Repository: lexfridman/mit-deep-learning Branch: master Commit: c5cc240d3482 Files: 10 Total size: 1.7 MB Directory structure: gitextract_cpu21i3y/ ├── LICENSE.md ├── README.md ├── tutorial_deep_learning_basics/ │ └── deep_learning_basics.ipynb ├── tutorial_driving_scene_segmentation/ │ └── tutorial_driving_scene_segmentation.ipynb ├── tutorial_gans/ │ └── tutorial_gans.ipynb └── tutorials_previous/ ├── 1_python_perceptron.ipynb ├── 2_tensorflow_intro.ipynb ├── 3_tensorflow_simple_neural_network.ipynb ├── 4_tensorflow_mnist.ipynb └── 5_tensorflow_traffic_light_classification.ipynb ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE.md ================================================ MIT License Copyright (c) 2019 Lex Fridman Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # MIT Deep Learning This repository is a collection of tutorials for [MIT Deep Learning](https://deeplearning.mit.edu/) courses. More added as courses progress. ## Tutorial: Deep Learning Basics This tutorial accompanies the [lecture on Deep Learning Basics](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U). It presents several concepts in deep learning, demonstrating the first two (feed forward and convolutional neural networks) and providing pointers to tutorials on the others. This is a good place to start. Links: \[ [Jupyter Notebook](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_deep_learning_basics/deep_learning_basics.ipynb) \] \[ [Google Colab](https://colab.research.google.com/github/lexfridman/mit-deep-learning/blob/master/tutorial_deep_learning_basics/deep_learning_basics.ipynb) \] \[ [Blog Post](https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0) \] \[ [Lecture Video](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U) \] ## Tutorial: Driving Scene Segmentation This tutorial demostrates semantic segmentation with a state-of-the-art model (DeepLab) on a sample video from the MIT Driving Scene Segmentation Dataset. Links: \[ [Jupyter Notebook](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb) \] \[ [Google Colab](https://colab.research.google.com/github/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb) \] ## Tutorial: Generative Adversarial Networks (GANs) This tutorial explores generative adversarial networks (GANs) starting with BigGAN, the state-of-the-art conditional GAN. Links: \[ [Jupyter Notebook](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_gans/tutorial_gans.ipynb) \] \[ [Google Colab](https://colab.research.google.com/github/lexfridman/mit-deep-learning/blob/master/tutorial_gans/tutorial_gans.ipynb) \] ## DeepTraffic Deep Reinforcement Learning Competition DeepTraffic is a deep reinforcement learning competition. The goal is to create a neural network that drives a vehicle (or multiple vehicles) as fast as possible through dense highway traffic. Links: \[ [GitHub](https://github.com/lexfridman/deeptraffic) \] \[ [Website](https://selfdrivingcars.mit.edu/deeptraffic) \] \[ [Paper](https://arxiv.org/abs/1801.02805) \] ## Team - [Lex Fridman](https://lexfridman.com) - [Li Ding](https://www.mit.edu/~liding/) - [Jack Terwilliger](https://www.mit.edu/~jterwill/) - [Michael Glazer](https://www.mit.edu/~glazermi/) - [Aleksandr Patsekin](https://www.mit.edu/~patsekin/) - [Aishni Parab](https://www.mit.edu/~aishni/) - [Dina AlAdawy](https://www.mit.edu/~aladawy/) - [Henri Schmidt](https://www.mit.edu/~henris/) ================================================ FILE: tutorial_deep_learning_basics/deep_learning_basics.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![MIT Deep Learning](https://deeplearning.mit.edu/files/images/github/mit_deep_learning.png)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "\n", " \n", " \n", " \n", " \n", "\n", "
\n", " \n", " Visit MIT Deep Learning\n", " Run in Google Colab\n", " View Source on GitHub\n", " Watch YouTube Videos
" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "# Deep Learning Basics\n", "\n", "This tutorial accompanies the [lecture on Deep Learning Basics](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U) given as part of [MIT Deep Learning](https://deeplearning.mit.edu). Acknowledgement to amazing people involved is provided throughout the tutorial and at the end. You can watch the video on YouTube:\n", "\n", "[![Deep Learning Basics](https://i.imgur.com/FfQVV8q.png)](https://www.youtube.com/watch?list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf&v=O5xeyoRL95U)\n", "\n", "In this tutorial, we mention seven important types/concepts/approaches in deep learning, introducing the first 2 and providing pointers to tutorials on the others. Here is a visual representation of the seven:\n", "\n", "![Deep learning concepts](https://i.imgur.com/EAl47rp.png)\n", "\n", "At a high-level, neural networks are either encoders, decoders, or a combination of both. Encoders find patterns in raw data to form compact, useful representations. Decoders generate new data or high-resolution useful infomation from those representations. As the lecture describes, deep learning discovers ways to **represent** the world so that we can reason about it. The rest is clever methods that help use deal effectively with visual information, language, sound (#1-6) and even act in a world based on this information and occasional rewards (#7).\n", "\n", "1. **Feed Forward Neural Networks (FFNNs)** - classification and regression based on features. See [Part 1](#Part-1:-Boston-Housing-Price-Prediction-with-Feed-Forward-Neural-Networks) of this tutorial for an example.\n", "2. **Convolutional Neural Networks (CNNs)** - image classification, object detection, video action recognition, etc. See [Part 2](#Part-2:-Classification-of-MNIST-Dreams-with-Convolution-Neural-Networks) of this tutorial for an example.\n", "3. **Recurrent Neural Networks (RNNs)** - language modeling, speech recognition/generation, etc. See [this TF tutorial on text generation](https://www.tensorflow.org/tutorials/sequences/text_generation) for an example.\n", "4. **Encoder Decoder Architectures** - semantic segmentation, machine translation, etc. See [our tutorial on semantic segmentation](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb) for an example.\n", "5. **Autoencoder** - unsupervised embeddings, denoising, etc.\n", "6. **Generative Adversarial Networks (GANs)** - unsupervised generation of realistic images, etc. See [this TF tutorial on DCGANs](https://github.com/tensorflow/tensorflow/blob/r1.11/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb) for an example.\n", "7. **Deep Reinforcement Learning** - game playing, robotics in simulation, self-play, neural arhitecture search, etc. We'll be releasing notebooks on this soon and will link them here.\n", "\n", "There are selective omissions and simplifications throughout these tutorials, hopefully without losing the essence of the underlying ideas. See Einstein quote..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 0: Prerequisites:\n", "\n", "We recommend that you run this this notebook in the cloud on Google Colab (see link with icon at the top) if you're not already doing so. It's the simplest way to get started. You can also [install TensorFlow locally](https://www.tensorflow.org/install/). But, again, simple is best (with caveats):\n", "\n", "![Einstein](https://i.imgur.com/vfPDHGN.png)\n", "\n", "[tf.keras](https://www.tensorflow.org/guide/keras) is the simplest way to build and train neural network models in TensorFlow. So, that's what we'll stick with in this tutorial, unless the models neccessitate a lower-level API.\n", "\n", "Note that there's [tf.keras](https://www.tensorflow.org/guide/keras) (comes with TensorFlow) and there's [Keras](https://keras.io/) (standalone). You should be using [tf.keras](https://www.tensorflow.org/guide/keras) because (1) it comes with TensorFlow so you don't need to install anything extra and (2) it comes with powerful TensorFlow-specific features." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.12.0\n" ] } ], "source": [ "# TensorFlow and tf.keras\n", "import tensorflow as tf\n", "from tensorflow import keras\n", "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense\n", "\n", "# Commonly used modules\n", "import numpy as np\n", "import os\n", "import sys\n", "\n", "# Images, plots, display, and visualization\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sns\n", "import cv2\n", "import IPython\n", "from six.moves import urllib\n", "\n", "print(tf.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: Boston Housing Price Prediction with Feed Forward Neural Networks\n", "\n", "Let's start with using a fully-connected neural network to do predict housing prices. The following image highlights the difference between regression and classification (see part 2). Given an observation as input, **regression** outputs a continuous value (e.g., exact temperature) and classificaiton outputs a class/category that the observation belongs to.\n", "\n", "\"classification_regression\"\n", "\n", "For the Boston housing dataset, we get 506 rows of data, with 13 features in each. Our task is to build a regression model that takes these 13 features as input and output a single value prediction of the \"median value of owner-occupied homes (in $1000).\"\n", "\n", "Now, we load the dataset. Loading the dataset returns four NumPy arrays:\n", "\n", "* The `train_images` and `train_labels` arrays are the *training set*—the data the model uses to learn.\n", "* The model is tested against the *test set*, the `test_images`, and `test_labels` arrays." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "(train_features, train_labels), (test_features, test_labels) = keras.datasets.boston_housing.load_data()\n", "\n", "# get per-feature statistics (mean, standard deviation) from the training set to normalize by\n", "train_mean = np.mean(train_features, axis=0)\n", "train_std = np.std(train_features, axis=0)\n", "train_features = (train_features - train_mean) / train_std" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "### Build the model\n", "\n", "Building the neural network requires configuring the layers of the model, then compiling the model. First we stack a few layers together using `keras.Sequential`. Next we configure the loss function, optimizer, and metrics to monitor. These are added during the model's compile step:\n", "\n", "* *Loss function* - measures how accurate the model is during training, we want to minimize this with the optimizer.\n", "* *Optimizer* - how the model is updated based on the data it sees and its loss function.\n", "* *Metrics* - used to monitor the training and testing steps.\n", "\n", "Let's build a network with 1 hidden layer of 20 neurons, and use mean squared error (MSE) as the loss function (most common one for regression problems):" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [], "source": [ "def build_model():\n", " model = keras.Sequential([\n", " Dense(20, activation=tf.nn.relu, input_shape=[len(train_features[0])]),\n", " Dense(1)\n", " ])\n", "\n", " model.compile(optimizer=tf.train.AdamOptimizer(), \n", " loss='mse',\n", " metrics=['mae', 'mse'])\n", " return model" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "### Train the model\n", "\n", "Training the neural network model requires the following steps:\n", "\n", "1. Feed the training data to the model—in this example, the `train_features` and `train_labels` arrays.\n", "2. The model learns to associate features and labels.\n", "3. We ask the model to make predictions about a test set—in this example, the `test_features` array. We verify that the predictions match the labels from the `test_labels` array. \n", "\n", "To start training, call the `model.fit` method—the model is \"fit\" to the training data:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", "....................................................................................................\n", ".............................................................................................\n", "Final Root Mean Square Error on validation set: 2.359\n" ] } ], "source": [ "# this helps makes our output less verbose but still shows progress\n", "class PrintDot(keras.callbacks.Callback):\n", " def on_epoch_end(self, epoch, logs):\n", " if epoch % 100 == 0: print('')\n", " print('.', end='')\n", "\n", "model = build_model()\n", "\n", "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=50)\n", "history = model.fit(train_features, train_labels, epochs=1000, verbose=0, validation_split = 0.1,\n", " callbacks=[early_stop, PrintDot()])\n", "\n", "hist = pd.DataFrame(history.history)\n", "hist['epoch'] = history.epoch\n", "\n", "# show RMSE measure to compare to Kaggle leaderboard on https://www.kaggle.com/c/boston-housing/leaderboard\n", "rmse_final = np.sqrt(float(hist['val_mean_squared_error'].tail(1)))\n", "print()\n", "print('Final Root Mean Square Error on validation set: {}'.format(round(rmse_final, 3)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's plot the loss function measure on the training and validation sets. The validation set is used to prevent overfitting ([learn more about it here](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit)). However, because our network is small, the training convergence without noticeably overfitting the data as the plot shows." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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ELbpU0TGjq9PVtTg7yOYGm7DPGJPaok0QFwIzVPXL9gNuldC33Z/1bdstGlSUnc5mq2IyxqS4aBNEQFU3bX/QbYcIxDakFJRZ4Dw3bQGgJCfIZqtiMsakuGgTxM4+7eyTMKvYeW6sBqA4J8iWhlba2nq+opMxxiRKtL2YxonI1i6OC85gt74tmOOsCdHoFLKKstMJtylbm0MdczMZY0yqiXbBIF+8A0lpIpBVAo2bAaeKCWBTfaslCGNMyrIFg2IlqxganARRlO0kBevqaoxJZZYgYiWrqKMEUZydDsBmW5vaGJPCLEHESva2KqZit4rJejIZY1JZtEuOztrZz905mvq2rOKORupCt93BxkIYY1JZtL2Yct3nUcAk4Gl3fzrweqyDSklZJc5I6kiIoD9AfmaAahtNbYxJYdH2YroJQEReBCaoap27fyPweNyiSyVZRc5z0xbI6UdxdpBNVsVkjElhXtsghvL1gXGtQHnMokll7YPlGpxqpuKcINVWxWSMSWFep/v+KzBfRJ50908FHoxtSCkqu8R5btzW1fXLTQ0JDMgYY3rGU4JQ1V+LyHPAIThrRl+sqh/GJbJU0zHdRnsJIp0Fq7YkMCBjjOkZT1VMIpIOjAaygQJguq0J4cr6egmiX2461Y2ttIbbEhiUMcZ0n9c2iHnAKUAYaOj0MO2N1O6EfYMLMlGF9bVNCQzKGGO6z2sbRJmqHheXSFKdLwDp+R2N1IMLMgFYu6WJYcXZiYzMGGO6xWsJ4m0RGRuXSHqD7OKOKqbBhU6CqKixEoQxJjV5LUFMAy4SkS+BFpzpvlVV94t5ZKkoa1uCGJifiQisswRhjElRXhPE8XGJorfIKoGtFQAE/Wn0y01n7RZLEMaY1OS1m+vqeAXSK2QXw/pFHbuDCjJZayUIY0yK8lqCQEQKgZF0WklOVW0+JoCcAVBfCW0RSPMxuCCTT9bWJjoqY4zpFq/jIL6LMznfC8BN7vONsQ8rReUOAI1s68lUmMn6mmZbm9oYk5K89mK6Amc219WqegSwP1AV86hSVe5A57luPQBlBZm0RtqosoWDjDEpyGuCaFbVZnBGVavqEpwpwA10ShAbAKcNArB2CGNMSvKaICpEpAB4CnhJROYB62IfVorKHeA8uyWI9rEQ1pPJGJOKvPZiOs3dvFFEXgHycNohDEBOP0A6ShDto6krLEEYY1KQ10bqs0SkfXW5Q4GLgX1jHlWq8gUguxTqnEJVbkaAouwga6ptuipjTOrxWsX0C1WtE5FpwDHAA8CdsQ8rheUPhtq1HbvDS7JZWWUJwhiTerwmiIj7fCJwp6rOA4KxDSnFFQyDLas6dkeUZLPSFg4yxqQgrwlirYjcBZwD/NNdH8LrNXq3wnKo/QranHUghpdmU1XXQl1zKLFxGWOMR14/3M/GaZQ+VlVrgCLgqphHlcoKh0GktaMn04gSZ6rvVZsaExmVMcZ45nWqjZ+5z2NEpPPxF6O9gIhk4IzGTnfv/zdV/ZWIDAfm4iSdD4ALVLXVY3yJVzDMea5ZDfmDGV6SA8DKTfWMLctPYGDGGOON1xJE51XkIjizu5Z7vEYLcKSqjgPGA8eJyBTgN8D/qupIYAtwicfrJofCcufZbYcYVpyFCHxp7RDGmBTjdRzE/3TeF5HfAU97vIYC9e5uwH0ocCRwnnv8AZw5nv7k5dpJoWAopAVg0xcAZAScSftWWE8mY0yK6WkDcxYwwuuLRMQnIouASuAlYAVQo6ph95QKYPAOXnupiCwQkQVVVUk4DZQvACV7QeXnHYdGD8hl6YatCQzKGGO88zpQ7hMR+dh9fAYsBW7zelNVjajqeKAMmAzs3dVpO3jt3ao6UVUnlpaWer317tFvb6hc3LE7ekAeK6oaaAlHdvIiY4xJLl4bqU/qtB0GNnb61u+ZqtaIyKvAFKBARPzu9cpI5Tme+u0Nn/4NWuogPZfRA3OJtCnLK+vZd5A1VBtjUoOnEoS7olwBMB04DdjH6w1FpNSd8A8RyQSOBj4HXgHOdE+bCczzeu2k0c/9Z6laCjglCIAl6+sSFZExxnjmtYrpCmAO0M99zBGRH3u850DgFRH5GHgfeElVnwGuAWaJyHKgGLjH43WTR7/RzrNbzVRenEW6P40l1g5hjEkhXquYLgEOVNUGABH5DfAO8H/RXkBVP8ZZaGj74ytx2iNSX0E5+DM7Gqr9vjT26p/L51aCMMakEK+9mIRt8zHhbssOzu270tKcUkSnhuoxg/P5qKLGlh81xqQMrwniPuA9EblRRG4E3iWVq4Liqd8+sHFbgpg4rJC65jDLKut38iJjjEkeXhupf4+zBkQ1zmjni1X11ngElvIGjoOGyo6pvyeWFwKwYHV1IqMyxpioeR4op6ofqOrtqnqbqn4Yj6B6hbKJznPFfACGFmVRkhNk4aotCQzKGGOi56mR2p3e+wyc+Zc6Xquqs2MbVi/Qfyz4M6BiAex7GiLCAcMKWbDaEoQxJjV4LUHMA07BGSTXeeI+sz1/EAaOh4r3Ow4dOLyYNdWNfFVtU38bY5Kf126uZap6XFwi6Y3KJsL8P0O4FfxBDt2rBIA3lm3ivAOHJjg4Y4zZOa8liLdFZGxcIumNRhwOkRZY+iwAe5TmMCg/g9e/SMJJBo0xZjtRJYj2SfqAacAHIrLUnbCv/bjpyh5HQl4ZLHoEABHh0L1KeWvFJsKRtgQHZ4wxOxdtFdPpQOqt7pZoaT4Ycxq8+ydorIasIg4ZWcrc97/io4oaDhhWlOgIjTFmh6KtYnpUVVfv6BHXCFPdmDOhLQyLnbkHp+1ZQprAy59XJjgwY4zZuWgThE2n0V0Dx0Hp3vDm7yHcQn5WgGkjS3n6o3U27YYxJqlFW8VUKiKzdvRDd4S16YoIfOs/Yc6Z8PGjMOFCTh0/iFmPfcTCNVuYVG7VTMaY5BRtCcIH5AC5O3iYndnjKCibBC/PhuZajt13AJkBH099uDbRkRljzA5FW4JYb6OleyAtDU74Ldx9OPzrJrJP/B++tW9/nv1kPb+avi9Bf0+XBjfGmNizNojdZdD+cNCPYcE98N5dnLr/YGoaQ/x7iTVWG2OSU7QJ4qi4RtFXHD0bRh4LL8/mkIHKoPwM7n3ry0RHZYwxXYo2QfxrVyeIyAc9jKX3S0uDY38N4Wb8b/w3l0wbzvwvq/lgjU3gZ4xJPtG2Qey9ixHTAuTHIJ7er2QkTLwY3v8LF46p486Mk7nrtRXcdcHEREdmjDFfE22CGB3FOZFdn2IAOP6/IauEwGu38ETeMg5d/FNWVNWzR2lOoiMzxpgOUSUIGy0dY2k+OOI6SM9hyIs3MDd4M/98vpEfX3B2oiMzxpgO1r8ykab+CKbfxlh/BT9e8T0a/vkLUBtdbYxJDlEnCHEMiWcwfY4IHHARmy96k6VtQ8iefzt8NDfRURljDOAhQaiqAk/FMZY+a8iQch6eMIf32kaj834IL/zcShLGmITzWsX0rohMikskfdxlR47iysiVLM8cB+/8AT79e6JDMsb0cV4TxBHAOyKywhYMiq3+eRmcPG08x1X/hLricfD3S5x1JIwxJkG8rkl9fFyiMABcedRe/POT9VzU9BMe2/NhfM9fB6WjYfhhziA7Y4zZjTx96rjdXQuA6e6jwLrAxk5m0Mf/O20sC6uD/LHwasjpD389Ff58BIRbEh2eMaaP8ZQgROQKYA7Qz308JCI/jkdgfdUhI0s5Y0IZt75VxeLTX4JDr4b1i+DDvyY6NGNMH+O13uIS4EBV/aWq/hKYAnwv9mH1bb88aR+Ks4PMenoVLYdcA2WT4fnrYN2HiQ7NGNOHeE0Qwten1IhgU4HHXH5WgFvOGMuSDXX85vkv4Jy/QmYh/O0SqPkq0eEZY/oIrwniPuA9EblRRG4E3gXuiXlUhiNH9+eig8q5960veblC4PQ/Q0MVPHgKbFmV6PCMMX2Ap5HUwOPAxUA1sAW4WFVvjVNsfd61x49m74F5XPW3j6nuPxVmzIUtX8KLv0h0aMaYPsDzSGpV/UBVb1fV21TVKsXjKCPg49ZzxrO1KcTsf3yGDjsIJsyEz5+Gt25PdHjGmF7ORlInuVEDcvnRkXvy1KJ1PPD2Kmeq8H1OhZd+AU9dBm02y7oxJj68DpQ7Avi+iKwGGnAaqFVV94v2Au6Efw8CA4A24G5VvU1EioBHgXJgFXC2qtpSa8DlR47k07Vbmf3MYoYUZXHUaXdBMAcWPQR16532ieySRIdpjOllvLZB/ADYAzgSZ6DcSe6zF2Hgp6q6N0432R+KyD7AtcDLqjoSeNndN0BamnD7jPHsOyifHz/yIZ9WtsCpdzilidVvw5Pfh7a2RIdpjOllvLZB/K+qrt7+4eWGqrpeVT9wt+uAz4HBwCnAA+5pDwCnerlub5cV9HPPzIkUZgX5zv3vs66mCQ78PnzrZlj+L3jl15YkjDExldA2CBEpB/YH3gP6q+p6cJIIzkjtrl5zqYgsEJEFVVVVsQolJfTLy+DeiybR1BrhO/e/T11zCCZ9F/aeDm/8Dh67ALauT3SYxpheojuzub4bi9lcRSQH+DtwpapujfZ1qnq3qk5U1YmlpaXduXVKGzUglz9+ewLLK+v54cMfEmpTOOsBOOpXsOxFuOtQqK1IdJjGmF7Aa4I4HhhBz9ogEJEATnKYo6pPuIc3ishA9+cDgUqv1+0rDhlZyq9PG8PrX1Txy3mfoZIGh8yCS1+Fljr459W24JAxpseiShAicjV0zOY6ebv2h+97uaHb2H0P8Lmq/r7Tj54GZrrbM4F5Xq7b15wzaSg/PGIPHpm/hrteX+kc7L8vHHEdLH3WFhwyxvRYtCWIczttX7fdz47zeM+DgQuAI0Vkkfs4AbgFOEZElgHHuPtmJ356zCimjxvELc8t4fEF7hxNU37oTO735Pfhk78lNkBjTEqLdhyE7GC7q/2dUtU3d/Kao7xcq69LSxN+d9Z+1DS2ct0TnzC8JJuJ5UVw7sPwyDnw9OUweAIUjUh0qMaYFBRtCUJ3sN3VvtmN0v0+/nDeBMoKM/nBQx9QsaURckrh7AfB54dnZiU6RGNMioo2QYwTka