\n    \"        probas = F.softmax(logits, dim=1)\\n\",\n    \"        return logits, probas\\n\",\n    \"\\n\",\n    \"model = SoftmaxRegression(num_features=num_features,\\n\",\n    \"                          num_classes=num_classes)\\n\",\n    \"\\n\",\n    \"model.to(device)\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### COST AND OPTIMIZER\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/010 | Batch 000/012 | Cost: 1.0986\\n\",\n      \"Epoch: 001/010 training accuracy: 44.00%\\n\",\n      \"Epoch: 002/010 | Batch 000/012 | Cost: 0.9489\\n\",\n      \"Epoch: 002/010 training accuracy: 66.00%\\n\",\n      \"Epoch: 003/010 | Batch 000/012 | Cost: 0.8236\\n\",\n      \"Epoch: 003/010 training accuracy: 72.00%\\n\",\n      \"Epoch: 004/010 | Batch 000/012 | Cost: 0.8275\\n\",\n      \"Epoch: 004/010 training accuracy: 95.00%\\n\",\n      \"Epoch: 005/010 | Batch 000/012 | Cost: 0.6650\\n\",\n      \"Epoch: 005/010 training accuracy: 72.00%\\n\",\n      \"Epoch: 006/010 | Batch 000/012 | Cost: 0.6465\\n\",\n      \"Epoch: 006/010 training accuracy: 78.00%\\n\",\n      \"Epoch: 007/010 | Batch 000/012 | Cost: 0.3825\\n\",\n      \"Epoch: 007/010 training accuracy: 95.00%\\n\",\n      \"Epoch: 008/010 | Batch 000/012 | Cost: 0.4917\\n\",\n      \"Epoch: 008/010 training accuracy: 77.00%\\n\",\n      \"Epoch: 009/010 | Batch 000/012 | Cost: 0.6003\\n\",\n      \"Epoch: 009/010 training accuracy: 81.00%\\n\",\n      \"Epoch: 010/010 | Batch 000/012 | Cost: 0.5938\\n\",\n      \"Epoch: 010/010 training accuracy: 93.00%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Manual seed for deterministic data loader\\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def compute_accuracy(model, data_loader):\\n\",\n    \"    correct_pred, num_examples = 0, 0\\n\",\n    \"    \\n\",\n    \"    for features, targets in data_loader:\\n\",\n    \"        features = features.to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        _, predicted_labels = torch.max(probas, 1)\\n\",\n    \"        num_examples += targets.size(0)\\n\",\n    \"        correct_pred += (predicted_labels == targets).sum()\\n\",\n    \"        \\n\",\n    \"    return correct_pred.float() / num_examples * 100\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        features = features.to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"            \\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        \\n\",\n    \"        # note that the PyTorch implementation of\\n\",\n    \"        # CrossEntropyLoss works with logits, not\\n\",\n    \"        # probabilities\\n\",\n    \"        cost = F.cross_entropy(logits, targets)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_dataset)//batch_size, cost))\\n\",\n    \"            \\n\",\n    \"    with torch.set_grad_enabled(False):\\n\",\n    \"        print('Epoch: %03d/%03d training accuracy: %.2f%%' % (\\n\",\n    \"              epoch+1, num_epochs, \\n\",\n    \"              compute_accuracy(model, train_loader)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 84.00%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Creating a ModelWrapper class for plot_decision_regions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"All we need is a simple class that implements a `predict` method. That's it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"class ModelWrapper():\\n\",\n    \"    def __init__(self, model, device):\\n\",\n    \"        self.model = model\\n\",\n    \"        self.device = device\\n\",\n    \"        \\n\",\n    \"    def predict(self, X):\\n\",\n    \"        features = torch.tensor(X, dtype=torch.float32, device=self.device)\\n\",\n    \"        logits, probas = self.model(features)\\n\",\n    \"        _, predicted_labels = torch.max(probas, 1)\\n\",\n    \"        \\n\",\n    \"        return predicted_labels.numpy()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"mymodel = ModelWrapper(model, device=torch.device('cpu'))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from mlxtend.plotting import plot_decision_regions\\n\",\n    \"\\n\",\n    \"plot_decision_regions(X, y, mymodel)\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       1.3.1\\n\",\n      \"matplotlib  3.1.1\\n\",\n      \"numpy       1.17.4\\n\",\n      \"torchvision 0.4.2\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.5\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch_ipynb/basic-ml/softmax-regression.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- Softmax Regression\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Implementation of softmax regression (multinomial logistic regression).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Settings and Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Image batch dimensions: torch.Size([256, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:0\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 123\\n\",\n    \"learning_rate = 0.1\\n\",\n    \"num_epochs = 10\\n\",\n    \"batch_size = 256\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_features = 784\\n\",\n    \"num_classes = 10\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),  \\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"class SoftmaxRegression(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_features, num_classes):\\n\",\n    \"        super(SoftmaxRegression, self).__init__()\\n\",\n    \"        self.linear = torch.nn.Linear(num_features, num_classes)\\n\",\n    \"        \\n\",\n    \"        self.linear.weight.detach().zero_()\\n\",\n    \"        self.linear.bias.detach().zero_()\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        logits = self.linear(x)\\n\",\n    \"        probas = F.softmax(logits, dim=1)\\n\",\n    \"        return logits, probas\\n\",\n    \"\\n\",\n    \"model = SoftmaxRegression(num_features=num_features,\\n\",\n    \"                          num_classes=num_classes)\\n\",\n    \"\\n\",\n    \"model.to(device)\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### COST AND OPTIMIZER\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/010 | Batch 000/234 | Cost: 2.3026\\n\",\n      \"Epoch: 001/010 | Batch 050/234 | Cost: 0.7941\\n\",\n      \"Epoch: 001/010 | Batch 100/234 | Cost: 0.5651\\n\",\n      \"Epoch: 001/010 | Batch 150/234 | Cost: 0.4603\\n\",\n      \"Epoch: 001/010 | Batch 200/234 | Cost: 0.4822\\n\",\n      \"Epoch: 001/010 training accuracy: 88.04%\\n\",\n      \"Epoch: 002/010 | Batch 000/234 | Cost: 0.4105\\n\",\n      \"Epoch: 002/010 | Batch 050/234 | Cost: 0.4415\\n\",\n      \"Epoch: 002/010 | Batch 100/234 | Cost: 0.4367\\n\",\n      \"Epoch: 002/010 | Batch 150/234 | Cost: 0.4289\\n\",\n      \"Epoch: 002/010 | Batch 200/234 | Cost: 0.3926\\n\",\n      \"Epoch: 002/010 training accuracy: 89.37%\\n\",\n      \"Epoch: 003/010 | Batch 000/234 | Cost: 0.4112\\n\",\n      \"Epoch: 003/010 | Batch 050/234 | Cost: 0.3579\\n\",\n      \"Epoch: 003/010 | Batch 100/234 | Cost: 0.3013\\n\",\n      \"Epoch: 003/010 | Batch 150/234 | Cost: 0.3258\\n\",\n      \"Epoch: 003/010 | Batch 200/234 | Cost: 0.4254\\n\",\n      \"Epoch: 003/010 training accuracy: 89.98%\\n\",\n      \"Epoch: 004/010 | Batch 000/234 | Cost: 0.3988\\n\",\n      \"Epoch: 004/010 | Batch 050/234 | Cost: 0.3690\\n\",\n      \"Epoch: 004/010 | Batch 100/234 | Cost: 0.3459\\n\",\n      \"Epoch: 004/010 | Batch 150/234 | Cost: 0.4030\\n\",\n      \"Epoch: 004/010 | Batch 200/234 | Cost: 0.3240\\n\",\n      \"Epoch: 004/010 training accuracy: 90.35%\\n\",\n      \"Epoch: 005/010 | Batch 000/234 | Cost: 0.3265\\n\",\n      \"Epoch: 005/010 | Batch 050/234 | Cost: 0.3673\\n\",\n      \"Epoch: 005/010 | Batch 100/234 | Cost: 0.3085\\n\",\n      \"Epoch: 005/010 | Batch 150/234 | Cost: 0.3183\\n\",\n      \"Epoch: 005/010 | Batch 200/234 | Cost: 0.3316\\n\",\n      \"Epoch: 005/010 training accuracy: 90.64%\\n\",\n      \"Epoch: 006/010 | Batch 000/234 | Cost: 0.4518\\n\",\n      \"Epoch: 006/010 | Batch 050/234 | Cost: 0.3863\\n\",\n      \"Epoch: 006/010 | Batch 100/234 | Cost: 0.3620\\n\",\n      \"Epoch: 006/010 | Batch 150/234 | Cost: 0.3733\\n\",\n      \"Epoch: 006/010 | Batch 200/234 | Cost: 0.3289\\n\",\n      \"Epoch: 006/010 training accuracy: 90.86%\\n\",\n      \"Epoch: 007/010 | Batch 000/234 | Cost: 0.3450\\n\",\n      \"Epoch: 007/010 | Batch 050/234 | Cost: 0.2289\\n\",\n      \"Epoch: 007/010 | Batch 100/234 | Cost: 0.3073\\n\",\n      \"Epoch: 007/010 | Batch 150/234 | Cost: 0.2750\\n\",\n      \"Epoch: 007/010 | Batch 200/234 | Cost: 0.3456\\n\",\n      \"Epoch: 007/010 training accuracy: 91.00%\\n\",\n      \"Epoch: 008/010 | Batch 000/234 | Cost: 0.4900\\n\",\n      \"Epoch: 008/010 | Batch 050/234 | Cost: 0.3479\\n\",\n      \"Epoch: 008/010 | Batch 100/234 | Cost: 0.2343\\n\",\n      \"Epoch: 008/010 | Batch 150/234 | Cost: 0.3059\\n\",\n      \"Epoch: 008/010 | Batch 200/234 | Cost: 0.3684\\n\",\n      \"Epoch: 008/010 training accuracy: 91.22%\\n\",\n      \"Epoch: 009/010 | Batch 000/234 | Cost: 0.3762\\n\",\n      \"Epoch: 009/010 | Batch 050/234 | Cost: 0.2976\\n\",\n      \"Epoch: 009/010 | Batch 100/234 | Cost: 0.2690\\n\",\n      \"Epoch: 009/010 | Batch 150/234 | Cost: 0.2610\\n\",\n      \"Epoch: 009/010 | Batch 200/234 | Cost: 0.3140\\n\",\n      \"Epoch: 009/010 training accuracy: 91.34%\\n\",\n      \"Epoch: 010/010 | Batch 000/234 | Cost: 0.2790\\n\",\n      \"Epoch: 010/010 | Batch 050/234 | Cost: 0.3070\\n\",\n      \"Epoch: 010/010 | Batch 100/234 | Cost: 0.3300\\n\",\n      \"Epoch: 010/010 | Batch 150/234 | Cost: 0.2520\\n\",\n      \"Epoch: 010/010 | Batch 200/234 | Cost: 0.3301\\n\",\n      \"Epoch: 010/010 training accuracy: 91.40%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Manual seed for deterministic data loader\\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def compute_accuracy(model, data_loader):\\n\",\n    \"    correct_pred, num_examples = 0, 0\\n\",\n    \"    \\n\",\n    \"    for features, targets in data_loader:\\n\",\n    \"        features = features.view(-1, 28*28).to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        _, predicted_labels = torch.max(probas, 1)\\n\",\n    \"        num_examples += targets.size(0)\\n\",\n    \"        correct_pred += (predicted_labels == targets).sum()\\n\",\n    \"        \\n\",\n    \"    return correct_pred.float() / num_examples * 100\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        features = features.view(-1, 28*28).to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"            \\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        \\n\",\n    \"        # note that the PyTorch implementation of\\n\",\n    \"        # CrossEntropyLoss works with logits, not\\n\",\n    \"        # probabilities\\n\",\n    \"        cost = F.cross_entropy(logits, targets)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_dataset)//batch_size, cost))\\n\",\n    \"            \\n\",\n    \"    with torch.set_grad_enabled(False):\\n\",\n    \"        print('Epoch: %03d/%03d training accuracy: %.2f%%' % (\\n\",\n    \"              epoch+1, num_epochs, \\n\",\n    \"              compute_accuracy(model, train_loader)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 91.77%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       1.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/cnn/cnn-alexnet-cifar10-grouped.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"UEBilEjLj5wY\"\n   },\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 119\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 536,\n     \"status\": \"ok\",\n     \"timestamp\": 1524974472601,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"GOzuY8Yvj5wb\",\n    \"outputId\": \"c19362ce-f87a-4cc2-84cc-8d7b4b9e6007\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Author: Sebastian Raschka\\n\",\n      \"\\n\",\n      \"Python implementation: CPython\\n\",\n      \"Python version       : 3.8.12\\n\",\n      \"IPython version      : 8.0.1\\n\",\n      \"\\n\",\n      \"torch: 1.10.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"rH4XmErYj5wm\"\n   },\n   \"source\": [\n    \"# AlexNet CIFAR-10 Classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"AlexNet [1][2] trained on CIFAR-10 [3].\\n\",\n    \"\\n\",\n    \"This implementation uses grouped convolutions like in the original AlexNet paper [2]:\\n\",\n    \"\\n\",\n    \"![](../images/alexnet/alexnet-paper.png)\\n\",\n    \"\\n\",\n    \"Here, the network is essentially split into two parts to train it on two GPUs with 1.5 Gb RAM each. This was purely done for computational performance reasons (and the video RAM limitation back then). However, there are certain benefits to using grouped convolutions ...\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**Taking a step back, how do grouped convolutions work?**\\n\",\n    \"\\n\",\n    \"In a nutshell, you can think of grouped convolutions as convolutional layers that process part of the input independently and merge the results. So, for example, if you consider grouped convolutions with two filter groups, each filter group would process half of the channels. \\n\",\n    \"\\n\",\n    \"![](../images/alexnet/grouped-convolutions.png)\\n\",\n    \"\\n\",\n    \"**One of the benefits of grouped convolutions is**, as noted by Yani Ioannou [4], that AlexNet has a slightly improved accuracy when using two filter groups:\\n\",\n    \"\\n\",\n    \"![](../images/alexnet/alexnet-groups.png)\\n\",\n    \"\\n\",\n    \"**Another benefit is the reduced parameter size**. \\n\",\n    \"\\n\",\n    \"Say we have kernels with height 3 and width 3. The inputs have 6 channels, and the output channels are set to 12. Then, we have kernels with 3x3x6 weight parameters with a regular convolution. Since we have 12 output channels, that's 3x3x6x12=648 parameters in total.\\n\",\n    \"\\n\",\n    \"Now, let's assume we use a grouped convolution with group size 2. We still have a 3x3 kernel height and width. But now, the number of input channels is split by a factor of 2, so each kernel is 3x3x3. The first group produces the first 6 output channels, so we have 3x3x3x6 parameters for the first group. The second group has the same size, so we have (3x3x3x6)x2 = 3x3x3x12 = 324, which is a 2x reduction in parameters compared to the regular convolution.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"**And how do we do this in PyTorch?**\\n\",\n    \"\\n\",\n    \"Implementing grouped convolutions in PyTorch is now really straightforward. We just used the `groups` parameter. For example, to implement a grouped convolution with two filter groups we use\\n\",\n    \"\\n\",\n    \"    torch.nn.Conv2d(..., groups=2)\\n\",\n    \"\\n\",\n    \"Note that a requirement for this is that the number of input and output channels is divisible by groups (here: 2).\\n\",\n    \"\\n\",\n    \"### References\\n\",\n    \"\\n\",\n    \"- [1] L13.7 CNN Architectures & AlexNet (20:17), https://www.youtube.com/watch?v=-IHxe4-09e4\\n\",\n    \"- [2] Imagenet classification with deep convolutional neural networks, https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf\\n\",\n    \"- [3] https://en.wikipedia.org/wiki/CIFAR-10\\n\",\n    \"- [4] https://blog.yani.ai/filter-group-tutorial/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"MkoGLH_Tj5wn\"\n   },\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"ORj09gnrj5wp\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torch.utils.data.dataset import Subset\\n\",\n    \"\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"I6hghKPxj5w0\"\n   },\n   \"source\": [\n    \"## Model Settings\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Setting a random seed\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"I recommend using a function like the following one prior to using dataset loaders and initializing a model if you want to ensure the data is shuffled in the same manner if you rerun this notebook and the model gets the same initial random weights:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def set_all_seeds(seed):\\n\",\n    \"    os.environ[\\\"PL_GLOBAL_SEED\\\"] = str(seed)\\n\",\n    \"    random.seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"    torch.manual_seed(seed)\\n\",\n    \"    torch.cuda.manual_seed_all(seed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Setting cuDNN and PyTorch algorithmic behavior to deterministic\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Similar to the `set_all_seeds` function above, I recommend setting the behavior of PyTorch and cuDNN to deterministic (this is particulary relevant when using GPUs). We can also define a function for that:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def set_deterministic():\\n\",\n    \"    if torch.cuda.is_available():\\n\",\n    \"        torch.backends.cudnn.benchmark = False\\n\",\n    \"        torch.backends.cudnn.deterministic = True\\n\",\n    \"    torch.set_deterministic(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 85\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 23936,\n     \"status\": \"ok\",\n     \"timestamp\": 1524974497505,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"NnT0sZIwj5wu\",\n    \"outputId\": \"55aed925-d17e-4c6a-8c71-0d9b3bde5637\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"RANDOM_SEED = 1\\n\",\n    \"LEARNING_RATE = 0.0001\\n\",\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 40\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"NUM_CLASSES = 10\\n\",\n    \"\\n\",\n    \"# Other\\n\",\n    \"DEVICE = \\\"cuda:0\\\"\\n\",\n    \"\\n\",\n    \"set_all_seeds(RANDOM_SEED)\\n\",\n    \"\\n\",\n    \"# Deterministic behavior not yet supported by AdaptiveAvgPool2d\\n\",\n    \"#set_deterministic()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Import utility functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.insert(0, \\\"..\\\") # to include ../helper_evaluate.py etc.\\n\",\n    \"\\n\",\n    \"from helper_evaluate import compute_accuracy\\n\",\n    \"from helper_data import get_dataloaders_cifar10\\n\",\n    \"from helper_train import train_classifier_simple_v1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"### Set random seed ###\\n\",\n    \"set_all_seeds(RANDOM_SEED)\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### Dataset\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"train_transforms = transforms.Compose([transforms.Resize((70, 70)),\\n\",\n    \"                                       transforms.RandomCrop((64, 64)),\\n\",\n    \"                                       transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"test_transforms = transforms.Compose([transforms.Resize((70, 70)),\\n\",\n    \"                                      transforms.CenterCrop((64, 64)),\\n\",\n    \"                                      transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader, valid_loader, test_loader = get_dataloaders_cifar10(\\n\",\n    \"    batch_size=BATCH_SIZE, \\n\",\n    \"    num_workers=2, \\n\",\n    \"    train_transforms=train_transforms,\\n\",\n    \"    test_transforms=test_transforms,\\n\",\n    \"    validation_fraction=0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Set:\\n\",\n      \"\\n\",\n      \"Image batch dimensions: torch.Size([256, 3, 64, 64])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\",\n      \"tensor([0, 2, 3, 5, 4, 8, 9, 6, 9, 7])\\n\",\n      \"\\n\",\n      \"Validation Set:\\n\",\n      \"Image batch dimensions: torch.Size([256, 3, 64, 64])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\",\n      \"tensor([6, 9, 3, 5, 7, 3, 4, 1, 8, 0])\\n\",\n      \"\\n\",\n      \"Testing Set:\\n\",\n      \"Image batch dimensions: torch.Size([256, 3, 64, 64])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\",\n      \"tensor([2, 6, 3, 1, 1, 1, 1, 2, 4, 8])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Checking the dataset\\n\",\n    \"print('Training Set:\\\\n')\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.size())\\n\",\n    \"    print('Image label dimensions:', labels.size())\\n\",\n    \"    print(labels[:10])\\n\",\n    \"    break\\n\",\n    \"    \\n\",\n    \"# Checking the dataset\\n\",\n    \"print('\\\\nValidation Set:')\\n\",\n    \"for images, labels in valid_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.size())\\n\",\n    \"    print('Image label dimensions:', labels.size())\\n\",\n    \"    print(labels[:10])\\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"print('\\\\nTesting Set:')\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.size())\\n\",\n    \"    print('Image label dimensions:', labels.size())\\n\",\n    \"    print(labels[:10])\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"class AlexNet(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super(AlexNet, self).__init__()\\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(64, 192, kernel_size=5, padding=2, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(192, 384, kernel_size=3, padding=1, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(384, 256, kernel_size=3, padding=1, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(256, 256, kernel_size=3, padding=1, groups=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"        )\\n\",\n    \"        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(256 * 6 * 6, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(4096, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Linear(4096, num_classes)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = self.avgpool(x)\\n\",\n    \"        x = x.view(x.size(0), 256 * 6 * 6)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"        probas = F.softmax(logits, dim=1)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"_lza9t_uj5w1\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(RANDOM_SEED)\\n\",\n    \"\\n\",\n    \"model = AlexNet(NUM_CLASSES)\\n\",\n    \"model.to(DEVICE)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"RAodboScj5w6\"\n   },\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 1547\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 2384585,\n     \"status\": \"ok\",\n     \"timestamp\": 1524976888520,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"Dzh3ROmRj5w7\",\n    \"outputId\": \"5f8fd8c9-b076-403a-b0b7-fd2d498b48d7\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/040 | Batch 0000/0175 | Loss: 2.3021\\n\",\n      \"Epoch: 001/040 | Batch 0050/0175 | Loss: 2.0457\\n\",\n      \"Epoch: 001/040 | Batch 0100/0175 | Loss: 1.8939\\n\",\n      \"Epoch: 001/040 | Batch 0150/0175 | Loss: 1.8882\\n\",\n      \"***Epoch: 001/040 | Train. Acc.: 32.408% | Loss: 1.743\\n\",\n      \"***Epoch: 001/040 | Valid. Acc.: 33.340% | Loss: 1.714\\n\",\n      \"Time elapsed: 1.02 min\\n\",\n      \"Epoch: 002/040 | Batch 0000/0175 | Loss: 1.7017\\n\",\n      \"Epoch: 002/040 | Batch 0050/0175 | Loss: 1.7092\\n\",\n      \"Epoch: 002/040 | Batch 0100/0175 | Loss: 1.6315\\n\",\n      \"Epoch: 002/040 | Batch 0150/0175 | Loss: 1.5158\\n\",\n      \"***Epoch: 002/040 | Train. Acc.: 41.415% | Loss: 1.555\\n\",\n      \"***Epoch: 002/040 | Valid. Acc.: 42.140% | Loss: 1.538\\n\",\n      \"Time elapsed: 2.06 min\\n\",\n      \"Epoch: 003/040 | Batch 0000/0175 | Loss: 1.5034\\n\",\n      \"Epoch: 003/040 | Batch 0050/0175 | Loss: 1.5797\\n\",\n      \"Epoch: 003/040 | Batch 0100/0175 | Loss: 1.4733\\n\",\n      \"Epoch: 003/040 | Batch 0150/0175 | Loss: 1.2810\\n\",\n      \"***Epoch: 003/040 | Train. Acc.: 48.246% | Loss: 1.380\\n\",\n      \"***Epoch: 003/040 | Valid. Acc.: 49.020% | Loss: 1.373\\n\",\n      \"Time elapsed: 3.09 min\\n\",\n      \"Epoch: 004/040 | Batch 0000/0175 | Loss: 1.3568\\n\",\n      \"Epoch: 004/040 | Batch 0050/0175 | Loss: 1.3999\\n\",\n      \"Epoch: 004/040 | Batch 0100/0175 | Loss: 1.4197\\n\",\n      \"Epoch: 004/040 | Batch 0150/0175 | Loss: 1.3423\\n\",\n      \"***Epoch: 004/040 | Train. Acc.: 52.406% | Loss: 1.289\\n\",\n      \"***Epoch: 004/040 | Valid. Acc.: 53.300% | Loss: 1.280\\n\",\n      \"Time elapsed: 4.13 min\\n\",\n      \"Epoch: 005/040 | Batch 0000/0175 | Loss: 1.2611\\n\",\n      \"Epoch: 005/040 | Batch 0050/0175 | Loss: 1.2486\\n\",\n      \"Epoch: 005/040 | Batch 0100/0175 | Loss: 1.2251\\n\",\n      \"Epoch: 005/040 | Batch 0150/0175 | Loss: 1.2385\\n\",\n      \"***Epoch: 005/040 | Train. Acc.: 52.710% | Loss: 1.277\\n\",\n      \"***Epoch: 005/040 | Valid. Acc.: 54.220% | Loss: 1.255\\n\",\n      \"Time elapsed: 5.17 min\\n\",\n      \"Epoch: 006/040 | Batch 0000/0175 | Loss: 1.3014\\n\",\n      \"Epoch: 006/040 | Batch 0050/0175 | Loss: 1.2493\\n\",\n      \"Epoch: 006/040 | Batch 0100/0175 | Loss: 1.1972\\n\",\n      \"Epoch: 006/040 | Batch 0150/0175 | Loss: 1.1217\\n\",\n      \"***Epoch: 006/040 | Train. Acc.: 58.699% | Loss: 1.130\\n\",\n      \"***Epoch: 006/040 | Valid. Acc.: 58.900% | Loss: 1.133\\n\",\n      \"Time elapsed: 6.20 min\\n\",\n      \"Epoch: 007/040 | Batch 0000/0175 | Loss: 1.1142\\n\",\n      \"Epoch: 007/040 | Batch 0050/0175 | Loss: 1.1751\\n\",\n      \"Epoch: 007/040 | Batch 0100/0175 | Loss: 1.1640\\n\",\n      \"Epoch: 007/040 | Batch 0150/0175 | Loss: 1.3103\\n\",\n      \"***Epoch: 007/040 | Train. Acc.: 59.344% | Loss: 1.130\\n\",\n      \"***Epoch: 007/040 | Valid. Acc.: 58.880% | Loss: 1.139\\n\",\n      \"Time elapsed: 7.23 min\\n\",\n      \"Epoch: 008/040 | Batch 0000/0175 | Loss: 1.0524\\n\",\n      \"Epoch: 008/040 | Batch 0050/0175 | Loss: 1.1116\\n\",\n      \"Epoch: 008/040 | Batch 0100/0175 | Loss: 1.0474\\n\",\n      \"Epoch: 008/040 | Batch 0150/0175 | Loss: 1.0964\\n\",\n      \"***Epoch: 008/040 | Train. Acc.: 60.248% | Loss: 1.114\\n\",\n      \"***Epoch: 008/040 | Valid. Acc.: 59.220% | Loss: 1.136\\n\",\n      \"Time elapsed: 8.28 min\\n\",\n      \"Epoch: 009/040 | Batch 0000/0175 | Loss: 1.1965\\n\",\n      \"Epoch: 009/040 | Batch 0050/0175 | Loss: 1.0852\\n\",\n      \"Epoch: 009/040 | Batch 0100/0175 | Loss: 1.1306\\n\",\n      \"Epoch: 009/040 | Batch 0150/0175 | Loss: 1.0086\\n\",\n      \"***Epoch: 009/040 | Train. Acc.: 61.824% | Loss: 1.058\\n\",\n      \"***Epoch: 009/040 | Valid. Acc.: 60.620% | Loss: 1.076\\n\",\n      \"Time elapsed: 9.31 min\\n\",\n      \"Epoch: 010/040 | Batch 0000/0175 | Loss: 1.0530\\n\",\n      \"Epoch: 010/040 | Batch 0050/0175 | Loss: 1.0641\\n\",\n      \"Epoch: 010/040 | Batch 0100/0175 | Loss: 0.9715\\n\",\n      \"Epoch: 010/040 | Batch 0150/0175 | Loss: 1.1926\\n\",\n      \"***Epoch: 010/040 | Train. Acc.: 64.944% | Loss: 0.977\\n\",\n      \"***Epoch: 010/040 | Valid. Acc.: 62.820% | Loss: 1.028\\n\",\n      \"Time elapsed: 10.34 min\\n\",\n      \"Epoch: 011/040 | Batch 0000/0175 | Loss: 1.0312\\n\",\n      \"Epoch: 011/040 | Batch 0050/0175 | Loss: 1.0072\\n\",\n      \"Epoch: 011/040 | Batch 0100/0175 | Loss: 0.9125\\n\",\n      \"Epoch: 011/040 | Batch 0150/0175 | Loss: 1.0450\\n\",\n      \"***Epoch: 011/040 | Train. Acc.: 65.922% | Loss: 0.945\\n\",\n      \"***Epoch: 011/040 | Valid. Acc.: 64.360% | Loss: 0.990\\n\",\n      \"Time elapsed: 11.37 min\\n\",\n      \"Epoch: 012/040 | Batch 0000/0175 | Loss: 1.0078\\n\",\n      \"Epoch: 012/040 | Batch 0050/0175 | Loss: 1.0750\\n\",\n      \"Epoch: 012/040 | Batch 0100/0175 | Loss: 0.8935\\n\",\n      \"Epoch: 012/040 | Batch 0150/0175 | Loss: 0.9567\\n\",\n      \"***Epoch: 012/040 | Train. Acc.: 67.286% | Loss: 0.907\\n\",\n      \"***Epoch: 012/040 | Valid. Acc.: 64.140% | Loss: 0.983\\n\",\n      \"Time elapsed: 12.40 min\\n\",\n      \"Epoch: 013/040 | Batch 0000/0175 | Loss: 0.9305\\n\",\n      \"Epoch: 013/040 | Batch 0050/0175 | Loss: 0.8980\\n\",\n      \"Epoch: 013/040 | Batch 0100/0175 | Loss: 1.0309\\n\",\n      \"Epoch: 013/040 | Batch 0150/0175 | Loss: 0.9596\\n\",\n      \"***Epoch: 013/040 | Train. Acc.: 69.614% | Loss: 0.853\\n\",\n      \"***Epoch: 013/040 | Valid. Acc.: 66.480% | Loss: 0.926\\n\",\n      \"Time elapsed: 13.43 min\\n\",\n      \"Epoch: 014/040 | Batch 0000/0175 | Loss: 0.8409\\n\",\n      \"Epoch: 014/040 | Batch 0050/0175 | Loss: 0.9902\\n\",\n      \"Epoch: 014/040 | Batch 0100/0175 | Loss: 0.8945\\n\",\n      \"Epoch: 014/040 | Batch 0150/0175 | Loss: 0.9077\\n\",\n      \"***Epoch: 014/040 | Train. Acc.: 71.190% | Loss: 0.822\\n\",\n      \"***Epoch: 014/040 | Valid. Acc.: 67.460% | Loss: 0.915\\n\",\n      \"Time elapsed: 14.45 min\\n\",\n      \"Epoch: 015/040 | Batch 0000/0175 | Loss: 0.9422\\n\",\n      \"Epoch: 015/040 | Batch 0050/0175 | Loss: 0.8516\\n\",\n      \"Epoch: 015/040 | Batch 0100/0175 | Loss: 0.7501\\n\",\n      \"Epoch: 015/040 | Batch 0150/0175 | Loss: 0.8076\\n\",\n      \"***Epoch: 015/040 | Train. Acc.: 70.270% | Loss: 0.832\\n\",\n      \"***Epoch: 015/040 | Valid. Acc.: 66.460% | Loss: 0.936\\n\",\n      \"Time elapsed: 15.49 min\\n\",\n      \"Epoch: 016/040 | Batch 0000/0175 | Loss: 0.8759\\n\",\n      \"Epoch: 016/040 | Batch 0050/0175 | Loss: 0.9273\\n\",\n      \"Epoch: 016/040 | Batch 0100/0175 | Loss: 0.8645\\n\",\n      \"Epoch: 016/040 | Batch 0150/0175 | Loss: 0.8602\\n\",\n      \"***Epoch: 016/040 | Train. Acc.: 69.623% | Loss: 0.859\\n\",\n      \"***Epoch: 016/040 | Valid. Acc.: 65.780% | Loss: 0.967\\n\",\n      \"Time elapsed: 16.53 min\\n\",\n      \"Epoch: 017/040 | Batch 0000/0175 | Loss: 0.9171\\n\",\n      \"Epoch: 017/040 | Batch 0050/0175 | Loss: 0.8242\\n\",\n      \"Epoch: 017/040 | Batch 0100/0175 | Loss: 0.7830\\n\",\n      \"Epoch: 017/040 | Batch 0150/0175 | Loss: 0.9317\\n\",\n      \"***Epoch: 017/040 | Train. Acc.: 72.188% | Loss: 0.794\\n\",\n      \"***Epoch: 017/040 | Valid. Acc.: 67.400% | Loss: 0.948\\n\",\n      \"Time elapsed: 17.56 min\\n\",\n      \"Epoch: 018/040 | Batch 0000/0175 | Loss: 0.7613\\n\",\n      \"Epoch: 018/040 | Batch 0050/0175 | Loss: 0.8159\\n\",\n      \"Epoch: 018/040 | Batch 0100/0175 | Loss: 0.8606\\n\",\n      \"Epoch: 018/040 | Batch 0150/0175 | Loss: 0.8943\\n\",\n      \"***Epoch: 018/040 | Train. Acc.: 74.721% | Loss: 0.720\\n\",\n      \"***Epoch: 018/040 | Valid. Acc.: 69.640% | Loss: 0.866\\n\",\n      \"Time elapsed: 18.60 min\\n\",\n      \"Epoch: 019/040 | Batch 0000/0175 | Loss: 0.7614\\n\",\n      \"Epoch: 019/040 | Batch 0050/0175 | Loss: 0.7849\\n\",\n      \"Epoch: 019/040 | Batch 0100/0175 | Loss: 0.8485\\n\",\n      \"Epoch: 019/040 | Batch 0150/0175 | Loss: 0.8462\\n\",\n      \"***Epoch: 019/040 | Train. Acc.: 75.212% | Loss: 0.704\\n\",\n      \"***Epoch: 019/040 | Valid. Acc.: 69.440% | Loss: 0.872\\n\",\n      \"Time elapsed: 19.64 min\\n\",\n      \"Epoch: 020/040 | Batch 0000/0175 | Loss: 0.6625\\n\",\n      \"Epoch: 020/040 | Batch 0050/0175 | Loss: 0.7826\\n\",\n      \"Epoch: 020/040 | Batch 0100/0175 | Loss: 0.7387\\n\",\n      \"Epoch: 020/040 | Batch 0150/0175 | Loss: 0.7622\\n\",\n      \"***Epoch: 020/040 | Train. Acc.: 76.560% | Loss: 0.663\\n\",\n      \"***Epoch: 020/040 | Valid. Acc.: 69.820% | Loss: 0.859\\n\",\n      \"Time elapsed: 20.68 min\\n\",\n      \"Epoch: 021/040 | Batch 0000/0175 | Loss: 0.7006\\n\",\n      \"Epoch: 021/040 | Batch 0050/0175 | Loss: 0.6893\\n\",\n      \"Epoch: 021/040 | Batch 0100/0175 | Loss: 0.6352\\n\",\n      \"Epoch: 021/040 | Batch 0150/0175 | Loss: 0.7598\\n\",\n      \"***Epoch: 021/040 | Train. Acc.: 76.339% | Loss: 0.674\\n\",\n      \"***Epoch: 021/040 | Valid. Acc.: 70.540% | Loss: 0.859\\n\",\n      \"Time elapsed: 21.70 min\\n\",\n      \"Epoch: 022/040 | Batch 0000/0175 | Loss: 0.7093\\n\",\n      \"Epoch: 022/040 | Batch 0050/0175 | Loss: 0.5893\\n\",\n      \"Epoch: 022/040 | Batch 0100/0175 | Loss: 0.6019\\n\",\n      \"Epoch: 022/040 | Batch 0150/0175 | Loss: 0.7325\\n\",\n      \"***Epoch: 022/040 | Train. Acc.: 78.268% | Loss: 0.623\\n\",\n      \"***Epoch: 022/040 | Valid. Acc.: 70.520% | Loss: 0.851\\n\",\n      \"Time elapsed: 22.73 min\\n\",\n      \"Epoch: 023/040 | Batch 0000/0175 | Loss: 0.6316\\n\",\n      \"Epoch: 023/040 | Batch 0050/0175 | Loss: 0.5694\\n\",\n      \"Epoch: 023/040 | Batch 0100/0175 | Loss: 0.7315\\n\",\n      \"Epoch: 023/040 | Batch 0150/0175 | Loss: 0.6656\\n\",\n      \"***Epoch: 023/040 | Train. Acc.: 79.455% | Loss: 0.589\\n\",\n      \"***Epoch: 023/040 | Valid. Acc.: 71.440% | Loss: 0.828\\n\",\n      \"Time elapsed: 23.77 min\\n\",\n      \"Epoch: 024/040 | Batch 0000/0175 | Loss: 0.5285\\n\",\n      \"Epoch: 024/040 | Batch 0050/0175 | Loss: 0.6959\\n\",\n      \"Epoch: 024/040 | Batch 0100/0175 | Loss: 0.5504\\n\",\n      \"Epoch: 024/040 | Batch 0150/0175 | Loss: 0.6831\\n\",\n      \"***Epoch: 024/040 | Train. Acc.: 80.174% | Loss: 0.570\\n\",\n      \"***Epoch: 024/040 | Valid. Acc.: 70.540% | Loss: 0.830\\n\",\n      \"Time elapsed: 24.82 min\\n\",\n      \"Epoch: 025/040 | Batch 0000/0175 | Loss: 0.6270\\n\",\n      \"Epoch: 025/040 | Batch 0050/0175 | Loss: 0.6128\\n\",\n      \"Epoch: 025/040 | Batch 0100/0175 | Loss: 0.5769\\n\",\n      \"Epoch: 025/040 | Batch 0150/0175 | Loss: 0.6409\\n\",\n      \"***Epoch: 025/040 | Train. Acc.: 80.933% | Loss: 0.547\\n\",\n      \"***Epoch: 025/040 | Valid. Acc.: 72.340% | Loss: 0.822\\n\",\n      \"Time elapsed: 25.85 min\\n\",\n      \"Epoch: 026/040 | Batch 0000/0175 | Loss: 0.5859\\n\",\n      \"Epoch: 026/040 | Batch 0050/0175 | Loss: 0.5577\\n\",\n      \"Epoch: 026/040 | Batch 0100/0175 | Loss: 0.6651\\n\",\n      \"Epoch: 026/040 | Batch 0150/0175 | Loss: 0.5483\\n\",\n      \"***Epoch: 026/040 | Train. Acc.: 79.150% | Loss: 0.589\\n\",\n      \"***Epoch: 026/040 | Valid. Acc.: 70.240% | Loss: 0.907\\n\",\n      \"Time elapsed: 26.90 min\\n\",\n      \"Epoch: 027/040 | Batch 0000/0175 | Loss: 0.6005\\n\",\n      \"Epoch: 027/040 | Batch 0050/0175 | Loss: 0.5660\\n\",\n      \"Epoch: 027/040 | Batch 0100/0175 | Loss: 0.6606\\n\",\n      \"Epoch: 027/040 | Batch 0150/0175 | Loss: 0.5047\\n\",\n      \"***Epoch: 027/040 | Train. Acc.: 81.045% | Loss: 0.539\\n\",\n      \"***Epoch: 027/040 | Valid. Acc.: 71.120% | Loss: 0.864\\n\",\n      \"Time elapsed: 27.94 min\\n\",\n      \"Epoch: 028/040 | Batch 0000/0175 | Loss: 0.5897\\n\",\n      \"Epoch: 028/040 | Batch 0050/0175 | Loss: 0.5210\\n\",\n      \"Epoch: 028/040 | Batch 0100/0175 | Loss: 0.5563\\n\",\n      \"Epoch: 028/040 | Batch 0150/0175 | Loss: 0.5192\\n\",\n      \"***Epoch: 028/040 | Train. Acc.: 83.891% | Loss: 0.464\\n\",\n      \"***Epoch: 028/040 | Valid. Acc.: 72.400% | Loss: 0.815\\n\",\n      \"Time elapsed: 28.98 min\\n\",\n      \"Epoch: 029/040 | Batch 0000/0175 | Loss: 0.5087\\n\",\n      \"Epoch: 029/040 | Batch 0050/0175 | Loss: 0.6121\\n\",\n      \"Epoch: 029/040 | Batch 0100/0175 | Loss: 0.5465\\n\",\n      \"Epoch: 029/040 | Batch 0150/0175 | Loss: 0.4414\\n\",\n      \"***Epoch: 029/040 | Train. Acc.: 82.757% | Loss: 0.493\\n\",\n      \"***Epoch: 029/040 | Valid. Acc.: 71.080% | Loss: 0.851\\n\",\n      \"Time elapsed: 30.01 min\\n\",\n      \"Epoch: 030/040 | Batch 0000/0175 | Loss: 0.5460\\n\",\n      \"Epoch: 030/040 | Batch 0050/0175 | Loss: 0.5083\\n\",\n      \"Epoch: 030/040 | Batch 0100/0175 | Loss: 0.4999\\n\",\n      \"Epoch: 030/040 | Batch 0150/0175 | Loss: 0.5453\\n\",\n      \"***Epoch: 030/040 | Train. Acc.: 83.397% | Loss: 0.469\\n\",\n      \"***Epoch: 030/040 | Valid. Acc.: 71.660% | Loss: 0.869\\n\",\n      \"Time elapsed: 31.03 min\\n\",\n      \"Epoch: 031/040 | Batch 0000/0175 | Loss: 0.4998\\n\",\n      \"Epoch: 031/040 | Batch 0050/0175 | Loss: 0.4808\\n\",\n      \"Epoch: 031/040 | Batch 0100/0175 | Loss: 0.4958\\n\",\n      \"Epoch: 031/040 | Batch 0150/0175 | Loss: 0.5201\\n\",\n      \"***Epoch: 031/040 | Train. Acc.: 84.167% | Loss: 0.447\\n\",\n      \"***Epoch: 031/040 | Valid. Acc.: 71.980% | Loss: 0.873\\n\",\n      \"Time elapsed: 32.07 min\\n\",\n      \"Epoch: 032/040 | Batch 0000/0175 | Loss: 0.3548\\n\",\n      \"Epoch: 032/040 | Batch 0050/0175 | Loss: 0.4062\\n\",\n      \"Epoch: 032/040 | Batch 0100/0175 | Loss: 0.4292\\n\",\n      \"Epoch: 032/040 | Batch 0150/0175 | Loss: 0.4786\\n\",\n      \"***Epoch: 032/040 | Train. Acc.: 85.027% | Loss: 0.430\\n\",\n      \"***Epoch: 032/040 | Valid. Acc.: 71.920% | Loss: 0.899\\n\",\n      \"Time elapsed: 33.10 min\\n\",\n      \"Epoch: 033/040 | Batch 0000/0175 | Loss: 0.4133\\n\",\n      \"Epoch: 033/040 | Batch 0050/0175 | Loss: 0.3402\\n\",\n      \"Epoch: 033/040 | Batch 0100/0175 | Loss: 0.3988\\n\",\n      \"Epoch: 033/040 | Batch 0150/0175 | Loss: 0.4555\\n\",\n      \"***Epoch: 033/040 | Train. Acc.: 87.281% | Loss: 0.373\\n\",\n      \"***Epoch: 033/040 | Valid. Acc.: 72.960% | Loss: 0.846\\n\",\n      \"Time elapsed: 34.13 min\\n\",\n      \"Epoch: 034/040 | Batch 0000/0175 | Loss: 0.4540\\n\",\n      \"Epoch: 034/040 | Batch 0050/0175 | Loss: 0.5704\\n\",\n      \"Epoch: 034/040 | Batch 0100/0175 | Loss: 0.5121\\n\",\n      \"Epoch: 034/040 | Batch 0150/0175 | Loss: 0.3992\\n\",\n      \"***Epoch: 034/040 | Train. Acc.: 88.328% | Loss: 0.340\\n\",\n      \"***Epoch: 034/040 | Valid. Acc.: 73.600% | Loss: 0.847\\n\",\n      \"Time elapsed: 35.17 min\\n\",\n      \"Epoch: 035/040 | Batch 0000/0175 | Loss: 0.3568\\n\",\n      \"Epoch: 035/040 | Batch 0050/0175 | Loss: 0.4348\\n\",\n      \"Epoch: 035/040 | Batch 0100/0175 | Loss: 0.3955\\n\",\n      \"Epoch: 035/040 | Batch 0150/0175 | Loss: 0.4242\\n\",\n      \"***Epoch: 035/040 | Train. Acc.: 88.502% | Loss: 0.338\\n\",\n      \"***Epoch: 035/040 | Valid. Acc.: 72.520% | Loss: 0.874\\n\",\n      \"Time elapsed: 36.20 min\\n\",\n      \"Epoch: 036/040 | Batch 0000/0175 | Loss: 0.2860\\n\",\n      \"Epoch: 036/040 | Batch 0050/0175 | Loss: 0.3446\\n\",\n      \"Epoch: 036/040 | Batch 0100/0175 | Loss: 0.4947\\n\",\n      \"Epoch: 036/040 | Batch 0150/0175 | Loss: 0.3412\\n\",\n      \"***Epoch: 036/040 | Train. Acc.: 88.482% | Loss: 0.325\\n\",\n      \"***Epoch: 036/040 | Valid. Acc.: 72.760% | Loss: 0.905\\n\",\n      \"Time elapsed: 37.24 min\\n\",\n      \"Epoch: 037/040 | Batch 0000/0175 | Loss: 0.3963\\n\",\n      \"Epoch: 037/040 | Batch 0050/0175 | Loss: 0.3388\\n\",\n      \"Epoch: 037/040 | Batch 0100/0175 | Loss: 0.3279\\n\",\n      \"Epoch: 037/040 | Batch 0150/0175 | Loss: 0.3965\\n\",\n      \"***Epoch: 037/040 | Train. Acc.: 90.065% | Loss: 0.294\\n\",\n      \"***Epoch: 037/040 | Valid. Acc.: 71.740% | Loss: 0.904\\n\",\n      \"Time elapsed: 38.27 min\\n\",\n      \"Epoch: 038/040 | Batch 0000/0175 | Loss: 0.3269\\n\",\n      \"Epoch: 038/040 | Batch 0050/0175 | Loss: 0.3193\\n\",\n      \"Epoch: 038/040 | Batch 0100/0175 | Loss: 0.3548\\n\",\n      \"Epoch: 038/040 | Batch 0150/0175 | Loss: 0.3414\\n\",\n      \"***Epoch: 038/040 | Train. Acc.: 90.460% | Loss: 0.280\\n\",\n      \"***Epoch: 038/040 | Valid. Acc.: 73.400% | Loss: 0.904\\n\",\n      \"Time elapsed: 39.30 min\\n\",\n      \"Epoch: 039/040 | Batch 0000/0175 | Loss: 0.2794\\n\",\n      \"Epoch: 039/040 | Batch 0050/0175 | Loss: 0.2914\\n\",\n      \"Epoch: 039/040 | Batch 0100/0175 | Loss: 0.3197\\n\",\n      \"Epoch: 039/040 | Batch 0150/0175 | Loss: 0.2898\\n\",\n      \"***Epoch: 039/040 | Train. Acc.: 90.545% | Loss: 0.276\\n\",\n      \"***Epoch: 039/040 | Valid. Acc.: 73.240% | Loss: 0.922\\n\",\n      \"Time elapsed: 40.33 min\\n\",\n      \"Epoch: 040/040 | Batch 0000/0175 | Loss: 0.3442\\n\",\n      \"Epoch: 040/040 | Batch 0050/0175 | Loss: 0.3222\\n\",\n      \"Epoch: 040/040 | Batch 0100/0175 | Loss: 0.4111\\n\",\n      \"Epoch: 040/040 | Batch 0150/0175 | Loss: 0.3112\\n\",\n      \"***Epoch: 040/040 | Train. Acc.: 89.846% | Loss: 0.292\\n\",\n      \"***Epoch: 040/040 | Valid. Acc.: 71.940% | Loss: 0.997\\n\",\n      \"Time elapsed: 41.36 min\\n\",\n      \"Total Training Time: 41.36 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"log_dict = train_classifier_simple_v1(num_epochs=NUM_EPOCHS, model=model, \\n\",\n    \"                                      optimizer=optimizer, device=DEVICE, \\n\",\n    \"                                      train_loader=train_loader, valid_loader=valid_loader, \\n\",\n    \"                                      logging_interval=50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"loss_list = log_dict['train_loss_per_batch']\\n\",\n    \"\\n\",\n    \"plt.plot(loss_list, label='Minibatch loss')\\n\",\n    \"plt.plot(np.convolve(loss_list, \\n\",\n    \"                     np.ones(200,)/200, mode='valid'), \\n\",\n    \"         label='Running average')\\n\",\n    \"\\n\",\n    \"plt.ylabel('Cross Entropy')\\n\",\n    \"plt.xlabel('Iteration')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(np.arange(1, NUM_EPOCHS+1), log_dict['train_acc_per_epoch'], label='Training')\\n\",\n    \"plt.plot(np.arange(1, NUM_EPOCHS+1), log_dict['valid_acc_per_epoch'], label='Validation')\\n\",\n    \"\\n\",\n    \"plt.xlabel('Epoch')\\n\",\n    \"plt.ylabel('Accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train ACC: 71.94%\\n\",\n      \"Validation ACC: 71.94%\\n\",\n      \"Test ACC: 70.93%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with torch.set_grad_enabled(False):\\n\",\n    \"    \\n\",\n    \"    train_acc = compute_accuracy(model=model,\\n\",\n    \"                                 data_loader=test_loader,\\n\",\n    \"                                 device=DEVICE)\\n\",\n    \"    \\n\",\n    \"    test_acc = compute_accuracy(model=model,\\n\",\n    \"                                data_loader=test_loader,\\n\",\n    \"                                device=DEVICE)\\n\",\n    \"    \\n\",\n    \"    valid_acc = compute_accuracy(model=model,\\n\",\n    \"                                 data_loader=valid_loader,\\n\",\n    \"                                 device=DEVICE)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"print(f'Train ACC: {valid_acc:.2f}%')\\n\",\n    \"print(f'Validation ACC: {valid_acc:.2f}%')\\n\",\n    \"print(f'Test ACC: {test_acc:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch      : 1.10.1\\n\",\n      \"torchvision: 0.11.2\\n\",\n      \"numpy      : 1.22.0\\n\",\n      \"pandas     : 1.4.1\\n\",\n      \"sys        : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"matplotlib : 3.3.4\\n\",\n      \"PIL        : 9.0.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"default_view\": {},\n   \"name\": \"convnet-vgg16.ipynb\",\n   \"provenance\": [],\n   \"version\": \"0.3.2\",\n   \"views\": {}\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": true,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"371px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch_ipynb/cnn/cnn-alexnet-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"UEBilEjLj5wY\"\n   },\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 119\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 536,\n     \"status\": \"ok\",\n     \"timestamp\": 1524974472601,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"GOzuY8Yvj5wb\",\n    \"outputId\": \"c19362ce-f87a-4cc2-84cc-8d7b4b9e6007\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Author: Sebastian Raschka\\n\",\n      \"\\n\",\n      \"Python implementation: CPython\\n\",\n      \"Python version       : 3.8.12\\n\",\n      \"IPython version      : 8.0.1\\n\",\n      \"\\n\",\n      \"torch: 1.10.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"rH4XmErYj5wm\"\n   },\n   \"source\": [\n    \"# AlexNet CIFAR-10 Classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Network Architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"References\\n\",\n    \"    \\n\",\n    \"- [1] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. \\\"[Imagenet classification with deep convolutional neural networks.](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)\\\" In Advances in Neural Information Processing Systems, pp. 1097-1105. 2012.\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"MkoGLH_Tj5wn\"\n   },\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"ORj09gnrj5wp\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"import random\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torch.utils.data.dataset import Subset\\n\",\n    \"\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"I6hghKPxj5w0\"\n   },\n   \"source\": [\n    \"## Model Settings\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Setting a random seed\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"I recommend using a function like the following one prior to using dataset loaders and initializing a model if you want to ensure the data is shuffled in the same manner if you rerun this notebook and the model gets the same initial random weights:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def set_all_seeds(seed):\\n\",\n    \"    os.environ[\\\"PL_GLOBAL_SEED\\\"] = str(seed)\\n\",\n    \"    random.seed(seed)\\n\",\n    \"    np.random.seed(seed)\\n\",\n    \"    torch.manual_seed(seed)\\n\",\n    \"    torch.cuda.manual_seed_all(seed)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Setting cuDNN and PyTorch algorithmic behavior to deterministic\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Similar to the `set_all_seeds` function above, I recommend setting the behavior of PyTorch and cuDNN to deterministic (this is particulary relevant when using GPUs). We can also define a function for that:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def set_deterministic():\\n\",\n    \"    if torch.cuda.is_available():\\n\",\n    \"        torch.backends.cudnn.benchmark = False\\n\",\n    \"        torch.backends.cudnn.deterministic = True\\n\",\n    \"    torch.set_deterministic(True)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 85\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 23936,\n     \"status\": \"ok\",\n     \"timestamp\": 1524974497505,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"NnT0sZIwj5wu\",\n    \"outputId\": \"55aed925-d17e-4c6a-8c71-0d9b3bde5637\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"RANDOM_SEED = 1\\n\",\n    \"LEARNING_RATE = 0.0001\\n\",\n    \"BATCH_SIZE = 256\\n\",\n    \"NUM_EPOCHS = 40\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"NUM_CLASSES = 10\\n\",\n    \"\\n\",\n    \"# Other\\n\",\n    \"DEVICE = \\\"cuda:0\\\"\\n\",\n    \"\\n\",\n    \"set_all_seeds(RANDOM_SEED)\\n\",\n    \"\\n\",\n    \"# Deterministic behavior not yet supported by AdaptiveAvgPool2d\\n\",\n    \"#set_deterministic()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Import utility functions\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"\\n\",\n    \"sys.path.insert(0, \\\"..\\\") # to include ../helper_evaluate.py etc.\\n\",\n    \"\\n\",\n    \"from helper_evaluate import compute_accuracy\\n\",\n    \"from helper_data import get_dataloaders_cifar10\\n\",\n    \"from helper_train import train_classifier_simple_v1\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"e73f98cd265c48ca8922f5968c31d202\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/170498071 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Extracting data/cifar-10-python.tar.gz to data\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"### Set random seed ###\\n\",\n    \"set_all_seeds(RANDOM_SEED)\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### Dataset\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"train_transforms = transforms.Compose([transforms.Resize((70, 70)),\\n\",\n    \"                                       transforms.RandomCrop((64, 64)),\\n\",\n    \"                                       transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"test_transforms = transforms.Compose([transforms.Resize((70, 70)),\\n\",\n    \"                                      transforms.CenterCrop((64, 64)),\\n\",\n    \"                                      transforms.ToTensor()])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader, valid_loader, test_loader = get_dataloaders_cifar10(\\n\",\n    \"    batch_size=BATCH_SIZE, \\n\",\n    \"    num_workers=2, \\n\",\n    \"    train_transforms=train_transforms,\\n\",\n    \"    test_transforms=test_transforms,\\n\",\n    \"    validation_fraction=0.1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Training Set:\\n\",\n      \"\\n\",\n      \"Image batch dimensions: torch.Size([256, 3, 64, 64])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\",\n      \"tensor([0, 2, 3, 5, 4, 8, 9, 6, 9, 7])\\n\",\n      \"\\n\",\n      \"Validation Set:\\n\",\n      \"Image batch dimensions: torch.Size([256, 3, 64, 64])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\",\n      \"tensor([6, 9, 3, 5, 7, 3, 4, 1, 8, 0])\\n\",\n      \"\\n\",\n      \"Testing Set:\\n\",\n      \"Image batch dimensions: torch.Size([256, 3, 64, 64])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\",\n      \"tensor([2, 6, 3, 1, 1, 1, 1, 2, 4, 8])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Checking the dataset\\n\",\n    \"print('Training Set:\\\\n')\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.size())\\n\",\n    \"    print('Image label dimensions:', labels.size())\\n\",\n    \"    print(labels[:10])\\n\",\n    \"    break\\n\",\n    \"    \\n\",\n    \"# Checking the dataset\\n\",\n    \"print('\\\\nValidation Set:')\\n\",\n    \"for images, labels in valid_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.size())\\n\",\n    \"    print('Image label dimensions:', labels.size())\\n\",\n    \"    print(labels[:10])\\n\",\n    \"    break\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"print('\\\\nTesting Set:')\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.size())\\n\",\n    \"    print('Image label dimensions:', labels.size())\\n\",\n    \"    print(labels[:10])\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"class AlexNet(nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super(AlexNet, self).__init__()\\n\",\n    \"        self.features = nn.Sequential(\\n\",\n    \"            nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(64, 192, kernel_size=5, padding=2),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(192, 384, kernel_size=3, padding=1),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(384, 256, kernel_size=3, padding=1),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            \\n\",\n    \"            nn.Conv2d(256, 256, kernel_size=3, padding=1),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.MaxPool2d(kernel_size=3, stride=2),\\n\",\n    \"        )\\n\",\n    \"        self.avgpool = nn.AdaptiveAvgPool2d((6, 6))\\n\",\n    \"        self.classifier = nn.Sequential(\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(256 * 6 * 6, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Dropout(0.5),\\n\",\n    \"            nn.Linear(4096, 4096),\\n\",\n    \"            nn.ReLU(inplace=True),\\n\",\n    \"            nn.Linear(4096, num_classes)\\n\",\n    \"        )\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        x = self.features(x)\\n\",\n    \"        x = self.avgpool(x)\\n\",\n    \"        x = x.view(x.size(0), 256 * 6 * 6)\\n\",\n    \"        logits = self.classifier(x)\\n\",\n    \"        probas = F.softmax(logits, dim=1)\\n\",\n    \"        return logits\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"_lza9t_uj5w1\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(RANDOM_SEED)\\n\",\n    \"\\n\",\n    \"model = AlexNet(NUM_CLASSES)\\n\",\n    \"model.to(DEVICE)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"RAodboScj5w6\"\n   },\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 1547\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 2384585,\n     \"status\": \"ok\",\n     \"timestamp\": 1524976888520,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"Dzh3ROmRj5w7\",\n    \"outputId\": \"5f8fd8c9-b076-403a-b0b7-fd2d498b48d7\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/040 | Batch 0000/0175 | Loss: 2.3033\\n\",\n      \"Epoch: 001/040 | Batch 0050/0175 | Loss: 2.0240\\n\",\n      \"Epoch: 001/040 | Batch 0100/0175 | Loss: 1.9445\\n\",\n      \"Epoch: 001/040 | Batch 0150/0175 | Loss: 1.8135\\n\",\n      \"***Epoch: 001/040 | Train. Acc.: 33.674% | Loss: 1.703\\n\",\n      \"***Epoch: 001/040 | Valid. Acc.: 34.880% | Loss: 1.670\\n\",\n      \"Time elapsed: 1.05 min\\n\",\n      \"Epoch: 002/040 | Batch 0000/0175 | Loss: 1.7606\\n\",\n      \"Epoch: 002/040 | Batch 0050/0175 | Loss: 1.5473\\n\",\n      \"Epoch: 002/040 | Batch 0100/0175 | Loss: 1.5496\\n\",\n      \"Epoch: 002/040 | Batch 0150/0175 | Loss: 1.5093\\n\",\n      \"***Epoch: 002/040 | Train. Acc.: 42.819% | Loss: 1.505\\n\",\n      \"***Epoch: 002/040 | Valid. Acc.: 43.840% | Loss: 1.491\\n\",\n      \"Time elapsed: 2.09 min\\n\",\n      \"Epoch: 003/040 | Batch 0000/0175 | Loss: 1.5411\\n\",\n      \"Epoch: 003/040 | Batch 0050/0175 | Loss: 1.5485\\n\",\n      \"Epoch: 003/040 | Batch 0100/0175 | Loss: 1.3723\\n\",\n      \"Epoch: 003/040 | Batch 0150/0175 | Loss: 1.3084\\n\",\n      \"***Epoch: 003/040 | Train. Acc.: 49.712% | Loss: 1.336\\n\",\n      \"***Epoch: 003/040 | Valid. Acc.: 50.300% | Loss: 1.327\\n\",\n      \"Time elapsed: 3.12 min\\n\",\n      \"Epoch: 004/040 | Batch 0000/0175 | Loss: 1.4301\\n\",\n      \"Epoch: 004/040 | Batch 0050/0175 | Loss: 1.4117\\n\",\n      \"Epoch: 004/040 | Batch 0100/0175 | Loss: 1.2894\\n\",\n      \"Epoch: 004/040 | Batch 