0iUgfs526374+NY3wmCvmZAf584URawhEuvGc+1Q2tkF/mLDa08hVY+MCuL2KMMduJKkGoqk9V81Q1V1X97nb7fiDeQZpdG9k/l3svmkRFTROXzVlISzjijJEYciD88yqoXpnoEI0xKcZrN1eTxCaVF/HfZ+zHuyur+cmji4j40p0xEr4gPGczlxhjvLEE0cucuv9gbjhxb/75yQZ+Me9TNHcATLsSlr0A8/9s4yOMMVHzOpurSQHfPWQE1Q2t/PHVFRRlBfnZId+Bjx+Df/7MWZXuiOsTHaIxJgVYguilrjp2FFsaW/nDK8upbwnzq8veRZ7+Ebz2G6cBe8KFiQ7RGJPkPCUIdxT0+cAIVZ0tIkOBAao6Py7RmW4TEW4+dSwBXxr3v72KgE+49sRb8dVtgH9cCVklMPqERIdpjEliXtsg/ghMBWa4+3XAHTGNyMSML0246eR9+faUofz5jS+55qnPiZx5PwwYA4+eDytfTXSIxpgk5jVBHKiqPwSaAdwV34Ixj8rEjIjwn6eM4SdH78XfFlYwa94KWs+fBwXDYO75ULU00SEaY5KU1wQREhEf7uhpESnFWTbUJDER4YqjR3L1caOYt2gd331sGY3nzQN/Otx7LFR/megQjTFJyGuCuB14EugnIr8G3gT+X8yjMnFx2eF78pszxvLmsirOf7yC+hlPQVsEHj7HFhoyxnyD1zWpXweuBv4LWA+cqqqPxyk2EwfnTBrKH88/gE8qajnmoSrWHv1HZ4Ghe78FGz5JdHjGmCTidU3qp1R1iareoap/UNXP4xibiZPjxgzg0e9PpTkU4eTnM/hg6u3QVAtzzobKJYkOzxiTJBK6JrVJnAOGFTL30qkMLMjgzJcyeX3ag85qdPccA1Vf2IhrY0y31qR+JxZrUpvEGzUgl0cvncr4IQVc+GwDD+39J2jZCndMgofPtiRhTB/ndST18XGJwiRMdrqfh783hZ8/+Sk3vFdB45DrmZkzn/RlL8JnT8CYMxIdojEmQTwlCFVdLSKFwEggo9OPVsdt06WjAAAat0lEQVQ0KrNbZQR8/O6s/divLJ//fEZ4LH8880qqyH7i+9B/DJSOSnSIxpgE8FTFJCLfxenJ9AJwk/t8Y+zDMrubiDDzoHIevGQyNSEfR669lCZfNtwx2VlPwqqbjOlzvLZBXAFMAlar6hHA/kBVzKMyCXPQHiW8etXhjN9nNCfXu7O+zr8b/vUriIQTG5wxZrfymiCaVbUZQETSVXUJYPUPvUxOup87v30A3z39eA7lLzzRdii8dRv64MkQbkl0eMaY3cRrgqgQkQLgKeAlEZkHrIt9WCbRRIRzJg3lsZ9M5+nhv+Ca0PeQ1W+x+dU/JTo0Y8xuItrNumUROQzIB55T1VBMo4rSxIkTdcGCBYm4dZ8SaVMefncVI16YyWQ+5dl9f8/xp19Aut+X6NCMMR6JyEJVnRjVuV4ShIj8sqvjqjo76ovEkCWI3auqqpLmv5zAgOaV3Jz5U4454/tMG1mS6LCMMR54SRBeq5gaOj0iOOMiyj1ew6So0tJ+DLn8BSI5A/lp8x/59/038pNH3qeyrjnRoRlj4qDbVUzgNFQDT6vqsbELKXpWgkiQVW+iD56CtIW5P3I8/+O7mKuPHcV5Bw7DlyaJjs4YsxPxLEFsLwsY0cNrmFRTPg25ajlkl3KR7znuzryD+59+kdP/+Bafrq1NdHTGmBjxOlDuE3cOpo9F5DNgKXBbfEIzSS2zEGYtgcOuYWrTa7yQ9UuKtnzESf/3JpfNWch7KzcnOkJjTA95baQe1mk3DGxU1YSNnrIqpiRRtRTmnEnEl8kf9/gTd7xTSXOojRmTh3L2xDL2H1qY6AiNMa649WJKNpYgksiSZ2HueQA0HPt7bl4/mb8vrKA10sb0cYP4wWEj2HdQfoKDNMbEs5vrrJ39XFV/H/XFYsASRJJZ9SbcfyLk9Icz72PrgMnc8cpy/vrOahpbI4wdnM8PDtuDY/ftj9/X0+YvY0x3xDNBPIwzF9PT7qHpOJP3fQWgqjd5C7VnLEEkoY2LYe4M2LIKEDhkFrUHXcfc+WuY+/5XfLmpgdLcdE6fMJizDhjCnv1yEh2xMX1KPBPEi8AZqlrn7ucCj6vqcd2KtIcsQSSp1gZ4/lr48CHQNrhwHow4nFCkjVeXVvHo+1/xytJKIm1KeXEWp+4/mDMPKKOsMCvRkRvT68UzQSwBxqlqi7ufDnykqqO7FWkPWYJIcuEWuG0cIDDlB1AyCsomQnYJlXXNPL1oHa8sreTtFU6PpwlDCxmQn8H0/QZxxOhSm8rDmDiIZ4L4OXA28KR76DTgUVX9fx6ucS9wElCpqmPcY0XAozijslcBZ6vqll1dyxJECqhYAM9dA2vdv1NhOcz8B+QOAp+zXlXFlkb+vnAtz326niUb6gDIzwxw9N792a8sn6l7FLNnaQ5pNgjPmB6Lay8mEZkAHAIo8Iaqfujx9YcC9cCDnRLEfwPVqnqLiFwLFKrqNbu6liWIFLL2A5j/Z1g8z6l20jY4ZrZTsuiksTXM859u4OUllby2tIr6FqcXdbo/jRPGDmRIYSaDCjKZNrLEqqSM6YaYJwgRmQR8paob3P2ZwBk43/ZvVNVqjwGWA890ShBLgcNVdb2IDAReVdVdrjNhCSIF1ayBN/4HFt4PCIw6AcbPgL2nf+PU1nAbX2ysY9FXNXxcUcPzn26gviVMm0LQl8b+QwsYUZrNyH65TB5exJCiLPIzA7v9VzImlcQjQXwAHK2q1W4JYC7wY2A8sLeqnukxwHK+niBqVLWg08+3qGqXo6tE5FLgUoChQ4cesHq1LYedklobYN4P4TO3tnLid2DceTBk0k5ftqWhlfdXVfPm8k18sraWVZsa2NLozDYf8AkjSnIYNSCXQQWZFGUHOGRkKXuU5hD0W7daYyA+CeIjVR3nbt8BVKnqje7+IlUd7zHAcrqZIDqzEkSKU4W1C+HZn8L6Rc4x8cEZf4Exp0d9mYotjSxcvYXP1m1lRWU9H6+tpaaxlVDEeW/704RBBZmUFWayX1kBtU0h8jL9nDxuEHkZAQqyAuRmWMnD9A1eEoQ/ymv6RMTvTqtxFO43eI/X2JmNIjKwUxVTZQyuaZKdiNOraeY/4L27oKESPpoLz/zEadweczoMHAe+nX94lxVmUVaYxSnjB3ccU1VWb27k/VXVrNrcwOfr61hRVc+7KzfjT0ujNdLGXa+tBCA76GPM4HwGF2ZSmBUkO+hjSFEWgwoyycsI0C8vnX656YhYI7npW6L9cH8EeE1ENgFNwBsAIrInEIvpO58GZgK3uM/zYnBNkyoy8uCwq5ztSd+DeZfBu3c4j7QA5A6AaVfCpO86pQ5wkstOiAjlJdmUl2R/7XikTWkJR/joq1oq65qpqmtheWU9K6saeHfFZjY1tNIabvvG9bKDPoYWZ5OT7mNYcTZBfxr5mQGGF2eTnxVgj9JsCrKClOSkx+SfxJhkEHUvJhGZAgwEXlTVBvfYXkCOqn4Q9Q1FHgEOB0qAjcCvcNa4fgwYCqwBzoqm4duqmHqxzSuchuzNy53Hpi8gswja3Lkhp1wGmQWw+Gk4+HLotw/kD4G0nrc1NIcifLmpgZrGEHXNIdbVNLF4/VY2bG2hqTXMV9VN1DaFaApFvvHavAw/xTnpNLVG2KNfNkMKs+iXm05pXgb9ctPJTfdTmpvekWRU1UomZreyyfpM79JU44zM/uwpCDft+LzikXDMTTD6xLiHpKo0h9pYVllHfXOYNdWN1LeEWV5ZT01jiIA/jRWV9VTWtbC5oYWu/psVZQcJRdooyAqwZ2kO/d0kUpqXQWlOOiU5QRpbIwT9aeRm+CkvziY7PRY1uqYvswRheqdwi9OI/d6fnAF3oWZo2QqLn4IvX9923pn3wp7HQONmZ7xF8R4JCxkgHGljc0MrVXUtbNza7D63sLGumXCkjVWbGtnaHGJTfesOkwlAmkBxTjr5mQEKMgPsNSCXvIwAuRl+BuRlMKggE4BhxVmU5qbjTxMrnZhvsARh+p4Nn8IXz8Fb/wct2zWL/WgBlIx0qq0yCyGrKDExRiEcaaO6oZVKN5lsbQ6xZEMdDS1h0v0+GlrC1DaF+GRtLZvqWwhHlHBb1/+Hc9P95GUGKMkJkp8VxCfQPy+DSJtSmB2kX246e5TmMKQoiwH5GWQGnKlNfGlCbWOI/Czr2dUbWYIwfVdrA3w1H/55FWxetu147iCoWweSBsV7QrgZSveGqT+EwRMgPRfCrc7PGyohb1DifgcPQpE2aptCLN1QR21TCAEq61qoaQxRWddMY2uETfUtVDe00hSKUN3QSo07bqQrvjRhWHEWK6saOHSvUkpygoQjyuDCTBpbwozsn8vgwkwy/D7qW8KM7JdDVtDHRxW1HL13PyuxpIB4zsWUjjOCupxOPaBUdbbHGGPCEoTZpaovnCqpzSuc6qh1HmaGOfcRGH0CNNeCP8PpUQUxaQhPFFWlJdxGfUuYSJvTFXhtTSMV1U00hyNsbQqztqaJmsZWqhtaqW8JU98Spjn0zZ5d28sM+CgrzKQ4J4gqBP1pFGcH8aWlMbK/M617+7iT7HQ/JTlBctL9rNrcSHlxFkOLsmgJtxHwpeGzebfiJp4J4nmcbq0LgY4uHKr6P16DjAVLEMYTVfjiBcgphYH7w9YKmHM2VH3u/HzAfk5V1Kd/3/aaQRNgndtJLz0PBoyFGY/Aq79xpggpHA7pOd+8T936lCmF7Eo40kZzuA1/mrCmupG65jAtoUhHw3x9S5hlG+spzglSudUprQA0hSKsqKqnsfWbvb26khnwEYq0EfSnUZAZoKwwi4KsAEXZTiJZU92IL01QhX0G5dHQGqasIJO9+ufSPy+D/MwAfp+QmxGgJRwh6EuzEk0X4pkgPm0f/ZwMLEGYuGhthJrV8P498OFfYc+jncTStoOqGX8mDNwPWuohmA0V853jl70L/fbefXEnqUibUtccItKmNLZGqG8Js7UpxPraZupawqDK1uYw1Q2t+NOELY2tLNlQR4bfR21TiC2NrWxtDkVVigHICKTRHGojO+ijMDsIQE66n+x0P3kZfgYVZFLTFKIlFGHPfrk0tIRZvH4rR47ux579cvCnCWkiZAV9DCrIxO8TstP95Kb7e0XCiWeCuBv4P1X9pLvBxZIlCBN3bRFI821bIe+r92DZi06jeHvJY0cGjIWt62DwAc5+IBN86dBQ5WyPPx++eheqv4TDroHar2D4obDyNee1hcPi/dulnMZWZxxMe/VYdUMrkTZlw9Zm6pvD1LWEaQ5F8KUJNY2t1LdEaAlFaFNla1OYrc0hNm5tJj8zQE1TiIaWMKrssKG/s3R/GkF/GnkZAZpCEfxpwoD8DMqLsynKDpIeSCPoS6MwK+iMws/0k+5PI+BLI9KmVDe0Mnl4Ef60NNLSSNh6J/FMEIuBPYEvgRZAAFXV/boTaE9ZgjAJ1VjtdKNdeD+MPskZ0LflS+fDfd2H8Mb/Oj2qAtkQavB+/QH7OY/cAVA6Cl79LxhzJhz5cwg1wZbVzv1LRjpjRbJLoHolFI1wRpqrOtOsDxzXsfZGh8+fgUH7Q/7gru/dx7S1KbVNIdbWNBFpUxTYVNfClsZWwm4JaFO9M8p+fW0TgrC5oYXmUBtbm0NUuyPwWyNtO+ymvL2i7KBbZaZkBf1kBX18sbGOieVFhCJtCDBqQB7pnQZUjh6QS3MowvKqem4+dWy3ftd4Joguv9KoakKmVLUEYVLG4qedUeCjT4L6DVC3AT55HObfDSOOcD7wyybC+o9g42fQ5GkG/a8rGuGs3rfmbaeBvV2aHwqGAgLVK5zk8+2/O6WZ9Fxo2OTEVTrKmf+qboOTnIwnX1U3UtsUYmtziMaWCA2tTpVav7wMllfWkyZCbVOIzfUt+H1CY2uElpCTeJZV1jMgP4OsoI/q+la3Cg7S0oSmUKRjGphR/XN57AdTuzW9fbwXDCoERgIZ7cdU9fUdvyJ+LEGYXqmtDRY/6VRN+YJOctn0BbTUAeoklvR8Zw2NRQ998/XBHGit93bPNP+2aUwAcvpD/UY45j+h/74QaoQhU5zpTVTh47mQkQ97HOV0LQ5mf7Ox3sRUc8jpsgwwKD+z2yssxrME8V3gCqAMWARMAd5R1SO7E2hPWYIwfdKm5U5PrIx8J5nUb4Dcgc74j4HjnOlI1n3ozE2V09/58F71Bqx5F0YdD4segfwyd4p1gU8ecy8szrnFezglme6YcKFT9QbO/Fhr3oGcfnDoVU6JJG8w+NMhkOV0BFj7gVOqyciHJc8454w8xkmOeYPc7sU+WPaS02OsZE/nnLbIN6vHVHc5iaOJb4L4BJgEvKuq40VkNHCTqp7TvVB7xhKEMTGgCs01TqkkLc0pEXzyN6c67IXrYNWbztQmq99yPsiba6FssvPatvC2bsDxIGkw/DBY+Yqzf/kiuOtQZ0zLaXfD+392qsrWf+T0Mjvtbie+h86AC9xSWFqak1A2feH8HqvecjoEpPlg2MHw0SPOcVWYcIFzn2d/5pTYjvsvWP027HmUUxXX2giNm5w2nPEznETnj2IG37qN8MGDcPAV4A9G//u3d5KIoXgmiPdVdZKILAIOVNWW7iwYFCuWIIzZTVSdkkF2sdNWkVn09QGDm1c4H5Z5A50P68//AZFW51t/3Qbnw3nzcqd0EAnB27c7pZ0DLnLW/2g3+iSnJPENAnRj1gdfOgSzoGlL9OePPmHbSoedq97yBjvVbp2r4nxBp5Qz5gzn91r0kHMsqwSGHeR0f17zHix91jl/+KFOd+g9joAv33Dae1a/5cwrVr/B6VKt6vx80zJnTM7BVzhruW/4GCZf6nSd3uu4bo+ziWeCeBK4GLgSOBLYAgRU9YTuBNpTliCM6QXaIrDi31B+CAQynA/Q1gaItDjf4nMHOoMUFz8F/77Z+WA87S5443fOh3b+EOdDc/VbThIKNTsfsF/Nd77tR0Kw5FlAnbm4Bh/gVGs1VjtdjtsiTndjcBr3m6qdRNFS5/Q+yypxzsvIiz7RxJsvHa5Z5SQ/j3bLXEwichiQDzyvqq3dukgPWYIwxkSltcH5Zg/fXKEw3OpUYe159Nerc9ranGorf/q29o22CHz5mlMlVV8JVUtgn1PdhNbqjJdp2gJZxZBd6jTqh1vgrducc/zpTvfiwmGQUeiUGr6a75yf0x9WvQ6DJzptNyV7OSWF+XfDlP9wEtagCc4cYxs/g7FnduufIp4lCAHOB0ao6mwRGQoMUNX53Yq0hyxBGGOMN14ShNdZx/4ITAVmuPt1wB0er2GMMSYFeF2e6kBVnSAiHwKo6hYR8dAkb4wxJlV4LUGERMSH251AREqB6GbQMsYYk1K8JojbgSeB/iLya+BN4L9iHpUxxpiE81TFpKpzRGQhcJR76BRVXRL7sIwxxiRaVAlCRJ7e/pD7fKyIoKonxzYsY4wxiRZtCWIq8BXwCPAe2xKEMcaYXiraBDEAOAane+t5wLPAI6r6WbwCM8YYk1hRNVKrakRVn1fVmTgzuC4HXhWRH8c1OmOMMQkTdSO1iKQDJ+KUIspxejQ9EZ+wjDHGJFq0jdQPAGOA53Cm9/40rlEZY4xJuGhLEBcADcBewOWybVGO9jWp8+IQmzHGmASKKkGoqtcBdcYYY1KcffAbY4zpkiUIY4wxXbIEYYwxpkuWIIwxxnTJEoQxxpguWYIwxhjTpaRKECJynIgsFZHlInJtouMxxpi+LGkShLtS3R3A8cA+wAwR2SexURljTN+VNAkCmAwsV9WVqtoKzAVOSXBMxhjTZ3laUS7OBuOsOdGuAjhw+5NE5FLgUne3XkSWdvN+JcCmbr42npI1Lkje2JI1Lkje2Cwu75I1Nq9xDYv2xGRKEF0tQqTfOKB6N3B3j28mskBVJ/b0OrGWrHFB8saWrHFB8sZmcXmXrLHFM65kqmKqAIZ02i8D1iUoFmOM6fOSKUG8D4wUkeEiEgTOBbZfC9sYY8xukjRVTKoaFpEfAS8APuDeOC9p2uNqqjhJ1rggeWNL1rggeWOzuLxL1tjiFpeofqOa3xhjjEmqKiZjjDFJxBKEMcaYLvW5BJHo6TxE5F4RqRSRTzsdKxKRl0Rkmftc6B4XEbndjfVjEZkQx7iGiMgrIvK5iHwmIlckQ2wikiEi80XkIzeum9zjw0XkPTeuR92ODYhIuru/3P15eTzi2i5Gn4h8KCLPJEtsIrJKRD4RkUUissA9lvD3mXu/AhH5m4gscd9vUxMdm4iMcv+t2h9bReTKRMfVKb6fuO//T0XkEff/RfzfZ6raZx44jd8rgBFAEPgI2Gc3x3AoMAH4tNOx/waudbevBX7jbp8APIczRmQK8F4c4xoITHC3c4EvcKY8SWhs7vVz3O0A8J57v8eAc93jdwL/4W5fBtzpbp8LPLob/qazgIeBZ9z9hMcGrAJKtjuW8PeZe78HgO+620GgIFlic+/pAzbgDChLeFw4g4i/BDI7vb8u2h3vs7j+QyfbA5gKvNBp/zrgugTEUc7XE8RSYKC7PRBY6m7fBczo6rzdEOM84Jhkig3IAj7AGWG/CfBv/3fF6QU31d32u+dJHGMqA14GjgSecT8wEh4bXSeIhP8tgTz3w06SLbZO9/gW8FayxMW2WSaK3PfNM8Cxu+N91teqmLqazmNwgmLprL+qrgdwn/u5xxMSr1sk3R/n23rCY3OrcBYBlcBLOKXAGlUNd3Hvjrjcn9cCxfGIy3UrcDXQ5u4XJ0lsCrwoIgvFmZ4GkuBviVN6rwLuc6vl/iIi2UkSW7tzgUfc7YTHpaprgd8Ba4D1OO+bheyG91lfSxBRTeeRRHZ7vCKSA/wduFJVt+7s1C6OxSU2VY2o6nicb+uTgb13cu/dFpeInARUqurCzod3cv/d+fc8WFUn4MyO/EMROXQn5+7OuPw4Vax/UtX9gQacqpsd2a3/B9x6/JOBx3d1ahfH4vU+K8SZuHQ4MAjIxvm77uj+MYutryWIZJ3OY6OIDARwnyvd47s1XhEJ4CSHOar6RDLFBqCqNcCrOHW+BSLSPtCz87074nJ/ng9Uxymkg4GTRWQVzuzDR+KUKBIem6quc58rgSdxEmsy/C0rgApVfc/d/xtOwkiG2MD54P1AVTe6+8kQ19HAl6papaoh4AngIHbD+6yvJYhknc7jaWCmuz0Tp/6//fiFbo+JKUBte3E31kREgHuAz1X198kSm4iUikiBu52J85/lc+AV4MwdxNUe75nAv9WtjI01Vb1OVctUtRznvfRvVT0/0bGJSLaI5LZv49Spf0oSvM9UdQPwlYiMcg8dBSxOhthcM9hWvdR+/0THtQaYIiJZ7v/T9n+z+L/P4tnYk4wPnN4HX+DUY/88Afd/BKceMYST6S/BqR98GVjmPhe55wrOIkorgE+AiXGMaxpOMfRjYJH7OCHRsQH7AR+6cX0K/NI9PgKYDyzHqQ5Id49nuPvL3Z+P2E1/18PZ1ospobG59//IfXzW/j5P9N+yU3zjgQXu3/QpoDAZYsPpBLEZyO90LOFxufe7CVji/h/4K5C+O95nNtWGMcaYLvW1KiZjjDFRsgRhjDGmS5YgjDHGdMkShDHGmC5ZgjDGGNMlSxDG7ISIRLab5TNmMwCLSLl0mtXXmGSTNEuOGpOkmtSZ5sOYPsdKEMZ0gzjrLfxGnLUq5ovInu7xYSLysrtGwMsiMtQ93l9EnhRnXYuPROQg91I+EfmzO9f/i+5ocWOSgiUIY3Yuc7sqpnM6/Wyrqk4G/oAzBxPu9oOquh8wB7jdPX478JqqjsOZe+gz9/hI4A5V3ReoAc6I8+9jTNRsJLUxOyEi9aqa08XxVcCRqrrSneRwg6oWi8gmnHUBQu7x9apaIiJVQJmqtnS6RjnwkqqOdPevAQKqenP8fzNjds1KEMZ0n+5ge0fndKWl03YEaxc0ScQShDHdd06n53fc7bdxZnYFOB94091+GfgP6FgAKW93BWlMd9m3FWN2LtNdza7d86ra3tU1XUTew/miNcM9djlwr4hchbNy2sXu8SuAu0XkEpySwn/gzOprTNKyNghjusFtg5ioqpsSHYsx8WJVTMYYY7pkJQhjjDFdshKEMcaYLlmCMMYY0yVLEMYYY7pkCcIYY0yXLEEYY4zp0v8HmnJJAHocGvoAAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def plot_history():\n", " plt.figure()\n", " plt.xlabel('Epoch')\n", " plt.ylabel('Mean Square Error [Thousand Dollars$^2$]')\n", " plt.plot(hist['epoch'], hist['mean_squared_error'], label='Train Error')\n", " plt.plot(hist['epoch'], hist['val_mean_squared_error'], label = 'Val Error')\n", " plt.legend()\n", " plt.ylim([0,50])\n", "\n", "plot_history()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "Next, compare how the model performs on the test dataset:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "102/102 [==============================] - 0s 44us/step\n", "Root Mean Square Error on test set: 4.244\n" ] } ], "source": [ "test_features_norm = (test_features - train_mean) / train_std\n", "mse, _, _ = model.evaluate(test_features_norm, test_labels)\n", "rmse = np.sqrt(mse)\n", "print('Root Mean Square Error on test set: {}'.format(round(rmse, 3)))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "Compare the RMSE measure you get to the [Kaggle leaderboard](https://www.kaggle.com/c/boston-housing/leaderboard). An RMSE of 2.651 puts us in 5th place." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Classification of MNIST Dreams with Convolutional Neural Networks\n", "\n", "Next, let's build a convolutional neural network (CNN) classifier to classify images of handwritten digits in the MNIST dataset with a twist where we test our classifier on high-resolution hand-written digits from outside the dataset." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Set common constants\n", "this_repo_url = 'https://github.com/lexfridman/mit-deep-learning/raw/master/'\n", "this_tutorial_url = this_repo_url + 'tutorial_deep_learning_basics'" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "The MNIST dataset containss 70,000 grayscale images of handwritten digits at a resolution of 28 by 28 pixels. The task is to take one of these images as input and predict the most likely digit contained in the image (along with a relative confidence in this prediction):\n", "\n", "\n", "\n", "Now, we load the dataset. The images are 28x28 NumPy arrays, with pixel values ranging between 0 and 255. The *labels* are an array of integers, ranging from 0 to 9." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [], "source": [ "(train_images, train_labels), (test_images, test_labels) = keras.datasets.mnist.load_data()\n", "\n", "# reshape images to specify that it's a single channel\n", "train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)\n", "test_images = test_images.reshape(test_images.shape[0], 28, 28, 1)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "We scale these values to a range of 0 to 1 before feeding to the neural network model. For this, we divide the values by 255. It's important that the *training set* and the *testing set* are preprocessed in the same way:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [], "source": [ "def preprocess_images(imgs): # should work for both a single image and multiple images\n", " sample_img = imgs if len(imgs.shape) == 2 else imgs[0]\n", " assert sample_img.shape in [(28, 28, 1), (28, 28)], sample_img.shape # make sure images are 28x28 and single-channel (grayscale)\n", " return imgs / 255.0\n", "\n", "train_images = preprocess_images(train_images)\n", "test_images = preprocess_images(test_images)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "Display the first 5 images from the *training set* and display the class name below each image. Verify that the data is in the correct format and we're ready to build and train the network." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,2))\n", "for i in range(5):\n", " plt.subplot(1,5,i+1)\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.grid(False)\n", " plt.imshow(train_images[i].reshape(28, 28), cmap=plt.cm.binary)\n", " plt.xlabel(train_labels[i])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "### Build the model\n", "\n", "Building the neural network requires configuring the layers of the model, then compiling the model. In many cases, this can be reduced to simply stacking together layers:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [], "source": [ "model = keras.Sequential()\n", "# 32 convolution filters used each of size 3x3\n", "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", "# 64 convolution filters used each of size 3x3\n", "model.add(Conv2D(64, (3, 3), activation='relu'))\n", "# choose the best features via pooling\n", "model.add(MaxPooling2D(pool_size=(2, 2)))\n", "# randomly turn neurons on and off to improve convergence\n", "model.add(Dropout(0.25))\n", "# flatten since too many dimensions, we only want a classification output\n", "model.add(Flatten())\n", "# fully connected to get all relevant data\n", "model.add(Dense(128, activation='relu'))\n", "# one more dropout\n", "model.add(Dropout(0.5))\n", "# output a softmax to squash the matrix into output probabilities\n", "model.add(Dense(10, activation='softmax'))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "Before the model is ready for training, it needs a few more settings. These are added during the model's *compile* step:\n", "\n", "* *Loss function* - measures how accurate the model is during training, we want to minimize this with the optimizer.\n", "* *Optimizer* - how the model is updated based on the data it sees and its loss function.\n", "* *Metrics* - used to monitor the training and testing steps. \"accuracy\" is the fraction of images that are correctly classified." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [], "source": [ "model.compile(optimizer=tf.train.AdamOptimizer(), \n", " loss='sparse_categorical_crossentropy',\n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "### Train the model\n", "\n", "Training the neural network model requires the following steps:\n", "\n", "1. Feed the training data to the model—in this example, the `train_images` and `train_labels` arrays.\n", "2. The model learns to associate images and labels.\n", "3. We ask the model to make predictions about a test set—in this example, the `test_images` array. We verify that the predictions match the labels from the `test_labels` array. \n", "\n", "To start training, call the `model.fit` method—the model is \"fit\" to the training data:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/5\n", "60000/60000 [==============================] - 7s 121us/step - loss: 0.1953 - acc: 0.9410\n", "Epoch 2/5\n", "60000/60000 [==============================] - 6s 100us/step - loss: 0.0842 - acc: 0.9753\n", "Epoch 3/5\n", "60000/60000 [==============================] - 6s 96us/step - loss: 0.0642 - acc: 0.9810\n", "Epoch 4/5\n", "60000/60000 [==============================] - 6s 94us/step - loss: 0.0526 - acc: 0.9835\n", "Epoch 5/5\n", "60000/60000 [==============================] - 6s 94us/step - loss: 0.0443 - acc: 0.9861\n" ] } ], "source": [ "history = model.fit(train_images, train_labels, epochs=5)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "As the model trains, the loss and accuracy metrics are displayed. This model reaches an accuracy of about 98.68% on the training data." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "### Evaluate accuracy\n", "\n", "Next, compare how the model performs on the test dataset:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10000, 28, 28, 1)\n", "10000/10000 [==============================] - 1s 50us/step\n", "Test accuracy: 0.9913\n" ] } ], "source": [ "print(test_images.shape)\n", "test_loss, test_acc = model.evaluate(test_images, test_labels)\n", "\n", "print('Test accuracy:', test_acc)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "Often times, the accuracy on the test dataset is a little less than the accuracy on the training dataset. This gap between training accuracy and test accuracy is an example of *overfitting*. In our case, the accuracy is better at 99.19%! This is, in part, due to successful regularization accomplished with the Dropout layers." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "### Make predictions\n", "\n", "With the model trained, we can use it to make predictions about some images. Let's step outside the MNIST dataset for that and go with the beautiful high-resolution images generated by a mixture of CPPN, GAN, VAE. See [great blog post by hardmaru](http://blog.otoro.net/2016/04/01/generating-large-images-from-latent-vectors/) for the source data and a description of how these morphed animations are generated:\n", "\n", "![MNIST dream](https://i.imgur.com/OrUJs9V.gif)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": {}, "colab_type": "code", "id": "" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mnist_dream_path = 'images/mnist_dream.mp4'\n", "mnist_prediction_path = 'images/mnist_dream_predicted.mp4'\n", "\n", "# download the video if running in Colab\n", "if not os.path.isfile(mnist_dream_path): \n", " print('downloading the sample video...')\n", " vid_url = this_tutorial_url + '/' + mnist_dream_path\n", " \n", " mnist_dream_path = urllib.request.urlretrieve(vid_url)[0]\n", " \n", "def cv2_imshow(img):\n", " ret = cv2.imencode('.png', img)[1].tobytes() \n", " img_ip = IPython.display.Image(data=ret)\n", " IPython.display.display(img_ip)\n", "\n", "cap = cv2.VideoCapture(mnist_dream_path) \n", "vw = None\n", "frame = -1 # counter for debugging (mostly), 0-indexed\n", "\n", "# go through all the frames and run our classifier on the high res MNIST images as they morph from number to number\n", "while True: # should 481 frames\n", " frame += 1\n", " ret, img = cap.read()\n", " if not ret: break\n", " \n", " assert img.shape[0] == img.shape[1] # should be a square\n", " if img.shape[0] != 720:\n", " img = cv2.resize(img, (720, 720))\n", " \n", " #preprocess the image for prediction\n", " img_proc = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", " img_proc = cv2.resize(img_proc, (28, 28))\n", " img_proc = preprocess_images(img_proc)\n", " img_proc = 1 - img_proc # inverse since training dataset is white text with black background\n", "\n", " net_in = np.expand_dims(img_proc, axis=0) # expand dimension to specify batch size of 1\n", " net_in = np.expand_dims(net_in, axis=3) # expand dimension to specify number of channels\n", " \n", " preds = model.predict(net_in)[0]\n", " guess = np.argmax(preds)\n", " perc = np.rint(preds * 100).astype(int)\n", " \n", " img = 255 - img\n", " pad_color = 0\n", " img = np.pad(img, ((0,0), (0,1280-720), (0,0)), mode='constant', constant_values=(pad_color)) \n", " \n", " line_type = cv2.LINE_AA\n", " font_face = cv2.FONT_HERSHEY_SIMPLEX\n", " font_scale = 1.3 \n", " thickness = 2\n", " x, y = 740, 60\n", " color = (255, 255, 255)\n", " \n", " text = \"Neural Network Output:\"\n", " cv2.putText(img, text=text, org=(x, y), fontScale=font_scale, fontFace=font_face, thickness=thickness,\n", " color=color, lineType=line_type)\n", " \n", " text = \"Input:\"\n", " cv2.putText(img, text=text, org=(30, y), fontScale=font_scale, fontFace=font_face, thickness=thickness,\n", " color=color, lineType=line_type) \n", " \n", " y = 130\n", " for i, p in enumerate(perc):\n", " if i == guess: color = (255, 218, 158)\n", " else: color = (100, 100, 100)\n", " \n", " rect_width = 0\n", " if p > 0: rect_width = int(p * 3.3)\n", " \n", " rect_start = 180\n", " cv2.rectangle(img, (x+rect_start, y-5), (x+rect_start+rect_width, y-20), color, -1)\n", "\n", " text = '{}: {:>3}%'.format(i, int(p))\n", " cv2.putText(img, text=text, org=(x, y), fontScale=font_scale, fontFace=font_face, thickness=thickness,\n", " color=color, lineType=line_type)\n", " y += 60\n", " \n", " # if you don't want to save the output as a video, set this to False\n", " save_video = True\n", " \n", " if save_video:\n", " if vw is None:\n", " codec = cv2.VideoWriter_fourcc(*'DIVX')\n", " vid_width_height = img.shape[1], img.shape[0]\n", " vw = cv2.VideoWriter(mnist_prediction_path, codec, 30, vid_width_height)\n", " # 15 fps above doesn't work robustly so we right frame twice at 30 fps\n", " vw.write(img)\n", " vw.write(img)\n", " \n", " # scale down image for display\n", " img_disp = cv2.resize(img, (0,0), fx=0.5, fy=0.5)\n", " cv2_imshow(img_disp)\n", " IPython.display.clear_output(wait=True)\n", " \n", "cap.release()\n", "if vw is not None:\n", " vw.release()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "" }, "source": [ "The above shows the prediction of the network by choosing the neuron with the highest output. While the output layer values add 1 to one, these do not reflect well-calibrated measures of \"uncertainty\". Often, the network is overly confident about the top choice that does not reflect a learned measure of probability. If everything ran correctly you should get an animation like this:\n", "\n", "![MNIST dream predictions](https://i.imgur.com/eMF9FOG.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Acknowledgements\n", "\n", "The contents of this tutorial is based on and inspired by the work of [TensorFlow team](https://www.tensorflow.org) (see their [Colab notebooks](https://www.tensorflow.org/tutorials/)), our [MIT Human-Centered AI team](https://hcai.mit.edu), and individual pieces referenced in the [MIT Deep Learning](https://deeplearning.mit.edu) course slides." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "tutorial_deep_learning_basics.ipynb", "provenance": [], "toc_visible": true, "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: tutorial_driving_scene_segmentation/tutorial_driving_scene_segmentation.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dvefM1LtKLST" }, "source": [ "![MIT Deep Learning](https://deeplearning.mit.edu/files/images/github/mit_deep_learning.png)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Mv9KQHwAKLSV" }, "source": [ "\n", " \n", " \n", " \n", " \n", "\n", "
\n", " \n", " Visit MIT Deep Learning\n", " Run in Google Colab\n", " View Source on GitHub\n", " Watch Videos
" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "KFPcBuVFw61h" }, "source": [ "# MIT Driving Scene Segmentation\n", "\n", "This tutorial demostrates the steps to run DeepLab semantic scene segmentation model on a sample video from MIT Driving Scene Segmentation Dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 35 }, "colab_type": "code", "id": "kAbdmRmvq0Je", "outputId": "f82ca270-f776-41b7-c542-dbf02116015c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.12.0\n" ] } ], "source": [ "# Tensorflow\n", "import tensorflow as tf\n", "print(tf.__version__)\n", "\n", "# I/O libraries\n", "import os\n", "from io import BytesIO\n", "import tarfile\n", "import tempfile\n", "from six.moves import urllib\n", "\n", "# Helper libraries\n", "import matplotlib\n", "from matplotlib import gridspec\n", "from matplotlib import pyplot as plt\n", "import numpy as np\n", "from PIL import Image\n", "import cv2 as cv\n", "from tqdm import tqdm\n", "import IPython\n", "from sklearn.metrics import confusion_matrix\n", "from tabulate import tabulate\n", "\n", "# Comment this out if you want to see Deprecation warnings\n", "import warnings\n", "warnings.simplefilter(\"ignore\", DeprecationWarning)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "OQ4WfdFGKLSc" }, "source": [ "### Build the model\n", "\n", "**[DeepLab](https://github.com/tensorflow/models/tree/master/research/deeplab)** is a state-of-art deep learning model for semantic image segmentation, where the goal is to assign semantic labels (e.g., person, dog, cat and so on) to every pixel in the input image. Some segmentation results on Flickr images:\n", "\n", "

\n", "
\n", "
\n", "

\n", "\n", "In the driving context, we aim to obtain a semantic understanding of the front driving scene throught the camera input. This is important for driving safety and an essential requirement for all levels of autonomous driving. The first step is to build the model and load the pre-trained weights. In this demo, we use the model checkpoint trained on [Cityscapes](https://www.cityscapes-dataset.com/) dataset.\n", "\n", "

\n", "
\n", "
\n", "

\n" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "colab": {}, "colab_type": "code", "id": "ixa_Cty2KLSc" }, "outputs": [], "source": [ "class DeepLabModel(object):\n", " \"\"\"Class to load deeplab model and run inference.\"\"\"\n", "\n", " FROZEN_GRAPH_NAME = 'frozen_inference_graph'\n", "\n", " def __init__(self, tarball_path):\n", " \"\"\"Creates and loads pretrained deeplab model.\"\"\"\n", " self.graph = tf.Graph()\n", " graph_def = None\n", "\n", " # Extract frozen graph from tar archive.\n", " tar_file = tarfile.open(tarball_path)\n", " for tar_info in tar_file.getmembers():\n", " if self.FROZEN_GRAPH_NAME in os.path.basename(tar_info.name):\n", " file_handle = tar_file.extractfile(tar_info)\n", " graph_def = tf.GraphDef.FromString(file_handle.read())\n", " break\n", " tar_file.close()\n", "\n", " if graph_def is None:\n", " raise RuntimeError('Cannot find inference graph in tar archive.')\n", "\n", " with self.graph.as_default():\n", " tf.import_graph_def(graph_def, name='')\n", " self.sess = tf.Session(graph=self.graph)\n", "\n", " def run(self, image, INPUT_TENSOR_NAME = 'ImageTensor:0', OUTPUT_TENSOR_NAME = 'SemanticPredictions:0'):\n", " \"\"\"Runs inference on a single image.\n", "\n", " Args:\n", " image: A PIL.Image object, raw input image.\n", " INPUT_TENSOR_NAME: The name of input tensor, default to ImageTensor.\n", " OUTPUT_TENSOR_NAME: The name of output tensor, default to SemanticPredictions.\n", "\n", " Returns:\n", " resized_image: RGB image resized from original input image.\n", " seg_map: Segmentation map of `resized_image`.\n", " \"\"\"\n", " width, height = image.size\n", " target_size = (2049,1025) # size of Cityscapes images\n", " resized_image = image.convert('RGB').resize(target_size, Image.ANTIALIAS)\n", " batch_seg_map = self.sess.run(\n", " OUTPUT_TENSOR_NAME,\n", " feed_dict={INPUT_TENSOR_NAME: [np.asarray(resized_image)]})\n", " seg_map = batch_seg_map[0] # expected batch size = 1\n", " if len(seg_map.shape) == 2:\n", " seg_map = np.expand_dims(seg_map,-1) # need an extra dimension for cv.resize\n", " seg_map = cv.resize(seg_map, (width,height), interpolation=cv.INTER_NEAREST)\n", " return seg_map" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "q0QhB_7SKLSf" }, "source": [ "### Visualization\n", "Now let's create some helper functions for decoding and visualizing the results." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "code", "colab": {}, "colab_type": "code", "id": "vN0kU6NJ1Ye5" }, "outputs": [], "source": [ "def create_label_colormap():\n", " \"\"\"Creates a label colormap used in Cityscapes segmentation benchmark.\n", "\n", " Returns:\n", " A Colormap for visualizing segmentation results.\n", " \"\"\"\n", " colormap = np.array([\n", " [128, 64, 128],\n", " [244, 35, 232],\n", " [ 70, 70, 70],\n", " [102, 102, 156],\n", " [190, 153, 153],\n", " [153, 153, 153],\n", " [250, 170, 30],\n", " [220, 220, 0],\n", " [107, 142, 35],\n", " [152, 251, 152],\n", " [ 70, 130, 180],\n", " [220, 20, 60],\n", " [255, 0, 0],\n", " [ 0, 0, 142],\n", " [ 0, 0, 70],\n", " [ 0, 60, 100],\n", " [ 0, 80, 100],\n", " [ 0, 0, 230],\n", " [119, 11, 32],\n", " [ 0, 0, 0]], dtype=np.uint8)\n", " return colormap\n", "\n", "\n", "def label_to_color_image(label):\n", " \"\"\"Adds color defined by the dataset colormap to the label.\n", "\n", " Args:\n", " label: A 2D array with integer type, storing the segmentation label.\n", "\n", " Returns:\n", " result: A 2D array with floating type. The element of the array\n", " is the color indexed by the corresponding element in the input label\n", " to the PASCAL color map.\n", "\n", " Raises:\n", " ValueError: If label is not of rank 2 or its value is larger than color\n", " map maximum entry.\n", " \"\"\"\n", " if label.ndim != 2:\n", " raise ValueError('Expect 2-D input label')\n", "\n", " colormap = create_label_colormap()\n", "\n", " if np.max(label) >= len(colormap):\n", " raise ValueError('label value too large.')\n", "\n", " return colormap[label]\n", "\n", "\n", "def vis_segmentation(image, seg_map):\n", " \"\"\"Visualizes input image, segmentation map and overlay view.\"\"\"\n", " plt.figure(figsize=(20, 4))\n", " grid_spec = gridspec.GridSpec(1, 4, width_ratios=[6, 6, 6, 1])\n", "\n", " plt.subplot(grid_spec[0])\n", " plt.imshow(image)\n", " plt.axis('off')\n", " plt.title('input image')\n", "\n", " plt.subplot(grid_spec[1])\n", " seg_image = label_to_color_image(seg_map).astype(np.uint8)\n", " plt.imshow(seg_image)\n", " plt.axis('off')\n", " plt.title('segmentation map')\n", "\n", " plt.subplot(grid_spec[2])\n", " plt.imshow(image)\n", " plt.imshow(seg_image, alpha=0.7)\n", " plt.axis('off')\n", " plt.title('segmentation overlay')\n", "\n", " unique_labels = np.unique(seg_map)\n", " ax = plt.subplot(grid_spec[3])\n", " plt.imshow(FULL_COLOR_MAP[unique_labels].astype(np.uint8), interpolation='nearest')\n", " ax.yaxis.tick_right()\n", " plt.yticks(range(len(unique_labels)), LABEL_NAMES[unique_labels])\n", " plt.xticks([], [])\n", " ax.tick_params(width=0.0)\n", " plt.grid('off')\n", " plt.show()\n", "\n", "\n", "LABEL_NAMES = np.asarray([\n", " 'road', 'sidewalk', 'building', 'wall', 'fence', 'pole', 'traffic light',\n", " 'traffic sign', 'vegetation', 'terrain', 'sky', 'person', 'rider', 'car', 'truck',\n", " 'bus', 'train', 'motorcycle', 'bicycle', 'void'])\n", "\n", "FULL_LABEL_MAP = np.arange(len(LABEL_NAMES)).reshape(len(LABEL_NAMES), 1)\n", "FULL_COLOR_MAP = label_to_color_image(FULL_LABEL_MAP)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pzxbIveNKLSi" }, "source": [ "### Load the model from a frozen graph\n", "There are two model checkpoints pre-trained on Cityscapes with different network backbones: MobileNetV2 and Xception65. We default to use MobileNetV2 for faster inference." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 70 }, "colab_type": "code", "id": "c4oXKmnjw6i_", "outputId": "19f93d51-1e69-4b7d-b9b5-16affad308b2", "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "downloading model, this might take a while...\n", "download completed! loading DeepLab model...\n", "model loaded successfully!\n" ] } ], "source": [ "MODEL_NAME = 'mobilenetv2_coco_cityscapes_trainfine'\n", "#MODEL_NAME = 'xception65_cityscapes_trainfine'\n", "\n", "_DOWNLOAD_URL_PREFIX = 'http://download.tensorflow.org/models/'\n", "_MODEL_URLS = {\n", " 'mobilenetv2_coco_cityscapes_trainfine':\n", " 'deeplabv3_mnv2_cityscapes_train_2018_02_05.tar.gz',\n", " 'xception65_cityscapes_trainfine':\n", " 'deeplabv3_cityscapes_train_2018_02_06.tar.gz',\n", "}\n", "_TARBALL_NAME = 'deeplab_model.tar.gz'\n", "\n", "model_dir = tempfile.mkdtemp()\n", "tf.gfile.MakeDirs(model_dir)\n", "\n", "download_path = os.path.join(model_dir, _TARBALL_NAME)\n", "print('downloading model, this might take a while...')\n", "urllib.request.urlretrieve(_DOWNLOAD_URL_PREFIX + _MODEL_URLS[MODEL_NAME], download_path)\n", "print('download completed! loading DeepLab model...')\n", "\n", "MODEL = DeepLabModel(download_path)\n", "print('model loaded successfully!')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "SZst78N-4OKO" }, "source": [ "### Run on the sample image\n", "The sample image is frame #0 in the MIT Driving Scene Segmentation (DriveSeg) Dataset." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 304 }, "colab_type": "code", "id": "edGukUHXyymr", "outputId": "68858de4-7d6f-464c-bfed-b576a2843ea3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "downloading the sample image...\n", "running deeplab on the sample image...\n" ] }, { "data": { "image/png": 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M/va3vxEMBtF1neeff/5Le4FFa2srY8eORVEUXnzxRYLBYI/yvfLKKwDs2rWLvXv3Ul1d\n/aXIIZFIjh5kf3to/e2MGTP405/+lJB75cqVzJgxA4CZM2eyceNG3njjjYT1WHV1Nc3NzWzatAmA\nmpoabrnllj6y9eZA/fWQIUOoqKjg0Ucf7WGlJpFIjj2kwkpyTDBs2DAuvPBCzjnnHC677DKGDRvG\n4MGDueCCC5g3bx4LFizo4W538skns2bNmkS8kc/DueeeS0dHB2eeeSb/8R//wY033sj+/ft58MEH\nueSSS3A6nZxxxhncc889nHPOOYm3pdx99928//77zJkzh29961uUl5dTUlLyueWRSCSS008/nc2b\nNzN79mzmzp3Lzp07ueKKKygsLOS+++7jmmuuYe7cuYk3wABcffXV7N69mzPPPJPf/e53nH766X3e\n7nQgFEXhscce49lnn2XOnDksXLiQqVOn9nClTkVyf3yg/nTixIk0NTVx6qmn9nBvmTNnDtOnT+f8\n88/n3HPPpbi4mMsvv/zwKu4g3HDDDVxzzTWcd955BAIB5s+fzx133MG+ffsAK5bNN7/5TS699FJu\nv/12aWElkfwbIPvbQ+tvb7zxRrxeb0Luq666KhHDKz09PRFza8KECQC4XC6WLVvGfffdx9y5c7nm\nmmuYM2fOQevrYP31OeecQ0tLSyJemEQiOTZRxMHU1xLJEWbWrFk8/PDDTJo06UiLkhIhRGJQfeih\nhzAMgyVLlhxhqSQSiaQvyf3V9ddfz8SJE1m0aNERlurYYMSIEaxatSoRwFgikUgOhOxvjyyvvvoq\nr732GkuXLj3SokgkPfAf13hY56XtLDp4ohirV6+mtrY28QKEOOeffz7Lli1j0KBBhyVDb9auXcuz\nzz7LsmXLmDJlyoBdlg8FaWElkXwO/vnPf3LBBRcQiUTw+/2sWrUqsWIkkUgkRxPLly/n6quvxjRN\nWltbWbduHccff/yRFksikUi+dsj+9sgSDAb57W9/y2WXXXakRZFIjgjTp0/vo6w6VpFB1yWSz8GM\nGTNYtWoVc+fORVVVZsyY0eMtJxKJRHK08K1vfYt169Yxe/ZsVFVl8eLFfV61LpFIJJLPj+xvjxxv\nvfUW99xzDxdccMFR650hkXzR1NfXc8stt6CqKoZhMG3aNPx+P7feeiv3338/GzdupKqqimg0CkBj\nYyM//elPiUajaJrG/fffz5/+9CdGjBjB2WefzZ133onNZuPOO+/k5ZdfZs+ePZxwwgksXboUu91O\nZmYmjz/+eEpZtm7dyj333MMzzzzzhbx8TCqsJEc9b7755pEWoV80TePee+890mJIJBLJQUlLS0v5\nNivJwNi+ffuRFkEikRwjyP72yDFz5kxmzpx5pMWQSL5SXnvtNaZNm8Y111zD5s2beffdd/H7/ezc\nuZMNGzbw/PPP09jYyJlnngnA0qVLWbx4MdOmTWPVqlU88cQTzJ07l1WrVnH22WfT0tKSePHBhg0b\nmDt3Li0tLTzyyCOUl5fz4x//mHfeeaePQqqtrY277rqLxx9//AtRVoFUWEkkEolEIpFIJBKJRCKR\nHJOcfPLJXHvttXR1dXHWWWeRn59Pe3s7O3fupLq6GlVVKSkpoby8HICNGzeye/dunnzySQzDIDc3\nl+OPP54nn3ySzs5O0tPT0XWdYDDIli1buO2229i4cSO33347hmFQU1PDSSed1EMpJYTgRz/6Ed/7\n3vcoLS39wsp2VCqsHkyK1WWLvyAi6UURpgGqCkpyBK4DhI43k9KpZtKBWJ6iv+MHSJeKg738Q+tn\nv6qC0wYZdjiuCEZmQkbS8Siwaj/sbgZTQMQBdh0Mpfu68WsrWGnsNtANQPQsf0oZU9SdqvU9nlzf\nIpZPf1WiKD0/Ux1TVVBjefeoW7Vv3iIpn7gcitk3f6Fa+QIowjoueqXpfU7y7wPJmxBPpUd7TMWB\n2kLKa8Rk3bcDSnIgQ0BBJWzaDIVV4A1AVpaVLt0Ge7fBoGHg67LqIS8POg3rvpvAICAM2AG/gHQF\nCILTCUMP0o7fbjJx5Kukq91NI9aULFnpbsvJfxcX8H9rGljzx+UMPm4c5Z48gg4dPSeL5b/9A3/7\n08/5/vd/TuPeHfzmzd+RDjQbUUZodmqAEiACPLBkGcseuB6AhZf8gGWPL6U9EiVkT2dMMfzk539h\n7+4GqidPo3RwFo//9G7SnCo3PXA/U6eX8n8fdXDW+GwUrP+OiG0G0AHUB6HEDXnADRfcQnPrNvbU\n1VGUn0OaXaOxsRGf38vg0jIK8wtAqHS1deBJT8PucqKHIwjDxGdG+GzPHkaPHIlNF6gOQdRv0NTa\nSDQaQgjBuzu2HLiyj3LOuu+VIy2CRPKVMyz9hiMtQr/s8B04ePDRKnuy3L1l3OFb+qXL/Z8/2vml\n5v9ls+Cp9xPfU04vhHWgx/ziAPPiHnOq5HTx+W7fXakZ+EvvDul0RQFVsZ4B0pzWvCf5gcUEWsMQ\niMTGeMUqh0g6P1We8ddMiV77+5Cq7lKlS3WdFMkORo956EAz6ud4790iRf69i3cILy/sP/3naAv9\nnVrqepqgH1x26/47PODtsj712JwXrHYS8IE7DXTd2udwgC66ZXVhtRsFaz5ow/qiquA5iOytYVAd\noCWlqw9e2WNe3Ed299OoQFt7mPa6WjxpGbg1B4YqEHY7tftqOHHiKDZ9tJNwwEf1tAnYgLAQpCsK\nwSSZd2zdzdhRVQBs/OAjxo4dS9QUGKpGhhO27thPMBAiMzsXl8fOZ1u3Y1NhyKiR5OS5aPdGKci0\nowC1wSsTMpa4nyYKhAxwadASvJLWT35EJOIjEArhdNjRVIVwOIxh6LidLpxOBwgFPeZGpmoqpilA\nCHRhEggEyMjIQDEFigqmIQhHwghhghD8YdmOA1e25LAZPnw4f/3rX3n33Xd57LHHmDJlCmApkVS1\n++Ev/lZQu93O0qVLKSws7JGPqqqsW7eO6upqQqEQ7733Hh6PB4fDwZIlS/jNb37D0KFDU3oY+Xw+\nRowYwR//+Edmz579hZXtmAy6rqqH3rke7ZgGhEzYVgdrmsCbdMwOnFEM3x4HU4qhOgMmVICWYkC1\nqzC5EqrLINMWG/SFtcW/f51Rj8kW3U1FBWSkWfdO2wc+f5BgAApywWaz2klLA5RnQU4EhmVD665G\ntmzswGOzFEk2oAtwAG++24zXCy1hMNyWsi/Qz7WbolAPVBWq2FRrkPRjDezJCKxjcWVVvMojwMb/\n/Scfb2rB7RmKmlvBhg07+f09/02ZUsTcky4n34Az5p5FLtZAXKXZWb3Fy++f+ifBMJwy4QKuv/F6\n2kMw74KFLP9/T/HqOx8wpDyddAe0ATf+ZB5P/+Zqxoyp5o/LX+TbN97Ms+/8jlOnl9IOnDY+OyFz\nXLmmxeR0A3an9ekBTBMURYARobOzE6/Xi82uMmrIcZQWFGFGdbr8PjIzM4lEIrS3t6Mrgqgq6Orq\nYmRFFfaoAN1ACIGu64TDYSKRyOdpBhKJ5AhxtCp84ODKqqORHb6lfeSO70t1THKYKJ9bd3TUIYQ1\n/+gKQXvEWoCKowIFTijNgBynNWfKcqeuA1WBbDdkuroXwZX4lkp78zXjWH1eKnU9DYDbbc1/bQoo\nQdB1E9MAp92a0woBkTC47WA3Ic0OUX+Yrs4omtJ9r3WsdtPSFkGPQsQEoVkHe89z40RMa/HX4+yu\nRwNLWZVMf03IBLy1zXR5I2haGjjcdHb6qdm+DxdO1vxrIw4BBYWFOGLyeRSF1i6d2j0tGCa8u2o9\nQ4ZUETXg/fc3cvzE8TS1deJxa9gU638xZFgx1dWVZGRkUlfbQOmQoZxwygTy8lxEgbxMO/XBK6kP\nXtnd9oGG4JU0Bq9kf8T6TNgqKIAw0fUouq6jKpDuScPldCJMgW7o2Gw2TGESiUYRCASg6zrpHg9K\nTIElsJQlpmkmlCSSL49XXnmFHTt2cMYZZ3DDDTfwu9/9DoCqqio2b96MEIK6ujrq6uoAqK6u5o03\n3gDgvffe46WXXkrsf/bZZzn++OOprq5m+fLliVhwPp+PkpISvF4va9euTcTDipORkcGSJUsoKCjg\nueee+8LKdlRaWNkGYDklxME74bjVTvJqhYj9G1UR07L3traJnXMAQ6sUP2K7DjYoxC2HkspkKtZm\nCIgIq2Ns9MI7BgzNg0EOiBvaZQMTkpSgohhq22DWcNhcA4PKoa4FclzQ7LdWFzQBbpvV2YdClkIn\ngPWgnhArhTVVj3KlUAKl0gsdqqUSdN+j+GqfonSv9okkuZLrLL5ChmZNRFQBugmK1mulUO1/FS35\ne7IFWrISML47sSt2jimSyn+wMifLneK4inUvbCp0ecHbCgU2y4quvkPntBPdmBGo2Q3OYkj3gD8P\n6j/o4BcP3MvEk09g1KmnMWVKEW2mpbCMW1b98AdPcc33f0D99gjrVr3Nd2+aTVSzFDWJsgPNAtIU\naArCpx/vJ21wMVV5Vjtx01cx1Vv+HXVeaj8x+eZZ2fx42ULuuWsV0xYPQ22ENXsGMeYbHm684gK2\n7+2irraZje+uJQ2467FXKR9WykknTWDk0DFMGH8eixZfwZXX38nJY0v5ywvLATjvgmkowIebd7Fr\nfyOzz57G/g5oC9Yy5dypeHI91HVAWTakYw3e8Y7NgTW5iK+qqUCVaqVRASWs0OlrQ7Mb2GwGnV0B\niosKcDgcNDY2kp+fT1VVFVt37iAS0UnPzGBnTQ35GVkUZ+cRCvpRnHZsdjv+QACb24nDZkdTVHQ9\nnKLGJBKJ5MvhaFO2DVQZdbTJfTQyEJ2DGEC6VPOQxPwrKZ+vjF7Xjl8/LoMZ+xLWoU1AmgNcSvcY\nbweyHEnnOiEUhfw0S9HlckEoAnYNwnHvDNOylFGU2Fw4lcLiMKybPq9lVe+MUlrvDOQiydZmqe73\nwa5P/xZ4B7z8gcyNeoqXUpZU6XQd9Ag4VGuuHooK8nKsFdVgAFSn9XxjAKHOKDt3fEp2bhbpufnk\n5tiJCus5wcSa83300V6qBg8m5BN0tLZQMbQAofT1gAljtbGwCf6uMJrbicdhXUcbQFGzlCdpaRIU\nF9oZOm4Qn25vJaciDSUM7QE3GcUaQypK8AV0QqEInW3taMD2XU2401zk5GSSnpbBqrfXUV5Rzoef\nbCc3w8XkydZbJotKcgDo7AoQCIcpKMwhrEPUCJFTlINm1whGLSWeDagLXkncOUSl538MYgu4WFZh\nTSboegRFFSiKQNejOJ1O1JillcPhwOPx4PP7MU2BZrPhDwZx2Oy47A4Mw0CxgaKq6IaBoqqoioJA\nQ4j+VIOSL4LKykruuusuPB4PmqZx8803U1NTw8iRIxk+fDjz58+nsrKSkSNHAnDttdeyZMkSXnnl\nFRRF4ec//zkAkydPZvny5YwYMYJoNMq6dev44Q9/CMCCBQu45JJLqKys5Hvf+x6/+tWvuOmmm/rI\nsmTJEubPn8+pp55KSUnJ5y6bIoT4SsemgfDo+p6/hdlTaWLGV0R6KQtUs+e+RLpkko5r8TRJxBUj\nPTwHB2D6mipNb2ufPq5lovv6Wuy4ooJNA7uwOuGheVCYCfkqZKUQ48MWGJ/frUxoEvDPT61BuzMA\n6Q4rn6IsKLLDmHT4Vy3sCECkv542lWl5P2U8mJIqVdrkeonvE0nXS7jyqd1plOTeNUkRqcRGGTN2\n/9UkGUSKMqSS1eyVLt4G+tyvVIq9g5Tf7NXe+shBtxVQ3XYoKQXDC1n5EDEsBdaK/36e2TPPYuP6\nD5i/cAatEVj7/N+YPnsmo0Zm8NuX1/ONiyfRGAK3Dr9/7CW+seA81q7/hHxPNoMGFVNeaaMw16q6\npiBs29TAuJNKaKmH5to6Rh9XRm6uJUcGsD1qmWAny5f8d4r/PwTdba+mFYIfdnDXj77LG5teIKLA\nB5v8nFCdRsAPddu7IAKb1rzHX1/7B8OGj2Tx96+io62Fb86bRXHBYD7d+hL/9T9vc/PlM/ACzth1\n9wMffKhz8gQby/+xk0EFVSz/399wz31XE4hEGZltJ4A16DqS6jUuq0m31ZkbePLFzUyfMob7L78S\nQSP1+3cRDAYpzMgmGIyQ5nJTWFJMa2sr2DQa9zczblw1ne0dOFWNNLtGMBjE4dBQFMV6I4cKGAr1\ntXUIw8BvRvngs20p7vqxg3QJlPw7cbQrTg6kADoaZe9P3iMh67HuErjwv97v8bv3om1/E/ne05KD\nTfiVgZ50mFY7B50n0nN6FZeRPmpZAAAgAElEQVRHUWKLTAp4HFYYDQeWwqo3nRHIdHSLGAZa/Nac\nUjdAU625otMGThUyNGgNgd9IvVjdR8D+fx4yB5pXp1RADkQZ1Ov83gqigVhc9UnXz7w4pfbpECql\nv6SlrqdRgJAfnC4gCjaHpcDUo9Cwr4GC/AK8HZ2UDsojYkJ7QyN5BXlkpNvY29hJcVkWYcNaiK75\nrJHisiLaO7pwaHbcbicut4Iz1k7CBvi8YTJynERCEAmGyEhzYY8dtwE+02o7YFkmxeXv/fcocT+d\nKFcwAkZnlO2bNzF1xiRMoNNrkJWpYegQ8utggre9nf1NTaSlp1MxeDDRaIR1697D6XRz+swT2VPT\nytDyvISVmAKEgM5OQW6WQm2TH5fDQ23dXkaOrMQwBel2JaFg29/LIix+uwpjbosasKehi9ycDHZs\n3IQgTDgUwDANnDY7hmGiqRpOl9PyIFAUwuEImZmZRKNRVBRsqoJhGKixBzFFUay6ERAKhWIug4L/\n/ZV0CQTwH9d4WOel7Sz6giU5NjgqLaxMpadFjapZWu34vrh2W0lKnyqP/pab4h1u8sAUd5kzYtdO\n9lOO53+gTj45HlPiPHruU7BWCOLZKLFVIjOukMGahER10BXLtHV3B+xthvI8qMiFXLotrgAm5Pe8\nZqECk4vg/1ot81m/AFsU9raCPw2iTpgyCPRG2OWzLLuSJz5qrM56K3H6Fjj22Z8FnNLTWipeR8n0\nUBIl5ZWQJZFZ8o9eCq94m0gRr+pAcbSS0eg7STloTIOD5B3fb0s6x1B7/jZjmpSWVjAjlpJq3dp6\nVr7+D55ctpgP3+4gZ1Q2V9xxIekanDhnBpv3wf4WH1v3bud7Y7/BJ1vaufziSdx/x3P8+O6LUF1w\nzZLz2LML/v63l/npbbeSpUIkAq0BKPNAmhsKi0qo3SUozlV47ZPtTD6xDD+QCewIgcMVs/7qp/gC\ny7zamVQdm979mAnZVeR40nj6+XX89j/u4fs3XsfK5fWUlgxi8vRTOfMkN2edNJuOSIjVq9/mo08/\n4Z3Vq2hu+Yi77l/B9Xf+idaWLVx7+QwcWANyI1AAnDHBxvK3djP7tOOo/wgaaneieqB+dxOjs8tw\nAJoBNVEfuqahR1U8fiuYgWEYPPvss0w64RTKysoYkRPlz08+SlPTTiJ6B5rmxDCC5BWVUJibx+7d\nu+lobaOqvIpPd++hcvBxYBo4NZ2cdA9Bfxin02m1N0UhahioQsVuV4kSxWZTMaNyJUkiOVY4GhU+\n/TEs/YavJO7T5+Vol+9Yovd0VlF67huIjumA1iz9zHkS86oBKsh655lKroPtU+mOSZVIE3MPVIBA\nFIJhcDtibmD0fJhJtrgCa56S7YS2iHW+ISzFhxEFXbPm5TkuEGFLaRV/yE7M8+Lz8xTlOSSUHh+p\nkyTPYQ8l61TPOvRVqAzUPTBVsoHOiw8lz1TtodT5NJGIZaxgc0BHe4jm5mbGjy2nsyWKI8NOxfAS\nNAVyCvPoCkIoYuAL+BicUYTXF6W8LItPt9Vz3IhSFAUqhxUR9EPT/kaGDTsOOzFXwljsJk0Dh9Np\nKcgc0NzlIzvHlYh15Td6LlrH5S5xP0198EpK3U8nrJaSy+Nt6yLT7sGuaeyr72Dv5u0MHlJFc20Y\nl9NFdl4eBTkqBTkFRE2T1tYWvP4uWltbOWvOaWz/tIGPt9UTiXRRVZ6XmJeHsRS2BVkKtS0BCvLS\nCHkhHAyABqFAmHS7y2oDAnIcT2EqCkKApotYGQR7dtWSnZWHy+UizS5o2LOLcNiPKaIoiooQBnan\nk3S7g0AgQDQSweP24A8E8LjTQAhUBHabhmmY3XGSFAVTCOv6qoKJiBkUHHU2MkeM84defljnvcZr\nX7AkxwZHpcIqeZCL239p0K81S1zZRGwANxUgZvY7kB5fwxqwBDGXsAF26AezoEols5Y86Cvd+xJ/\n4fhxAdEotBvg1GBnu6W8ysuCogwocVhBqmOn0GZCXkyetDQYLeDjplj9KbEAlX5L5ooiS6nlTIed\nTVbsLLpFSlmW3vsSqy+9BvTktKrad39yPv1ZcA14QBVJwb8P8dzeHCi21+eJi5Xs6mhToKPZcs0M\nBKCsGEpzwcyHxjoYVAbHDSrFZU7nH8+s4/XX3+CKu2/DnqXiN+H5/1zHN79zIp70dAovuZi7F9/C\nWVdcwZ//vJ3Fl17EHx7+OydNOYHnXn2ZORdcSGVZOeMGK3R5Qc0Gt8tSAKUBPl+ADIcLR0Rh4vgT\n8GENfl1YrqlZWANivDqTVS8K0ByFoB+CbfDUQ7/nmV8sYvzkcTx982ME7GkURlz86ve/4e1Vb1DT\n3MzWT3fy+yd+zWUXL2D0GacyavwkfnjNN7j43Ju4+bafUtcA/+//PcvezS/QEIVPAoLKWBTMKqxg\n6Y/8/k3mL5zFw3f+L5WefJ787aMMATbHFEOnnvhDNO9e0lxtaCg4nW5CsRubmZlJe3s7rzy7HFVV\n8XX6CPg68DgV7KqDEyZN5MMPP6Szo4u25nbKCosQhkmrtxOXywFGlK7OMEG/nwxXBna7nUgkgonA\n5XLFzKQ1tm77GI87k7r99ZRVVhx+w5FIJF8Zx4pi5ViRU/LVcEDFR6/fcfcwZYBzXAVr/pJIPtB5\n2WEoLZTk/amUOkkyCwFRw7KQ8kct5ZXDHrOWUqzYmPFToljzGrAUEhl28PYKLxk1QImA22kptVQD\n/OFeC+EDUDSlLGB/898DzIsHlO8hJj3sGFZfgW5BUUAPg2GCYcCQrKdxaFbbCweteWuay4Uqcmna\n10FzczPlw4eh2Cz5GnZ3UFyejaZpOMvK2P7hFgorKqiv91ExqJTanU3k5GRR39hIYWkpbrebTHcs\nMLvNahem9RVDN7CpGqoJWZlZCWsmHSuNg+5F3FL3032qKBIrgxGFvTtqmDCmnMzsDPZt+QxD1XCY\nKuOOr6altZlgOIzP56dmzx4GlZWRkZ9LemYWlZVFfLBuM0OPG04oBHV1dZw5cxIhE7oMcMceejxY\n7XtHTQulg/LZua0Oj+Zg/ITRpAFdMV/ad1d/jKIH0LSo5S2haon5vM1uIxqJ0lhbi6Io6FEdw9DR\nVFBQyc7OotPrRY/qRMNRXE4nCIjoekwxZaLrplVvmg1FUWNxqqwA36Zpoijg83WhaTZCoRAuj/vL\naUiSrz1HpcIqmQNZsKRyZlSUmBLDHIBp7zFAXJFiGKCrUN8Opg5dLohkJQWUVqEJa6Up3W4ptsY5\n4ZP9sbcFYg3AwaCVxgA6O7vrUDHB5gSjn1jRqpq6vr8oDjSgxlcSj0XilmrCsKz2cgtjPvm6ZWEV\nFNYbcGrWbKfSPoIdwCnnHkfXLqhrbcEmVGw2CHfAtxedyDvrOpkyJYvaTj8TTz2T5tp6xk46nkCo\nk4xsQVFxHt//4XcxHeBv60BJg6AHhBeiAhxuwAf5Lg/vv/UvXtlTz0U3zseBpaByYgUu7YhYq5fJ\nLoBx5aAAcu1gy7YUYf/9X4v4uBFeeP4dvnvrTTTWd+Jv87Li+dc5cc4p/OjuYTTugYd/8is++2wP\n4Q8djJ9wKjYX1O9vYtWqVby8VufTzS/gA3xRqPAofNIYJC/Tzft7o0QMjfKSKh79xYv84IoF/PrB\nJ3jvnzmMnDeZGRMq8AMXfWcBzz32AJWVlTTU1RCNhnGnua1BUlOxIxhUNgiv14tDKBRkptPR3kxR\ncQHbP9lCxB+kLlDHpBMmIiJRVAFtbW1kZ2fjdjrp6GyjpLgMPRwlGgkRjUaJ6FGi0ShutxubTVBe\nPpi9NXWcetp0du3b+9U2NolE8m+DVF5JDoWvk3FD8oK2UKyYVcKMWUvZuhVgCta8RsVaMHTYIVOF\nrnB3bCyBpSyJx/WJv2EuflD5kue+h0s/eq9jgrjyFGEpUO1Oy/tAxAJNGcKK69rR7sOjphMG8orS\n0AMQikQslzgVzCiUlmfT2qGTk20jqOtk5RUQDobIyM7CMHRsdnA6HQyuqkAoYESiYANDA/SYA0fs\nu0PT6GhpIxgIUTqkNGHNpGLFQNPNngvYyVZWAiuGrFMF7DChuhxvGBoa2qg4bgjhkI4e0WloaCa7\nIJehI9IIB2Dn1t0E/AFMh0pmZi6KBqFwhNbWFho7BLNmTkLHetuhW4OusInDptIRFJhCwe3y8NnO\n/VRWlLF7xx7aWuykF2eTn+XGAEoryqjftQOP20M4FMQ0TTSbhmEYaAoYCJwuN1FdRwEcdhvRSASn\ny4GvqwtTNwgZBllZ2Yngx9FIBLvdjqqq6HoUl8uFMEyEsAKrm0KgCoGmaSiKwO12EwiGyM3LIxAM\nfhVNTPI15Kh9p5oR2+JByXtvcTe2HibHfZYV6NOjJ85J2pJXUoRqbfFjyd9tsU1VY5+ie6MfWeP5\nJcdj6oNidb7xLXm0VRRL4WRgDbBCQJMX9rbBmlrY3gYbm2BzK2zvgL1hqDGhKQDtPsiyWysI8fyC\nGnymw/r9loVV3HVPs4EnFj+rt4tlXDnYp+6E1YBU6P6S2BGrT6V7S9SpYqVJdt+L70O1XOdIylPR\nYi6XarfVVvyaRmxLnJ/iXiffm1TtoM/tSNqn9rof8XuVfE9755dsKaYoJAJ8xs9X7ZbiyJUGAWGV\nb/qlIzBLISvLWinsEgZbtnzE4Hxwh60A+iEN0jLSCXjhw00b+OY3ZnNcYS4bX3uFz9b8H5vWrUfJ\nsFE6BBoaggwpKaKt07r+sDx4/c+b2PE+7P3Qy4hhUHF8Nd+8dj6u7G63yPjgvG+3IAC0BLrd/8yk\nTYnt82G9ua+kCL5z7SkMGg+nnJFF6YhyKqZM47TzhqEAN93+FKNPmsioM2YxcuRJLF68mI2fGNzx\n2IPUBVsYPKKCtzaG+Ml9L1Hb1o4PqC5y0+YFQ7GRm6syf3YVJ1VPIOCHqafPYOPGjUSxZN+9W1Ca\nnoUZ1andW4tp6hhKlGDAi0MVtDfvp7Wxnprdn9LV3gR6GNMIUzF4EM37G7E7NJwuO2WlxYRDfoyo\nTsiIEvT7CQVChAI+IsEQoUAQXzBAVDexuZ1kZ6bjtGsIobN521baOrwYqp13122gvqGtb+OSSCQS\nieQwEAfZBsQANR29QwD0/h1HVaxNiX/S02JqIHL2u17ZO7NkGWKeCSI2Lw7rEIhAe8ha9OqMQFcU\nfDoETAhixSmKGpYyJHmeZyhW+IyOcE+3L0Wx5m4DflCKK2HiIveaO6YqV2JefIjlTz43MeeM103S\ndiDLrENpNyljV/VXvn6S9JBdpNindYeAQYG8QekIlxWHV1VBF4KuLi9uB2gx5ZGhgM1mxYPyejsp\nLi4gzWnH29xIoL0Nb0cH2BRcHgiHTTwuJ/GXmqU5oLnei78DAl6d9DRwZ2VSXFWKZu9bpEDAki1i\npK47BWt+bGBZP7mcUF6ViysTcgtsuNJduLNzyCu2Arts3raXjJxs0gvySU/Psaz8uwTDx4wiZERw\np7lp7TTZ+mkjoUgUHch0qkR0ECg47FBa4CEnKxPDgNyCfDo7OxPGGoEAuDQ7mIJQMIhAIBQTw4ii\nKhANh4mEwwQDPvRo2FJICROPx0UkFEZRFVRNweV0Yho6whSYwsQwDAzDxDQMTMPA0A1008A0BYqm\nYrdpqIoCwqTL5yMS1REotHV0EgrLN2hLDo+j0sIq1RsY4nGtDNXqqHr3JApWx5Uc2DrZ9S4ZoVh5\nHKobWbLCo3eMpz59eUze5B7tYFZE/aGq3Zp8QezNhjr4DAiFrY4fzbKsCpqWK6GqQovPWjkipujR\nYnG2PmuxPoXPGhzicYjSPRD0gif2VpWE9VU/sqWK/ZQqoHocAYmA6vHjiSRJ+zQ44AwhlTyiH/dD\n6KuQTOX+lyrPg7l89t6X8ngsuKeSoh0oAlwOMGKrPB3tloVbVzqkhzTmfetiMpzQlmkFlHeqsH/X\nTk4aMoILZ5/Dq2+uoaumlkU3XM47n9Rz6Ukz0bJhTx388+VXuPSSC/nbH1cx5fTTUIph3qXVmBqE\n2jJ5618tjJ6ST3sHZDgsV0AX0GHCpx+E2b93F6XFo8nNtAbe+EAM3bdGwwpgvqNBZ3yJja2fBDlh\nnJttIWjq8DK4soSXnt1GYU4R5ZkeTp07jSeffRnbp7u4+j9uxp2tkRHKxua0cdrMk9i8ZTv333Ee\nGlab9AN7djfz/tp15HjSOeHK0/j23Cqef2E3f/zD09iMMC++sZaC3CruvOpGvvXt83GnCcrKytjf\nuA+XzY7X6yWgRvH7/bhcLrq6ugiHw7jtNgzDoGF/HUMrqzBNE13XiQiDlo52GurqKS4qJT0zE1NE\nrFWx7GxMESYUDpKXl0lrexuZbss9cF9dA6VVVezduxfTVFBtGocwzZVIJEeQeHBwabUkOVpJOQ2L\nTQp7x3vqcVjpee6BlBgD1GX1oD9FVn/5D2TfQOgzv4wJb5g9w1zYVMuaRkStc8J6z/Pii6+BWHwr\nEQuUFR+9bZoVJkFTYvPpg8k1AFl7yE0vhc7h3IR+rnswZVWPdIfjpngosvQ+3qtdxr83hK5EVaHE\nabnc6XHrOQM0U6G4pAybCpGYS6CqQMjvJ8eTTklBEU0t7ejBEIOqBtHWFaIsJx/FDoEQNDc2Mqis\nhP11reQU5BFwQvGgTIQCZsRGS2uEjBwH0aj1vGRgtYOoAH+nSSgQwOVMx27vrq7ez6rxe+kPCTJd\nCj6vSXamSpcBkaiO2+OisdaH0+7EZdPILcxhT10jqj9A5dDj0GwKNrsdRVPJz8+hq8vHyOFFKDFZ\nDCAYiNDR3oFd08ganEdJoYeGhgB1tXtRhMn+5g4cDg/bN31CcWkJqo1Y6IogqqKh61EMRWDoOqqm\nousGpmmgqSpCCEKhEGkeD0IIS0kFRKJRwqEQTqcLm80GmITCYew2O2BimgYOu41INIpNs6EqCsFQ\nGJfHQzAQjIWRURCH/Y+XDJQVK1awY8cObr311gOmW716NbW1tUyfPp3rr7+eFStW9Dj+0EMPMWzY\nMPLz86mtrWXBggVfptgH5ahUWEGSRZLoGYRdiw0YyQojNaZ86v0WtlRv/NMgMUIMtDO2JS9bJMl2\nMEylZ/D2RFDxA1lb9ULtJ22iToQVf8s0Lb/7iAEODevNFrFn5uSBUFGsYJUx5Tdo1nGbCt4uy6TV\nMCyrLCNeXqPv9dWYDPG39KmClAHPe5+TWIHqveyS4n5oSfXexxqr1zXik7VU1+0d56o3yW+gTKVw\nSwTcTC6LSJFfqjLHz1Ws6wh6tomICZrdimNWWgbNTUAA9jU2s+j8Sr7/+Ft87/qZtO6F/Xu93HDx\nCN5vhS2f7eWVv7/OjXfczJzpF3HDNVfhHj2eQMhFNAwnnXwaQRNOm3UaHVEdNBsfbjEYM0rjuvm3\nsHzlL1izyceUasvc2g1EAE2HcyY7eS9zNGVZltLIZ0JWLzNoE8u1NAK0NtowS2DUODcbw/DJH/Zz\n6dXF1AHZZNIBnHn25XQCCy+dydThaZz9jR+TpoQJ6ipXXLmYsYXwg0W/YvjY/2T7tgaIWoHM6+p3\nc/8N56BhKc5agQUXVBFuvJA//c8vWHzGFE4//ybufuinPPngLxg6OBeh+wh2efFGo5gKeL1ehg8f\njs/nw4jqlAwtY/eOTykuLsDv9xMKhQiFQgSDQXQ9zO49exlSMZiS4gIikQhGJIo9J5fOzgAOmx1V\nNdi3ew8lVRW0t7cTaA3R3NZGW9BHdnY2bZ2thCPWfZVIJBKJ5AsheS6apGRQej81x/alChLeazrb\n+7QBo/Y66VB0LD3maSlkP6TzexzomZ/AcuMyRbcVWNyaSel1nmEmnR5/tsByEVTo9jJIlLMfBWHy\nvHYg8cIOVdHUX9nFQY73e4n+5sWptEkpdh30vh9MWaf0TRaPvatgvSEwEgYMCIYjlJe4+eizViqq\n8ogEQQR0hpSl0xGBLn+AxsZmhgwfytr/+4CqysFoGZkYhgNhQk5uHoaAvPw8oqZ1Qzu7BBkZCh9/\nsIUTpo6m3WuQk6klvA1MrPtYmK3SbkvHbbe8CyIC7Cn+A/FzImEF4YL0TJVOE7y1YQZVOgkBdtKJ\nAgVFg4gCg8ryyU3XWLNuC5piYJoKFRXlZDhh08bdpGWOw+cLg0lMoRRg5JDCRHuNAmUlHsxwCXU1\nuygvyOa99zczfNQw9u7cRZrbDsLA0HVMEXPr03XS09LRY5ZTnrQ0/H4fLqcT3dAxTAPTMDFMAyEM\nAoEgHrcbp8uJME2EaaLYHehRA1U1URAEA0GcHjfRaBTDMIhEo0QMHbvdTlSPWP8xuY571DB9+nQA\namtrB5TuSHPUKqxSEVcQaHQPPHD4qwDJ9LamUZMH0QGOxAezwOm97/ME81ZVKHFDfoFl9umwQ1Mz\nhFUrLpI3GHttrwZRM+F6jBCx4JR28AW7BwkTS/FlJep7raPRh/9IEncVTGU51ft7j3PoO35HwtDV\nAEEfZOdCQweklRbQAtxzzUw+6YTmXU24bCq//s0aKgaVUT7iOI5vOpEt6z7khMHDCNQ3Mnx2PscV\nQH0BOIwC3n57PR9uWs/9D/2A2+/5PY8+vIhnn9nAr1f+AgMYMSodg+7Jm4kVtyoCZGfBln1g2GBI\nqTVAB7GCTsabSSxmJadNgIYgNO/SUdw2hswrZh+w9uMGpo0rQQE2d8BJ2eAYnoYKXP6Nc3jksV/y\ng9vvIQys3CQYXD6CgB8mTiqh1AXvbmtl/hmnsCMEWVFYeMEV2IRg0qRJ+D77DF1EaAau+MmP2Lt2\nPdGQl9r6nTgdOqpdozC/gEAgTEZGBuFwmEAggMfjsQKku5xk5WSTnp5OdkYOW7ZsISMjg4qKCmyK\nSjQUprGxkXA4TF52Dl6vF7CxffsOKisrMRWora8nHA7j9fpA0QgFI/i0AIYBpmliRHot5UokEolE\n8kWS9MDfx2rm82bdK4/DyfKg5/S+xueU22WzQigYhjXnCscCqBvErKew5sZmcmXFFFpqbME22Xom\nsVjaaw7cXxzdf2cUui3d+js+UEwTjLDlgWC3W3HKbC4HEWBEZR5dOkT8EVQF9uxtx+1y405PIyuS\nja/DS5Y7DSMUJq3AQZozFstMOGhp6aTT28GoUYPZ+mkNY0aXU7evk/FTRyOA9HQt4e6X8CaILTbb\n7dAVtMroccU8XmLpkpuJihVHOGxA2C9QNIW0YidBoN0bJjfT8mvpikKOHdR0a1m9vKiIXZ99xuBh\nIzCAZi943OkYOmRnOXFp0OaLUlqQi88Au4AN73+IAmRnZ6P7/QhMIkDFsKEE2zswzSjBUABVNUFV\ncNqcGIaJzWaz5qmGgaZpGKb1hj+b3YZm07Db7Pi6fNhsNtxuDwoKwjQJh8OYhoHD7kDXdUDB5/Pj\n8biteHKhEKZpEo0dE6aJrhuJ/4ppDsBMUfK5qa2t5corr2T//v0sWrSIJ554gpdeeom0tLSE5RTA\njh07uPTSSxPn/fWvf+W3v/0tRUVFuFwuhg0blrDYuvTSS7ntttsoLy9n+/btjBo1ip/97Gds27aN\n2267jYyMDMaOHUt7ezsPPvjgF16mo1JhJdTY2/qwlChazIooYYwTX1WKtXvjAANK8mfvNAdSGIlk\n5UJvBU6Sdc2BgsJDT+uwgXKwAdumWZZQHWFoqQOHsOIi2ZyQplkBscMhyMiEtk6rfkJRMA0QGigO\nyHRa7miNnVZZ46bOIsnvK2GZJPrKFK+7uJufKqzr9Jng9FP30L0C+FWRfB/6xKlKsb/PvqQVOkiK\nS9UrnarS7yQsbn6ejMNhmT3n5ENFKQSKwReBRqB2O+RWQcUpheS44OX2dvbV1nDeyeU0bC2mdFAJ\nOQsupWPvp+TnqfiBjzb4+NtLf+Gic8/D8HfS8BksOPcbfHPOzVx5w/XsfKuLIadmYBiWEsqO9dnZ\nAYXZ8Me/7OLyeUMRwHubDdpdGoVOyEyzLK6SPXJ1oBModEPxWBsfbutgfEk2e0Mwa1wJP77tGf7z\nwe+y8Oq7+Z//upuXXviAX9/xc7bXPk/5yeM5ZVQOu9vhhKEKrReeT2uTl7SsTN7ZU8/JI0sxgJ9e\ndjeecD1aoJVAwIvZOYTRkycx9qSTWTDvGoK+MKa3HkUPEg0HiRomqk2jw1tHUUEhpgG+rgCaakcI\nQXNzM2keDw0NDRQXFrFz9w4CgQAul4v6mlqcTjtup4PafbW0traiDBmKt8uHTdUYNKiU1rZGwhGd\nbE8hQigMGVJCwB+isLCYHbt2okfBRGAacjYrkUgkki+AJGupuOVUqvnTgC1f+rvMQSxi+j0v6Xi/\nWSjd+XzRUz8VS/aoCZFQt0W/olpzNbtqzYFt9ljYDCXmRRAXRrXSaCqEoyTekJgQOJ5fP/PIPoj+\n17u/wmnvwemvPIdpqaX02HlgS66DhUKJmpa3iNsFhsuylgsDIR/YPZCT68Cuwf9n78zj7KrL+/8+\n+93v3Nm3TFayh5BAAgEFZBHUWi1FBAS1UorrD0RcWq27Yt3QWlRKVaRqqxQUhYIUrKKASSD7Plkm\ny2T2ufty9t8f596ZM5M7kwQThXo/r9eBk7N8t3Pmfp/z+T7P5xkwTYqlIi31AUq5AIGAhtLRiVnM\noakeqZRJ2/T399He0oprm5QK0NnSyvrf76BrzmzywxahBhnXHSehbLx3RVOgt79AZ2sIgGTWxRQF\nNNHT1/Iv/MK4jpUqgRYTyORMYgGFgg2NMY0dOw+xbFEXG7bsZsXyBQwcTdOzu5tLLj+HYH2M+qhC\nwYSQAkZ7G4ZhISkyI8US9ZEALrBr424kR0ewDWzbAjNENFFHrL6eDeu2YtsOrllCcG1cx8Yuuwha\nZglNU3FdAcuyEcrpBgxdR5IkdF1HUzXyhTyWbaOKCqViEVEUEUSRYrGIYZgIYRfTshARCAY0DEPH\ncVwUTUMAwqEwtu2gahr5fN6Tx8J98T9MNZwUenp6eOihh8jlcrzhDW9AkibHoB0L13W56667ePDB\nB4nFYlx11VXHXLN9+5MMg5IAACAASURBVHbuuusuGhoauPDCC8lkMtx999285z3v4fLLL+fWW28l\nGDw9mSBfss55FZHsCkFQyYbn17fyY7JAekVM3R+2JYpMLKgMvzD42LXu+DZZiN2/+XXGJwinA5Tb\nPSZKWT5XEQ8vX+JtVcqearPLjIEpeasQJRcKNqSKkCxAf9rL8nZkBIq2N8EG1XESrlCCo3kYLQIy\nY1pSAuN9ldyJGQgniKdL4/sVI6EyPv7ncYwQuW+wKvdXiMjJBNIxdU7zXP3vy/GOTXgGZYHHiq6W\nX9hdEsY3yu+NVNGjqjy4Se/f2Bj6wlbH3rGpVpwEKKUhm07T3AQDObBkaAqBU4Rk/yHabVj70CbC\nwLwzZ3PT355P3/4SucwobWe0cNHrFvM/67bynXue5Hs/3sa2PftZdtZ5OGqU9956KYjwre/cx2d/\n9GVWv6aL5hVRT9h9GAa7XcJ4KXIFHYwUvOmNc3nsiX4sYMUSifX//TxuGI4UvXdqKAdPPXNg7P1N\nlP8/nIF0QafHgPYAPPnCCPd84SY04Ac/+CTbNqS45OKzuer91+IA5y1KALA0AXHg2ld3kMpnGRrt\n58KF7ZSAzYfhbXfcQed5b+TGN9+O7MbYuOE5PnXXl/m3f/8F+9fvZ8XsOfQfHaBQyBAIBIhEIkiC\nSFANkc8VyWbzgIhhWAiqSkNDA5FIBEUJUCwWScTi1DXUYzo2PYd70FQRxzZoaIyz+tyV5Io5ZsyZ\njRqOMDyaxLQcbFEgqIWYN3sR2UyRTC5P/1AveTOH5eogWGiB408QNdRQQw011HAiqPArFQi+bbp7\npi3DX8hxSAr/ZZPL8Z+f8ibfoWMuq9iJJ1J5la3SlkpSJru8WY4ntK6bnhZRyRiXu5DEcbvfdqBk\ne7IafnLQX3dFKLxyzvVt/raM2bST7cTKoen6MlX3T3R8fKj2jKo+N3/Z/gd8vGb6bfMpmjSh4kkH\np7SL8TIAWqaFpnq6Y67gRYxgg6kXCQDJvgwSEI6F6OpKUCo42JZBIKLR0BJhKJnh4MFhDvdmyeTy\nxOIJXFFm9pxGEKDn0GEWrlxMojmIGpcRBbAM0PPj3z+CU85G2BpicEjHBeJRgdRgGlf23pmiAYYF\nw6OFsfYr5f8bFpi2Q8GBgATDKZPli7oQgZUrF5BNmTQ2xmmb04ELJKKelkRU8TxKOpoCmJaFYeo0\nRAI4eBE0M+bOI5hopbNjLgIy6XSSPfv2cfDwAPlUgXg5msC2LURRRJJkBAREUcKyPK8nKHs8iSKq\nqiLLMoIgYjs2qqygqAoOLoVioRxp46CqColEHMu2CYZCCLKnW1XJ2CmJEuFQFMtyMC0bXS9huzae\nEpaLKE33EtdwqrBy5UoURSGRSBCJREilUse9J5lMEg6HaWhoQFEUVq5cecw1XV1dNDU1IYoizc3N\nZLNZ9u3bN3btJZdccsr7UsFLkrDyc0pTCWT7wwIr14nuRNLBTwxVQ6VsPxlSKXdyfYIAjuRttnjs\nuco9YwSLeGw5x2vPyUC3PObfcr3NKVP8RjkLils+bjqemKRugqqNt9m2vXOVTCtTQSj3RRKObf9U\n43QiLt0TPI5e5O+Xc5yxrEZwjWUaxDM+LNF7piEbEhZ02NCiQ10J6gTQhLJXmjDFViHAJmcyrLyX\nvk1wGfNGq2yCA/X1sOUXv+ThD36FxRIc/eF6/vkDX+MDr/k7li7s4sBBndFDR/n4h+4mkB7l6r+8\nlX/4+w+itLUxpwPu/95v+eCH72DxOWdy1uqlzG1t4cY3zOOSpSIfuvFzdAg6H//a+0nIEAb0HHQC\n2+/7CU6+HxtY+3gfyUMFhLJgqeU6BICeIrz6hnM4MATJFITD0ByByy6YTakE777hCyjAM5v76YrB\nq1a2YFmwtRcuPLuBb/54E7d+9kF27rRJWTlu/rv38YEPXE0ST+TdxvPa+snzGZ47BI0tKmvmtHIU\nT1drcHiIpq4Ib3nHaxFb27HUCKIa4t3vvY3fbbiPCy68gmuuv5GOziaKuTy6Y7F0xXLyRgklFCBd\nyBGIRNFtsJBIJpNYRZ2jh/soZIuAxMjIiOfCbJoElAAHeo6CpDE0nGTr9u3MXTiP/sE+DEsnVyzg\nWC5t8UbaG9vY3d1NupCjaBkcPHgQy6oYBxKFQuHFvdg11FBDDTXU4MOJ8BrCpP2xrYrtMl09J3pM\noPqC78nagqdC1gPKmbQdHylT3qlk2MYdz3Rsl6Uy/IvHlYyD1UidiY0eJ2lOZDyPw0VVL/9krvfh\neA4sY+31N2oSC1ch4GTXCzsLAJoDiuMRMRJUbeDk920CoeUfM3+Vle8wd3zD9TyrMgOD9O/YR1SA\n0pEU+7fvZ/vvtxCNBCkUHMxiid07ehAtk/XrtrFr5w4ELUAoAIcPjzLvjHlE62LEElHCmkZna5jG\nGOzY0E0Ah/lL53hedYBjeTZn9vBRsDxiKjmoYxS9GFHb9TxQRDwHgabOOAXd+w6TJU9qpbE+hGPD\nlg17EYHRjE5Qhsa4hutCpgT1dQo9vRm27ekjl3UxXZvNm7cxZ24bJpT9nTzb+GjKIlkETRNJhDz9\nKwnQDQM1KNHR1YygBXBFGUSJWbPn8IqLzqK+oYn2jhkEAiq2beO4LrF4DNuxPZF120KUZe/vAgHT\nNHEdh1KxhG15HhGG4WXech0XSZQoFEogiBiGSSabJRwJo+slXMfGsm1c1yUgqwRUjVw+h2l7elnF\nYhHHcRAEAUEQsG37hN/lGl48hEk/SolEYmzfrKTJrALR95HvVmGUJ3tqua6L67pj9U2u91TiJUlY\nCRxLjhxDQknHHpuKDPKTCBM8dSpEhP+HkokePX6Bdf91/vN+D6wKOVGBI0w9+fknDX85k6+ZytAY\nC0tkvI5KfQ6+sL2ya61pTFwRsn1ljK0Mlcd2zFOsXPhU7ahmrEwFP8lTqUt0JhoW/rGeME4+jJGV\nU4xPZZPxiLZKX9xJDi/zTLg2DG8Kw2vi8Ko4rI7C+Qm4uAEuDsFrQrDKgaBTJjjLz0qqbEK5DoEJ\n7NQYSVWF3HKl8U0QoWDCX3/mGl77tQ9wMAit167i5ttv464f/CsPP/E0bS0atLXx9DMHmTG7mS/e\n9XUef/wbdLS3MpyHv7zqlcw8K8i6369l6WyY3xrlda++mbX9cOPttzLs2JRGYc++PA/+bA+5NBxx\n4YLXvAYyFv/x+f+if88OuppCfOdbD/L5zz7I1Ve0s2OvF+qn4q06NSQgGoAC3rGgAh/45AdxgSXL\nW8nijfn3/vVxSjloAXp3HebCledxaMde5s3oZP3/fGNM++rL9z3HaNkYcgZ1nJLLZYua2DVq04Dn\nuZXs72dGG8xrhV/c+0+EhAx9qSQLVqxh7yFY/8JveMv1f81ochhBloiEwjzz66eJB2PoeZNYtB5T\nLxILh9BkiYAWYng0CYBhlhgeHsB0TMBEll1KZomOllayyRSCJNM5YzZ6wUBwoVQykSQVV5Cob2/n\nUP9hTNvAsHTypTyKFsS1IB4NUyrkiIZPj1tsDTXUUEMNf36YbAMeY5NNYQudSFkVnGzEzgT7uWLP\nchzS50S/aaZgeyaTHv7T7qT9CvlS2R+7VvDsZD/Bddw2T6psqrE9blnHw4uInDrZIZ3qxrALHRK0\nS9AsQ6MMibLUSKMCjRI0S1CHF4nhH89jSKmpHtKk40KVY7YD7QvbaV46l4IEWkcdM+fOYcnKM+kf\nGiWgiRDQGBktEAypLF66lPPOW+qFp1nQ2lZPMCaSSiaJhSAckFn73GZSJeicOwfDdXEMyOVt+vrz\n2JYnjVHf3AyWS293H3ouS0iVONTTR3d3H23NAXJ5z9NLxCM3FcUjrCphhKIIcxd4khrRmIZV7tKh\ng4M4lpcBu5gr0hBPUMzlCQcDXLhm6di7ue9wEqNclqs7uDY0RlVyhouKRxiauk4wAGEN+g/uRcJC\nN00i8QT5IqTSI2zYsB7TNEAQkCSJ0ZERZEnBsV1kWcF1bGRZQhQFJFFCNzwSw3FtDF0vi7O7CIKL\n7