0150/0175 | Loss: 1.1508\\n\",\n      \"***Epoch: 004/040 | Train. Acc.: 54.138% | Loss: 1.231\\n\",\n      \"***Epoch: 004/040 | Valid. Acc.: 54.300% | Loss: 1.226\\n\",\n      \"Time elapsed: 4.16 min\\n\",\n      \"Epoch: 005/040 | Batch 0000/0175 | Loss: 1.1781\\n\",\n      \"Epoch: 005/040 | Batch 0050/0175 | Loss: 1.2942\\n\",\n      \"Epoch: 005/040 | Batch 0100/0175 | Loss: 1.3343\\n\",\n      \"Epoch: 005/040 | Batch 0150/0175 | Loss: 1.1216\\n\",\n      \"***Epoch: 005/040 | Train. Acc.: 58.536% | Loss: 1.139\\n\",\n      \"***Epoch: 005/040 | Valid. Acc.: 58.220% | Loss: 1.152\\n\",\n      \"Time elapsed: 5.23 min\\n\",\n      \"Epoch: 006/040 | Batch 0000/0175 | Loss: 1.1030\\n\",\n      \"Epoch: 006/040 | Batch 0050/0175 | Loss: 1.1732\\n\",\n      \"Epoch: 006/040 | Batch 0100/0175 | Loss: 1.1508\\n\",\n      \"Epoch: 006/040 | Batch 0150/0175 | Loss: 1.0059\\n\",\n      \"***Epoch: 006/040 | Train. Acc.: 58.882% | Loss: 1.132\\n\",\n      \"***Epoch: 006/040 | Valid. Acc.: 58.600% | Loss: 1.158\\n\",\n      \"Time elapsed: 6.28 min\\n\",\n      \"Epoch: 007/040 | Batch 0000/0175 | Loss: 1.0091\\n\",\n      \"Epoch: 007/040 | Batch 0050/0175 | Loss: 1.2888\\n\",\n      \"Epoch: 007/040 | Batch 0100/0175 | Loss: 1.0148\\n\",\n      \"Epoch: 007/040 | Batch 0150/0175 | Loss: 1.0491\\n\",\n      \"***Epoch: 007/040 | Train. Acc.: 65.203% | Loss: 0.966\\n\",\n      \"***Epoch: 007/040 | Valid. Acc.: 63.880% | Loss: 1.007\\n\",\n      \"Time elapsed: 7.31 min\\n\",\n      \"Epoch: 008/040 | Batch 0000/0175 | Loss: 0.8920\\n\",\n      \"Epoch: 008/040 | Batch 0050/0175 | Loss: 0.9769\\n\",\n      \"Epoch: 008/040 | Batch 0100/0175 | Loss: 1.0159\\n\",\n      \"Epoch: 008/040 | Batch 0150/0175 | Loss: 1.0733\\n\",\n      \"***Epoch: 008/040 | Train. Acc.: 67.181% | Loss: 0.920\\n\",\n      \"***Epoch: 008/040 | Valid. Acc.: 65.020% | Loss: 0.974\\n\",\n      \"Time elapsed: 8.35 min\\n\",\n      \"Epoch: 009/040 | Batch 0000/0175 | Loss: 0.9276\\n\",\n      \"Epoch: 009/040 | Batch 0050/0175 | Loss: 0.8630\\n\",\n      \"Epoch: 009/040 | Batch 0100/0175 | Loss: 1.1130\\n\",\n      \"Epoch: 009/040 | Batch 0150/0175 | Loss: 0.9105\\n\",\n      \"***Epoch: 009/040 | Train. Acc.: 66.795% | Loss: 0.920\\n\",\n      \"***Epoch: 009/040 | Valid. Acc.: 64.980% | Loss: 0.984\\n\",\n      \"Time elapsed: 9.38 min\\n\",\n      \"Epoch: 010/040 | Batch 0000/0175 | Loss: 0.8506\\n\",\n      \"Epoch: 010/040 | Batch 0050/0175 | Loss: 0.7531\\n\",\n      \"Epoch: 010/040 | Batch 0100/0175 | Loss: 0.9312\\n\",\n      \"Epoch: 010/040 | Batch 0150/0175 | Loss: 0.9103\\n\",\n      \"***Epoch: 010/040 | Train. Acc.: 70.491% | Loss: 0.832\\n\",\n      \"***Epoch: 010/040 | Valid. Acc.: 67.560% | Loss: 0.934\\n\",\n      \"Time elapsed: 10.42 min\\n\",\n      \"Epoch: 011/040 | Batch 0000/0175 | Loss: 0.8196\\n\",\n      \"Epoch: 011/040 | Batch 0050/0175 | Loss: 0.7955\\n\",\n      \"Epoch: 011/040 | Batch 0100/0175 | Loss: 0.9367\\n\",\n      \"Epoch: 011/040 | Batch 0150/0175 | Loss: 0.7501\\n\",\n      \"***Epoch: 011/040 | Train. Acc.: 70.826% | Loss: 0.819\\n\",\n      \"***Epoch: 011/040 | Valid. Acc.: 66.220% | Loss: 0.950\\n\",\n      \"Time elapsed: 11.50 min\\n\",\n      \"Epoch: 012/040 | Batch 0000/0175 | Loss: 0.7863\\n\",\n      \"Epoch: 012/040 | Batch 0050/0175 | Loss: 0.8496\\n\",\n      \"Epoch: 012/040 | Batch 0100/0175 | Loss: 0.7997\\n\",\n      \"Epoch: 012/040 | Batch 0150/0175 | Loss: 0.9733\\n\",\n      \"***Epoch: 012/040 | Train. Acc.: 73.600% | Loss: 0.757\\n\",\n      \"***Epoch: 012/040 | Valid. Acc.: 68.640% | Loss: 0.901\\n\",\n      \"Time elapsed: 12.72 min\\n\",\n      \"Epoch: 013/040 | Batch 0000/0175 | Loss: 0.8286\\n\",\n      \"Epoch: 013/040 | Batch 0050/0175 | Loss: 0.8397\\n\",\n      \"Epoch: 013/040 | Batch 0100/0175 | Loss: 0.7478\\n\",\n      \"Epoch: 013/040 | Batch 0150/0175 | Loss: 0.8451\\n\",\n      \"***Epoch: 013/040 | Train. Acc.: 76.750% | Loss: 0.672\\n\",\n      \"***Epoch: 013/040 | Valid. Acc.: 70.260% | Loss: 0.848\\n\",\n      \"Time elapsed: 13.96 min\\n\",\n      \"Epoch: 014/040 | Batch 0000/0175 | Loss: 0.6818\\n\",\n      \"Epoch: 014/040 | Batch 0050/0175 | Loss: 0.7883\\n\",\n      \"Epoch: 014/040 | Batch 0100/0175 | Loss: 0.7845\\n\",\n      \"Epoch: 014/040 | Batch 0150/0175 | Loss: 0.6714\\n\",\n      \"***Epoch: 014/040 | Train. Acc.: 76.462% | Loss: 0.669\\n\",\n      \"***Epoch: 014/040 | Valid. Acc.: 69.560% | Loss: 0.876\\n\",\n      \"Time elapsed: 15.17 min\\n\",\n      \"Epoch: 015/040 | Batch 0000/0175 | Loss: 0.7720\\n\",\n      \"Epoch: 015/040 | Batch 0050/0175 | Loss: 0.7569\\n\",\n      \"Epoch: 015/040 | Batch 0100/0175 | Loss: 0.6428\\n\",\n      \"Epoch: 015/040 | Batch 0150/0175 | Loss: 0.7415\\n\",\n      \"***Epoch: 015/040 | Train. Acc.: 78.196% | Loss: 0.622\\n\",\n      \"***Epoch: 015/040 | Valid. Acc.: 70.460% | Loss: 0.852\\n\",\n      \"Time elapsed: 16.39 min\\n\",\n      \"Epoch: 016/040 | Batch 0000/0175 | Loss: 0.6150\\n\",\n      \"Epoch: 016/040 | Batch 0050/0175 | Loss: 0.7300\\n\",\n      \"Epoch: 016/040 | Batch 0100/0175 | Loss: 0.4870\\n\",\n      \"Epoch: 016/040 | Batch 0150/0175 | Loss: 0.6177\\n\",\n      \"***Epoch: 016/040 | Train. Acc.: 80.033% | Loss: 0.571\\n\",\n      \"***Epoch: 016/040 | Valid. Acc.: 71.500% | Loss: 0.832\\n\",\n      \"Time elapsed: 17.62 min\\n\",\n      \"Epoch: 017/040 | Batch 0000/0175 | Loss: 0.6556\\n\",\n      \"Epoch: 017/040 | Batch 0050/0175 | Loss: 0.6564\\n\",\n      \"Epoch: 017/040 | Batch 0100/0175 | Loss: 0.5505\\n\",\n      \"Epoch: 017/040 | Batch 0150/0175 | Loss: 0.6272\\n\",\n      \"***Epoch: 017/040 | Train. Acc.: 81.415% | Loss: 0.532\\n\",\n      \"***Epoch: 017/040 | Valid. Acc.: 71.980% | Loss: 0.836\\n\",\n      \"Time elapsed: 18.80 min\\n\",\n      \"Epoch: 018/040 | Batch 0000/0175 | Loss: 0.5772\\n\",\n      \"Epoch: 018/040 | Batch 0050/0175 | Loss: 0.4951\\n\",\n      \"Epoch: 018/040 | Batch 0100/0175 | Loss: 0.4850\\n\",\n      \"Epoch: 018/040 | Batch 0150/0175 | Loss: 0.6942\\n\",\n      \"***Epoch: 018/040 | Train. Acc.: 82.944% | Loss: 0.486\\n\",\n      \"***Epoch: 018/040 | Valid. Acc.: 71.520% | Loss: 0.839\\n\",\n      \"Time elapsed: 19.84 min\\n\",\n      \"Epoch: 019/040 | Batch 0000/0175 | Loss: 0.4757\\n\",\n      \"Epoch: 019/040 | Batch 0050/0175 | Loss: 0.4909\\n\",\n      \"Epoch: 019/040 | Batch 0100/0175 | Loss: 0.5568\\n\",\n      \"Epoch: 019/040 | Batch 0150/0175 | Loss: 0.5895\\n\",\n      \"***Epoch: 019/040 | Train. Acc.: 81.592% | Loss: 0.515\\n\",\n      \"***Epoch: 019/040 | Valid. Acc.: 70.840% | Loss: 0.911\\n\",\n      \"Time elapsed: 20.87 min\\n\",\n      \"Epoch: 020/040 | Batch 0000/0175 | Loss: 0.5108\\n\",\n      \"Epoch: 020/040 | Batch 0050/0175 | Loss: 0.5133\\n\",\n      \"Epoch: 020/040 | Batch 0100/0175 | Loss: 0.4775\\n\",\n      \"Epoch: 020/040 | Batch 0150/0175 | Loss: 0.5364\\n\",\n      \"***Epoch: 020/040 | Train. Acc.: 85.272% | Loss: 0.431\\n\",\n      \"***Epoch: 020/040 | Valid. Acc.: 72.240% | Loss: 0.850\\n\",\n      \"Time elapsed: 21.89 min\\n\",\n      \"Epoch: 021/040 | Batch 0000/0175 | Loss: 0.4184\\n\",\n      \"Epoch: 021/040 | Batch 0050/0175 | Loss: 0.5490\\n\",\n      \"Epoch: 021/040 | Batch 0100/0175 | Loss: 0.4124\\n\",\n      \"Epoch: 021/040 | Batch 0150/0175 | Loss: 0.3877\\n\",\n      \"***Epoch: 021/040 | Train. Acc.: 86.616% | Loss: 0.384\\n\",\n      \"***Epoch: 021/040 | Valid. Acc.: 72.900% | Loss: 0.850\\n\",\n      \"Time elapsed: 22.93 min\\n\",\n      \"Epoch: 022/040 | Batch 0000/0175 | Loss: 0.3587\\n\",\n      \"Epoch: 022/040 | Batch 0050/0175 | Loss: 0.4164\\n\",\n      \"Epoch: 022/040 | Batch 0100/0175 | Loss: 0.4908\\n\",\n      \"Epoch: 022/040 | Batch 0150/0175 | Loss: 0.5300\\n\",\n      \"***Epoch: 022/040 | Train. Acc.: 87.763% | Loss: 0.353\\n\",\n      \"***Epoch: 022/040 | Valid. Acc.: 73.160% | Loss: 0.867\\n\",\n      \"Time elapsed: 23.97 min\\n\",\n      \"Epoch: 023/040 | Batch 0000/0175 | Loss: 0.3409\\n\",\n      \"Epoch: 023/040 | Batch 0050/0175 | Loss: 0.3932\\n\",\n      \"Epoch: 023/040 | Batch 0100/0175 | Loss: 0.4906\\n\",\n      \"Epoch: 023/040 | Batch 0150/0175 | Loss: 0.3842\\n\",\n      \"***Epoch: 023/040 | Train. Acc.: 88.516% | Loss: 0.325\\n\",\n      \"***Epoch: 023/040 | Valid. Acc.: 72.940% | Loss: 0.867\\n\",\n      \"Time elapsed: 25.01 min\\n\",\n      \"Epoch: 024/040 | Batch 0000/0175 | Loss: 0.3903\\n\",\n      \"Epoch: 024/040 | Batch 0050/0175 | Loss: 0.4127\\n\",\n      \"Epoch: 024/040 | Batch 0100/0175 | Loss: 0.3478\\n\",\n      \"Epoch: 024/040 | Batch 0150/0175 | Loss: 0.4306\\n\",\n      \"***Epoch: 024/040 | Train. Acc.: 90.315% | Loss: 0.284\\n\",\n      \"***Epoch: 024/040 | Valid. Acc.: 73.220% | Loss: 0.911\\n\",\n      \"Time elapsed: 26.04 min\\n\",\n      \"Epoch: 025/040 | Batch 0000/0175 | Loss: 0.2716\\n\",\n      \"Epoch: 025/040 | Batch 0050/0175 | Loss: 0.3371\\n\",\n      \"Epoch: 025/040 | Batch 0100/0175 | Loss: 0.4309\\n\",\n      \"Epoch: 025/040 | Batch 0150/0175 | Loss: 0.4343\\n\",\n      \"***Epoch: 025/040 | Train. Acc.: 88.908% | Loss: 0.311\\n\",\n      \"***Epoch: 025/040 | Valid. Acc.: 73.000% | Loss: 0.909\\n\",\n      \"Time elapsed: 27.07 min\\n\",\n      \"Epoch: 026/040 | Batch 0000/0175 | Loss: 0.2467\\n\",\n      \"Epoch: 026/040 | Batch 0050/0175 | Loss: 0.2832\\n\",\n      \"Epoch: 026/040 | Batch 0100/0175 | Loss: 0.3431\\n\",\n      \"Epoch: 026/040 | Batch 0150/0175 | Loss: 0.3218\\n\",\n      \"***Epoch: 026/040 | Train. Acc.: 90.547% | Loss: 0.272\\n\",\n      \"***Epoch: 026/040 | Valid. Acc.: 72.900% | Loss: 0.925\\n\",\n      \"Time elapsed: 28.10 min\\n\",\n      \"Epoch: 027/040 | Batch 0000/0175 | Loss: 0.3064\\n\",\n      \"Epoch: 027/040 | Batch 0050/0175 | Loss: 0.2874\\n\",\n      \"Epoch: 027/040 | Batch 0100/0175 | Loss: 0.3545\\n\",\n      \"Epoch: 027/040 | Batch 0150/0175 | Loss: 0.3866\\n\",\n      \"***Epoch: 027/040 | Train. Acc.: 92.277% | Loss: 0.230\\n\",\n      \"***Epoch: 027/040 | Valid. Acc.: 73.760% | Loss: 0.935\\n\",\n      \"Time elapsed: 29.13 min\\n\",\n      \"Epoch: 028/040 | Batch 0000/0175 | Loss: 0.1964\\n\",\n      \"Epoch: 028/040 | Batch 0050/0175 | Loss: 0.2317\\n\",\n      \"Epoch: 028/040 | Batch 0100/0175 | Loss: 0.2595\\n\",\n      \"Epoch: 028/040 | Batch 0150/0175 | Loss: 0.3056\\n\",\n      \"***Epoch: 028/040 | Train. Acc.: 92.049% | Loss: 0.225\\n\",\n      \"***Epoch: 028/040 | Valid. Acc.: 73.340% | Loss: 0.994\\n\",\n      \"Time elapsed: 30.16 min\\n\",\n      \"Epoch: 029/040 | Batch 0000/0175 | Loss: 0.2118\\n\",\n      \"Epoch: 029/040 | Batch 0050/0175 | Loss: 0.2198\\n\",\n      \"Epoch: 029/040 | Batch 0100/0175 | Loss: 0.2389\\n\",\n      \"Epoch: 029/040 | Batch 0150/0175 | Loss: 0.3052\\n\",\n      \"***Epoch: 029/040 | Train. Acc.: 93.170% | Loss: 0.198\\n\",\n      \"***Epoch: 029/040 | Valid. Acc.: 73.520% | Loss: 1.004\\n\",\n      \"Time elapsed: 31.20 min\\n\",\n      \"Epoch: 030/040 | Batch 0000/0175 | Loss: 0.1664\\n\",\n      \"Epoch: 030/040 | Batch 0050/0175 | Loss: 0.1880\\n\",\n      \"Epoch: 030/040 | Batch 0100/0175 | Loss: 0.1938\\n\",\n      \"Epoch: 030/040 | Batch 0150/0175 | Loss: 0.2032\\n\",\n      \"***Epoch: 030/040 | Train. Acc.: 93.333% | Loss: 0.188\\n\",\n      \"***Epoch: 030/040 | Valid. Acc.: 72.820% | Loss: 1.061\\n\",\n      \"Time elapsed: 32.23 min\\n\",\n      \"Epoch: 031/040 | Batch 0000/0175 | Loss: 0.2679\\n\",\n      \"Epoch: 031/040 | Batch 0050/0175 | Loss: 0.2778\\n\",\n      \"Epoch: 031/040 | Batch 0100/0175 | Loss: 0.2026\\n\",\n      \"Epoch: 031/040 | Batch 0150/0175 | Loss: 0.2144\\n\",\n      \"***Epoch: 031/040 | Train. Acc.: 94.058% | Loss: 0.170\\n\",\n      \"***Epoch: 031/040 | Valid. Acc.: 73.500% | Loss: 1.044\\n\",\n      \"Time elapsed: 33.27 min\\n\",\n      \"Epoch: 032/040 | Batch 0000/0175 | Loss: 0.1634\\n\",\n      \"Epoch: 032/040 | Batch 0050/0175 | Loss: 0.2475\\n\",\n      \"Epoch: 032/040 | Batch 0100/0175 | Loss: 0.1528\\n\",\n      \"Epoch: 032/040 | Batch 0150/0175 | Loss: 0.2810\\n\",\n      \"***Epoch: 032/040 | Train. Acc.: 94.471% | Loss: 0.161\\n\",\n      \"***Epoch: 032/040 | Valid. Acc.: 73.000% | Loss: 1.065\\n\",\n      \"Time elapsed: 34.32 min\\n\",\n      \"Epoch: 033/040 | Batch 0000/0175 | Loss: 0.2095\\n\",\n      \"Epoch: 033/040 | Batch 0050/0175 | Loss: 0.1590\\n\",\n      \"Epoch: 033/040 | Batch 0100/0175 | Loss: 0.1752\\n\",\n      \"Epoch: 033/040 | Batch 0150/0175 | Loss: 0.2319\\n\",\n      \"***Epoch: 033/040 | Train. Acc.: 95.118% | Loss: 0.141\\n\",\n      \"***Epoch: 033/040 | Valid. Acc.: 73.440% | Loss: 1.075\\n\",\n      \"Time elapsed: 35.35 min\\n\",\n      \"Epoch: 034/040 | Batch 0000/0175 | Loss: 0.1156\\n\",\n      \"Epoch: 034/040 | Batch 0050/0175 | Loss: 0.1456\\n\",\n      \"Epoch: 034/040 | Batch 0100/0175 | Loss: 0.1519\\n\",\n      \"Epoch: 034/040 | Batch 0150/0175 | Loss: 0.1831\\n\",\n      \"***Epoch: 034/040 | Train. Acc.: 95.286% | Loss: 0.142\\n\",\n      \"***Epoch: 034/040 | Valid. Acc.: 73.820% | Loss: 1.064\\n\",\n      \"Time elapsed: 36.40 min\\n\",\n      \"Epoch: 035/040 | Batch 0000/0175 | Loss: 0.1532\\n\",\n      \"Epoch: 035/040 | Batch 0050/0175 | Loss: 0.1267\\n\",\n      \"Epoch: 035/040 | Batch 0100/0175 | Loss: 0.1633\\n\",\n      \"Epoch: 035/040 | Batch 0150/0175 | Loss: 0.1263\\n\",\n      \"***Epoch: 035/040 | Train. Acc.: 94.638% | Loss: 0.154\\n\",\n      \"***Epoch: 035/040 | Valid. Acc.: 73.440% | Loss: 1.093\\n\",\n      \"Time elapsed: 37.43 min\\n\",\n      \"Epoch: 036/040 | Batch 0000/0175 | Loss: 0.1787\\n\",\n      \"Epoch: 036/040 | Batch 0050/0175 | Loss: 0.1622\\n\",\n      \"Epoch: 036/040 | Batch 0100/0175 | Loss: 0.1840\\n\",\n      \"Epoch: 036/040 | Batch 0150/0175 | Loss: 0.1143\\n\",\n      \"***Epoch: 036/040 | Train. Acc.: 95.480% | Loss: 0.132\\n\",\n      \"***Epoch: 036/040 | Valid. Acc.: 73.020% | Loss: 1.159\\n\",\n      \"Time elapsed: 38.46 min\\n\",\n      \"Epoch: 037/040 | Batch 0000/0175 | Loss: 0.1282\\n\",\n      \"Epoch: 037/040 | Batch 0050/0175 | Loss: 0.1299\\n\",\n      \"Epoch: 037/040 | Batch 0100/0175 | Loss: 0.1869\\n\",\n      \"Epoch: 037/040 | Batch 0150/0175 | Loss: 0.1387\\n\",\n      \"***Epoch: 037/040 | Train. Acc.: 95.129% | Loss: 0.138\\n\",\n      \"***Epoch: 037/040 | Valid. Acc.: 72.640% | Loss: 1.174\\n\",\n      \"Time elapsed: 39.50 min\\n\",\n      \"Epoch: 038/040 | Batch 0000/0175 | Loss: 0.1137\\n\",\n      \"Epoch: 038/040 | Batch 0050/0175 | Loss: 0.1053\\n\",\n      \"Epoch: 038/040 | Batch 0100/0175 | Loss: 0.1298\\n\",\n      \"Epoch: 038/040 | Batch 0150/0175 | Loss: 0.1280\\n\",\n      \"***Epoch: 038/040 | Train. Acc.: 95.429% | Loss: 0.134\\n\",\n      \"***Epoch: 038/040 | Valid. Acc.: 73.040% | Loss: 1.230\\n\",\n      \"Time elapsed: 40.53 min\\n\",\n      \"Epoch: 039/040 | Batch 0000/0175 | Loss: 0.1410\\n\",\n      \"Epoch: 039/040 | Batch 0050/0175 | Loss: 0.1084\\n\",\n      \"Epoch: 039/040 | Batch 0100/0175 | Loss: 0.1578\\n\",\n      \"Epoch: 039/040 | Batch 0150/0175 | Loss: 0.1516\\n\",\n      \"***Epoch: 039/040 | Train. Acc.: 97.002% | Loss: 0.090\\n\",\n      \"***Epoch: 039/040 | Valid. Acc.: 73.420% | Loss: 1.159\\n\",\n      \"Time elapsed: 41.57 min\\n\",\n      \"Epoch: 040/040 | Batch 0000/0175 | Loss: 0.1143\\n\",\n      \"Epoch: 040/040 | Batch 0050/0175 | Loss: 0.1153\\n\",\n      \"Epoch: 040/040 | Batch 0100/0175 | Loss: 0.1493\\n\",\n      \"Epoch: 040/040 | Batch 0150/0175 | Loss: 0.2771\\n\",\n      \"***Epoch: 040/040 | Train. Acc.: 96.071% | Loss: 0.111\\n\",\n      \"***Epoch: 040/040 | Valid. Acc.: 73.520% | Loss: 1.218\\n\",\n      \"Time elapsed: 42.61 min\\n\",\n      \"Total Training Time: 42.61 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"log_dict = train_classifier_simple_v1(num_epochs=NUM_EPOCHS, model=model, \\n\",\n    \"                                      optimizer=optimizer, device=DEVICE, \\n\",\n    \"                                      train_loader=train_loader, valid_loader=valid_loader, \\n\",\n    \"                                      logging_interval=50)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"loss_list = log_dict['train_loss_per_batch']\\n\",\n    \"\\n\",\n    \"plt.plot(loss_list, label='Minibatch loss')\\n\",\n    \"plt.plot(np.convolve(loss_list, \\n\",\n    \"                     np.ones(200,)/200, mode='valid'), \\n\",\n    \"         label='Running average')\\n\",\n    \"\\n\",\n    \"plt.ylabel('Cross Entropy')\\n\",\n    \"plt.xlabel('Iteration')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(np.arange(1, NUM_EPOCHS+1), log_dict['train_acc_per_epoch'], label='Training')\\n\",\n    \"plt.plot(np.arange(1, NUM_EPOCHS+1), log_dict['valid_acc_per_epoch'], label='Validation')\\n\",\n    \"\\n\",\n    \"plt.xlabel('Epoch')\\n\",\n    \"plt.ylabel('Accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train ACC: 73.52%\\n\",\n      \"Validation ACC: 73.52%\\n\",\n      \"Test ACC: 72.11%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with torch.set_grad_enabled(False):\\n\",\n    \"    \\n\",\n    \"    train_acc = compute_accuracy(model=model,\\n\",\n    \"                                 data_loader=test_loader,\\n\",\n    \"                                 device=DEVICE)\\n\",\n    \"    \\n\",\n    \"    test_acc = compute_accuracy(model=model,\\n\",\n    \"                                data_loader=test_loader,\\n\",\n    \"                                device=DEVICE)\\n\",\n    \"    \\n\",\n    \"    valid_acc = compute_accuracy(model=model,\\n\",\n    \"                                 data_loader=valid_loader,\\n\",\n    \"                                 device=DEVICE)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"print(f'Train ACC: {valid_acc:.2f}%')\\n\",\n    \"print(f'Validation ACC: {valid_acc:.2f}%')\\n\",\n    \"print(f'Test ACC: {test_acc:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sys        : 3.8.12 | packaged by conda-forge | (default, Oct 12 2021, 21:59:51) \\n\",\n      \"[GCC 9.4.0]\\n\",\n      \"matplotlib : 3.3.4\\n\",\n      \"PIL        : 9.0.1\\n\",\n      \"torchvision: 0.11.2\\n\",\n      \"numpy      : 1.22.0\\n\",\n      \"torch      : 1.10.1\\n\",\n      \"pandas     : 1.4.1\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"default_view\": {},\n   \"name\": \"convnet-vgg16.ipynb\",\n   \"provenance\": [],\n   \"version\": \"0.3.2\",\n   \"views\": {}\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.12\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": true,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"371px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch_ipynb/cnn/cnn-allconv.