dgEtACW6aXaDAZD2GUdGdtxEcofdUogQEEv4boujutgORZCOc29LEs4toUsvSRph/9z2LRpE7Zt\nMzo6SrFYJBKJMDQ0hG3bbN68ueo9dXV1ZLNZMpkMpmmyYcOGE6qrq6uLbdu2AV7mwdOFl6SGlR9T\nZckTKizNCWDKCftFzyanHiejczXBO4lxwsqPinCkv+/uhNn6T4vTSMJ65YtMFFtinHACiNuwvOHE\nypoRBKEEmyplvMjf22p9FgRPU6GiL9bTA4tmQTYIg8PQILSwa7/DK966gsvftoJzF7+dlWcv5dp3\n3UhkqMC+bIHv3vcd3vjGN/LTH/6ApXNamTdzFp//3GdorId8KcINb7qe6975Pm7/2zV84+lDrHrj\nfAo52D7Sx+/XrWXV2edw8SWLaA3Axz/61/zn44coAAPDWTJClIZ2aG2HoOBN6Kks1EchKMHKeRIW\nnvFiAn15uOO2KxGBg2l4762vR456E7wO9OKFH9rAbW9fg4P3I/TAoz/ju3ffTAjQdZ04IW659U62\nbVjPvqfO4ReP/JSIIxCOaDS4Ipldm7njS5+mrV0inw+iBkIM9B6lpJsYpo0riJRKBSKaimEY5HI5\nALRQkHA4SKmYJxwOoygiyeERVpy3nNHkMPWJpezZsRtXEGhubqa1tZWjRw4jCAKNjY3s7t5DOBxm\n+/5ubNv2VovKz9VybFxcMrksDU2NlEqlF/ei1FBDDTXUUMMUON3208sZlW+Dqp8IPu8h36E/G4x9\nN/lJN995xYX4CWY3DpZt7Ax/2GfFVPeOJXsSvMRS0ZAXEWEaoKKSK0D9jDhNM+L89n83EY9H6ZjV\niWzYFCybQ4cP0draSt+RI0RDGuFgiEWLFnqasY7Exuc30DFzNnNmJjgwUqSuNYxtQdbQSaaS1MXr\naGyMoEmwYH4bvYNFbEA3LExk1ICXwbAcpYhpgSJ7C9jxsOCJtHtDRMmCueWsfkUTZs9pQShfa8OY\n55QLzJmRGBNu7xvs56xlXZ4HmOOgILF5216y6SSFoTr6B/q9hXlJREHAzGXYsXcPAU3AkiVESUIv\nlnAcF8dxkWSwbRtZ9ATXK2GBoiQiS14ooCxJCKKAqRvEY3FM00BRVPK5HC6gaRqaplEqFQEBVVXI\n5XPIkky2kB/zuKmgsm9ZFoqq4Tg1D6s/BubMmcOtt97KwYMHue2229B1nXe+853Mnj2befPmVb1H\nFEXe+973csMNN9DR0TEmzH48vOtd7+JjH/sY3//+95k3bx7ZbPZUdmUML0nCShB9IVxVSJaKkLl/\noqk2gY851EzjrTMZjsgxZNjx7jkR46Hi4TXVvdOteFSrRxAm9n/Mu6w8Nn7vozF3aDgmFTEcKww/\nmcgbq/ckZqUXbVD5vNymKq8Sblc1a0v5Okn0ssUUXM+tGbzm2wIoBlxWf3LN6gyAYcABx8uiN/kn\ndyyF9DTtrtoXxr2xNKBrZjmbSRiW1kOkZQGhuDfxacCGHffxjtf9PcU9h3jq97/ijPkzaO+M8egD\n/87rr7yQxx99nBs++kG+8E93cXTPfl7/ur9ACEV499+u4Yabv86D997Ku2/7Fje8+c187H2387Ev\nfJ7zL1yEFICeXth3pIDiOGztBt0xeNVcGKqMHZ5nVWvUC+NT8LyjDhZgx7ZBrlzdTF0YjhpQp4Kj\nQkMQDhRhfhCG8ciqETyx9tl4BJYCZIvD5MvnZ7aFWLe+yMxAlK0jw/Tu2cmTv/gZb7vxbaRG0oQj\nQV535QV8/wffpX9wlHe84+388snHqW+ZgWDlEBSBbD6PK7pIskAxn0NVVQRBwLFK6IaObdvk83la\nW5uor2vk+XUvEA4HSY2kEQSBc84/j83Pb+DIocPopkH/wCC6fpjZs2cTDEY4cNhLA+uIXiihpgax\nbRtRlLBxyOSLtWwoNdTwMkN37uucEbn1T92MGmo4Bn6br5p5daLrkS+aXJjMAJ0Cu3jKa6rZxS8C\nY5EATLRjx7oy3YBOxhTM1km18RQTjcfYm8e7RhgnScYOV+xjB5pOkKyqICh63xeFqcIop3gpjzcM\nAnC0eDPtgXsRgVCovP4sQ0wFWYsgKeO280WvOotNa3di54sMJ4cJh4MEgjIDR4/Q2tzA4MAgnfPn\nsbd7P6V8npaWFpBkZs1MsGHzAVYtn82WbT10dnSwa9t25i9aSH1DBCRP8zdftBFcyObBdl0awt53\nQKWLIl5IYGVcZTz94GzGoDmhosie1IZSjqLRJC+sUC0v5Ep45bl4NnCpXKZlG1jl88GARDLlEBJl\nsoZBMZ9jzbmr2LhhI6ZhIcsiLc31HDlyiJJu0tU1g8GhQVQtCK4FImMElSAI2JbtCakDuA6O6+C6\nLpZtE5BVBEUllUohS9JYCFldfYJMKu2F+bnljIFOkVAohCTJ5IvF8gP0CDJRLNNw5d8uy7aZUris\nhlOGq666qqpg+jXXXDPlPQ899BAAV199NVdfffVxr/PvDw8P8+Uvf5mFCxdyzz33TAg/PJV4Sfrm\nTfAKqhImJ/quq+Yi7Ujj942F/VXIG982Vd3+UK4JOloTxLW8bUrvLcnbRBEk6QQmb797bJVtckgj\ngCyUhcDF8XvFclkV4qacGKJqyKRfS8l/DCaFOlZC+Hxhk9VQLazRlaocE8Z1BMb6Lfs2mBBuWUlJ\nPPm5TQ63kwG5PBkEZGgKw4yIlxGxUYVLY7AyAn91kmRVBXNUuDQAUdt7plKlyZV3pryJUzxD/ztV\nea8q46BIHiGlCtA3CKIG//18hkgM8hnoH4EDvfD4cyU+e/ednHPFKnIlnVR/ljf/1fUMyDY3f+49\nnLHkHMy+YRY2N3PuynN41WuvIJvN88Aj3Xz53lu5+5HdfOpr7yK4qJ4vfus+zpi7jM9/+n5++3yK\nPQMFzj03hGs47NuynTWrGtiZG5+Qy9GiBPC8qQQ84mo0afLK1c3owO5BaFfh8cd7qQvC7gIMjuoc\nARrwVuPqgCYghye2fue/PkxY8ybsTf02//7dZ3js5w9z73e/w6xZLZTyI7zlzdeyd083iqLQ3NzM\nRRe/kmJhlMsvOxdBT3F07zb6D+/GKJYIqgquZSM4No5jIcoSJUMnpxfJZooUCzq2A+efv4rM6BDt\n7S24rk0mkyEQCmILDod69iIHFYZSo5RKJu3tM2hpaSeTyWBZOmaxgF4sYes2puGSy+uk01kkSSKb\nyaPIGpJ4ktZfDTXU8CdFjayq4eWAauTA8UxM/yLgSWdnFiaYqN7mJ3sqW+XQdESUcJxr/kBUFmun\n8iIC39gJVU4et/CTu+9kZDOmra/KseOFHvqlM8QyWaVKnvi3LHr2ZpMMcQnaXqS5EhagSSyHB1Z5\nT6brSrWuUu5Pe/BeL2FV+XhJB0QYSFnICtiWJ6BfKMFg0mHhskXUNdVh2Q6mbtHe2okuunQtnEUk\nWodbMohoKol4HY3NzViWxdGBPEuWz6ZnIMfCpbOQIgqLl51FOByje88RRlMmed0mkZDAdclnstTX\nKeSsY/tUCeoA73vFMF0aEioOkNMhIMLgYAlFgpwNuulQoiy5wfiitF3e7z7YjyR6ZaZ1lyOHRhns\n7+fgoYMEQxqOZbDh+RfI5/OIooCqaTz7zDPYtklTUx04Jno+S6mUw7EdpDGXNRcXB0EUcBwby7Gx\nTNsL8XOhPlGHaRhoAQ0oZwKUJFygWMgjSAKGaeLYLoFAEE0LYFkWjmvj2jaO7Xhhtg7YtoNpWgiC\n4Om7TshKVsP/Faiqykc/+lHe8pa3sG7dOq699trTUs9L1sOq8sMlln8BJq8STAVHmEiqCO5EksMv\nY1RVtLui+zTZe2dytQLHzBITCCHXIzXGyCDXW4mYbpI+njdO1Xqmuc7Fm6Smm8yqYbL3UiVb45ih\n4VaffP2rWGMTj2+FxS1rVk0wdPD64/dFcQSPBJpqPFyh7EU26blGQ2BZ5fZbMFTw6g+6EI94REld\n9S6fFF4RKO+IcAToxhO7r4zJlCTmpH/L5QOm47k6D2YgFoPWZu81LI6kyQ/HaGwC3YHe/dDeFaBf\nh00/eYabb/9HuhICN1/7Nuqb4hzoznLnlz/NZz75cX731FM88+yPOJAHilnOXngGzz2xl1dduYDn\nD8Cq2bBfkcgFXb7wxbfy7OYCCS3ET/7tV6TNHM/8+gUWrPoUM7tg7XaDC5aoJPEE/eukcS+zPd1F\nzj4jyK4jFud3yvx6xwEuaJ7NkhUd7BqEZc0ghTRyeBNzEe89eXZPlmXzo7QBixOz2LjrZ/zsn35E\noCVOtKDxxGM/YM6sGPsP7mZgMMuP/v0RPvD/PkGpNMSePYcBuOqNf8ETjz/B+pGnCWlhZFUhX3CR\nRBNBcLEsi9GhUQzDIJFIkMyky/H4JosWLiGTTKFqQQaHh4jVJWiIxyiZBYr5Art37SNaFycej3Pm\nmWfywrr1NDTUUSwWCQaDLF95Jpu3bsd2BSTFW1ma3dlBcmSUcDhcNiJekj+vNdRQQxXUyKoaXi54\nUdzHdF70vv3piJVq545nF08sYOKiK5w6qYqqdtc0ttiUdvF0lfhucid/FEzTj2nLnKIxk6MojmfP\nV8qZPA6y5JEHQrkQoywXIuEtlCp42x+KBl+URxFvMfPF+JiLQFvwXtzyt5tlgSyDpnl9sA0LS5dR\nNe+bqliAQFCk5EDm6Cgz584nqMDmFzaiqAqFvEX3vj0sXDCf0eFhXvGKlRQswLaoi4RJDuVpaI6Q\nKkBdCAqigCXCosWdjGZsFFHi6MFhLNdmdDhFpG4BwSCksi71UcHzjHJBEcqaaEA+7xAPi+SKLvVB\ngeFsgXotRDQeIKd7i+iCJGKV+1uJ2BjNWUQjMgEgqoRI5/rp39uLpCnItkjf4BFCIYVCIY+uW5y9\ncjXbt+3GsQ3yOc+7qa21hcHBQVLGCJLohffZtovgeC+B6ziYuonjOiiKgmlaILi4jkMkGsUyTURJ\nwjAMZEVBlRUc18a2LXI5C1mRkRWZWCxGOplCVRVs20YSJWLxGJls1vtTEAUs2yYUDGAaJrIkY9k2\nwktFl+YlgFmzpvZ4ejlh8eLFPPjgg6e9nhrVeRKo5pE0FSTKgtx4E0hIO3UZAk8ULwWdg6pecO70\n3lpTEVXT9UcSvFUMw4ZCWTzRKGdCzAiettIm13O1PZXoBF4FXCL5vN2OA8fxyBt/210H6mJw+DBg\neistr7liBv2HDdY+myIsgiNDvAHyebjosgtYME9A6oQfP/N92prbeew73+e8ZYu57FUXs3zFWXzh\n7p8zuj9LV3sTv/j5f/Hkfz1A/+Ykg3sO8KWv/IKtzz3Drx9+hJ8/sodXLw+xefNmOjpnsWvrNlyz\nxL5N2ylmYH6rygc/fD8JxnWs6oHkMLz+jCAKMDyQZBRYffFsvvPrHppaYEGzN4EfwvuhWbsvzYzy\nfvfu7TQAT28q8JU7v0pyZJjV569gZLifH977GTIDfei6iWOrbDhwgFI4TEtXJ5/81GcpGhJXXn41\nWzbsIjWYJtFYR1DVqKurG9Orsm3PL0wQBCKRCIVCgUDAYxolSSIaCiCKIoVCCdM0CQaDjI6m6Dsy\nwAVr1rB48TIymQylUondu3cTCgU4fPggHR0dFItFcrkM0WgUs6Qzs72Ttvp6Crk8muyZfqVSibb2\nplPwhtVQQw2nE2dEbq2RVTXU8GJxPDcfHyZ72UjTRCn8n0cVL7BpPZBe5DhZjmeHWW45UoFyeBZQ\nsiHNsRIXfyiCQGN5O1FnNBfoCN5LW/BegDFpFkWGYtFruAi0NAfQSy7JUROpXHjF46qhqZ5wGIQg\nnP2KFQS0AIOHjpCIRWlsbCQWj9F9YACjYBEMaPT39zF0tA89baLnCuzdN0AmOcpI/wD9A3maYxLp\ndIZAMEQ2k8F1HfLpLI4FYU1g+44jqIzrWKl4GlstYRERMHQTA0g0hjg0XETTIKJ5fS2WxyWZtwiW\n93O5LCowkrbZ170P0zBIJOIYRoneg3uw9BKO4+C6Ahdddim2JKMFg8xfuBDbEWhqaiOTzmEZFoqm\nIIoiiqJ4AuiWXdaT8tzgZEn2ZCzKIuiCICCXw4G8zIIOkuiFA5aKOvWJeqKRKJZp4dgO+VwOSZIo\nFj3b2nY8UkuWZVzbIRgIEFBVL/Sw/PI6tk0g4P/6qaGGE4fgVstb+CfG3duZEFs+prE06Zh/gpT8\nvqSM3zNV6B/4PBOrxOVP8MSapoypvJ0qHkKqC2jeKkFF0qbaiFcmI1v0NJckvHhno9KXSjsF36qF\nME6e+YXYJ7d7UnK8MVTC9PwE3HR9dX1jXPFOE/1LKAITtMccwXMTnrwKNXaveCz5V2mT5Nuf3Ea3\nHGI54V7p2OvAey8UAUwXAipcqHqTacUF91TbSo8Bkk/svZK1UQAEG0TJGxNJgILhZRjJJGFmvZd9\nz9Thoe89xy3vXMML6ws0iQG6zhZJAZkRKIzAgz/6GV/8xBu57W8+R3NDlI6l87juutdy5Tmv5Ybr\nr2femUvQbYfBw4fB1bn8kkv53v3/zuIlyylJKoop8PDPH+T++7/K89v7qWtrJdoGPVtGefQ//oN0\napiGujiWWaSxqQ1J1Zg5fwkbd+zh7951NQ89sZHLr1xBVxwOOhARIVoeUwk4koJZdR45WAJm4o3B\npoPw5AOPMXpwH1/4xnvZ1g2zzoDLz3onM1vyfPGb/8z7b3onsbjK/n2HePvfvJWVZ5/L377rXSw5\nazUxLciG3z/LBa+8kNlzlvHD7/4EjL045JjbNQspoLJ9+3YKRR1JVbAcG1URCAeC5POeGKQjgCYr\nNNaF0EsFXEdADQSZOXMGsiRwYP9BsvkcluWApFAsFhFEmYZYPQPDA4RCIWzbJhINkUrmMHQLNaAh\nB2T0Qh4toJDPFRFFGVeQGEoePcVv2B8XV3zm0T91E2qo4ZSjRlD9+eFf3r/3T92EPwhvvXf9xAPV\nPHMmHRN8NhuU2gtz4wAAIABJREFUdTan8JA/ERzjTXWSN1dMOrH8H9c9MUmbSrsFPC+jCuky2cGp\nUs9kL/4pmjNlXZPP/1E+koSquxMOTH7k1Z7HiTwjgfEQNkmEemFc9PtECaaTweA059oD906IzLAd\n79vAMiColkk2B/oPJZk5K0EqZaMhEazzpCksA2wDjvb2s3hBK9s3dqOqMoFomM6OZn7/9Fo6OzsI\nx6LYLhhl9qupsZFDh48QjcZwBBHBhf7+PlasWEIqq6MENGQNihmTgd5eTNNAVWRc10FVNQRRJBiJ\nks7mmTmrjb7BNE3NcYIKFF3vG1BmPPdTyYSQMk4aVnJIpwsw3DeIUSiwaNkssnkIheG532whqNks\nXraU7Zu2ICsihXyRGV2dxOMJNm/dQjSWQBZF0skk9Q31hEIxjhw6Ck4esAiFQgiiSDabxba9MEDX\ndRFEAVkUPU2pMkRBQFUkHMfGdQVESSQUDCIIUCgUsSyr7A0peOGDgoAqK+iGjiRJuK6LLEuYpl3W\nrxIRJAHHthFFTzOr8tH902/3nLqX62WMW275zou67557bjrFLXl54CXpYVVN96eipSSUdY0kxv89\nWcvohLcq9UznyVPRgfJvU91bmYgN0fuhsnzkmf9aWR7XoJJlCAOqBZfOhqtnwqUz4QwFzmoqk2C+\n+yW8yVs8TrudyvhN0VfnOPefzHOr6DONlS36tKekY8ducrv8909VhyCMGzljZdtl7zfKMe/l8wEN\nZBVCNqxUy6tKlbJ85Z7gAuFx8Qoge2g83K9iGCiAaEL/RgtrxDsnAYID8+q9554+DBufGeLqq9aw\ncwvUBUOs27yRr97xH9SnIb21Fy2TRiLHw786yLv/8cNcfPErOLJtKze+4a20z2jnH//hBs5Zvpzt\na9cSdiwe+8VPObhtK2tWruTxn/4McGhpS3DNVX/Fumd3MnNuK4oIz/9sF90bX2DDuufoP9pDY2M9\ns+fMRxZjbN10ANGQWLHyXAaGoTiaxnZh2IHcYXCLXh9HbK8f0ToYcSHjQHd3FhHYMuRw4Ux44PEH\nuPMb7+VAAZadATHgX374dbJWC489sp17fvRjitZsVr3yBj71qe9y+//7LF3tCwg5Mv1D/Vzzjnfx\nkS9/gjdccx4dCwyUQA7XLBELSViZLPlUBsGFkBYgFNSQJAmjpGPqBpLgeVxJssDeA/uxTZO2tjaS\nmTTbd+0kmU5x5vKl1MXi2LZNNpvFsUEQJNLpNLKsUiwWKZVKDPQPsXLlWWCZuKZBU109pmNjGuWs\nKyIE1FpIYA01/ClRzXuqRlbV8HLEBH2givHit93K56cVEGLS8anqESYWc9L2oVBlY9zGcvCICcet\nbndN8Miv2LouNIWgLQiNQQiL5dCqKu0+EdZlTFdq0lbZPW0kVbU6J/V3cttPlkQas2crC9qV+8v/\nlsrfQJILccE7XFl7Ph12cT3Q7N5LR9l7qoKO4L0IDpTSLq7hq9+FkOo9d7MImVGDtrYE2QwookQy\nk2bf9l4UE8xMCdGyELDoHyoya/48GhvqKWUzbFi/kUAwwPwzOonH4mSTSSTXZbC/j0ImS308zmB/\nPwCaptLe1kYqmSMU1hCBVH+OXDpFOpVE1wuoqkooFEYQZDLpAoIjEI8n0A1wTAsXMFywiuCWuSDT\nLWvdKp4DguVCLm8hABkdGkJwdPAoi5bNomBDLOxdv2zlUixHY3Agy5lnn43thqhr6GT37kNs37aH\noBZBcgV0Q6e9axbzFi+gtT1BIOIgShau6yBLAq5lYZtW+blLSJKEIAiep5bj+P5+BPKFAq7jEgho\nmKZFNpfDNC1isSiKrHii7BXiCqGsTyVi2w6O46DrBvF4zGMYXaccTujiOm65Di8TXQ01vBi8JD2s\n/nXHuGcKlOOvX+Q7Plmw3Q/RR3occ66KQLm/rIqHT0XUXMDLQFeB7CNV/J5Mfg8kVYWg5okGyi60\n1EGjBvUaJPCyRwhABI/w2GPCviEYtstkkC9e3U+GTe7flJ5Kvpjz8YPHjsVknOjqXEWLaspy/N5U\nvr74x6tCTrk+N7FqfRDdiZkYZRHmh6AZ2GZAowxtvvrCnD4Bt24Ldg2DGoVoeNyba/MLORq0MLIs\n0LIQnluXZf68KE31YA3D+mePsHBZJ0bSpG2GQroJeneVmB8I8IW//wjnX3oh/f1HueiySygYLmvX\nb+SbH/8Qd9z2Pq5/yzWEGhN89h8/QTQa5f2338GnP/FPLFy8CNu12bVrF7e85xbURB2C4/IP7/sQ\n73nPe/jtxhe4/e/fRh3whtf+HV/96ldZv3MnsZlzOWdlPQLw9As5Hv3Bj9mxcQs/ue/rvPMjn+b+\n//w4NrDjCGzfuodlr5zP6gg8siHJRSsT7ExBos7rdwNw110/4jc/eZSfPPdDokw0jgJ4bumDSfjd\nr3Zz1VUL6BTgi19+kme3bOC/7/8Ql77hk3z7B59kbhTmtq9GEUaJxoKAzejwCEFNYfGCZTyz9gWC\n9Q2oqophFtB1HU3TMAwDSy+hKSqSLBANaLQ0NXG0f8hbhRIcEok4fb29tLa2sq+nD92yKRaLXH7x\nJfT29nK49wjpXJpoNIrl2GTTGZobmgmHw2QyKe83SxDRRBHbtlEViV1HDpymt+yPg5qHVQ0vV9SI\nqRr8eLl7WL3939ZPNM8muxidDKa77wS8k/zF+O3Bsd3xyKMpPYL81fj3KwuatuPZdZoCquhFHCiM\ne6xUch/lXM9bXXenIZr8bfR5m1Vr14vFCX9ICcepz7fYOcEunni6yj+mKMr/TQCEywl+sq4nuq75\nrq2M6alGW/Be8i7kDC/KQJbHX99MykYVJQQRtAgkkxbhsIyqgmtAarREJBbAMV0CAQFLg2LOISKK\ndO/cSX1jA7peoqGpEduBZCpNz64dzJ0zm47OdmRVYc+u3ciyzJy5c9mzey+RaBTXdcnlcsycPRNR\nUcB12bV1B7Nmz2Y0nWLOGTNQgHVrt7BkyRJS2SxKKEw8riAAIymbgSO9ZNMZzlmxlC0797Di7Pm4\nQLYI2WyeWH2YOhkG0iYNcYWc6UVVVBax9+/vZaR3gHNeuRKZie+QhOdBppswOpyjrS1CENi7b5jR\nTIpzV8zjuXW7OXPlAsIyPPXEbxEwkRURcDF1E1ESiIZjjKZSSIqKKIo4jo3jON6+65FWoiCUQwJF\nNFWlpBtIkudzpyoKpVIJTdMoFEvYjovjODQ1NFIqFSmWSpiW6YUClgktTfUWiy3LLPdJQBQ87y5R\nFPjxv+w7DW/Zyw8vFQ+rj3zkI1xxxRUkk0m6u7v58Ic/fErLP1V4SboA2JN+hIWy63CFRPL/iB8P\nkjCRJLJ8vwhiNcKmCiqeQn7NJdf1XDulyZOJr6xqZNlYKJvgTchZ3WPiHRGOZj3G3a7zdH8AZipe\nRglBgkYF4u3w9EEoljWTnEkhgZPr8ret0qYxIqtqZ489IZbJIqe8YiAJYB9vdpbGs3tMhbHhn0S2\nTWhvhbTyN7GK8eF/N6JhUDKwKOQdu0j1vKosJqadPV0I65DedYizL+5i7doUc8+to+8otDdFyPZl\naE7EkICVy6LoOuTT0HNIZ/ayTho74eGnnuPiwCp+ec9P2LR5Hde97e2sWbWch773HSIOWAf2cVjP\ncMObr+e8+++h70gvj/z8Yc48ewVWLs+69S/wxWyBzRs3USyl0Q3Yv38/z/zq14RCIe7+52+wdPmZ\nBIMCF150Hk//di+dzR1cf8Nb+dKXvsL7PvQROro0fvfUAHPmtbC0PcJld93E0DB89BN3Y9s2gyPw\n7BNb6Dmyn127N9EU/zu657dzwcqER7ZmoLEORvCe8282beLB537IP3zlm3zpA+9GLh8vAk8fzLJq\nZpTmBKxZs4ChoyB0wM13XEbrg538YpfFPQ9/km997zdccM55dLXPoaenlzNa5rKnexeIMrG6JlKZ\nHPUNTWRtk9TwELFYBE0NYhs62A4BVUMNaEQiEbLJUUZTKXKFPJlclpaWRta/sIE5c85gKJmlUCgg\nSRJBVeH559dhOS4Llywmk8mwd+9eZFXiNa++gt/87rcYtkUsFCeTHiFWlwDHRRQtiqX8aXzLaqih\nhhpq+HPBMSbXH8CyTCaJqtlXxyVghGOvmWwHVytjSo+qil3qerax63rkVMkCU/TCqYrl60OCZ8+B\nR7ooGowUvW+HCf2pNkangp3yl+OzwY+7/H+CJODY/km0teql/kV0CUTLk28AaBA8e9jvRXU6fV9k\nG6xskXhjkGTSJJxQ0EsQ0CSskkVAlBGAeEzGccA2oVB0CMUCqAHoH0rSWF/H4MFe0pkUHTNmUF8X\no+/wIWQX3EKeomPR2d5BYuWZ6MUSA/39xOriuLZNMpVmr2WTSaexbQvHgUIhz+jwMJIk0bP/ANFY\nDEmC+oYEIyN5glqQzo5O9u3dx+x58wgERUaHdEJhjWhAomlpF7oBu3b34LouugHJoQyFYoFcLo0m\nzyQfCVAfV1ABxwK17GklACPpNKteuZKd+3pYPHfWmCecDYwULBIhGU2B+kQEowQEoGtuI1pfgIGc\ny/LVC+g5NEJ9XYJgIEShMIqmhsjncyAIKLKKaVmoioaFg27oyLKMKIq4rvdHJgoioiQiSxKWaWKY\nJpZtecSTppJMpwmHwhimhW173hKiKJBKJXHBE2q3TPL5AoIAzU3NjIyO4LgusqRgmQay4jGUguBi\n29aU70gNNUyHlyRhBcdOorI8rgF1Uq7Jf8gqlA+SM0XonK9NfuJF4PgTmCN4+kpu+Sm4LmRLsH2A\nsZDFERE6mryUrm0aFEyY2QIDBe9HLVfyPND+GDidnpwTxm6SITAd/G2q3DtThIWT0gFWEvudotdh\nWuSKNolYM889eYjVl3WhA4kEmCOwfvsOVv/1efz86V6Wn9WBa4JrQsCChAayDi/8+pcEimkuuuIK\nOs+Yxdc/fye3vOMmLv2L1/Ddb3+LvcM9WJbBr598grp4PQ3xGGevPJOHf/kL7rv/ft7zN7fgui4B\nNch1172FJ558hnQ6zYM/fQhTN2hpauamt7+NTWvXMZTNc8uH30siAg/ct4Vr3nIDDz/0MAuWncWC\nRfOxbVi3djeLzlzA2t+t5YMffg/9hzJsenQ7T/zqSbpmtVIXjtK9ZTsrzmzngR9tYPX5K1k0C0ZK\nUBeAwzo8+P0vogBf+sC7xyZlA88A/dy7P8N/PvpFNOD557fTKkl86b3fJNAU4nP/+gUee2QHjXWL\n+dDfXMTf3PoRXPEIK1Yu58jho8yYMYN8Pk+pYBBQHA4dOkSkuRFBECgWdSRBRHRddENHDYcoFnVc\nV0AvmRCXiUajpFIp0uk0kUiEdDZPoVBgzZo1bNy4kQsuuIBdO3YSiscxDMMjq2RPx2DLli0oikIg\noJLJpmhIJAhFYgz09aGJIk119af5TauhhhpqqOHPFSdEkpzO+t2yvtRU56kis3WcNrtCmTjxkV5W\neXG3cszAi06wHQiI3gJqUAO9nAGvQnj9UXC6DcpKNSdhF1cLKQwBkUlffJXAhT+GXWw5LoqikRwq\nUtcUxMHzNnIMyGWz1LUlGBgpEYsHvKga1/vuUkRPKzc9MojkmDQ0NxMIhzjQ3c3MGTNpbGnmcE8P\neaOA6zoMDw2hlLPbxeti9A8NsGLFCrZu2gyAKEp0dHQwNDyKaZn09fXhOi6aqtHVNYN0MoVh2cyc\nNwtFhqOHM7R3dtLf108kFicSCeMCqVSOSCxCaiTF3Hmz0IsWmYEsg8PDhEIaiiyTy2SJxwIcPZIm\nUR8nEvKSQSkSFB1YtWIxArB47qyx5+Tgfdt1b93D2ecu9kITU1k0QWDv1h4kVWLh8kUMDuRQlQhz\nuxrYtG0nUCIej1EslQgGg1i2hW27iKJLsVhE0lRAwLEdhLIXgOM6yJLkaVK54DgOCAqyLGOaluc5\nJUmYlieonkjUk06nqa+vJ5fNIikKjuOUySrvrz2TySAIIqIoYlkmqqIgyQp6qVTWyaqJrp9uXHnl\nlTz66KO4rsuqVau4//77WbZsGTfddBPNzc309PSg6zrXXXcdb3rTm/7UzT1hvCQJq0rqVgGfeLoz\nTk5UiCs/WeE/f0x5vh942edxVU1csWoGwErcd2XfFx449n+m93Kq1HNMZjy3uotvxYMrY0NhGOKq\nd68gQDQAYtbLgCcyMXxyssB6RddrrL+OLwSy4nk2ud2T/l3NFXnsWUwxy1WbXCuEX9Ux9hknlcfo\nSMcUcUz9/pBIqTyWh0uwMFDlJqaelE3GU/sOASm8cEKNcbLrRDE0muL8lQ28sKeLnbsc5pwh0n/Q\ne4aLzl7J80/uJhq3cbMduEWYPw969+S49xsP8vorLuTCSy5k9ZKFGGaKwf07efXFayhkhiiNDLJs\n0Twk0eH6697KQGqEoqGwf+sWBEfndVf+Bd/+2t2Mjqb4/ehG/vLat7N5Ty8f/8ptvGrVG2iKR7j3\n2//GunXriMXqeOp/n+ScS16NacC/fO2/mRGP8opL5lKIxFjY1cTzG/eyfdtObvl/r2fLpjR9fX28\nsHkH9V3zyHfU80/fvp1P3PEvXP7qK5k5bx5LItC9chmRRugfAUGGXgvaIoy5O28a7uesxlZG8EJd\nXSAc0jCAVuA3v36MBneEXXu3kMjFePZ/t/LkD37Ev310K02xNOFwFEMW6es9iiAIiKJIJBSmqa6R\nfL5IItFAKl8kGo0SDoeRRRcZF9eNUSwWkSWRhqYW9qVSpLN5j9jSTUKRGIqmkMlkWLP6XLbt3EE4\nHObw4cMMjA4TKBUZHR3FsiyKeolARCWZSSMIAoODg8TjcY4ODqClU3Q0NGAUS5QKuZN8c2qooYYa\naqjhOCgzQRNCxqp4208XjSBM2h+T4Khmx05jF09HdEzFrUxok1v9wqrllgkyCy+8TBE973kBTwbC\nsHzjQHXbdXL5bjVWbcoGTI/jeadVJZ2qfIdMX8nJnascKpYT5JxMkQ7j9riOZydr5WNTJXOaCoZh\nkYgrpHNBcjlPWLxU9J5hpK6O1HAOSWYsFCIchlLO5tCBPlqaGmhobKAuGsFxTIxCjqaGemxLxzF0\notEwguDS2TED3TSwHZFCJoPgOrQ0t9Cz/wCGYWEYaVo7ZpDJl5i/ZA7PPr0eTZZYetYyUqkkiqww\nPDxEXWMTrgM9+wcJyjL1jSFsSSYSUkml82QzOWbNaSGTtijpJdLpHEoojBVQWXzmHHbv6KGpqZlg\nOExUhlxdDEkFvexaVXIhII+Pe9rQiaueHVwZV0mScMrjPTIyiOIa5PIZFEtmdDjL0JEjHNyZRVNM\nJEnGEb3s1EL5gcqSjKRIWJaNoiqYto0sy2UNK+8Zuq7shQcKoGoa+byJZVmAp3ElSyqi5BFPiboE\n2Wy2nBmwiG4aiI6NaZi4ruuF+8kipmUCAoauIysyJUNHtEwCqopjOzg1D6vTjiVLltDd3Y1hGCxd\nupRNmzaxZMkSent7ueiii7jzzjsplUpcdtllNcLqD4UoTiJgJrkeVyOmxkS8J80U1Sbryg+CU+Wa\nym5VXasq56Yiqao5PY1pLE0xO1T6ZTveJaJbXi2yYbQIyRKERBhRwBS8DXH8On+fKu3wGxpChQWc\nFEZ3vBUbodqEKhzHGPKRgv5MhhWhdShnGBQmXl+tjMmoiMz7IVdCBwXI2vBYEl6TmL5fJeAoXira\nTt9xq7wNlq9ZNn0xx+CC+Q0AXDYffntIpG+vTVST6O1LMmdGgp/t3cJFi86gVYSfPvUIF877C2bP\nbeB9H7yeXBIeevDn1Adg4++fZaCvl4ZEPcOD/ViWxcED+5nRHucnP7oPOxAmqDVwx+3v56/e8Dre\n8dZm9u3bR2fXDK549etpXbSMRecs4N57nuK6t97AOUuW8PP/foBwOMxzv+/lpne8Fa2pniN7D7Nt\n068pZtOseP4yhnv6eXTnPi64+Dw2b9rOzm6bpllxrr/xjTzwn48RCtexsKWez3/sHlrqm3nqN7/j\ng+cs4c5v/o433/IKJBse/OnzdLU3cd6lMwni/c31A/VSKwDf/cJ/sWvd/6Ja/biGzG3XfIDU0f0s\n7pzNwaNbaG6I8ND//JwAsPZ3axjZf5BU5hADQ8NEAhFaEw0MpTNkC3k0UcYxHdraOti6Yw9yQKVU\nKlEqFQhqKtlcmqamJvJGiXyuyODgIJF4mLxlYhkmdU0NGLaNburYts2+fftQJZnly5ezbds2FE0l\nU8zjCNDW1sbczpn0DxxhNJklkYgjSpBOpzln9Sp0Xaev9zCBcACj+Cdc+q6hhj9zdOe+XtOxquH/\nDI6xhyZ70ZyEDVW1/PL/T1Yr6UQdf45nK07rpkXZ3mTcpnXxvFVM25OpMMp291gGcT9xV6W9Y/8+\nxgVsul5M2bwJ9Rx33IVjx21sAX26xp5oQ6ocslwYNKFZOfY6P2w8u1dkPJNdpZ0uHnHl4CXMOVH0\nFW+mLeIJrjdGvO+ZUt5FFgVKJZNQUKE/n6UhEkYD+oYHaAi3EAorzI51YJnQ19ePIkI6OYqul1AV\nBUPXcV2HYqFAMCDTe+QwSBKiqDJvzhzWrV9LV6dKIV8gGAzQ1NxKIBIlUhfhYM8wHZ2d1EWj9A8e\nRZYkRpPDdHXNQFQVivkSmfQwtmURTzVhFEsM5PLUNyTIpLNkcy5qSKYj2Epf7yBRWSaqqXTvOoim\nqAyPjDK3Lkp3zyjtM+sRXOjrTxPUVBJNQS/pUnmsVcFTEjvU3UcuNYzo6uAIbH9+B2YpTyQYoljK\noCkyq9as9ryuRusx8kVMs4iuG8iSTEBR0S0Ly7aRELAcl0AgQDabQ5A8bVXbtpFEEcs20VQVy7Gx\nLRu9TDDZrovj2CiqiuN6elWu61IoeAu88XiMTCaLUM4y6AqgaRrhQIiSXsQ0LRRFQRDAtEzqonU4\njkOpVEKSxDFpmRpOH1avXs2mTZsolUrceOONPPHEE6xatYoVK1aQTqe59tprURSFZDL5p27qSeEl\nSVgp7jgpVNGz8nNUY+SM79fYT4pUcLxJo3Le7/FTTbx87FyVe6uVB+OTjzKZDPORLRXPJrd83CmX\nIfmWLipNswXvo7/gAPpEUs9PQvnF6WU8baxjPKimmaCnGrOxjCplOOX7JgugVzziRN8xoTxwFb2v\nam2tpvc1wRPLv5wz6VkHFYiqkDVAtwARdA1+WYRzg/+fvfcOs+Qqz31/q3Lt2L07z/RkzWg00iij\nABZgkwwIMBicwAhsMPZ5zrUPB18D1+k+nGsb4wPH+YBEOuYYDIgkhECCQRLK0mhGYXLunHaOldf9\no/bu2d3qCSIcCej3efrpvXdVrVq1qrrX2u/3fu8Hy7IDAThMTEgJ4ol3rv27DjSI1VY28bHTQH9X\nF84dWQo5cGCK8VIVzB4sO0mp0OC6S0bRFXjX77+FPd99mIe/dy/9mRyf+vJurrz6amaP1VmzMcWV\n11xPvjbNS176Sm775nfYeelODh1+gk2bt9GXNHnggQcgbbN988XMVqrc/9B9vPTFN/AfX/w8f/YX\n/y8f//jH2bhhhAfu+y5P7LqD/g2bCbWQenGeNCZ79z7BxNNHufiay2mGJVolOHngMNuvuJJGq0mx\nXuDtN72cL3z9SV76ohsYSaj09sFtX3gAS5TZPjzI7Ow0b33da6nUK6zftI4PvO/Pec1rbuQdr3oP\nab3C5gsGGXj979KsbuC9v/3HpGUTIct87pufpwX88pWXM3XvPVTLHqmsQfHECXoSNvMzx+i304Qi\n4t/+4V/5wT13U85XifCo1116MllM3eLkxCS6bYEP2WwKTShE7YkS0yTwfNLZLLVahUQyTbFUQUoJ\niiCVTOP7Lp7nkbBsfD/E8xwMw0BLKoRIFFXhBw/cTy6Xo1wuMzIywoIzT8o0ablNHNdHUw18PyRr\nJ6iGPjoKDc8nUDUqtTJ+tEpYrWIVP05sTf0RR+v/cN77rmIVPyuIrZRjnG1mWWIm/kMoh56tsOjZ\nEFVn9EpaIWj8jM9X6Nji+rjDppyvmuxHIKWWnF+c4fqXkU6drq2k6hLLj3k29+csJJWqnPabDdvB\n4VCB+Qh6ldMZBd2oExNSEG93ib9HhMRB3I6ySgdy9i3kW+9ePN/Zuj1i3wJI6jWHlh+AoqOqKr4T\n0puxEMD6DSNUFkqU8gUMzWB8ukK2J4vTCLESKtneXrzQpa9/gLm5BTKZNPV6lUQyia4pFItFdE0l\nlUzj+gGFUpH+XB9T01Ns23Yhp06dImGbFIt5Kvl5DDuBVCSB76KhUK1UaVUbpHszhNKPPbRqdVLZ\nLEEY4gU+69b1MzVbpT/Xh6UJdANmp4oowidlmriuw+jwEH7gYydsDu4/zODQEE88/BSa8EkkTYzh\n9YSBzf49B9AIQfpcce2VRMBgTwankCfwIzRdw2s20FUVz2lgqBoSmDhxikIhj+8FQEQQhuiajiIU\nmq0Wov0FUtFjTzAZSYSiIBQFGUk0XSUIAlRVw2tXD0QINFWNUwKJqwlGbeJKURRU1Pb3VEGhWETX\nDXzfx7KsWEmlKIRRSBRJhFCQUsZKLikRCGQkkULgL5qwr+IniWuuuYabb74Zx3F485vfzFe/+lUe\nf/xx1q5dy0MPPcTnPvc5dF3niiuueK67+qzwvCSspLJCtGHZJBy2iZFnBFPOk6Tqfi1W2H6+ZFc3\nVlJ+LSFixBmItWXqsOVtK21z9ahrLMJl46KqQLBM2bXCtXQr0J5NRO4ZUan2wEdd19eJGNBNbnWR\nj8pZ2l8kp9Su9M7w9GfdJJGixOfVonh8VWKpraLGaik/is/vq3C/D5frSxVU48Bku19SQiDitL8p\nTlegiYjNKTvG4OebdR168LbfuIlLL7+O3/u//zOFSp1PfOyfqRXr7N+6jWLJ401vexN3fft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o4mFCxLp783h2N5JAyTjevWMz87R7NVoV6vc/DgwcVqLKtYxSpWsYpV/DhwPkvHHxd/tOT4FRfT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x8lk03h+W0mJiZ44YuuZe2G9Qy/9HrubTY5YemcmD4Dnsf0uSnp7tE0Go0GvV293PSZ/0HY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\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ddddhreXFL34xmzZt+p6+l7DfDR/rcapX/MafsbCwxPLiAlK3yNsJ1iToLKdaijjbhgzP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\nnfMNBFpZNAqp4nlU/lrx27AirCrAj1HluJu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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "SAMPLE_IMAGE = 'mit_driveseg_sample.png'\n", "if not os.path.isfile(SAMPLE_IMAGE):\n", " print('downloading the sample image...')\n", " SAMPLE_IMAGE = urllib.request.urlretrieve('https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_driving_scene_segmentation/mit_driveseg_sample.png?raw=true')[0]\n", "print('running deeplab on the sample image...')\n", "\n", "def run_visualization(SAMPLE_IMAGE):\n", " \"\"\"Inferences DeepLab model and visualizes result.\"\"\"\n", " original_im = Image.open(SAMPLE_IMAGE)\n", " seg_map = MODEL.run(original_im)\n", " vis_segmentation(original_im, seg_map)\n", "\n", "run_visualization(SAMPLE_IMAGE)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mnrA2FABKLSp" }, "source": [ "### Run on the sample video\n", "The sample video is frame #0 to #597 in the MIT DriveSeg Dataset." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 521 }, "colab_type": "code", "id": "oXTrq3DgKLSp", "outputId": "de91e86a-1b2a-43d9-cd3d-42a0bd92320b" }, "outputs": [ { "data": { "image/jpeg": 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sRdX/APSKWunB/wC+Uv8AFH/0qJzYz/c6v+GX/pMj8aaKKK/oS5/PgUUUAZNF\n2AUUu1vSlCgDJpXGNoIxS4JJ2ilC+q0C3DYOu6gIPXNOwMYpAoHSgdgKDHAoCgDkCloPNAJKwYHp\nRS7WPalCcc0roQ2lCnPSnKoWlpOQ7DdhHRqcOlFA5OKV7jSDAHQUq5DCnbV9KXA9KRRNaXclvcLK\nJCCvQ5ro9J8SCZAs7e2SetctTkkZDwxFYVsPCstTalXqUXeLO/SWG4XAwRUVxpdhcRPG1nEdy4yU\nFclaaxe2+BFMRgcDNbul+JFlRRcsAc8+1eXUwtWlrFnqUsbRq+7NalWy8FRMXS9lkBH3WQgD/wCv\nUlz4etNFjXUY0MghxvHr2zWub+3IBWQc9DnrSmeKRCjEHIwQe9J4jEOV5PTsWqGF5LLfucjq97Bf\nXAK8L246UthpVrfDatztbrgr2q3ruigXDXNntAI+ZB2PtWUnmQycZBBr0qbU6S5HY8ionCp7yuXN\nV8O3NgTLEvmQ9n4rPClTya6rRpY73T2WRidwwyuawtVt0gumjSMCpo1pyk4S3RVejGMVOOzKdFSG\nFwokZflPQik2L6V0HGMwfSnBARk06igBMAdqWiigAoop4QCpbAZTlXPJpQPmJpakpIKKVRk4p20Z\nyfwoG3YCCTwcUtABJwKUIc8ii5AlKFOenan0VDdxq4ijA6U4LnrxSooPJp3SkD3EVdooAIGc0tFA\ngoooAJ4FK4Dgh9acOBigcCipeoBRRRSAKKKKACilA9aQBzkjC8dCuT/Osq1VUaUqnK5WV7RV2/JK\n6u/mvUqMeaSV7eoAEnGOaWRpIIjKsW4DrtPI/p+tLJHcSwGOKVIycfOY92Bxk4Jx61WZtRx5MWnC\nRhHhphINmcehG4HIHygHr1r+bON+PsZxBGWXQpeypRl7yl8cnFtK+yir/Z1d927H2GV5VTwzVVy5\npNfLX8/UratcW9zKkdzIAgLeYBvCx4O6MvtYHBOFxgZ3tnjcCQTXStPNHErCIRQp5SZxtVidgyPl\nBcHuTtIx0FU7221PUrR5dMS1hkYeXFc2W5ZVUY3FgArbs7iEy3O3K9TSfDy8vZdPtor66eR5ke6V\nriQEmJpWaNxgAAbGXgAYOV42hV/NmkkfR2XJddDYttX1GIlp9FlMCpvafCL0OCCu9jkccnAwfTpy\n3ji/e9vN+iJdpBp8XnXt4gdTEgJWRY9xXcGjdiWTqdpLZWulS0uzfXF3HdXAik2MYS+xPlx0DAlQ\nQRuwASORz14f4yRvf6hp1xpmsm2j1O1uIXdbiRFlTYshHyHgYHzHGDu5z2zaSNKHJ7VWR2HhbSES\nJbO3ladLe4825kdmB84gjCkndtUEKv3h8uAQVydm6u9RtPKit7QXAldQpVgpRf4icn5iBjjjJIGc\n1zNvcaVd+E4Zry13iOFZZrWK33r5gO1gVAJGJSnQhsqMYwTRpk2qXFtFENZuH8yZZYjMd0vkyb3i\nXeV2gMBkggNgADlcVNlJl1KUptyfQ0JY4jfahHLcNKpuYp0iA4G6HbuBB5IEYx6EA9a0/DMzzXNz\ncnUvPhiIjTKn92VUKy5xyS6uTyeidc5rmbuSW88Uz+F7O/FtPPZQibUIxmSJEknQhNp+RzuTrhQH\nbglStdBa29lomnpp8Nkoto4xmQyswiHABbrx94k5xnJJ+bJKivGxn8Hqbd1qS6fGbgE+XEpaRWOB\ntOSSSTxjHHbt05D7zXbe2hEqgsWZVVenJzjOeg4/CuT1/wAVadczNoUEwuDNbyO628hYmJWCyHJ+\nQfIzAEnOSoHJClPDp1+5v31bWNOa0SOQRwSzumTEFPzBNx2uSxBYn5VAUZ5ZsPq8bXZ0KvVS30Ne\nfWryK8F/LCPI8sBnKEKpZlB2sxG/+9nAG0d8ZrRUkDBLEnks3emSol3E0dwj7HBBVWYEjv05pto0\nvlEXLkuhKFj/ABBTgMe2T14qXZx0WxzTm5O7JNjO3THvTQjE4UE1IGIGKTJHQ1+pYDxYzXL8FHDQ\nw0OWEIQhrLTlVnJ9ZX00ukrPV308OpktCrPncnq23t16CLE3Xdg+o7VzfiWdfCen6dpUK/2gdR1i\nOCWG+/e+ZGwKgHJzhQE+YBj8u5hyzDZ1i01W402dNJ1aWG4ZAIn2RnYe5wy88Z4JGem5c7hzvjzw\nzr3i7RNO0i8RIZJiE1W9tp/KMUTKRIsSktlnBKgMWXnnPFfEZzn2Y8R5g8XjZpvZJaKKV7JLt53u\n3q23t62Dw1HCQUIaLr39TC8MeOfCOueFRNf6emkKspu3WeHfEgYkKF8o7dg8yNWBK7jIxYA7gYm1\nrwFr1rJoV9rdhcwajZmJlso1knu5iFlBKwoTGkZOMtwo+9jOWy9Z8E6j4I8NafoPh7S7bVEkvBeT\n2U9vHI4uEiKiRGi2ZAKqdpyzBshsg5tWnhHVdum3+rS2sFxrSMt8senLEzmQjaU85iokT5UXKEne\ncEferiUZJev9foei1C90yKHSfDun6XbyWem6jp2sWz+XpNnaW10qXE9s55YL8txuAYtIrcRueAfv\nb1z421q5uY/Ek8eoaXpOnL5Or6P5PlyvMYsBYCUDOQQwKqVyMFSTla5/4ePq93JrV/8AEq0u7xrT\nUbpbS8Ee2JIWTbO0Wxl2KQ4bcDhuMEsorIv/AA7qeglv7D1PUm1TVb6aF3iKtHEglVZ0/fM5dU3e\nWrL1eQq0hz5Yhx5nqVo9GyD4l/ESHxVdP4O8J2jNp82pA6gZJUtorq7+dvLHH+oTh5NwG4gZ2l9z\nei/CfRxcSXPhmwintfDq29vdwW8iAG8Db0JIJJWFzHuKnmTkkhWKtn+CfCmlSRQeEtNt7qCGxuJr\nm/uTdBmeZy0Rt3IbczHfJvkUrnyioIBZR3nhFLGSK68RWsjhNRdZYjKCNsCrtQAMflQ4ZwOMeYeB\nWFdpQshwqRjJJGncWdqlzGy25IXDIodtqlRtUBeij5j09M1mXsM8epJJH5ttbI7S3BVFbe2cKqqo\nJbczbtxyQUwB8xxbl1GVYg0kbBvLLFwmc4ByFXJYnp0DZyMZ7YtuNZu9buNUv7aCOIqiKZJfktok\nMm4s27BlyzqdvQEAkAZfGnFpainUc3ocj4lsbnVviTa6XKkNxEbG5Q6bMVJAkluJTIRwrEPbwkZy\nqknBztdfSYLGQPcyylHYz7sN/Dwvy9PQLzXnXgPXk1/4hf28LCCeGVJbG01ZmdyrpmUoM4G51kL5\nUKMZUD5ct6FrtuLrR7/T0vnhlurdxFMr7GjdkKqVIwcjGeua2rOSkokcuvvFyOzEUz3BZ2L43Zcn\np6Dt+H16k1Lg+h/Kvi63+IHja5sVa78YaiyoSdw1RzIeenUkDjv/ACqJ/GPi1izSeJtUbptD38mf\n5+lfomTeJuY5Dl0MBHDU5RpqyfvRb83a6bfV9fkclfI6deq6nO1f5n2jOJQNqxFmYfKucfj7AVmX\nt86QT2tnauxWGXzZmjZEUheuWwCMkY5AwrDdlcV8fSeKNdkuVJ8RXZdxjL3T5XnOOT09e3NUdWuv\nEG3y5teuSFwSqu3QdOAeMcflWGYeKXEOY050+WnCMk46Ru0pJp2k3dNxdrq3p0Cnw/hqc1K7bWv9\nL1E1a6SEhkLOHIwdxKkcE8EAen+FVLEXEzODGoVGBO1lbO75R05/I8de1Pe83f6W8KqxkYW1qiHZ\nsI/2cHOf0HJOeJ/Cs7XLSwBEAeIea0aY7kZPrxn/ACa/NJy55Nn0SLen3jymRYohwC2CfTqST1/+\ntjHFNkvZrsvbW0yRxKCJSFwMdD0zk9+P61NePDp8EkkChQx5OOT7moIhBIqSyygoECy7MjjHU4xn\nPrS0Ro7j0si6ruuDJEi7VRE2ggdAeTn/APVx2qxGgUHcevQAkURmK2j3xsWQ8g5GPT1xTWM1yQET\n5TgZI4x9O/8AL60rlqyRFqcrHT5FUjBxggY4+n9f/rV2v7Epx+2j8Ido6/FDQOT/ANhGCuI1i8ji\ntjAxZdwJUgAhiOoJ7cnP4da7b9iO4tx+2b8I3a6bzn+KPh8LD5ZG3/iZW+Tu9COw9s9K3wjvjKX+\nKP8A6VEd7M/otooor99OwKKKKACiiigArhf2of8Ak2f4if8AYi6v/wCkUtd1XC/tQc/s0fEQf9SL\nq/8A6Ry104P/AHyl/ij/AOlRObGf7nV/wy/9JkfjTk4xmlIIApwQA5pa/oM/n1K40Db1GacOnSij\nHegdgopcH/Jo2t6UBbQTAHQUUuxvSnbB6mldC1TGYPXFFSAYGKKXMDGBS3Sl8s9zTqKXMwADAxRR\njNKqkmkFmJRTtg7frTgAOgoDRDApbpTlUClowT0FBaCinhADS4HoKnmQDNrHtSqvc08DNKE9aV2K\n6Q0cdKkBIPy9aBwMU6JlU7j1HSkwTuyT/TFQfK23qDirFtqEgGJS3TqDioxqt5wpnJA9RUo1qYoE\nlCvj1UHNYSUmtYmqcVsyO+uTMwKyk8c+tVzkn5znvmrK3Vvhi0HJ+6R25pjJE6ZiHI7U4vlVrClr\nrcdbSTRRlYzw/ao7iX7SwLjkDGfWn28pt3Dr26/SkumgZt8SFc9VzxRtLYT+EbtCJ5eM56mopFCs\ncU7J9aCARg1S0JepHRTmXHPbFIq55NVfQzYg5OKUKSM04LjoTQBjjNJu4bgqletLRTlUEZNIdhuD\n6U4ITzmlwMYpaB3EAApQMnAopyDJzQ9iQUdDTqAMDFFZgFO2gcetIq569Kf1oG2hFBAwaWjHOc0U\nCCgHFLtOcGnBQvSpugEALZNKABS0u1v7p/KpuPViUUux/wC6aNjZwRRdCs0JRTgh70oUDoKV0A0K\nT0pQhzzT1XJ5FOCDGKnmYDMZ49aUI3pUojUDI5pQB024zU3HYjRdp++RnuKWURw5aWchcYbcBj8c\nipFUAjiszWNa0PS9UjTV9WFs/l+YhZQBgH157Z9sZ6c1/PvjDhsLDNMNWhFqc4S5nbRqLSj/ANvK\n7X+G3ZH1fDzqVKU4dE9P1+X6lHxDZXs8YvrWK5iuI5CI3kkCiVRyFby8lUJ6sfmXqud2DQ8CaLpV\np4g1gwXF063UUMtxDdO8bwSlcNuUnIL46EnHlnBIYV0Nxq3h3S5pZZr6OKSOPzXiLHeF5+cK3QdR\nkDHPvWPdana6jOPEvhSMO0qrFNuDAXMSPITGcOGSRSrlSwHLbSRu+X8jUnJcp9TFVOXls0HiXxfD\np921pb2dwUs1867njIcRk7lRss2V5D8spHyexZed1zytR1KTw7JFqDQahdLbxTQym3R38suyFnbc\npZhyVi5C9WOSNLT/ABbpfiW3t5i8MH2hmhitzH/pF0zRx4RVwuMIMMMAEqMHbhzU0XW59a+zyw2j\n3FrJq2Y7yZWc/ZYZFGGJPzN5gRlJ3HBkO3KYLVuWx004OHTUy11nwj4X159EGjpqDzD7bPJdiOK3\nWMKUlwJewMIkABYNuYZyMntP9K0zTofsCG61G7jRdOAi2qg2hgxHIjiygLn5icqMMWRKwPHtpcrp\nNxrv2SJ30ib7bBNbxbXIQqpcHP3CGlwMnf5a9OSbtrYeIr6C31TNlFJdyCW3FxBI620YLSeUSSCS\nArsSyks5YZXYuM7WfkXU5ZxTW/Ul0S0isNStCuqTQ3EryxTzEBZJRKnmgcqdij7KyjBLAHqTljri\n18O6Xczanp2lPJeyR+ZPKzZdEXPzSMclQcHJALHLEKcHHI+Io9Y0rR5n8R69BHLY6pFNpgMZ/e+X\nIgx8mMgBps4bIDEtggNWzpOlWmri48y7kjguLljqdwLWOJr9woj2xMBuWNCPmbGc8bgA+6pWu3cy\nqRkoptl/R0m127a/nuJZLQyf6RIw+S5kXYAkfBby0beDnl3K4bAxXR2kYCiZlKEk4ixgJzjoOM4x\nzz3x1qpHp9tY7bGO7ZbeOAotqiqiqoXGflC7Rg9+OmAPlwtprMMti19bAPFEh85tx4K9SOvB5PJ6\nc85rCd57GLvJXRdklBPlAspLBdwx3wARnryRWZol2dRM0b3rBXZZvLJ2sFYEADbgjlHJ9cenUvNW\ng1KF7azVZzKrxRKACFc4XLj0GWPphT3HOZa2y3Pi2PfAskgsAIXibzEGxm5MjZ4BdePvBlH3gN1V\nGFoNPQcY6HT20puIRNsKhuQD2H+fw+tPrNt7O+vLaSXWh5chxG8MUjNE6DrhSB13MM9enPANaAm3\nsAkZIIyDj349vfrXPKNnoS48ulxc/wB0Eken/wBeq9xa208gS7kjEjE8KdhYdMHnJHQcHBzyKmS6\ntXunsBcoZ44lkkhVssqsSAxHXBKtz7VGs80irbK7yFzkTwrgBQf4ieAe2Bk9wB2cbrYEncq7L2eC\n5muJomjlJFnGrq6yxMqjDEqMMzk9z1HJrO8T+EDqOnWVpqGs3SQ2s0LfaIVQmNg45JkDHGOA33lx\nlmYFjWrdrcgpNpkkL3Em8RMV3KM4ySAQcDjJ3d8AcgVzviTTLHUtIuII9SkvJ5f3+69Yyx7UyF/d\nMfKCyPGccKGXfjkc6w5nJWZpFtMfeeFdF1a6uZNT8yO12NLcOrC2JUr8zO8QTBZW+bdyEYYwxOOa\n8N6xqfiRWsNGhi8i7tVjt59PKiaxtQpNtEwG4ITG4kJA43sFGVLQ48vwnj8QeI7TSNKhstOuknDa\ng9iXljmXyztuCrYMI3Kdu3/Wsd4KbA9eqeC/h9pvhXRP7Ja1tWDxKk7QW+3zSM5Z2JLOxznJP9Ku\ndRU92dCjZaO7MfxB5vh3QNP8MeF7KaS4u76K1GxkzESjZk4O3CxoWVRgAIvTiumXUWfDDS5li3gR\nqGj5X+/w/Cgc+3HBOAMzWbmyi1/S/Dk9woni828ijU4JQIYMjHC5NwO3VWAB+WtHT7c30f2u5XKS\nYLCSNcyuF25OO304IHHy5L4zcXFNkXd9tTG1K0F6h8UXGoy2NnbLJs+zQ467Y9wG0sxxvAcbSAw2\njBJaEax8P7LUbfwvY6raT3lxa+THbSz4klwyIqkdFGUJZQPuqflxwa/xx8La5468OjRtA1WK3ltp\nEmlRnZWlBJAwwBC4wzZOeV7DmvBfG+g+JfBPjK2j1DXbyZ4ZJC19pvmo0WAHljhkkVcsofcGXgbw\nw5JWq9501JM2p04T0vqdL4i8WeIG+JWkeHfD5s7gprKXMPlzrKslxJMHDMQUUksWYgOcLIq7lIYV\na+IPj3xJr2oWcGoRx20dpbpqWnG/ntds6xysiyyNhTtLlx5Sxh2CJ0JOOSsvAr+JPCWqeIINPkt1\ntnkYGZThYd7EO8SqWBkJWJDHlR5b52gE1nfEjxdqnim8vdWvJ1srG7u47qy01o/nkQLJGkpbau/G\nHUtk5Z26/MQSk0nNnVGnG6t0OZWaztbf7NCzsqDdhgAxz/XoPTrVaVrmAJPJAfmI2qfmbI9qidbx\nyz7tyRn769EJ9T2FLPcXUscMTBXKuxDL3HTr1Pf864ZNyldnTogmjmLC4lmJaUgBe5BXkY9jwfcU\nskzTR/Y4nf5wC4IBJPoMDP8A+uoVdo5hI/QMRxk4GOePy/P60srpZ3XmSOQxYjcv3sd+Oh4/D3pa\npBdDpbZbORkeRJFZVG/eWXb1GMY9P0qfSkGnrPKC/wB5Y2dOVzyR07AEkk9+Ki1BLQySRWtsELKQ\nrtuG4E9Qc9/yo0q7bYLQysqRRt8qDKuSe/oMHr1z0qblaF+1Y6hJmXUMiN8iNlwCcnAJPU/T15p9\nw01qrXdxIxREXODnZ2zyc5J44qlEyyzTG4IQlAIonVg6nIAAB/l79qWVUtrMxu+xpFUSRM2SoBzz\nzwc9QO9K5asW7HWRcLdXM0yPiUeWx+UKuOnP4/nSXWtvp9l5mxnkIAyw24J9R2GPfJrNRpL6CSNF\nZ1j2pGM8Kuc/XtU88jQxPBfq5LOSAzfLngbuvt+nahau42yKO7TUGDTxjcsf72RSfmOegB4BJP8A\nnv6D+xI+/wDbb+ECJjj4peHuntqUGa8zvL+N9/2dQhKhcKBjb7nPU8fT+fov7DLD/htr4Ohgc/8A\nC0vD/wBD/wATGCuvBr/bKf8Aij/6VEzTvJH9IVFFFfvZ6YUUUUAFFFFABXDftP8AP7NPxDH/AFI2\nr/8ApHLXc1w37T3/ACbV8Q/+xG1b/wBI5a6cH/vlL/FH/wBKic2M/wBzq/4Zf+kyPxu2DHPWl2LS\n0pGADnrX9AXZ+ADdqjtRtX0paKV2ITYM5FLRS7T2I/OgFoJRTth9aPL96BO1xtA5OKcE96Xao7UC\n0G49M/lQEJp4HoKXB9DQF+wzyz3NOAA4FGD6U4ICOaV0gd+obD6ijyz3NOoxnipuxCbBjFKAAMU7\ny/egxMpw1K5WqGjk4p4QDrS4HpRQF2wwB0FFFFA7IKKKds/2qTdh2QgUk0oU5yTSrnHNLUtsBQRj\nbjqetKxKnikUZPXFBDdxSAN7etJShCeop2wepoFcZRT9go2LQFxh54NIBj6dqkKDGBTSpBoFdMSl\nAJ6UoTPU04DHegNEMCEjNPAA6UUUCbuFFFKOooEJyaeoAHSlwB0FAGTiobuAU5VzyelKq4HIpRjt\nSAQg4wtGDjANCqxPJp/l+9K6AYM9zT1HQ1IFiBBJzxzinrNGEKNGDzkVm5MpJdyHaM5xVixsZb2U\nIinaCN5HYVZsLfS7kFJAQxBx83T6VqwwQwJtgjVR7DrXPVr8miWp6GFwKqtTlJWIY9I0+MhhBkj1\nY1YWONBhI1H0FLRXE5Sluz24U6cF7qSGvDDIux4lIPYiqGo6VCkfn26EY+8o/nWjRThOUHdEVqFO\ntC0kc95eDg0BQOgrauNOt7ht/Kt3K96ry6OQCYZAfYiuqNeD3PGqZdXhflV0UVKnoKXA9KdLbyW7\nbZEwetNq7p7HC4SjKz0Ciijk9B+VTOcKcXKTslu3ol6t2S+8aWtkFV9a0XR9dijtNb037RHvDJkn\nhsH0IPTI9Ocd6tLGW5xx7U4sJo/kI2scEEdR6e3p7V+A+LWdZRj54bD4Wop1Kbk5OLuoppLlum1d\ntX0bslrufU5DQxFFynJNJ2t0fqUZvD+kSFp10W2jmdmPnGBN4LLtJyQeSOD1yM8GuZj8EXcVysOj\nm5hXZCLq/F7Kj3ATGDiOTHPBJwOWZhySa7CC5d9nmRsH3mOUFuhxkH8eP++qq285sWksmKnEsjEL\nzsX5WweAOQwUDsM88YH4/CUldbn0sKlRJpM5DXvhroujhNVsYLq8Ql21rT5DNIt4hVAZAuT88aqC\nqHO8AKctsYUtPn023t/7f0jUw0+qyxy2fmhbWO5jyjxxMACJAAxHyqSJFcEHcVHfTQ3kwLTowjDZ\njJZtwAGd2RnByepxwP4TXDa3omiaXeT+Eb1bZo9Rle80K5a1Z2t5Iz5sttlWVtoYbk2kErK6jGzJ\n0i21bc6KVRyVpDYvEOgX2uQ6pcfYHS4d7NoLi6GY3kVjxkOSARIu8Z3mfsoBGNaa94z8T2dt4cgv\nJLYQRP8AaLs2wlCurNC7CRCoQsRKwViMI75IwA8+sjVvh1rkGoarp8smifaIil3v3PGieWXGckBM\nL8gyshCsGyGAqrN4v019Y1v+ztPWFtSs0vrOeWeXESOwilkKD7zmWG3BRVfzGfaWZcusN33OuKS1\nRt66bDWvBsfh3RNVM8mqSTm4kVBM8mRLlsqwUNIVOwEYC7zhiuK0/C/i5ZPA+kW99YRRxXOnxfYY\n1lYBz5QbZtCA4AbG1NxwoHPNc/oOn2mneDJp7+S5tNe1O2uJG1QyeViZ2+SNTlcE8DLBRhABkKgW\nD4eeJNSi8DaVBdXNrHusYo0szYtI8UQDqGUoxbeVV8Fk+8UUEg5NLlaM3BtNbne6hrdrbaZc297J\nGl3a2qrd+a4dISwO5izYVQ23OWKqcrzycc49xPqXhS30LQbqZbe9t0CXenWwkhCC3C53rGwiBK4O\nfmLKSoOQa2dTm0DRNJEOoyqG8iW4iN7IWbzDuYS9lRiBuwoUBuAMjis2qaTLm8m8QXWkzxOo+zWS\nrKzvkeWjRtGxY/PjbxncO5OBaakxko7IYWvjrUytJcpaxWyrGv8Ao8cZZncxr8252jVIz0BGSy9i\nF07IQz+L7eSzmtZ4E0bdHFGdyw/vF2kcEu37tucqcLgKeduBq+t6pJ581xo6TECOQwwXjslpIoVW\nRXCg78D/AFhGFEgABVmdrng7X9Om1y/1+XU7a4MpjtIpmuVJMaKZmbGFCf6wLnaoBQZOSaJN2CUH\ny3O0a3N5OJJE2KiuvRSckjvz0xyOh755AmaOQxmNHKnBxJnJU+uCef8A9XBqm2vCBjG9pMxkB+yJ\nFASHAIB+b7o5IwSQORzVuG4LRK8jFTsDOrLtK/XriuWSmjlsQQaWsWoSahNKZ3eJIwzxYbaGY4JX\nAYDdkAjg55OeHvqE07+VpaJNwd05O6MHkbcg8tkcjjA6nOAS6ulc7Gn+zxsoxOzhdxyeADzjjrxw\nRtPOREkk10qGKNhbkMOojiCg4AyQWyeo2jbgdemSzerGr3M/UreOwDy2rmee5ZRclg8hRQveQD92\nO+flABYgFuGpeH/DmradCkN1qvkiW4L5t7QRS3bAkf6vH7mNcqMEvwAxKnO7eSymu9Zk1VdQ3IgE\nUMQUqq7WPmE88knAz0+UcHFXXxErSXEi/cwd3AP19qp1WlZGqdjP0Dw5pGjLeX00UYnv5M3tw7Hd\nKEyiBmPLYXvxkknHNXJLJIl8yxuCqNtwqxeYOw49OOPTv65b/aejx3kNpHLEZpZGSNAMFXCZIPdf\nl9fUeozV1nWLKXRbiW11e3tblGaOKZgrmKUP5edrjkbuOnOeDyDWfvyn6lrbUxJdNR/iI0l/ftcN\nBoJE7M4QFnlXYpA5QfK2ASSQTjJL52/F/inSfCOhT69rDyRwRqMlFcvhmAztXlRkj5v0PAPi1h8Z\n9Y0/VZ9MtdUt72a+tY0GsTSKVt4/OmXzHUO8mAsvMZKuCoGAv3o/i/8AGddS0az8M6JcjWLiSFDq\nN7bpLGEmDF1VY1+ViBzuBYHaelaTV5Lm6GyoS5zTT9qDR7TU7ppvAMk9yynEi6m7OWCqFUBosxg8\nAlcEkbtueK8q8U+OJtV8W3GqeJ7K4lMly73Wmi4aLa5UZTJLFQrqF7EhP4c4Wro2g614w1K40fSL\nGV7hIpJp1nk+5GvDFycFmDFRtCksScc8GLxT4K1Dwvf3+kPqayfZV3yvLbFAYwm6PB+Zl3kABOMk\nIckYYZpVJptbHZGFOEtNz0K1+N1hofwvg8N2uhW6TX9vNGYIi0haMeaqzSB4yjOGXcWDKTjkV5x4\nh0LUF1KTbplvZx2MKx/ZheROpIb5tsm7Ezl5GOELEBiMYXirZajqumaeZ4ruRIpGSR4okzF3AEiM\nMPx0J3KOQAdxxTs7K+vo47XTbeWeeZmRYIN5eQKvmbiNuGUDfjuNhJA6s3J8vK0OMbO6ILjyUuAx\ngO3cSxYkEjt+FNaRzG9wYXC8KhBPTHfnFJqc9kt3N9iJ8gysIUbaW2Z+UMygAkAckDBOarm9uJIh\nEjhBz74/P8Olc0o8rNdyZNrKiyQoABxu747fjiowVFyFu8SYOWXH3sHpx05qo7yA+bJIxY9DnBz/\nAFrR0C3Xa+pCRi8ch28DGcck57YP8vWpb0Bb6Etu5N2JtQvJcunzEnJGeigk5J9+MfrUogs7WCG8\ntJWaWdwEib5S2SDyMew/zxVeZIbyMW6xhHY4ZmfcQfXJwB9Pf2q8srssNiLQK/lkeY0YG8DBOSSS\ncA9j04xWdzS2pFqdo0F15uo3aCXaw3AAozEcAcrhcEcgYBB7VVZY5d0LX0TskKiMKURQeAFGMZ4+\ntNuZIYd9ldRYeEctGoAxjI4zzx79/wATPaLpkV5Bc+U3lBzhGAYuSOAewOSO9NAQ211d6K489Rtk\nUEYIOOSOvr1+lQalexXGIY0wO3zZz7knpV++0uK4vFtbi6ABjkcLwSpAzt4+mT27Z7VgmCdspGCx\nQE8HgDPqapasmTkhzMzHpxjuP0r1n9hto2/bU+DrBDx8UfD/AOH/ABMoK8oNrCqhVuy0hxuOOAPa\nvV/2D4fP/bV+EcdvHlYvif4faRiDgf8AEyt/19K6cJ/vlP8AxR/9KiKN+ZH9HtFFFfvZ6oUUUUAF\nFFFABXDftO8/s1/EMf8AUjat/wCkctdzXD/tO/8AJtfxD/7EbVv/AEjlrpwf++Uv8Uf/AEqJzYz/\nAHOr/hl/6TI/HLYPU0FQRj0paK/fz8AECgc0tFKEJGaBWQmB6UAEnApdpyR6UqqQckUrhYaQR1pd\nrHtT8D0opcwmnuM2N6UuwetOopXZIgTBzmlopQCegpXuAgAHSinBPWl2j1oH1GhWPalVcHJp1FAJ\nO4qPszwDkd6QnNFFLTcq7YoBPQU4wuCo4y3TmkQHPSn8E8E0m2NE9vot5ORtjyO/zCtKXwpAYh5F\nwd+OjAYzWda39zZ8RueferNtrVzG+6Vt3rxXLU+sN3TOilLDpWkhl54bvbOJZBh88Hb2qiwKsVYY\nI6iuitdTNx0cbcdCKo6taQOftKqF3HnB61NOtPm5ZlVaVK3NTMqgAnoKldFVsDGPpSYA6Cum6scn\nUZsb0p4zjmiildA7hRRShCevFJsFoJRTvL96Nh9aE7CdmNop+wUbRjGKfMLQZRT9nOQaQJzzTuhC\nBSegpVUg804DsBRg+lTdgJsHNLgelOCdDmnYHoKVwGBSacFAORS46YopN2QBSKCBjHelAxn60oxn\nmi6sAqsB+VKrZzTQuecU8ADpUAGPWpIbaWbLCMlR1IFLZJFJcqs/3SfmrftbWK1j2RjPqx6msK1b\n2eh24TCPE3beiMnT7ItP5pyFU5HvWsrFgADTjDH2XH0ojj2ZyfpXHOpzu56+Hwzw6shw6Ugz3paK\nyOwKKjjuI5BkcfWpKbTQoyjJXTCiiikMiurZbmPYRz2NYqTjeY5wY2C5ww7EjH6nH4VvggHJ6D0F\nc74oguExc2sEjrFhXgVDswTwevscjkjIP+9+O+IfGGbZTnFDCYCrKnyrmlppJt6LVWlFRWqWib3v\nt6GFynDYuhKrUim3p/Vtiw0bRjc64A5OarrqT+cY0Ee1VO8+hycd+mMc/X14iTUwIAJblwHf90Zc\nbnLnIA9eueB09AMVj6356PFdCSRkDs3m2wJbJXATB+9liMAkDnORX59xFxdnHFEorFNRjFW5Ycyi\n3e92nJ3fa97Lbua4HKsPhJNR1b6u1zpTKjCNoHI8wZUY4PufzHvzWdqeoS6Ykl4IJlZAr7URpEds\nFTnGOoAC7ivI9uY/DWtJrWh2WowAuDGg2hduRgAcHBB5Qn0yfaq97qslhIj30+2N0jHmNESSu9dr\nYxy24gevzLxXycIanfGk1KzRDq2uy2ut26z30dnIbpYYZpZw8Uqsj7SQPRyFHI/1gz72JWutMnn1\nN7WCeRoSsxadljZUHzDhdoyGc44+6TzjA5/Wte1FoA+nR2sUd/bn7UXZGWTcGD5QMU3MqjDEq2cg\nqRkDh/FF/f3uhi2mmur+4ktgy2l3exCFTnawEa3BEe5Dgfe55APzYJTitDrp4dtI9C/4WXolzHFB\nfx5hliMaz3HzF3Chwh5HOwvknHPXaOaqapNeeItKuLfTdYiW4W4D6Ml1ayD94hJB+fgqwPPABR3U\nDA48n1a50O8a3uNO0a6hvIHkuJY7i8MDM5C+V5awoy4IBGQFyQDuBJALPW/EpuYxZ2Ucolm3xwXF\n8XWPAVpOFgDMwDBtxycAnLEbqhVktEjdYWMdUdraeJta1/QoYb7T47iHUIkN097EFWNCcyKrBsFg\nyFd27f8AKDgFQTgWUCaRdQy3+qSRXtvDdWFxctOGj2bIWjcSKrIv7qN0yFBbYckndjPvjqPha/On\neLbaC3l1KMX8t1asZvNRw3nlWYblOCjsq9NzvnH3aWrXlxo9zpXjuG/8zU7K+AaERKEWDduTO3pu\n+YdBkuxHQk5OpK5sqcVt1Oz8TeIdY+0okF7HHfSz4aOS9/dYLKjLuj3bgFXG4MOHD4VQc1/BMtxp\nuorYLcWsx07UcTIZiiGNz5iRooVsjDoCq7mGXXJDEHN0bTb++ln1C/uLuK4NvJfWcjxyySyRI42A\nAEKse7L54HCfOoPMOraDNomvXugavo08lzqBsWjsmmSaSRmSQSHzFUEbmjIbDYG4D5xg1acrXYrR\n+FHV+IbsHT5rvVJWWS8At9LtlkjxNhY1YYZy7qS24AFVAw7AHBTb0DWJZY38Y31kZL9LrfBp7Tl4\nYo2mC/3m3ybZGG7kx5ICqrEP543hTUrP7T/aKW0kshiEttbyebnbKiExquEVVLFQdrZLMPmJyO1/\nsp9c8H3ct3Ib2C8dbVLie5V2hjnlVS8abFUALMVJUAhkAJOCBUZ8z1InDliZ3ivx1pqebf6F5TWs\nPMAtCsMjIoAVEb7jbXkKAJlgPM+T+Iu0Dwf428VaNBLJpspBuWuv7QiuViVZGcHCoyuXQBVGGTJE\nY+YBuKvgnRr/AMWeNbj7KqKkF7JqqfbVSN98kjrExVVKsQqvjGBnYwyFxXXXD3N3qFtPeahFJbxK\nWeGHT8xOigF8JGd/DFV+bOCC2ADzcbyZMny6IxopvHfh62m/4S/V2svOYM+oXFs8haPB3FNuem85\n6ABiwUYIrp7TxbZX32KHVL20S1ngJs743HmWdwCWUhWHyMAA3EhViAu3cQwqutosumSXl9M8MfNz\nCqQRt5bAsSgYqCqDJBBTGM5yDiq2jeHdP1CO11zS7f7JLawB3mG/esxXDB8gxjyyHyMZXlVIO6tW\ntDCSjLVnbNPFBYzXkVvbrGsYZsr806gZBK9AT6Ek8546VaVrGO2zYxL98xrJHGcl+QeRz1ByfY81\n5Tqy+IYdXj8L3LrpOp6lGP39lI40+6kMyFSikbw4wxIXa+4E5KjnpvDGsX19afLcQ2k1uVUWFyUk\nWNO0kTffZCNwUnpucHbjaMZUm1uQ6dtbnaW0a2ix2MA3Ep80kh+bA9e5OT7dSc+sGt6qug6Fd6xe\n3IjEMbN5kkZfyzg4YgY+UA5PoFJJ4JqtZpDa3pltZY2uJXG6NOVVM4wuwKDjryOMjJ9bWq6Pp3ii\nzOn6pCrwrKkqrlgwYcgkcFSDz+HPGRWE42ldkxsmeazeA9C+KfiKXWdktoslpLJY3GnWoiOoTZ2T\nybmRlVN23axy77yQ5UBR3Gg+Drm30abTdfi+0I5MX2aNgYXgwoClGOCRgjcfmPXjOBtbNH0SHfHH\nFbokSxxxrhEAycKo4UEk9uvGe1WEaVlURR7s9GLcfpSdWXTY2bu7XPBLH4N+J31TxLqkD2TQNdLB\nHdanPmezdNknm+aSxyikx7gw5A425rG8Vfs5arpFjLqqarb3WnwxNK90h2P5Z2NHGsTjcsrjcFO5\nlyQDjk17Z8PL6GLwA3iHU7xRHPNc3l24ACr5kruQS3G0AgZOBgeleT+I/jZqGim2ttN1aw1ZUvri\nYapcQbvOfIEabfl2KASC+QXQYUr90bOSbfNbRm9OdaWxi+HvD3jlY77Wbjwumn22gW01xLd6eJ7N\nLsRurTwHAXzUG5hsXy1+ViG5bMXiDw5fato//Ce+NdK1L7MwE9zHavEGNsGVVklB2Mz72kVnZSzb\nGcMVUtXdaT8R4NO8CT+N/FOsag15qTBJLC2kRfK3SFZDbKSGHyiJjv4U4AIzkc18U/j14d8eaDNp\nNn4ZuGS4DFpLy6QCN+AmU25O0ElcMACdxBIFKU4Q1eiZa9pOWiOS+I/j/wCGfinR/wCy/D+kapH5\nXkraLeXSLDbhY3QhYkBDZQogdiXAQfNhRni9EaO1WS4S4ALuY4/3ecbuD8x4HXH0J+lSz2wmBjSZ\nlMz+YzAfdJ5wO3Yc9eDzSW9nHbzZjmBUNuYknaPxz/nFcc6kpvc7oQUFYx9Ttmi1eezjdmSJ8BmH\nJOOf1qKMLGxBb5l5A9/Til1SdZdSmhE2d0rBcDAAH546U9bJzGGWcSKFGNgOGJ4HJA4yCM+1S9US\n7XsVpV3/ADSNk5/OtPRI2kD2dtESZR8wwT/EpHTpjBJPXpjHOanlSM6ROxCoTgtkheasIY47gSqQ\n6RQF0Rm2ZGcA4z15zgZ7mpauhqyZaiuIrbU5JbRVkXYNqyR5y/U/KMcfTH+M+q38d3eTJ57CXIjB\nH8CnO7POPrx7ZNZQvFj1H7fbIqEksFkJJ/SorCGe5vIrSORQzvlm2845JP1x0Hc1NtSuazI5fOuZ\nFVpCzswVhgbuePxAwByR1FW9UsdRtIoYJrcROjbYk84Fjz6Dv7jr2p66dFFeiKwSSZSwLb0BIZfU\nnoM8Z/Q4ps2o2lpOJxD5sjRssjv/AHsHle/4n9OlWkJ26lBryaCVhcSshLES8cj1GDUwiXUnb7FG\nttEXCxQnJyP73X72DVIq0oNxIc5yN2MYP+P9K1Lm+e+HnXLmRxGqK5JGDzgdAAcA9OKWwijICRIY\nOmc5A6KOP8K9R/YUgmX9tb4PsZCF/wCFp+HmYe/9owf/AKq89u7q3k0l7e5smNyoCtKYx8uT/e7d\nT8ox7nPX079hy0H/AA2r8IZp2DuPih4fCAD7v/Ext66MJ/vdP/FH/wBKiXCHvI/o3ooor97PSCii\nigAooooAK4f9pv8A5Nt+IX/Yj6t/6Ry13FcR+01/ybd8Qf8AsR9W/wDSOWujB/73T/xR/wDSonNj\nP9zq/wCGX/pMj8dyuO4pME9BUhAPBoAA6Cv3w/n/AFI8EdRShSeQKcVB6ilAA4FAO4wLkE0oQ55H\nFOooB3EKLRsHqaWigVmJsFLgegoooDlDA9KKME9BTghIyTRdILaDaKd5Z7GjZzxSuhWY2gDPenBP\nU0u1R2qbsdmIEB6n8qUAAYFO2n0pQhPJNF2UAQY5pdqjnFLRxiobAOv4UYPTFKATwKUK2c5FK7AP\nmTvTzcu67HPFNK56/pRsX0paBdiMdx4ptPCgdBRsX0ovYTVxFQEZpdi0oAHApRgnGefSgWo0KAcg\nUtOKe/6UBMHk0C0GgE9KXY3pTgoByBS0BdDNjelG09hT6AAOBRdDuxhUilCe9O60YAGKCRqg9x0p\nSoJ3ZpetAQ4wFpNiEyd2AKWlEf8AF3p6xhjgD9alse40AMOnekC9c1IU2CgMD94ZpXHYaB7dBRtG\nd1PZAfujFAQ55pXQhArHkAnHWpbaJGyZR9DSwMVbIHB4IqX5egGB6VEpPYtJbkMsHlyfIeOxBq9p\n2qtEq280fyjow6iq9OVAfmNZytONma0alSjPmgzZDow3K4I9QagvL5YBhMEnvms0yODgMeKQszcs\nawVFJ6nfUzKUoWirMsR30rPuaSpX1OTbtUcjqaogkHIpyA8k1o6cX0OWOKrpWTJo3LHB69var1qZ\nHj++QB6iqEAQv8x+lX4H+VUUgD6VlVO7BO7u2TDp1zRSM6R/eOKRpVUjjg9xWFmenzJdRxHyklSR\nj0rmdI+JWm+I2Nvb6NqlrC0LML65t1WMMN+UJJIDDYxweMcnGRm5f2Gp2epprb3aTqgckiaSLYgY\nlV2LlZPlZhk4GQpwT0zL7V9Z1O0g1SW3e0gMazpYyGIPKSy4RwVcLvywyGyNxBB5r+XOLOOcz4op\nLCygqdKMm7K9202lzX2svspWUr66K32GDwGHwcuZO8mvz7f1scndeOI/B7TaF4p3QSI6eRLDJmJl\nKpgKx5YMd4xg4JODgA1b1XXLBPtFjMySgo21pXVI522hkCk/ecuQdq5yFfuNo57x9e+FxcwXNvoH\nnTecsptjCpBKEqzBm2l0G5wQVIY7mPCEJNF4vS80N9OtkAubFRHGnlsWQAF9pRgefL8xSME5RgFA\nYY+NjWa0bOqVCNk0igdY0nQNR+yTzE2N25Bla3Ri4UniQk/MMZByRld2CXQbuknuNFl0/amrx+RF\n5ZV7VY5ljWI5VGLI2DhBgHk7Sc5IJ5TUPiBoWs2A+26M0kkVq1tARbLJ9kJYADYRjDBXHH8RK9zW\ndHrt7oetXF0dQuQtvEVETxRyu+FkhEbSOp2hd5QsF5CnO0jhe3UX3LdFyXY7jxHomiajcXN/axmb\nyv38Zgh8hc7NzEOXCsT80hUkkjGFZScedat4N1Cy03+17JprkI05mhikZWWRF2K2HPzjB4HytlsE\n8HHXWT3ei6UdQn1VbhruBkeNbVnnQ+WWbgDJwM7n4GF298nnNQ1rTL+J7K2t5pgbcyKVuCZAsi5b\ncqghm+ZEHCgkEFhhSKnKLV2TTUouyObv49Nu7KzlmtRGbaRg0UZ2mGZAuSe6ghVHzHnaewC1KLPU\ndOvra1t5bLVXnjUTbLnbsc/OoEmMLtDBT95WLDI6U9La/TSLTxE+oW8c1pGGKW6pHNGxXdHIyFf3\nikFMkZICKMhulWOc6lA2pFozcsm6YS3QR5CSg287QOjMcZ6YIBINcknyu7OqKurGjrkMMegS2EWg\nxK+VD6nBIsgWRJFBkSV9rJyMgY+XY+QcybcKDR/Ev/CP6tbWWnQs6QJPqMDTqWSHZG3nKqqU2uCW\n+9neoAG9BmHSjZTarawaq6/ZrmZo5prlnCrn5fOIBzhchtvOduMjINd5qfinwX4n17wu2istjboo\ntLxIgzKInlVwkkjbVZBKFG5sNukL54G64zU92Q1KGiVx6ePLvRxp3hrwBqNpLZ3FjFBJDLalrdJZ\noYsQM/ILFzI20YPOGI6rmeM7W38Oa5Z6dqMizzS6XLBcXFyzoWEMUMkcmdoIyYmCoqjknH3snH8M\neItD0Xw9anUtIt7u4s7zfHHIBkgNgI6/MSpXzDjGNzKTu4FUPG/iG51q7t9S060NrDDLBM+yTd9k\nUqSqIdxAwWfKYG35c/ewK54tbiULTvY7G40a/wBP1V9AfVYbSUxNPHMLVNsBjWWfY+SXRyI8kgZX\nI5weLOvaneWnh+2vdA1kXpgkgmhtYrfMjTSPE0Yj5wwEvRU4wq9ckHhLbWZrrV3s4DaxQ3ErtKzR\nHC5V0CIwydg3qoTGPmUKOlTDUNZl1aTTNOvlW0s7ucWHlkq7SqpRJAzAM525VQeQW4x1rP2kUrlO\nMnod14J0C50G6Nt4it5p7+RtxjuLhIpGdWEXykvwg8thuJXaF4ToG9A0i4gt9Ai06XS9PsjeS4V9\nzojKc/Km1AF/dsdqhs4OR1zXlVn4p0C78LtLeeJri61SS2eMQXqtMWnaQ9HKkoAMkEcg567uOZ0/\nWdQstSXWbtppHhhcWrpJyOuTlSDgbmPBHOMVr7dRSRnLDyqXbZ7r4o13R9JhuZLrxFa211NbObee\n6u1QrIqsQQhUMQCcAHdkHAB5rj9D+JujPfTRw35sZtQgjaaa0bZDEyliUAYuWbYQu1sDO4KW6jjf\nHvxFtvFmnabZ29mYGs5J/tM+4bZBLswAmOPudupG4/MxasGw1m5tNPuNOQsEuXcSzBiNyEINu3v9\n05zntjkU5Yl3SQRwq5NdzvIPil4Q0i8u11Tw/qV28ix21+91ciTzGi2n5juJVtysA6gDhTtGG3af\niXxu/h3TbTVNPvI9Tms9txZXcvl/PayyojQNtyjMrHa20EghW3gsVHklx57edcz3GVkl3O0kpLZY\nnkk8sM5+Ykn5hnrmrGv6zqWpxFZtQWRJJ/PkghhEaI/zDcEUBRwx6AD5zx1zKxLTdy3houx6r4L+\nJesfEHxlDpMGgWdlalJZZEtVkaQnywNxK53SDjBIAHfoK0vjR8U/Hfg3VdNs9C1aOz8yxLXVssMU\no8xXKnBZSQOCOvUH0zXhsd9c2yKFdvmTaSpwfcGrEM1vcrJLcSfvJHXDu+SMDGMdxgD6flWbqycb\nS1HHD0+dNI9u+EXxitZdIu9V+IHjRDeNd7baOdwoVRGvOFwByx5PpgY5z5R8Rtd/tnxhqt1ZX5ur\nWXUpnt2kY7TGZGI2g9BzxWHLOqAoACV4JH+faohLlwrZ2jklunXNSqj5bGiw8ITckdPffFC01PTt\nE06aK/eyh0NbK5tUQIC4SRd8ZWQO4LMCQxUHacqRgDI8O6rFoGvWmvNY+ebW+iuWgd9ofZIrbc84\nzjHfFY+mISTYRI0rQyFdqjnOc4A9gRWgbaZkHlxPk9QVyaUpSkXCEIo7b4n/ALROteNPD0/h5vD9\nlaW1wyvMROzy/K28YJIHXPGPpXlsLW8jM5QbCvCCTZn1H8jV3WdKv5ZhCqsSgzIgIyAOucdCcYAP\n5VmrpFysi/aLQ7Xz8jjiNiMqvPXPAH+cy22rMIqMNIks12jtsAQKD0xt5xgE+pP8utT2ssBVZ4nX\n2JYYA7niqcukxRpILmVYxGwDKG3EtuwTknJxkH3wRx1Gjay6dBdoltpaSsYyxSbJ2Z2/dJ7gluwH\nPajQabucpewqLue5M6HfM+0Fucbjz+VMieBUO2T7oxuyeCfw/wAasaiWeWe182BFSWVvn2h22sxw\nT1z2APXj2qCWLZi1AjDqCz5cY+n+fWr6Gb3IZ3kc7luVODxjIx+lCb2YkZz05PJqdY2S1MJFufmV\nvMBBcZB4HPT14yOM1J5XlKC0ZJyCSV4P1pCGQwSmBAi/LyQxGPqfoMU+yVVuI57R2MxOVUgYB6fi\nPr9Kv+H7K31C8ksNQdY42t2ZEL7VByuM4x9cVWt7GOLXLOzh1CBzI4DNbF/lxzj5lHoPUUmmhoJd\nRvrWFtNC/MQWmZOGyeCWOO+QPpgd6qGyE3lxvMoZiCST0HFaWvmO0nfT7W3UIVG8tyxOd2c9ecjr\n2ArLcux8x3OfX1Ht+tSW9DXjvNLXTjaRELkrviCZ81hgZBPA+vbHSmWdvbpJGtzKFzGXZUYgsScB\ne2DwRjH55qla2DyN5scuVXq4BwgI7npnkDHvVmLRdVkmVrWdDJtJQiToB39j0/P8i+g1qy/qL2f/\nAAjX2eCUMyxITtOcDI5Of4eDz3rvf2ILhz+2n8HlXlR8UtAHXp/xMrevMJrk2tvJpjKojLfMVADM\nc9z9OPwFenfsOLE/7avwgaIscfFLw/k4z/zEoOpH862wj/2ul/ij/wClRNVuj+jmiiiv307goooo\nAKKKKACuI/aa5/Zu+IP/AGJGrf8ApHLXb1wn7UlwLT9mX4i3RUEReBNXfBOM4spTW+Fko4qnJ7KU\nf/SomGKjKeFqRW7jL/0mR+PxBHUUoUkZrzH4j/HHxZ4XtoZfD/hGOYGYGaedGeNVIYYOxgQcheTx\n8wHJPHJL+1h41k057o6PoyOJ0jH7qUjlXJ6v6qOPc1+s1uK8mo1HDnbt1SbX33PyShwjndemp8iV\n+8rP7rHvZXnAPWgqR+Z/niuIm+IeumMt5sCDbuZprVgqj6grj8TXnOs/G25McsMvj24kit7pFlms\njJGFLk7csoXcMK54JHHriueXGWUW9zmb9LfmzohwXm9/fcUvW/5I99CE/d59aZe3Vpptq13qN1Fb\nwpjfNO4RVycck8Dmvnk/Ga7ksZrweItWmiiZEk33RYqXbC/xnjjrTNM1Pw/rVtNq8up2Vtcea25L\nlx5kmcHfwMnOe2ea5anGeGS9ym7+b/yR1UuCcVJ+/UVvJf5s9lv/AIw/D2zWUQa4LuSJtpis0Llj\nnBwcBT65z06ZqnH8c/CMp2jS9WU/7VkAPz3V5A/ifRI5Ck2uRvsJGBZzDP0J4NN/4TDQGYpFPchg\njbHjiUrnHA5yf0rz6nFeOk7xcUvS/wCbO6HCOCirSUn87fkj19fjd4fa/EH9jX32cp/r/wB3uD56\nFN33cd85z271veHvHPhrxIY47G9KTSZxbzLtYHdgDuMnjAzk56da+fU8aaPazCaUXsgXkCaFEU8d\n+RRb/GXQ9JnaaHTrZjjAH9pqMfhgn9azXFuPpSvLlku1rfinp+Jo+DsDWjaKlF973/B7/gfTpjZe\nCuMdRRsavAPBP7QWteKvFVroVtq726rC5jVZnkACqcAbspgDPBHbjGBXrmk/EW3aFzrVqxfcDC1o\ngwV99zdfcdc9sc+3g+L8sxD5a16b89V96/VHh43gzNcMuajaovLR/c/0Z0exvSjY1VYPE/hq5lMU\nOsxfKuS0gMYx9XA/Kr7RSKcFTX0VDF4bEx5qM1JeTT/JnzOIwmKwsuWtBxfmmvzRHtOMYH1pQgB5\n5p2D6UbW9K35kc2txKAM0oQ55FO2r6UrsBuxvSnBAOtLR1pDECgdBS0u3B+ahQpOD+FAXEwfSnBM\njrTgMcCjBIOKT2JvqMCruwzgcZ5pl7crawtIqbgqnO05I47gc9aS7mZINxgk3YPCJu/Ecf1FYkms\nXInNu1tJJgEPtdUYjjOFchgAD0B7d6/mTj3i7NczzmrhIzdOlRnKKjFtXcXZyk01fVe6tkrac12f\ncZRllGnh41GruST18+iNWHXLW4RpLZA+wENg/wAQIGP1pLvUYXVJoJMrkEbP4hn19OP0/Pn9U1Vr\nXVI5rTaVkJWcNCwYSKxwGI4VzuIIJBOweoqzp+oaFrVvF9maNWKFot8QJIPTttPAyevAHPOa+JrZ\n7m2KofV62JqShp7spya020ba06bnswy/DU5+0jBJ97IvtrkkMzPID5AAUMwwGyCwI+v64OOtWY9W\nSQjoCqnePpnPb2Pp2rk7nXTCYhb2mI7lUe6t8BVj6HIBxg98gAdc46lPC/iTTbm6n0Kyup58ynyD\nIGLmNnzvwwzleevXavGSBXDQzHE4aThSqShf+WUo3+6SubzwVOcOaUU/kv8AI7eK8tZbZbnfgMoO\nM+vNI92oAnRPk7Djn9ea5OHU7icWlzLMiQ7szqm5FKO2Co3c9VOBjJAOO2dZtUmtGLM6sY8BwB93\ndgZA6gk8d84Hc4r18XxJnuY0VRxOKqTiujk7WtbW1r6fzcxwwyvC0J80IJP0/r9DZtpVuFyVA4yM\nEcj1/lTZLqCGbyXI9yT0/wA9azIr0Q3It7ad2jA+ZZGwV5JK8/wgAn6EelVp78/2mY/LLkQ/fYYO\n7J4GRjsCPwNa0eKuJMNhvYU8ZUUNdOZ9fN3l/wCTeljN5VhJ1OaVNX9P6X4G/G8bu8Y4KHueaULk\nblIx9a5qTXDBugkkO7zAIwAw3DnIwAecYPGcA+xqzea0bGzNzPG6qrZOzBJ+bkD/AL5I59u1fQ5d\n4ncT5fg/q/PGpbaVROUl8+Zc1unNftdo5a/DmEq1OdXXktF+X5G6gB7dDg06oLO6WU+WBjOWY+24\njP6VYIKnkV+v8C8ariihOniVGFeD+FO3NHpJJtvTaW9tHomfL5rljwFRct3F/g+3+QlHToaUKTzT\nvLB9a+/ujyRFG4ck0oQA5zT1QDOVp4THQfrUtjsMSMt3p/ljP/1qcFA6UtQ5FJIQKBS0UoAI681I\nwCk05Y3PANKDgYwKliuWjG1gCPcUm2jaEIN6sDYXKnBiz7imSwSwkCVMelXIr5inQHA4NMmvvMQx\nyRrzWSnUvqjsnRwqheMncqUEZ70YA6UVrdHDZguVIxUyzSdmI9qiUEmn1MrNmsHKK0HtPJIdzNmn\npdMp6EgdRjNQgYGKp+Ib9tN0a4uotQS2lEZ8mR4Gl+bBPCKQW4BPHYE9BXzfFebSyLh+vjKavKKt\nH/FJ8qfnZvmt1t6noZfCeJxcYN7vX5akfiLX7WGzaZlhLK+0rKWWN3BC4LDgHOMZ6cdOtcsfFuhJ\no0fiQyQ2VjGxiUSwXLK+XZFGzaASSowyncPlJyMiq+qeNbQNPYGa2nhOFNvKqwvv352qCpBHlDht\nwDbhtw4NcRdX2qeCb+6t/tEggncxzQ3rG4R/lkGFcDbzkHBx8r4JOCy/yJVqTlNybu22231bbbf3\ntn6TRo6am1far4b1q61bxRertuPNjgtzBJM8XnSYG7lFe3PQB2ABOCPuha5DVdXWa8tdbsL60Yl4\nGu7LywY1CNjHyqBiUBSQW3fPhs8Gp9U0qeBoIdMvLHULOR0iGyQmKGYhD5hTIdQdgO4nBUH5jurJ\n8UaayRz6no+rPNCdgBl03ylm3g/MrLgADDrsPdMDdjjmk20diSRV1HxHqAvWWxvZLaVZGMLW7mKO\nPCN8qoccgM33jklu5NS6fqk8FtKhkGIrkw3CxEoI0kLMGU43L0HAY7jGeSADWR5etaTdyaNeWsiX\njT+RPAsZSSFwNgBHGclh+HPOaWHThPJ5UjEjyZgHdAAzCLftBLDd8yhgm7nHCk5B5uZxlqbcqlHQ\n6i41t3BsrK8juoZFid3aDZLMx6qzHkgKp65GMZHzbaqXXie2sZkiEHygMEjaz8qTcAAFAGVxuOTg\nnPJOSeMe61aQ2EFzHrcsV07iMIGd2VMFcLhSFUA7QByNx/hJqtcPdXlkt2baOBGhU/abiQkvjYqg\nKpOGwCRnAZTkcgVftG0Q6djXsrzEEItYBLHxvhhKMQmEDCYg4jjL7RuYLwSWPIJ0tavdT1vS7PVL\nfRdL0+3AEckNuS8rkMAqzMiAxkK3UYJAxnKsKqeBfEWo6W+q2+h6INTjuo44b2W5CMdhXbySyBFy\nf4lYktz1Je4+nabr/gC91TUNamub22W2iPmzlzFOOBFIsgHyOokKBAwO0lsHaBolHl8yHzcxxM40\nq1u1S6vImxkyGSQNzngYJBzkHp0z3ptzd25gNvbSxW1wCsgnt2UMGVhIpG04IBVSOuMkjoMTQWUo\nYLpqn5yWEEDZUHkAccYB6n0796s2d26yQ3TRlrUnpcKH3rgocjoQFJAGOOwHSudOxt0KqxC7t/7U\nuSGmvRLdXJBypHmiMcfdByzHoeo6Vcg8hYLW1bWAFS8WO4t2UkAM69x1Bw3IP8K9M1Wt2MDNZxOW\ng+aOItGCxMsZwACCR86LnHPHHOKqy3ABmjKKNsJy4J525POODznHrRe4WVh7WgWRNMhhhxJCVeVB\nk8suCSxOD0XIwO59CxJJ4mjaGBl8qXzEBPIIwQvH06joKsfbRbXV3LFclVnliKRiPcjpmQkHBGDw\nBx79wDUUwjkvJbmLO9t2NjbVTPPGc/wg9Se3XuwLEf2draYWLJIsBJRTx8n94BscjgcZPPA45juo\nJbiBJXdFUSMqoy9MAdSep5UfzwBVRJruAPaOoSNDngEAHPfHII569Pyqzc3FtFDItntuk/euJS7o\ndoIOM5GWweeCQB27KzuU2mhbG0d2WKSNHk8xVCROCWZt20An5VyeCc8ZPBPy1JBLfwXXyApJuPKP\nnyT9R0I9eOeaowTK0bCRmeRhhYwo+c9cE9/xz+FOfUL6CCPy5pFXHCGU/Ljt1xTCzNC5FnK4jjli\ndjErSFZAQD3H+evpUEdrHHGiXk4jSUsqy53EkbcRnnHTnPvg84qhrBlS6VhKHGF2yjOeSCc56VJN\nftqFxMfsUcMTOxNugJRD/sk5OMY4JJ46mgSWpJdpbW94kP2pWhf+JGJKnvwfb0/pU0MOnhpHD7iB\n8nJwePesxbhsNBO5cMMqHA6ZHSmmRmtDE8pUq+ACeSD7/wCetVqFlc157ixiAdrclscjaAPx5/oO\nlQi+toxuFsquTkOzZAHpjFUHZZLdIwACu7nHJp8sbIgx/cBBz0NCaQ+W47S7u5tNQuYBKqlp/M5Q\nHPAB5IyOnr6/jM2uXQJikvXIAIY4AXA69P8ACoTETfSMoyVK8/iT/I0s0JkkVUCfvGO/I/hGM/nk\nD6E0m9QUdBbS6le3ebySjliW5AIHYYB/ySaq3Iuboea8hDoCQynHJGPz/wAPzvRwhGyy9YyOuBzS\nTRRRoGMmFYZZicYqb3L5UtyrcIvlNn5ZcYVV6Y5OR+PamLcSykuwbdGV4B69Tn+X50XEjzAlYx5f\nGN2QT7+34/iKcspQ4hQsTwQFP5e9UrEPcjktYJSJFtUDNJgNt6Ek89O3Wg2VlDIqyxqxb+DaPzJ6\nn9ac8lzIAIbcgsPUYz74NS2tiz7ZZrhsr1WPgMc9+5oY0mQi0tpQ9vbW0Z3fekK8Jg9AO57V9A/s\nl+DbW+8O6nqlxYW/kPdRW/mmJTI/lpuZc4yAS65Of4ePWvDgwx5QyOcYUZ/T+lfQ3w8+I3hH4eeB\nbDSI73T3cjzbxba9Z2jd9zPkCM7SoAG0nuoBORVU1Jv3THEWjHVHmX7QmmabpfxW1Y2uk20fleS0\nKiEBR+5Q5xwMZ5xXjNxPMdUWebauGyqxIsYDewQAD8AO/SvSfix4guvHnju88R/aFlgnjiZWgSSF\nCojXAKv82Rxk/dJ5BxXmUvzaioUgkuOT35rWo/e11D7KLEOnXN5IkaFSSDvG7/VKOPm9OMGtZtFS\n5tPs6AMSqFZ5hkkD+EdwowKs2lhb+czJGSgQKmQBg9cgD7pOQeMfpViVI1Ak8x1CZDHd/n/JrBs6\nIRVrswv7Ka21EWEOZCVzkLzxyRgf55FaFtNCtk16jZx8gkBCq3rwPvEn25x160+4li8szy7gzjiU\nNyi+g9ePz5qpNJBbQpBPNMsedx28k+nXqT+mDUtoSSWpR1AKbxVihiRY8F8DAc9MYA9+or1D9h6w\n1CD9s/4QTBkKyfFLQCRuBJA1KDOBXmmo2/2eRRCsm5+gcLkE9sAZBr079hm61GD9s/4RW284PxQ0\nBXw//USgOMZ5rowa/wBrpf4o/wDpUQTXMj+jKiiiv309AKKKKACiiigArzv9rxpE/ZO+KDw/fHw7\n1sp8hbn7BNjgcn6V6JXn37Wawv8AsrfExLmVkjPw+1kSOrYKr9hmyQe1Z1ansaUqlr8qbt3sm7fO\n1hN2TZ+ESeL0mae3js5TiMnzUiBMYBB3fMwAPUfMMc+uCMLUtQs9atJtOn1HUpIbiDyp02wL5ic/\nKW2Enqe/c+tRaj4Rn0fxBrOo6d4iubiK5lKQQ3U58uGRwQfnwSBuYgYAwM8NxWlP8NYZvASRzSW9\nlq4Mfm3drEZk3AgNgPjgjJ7EE98cujxxwZLCqrONpXSceVtq6u3uk0nfVa+TOR1lb4jJ1G20HUrZ\nRqOkyTldpLXeouwZlUgNjgA4z0Hc1zHinwffWdijeGP3ksY3zQ3LcsvmbBt27cEHrnrzgjGD6xqv\nhnR5Nlrp0aoS8Z2eSFYLvXJPTAznkjvWlfLbObXydNdGN9CzyMBz8wKpyTg85x0/PnyeIOPslp06\nccspRqcyfNdSg47W2Wr3vutN9SHiOXbU8Dg8OeN7qyeWS0SO4UkpAYXcyDYSNp+YAkgAZPXOccZ5\ny9vfElkGTUEu7eRP9YrWscbKdwGSNoYcEH6fWvdf2jfFGq6V/ZukeH7vy3vJybqSTYZNjABSuCdu\nT5nIGflGCOa8507XotFD2tzpkTR3llPbBhOCFYqSrEFccMmTz057V6tPH4bG5VSxGFi5Sa1WqTd7\nO3NrZa2b3S21OmE3KnzWOEvNR1/lrm5voVILIklwwynbgHGKNG0fW/EsPnwWsQGcA3M5zn/vk1f8\nVQyebIzXsN1ut/ke2cMqckbev+cj1ra8FadssDLaX/lR7juKKD5jDjeNwOAT+gHrXRGKlKzNG7Q5\njA0z4f8AiHUEaRJ7KNUkaMn5icqxB9PSrVh4Au7vUbnSrrUIx9mEe544Dk7l3d24rd8N6jDFYv51\n5Pk3Mh2palxksT1VD169al0dLa91i/lSWYFmj34kdCMAjBAIOeO/rWipwMfaS1K/wf05dM+LkWnL\nJvEEc6FmHJ/d9f1r128uPF+nXryw6Pa3dgxURG3uzHcKCAMFXGxzu77147evlPwshA+NMiKT+7W5\nALHJ+4O5617e4/cKCP4k/mK4qkIyrNPodcZPkTOci+JujW0otfEQn0eZjhU1iHyQ30kBMZ+m7PFd\nLpfim8sE83TdSntxPgF7WcgOOccqRnqfzpJeSmDzvFYd58OfCMmqLq9nphsrt2w9xp1xJbs3yn73\nlsA/IHDAjjkU4+2pyvTk1/XdWZMlTqRtON0d9pXxK1m1QQXa295GNoPmIFkVR1wy4OT6tu559c25\nPivpttG0l9oN8MNx9m2SjbjqclTnr2x715F4Ou9ffxt4j8Oatrct5badJa/YWniiV1Eke5gSiLu5\n7nnj61oyeLdX0txDrPhLUlAba9xYxi7iBwOyfvcc/wDPMV7GE4nzrB+6puSXf3vz1/E8TGcMZLjn\nzSppPvH3fy0/A7S5/aa+GVoxS4nuFI4KmSDI/Dzc1k6V+05Z+JfEEWj6FBZJ5kbYSRpJWLAFidwC\nrjA6frXP3F58PvHT/wBl339n304TiAuBcR46jacSRkd+hqDSPhT4N0LXbfxJoCy2skAYeUZi6MCh\nU53knPPr+FdNbi7OK8bKoo+iSf6nPQ4QyKhK7puXq21+h7N4f8b6TrJaK9eOyk3fuxLN8rjGc7iA\nF6Hg+2M5wNqEwzxLcW0yyRtnbJGwZTg44I68ivHZmktpMLJnIyCPSnQalcRSrKrEMrBkYHBUg8EE\ndPrXdg+OsVRShiqal5p2f+X5HnY3gPBVm54ao4X6PVf5/iz2MgjqKCSBkDJ9O9cNpXxX1OFguqWs\nd0hYszY8tunCgr8oAPP3fXn06zwxrOneOBNBpoZJoI0aWCR1BOeu3nkA9yB1HrXu4zjXKaWU18VC\naU4Qk0p3V2lotL3u7LRo+Rr8I5vhKsVOHNC6u4u9l6OzX3FtZYXBAlGcjvxU37sKGGfmIFZUzXVp\nKxms5XjQlJGwu4DPJwDyB7epPaoE1CO1k23EkixbAzF42+UHnaM4/wBrjr36dPwGfH3F88Q6yxkl\nd3slHl9FHlat5Xfqet/YuA5OXkX43++5sXlvaTRmKZVYgfd7msPV76J1NjLiIZULiNlCEHgg4Crj\nrweMdQQMW31A29gsrW8hGz99G6MHVse/49v0rIvL8a1Moht2cIS5tyP3rIMfMFP3sdMZzk4wCc18\njWrVq1SVWrJylJttvdt6t/Nnp4ekqaStojI1HTtU1IGBRLPKrebDKsiSOrZwcOR0K/wlxnccdKwo\nLu8sdS+zwWqRS42QW7yP5bEcEYcBgCSV5UEFyDjcpPTSW97ezSLpmoxRKwUkmPAkYOQdynHOAAR9\n7jkjoOa1y2bUJDC7LHcBX8ow3cRJ4JLMzlVcKCQdpHGR/FmvPqRcVdHqU5J6M3L7ULq60oQ29ibi\nAynmYBW4dnDZ3EOuI8/L03H1Fc/rFlL4a1Oe6tklhtLuQwRvsHliTIZR8rBHGBuOMfdOCCaPB/jC\n/sr6Kz1C5MTRmRW4BeMtIzDywRgdBgEBCJSxBBK1P4m1W0g8GxR6asTtYtEqxSQq43iVT5mCRy2C\nx5YD5RgZYiXKEo3e5SjOMrLY3bXWNO0rRn0g5mupT5flrCqneSMkEdD/ABH0wQMBVAms9Sto7NdM\nkuojNewxMkJRpNofaCzEj03EZwMY44FcqthpS2N14kutQa6kKg+VujgUrh/kVMDKp8oBGCM7eQw2\n7Kx6fHppWOW0iKOH8s3G05j5wrLzyF9VI981pGrJrUh0ojo7yO0mQWiKzWjH5pLZsldoLKF3DOCj\nEEkH3znMcXiYWcq3LyH97eqJGDK5A3ZIxwSDgbcjAyyk/McY9zqp1C9eWZI4w6KrC8YxySnG3eql\nsgckgRguRlgBgZjj1aYxxW+oRJPBHhY1BVCAFHG8kAbc5z6gHOMGsXVadjZU1Y6TVtXk1HVAsSXM\nO+MShUjKlQqZWYAjjbvUjOD06H5BXGt6iTFokjxzpM7Ru0ZJ3SK204OGPIXI4x8/UDINbRr2Gz1C\nHUdSlidZEkijldDEdrK2zaZANzB1QA4+TDMFxgrnRa+sHitxaW8dxLGoiW6ZNpnYtjzMEZGFDHA6\nnv1AUpLe4ox0slsdxo+uajbWbR3KqJIVEcgSUt+9KjCHPuwXjjIP1rodP1y21KfFtcvIWiGNoPl9\n8ED9eecdeMY8pg1aeGJbu31ID7A/mNCJNzEjgMccbiWXgE8LnAPI6/w25n0uGCayEbPEfJ83euQU\nVt2DnO7OQ3QDaODkV00azbVumzOTEYaEldnam4hWTY2Qec5OM9On51LEUcfKwzxwevf/AArg31O8\ne+DSXBbfKoWAOwKfxlixJ6rnnOBuIPpXR2N5KCq3ZlWbcFWNoyG2naASDyD1649BX32U+IvFOWcs\nPbe1hHTlqLm0/wAWk9Omrt5ngYrIMDUTklZvqv8ALY3lXHWlqGyu1u4ckBXH3l3AnHuO3OR+FTV/\nQ3Duf4XiTLI4ygmtWpRe8ZK11p63T6pp6apfFYvCVMFXdOf390FFFKEJGa9w5hKcikc0qrjmloKS\nClUAnBpKkGO1Q3c1SADAxSFc0tFIuzY3yx3NLsHqaWigOUAMDFFFBDBS20nAyQBk/lXDjc0y3LYO\neKrQppK/vSSdvS938os0p0a1V2pxb+QU6NirYBwO9Z2u67puj2Ul7c3ZaGNHFwLchnUbc7lA5z6D\nHfJwASPPbv4xS6g5s77VfsxguGE8ZSSF9h+UYaJvndDIu9cr8qEgZOD+OcU+J2SY7D1cBRwzrUpx\na5pPl1s7SjFxk/ddmm+V3Wlj6fLuH8bzKq58rT23/VGx4+0YT6xchdMt/Ih04tIlu0nnFAHOfLXa\nr4fcCpJyrnOdwWuD1nwd4k0i1i1e8tVuHuNz3sNvEdlsVUgxl2PRdhyfuYwMEYrd07xJqWkQ3miW\nWmXcd1Jbvc28aSs8bxvGsrJKkixuW3HAfBb5mBdttYnjn4tXXinw9LY3Fr5IigfzrIQDYOB5ZQFg\nzDjaxOBg/dIOK/DZzhyn2dGnVhZLY5eWfSppUN3YrExtz+8eFQNm0k7QBjJYfK2FA3EYON1Vra4f\nTbiK805cRlt4u7232h9rYDfeYL0AxuXkgcYzVnWrm51C2mkh09oLE3Lz2rSEM0a7ipRZGUFmKn7p\nxyGOMgmqGoWF3dSsw1e3meeHeslsyhosKpBIjG0nGAfxOA1ckn3OyKeyF1yKN51u5JYlee3AMsoD\nLgfKmOpPCbdy5A6bskisS4vpL6+F3HaojtAiQm2iUKJFACjHQ44z14J/C2k+rx2yyx24a1aUW3mu\nSIf4TtZySFADJ6EDB4zVeGK3luXdYtjMeI4nChflbCKWyxJyAPw+bJ4htFJPYjMjyxGXcZGKIXhc\ngLIwJAJGPm4JGT1wc5JJNoEaq8tyLmCKPcWEk1sy78/eA8sNtwAx9QAcEnkQpZJfo0GoXCwxoGZI\nZIcM3ABG7IwDgYye5PHezFaagsM1/La3TWsoDi4WYsFAJXc+0A5yeGYBfmHBzSWrFqjb8N6z4kst\nOtJofDelLbJKBb3zz3HkyuCMjcqBG2s249cHjnbg5XjS+1a0vzeTXelwzFEjFvp140oiVGXahDMe\nMFSCpCnY2MbcDp/hpc6drF6k3ifZfJKv2e2sJLtkBxsy7KNol3sSFjJ2sFYEqFJrpfjbpHh21+Gz\npp+gJbSfa0Mc0lm+6FF3qqbgNiLkMBtJU7ztAzXZGKlTORz5aiVjyS9vUnt/IleaRZUynkQqAWjD\nLt3Pg7RknAHQ9COaqWly23y/O/1qNmOReFJJwQSBnqOSc/yq1bTR3FnLa2eozHLclCQpxI7kMOpK\nleOCd2SOvNKOY2jP51ukoGGiYsNrDByp24weV4BHpjNcex2aNj2u7i7trq6icxyGLLZA5xIgGQOC\nMdumB0qDV7u2vY45EdxNJA/2jfbqqk4ydu0jnnb0A44zk4urdW1reM9q3yyWzxv+7CgswO04JwQG\n2jnjPPoKz1ga5mNrPIBFbp5qIRjBznGcHjPOPX8ciE1ZjpTdXA+dnlGd7hWxtBYkYZ9pVfmz2z1I\npbOOeRWtZpikceS7SzbgOc/MR1745PPSrGgXltcWVzeXE7ho7e3LMqq2do4TqMDa2znOGODjpUIV\n5rjymCoPnaPzDgD5WPPP3uw9+KL3YJaEr2catHGbqKXzgjvNHvZI9y529AcqCQQAfmBwSMGkn0v7\nPYu3nhJmXIhA3KVII3E9CO2Rnqc9ssnjjhchVT92xBw2Qcd8jqPpT9Zu4IdTGn2mpPdwBQ0Jmfqz\nD5hwTgjABxwSM9+Dm1K5NCuptp2xDCyfMMozZAHsfxrRS5drWCJIXk8tyJUx8jZJwAM9SC2emc9O\n9RajpEULi40yZmieMMA8f8J6ZHII3Ar1OGUqeRzTN5IPMDN97Axu+Zfof4u3J5p6MNiW8hiSbEca\nANgrt3fLz90FjyPc5+tQu8AuA4TarZ3Kp4H0zT5ClxvaBkVeSu1shTzgdz1GOaijjPmAyMvGGXDA\njpnr+OPbp60Dei0H7Ip0BbJKnKMTyD/hyRj0NRs+T5EyAM/Ksp4P596naSNBgckn06VDPF9piOUb\nZ1yMce/51SuS7JBbFZFIRshOQOhGe1OkeX5lD54xge9VVkFtIxkztwd5A+978fWrMci3G2VDwwG0\nL/n3pagmrCi4uHeeHOSdgOR2A60638x7ppHwAFCALzgDr+v8qht51huZ5ZHDKY1kwOw+bH48Cui8\nJeCU1qxa91C9lSKGcrL5UqqQAAxbBHueeg9KGO6S1M5pzhY44/unqRgmqrl71fMi27RypP3T6nHf\ntjt39g9YN80tvFNvt0kdUlznzVyR+WMc96kEUaodrY4GRSfumiXOvIrSQsIyRKxJ/vementUMMsc\nkrQqyMVHzKW5FWZyEU7mGCO1Z9kqjUH6cx8/nQmJxSZo28ToM8+2RT1cCMrnHXcwYjFA2kYAyT71\nCYmaYJDGACMsWPTtQtSmrLQfE3nSlI1LKFxuJ47V6H4ghjubeK1upIbh5dNhUQICkKqYFx8u4k4H\nHPGecLXCIojiLYx+Heuq8Y69bT6pC9ndRvENPt04cbBtjG72wOn4Y56VUH75lWi+Q57xWtnBq0tj\nao/l7UYR+YXyAgAUEk9x06YU1xVjDFcayUnidyBuCr/eyMA+1dFeaxDJMXjDb3kzI2MAjGOB2z/I\nD1NZFpY30Rl1ezcxfwGTy2OTkfLkjb+ueOmKp2s7EN7I1YLgTwtCLhBIkhywb5cfXH4Cq0i28Bl8\n0SGQqWeQY5PABzggDP489e9UntJoLxZFnAWQ4JVeRnqTx/KpbuxWedNuACMOGBJ6cen9Kx1NiKS9\ntWtjFIGMw4j4GF57etLOrWSF5LbEpwUfawPI4yCM++aLbTH+1kmVciLbH0BIyeT/AJ6+1LNO1xZf\nLjaHyCqDCsO9FibWIphbQWazpIWkIxuAxnnnHevRf2H2A/bV+DsfmE5+Kfh8kEf9RG3rzZXsvIzc\nRkkkBd2cA5znj8a9L/YhaN/22PhCwQH/AIun4e2lRx/yEreunB/75S/xR/8ASogl7yP6O6KKK/fj\n0AooooAKKKKACuA/awuYbL9ln4l3lycRxeANZeQ+iixmJrv687/a+uFtP2TPihdNCsgi+HetuY26\nNiwmOD7GufF/7pU/wy/9JkTL4Wfg9q8kd5qFxHY72ae8EiN5hYBtoUED0woOBxwT3OdfxnZWXiLw\nudL12JpELpJOoleJWk4bBK4yMtnGcDAPbjmtQvxqFwpnVYoZZQzvtywXdgYHp19fr1zesr/U9Sim\ntJ3CxwuvmKVHlxjaAF3Z5YsuNuPm/E1+DQrTpT54O0ls1ueXZ6G5Gxvofs6GQO9wHAOQq4xkbsY4\n5GOp5xRr8Nrq1zBPcWupKttexSqItQki3JwMBUcd8kk9MAZxiuZ1nWtWlRLOG/YkfM67ANhBIwT3\nwc+g61ftrrVrh/tV7O80ClprQX7EgqBIfLJ6gb0RSMjdtHrRTqypvmi7MOVmd8R/g7b60o1F/Edx\nYOJi8sdxL9rckIFRFyARhRj94zdhxjnmtK+Dt9qWpxySeMCVSZTH5unAFG3NuTh/ugAdTyWJwMc6\nuq+PNO8N6jbaj4z1u5jhuJWZPkfYEUclQg+RVHZQAABxwMVvD/jTwr4t1ADTNUSchFe4idGU4cBg\nwVwCy54wBxtwcZFfR0M74moYRTg5ezS0fKmktt+XpsbxlXjDTb0Oc+JfgG/8MeH7rxbrmuW6TRwh\nLOzWIhpCJiq7dzAk4V5cY5VM8AnHO3mhTaR4KtvEmmeJBLHdH95AANiHaS3zbiOCCp49Oc11fxi1\nDRJfD66PeWFo2o2KgWkG4tKuZfnZVHG1lLDp/ABnO0VP4S8P+Etb+GVhpMcwntntAyyoqrsmDkOV\nLIM7ZGcbwxDHj5h81fQUuI8bTwFLG13KzqtSSirOHLsn26p7t3V9Ebqo/Zpvv2OY8CeHPDP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hRznkjimv4c1fSrW3muNPkCSx+askTiRXJCnOVyOmwHv0z2p1rNO0ETzW0kkZuApYAg53DIB7ng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M4OD15APqBX0rsPqKURE9DXXT43zuD97kl/27b8mcs+Fcrmvd5l87/mj5Xtvhj8QLkf8idq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\nMqX/AIhmWDFk/bZC5zwPht2Pb/kJU/7Wy/8An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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "def vis_segmentation_stream(image, seg_map, index):\n", " \"\"\"Visualizes segmentation overlay view and stream it with IPython display.