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.6.8\\n\",\n      \"IPython 7.2.0\\n\",\n      \"\\n\",\n      \"torch 1.0.1.post2\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"- Runs on CPU or GPU (if available)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Model Zoo -- All-Convolutional Neural Network\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Simple convolutional neural network that uses stride=2 every 2nd convolutional layer, instead of max pooling, to reduce the feature maps. Loosely based on\\n\",\n    \"\\n\",\n    \"- Springenberg, Jost Tobias, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller. \\\"Striving for simplicity: The all convolutional net.\\\" arXiv preprint arXiv:1412.6806 (2014).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import time\\n\",\n    \"import numpy as np\\n\",\n    \"import torch\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Settings and Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Image batch dimensions: torch.Size([256, 1, 28, 28])\\n\",\n      \"Image label dimensions: torch.Size([256])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Device\\n\",\n    \"device = torch.device(\\\"cuda:1\\\" if torch.cuda.is_available() else \\\"cpu\\\")\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"random_seed = 1\\n\",\n    \"learning_rate = 0.001\\n\",\n    \"num_epochs = 15\\n\",\n    \"batch_size = 256\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"num_classes = 10\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"##########################\\n\",\n    \"### MNIST DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Note transforms.ToTensor() scales input images\\n\",\n    \"# to 0-1 range\\n\",\n    \"train_dataset = datasets.MNIST(root='data', \\n\",\n    \"                               train=True, \\n\",\n    \"                               transform=transforms.ToTensor(),\\n\",\n    \"                               download=True)\\n\",\n    \"\\n\",\n    \"test_dataset = datasets.MNIST(root='data', \\n\",\n    \"                              train=False, \\n\",\n    \"                              transform=transforms.ToTensor())\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=batch_size, \\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=batch_size, \\n\",\n    \"                         shuffle=False)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class ConvNet(torch.nn.Module):\\n\",\n    \"\\n\",\n    \"    def __init__(self, num_classes):\\n\",\n    \"        super(ConvNet, self).__init__()\\n\",\n    \"        \\n\",\n    \"        self.num_classes = num_classes\\n\",\n    \"        # calculate same padding:\\n\",\n    \"        # (w - k + 2*p)/s + 1 = o\\n\",\n    \"        # => p = (s(o-1) - w + k)/2\\n\",\n    \"        \\n\",\n    \"        # 28x28x1 => 28x28x4\\n\",\n    \"        self.conv_1 = torch.nn.Conv2d(in_channels=1,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      padding=1) # (1(28-1) - 28 + 3) / 2 = 1\\n\",\n    \"        # 28x28x4 => 14x14x4\\n\",\n    \"        self.conv_2 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=4,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(2, 2),\\n\",\n    \"                                      padding=1)                             \\n\",\n    \"        # 14x14x4 => 14x14x8\\n\",\n    \"        self.conv_3 = torch.nn.Conv2d(in_channels=4,\\n\",\n    \"                                      out_channels=8,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      padding=1) # (1(14-1) - 14 + 3) / 2 = 1                 \\n\",\n    \"        # 14x14x8 => 7x7x8                             \\n\",\n    \"        self.conv_4 = torch.nn.Conv2d(in_channels=8,\\n\",\n    \"                                      out_channels=8,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(2, 2),\\n\",\n    \"                                      padding=1)      \\n\",\n    \"        \\n\",\n    \"        # 7x7x8 => 7x7x16                             \\n\",\n    \"        self.conv_5 = torch.nn.Conv2d(in_channels=8,\\n\",\n    \"                                      out_channels=16,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      padding=1) # (1(7-1) - 7 + 3) / 2 = 1          \\n\",\n    \"        # 7x7x16 => 4x4x16                             \\n\",\n    \"        self.conv_6 = torch.nn.Conv2d(in_channels=16,\\n\",\n    \"                                      out_channels=16,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(2, 2),\\n\",\n    \"                                      padding=1)      \\n\",\n    \"        \\n\",\n    \"        # 4x4x16 => 4x4xnum_classes                             \\n\",\n    \"        self.conv_7 = torch.nn.Conv2d(in_channels=16,\\n\",\n    \"                                      out_channels=self.num_classes,\\n\",\n    \"                                      kernel_size=(3, 3),\\n\",\n    \"                                      stride=(1, 1),\\n\",\n    \"                                      padding=1) # (1(7-1) - 7 + 3) / 2 = 1       \\n\",\n    \"\\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"    def forward(self, x):\\n\",\n    \"        out = self.conv_1(x)\\n\",\n    \"        out = F.relu(out)\\n\",\n    \"        \\n\",\n    \"        out = self.conv_2(out)\\n\",\n    \"        out = F.relu(out)\\n\",\n    \"\\n\",\n    \"        out = self.conv_3(out)\\n\",\n    \"        out = F.relu(out)\\n\",\n    \"\\n\",\n    \"        out = self.conv_4(out)\\n\",\n    \"        out = F.relu(out)\\n\",\n    \"        \\n\",\n    \"        out = self.conv_5(out)\\n\",\n    \"        out = F.relu(out)\\n\",\n    \"        \\n\",\n    \"        out = self.conv_6(out)\\n\",\n    \"        out = F.relu(out)\\n\",\n    \"        \\n\",\n    \"        out = self.conv_7(out)\\n\",\n    \"        out = F.relu(out)\\n\",\n    \"        \\n\",\n    \"        logits = F.adaptive_avg_pool2d(out, 1)\\n\",\n    \"        # drop width\\n\",\n    \"        logits.squeeze_(-1)\\n\",\n    \"        # drop height\\n\",\n    \"        logits.squeeze_(-1)\\n\",\n    \"        probas = torch.softmax(logits, dim=1)\\n\",\n    \"        return logits, probas\\n\",\n    \"\\n\",\n    \"    \\n\",\n    \"torch.manual_seed(random_seed)\\n\",\n    \"model = ConvNet(num_classes=num_classes)\\n\",\n    \"\\n\",\n    \"model = model.to(device)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/015 | Batch 000/235 | Cost: 2.3051\\n\",\n      \"Epoch: 001/015 | Batch 050/235 | Cost: 2.2926\\n\",\n      \"Epoch: 001/015 | Batch 100/235 | Cost: 2.0812\\n\",\n      \"Epoch: 001/015 | Batch 150/235 | Cost: 1.4435\\n\",\n      \"Epoch: 001/015 | Batch 200/235 | Cost: 0.9232\\n\",\n      \"Epoch: 001/015 training accuracy: 76.06%\\n\",\n      \"Time elapsed: 0.23 min\\n\",\n      \"Epoch: 002/015 | Batch 000/235 | Cost: 0.7001\\n\",\n      \"Epoch: 002/015 | Batch 050/235 | Cost: 0.5710\\n\",\n      \"Epoch: 002/015 | Batch 100/235 | Cost: 0.5925\\n\",\n      \"Epoch: 002/015 | Batch 150/235 | Cost: 0.4022\\n\",\n      \"Epoch: 002/015 | Batch 200/235 | Cost: 0.4663\\n\",\n      \"Epoch: 002/015 training accuracy: 85.68%\\n\",\n      \"Time elapsed: 0.45 min\\n\",\n      \"Epoch: 003/015 | Batch 000/235 | Cost: 0.4332\\n\",\n      \"Epoch: 003/015 | Batch 050/235 | Cost: 0.3523\\n\",\n      \"Epoch: 003/015 | Batch 100/235 | Cost: 0.4114\\n\",\n      \"Epoch: 003/015 | Batch 150/235 | Cost: 0.4587\\n\",\n      \"Epoch: 003/015 | Batch 200/235 | Cost: 0.4517\\n\",\n      \"Epoch: 003/015 training accuracy: 89.33%\\n\",\n      \"Time elapsed: 0.68 min\\n\",\n      \"Epoch: 004/015 | Batch 000/235 | Cost: 0.4083\\n\",\n      \"Epoch: 004/015 | Batch 050/235 | Cost: 0.3158\\n\",\n      \"Epoch: 004/015 | Batch 100/235 | Cost: 0.2728\\n\",\n      \"Epoch: 004/015 | Batch 150/235 | Cost: 0.3023\\n\",\n      \"Epoch: 004/015 | Batch 200/235 | Cost: 0.2709\\n\",\n      \"Epoch: 004/015 training accuracy: 90.40%\\n\",\n      \"Time elapsed: 0.90 min\\n\",\n      \"Epoch: 005/015 | Batch 000/235 | Cost: 0.2514\\n\",\n      \"Epoch: 005/015 | Batch 050/235 | Cost: 0.3704\\n\",\n      \"Epoch: 005/015 | Batch 100/235 | Cost: 0.2972\\n\",\n      \"Epoch: 005/015 | Batch 150/235 | Cost: 0.2335\\n\",\n      \"Epoch: 005/015 | Batch 200/235 | Cost: 0.3242\\n\",\n      \"Epoch: 005/015 training accuracy: 91.36%\\n\",\n      \"Time elapsed: 1.13 min\\n\",\n      \"Epoch: 006/015 | Batch 000/235 | Cost: 0.3255\\n\",\n      \"Epoch: 006/015 | Batch 050/235 | Cost: 0.2985\\n\",\n      \"Epoch: 006/015 | Batch 100/235 | Cost: 0.3501\\n\",\n      \"Epoch: 006/015 | Batch 150/235 | Cost: 0.2415\\n\",\n      \"Epoch: 006/015 | Batch 200/235 | Cost: 0.1978\\n\",\n      \"Epoch: 006/015 training accuracy: 92.82%\\n\",\n      \"Time elapsed: 1.35 min\\n\",\n      \"Epoch: 007/015 | Batch 000/235 | Cost: 0.1925\\n\",\n      \"Epoch: 007/015 | Batch 050/235 | Cost: 0.2179\\n\",\n      \"Epoch: 007/015 | Batch 100/235 | Cost: 0.3337\\n\",\n      \"Epoch: 007/015 | Batch 150/235 | Cost: 0.1856\\n\",\n      \"Epoch: 007/015 | Batch 200/235 | Cost: 0.1333\\n\",\n      \"Epoch: 007/015 training accuracy: 93.68%\\n\",\n      \"Time elapsed: 1.58 min\\n\",\n      \"Epoch: 008/015 | Batch 000/235 | Cost: 0.1776\\n\",\n      \"Epoch: 008/015 | Batch 050/235 | Cost: 0.2973\\n\",\n      \"Epoch: 008/015 | Batch 100/235 | Cost: 0.1685\\n\",\n      \"Epoch: 008/015 | Batch 150/235 | Cost: 0.2062\\n\",\n      \"Epoch: 008/015 | Batch 200/235 | Cost: 0.2165\\n\",\n      \"Epoch: 008/015 training accuracy: 94.42%\\n\",\n      \"Time elapsed: 1.80 min\\n\",\n      \"Epoch: 009/015 | Batch 000/235 | Cost: 0.2038\\n\",\n      \"Epoch: 009/015 | Batch 050/235 | Cost: 0.1301\\n\",\n      \"Epoch: 009/015 | Batch 100/235 | Cost: 0.1977\\n\",\n      \"Epoch: 009/015 | Batch 150/235 | Cost: 0.2160\\n\",\n      \"Epoch: 009/015 | Batch 200/235 | Cost: 0.1772\\n\",\n      \"Epoch: 009/015 training accuracy: 94.61%\\n\",\n      \"Time elapsed: 2.02 min\\n\",\n      \"Epoch: 010/015 | Batch 000/235 | Cost: 0.1709\\n\",\n      \"Epoch: 010/015 | Batch 050/235 | Cost: 0.1695\\n\",\n      \"Epoch: 010/015 | Batch 100/235 | Cost: 0.2144\\n\",\n      \"Epoch: 010/015 | Batch 150/235 | Cost: 0.1548\\n\",\n      \"Epoch: 010/015 | Batch 200/235 | Cost: 0.1033\\n\",\n      \"Epoch: 010/015 training accuracy: 94.90%\\n\",\n      \"Time elapsed: 2.25 min\\n\",\n      \"Epoch: 011/015 | Batch 000/235 | Cost: 0.1651\\n\",\n      \"Epoch: 011/015 | Batch 050/235 | Cost: 0.1899\\n\",\n      \"Epoch: 011/015 | Batch 100/235 | Cost: 0.1727\\n\",\n      \"Epoch: 011/015 | Batch 150/235 | Cost: 0.1216\\n\",\n      \"Epoch: 011/015 | Batch 200/235 | Cost: 0.1859\\n\",\n      \"Epoch: 011/015 training accuracy: 94.82%\\n\",\n      \"Time elapsed: 2.47 min\\n\",\n      \"Epoch: 012/015 | Batch 000/235 | Cost: 0.2490\\n\",\n      \"Epoch: 012/015 | Batch 050/235 | Cost: 0.1022\\n\",\n      \"Epoch: 012/015 | Batch 100/235 | Cost: 0.0793\\n\",\n      \"Epoch: 012/015 | Batch 150/235 | Cost: 0.2258\\n\",\n      \"Epoch: 012/015 | Batch 200/235 | Cost: 0.1356\\n\",\n      \"Epoch: 012/015 training accuracy: 95.35%\\n\",\n      \"Time elapsed: 2.70 min\\n\",\n      \"Epoch: 013/015 | Batch 000/235 | Cost: 0.1512\\n\",\n      \"Epoch: 013/015 | Batch 050/235 | Cost: 0.1758\\n\",\n      \"Epoch: 013/015 | Batch 100/235 | Cost: 0.1349\\n\",\n      \"Epoch: 013/015 | Batch 150/235 | Cost: 0.1838\\n\",\n      \"Epoch: 013/015 | Batch 200/235 | Cost: 0.1166\\n\",\n      \"Epoch: 013/015 training accuracy: 95.61%\\n\",\n      \"Time elapsed: 2.92 min\\n\",\n      \"Epoch: 014/015 | Batch 000/235 | Cost: 0.1210\\n\",\n      \"Epoch: 014/015 | Batch 050/235 | Cost: 0.1511\\n\",\n      \"Epoch: 014/015 | Batch 100/235 | Cost: 0.1331\\n\",\n      \"Epoch: 014/015 | Batch 150/235 | Cost: 0.1058\\n\",\n      \"Epoch: 014/015 | Batch 200/235 | Cost: 0.1340\\n\",\n      \"Epoch: 014/015 training accuracy: 95.53%\\n\",\n      \"Time elapsed: 3.15 min\\n\",\n      \"Epoch: 015/015 | Batch 000/235 | Cost: 0.2342\\n\",\n      \"Epoch: 015/015 | Batch 050/235 | Cost: 0.1371\\n\",\n      \"Epoch: 015/015 | Batch 100/235 | Cost: 0.0944\\n\",\n      \"Epoch: 015/015 | Batch 150/235 | Cost: 0.1102\\n\",\n      \"Epoch: 015/015 | Batch 200/235 | Cost: 0.1259\\n\",\n      \"Epoch: 015/015 training accuracy: 96.36%\\n\",\n      \"Time elapsed: 3.37 min\\n\",\n      \"Total Training Time: 3.37 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"def compute_accuracy(model, data_loader):\\n\",\n    \"    correct_pred, num_examples = 0, 0\\n\",\n    \"    for features, targets in data_loader:\\n\",\n    \"        features = features.to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        _, predicted_labels = torch.max(probas, 1)\\n\",\n    \"        num_examples += targets.size(0)\\n\",\n    \"        correct_pred += (predicted_labels == targets).sum()\\n\",\n    \"    return correct_pred.float()/num_examples * 100\\n\",\n    \"    \\n\",\n    \"    \\n\",\n    \"start_time = time.time()\\n\",\n    \"for epoch in range(num_epochs):\\n\",\n    \"    model = model.train()\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        features = features.to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"\\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        cost = F.cross_entropy(logits, targets)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        ### LOGGING\\n\",\n    \"        if not batch_idx % 50:\\n\",\n    \"            print ('Epoch: %03d/%03d | Batch %03d/%03d | Cost: %.4f' \\n\",\n    \"                   %(epoch+1, num_epochs, batch_idx, \\n\",\n    \"                     len(train_loader), cost))\\n\",\n    \"    \\n\",\n    \"    model = model.eval()\\n\",\n    \"    print('Epoch: %03d/%03d training accuracy: %.2f%%' % (\\n\",\n    \"          epoch+1, num_epochs, \\n\",\n    \"          compute_accuracy(model, train_loader)))\\n\",\n    \"    \\n\",\n    \"    print('Time elapsed: %.2f min' % ((time.time() - start_time)/60))\\n\",\n    \"    \\n\",\n    \"print('Total Training Time: %.2f min' % ((time.time() - start_time)/60))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 96.42%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"print('Test accuracy: %.2f%%' % (compute_accuracy(model, test_loader)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"numpy       1.15.4\\n\",\n      \"torch       1.0.1.post2\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.1\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "pytorch_ipynb/cnn/cnn-densenet121-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"UEBilEjLj5wY\"\n   },\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 119\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 536,\n     \"status\": \"ok\",\n     \"timestamp\": 1524974472601,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"GOzuY8Yvj5wb\",\n    \"outputId\": \"c19362ce-f87a-4cc2-84cc-8d7b4b9e6007\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Sebastian Raschka \\n\",\n      \"\\n\",\n      \"CPython 3.7.3\\n\",\n      \"IPython 7.6.1\\n\",\n      \"\\n\",\n      \"torch 1.1.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"rH4XmErYj5wm\"\n   },\n   \"source\": [\n    \"# DenseNet-121 CIFAR-10 Image Classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Network Architecture\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The network in this notebook is an implementation of the DenseNet-121 [1] architecture on the MNIST digits dataset (http://yann.lecun.com/exdb/mnist/) to train a handwritten digit classifier.  \\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The following figure illustrates the main concept of DenseNet: within each \\\"dense\\\" block, each layer is connected with each previous layer -- the feature maps are concatenated.\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![](../images/densenet/densenet-fig-2.jpg)\\n\",\n    \"\\n\",\n    \"Note that this is somewhat related yet very different to ResNets. ResNets have skip connections approx. between every other layer (but don't connect all layers with each other). Also, ResNets skip connections work via addition\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{x}_{\\\\ell}=H_{\\\\ell}\\\\left(\\\\mathbf{X}_{\\\\ell-1}\\\\right)+\\\\mathbf{X}_{\\\\ell-1}$$,\\n\",\n    \"\\n\",\n    \"whereas $H_{\\\\ell}(\\\\cdot)$ can be a composite function  of operations such as Batch Normalization (BN), rectified linear units (ReLU), Pooling, or Convolution (Conv).\\n\",\n    \"\\n\",\n    \"In DenseNets, all the previous feature maps $\\\\mathbf{X}_{0}, \\\\dots, \\\\mathbf{X}_{\\\\ell}-1$ of a feature map $\\\\mathbf{X}_{\\\\ell}$ are concatenated:\\n\",\n    \"\\n\",\n    \"$$\\\\mathbf{x}_{\\\\ell}=H_{\\\\ell}\\\\left(\\\\left[\\\\mathbf{x}_{0}, \\\\mathbf{x}_{1}, \\\\ldots, \\\\mathbf{x}_{\\\\ell-1}\\\\right]\\\\right).$$\\n\",\n    \"\\n\",\n    \"Furthermore, in this particular notebook, we are considering the DenseNet-121, which is depicted below:\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"![](../images/densenet/densenet-tab-1-dnet121.jpg)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**References**\\n\",\n    \"    \\n\",\n    \"- [1] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708), http://openaccess.thecvf.com/content_cvpr_2017/html/Huang_Densely_Connected_Convolutional_CVPR_2017_paper.html\\n\",\n    \"\\n\",\n    \"- [2] http://yann.lecun.com/exdb/mnist/\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"MkoGLH_Tj5wn\"\n   },\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"ORj09gnrj5wp\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"from torch.utils.data import DataLoader\\n\",\n    \"from torch.utils.data.dataset import Subset\\n\",\n    \"\\n\",\n    \"from torchvision import datasets\\n\",\n    \"from torchvision import transforms\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"from PIL import Image\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"if torch.cuda.is_available():\\n\",\n    \"    torch.backends.cudnn.deterministic = True\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import matplotlib.pyplot as plt\\n\",\n    \"%matplotlib inline\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"I6hghKPxj5w0\"\n   },\n   \"source\": [\n    \"## Model Settings\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 85\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 23936,\n     \"status\": \"ok\",\n     \"timestamp\": 1524974497505,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"NnT0sZIwj5wu\",\n    \"outputId\": \"55aed925-d17e-4c6a-8c71-0d9b3bde5637\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# Hyperparameters\\n\",\n    \"RANDOM_SEED = 1\\n\",\n    \"LEARNING_RATE = 0.001\\n\",\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 20\\n\",\n    \"\\n\",\n    \"# Architecture\\n\",\n    \"NUM_CLASSES = 10\\n\",\n    \"\\n\",\n    \"# Other\\n\",\n    \"DEVICE = \\\"cuda:0\\\"\\n\",\n    \"GRAYSCALE = False\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### CIFAR-10 Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"train_indices = torch.arange(0, 48000)\\n\",\n    \"valid_indices = torch.arange(48000, 50000)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_and_valid = datasets.CIFAR10(root='data', \\n\",\n    \"                                   train=True, \\n\",\n    \"                                   transform=transforms.ToTensor(),\\n\",\n    \"                                   download=True)\\n\",\n    \"\\n\",\n    \"train_dataset = Subset(train_and_valid, train_indices)\\n\",\n    \"valid_dataset = Subset(train_and_valid, valid_indices)\\n\",\n    \"test_dataset = datasets.CIFAR10(root='data', \\n\",\n    \"                                train=False, \\n\",\n    \"                                transform=transforms.ToTensor(),\\n\",\n    \"                                download=False)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader = DataLoader(dataset=train_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=4,\\n\",\n    \"                          shuffle=True)\\n\",\n    \"\\n\",\n    \"valid_loader = DataLoader(dataset=valid_dataset, \\n\",\n    \"                          batch_size=BATCH_SIZE,\\n\",\n    \"                          num_workers=4,\\n\",\n    \"                          shuffle=False)\\n\",\n    \"\\n\",\n    \"test_loader = DataLoader(dataset=test_dataset, \\n\",\n    \"                         batch_size=BATCH_SIZE,\\n\",\n    \"                         num_workers=4,\\n\",\n    \"                         shuffle=False)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 1 | Batch index: 0 | Batch size: 128\\n\",\n      \"Epoch: 2 | Batch index: 0 | Batch size: 128\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"device = torch.device(DEVICE)\\n\",\n    \"torch.manual_seed(0)\\n\",\n    \"\\n\",\n    \"for epoch in range(2):\\n\",\n    \"\\n\",\n    \"    for batch_idx, (x, y) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        print('Epoch:', epoch+1, end='')\\n\",\n    \"        print(' | Batch index:', batch_idx, end='')\\n\",\n    \"        print(' | Batch size:', y.size()[0])\\n\",\n    \"        \\n\",\n    \"        x = x.to(device)\\n\",\n    \"        y = y.to(device)\\n\",\n    \"        break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([3, 0, 1, 3, 3, 5, 0, 4, 9, 4])\\n\",\n      \"tensor([1, 0, 4, 1, 8, 2, 0, 3, 5, 3])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Check that shuffling works properly\\n\",\n    \"# i.e., label indices should be in random order.\\n\",\n    \"# Also, the label order should be different in the second\\n\",\n    \"# epoch.\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    pass\\n\",\n    \"print(labels[:10])\\n\",\n    \"\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    pass\\n\",\n    \"print(labels[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"tensor([5, 0, 3, 6, 8, 7, 9, 5, 6, 6])\\n\",\n      \"tensor([7, 5, 8, 0, 8, 2, 7, 0, 3, 5])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"# Check that validation set and test sets are diverse\\n\",\n    \"# i.e., that they contain all classes\\n\",\n    \"\\n\",\n    \"for images, labels in valid_loader:  \\n\",\n    \"    pass\\n\",\n    \"print(labels[:10])\\n\",\n    \"\\n\",\n    \"for images, labels in test_loader:  \\n\",\n    \"    pass\\n\",\n    \"print(labels[:10])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"# The following code cell that implements the DenseNet-121 architecture \\n\",\n    \"# is a derivative of the code provided at \\n\",\n    \"# https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py\\n\",\n    \"\\n\",\n    \"import re\\n\",\n    \"import torch\\n\",\n    \"import torch.nn as nn\\n\",\n    \"import torch.nn.functional as F\\n\",\n    \"import torch.utils.checkpoint as cp\\n\",\n    \"from collections import OrderedDict\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"def _bn_function_factory(norm, relu, conv):\\n\",\n    \"    def bn_function(*inputs):\\n\",\n    \"        concated_features = torch.cat(inputs, 1)\\n\",\n    \"        bottleneck_output = conv(relu(norm(concated_features)))\\n\",\n    \"        return bottleneck_output\\n\",\n    \"\\n\",\n    \"    return bn_function\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _DenseLayer(nn.Sequential):\\n\",\n    \"    def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, memory_efficient=False):\\n\",\n    \"        super(_DenseLayer, self).__init__()\\n\",\n    \"        self.add_module('norm1', nn.BatchNorm2d(num_input_features)),\\n\",\n    \"        self.add_module('relu1', nn.ReLU(inplace=True)),\\n\",\n    \"        self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *\\n\",\n    \"                                           growth_rate, kernel_size=1, stride=1,\\n\",\n    \"                                           bias=False)),\\n\",\n    \"        self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),\\n\",\n    \"        self.add_module('relu2', nn.ReLU(inplace=True)),\\n\",\n    \"        self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,\\n\",\n    \"                                           kernel_size=3, stride=1, padding=1,\\n\",\n    \"                                           bias=False)),\\n\",\n    \"        self.drop_rate = drop_rate\\n\",\n    \"        self.memory_efficient = memory_efficient\\n\",\n    \"\\n\",\n    \"    def forward(self, *prev_features):\\n\",\n    \"        bn_function = _bn_function_factory(self.norm1, self.relu1, self.conv1)\\n\",\n    \"        if self.memory_efficient and any(prev_feature.requires_grad for prev_feature in prev_features):\\n\",\n    \"            bottleneck_output = cp.checkpoint(bn_function, *prev_features)\\n\",\n    \"        else:\\n\",\n    \"            bottleneck_output = bn_function(*prev_features)\\n\",\n    \"        new_features = self.conv2(self.relu2(self.norm2(bottleneck_output)))\\n\",\n    \"        if self.drop_rate > 0:\\n\",\n    \"            new_features = F.dropout(new_features, p=self.drop_rate,\\n\",\n    \"                                     training=self.training)\\n\",\n    \"        return new_features\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _DenseBlock(nn.Module):\\n\",\n    \"    def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate, memory_efficient=False):\\n\",\n    \"        super(_DenseBlock, self).__init__()\\n\",\n    \"        for i in range(num_layers):\\n\",\n    \"            layer = _DenseLayer(\\n\",\n    \"                num_input_features + i * growth_rate,\\n\",\n    \"                growth_rate=growth_rate,\\n\",\n    \"                bn_size=bn_size,\\n\",\n    \"                drop_rate=drop_rate,\\n\",\n    \"                memory_efficient=memory_efficient,\\n\",\n    \"            )\\n\",\n    \"            self.add_module('denselayer%d' % (i + 1), layer)\\n\",\n    \"\\n\",\n    \"    def forward(self, init_features):\\n\",\n    \"        features = [init_features]\\n\",\n    \"        for name, layer in self.named_children():\\n\",\n    \"            new_features = layer(*features)\\n\",\n    \"            features.append(new_features)\\n\",\n    \"        return torch.cat(features, 1)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class _Transition(nn.Sequential):\\n\",\n    \"    def __init__(self, num_input_features, num_output_features):\\n\",\n    \"        super(_Transition, self).__init__()\\n\",\n    \"        self.add_module('norm', nn.BatchNorm2d(num_input_features))\\n\",\n    \"        self.add_module('relu', nn.ReLU(inplace=True))\\n\",\n    \"        self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,\\n\",\n    \"                                          kernel_size=1, stride=1, bias=False))\\n\",\n    \"        self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"class DenseNet121(nn.Module):\\n\",\n    \"    r\\\"\\\"\\\"Densenet-BC model class, based on\\n\",\n    \"    `\\\"Densely Connected Convolutional Networks\\\" <https://arxiv.org/pdf/1608.06993.pdf>`_\\n\",\n    \"\\n\",\n    \"    Args:\\n\",\n    \"        growth_rate (int) - how many filters to add each layer (`k` in paper)\\n\",\n    \"        block_config (list of 4 ints) - how many layers in each pooling block\\n\",\n    \"        num_init_featuremaps (int) - the number of filters to learn in the first convolution layer\\n\",\n    \"        bn_size (int) - multiplicative factor for number of bottle neck layers\\n\",\n    \"          (i.e. bn_size * k features in the bottleneck layer)\\n\",\n    \"        drop_rate (float) - dropout rate after each dense layer\\n\",\n    \"        num_classes (int) - number of classification classes\\n\",\n    \"        memory_efficient (bool) - If True, uses checkpointing. Much more memory efficient,\\n\",\n    \"          but slower. Default: *False*. See `\\\"paper\\\" <https://arxiv.org/pdf/1707.06990.pdf>`_\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),\\n\",\n    \"                 num_init_featuremaps=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False,\\n\",\n    \"                 grayscale=False):\\n\",\n    \"\\n\",\n    \"        super(DenseNet121, self).__init__()\\n\",\n    \"\\n\",\n    \"        # First convolution\\n\",\n    \"        if grayscale:\\n\",\n    \"            in_channels=1\\n\",\n    \"        else:\\n\",\n    \"            in_channels=3\\n\",\n    \"        \\n\",\n    \"        self.features = nn.Sequential(OrderedDict([\\n\",\n    \"            ('conv0', nn.Conv2d(in_channels=in_channels, out_channels=num_init_featuremaps,\\n\",\n    \"                                kernel_size=7, stride=2,\\n\",\n    \"                                padding=3, bias=False)), # bias is redundant when using batchnorm\\n\",\n    \"            ('norm0', nn.BatchNorm2d(num_features=num_init_featuremaps)),\\n\",\n    \"            ('relu0', nn.ReLU(inplace=True)),\\n\",\n    \"            ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),\\n\",\n    \"        ]))\\n\",\n    \"\\n\",\n    \"        # Each denseblock\\n\",\n    \"        num_features = num_init_featuremaps\\n\",\n    \"        for i, num_layers in enumerate(block_config):\\n\",\n    \"            block = _DenseBlock(\\n\",\n    \"                num_layers=num_layers,\\n\",\n    \"                num_input_features=num_features,\\n\",\n    \"                bn_size=bn_size,\\n\",\n    \"                growth_rate=growth_rate,\\n\",\n    \"                drop_rate=drop_rate,\\n\",\n    \"                memory_efficient=memory_efficient\\n\",\n    \"            )\\n\",\n    \"            self.features.add_module('denseblock%d' % (i + 1), block)\\n\",\n    \"            num_features = num_features + num_layers * growth_rate\\n\",\n    \"            if i != len(block_config) - 1:\\n\",\n    \"                trans = _Transition(num_input_features=num_features,\\n\",\n    \"                                    num_output_features=num_features // 2)\\n\",\n    \"                self.features.add_module('transition%d' % (i + 1), trans)\\n\",\n    \"                num_features = num_features // 2\\n\",\n    \"\\n\",\n    \"        # Final batch norm\\n\",\n    \"        self.features.add_module('norm5', nn.BatchNorm2d(num_features))\\n\",\n    \"\\n\",\n    \"        # Linear layer\\n\",\n    \"        self.classifier = nn.Linear(num_features, num_classes)\\n\",\n    \"\\n\",\n    \"        # Official init from torch repo.\\n\",\n    \"        for m in self.modules():\\n\",\n    \"            if isinstance(m, nn.Conv2d):\\n\",\n    \"                nn.init.kaiming_normal_(m.weight)\\n\",\n    \"            elif isinstance(m, nn.BatchNorm2d):\\n\",\n    \"                nn.init.constant_(m.weight, 1)\\n\",\n    \"                nn.init.constant_(m.bias, 0)\\n\",\n    \"            elif isinstance(m, nn.Linear):\\n\",\n    \"                nn.init.constant_(m.bias, 0)\\n\",\n    \"\\n\",\n    \"    def forward(self, x):\\n\",\n    \"        features = self.features(x)\\n\",\n    \"        out = F.relu(features, inplace=True)\\n\",\n    \"        out = F.adaptive_avg_pool2d(out, (1, 1))\\n\",\n    \"        out = torch.flatten(out, 1)\\n\",\n    \"        logits = self.classifier(out)\\n\",\n    \"        probas = F.softmax(logits, dim=1)\\n\",\n    \"        return logits, probas\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     }\n    },\n    \"colab_type\": \"code\",\n    \"id\": \"_lza9t_uj5w1\"\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"torch.manual_seed(RANDOM_SEED)\\n\",\n    \"\\n\",\n    \"model = DenseNet121(num_classes=NUM_CLASSES, grayscale=GRAYSCALE)\\n\",\n    \"model.to(DEVICE)\\n\",\n    \"\\n\",\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)  \"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"RAodboScj5w6\"\n   },\n   \"source\": [\n    \"## Training\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def compute_acc(model, data_loader, device):\\n\",\n    \"    correct_pred, num_examples = 0, 0\\n\",\n    \"    model.eval()\\n\",\n    \"    for i, (features, targets) in enumerate(data_loader):\\n\",\n    \"            \\n\",\n    \"        features = features.to(device)\\n\",\n    \"        targets = targets.to(device)\\n\",\n    \"\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        _, predicted_labels = torch.max(probas, 1)\\n\",\n    \"        num_examples += targets.size(0)\\n\",\n    \"        assert predicted_labels.size() == targets.size()\\n\",\n    \"        correct_pred += (predicted_labels == targets).sum()\\n\",\n    \"    return correct_pred.float()/num_examples * 100\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 1547\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 2384585,\n     \"status\": \"ok\",\n     \"timestamp\": 1524976888520,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"Dzh3ROmRj5w7\",\n    \"outputId\": \"5f8fd8c9-b076-403a-b0b7-fd2d498b48d7\"\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/020 | Batch 000/375 | Cost: 2.4002\\n\",\n      \"Epoch: 001/020 | Batch 150/375 | Cost: 1.3511\\n\",\n      \"Epoch: 001/020 | Batch 300/375 | Cost: 1.3326\\n\",\n      \"Epoch: 001/020\\n\",\n      \"Train ACC: 61.52 | Validation ACC: 60.25\\n\",\n      \"Time elapsed: 0.99 min\\n\",\n      \"Epoch: 002/020 | Batch 000/375 | Cost: 1.1005\\n\",\n      \"Epoch: 002/020 | Batch 150/375 | Cost: 1.0096\\n\",\n      \"Epoch: 002/020 | Batch 300/375 | Cost: 1.2628\\n\",\n      \"Epoch: 002/020\\n\",\n      \"Train ACC: 66.19 | Validation ACC: 63.55\\n\",\n      \"Time elapsed: 1.90 min\\n\",\n      \"Epoch: 003/020 | Batch 000/375 | Cost: 0.6471\\n\",\n      \"Epoch: 003/020 | Batch 150/375 | Cost: 0.8107\\n\",\n      \"Epoch: 003/020 | Batch 300/375 | Cost: 0.8848\\n\",\n      \"Epoch: 003/020\\n\",\n      \"Train ACC: 73.32 | Validation ACC: 67.40\\n\",\n      \"Time elapsed: 2.79 min\\n\",\n      \"Epoch: 004/020 | Batch 000/375 | Cost: 0.6951\\n\",\n      \"Epoch: 004/020 | Batch 150/375 | Cost: 0.6852\\n\",\n      \"Epoch: 004/020 | Batch 300/375 | Cost: 0.8775\\n\",\n      \"Epoch: 004/020\\n\",\n      \"Train ACC: 76.38 | Validation ACC: 67.90\\n\",\n      \"Time elapsed: 3.65 min\\n\",\n      \"Epoch: 005/020 | Batch 000/375 | Cost: 0.4929\\n\",\n      \"Epoch: 005/020 | Batch 150/375 | Cost: 0.5942\\n\",\n      \"Epoch: 005/020 | Batch 300/375 | Cost: 0.6016\\n\",\n      \"Epoch: 005/020\\n\",\n      \"Train ACC: 76.70 | Validation ACC: 67.95\\n\",\n      \"Time elapsed: 4.53 min\\n\",\n      \"Epoch: 006/020 | Batch 000/375 | Cost: 0.5888\\n\",\n      \"Epoch: 006/020 | Batch 150/375 | Cost: 0.5500\\n\",\n      \"Epoch: 006/020 | Batch 300/375 | Cost: 0.4939\\n\",\n      \"Epoch: 006/020\\n\",\n      \"Train ACC: 84.21 | Validation ACC: 74.85\\n\",\n      \"Time elapsed: 5.51 min\\n\",\n      \"Epoch: 007/020 | Batch 000/375 | Cost: 0.3982\\n\",\n      \"Epoch: 007/020 | Batch 150/375 | Cost: 0.4272\\n\",\n      \"Epoch: 007/020 | Batch 300/375 | Cost: 0.3908\\n\",\n      \"Epoch: 007/020\\n\",\n      \"Train ACC: 87.29 | Validation ACC: 74.65\\n\",\n      \"Time elapsed: 6.43 min\\n\",\n      \"Epoch: 008/020 | Batch 000/375 | Cost: 0.4332\\n\",\n      \"Epoch: 008/020 | Batch 150/375 | Cost: 0.2335\\n\",\n      \"Epoch: 008/020 | Batch 300/375 | Cost: 0.3678\\n\",\n      \"Epoch: 008/020\\n\",\n      \"Train ACC: 84.10 | Validation ACC: 71.00\\n\",\n      \"Time elapsed: 7.35 min\\n\",\n      \"Epoch: 009/020 | Batch 000/375 | Cost: 0.2343\\n\",\n      \"Epoch: 009/020 | Batch 150/375 | Cost: 0.2704\\n\",\n      \"Epoch: 009/020 | Batch 300/375 | Cost: 0.3429\\n\",\n      \"Epoch: 009/020\\n\",\n      \"Train ACC: 88.51 | Validation ACC: 74.35\\n\",\n      \"Time elapsed: 8.26 min\\n\",\n      \"Epoch: 010/020 | Batch 000/375 | Cost: 0.1757\\n\",\n      \"Epoch: 010/020 | Batch 150/375 | Cost: 0.1748\\n\",\n      \"Epoch: 010/020 | Batch 300/375 | Cost: 0.4755\\n\",\n      \"Epoch: 010/020\\n\",\n      \"Train ACC: 92.77 | Validation ACC: 74.20\\n\",\n      \"Time elapsed: 9.27 min\\n\",\n      \"Epoch: 011/020 | Batch 000/375 | Cost: 0.2347\\n\",\n      \"Epoch: 011/020 | Batch 150/375 | Cost: 0.1618\\n\",\n      \"Epoch: 011/020 | Batch 300/375 | Cost: 0.3075\\n\",\n      \"Epoch: 011/020\\n\",\n      \"Train ACC: 90.78 | Validation ACC: 74.10\\n\",\n      \"Time elapsed: 10.24 min\\n\",\n      \"Epoch: 012/020 | Batch 000/375 | Cost: 0.1233\\n\",\n      \"Epoch: 012/020 | Batch 150/375 | Cost: 0.1385\\n\",\n      \"Epoch: 012/020 | Batch 300/375 | Cost: 0.2406\\n\",\n      \"Epoch: 012/020\\n\",\n      \"Train ACC: 93.26 | Validation ACC: 76.00\\n\",\n      \"Time elapsed: 11.15 min\\n\",\n      \"Epoch: 013/020 | Batch 000/375 | Cost: 0.0757\\n\",\n      \"Epoch: 013/020 | Batch 150/375 | Cost: 0.0658\\n\",\n      \"Epoch: 013/020 | Batch 300/375 | Cost: 0.2070\\n\",\n      \"Epoch: 013/020\\n\",\n      \"Train ACC: 91.64 | Validation ACC: 75.80\\n\",\n      \"Time elapsed: 12.13 min\\n\",\n      \"Epoch: 014/020 | Batch 000/375 | Cost: 0.1285\\n\",\n      \"Epoch: 014/020 | Batch 150/375 | Cost: 0.1468\\n\",\n      \"Epoch: 014/020 | Batch 300/375 | Cost: 0.1070\\n\",\n      \"Epoch: 014/020\\n\",\n      \"Train ACC: 95.18 | Validation ACC: 77.80\\n\",\n      \"Time elapsed: 13.10 min\\n\",\n      \"Epoch: 015/020 | Batch 000/375 | Cost: 0.1204\\n\",\n      \"Epoch: 015/020 | Batch 150/375 | Cost: 0.0461\\n\",\n      \"Epoch: 015/020 | Batch 300/375 | Cost: 0.1005\\n\",\n      \"Epoch: 015/020\\n\",\n      \"Train ACC: 93.26 | Validation ACC: 74.80\\n\",\n      \"Time elapsed: 14.10 min\\n\",\n      \"Epoch: 016/020 | Batch 000/375 | Cost: 0.0591\\n\",\n      \"Epoch: 016/020 | Batch 150/375 | Cost: 0.1024\\n\",\n      \"Epoch: 016/020 | Batch 300/375 | Cost: 0.0679\\n\",\n      \"Epoch: 016/020\\n\",\n      \"Train ACC: 95.66 | Validation ACC: 76.80\\n\",\n      \"Time elapsed: 15.02 min\\n\",\n      \"Epoch: 017/020 | Batch 000/375 | Cost: 0.0359\\n\",\n      \"Epoch: 017/020 | Batch 150/375 | Cost: 0.0615\\n\",\n      \"Epoch: 017/020 | Batch 300/375 | Cost: 0.0725\\n\",\n      \"Epoch: 017/020\\n\",\n      \"Train ACC: 92.06 | Validation ACC: 75.85\\n\",\n      \"Time elapsed: 16.01 min\\n\",\n      \"Epoch: 018/020 | Batch 000/375 | Cost: 0.0831\\n\",\n      \"Epoch: 018/020 | Batch 150/375 | Cost: 0.1023\\n\",\n      \"Epoch: 018/020 | Batch 300/375 | Cost: 0.0666\\n\",\n      \"Epoch: 018/020\\n\",\n      \"Train ACC: 94.32 | Validation ACC: 75.10\\n\",\n      \"Time elapsed: 17.01 min\\n\",\n      \"Epoch: 019/020 | Batch 000/375 | Cost: 0.0499\\n\",\n      \"Epoch: 019/020 | Batch 150/375 | Cost: 0.1068\\n\",\n      \"Epoch: 019/020 | Batch 300/375 | Cost: 0.0657\\n\",\n      \"Epoch: 019/020\\n\",\n      \"Train ACC: 96.89 | Validation ACC: 77.75\\n\",\n      \"Time elapsed: 17.99 min\\n\",\n      \"Epoch: 020/020 | Batch 000/375 | Cost: 0.0751\\n\",\n      \"Epoch: 020/020 | Batch 150/375 | Cost: 0.0423\\n\",\n      \"Epoch: 020/020 | Batch 300/375 | Cost: 0.0398\\n\",\n      \"Epoch: 020/020\\n\",\n      \"Train ACC: 94.66 | Validation ACC: 76.70\\n\",\n      \"Time elapsed: 18.90 min\\n\",\n      \"Total Training Time: 18.90 min\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"start_time = time.time()\\n\",\n    \"\\n\",\n    \"cost_list = []\\n\",\n    \"train_acc_list, valid_acc_list = [], []\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"for epoch in range(NUM_EPOCHS):\\n\",\n    \"    \\n\",\n    \"    model.train()\\n\",\n    \"    for batch_idx, (features, targets) in enumerate(train_loader):\\n\",\n    \"        \\n\",\n    \"        features = features.to(DEVICE)\\n\",\n    \"        targets = targets.to(DEVICE)\\n\",\n    \"            \\n\",\n    \"        ### FORWARD AND BACK PROP\\n\",\n    \"        logits, probas = model(features)\\n\",\n    \"        cost = F.cross_entropy(logits, targets)\\n\",\n    \"        optimizer.zero_grad()\\n\",\n    \"        \\n\",\n    \"        cost.backward()\\n\",\n    \"        \\n\",\n    \"        ### UPDATE MODEL PARAMETERS\\n\",\n    \"        optimizer.step()\\n\",\n    \"        \\n\",\n    \"        #################################################\\n\",\n    \"        ### CODE ONLY FOR LOGGING BEYOND THIS POINT\\n\",\n    \"        ################################################\\n\",\n    \"        cost_list.append(cost.item())\\n\",\n    \"        if not batch_idx % 150:\\n\",\n    \"            print (f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d} | '\\n\",\n    \"                   f'Batch {batch_idx:03d}/{len(train_loader):03d} |' \\n\",\n    \"                   f' Cost: {cost:.4f}')\\n\",\n    \"\\n\",\n    \"        \\n\",\n    \"\\n\",\n    \"    model.eval()\\n\",\n    \"    with torch.set_grad_enabled(False): # save memory during inference\\n\",\n    \"        \\n\",\n    \"        train_acc = compute_acc(model, train_loader, device=DEVICE)\\n\",\n    \"        valid_acc = compute_acc(model, valid_loader, device=DEVICE)\\n\",\n    \"        \\n\",\n    \"        print(f'Epoch: {epoch+1:03d}/{NUM_EPOCHS:03d}\\\\n'\\n\",\n    \"              f'Train ACC: {train_acc:.2f} | Validation ACC: {valid_acc:.2f}')\\n\",\n    \"        \\n\",\n    \"        train_acc_list.append(train_acc)\\n\",\n    \"        valid_acc_list.append(valid_acc)\\n\",\n    \"        \\n\",\n    \"    elapsed = (time.time() - start_time)/60\\n\",\n    \"    print(f'Time elapsed: {elapsed:.2f} min')\\n\",\n    \"  \\n\",\n    \"elapsed = (time.time() - start_time)/60\\n\",\n    \"print(f'Total Training Time: {elapsed:.2f} min')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"colab_type\": \"text\",\n    \"id\": \"paaeEQHQj5xC\"\n   },\n   \"source\": [\n    \"## Evaluation\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"colab\": {\n     \"autoexec\": {\n      \"startup\": false,\n      \"wait_interval\": 0\n     },\n     \"base_uri\": \"https://localhost:8080/\",\n     \"height\": 34\n    },\n    \"colab_type\": \"code\",\n    \"executionInfo\": {\n     \"elapsed\": 6514,\n     \"status\": \"ok\",\n     \"timestamp\": 1524976895054,\n     \"user\": {\n      \"displayName\": \"Sebastian Raschka\",\n      \"photoUrl\": \"//lh6.googleusercontent.com/-cxK6yOSQ6uE/AAAAAAAAAAI/AAAAAAAAIfw/P9ar_CHsKOQ/s50-c-k-no/photo.jpg\",\n      \"userId\": \"118404394130788869227\"\n     },\n     \"user_tz\": 240\n    },\n    \"id\": \"gzQMWKq5j5xE\",\n    \"outputId\": \"de7dc005-5eeb-4177-9f9f-d9b5d1358db9\"\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(cost_list, label='Minibatch cost')\\n\",\n    \"plt.plot(np.convolve(cost_list, \\n\",\n    \"                     np.ones(200,)/200, mode='valid'), \\n\",\n    \"         label='Running average')\\n\",\n    \"\\n\",\n    \"plt.ylabel('Cross Entropy')\\n\",\n    \"plt.xlabel('Iteration')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\\n\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.plot(np.arange(1, NUM_EPOCHS+1), train_acc_list, label='Training')\\n\",\n    \"plt.plot(np.arange(1, NUM_EPOCHS+1), valid_acc_list, label='Validation')\\n\",\n    \"\\n\",\n    \"plt.xlabel('Epoch')\\n\",\n    \"plt.ylabel('Accuracy')\\n\",\n    \"plt.legend()\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Validation ACC: 76.70%\\n\",\n      \"Test ACC: 74.97%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"with torch.set_grad_enabled(False):\\n\",\n    \"    test_acc = compute_acc(model=model,\\n\",\n    \"                           data_loader=test_loader,\\n\",\n    \"                           device=DEVICE)\\n\",\n    \"    \\n\",\n    \"    valid_acc = compute_acc(model=model,\\n\",\n    \"                            data_loader=valid_loader,\\n\",\n    \"                            device=DEVICE)\\n\",\n    \"    \\n\",\n    \"\\n\",\n    \"print(f'Validation ACC: {valid_acc:.2f}%')\\n\",\n    \"print(f'Test ACC: {test_acc:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"torch       1.1.0\\n\",\n      \"matplotlib  3.1.0\\n\",\n      \"pandas      0.24.2\\n\",\n      \"torchvision 0.3.0\\n\",\n      \"numpy       1.16.4\\n\",\n      \"re          2.2.1\\n\",\n      \"PIL.Image   6.0.0\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -iv\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"accelerator\": \"GPU\",\n  \"colab\": {\n   \"collapsed_sections\": [],\n   \"default_view\": {},\n   \"name\": \"convnet-vgg16.ipynb\",\n   \"provenance\": [],\n   \"version\": \"0.3.2\",\n   \"views\": {}\n  },\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.7.3\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": true,\n   \"toc_position\": {\n    \"height\": \"calc(100% - 180px)\",\n    \"left\": \"10px\",\n    \"top\": \"150px\",\n    \"width\": \"371px\"\n   },\n   \"toc_section_display\": true,\n   \"toc_window_display\": true\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  },
  {
    "path": "pytorch_ipynb/cnn/cnn-embetter-mobilenet.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"4936d1e6-5e7d-4e22-ae35-8e888927ce2d\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Use Pre-trained CNN as feature extractor\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"bf9e9fb5-7383-475a-93e1-decdbd59c247\",\n   \"metadata\": {},\n   \"source\": [\n    \"Use MobileNetv3 as a feature extractor via the [embetter](https://github.com/koaning/embetter) scikit-learn library and [timm](https://github.com/rwightman/pytorch-image-models). Train a logistic regression classifier in scikit-learn on the embeddings.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"id\": \"96b717c7-54c9-40dc-ba80-0fb47da2c0bd\",\n   \"metadata\": {},\n   \"source\": [\n    \"![](images/feature-extractor.png)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"id\": \"64d1dd64-c45b-4092-84d1-1bfcd0998f15\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"\\n\",\n    \"# pip install gitpython\\n\",\n    \"from git import Repo\\n\",\n    \"\\n\",\n    \"if not os.path.exists(\\\"mnist-pngs\\\"):\\n\",\n    \"    Repo.clone_from(\\\"https://github.com/rasbt/mnist-pngs\\\", \\\"mnist-pngs\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"id\": \"3a892538-8d9b-4420-9525-26d1a4b37ae3\",\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import os\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"for name in (\\\"train\\\", \\\"test\\\"):\\n\",\n    \"\\n\",\n    \"    df = pd.read_csv(f\\\"mnist-pngs/{name}.csv\\\")\\n\",\n    \"    df[\\\"filepath\\\"] = df[\\\"filepath\\\"].apply(lambda x: \\\"mnist-pngs/\\\" + x)\\n\",\n    \"    df = df.sample(frac=1, random_state=123).reset_index(drop=True)\\n\",\n    \"    df.to_csv(f\\\"mnist-pngs/{name}_shuffled.csv\\\", index=None)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"id\": \"5885e9bb-d43f-46ca-83ae-e2d63edcbb37\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"1fba0fcb2b1f408f85013da0d1694dd3\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/60 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"from sklearn.linear_model import SGDClassifier\\n\",\n    \"from tqdm.notebook import tqdm\\n\",\n    \"\\n\",\n    \"# pip install \\\"embetter[vision]\\\"\\n\",\n    \"from embetter.vision import ImageLoader, TimmEncoder\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"embed = make_pipeline(\\n\",\n    \"    ImageLoader(),\\n\",\n    \"    TimmEncoder(name=\\\"mobilenetv3_large_100\\\")\\n\",\n    \")\\n\",\n    \"\\n\",\n    \"model = SGDClassifier(loss='log_loss', n_jobs=-1, shuffle=True)\\n\",\n    \"\\n\",\n    \"chunksize = 1000\\n\",\n    \"train_labels, train_predict = [], []\\n\",\n    \"\\n\",\n    \"for df in tqdm(pd.read_csv(\\\"mnist-pngs/train_shuffled.csv\\\", chunksize=chunksize, iterator=True), total=60):\\n\",\n    \"    \\n\",\n    \"    embedded = embed.transform(df[\\\"filepath\\\"])\\n\",\n    \"    model.partial_fit(embedded, df[\\\"label\\\"], classes=list(range(10)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"id\": \"999a24ea-be5d-425f-923c-266372c66b5d\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"157302965ac8460c97c77935cc08e1fc\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/60 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"train_labels, train_predict = [], []\\n\",\n    \"\\n\",\n    \"for df in tqdm(pd.read_csv(\\\"mnist-pngs/train.csv\\\", chunksize=chunksize, iterator=True), total=60):\\n\",\n    \"    df[\\\"filepath\\\"] = df[\\\"filepath\\\"].apply(lambda x: \\\"mnist-pngs/\\\" + x)\\n\",\n    \"\\n\",\n    \"    embedded = embed.transform(df[\\\"filepath\\\"])\\n\",\n    \"    train_predict.extend(model.predict(embedded))\\n\",\n    \"    train_labels.extend(list(df[\\\"label\\\"].values))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"id\": \"c816cd7b-ed3a-4cb2-8aa6-400068a2e414\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7869826407314279a2806bf602a796a8\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"  0%|          | 0/10 [00:00<?, ?it/s]\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"test_labels, test_predict = [], []\\n\",\n    \"\\n\",\n    \"for df in tqdm(pd.read_csv(\\\"mnist-pngs/test_shuffled.csv\\\", chunksize=chunksize, iterator=True), total=10):\\n\",\n    \"\\n\",\n    \"    embedded = embed.transform(df[\\\"filepath\\\"])\\n\",\n    \"    test_predict.extend(model.predict(embedded))\\n\",\n    \"    test_labels.extend(list(df[\\\"label\\\"].values))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"id\": \"4a78add1-7f93-40fc-b119-9dbbe0aa55b4\",\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Train accuracy: 0.92\\n\",\n      \"Test accuracy: 0.92\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.metrics import accuracy_score\\n\",\n    \"\\n\",\n    \"print(f\\\"Train accuracy: {accuracy_score(train_labels, train_predict):.2f}\\\")\\n\",\n    \"print(f\\\"Test accuracy: {accuracy_score(test_labels, test_predict):.2f}\\\")\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3 (ipykernel)\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.9.7\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n"
  },
  {
    "path": "pytorch_ipynb/cnn/cnn-mobilenet-v2-cifar10.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\\n\",\n    \"- Author: Sebastian Raschka\\n\",\n    \"- GitHub Repository: https://github.com/rasbt/deeplearning-models\\n\",\n    \"\\n\",\n    \"---\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Author: Sebastian Raschka\\n\",\n      \"\\n\",\n      \"Python implementation: CPython\\n\",\n      \"Python version       : 3.8.8\\n\",\n      \"IPython version      : 7.21.0\\n\",\n      \"\\n\",\n      \"torch: 1.8.1+cu111\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%load_ext watermark\\n\",\n    \"%watermark -a 'Sebastian Raschka' -v -p torch\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# MobileNet-v2 trained on Cifar-10\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Imports\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import torch\\n\",\n    \"import torchvision\\n\",\n    \"import numpy as np\\n\",\n    \"import matplotlib.pyplot as plt\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sys\\n\",\n    \"sys.path.insert(0, \\\"..\\\") # to include ../helper_evaluate.py etc.\\n\",\n    \"\\n\",\n    \"# From local helper files\\n\",\n    \"from helper_utils import set_all_seeds, set_deterministic\\n\",\n    \"from helper_evaluate import compute_confusion_matrix, compute_accuracy\\n\",\n    \"from helper_train import train_classifier_simple_v2\\n\",\n    \"from helper_plotting import plot_training_loss, plot_accuracy, show_examples, plot_confusion_matrix\\n\",\n    \"from helper_data import get_dataloaders_cifar10, UnNormalize\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Settings and Dataset\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"##########################\\n\",\n    \"### SETTINGS\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"RANDOM_SEED = 123\\n\",\n    \"BATCH_SIZE = 128\\n\",\n    \"NUM_EPOCHS = 150\\n\",\n    \"DEVICE = torch.device('cuda:3' if torch.cuda.is_available() else 'cpu')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"set_all_seeds(RANDOM_SEED)\\n\",\n    \"#set_deterministic()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Files already downloaded and verified\\n\",\n      \"Image batch dimensions: torch.Size([128, 3, 64, 64])\\n\",\n      \"Image label dimensions: torch.Size([128])\\n\",\n      \"Class labels of 10 examples: tensor([4, 7, 4, 6, 2, 6, 9, 7, 3, 0])\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### CIFAR-10 DATASET\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"### Note: Network trains about 2-3x faster if you don't\\n\",\n    \"# resize (keeping the orig. 32x32 res.)\\n\",\n    \"# Test acc. I got via the 32x32 was lower though; ~77%\\n\",\n    \"\\n\",\n    \"train_transforms = torchvision.transforms.Compose([\\n\",\n    \"    torchvision.transforms.Resize((70, 70)),\\n\",\n    \"    torchvision.transforms.RandomCrop((64, 64)),\\n\",\n    \"    torchvision.transforms.ToTensor(),\\n\",\n    \"    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"test_transforms = torchvision.transforms.Compose([\\n\",\n    \"    torchvision.transforms.Resize((70, 70)),        \\n\",\n    \"    torchvision.transforms.CenterCrop((64, 64)),            \\n\",\n    \"    torchvision.transforms.ToTensor(),                \\n\",\n    \"    torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"train_loader, valid_loader, test_loader = get_dataloaders_cifar10(\\n\",\n    \"    batch_size=BATCH_SIZE,\\n\",\n    \"    validation_fraction=0.1,\\n\",\n    \"    train_transforms=train_transforms,\\n\",\n    \"    test_transforms=test_transforms,\\n\",\n    \"    num_workers=2)\\n\",\n    \"\\n\",\n    \"# Checking the dataset\\n\",\n    \"for images, labels in train_loader:  \\n\",\n    \"    print('Image batch dimensions:', images.shape)\\n\",\n    \"    print('Image label dimensions:', labels.shape)\\n\",\n    \"    print('Class labels of 10 examples:', labels[:10])\\n\",\n    \"    break\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"<matplotlib.image.AxesImage at 0x7f89d98d9ac0>\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 576x576 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"plt.axis(\\\"off\\\")\\n\",\n    \"plt.title(\\\"Training Images\\\")\\n\",\n    \"plt.imshow(np.transpose(torchvision.utils.make_grid(images[:64], \\n\",\n    \"                                         padding=2, normalize=True),\\n\",\n    \"                        (1, 2, 0)))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Model\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Using cache found in /home/raschka/.cache/torch/hub/pytorch_vision_v0.9.0\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"##########################\\n\",\n    \"### MODEL\\n\",\n    \"##########################\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"model = torch.hub.load('pytorch/vision:v0.9.0', 'mobilenet_v2',\\n\",\n    \"                       pretrained=False)\\n\",\n    \"\\n\",\n    \"model.classifier[-1] = torch.nn.Linear(in_features=1280, # as in original\\n\",\n    \"                                       out_features=10) # number of class labels in Cifar-10)\\n\",\n    \"\\n\",\n    \"model = model.to(DEVICE)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Epoch: 001/150 | Batch 0000/0351 | Loss: 2.3627\\n\",\n      \"Epoch: 001/150 | Batch 0100/0351 | Loss: 1.9649\\n\",\n      \"Epoch: 001/150 | Batch 0200/0351 | Loss: 1.7862\\n\",\n      \"Epoch: 001/150 | Batch 0300/0351 | Loss: 1.9222\\n\",\n      \"Epoch: 001/150 | Train: 32.20% | Validation: 32.60% | Best Validation (Ep. 001): 32.60%\\n\",\n      \"Time elapsed: 0.99 min\\n\",\n      \"Epoch: 002/150 | Batch 0000/0351 | Loss: 1.9170\\n\",\n      \"Epoch: 002/150 | Batch 0100/0351 | Loss: 1.7127\\n\",\n      \"Epoch: 002/150 | Batch 0200/0351 | Loss: 1.8019\\n\",\n      \"Epoch: 002/150 | Batch 0300/0351 | Loss: 1.6782\\n\",\n      \"Epoch: 002/150 | Train: 34.07% | Validation: 33.52% | Best Validation (Ep. 002): 33.52%\\n\",\n      \"Time elapsed: 2.00 min\\n\",\n      \"Epoch: 003/150 | Batch 0000/0351 | Loss: 1.7150\\n\",\n      \"Epoch: 003/150 | Batch 0100/0351 | Loss: 1.6087\\n\",\n      \"Epoch: 003/150 | Batch 0200/0351 | Loss: 1.4612\\n\",\n      \"Epoch: 003/150 | Batch 0300/0351 | Loss: 1.5310\\n\",\n      \"Epoch: 003/150 | Train: 44.74% | Validation: 45.50% | Best Validation (Ep. 003): 45.50%\\n\",\n      \"Time elapsed: 2.99 min\\n\",\n      \"Epoch: 004/150 | Batch 0000/0351 | Loss: 1.4913\\n\",\n      \"Epoch: 004/150 | Batch 0100/0351 | Loss: 1.4658\\n\",\n      \"Epoch: 004/150 | Batch 0200/0351 | Loss: 1.4478\\n\",\n      \"Epoch: 004/150 | Batch 0300/0351 | Loss: 1.5523\\n\",\n      \"Epoch: 004/150 | Train: 48.72% | Validation: 49.52% | Best Validation (Ep. 004): 49.52%\\n\",\n      \"Time elapsed: 4.00 min\\n\",\n      \"Epoch: 005/150 | Batch 0000/0351 | Loss: 1.2705\\n\",\n      \"Epoch: 005/150 | Batch 0100/0351 | Loss: 1.5536\\n\",\n      \"Epoch: 005/150 | Batch 0200/0351 | Loss: 1.2093\\n\",\n      \"Epoch: 005/150 | Batch 0300/0351 | Loss: 1.2654\\n\",\n      \"Epoch: 005/150 | Train: 54.51% | Validation: 55.88% | Best Validation (Ep. 005): 55.88%\\n\",\n      \"Time elapsed: 5.02 min\\n\",\n      \"Epoch: 006/150 | Batch 0000/0351 | Loss: 1.1654\\n\",\n      \"Epoch: 006/150 | Batch 0100/0351 | Loss: 1.3058\\n\",\n      \"Epoch: 006/150 | Batch 0200/0351 | Loss: 1.3688\\n\",\n      \"Epoch: 006/150 | Batch 0300/0351 | Loss: 1.0430\\n\",\n      \"Epoch: 006/150 | Train: 58.77% | Validation: 59.56% | Best Validation (Ep. 006): 59.56%\\n\",\n      \"Time elapsed: 6.02 min\\n\",\n      \"Epoch: 007/150 | Batch 0000/0351 | Loss: 1.0396\\n\",\n      \"Epoch: 007/150 | Batch 0100/0351 | Loss: 1.0419\\n\",\n      \"Epoch: 007/150 | Batch 0200/0351 | Loss: 1.0635\\n\",\n      \"Epoch: 007/150 | Batch 0300/0351 | Loss: 1.0383\\n\",\n      \"Epoch: 007/150 | Train: 66.43% | Validation: 66.68% | Best Validation (Ep. 007): 66.68%\\n\",\n      \"Time elapsed: 7.03 min\\n\",\n      \"Epoch: 008/150 | Batch 0000/0351 | Loss: 0.8938\\n\",\n      \"Epoch: 008/150 | Batch 0100/0351 | Loss: 0.8783\\n\",\n      \"Epoch: 008/150 | Batch 0200/0351 | Loss: 0.9044\\n\",\n      \"Epoch: 008/150 | Batch 0300/0351 | Loss: 0.8579\\n\",\n      \"Epoch: 008/150 | Train: 71.17% | Validation: 70.60% | Best Validation (Ep. 008): 70.60%\\n\",\n      \"Time elapsed: 8.03 min\\n\",\n      \"Epoch: 009/150 | Batch 0000/0351 | Loss: 0.8389\\n\",\n      \"Epoch: 009/150 | Batch 0100/0351 | Loss: 0.7041\\n\",\n      \"Epoch: 009/150 | Batch 0200/0351 | Loss: 0.6849\\n\",\n      \"Epoch: 009/150 | Batch 0300/0351 | Loss: 0.9598\\n\",\n      \"Epoch: 009/150 | Train: 71.66% | Validation: 71.24% | Best Validation (Ep. 009): 71.24%\\n\",\n      \"Time elapsed: 9.04 min\\n\",\n      \"Epoch: 010/150 | Batch 0000/0351 | Loss: 0.8388\\n\",\n      \"Epoch: 010/150 | Batch 0100/0351 | Loss: 0.8849\\n\",\n      \"Epoch: 010/150 | Batch 0200/0351 | Loss: 0.6462\\n\",\n      \"Epoch: 010/150 | Batch 0300/0351 | Loss: 0.7841\\n\",\n      \"Epoch: 010/150 | Train: 74.38% | Validation: 73.74% | Best Validation (Ep. 010): 73.74%\\n\",\n      \"Time elapsed: 10.05 min\\n\",\n      \"Epoch: 011/150 | Batch 0000/0351 | Loss: 0.6932\\n\",\n      \"Epoch: 011/150 | Batch 0100/0351 | Loss: 0.7479\\n\",\n      \"Epoch: 011/150 | Batch 0200/0351 | Loss: 0.7884\\n\",\n      \"Epoch: 011/150 | Batch 0300/0351 | Loss: 0.6257\\n\",\n      \"Epoch: 011/150 | Train: 76.58% | Validation: 75.58% | Best Validation (Ep. 011): 75.58%\\n\",\n      \"Time elapsed: 11.06 min\\n\",\n      \"Epoch: 012/150 | Batch 0000/0351 | Loss: 0.8433\\n\",\n      \"Epoch: 012/150 | Batch 0100/0351 | Loss: 0.8349\\n\",\n      \"Epoch: 012/150 | Batch 0200/0351 | Loss: 0.6882\\n\",\n      \"Epoch: 012/150 | Batch 0300/0351 | Loss: 0.6866\\n\",\n      \"Epoch: 012/150 | Train: 77.64% | Validation: 76.52% | Best Validation (Ep. 012): 76.52%\\n\",\n      \"Time elapsed: 12.07 min\\n\",\n      \"Epoch: 013/150 | Batch 0000/0351 | Loss: 0.5299\\n\",\n      \"Epoch: 013/150 | Batch 0100/0351 | Loss: 0.6179\\n\",\n      \"Epoch: 013/150 | Batch 0200/0351 | Loss: 0.5531\\n\",\n      \"Epoch: 013/150 | Batch 0300/0351 | Loss: 0.7265\\n\",\n      \"Epoch: 013/150 | Train: 78.00% | Validation: 77.24% | Best Validation (Ep. 013): 77.24%\\n\",\n      \"Time elapsed: 13.09 min\\n\",\n      \"Epoch: 014/150 | Batch 0000/0351 | Loss: 0.4452\\n\",\n      \"Epoch: 014/150 | Batch 0100/0351 | Loss: 0.5744\\n\",\n      \"Epoch: 014/150 | Batch 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min\\n\",\n      \"Epoch: 022/150 | Batch 0000/0351 | Loss: 0.4494\\n\",\n      \"Epoch: 022/150 | Batch 0100/0351 | Loss: 0.4051\\n\",\n      \"Epoch: 022/150 | Batch 0200/0351 | Loss: 0.5057\\n\",\n      \"Epoch: 022/150 | Batch 0300/0351 | Loss: 0.3307\\n\",\n      \"Epoch: 022/150 | Train: 85.38% | Validation: 80.78% | Best Validation (Ep. 020): 81.80%\\n\",\n      \"Time elapsed: 22.17 min\\n\",\n      \"Epoch: 023/150 | Batch 0000/0351 | Loss: 0.4090\\n\",\n      \"Epoch: 023/150 | Batch 0100/0351 | Loss: 0.5980\\n\",\n      \"Epoch: 023/150 | Batch 0200/0351 | Loss: 0.3950\\n\",\n      \"Epoch: 023/150 | Batch 0300/0351 | Loss: 0.4792\\n\",\n      \"Epoch: 023/150 | Train: 86.38% | Validation: 82.06% | Best Validation (Ep. 023): 82.06%\\n\",\n      \"Time elapsed: 23.17 min\\n\",\n      \"Epoch: 024/150 | Batch 0000/0351 | Loss: 0.3847\\n\",\n      \"Epoch: 024/150 | Batch 0100/0351 | Loss: 0.4703\\n\",\n      \"Epoch: 024/150 | Batch 0200/0351 | Loss: 0.3416\\n\",\n      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\"Epoch: 027/150 | Batch 0000/0351 | Loss: 0.3095\\n\",\n      \"Epoch: 027/150 | Batch 0100/0351 | Loss: 0.3755\\n\",\n      \"Epoch: 027/150 | Batch 0200/0351 | Loss: 0.3470\\n\",\n      \"Epoch: 027/150 | Batch 0300/0351 | Loss: 0.3880\\n\",\n      \"Epoch: 027/150 | Train: 88.26% | Validation: 83.06% | Best Validation (Ep. 027): 83.06%\\n\",\n      \"Time elapsed: 27.19 min\\n\",\n      \"Epoch: 028/150 | Batch 0000/0351 | Loss: 0.3093\\n\",\n      \"Epoch: 028/150 | Batch 0100/0351 | Loss: 0.5271\\n\",\n      \"Epoch: 028/150 | Batch 0200/0351 | Loss: 0.3308\\n\",\n      \"Epoch: 028/150 | Batch 0300/0351 | Loss: 0.4117\\n\",\n      \"Epoch: 028/150 | Train: 86.03% | Validation: 81.22% | Best Validation (Ep. 027): 83.06%\\n\",\n      \"Time elapsed: 28.20 min\\n\",\n      \"Epoch: 029/150 | Batch 0000/0351 | Loss: 0.3505\\n\",\n      \"Epoch: 029/150 | Batch 0100/0351 | Loss: 0.3633\\n\",\n      \"Epoch: 029/150 | Batch 0200/0351 | Loss: 0.3977\\n\",\n      \"Epoch: 029/150 | 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\"Epoch: 039/150 | Train: 92.33% | Validation: 84.20% | Best Validation (Ep. 038): 84.58%\\n\",\n      \"Time elapsed: 39.26 min\\n\",\n      \"Epoch: 040/150 | Batch 0000/0351 | Loss: 0.2201\\n\",\n      \"Epoch: 040/150 | Batch 0100/0351 | Loss: 0.4122\\n\",\n      \"Epoch: 040/150 | Batch 0200/0351 | Loss: 0.2909\\n\",\n      \"Epoch: 040/150 | Batch 0300/0351 | Loss: 0.2352\\n\",\n      \"Epoch: 040/150 | Train: 92.51% | Validation: 84.56% | Best Validation (Ep. 038): 84.58%\\n\",\n      \"Time elapsed: 40.27 min\\n\",\n      \"Epoch: 041/150 | Batch 0000/0351 | Loss: 0.2002\\n\",\n      \"Epoch: 041/150 | Batch 0100/0351 | Loss: 0.2190\\n\",\n      \"Epoch: 041/150 | Batch 0200/0351 | Loss: 0.2712\\n\",\n      \"Epoch: 041/150 | Batch 0300/0351 | Loss: 0.2431\\n\",\n      \"Epoch: 041/150 | Train: 92.84% | Validation: 84.68% | Best Validation (Ep. 041): 84.68%\\n\",\n      \"Time elapsed: 41.27 min\\n\",\n      \"Epoch: 042/150 | Batch 0000/0351 | Loss: 0.1842\\n\",\n      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Train: 98.48% | Validation: 87.62% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 139.90 min\\n\",\n      \"Epoch: 141/150 | Batch 0000/0351 | Loss: 0.0385\\n\",\n      \"Epoch: 141/150 | Batch 0100/0351 | Loss: 0.0582\\n\",\n      \"Epoch: 141/150 | Batch 0200/0351 | Loss: 0.1261\\n\",\n      \"Epoch: 141/150 | Batch 0300/0351 | Loss: 0.0202\\n\",\n      \"Epoch: 141/150 | Train: 98.70% | Validation: 87.38% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 140.78 min\\n\",\n      \"Epoch: 142/150 | Batch 0000/0351 | Loss: 0.0530\\n\",\n      \"Epoch: 142/150 | Batch 0100/0351 | Loss: 0.0471\\n\",\n      \"Epoch: 142/150 | Batch 0200/0351 | Loss: 0.1733\\n\",\n      \"Epoch: 142/150 | Batch 0300/0351 | Loss: 0.0358\\n\",\n      \"Epoch: 142/150 | Train: 98.41% | Validation: 86.80% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 141.67 min\\n\",\n      \"Epoch: 143/150 | Batch 0000/0351 | Loss: 0.0332\\n\",\n      \"Epoch: 143/150 | 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Validation: 86.60% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 144.38 min\\n\",\n      \"Epoch: 146/150 | Batch 0000/0351 | Loss: 0.0297\\n\",\n      \"Epoch: 146/150 | Batch 0100/0351 | Loss: 0.0962\\n\",\n      \"Epoch: 146/150 | Batch 0200/0351 | Loss: 0.0912\\n\",\n      \"Epoch: 146/150 | Batch 0300/0351 | Loss: 0.0400\\n\",\n      \"Epoch: 146/150 | Train: 97.96% | Validation: 86.04% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 145.29 min\\n\",\n      \"Epoch: 147/150 | Batch 0000/0351 | Loss: 0.0399\\n\",\n      \"Epoch: 147/150 | Batch 0100/0351 | Loss: 0.0214\\n\",\n      \"Epoch: 147/150 | Batch 0200/0351 | Loss: 0.0363\\n\",\n      \"Epoch: 147/150 | Batch 0300/0351 | Loss: 0.0488\\n\",\n      \"Epoch: 147/150 | Train: 98.11% | Validation: 86.34% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 146.17 min\\n\",\n      \"Epoch: 148/150 | Batch 0000/0351 | Loss: 0.0603\\n\",\n      \"Epoch: 148/150 | Batch 0100/0351 | Loss: 0.0407\\n\",\n      \"Epoch: 148/150 | Batch 0200/0351 | Loss: 0.0466\\n\",\n      \"Epoch: 148/150 | Batch 0300/0351 | Loss: 0.0600\\n\",\n      \"Epoch: 148/150 | Train: 98.57% | Validation: 87.24% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 147.06 min\\n\",\n      \"Epoch: 149/150 | Batch 0000/0351 | Loss: 0.0535\\n\",\n      \"Epoch: 149/150 | Batch 0100/0351 | Loss: 0.0428\\n\",\n      \"Epoch: 149/150 | Batch 0200/0351 | Loss: 0.0309\\n\",\n      \"Epoch: 149/150 | Batch 0300/0351 | Loss: 0.0754\\n\",\n      \"Epoch: 149/150 | Train: 98.42% | Validation: 87.24% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 148.01 min\\n\",\n      \"Epoch: 150/150 | Batch 0000/0351 | Loss: 0.0446\\n\",\n      \"Epoch: 150/150 | Batch 0100/0351 | Loss: 0.0869\\n\",\n      \"Epoch: 150/150 | Batch 0200/0351 | Loss: 0.0135\\n\",\n      \"Epoch: 150/150 | Batch 0300/0351 | Loss: 0.0404\\n\",\n      \"Epoch: 150/150 | Train: 98.73% | Validation: 87.46% | Best Validation (Ep. 140): 87.62%\\n\",\n      \"Time elapsed: 149.14 min\\n\",\n      \"Total Training Time: 149.14 min\\n\",\n      \"Test accuracy 86.33%\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\\n\",\n    \"\\n\",\n    \"minibatch_loss_list, train_acc_list, valid_acc_list = train_classifier_simple_v2(\\n\",\n    \"    model=model,\\n\",\n    \"    num_epochs=NUM_EPOCHS,\\n\",\n    \"    train_loader=train_loader,\\n\",\n    \"    valid_loader=valid_loader,\\n\",\n    \"    test_loader=test_loader,\\n\",\n    \"    optimizer=optimizer,\\n\",\n    \"    best_model_save_path='mobilenet-v2-best-1.pt',\\n\",\n    \"    device=DEVICE,\\n\",\n    \"    scheduler_on='valid_acc',\\n\",\n    \"    logging_interval=100)\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"plot_training_loss(minibatch_loss_list=minibatch_loss_list,\\n\",\n    \"                   num_epochs=NUM_EPOCHS,\\n\",\n    \"                   iter_per_epoch=len(train_loader),\\n\",\n    \"                   results_dir=None,\\n\",\n    \"                   averaging_iterations=200)\\n\",\n    \"plt.show()\\n\",\n    \"\\n\",\n    \"plot_accuracy(train_acc_list=train_acc_list,\\n\",\n    \"              valid_acc_list=valid_acc_list,\\n\",\n    \"              results_dir=None)\\n\",\n    \"plt.ylim([60, 100])\\n\",\n    \"plt.show()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Test accuracy: 86.66%\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"model.load_state_dict(torch.load('mobilenet-v2-best-1.pt'))\\n\",\n    \"model.eval()\\n\",\n    \"test_acc = compute_accuracy(model, test_loader, device=DEVICE)\\n\",\n    \"print(f'Test accuracy: {test_acc:.2f}%')\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 15 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model.cpu()\\n\",\n    \"unnormalizer = UnNormalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))\\n\",\n    \"class_dict = {0: 'airplane',\\n\",\n    \"              1: 'automobile',\\n\",\n    \"              2: 'bird',\\n\",\n    \"              3: 'cat',\\n\",\n    \"              4: 'deer',\\n\",\n    \"              5: 'dog',\\n\",\n    \"              6: 'frog',\\n\",\n    \"              7: 'horse',\\n\",\n    \"              8: 'ship',\\n\",\n    \"              9: 'truck'}\\n\",\n    \"\\n\",\n    \"show_examples(model=model, data_loader=test_loader, unnormalizer=unnormalizer, class_dict=class_dict)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {\n      \"needs_background\": \"light\"\n     },\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"mat = compute_confusion_matrix(model=model, data_loader=test_loader, device=torch.device('cpu'))\\n\",\n    \"plot_confusion_matrix(mat, class_names=class_dict.values())\\n\",\n    \"plt.show()\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.8.8\"\n  },\n  \"toc\": {\n   \"nav_menu\": {},\n   \"number_sections\": true,\n   \"sideBar\": true,\n   \"skip_h1_title\": false,\n   \"title_cell\": \"Table of Contents\",\n   \"title_sidebar\": \"Contents\",\n   \"toc_cell\": false,\n   \"toc_position\": {},\n   \"toc_section_display\": true,\n   \"toc_window_display\": false\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n"
  }
]