\"\"\"\n", " plt.figure(figsize=(12, 7))\n", "\n", " seg_image = label_to_color_image(seg_map).astype(np.uint8)\n", " plt.imshow(image)\n", " plt.imshow(seg_image, alpha=0.7)\n", " plt.axis('off')\n", " plt.title('segmentation overlay | frame #%d'%index)\n", " plt.grid('off')\n", " plt.tight_layout()\n", "\n", " # Show visualization in a streaming fashion.\n", " f = BytesIO()\n", " plt.savefig(f, format='jpeg')\n", " IPython.display.display(IPython.display.Image(data=f.getvalue()))\n", " f.close()\n", " plt.close()\n", "\n", "\n", "def run_visualization_video(frame, index):\n", " \"\"\"Inferences DeepLab model on a video file and stream the visualization.\"\"\"\n", " original_im = Image.fromarray(frame[..., ::-1])\n", " seg_map = MODEL.run(original_im)\n", " vis_segmentation_stream(original_im, seg_map, index)\n", "\n", "\n", "SAMPLE_VIDEO = 'mit_driveseg_sample.mp4'\n", "if not os.path.isfile(SAMPLE_VIDEO): \n", " print('downloading the sample video...')\n", " SAMPLE_VIDEO = urllib.request.urlretrieve('https://github.com/lexfridman/mit-deep-learning/raw/master/tutorial_driving_scene_segmentation/mit_driveseg_sample.mp4')[0]\n", "print('running deeplab on the sample video...')\n", "\n", "video = cv.VideoCapture(SAMPLE_VIDEO)\n", "# num_frames = 598 # uncomment to use the full sample video\n", "num_frames = 30\n", "\n", "try:\n", " for i in range(num_frames):\n", " _, frame = video.read()\n", " if not _: break\n", " run_visualization_video(frame, i)\n", " IPython.display.clear_output(wait=True)\n", "except KeyboardInterrupt:\n", " plt.close()\n", " print(\"Stream stopped.\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o_FjC5CNKLSr" }, "source": [ "### Evaluation\n", "Now let's evaluate the model performance with the ground truth annotation. First read the annotation from a tar file." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 304 }, "colab_type": "code", "id": "yIxt5IvCKLSr", "outputId": "ffec8782-d7c7-4370-975f-dec9e4e0f86e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "downloading the sample ground truth...\n", "visualizing ground truth annotation on the sample image...\n" ] }, { "data": { "image/png": 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HJ3AI0NMGseg7U3jjXxXsqdN4/tVXuGDOqUycVMLwTNOemAeVHLVdjdqoAd7o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IkPfOAD3H777fze7/0et912G+9973t5//vfz5YtW1Y8RlEU3v/+93Pbbbfxu7/7u0tm7efD+973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\nUBSqlSrKyqtQNYVM3MLutBkZHmLTxnF8z6XdbJBMJuj3bDZsXI9l6th9G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igAACAASURB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3kyWgKGABmCaYKmKy5PHOM6bred0ODYPRnu/XSbnmIO21TrF8dWDpjZ2VlkIogj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\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O3ThQgiwekKQJXlhCAp21derNBtq4/RYEPirwydMMYd1+yawDRXOtMcYihRvraerOs8xo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ElCCdDdz14heUAMMcw0Y9RPWqbS1GGbQpAmM6z2RzzvyKUcPHiQLMu47ytfRMoY45mHVVWNLzhb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\n1pS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SknhwaZkENBGAz2RmljF2b/FR74Gxcj3l3iOsJWXTwGQ+r0CP3i8BpIRxtYcUZRBP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\n/buJsZQBAmud1KNRidN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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "class DriveSeg(object):\n", " \"\"\"Class to load MIT DriveSeg Dataset.\"\"\"\n", "\n", " def __init__(self, tarball_path):\n", " self.tar_file = tarfile.open(tarball_path)\n", " self.tar_info = self.tar_file.getmembers()\n", " \n", " def fetch(self, index):\n", " \"\"\"Get ground truth by index.\n", "\n", " Args:\n", " index: The frame number.\n", "\n", " Returns:\n", " gt: Ground truth segmentation map.\n", " \"\"\"\n", " tar_info = self.tar_info[index + 1] # exclude index 0 which is the parent directory\n", " file_handle = self.tar_file.extractfile(tar_info)\n", " gt = np.fromstring(file_handle.read(), np.uint8)\n", " gt = cv.imdecode(gt, cv.IMREAD_COLOR)\n", " gt = gt[:, :, 0] # select a single channel from the 3-channel image\n", " gt[gt==255] = 19 # void class, does not count for accuracy\n", " return gt\n", "\n", "\n", "SAMPLE_GT = 'mit_driveseg_sample_gt.tar.gz'\n", "if not os.path.isfile(SAMPLE_GT): \n", " print('downloading the sample ground truth...')\n", " SAMPLE_GT = urllib.request.urlretrieve('https://github.com/lexfridman/mit-deep-learning/raw/master/tutorial_driving_scene_segmentation/mit_driveseg_sample_gt.tar.gz')[0]\n", "\n", "dataset = DriveSeg(SAMPLE_GT)\n", "print('visualizing ground truth annotation on the sample image...')\n", "\n", "original_im = Image.open(SAMPLE_IMAGE)\n", "gt = dataset.fetch(0) # sample image is frame 0\n", "vis_segmentation(original_im, gt)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PaDegfu-KLSu" }, "source": [ "### Evaluate on the sample image\n", "There are many ways to measure the performance of a segmentation model. The most straight forward one is pixel accuracy, which calculates how many pixels are correctly predicted. Another commonly used one is the standard Jaccard Index (intersection-over-union) as IoU = TP ⁄ (TP+FP+FN), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 141 }, "colab_type": "code", "id": "c2E4PMGRKLSu", "outputId": "9ac55399-1490-4d25-be56-267a0457739a" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "evaluating on the sample image...\n", "pixel accuracy: 0.90720\n", "mean class IoU: 0.5383739999999999\n", "class IoU:\n", " road sidewalk building pole traffic light traffic sign vegetation terrain person car\n", "------- ---------- ---------- ------- --------------- -------------- ------------ --------- -------- ------\n", "0.96126 0.34571 0.52489 0.16418 0.49512 0.52207 0.82085 0.05721 0.52215 0.9703\n" ] } ], "source": [ "def evaluate_single(seg_map, ground_truth):\n", " \"\"\"Evaluate a single frame with the MODEL loaded.\"\"\" \n", " # merge label due to different annotation scheme\n", " seg_map[np.logical_or(seg_map==14,seg_map==15)] = 13\n", " seg_map[np.logical_or(seg_map==3,seg_map==4)] = 2\n", " seg_map[seg_map==12] = 11\n", "\n", " # calculate accuracy on valid area\n", " acc = np.sum(seg_map[ground_truth!=19]==ground_truth[ground_truth!=19])/np.sum(ground_truth!=19)\n", " \n", " # select valid labels for evaluation\n", " cm = confusion_matrix(ground_truth[ground_truth!=19], seg_map[ground_truth!=19], \n", " labels=np.array([0,1,2,5,6,7,8,9,11,13]))\n", " intersection = np.diag(cm)\n", " union = np.sum(cm, 0) + np.sum(cm, 1) - np.diag(cm)\n", " return acc, intersection, union\n", "\n", "\n", "print('evaluating on the sample image...')\n", "\n", "original_im = Image.open(SAMPLE_IMAGE)\n", "seg_map = MODEL.run(original_im)\n", "gt = dataset.fetch(0) # sample image is frame 0\n", "acc, intersection, union = evaluate_single(seg_map, gt)\n", "class_iou = np.round(intersection / union, 5)\n", "print('pixel accuracy: %.5f'%acc)\n", "print('mean class IoU:', np.mean(class_iou))\n", "print('class IoU:')\n", "print(tabulate([class_iou], headers=LABEL_NAMES[[0,1,2,5,6,7,8,9,11,13]]))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dN_NN_KVKLSw" }, "source": [ "### Evaluate on the sample video" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 159 }, "colab_type": "code", "id": "YXqR6Vv7KLSw", "outputId": "9bd3a60e-164a-4d43-a258-718e8b8e176e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "evaluating on the sample video...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 30/30 [01:03<00:00, 2.12s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "pixel accuracy: 0.8995\n", "mean class IoU: 0.5276\n", "class IoU:\n", " road sidewalk building pole traffic light traffic sign vegetation terrain person car\n", "------ ---------- ---------- ------ --------------- -------------- ------------ --------- -------- ------\n", "0.9554 0.4104 0.5283 0.1594 0.4487 0.5508 0.8295 0.0474 0.3879 0.9582\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print('evaluating on the sample video...', flush=True)\n", "\n", "video = cv.VideoCapture(SAMPLE_VIDEO)\n", "# num_frames = 598 # uncomment to use the full sample video\n", "num_frames = 30\n", "\n", "acc = []\n", "intersection = []\n", "union = []\n", "\n", "for i in tqdm(range(num_frames)):\n", " _, frame = video.read()\n", " original_im = Image.fromarray(frame[..., ::-1])\n", " seg_map = MODEL.run(original_im)\n", " gt = dataset.fetch(i)\n", " _acc, _intersection, _union = evaluate_single(seg_map, gt)\n", " intersection.append(_intersection)\n", " union.append(_union)\n", " acc.append(_acc)\n", "\n", "class_iou = np.round(np.sum(intersection, 0) / np.sum(union, 0), 4)\n", "print('pixel accuracy: %.4f'%np.mean(acc))\n", "print('mean class IoU: %.4f'%np.mean(class_iou))\n", "print('class IoU:')\n", "print(tabulate([class_iou], headers=LABEL_NAMES[[0,1,2,5,6,7,8,9,11,13]]))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "qbiDYrpCKLSy" }, "source": [ "### Optional: leverage temporal information\n", "One thing makes video scene segmentation different from image segmentation is the availability of previous frames, which contains valuable temporal information that may help with perception. The open question is how can we use such temporal information. Let's try conbine the prediction of two frames instead of only one frame, making smoother predictions over time." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 159 }, "colab_type": "code", "id": "3CstN0dKKLS0", "outputId": "af17a11a-09ac-4f16-b75d-8aa8c65f25f4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "evaluating on the sample video with temporal smoothing...\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 30/30 [01:06<00:00, 2.20s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "pixel accuracy: 0.8998\n", "mean class IoU: 0.5284\n", "class IoU:\n", " road sidewalk building pole traffic light traffic sign vegetation terrain person car\n", "------ ---------- ---------- ------ --------------- -------------- ------------ --------- -------- ------\n", "0.9554 0.4064 0.5285 0.158 0.4504 0.551 0.8297 0.0453 0.4008 0.9586\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print('evaluating on the sample video with temporal smoothing...', flush=True)\n", "\n", "video = cv.VideoCapture(SAMPLE_VIDEO)\n", "# num_frames = 598 # uncomment to use the full sample video\n", "num_frames = 30\n", "\n", "acc = []\n", "intersection = []\n", "union = []\n", "prev_seg_map_logits = 0\n", "\n", "for i in tqdm(range(num_frames)):\n", " _, frame = video.read()\n", " original_im = Image.fromarray(frame[..., ::-1])\n", " \n", " # Get the logits instead of label prediction\n", " seg_map_logits = MODEL.run(original_im, OUTPUT_TENSOR_NAME='ResizeBilinear_3:0')\n", " \n", " # Add previous frame's logits and get the results\n", " seg_map = np.argmax(seg_map_logits + prev_seg_map_logits, -1)\n", " prev_seg_map_logits = seg_map_logits\n", " \n", " gt = dataset.fetch(i)\n", " _acc, _intersection, _union = evaluate_single(seg_map, gt)\n", " intersection.append(_intersection)\n", " union.append(_union)\n", " acc.append(_acc)\n", " \n", "class_iou = np.round(np.sum(intersection, 0) / np.sum(union, 0), 4)\n", "print('pixel accuracy: %.4f'%np.mean(acc))\n", "print('mean class IoU: %.4f'%np.mean(class_iou))\n", "print('class IoU:')\n", "print(tabulate([class_iou], headers=LABEL_NAMES[[0,1,2,5,6,7,8,9,11,13]]))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "s9ReH8nAKLS2" }, "source": [ "Can you make it better?" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mAUptUM1KLS2" }, "source": [ "## Acknowledgements\n", "\n", "The contents of this tutorial is based on and inspired by the work of [TensorFlow team](https://www.tensorflow.org), our [MIT Human-Centered AI team](https://hcai.mit.edu), and individual pieces referenced in the [MIT Deep Learning](https://deeplearning.mit.edu) course slides." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "tutorial_driving_scene_segmentation.ipynb", "provenance": [], "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: tutorial_gans/tutorial_gans.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![MIT Deep Learning](https://deeplearning.mit.edu/files/images/github/mit_deep_learning.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " Visit MIT Deep Learning\n", " Run in Google Colab\n", " View Source on GitHub\n", " Watch YouTube Videos
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Networks (GANs)\n", "\n", "This tutorial accompanies lectures of the [MIT Deep Learning](https://deeplearning.mit.edu) series. Acknowledgement to amazing people involved is provided throughout the tutorial and at the end. Introductory lectures on GANs include the following (with more coming soon):\n", "\n", "\n", " \n", " \n", "
\n", " \n", " \n", " (Lecture) Deep Learning Basics: Intro and Overview\n", " \n", " \n", " \n", " \n", " (Lecture) Deep Learning State of the Art 2019\n", " \n", "
\n", "\n", "Generative Adversarial Networks (GANs) are a framework for training networks optimized for generating new realistic samples from a particular representation. In its simplest form, the training process involves two networks. One network, called the generator, generates new data instances, trying to fool the other network, the discriminator, that classifies images as real or fake. This original form is illustrated as follows (where #6 refers to one of 7 architectures described in the [Deep Learning Basics tutorial](https://github.com/lexfridman/mit-deep-learning/blob/master/tutorial_deep_learning_basics/deep_learning_basics.ipynb)):\n", "\n", "\n", "\n", "There are broadly 3 categories of GANs:\n", "\n", "1. **Unsupervised GANs**: The generator network takes random noise as input and produces a photo-realistic image that appears very similar to images that appear in the training dataset. Examples include the [original version of GAN](https://arxiv.org/abs/1406.2661), [DC-GAN](https://arxiv.org/abs/1511.06434), [pg-GAN](https://arxiv.org/abs/1710.10196), etc.\n", "3. **Style-Transfer GANs** - Translate images from one domain to another (e.g., from horse to zebra, from sketch to colored images). Examples include [CycleGAN](https://junyanz.github.io/CycleGAN/) and [pix2pix](https://phillipi.github.io/pix2pix/).\n", "2. **Conditional GANs** - Jointly learn on features along with images to generate images conditioned on those features (e.g., generating an instance of a particular class). Examples includes [Conditional GAN](https://arxiv.org/abs/1411.1784), [AC-GAN](https://arxiv.org/abs/1610.09585), [Stack-GAN](https://github.com/hanzhanggit/StackGAN), and [BigGAN](https://arxiv.org/abs/1809.11096).\n", "\n", "First, we illustrate BigGAN, a state-of-the-art conditional GAN from DeepMind. This illustration is based on the [BigGAN TF Hub Demo](https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/biggan_generation_with_tf_hub.ipynb) and the BigGAN generators on [TF Hub](https://tfhub.dev/deepmind/biggan-256). See the [BigGAN paper on arXiv](https://arxiv.org/abs/1809.11096) [1] for more information about these models.\n", "\n", "We'll be adding more parts to this tutorial as additional lectures come out." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 1: BigGAN\n", "\n", "We recommend that you run this this notebook in the cloud on Google Colab. If you have not done so yet, consider following the setup steps in the [Deep Learning Basics tutorial](https://github.com/lexfridman/mit-deep-learning) and reading the [Deep Learning Basics: Introduction and Overview with TensorFlow](https://medium.com/tensorflow/mit-deep-learning-basics-introduction-and-overview-with-tensorflow-355bcd26baf0) blog post." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/device:GPU:0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# basics\n", "import io\n", "import os\n", "import numpy as np\n", "\n", "# deep learning\n", "from scipy.stats import truncnorm\n", "import tensorflow as tf\n", "import tensorflow_hub as hub\n", "\n", "# visualization\n", "from IPython.core.display import HTML\n", "#!pip install imageio\n", "import imageio\n", "import base64\n", "\n", "# check that tensorflow GPU is enabled\n", "tf.test.gpu_device_name() # returns empty string if using CPU" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load BigGAN generator module from TF Hub" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading BigGAN module from: https://tfhub.dev/deepmind/biggan-512/1\n", "INFO:tensorflow:Using /tmp/tfhub_modules to cache modules.\n", "INFO:tensorflow:Saver not created because there are no variables in the graph to restore\n" ] } ], "source": [ "# comment out the TF Hub module path you would like to use\n", "# module_path = 'https://tfhub.dev/deepmind/biggan-128/1' # 128x128 BigGAN\n", "# module_path = 'https://tfhub.dev/deepmind/biggan-256/1' # 256x256 BigGAN\n", "module_path = 'https://tfhub.dev/deepmind/biggan-512/1' # 512x512 BigGAN\n", "\n", "tf.reset_default_graph()\n", "print('Loading BigGAN module from:', module_path)\n", "module = hub.Module(module_path)\n", "inputs = {k: tf.placeholder(v.dtype, v.get_shape().as_list(), k)\n", " for k, v in module.get_input_info_dict().items()}\n", "output = module(inputs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions for Sampling and Interpolating the Generator" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "input_z = inputs['z']\n", "input_y = inputs['y']\n", "input_trunc = inputs['truncation']\n", "\n", "dim_z = input_z.shape.as_list()[1]\n", "vocab_size = input_y.shape.as_list()[1]\n", "\n", "# sample truncated normal distribution based on seed and truncation parameter\n", "def truncated_z_sample(truncation=1., seed=None):\n", " state = None if seed is None else np.random.RandomState(seed)\n", " values = truncnorm.rvs(-2, 2, size=(1, dim_z), random_state=state)\n", " return truncation * values\n", "\n", "# convert `index` value to a vector of all zeros except for a 1 at `index`\n", "def one_hot(index, vocab_size=vocab_size):\n", " index = np.asarray(index)\n", " if len(index.shape) == 0: # when it's a scale convert to a vector of size 1\n", " index = np.asarray([index])\n", " assert len(index.shape) == 1\n", " num = index.shape[0]\n", " output = np.zeros((num, vocab_size), dtype=np.float32)\n", " output[np.arange(num), index] = 1\n", " return output\n", "\n", "def one_hot_if_needed(label, vocab_size=vocab_size):\n", " label = np.asarray(label)\n", " if len(label.shape) <= 1:\n", " label = one_hot(label, vocab_size)\n", " assert len(label.shape) == 2\n", " return label\n", "\n", "# using vectors of noise seeds and category labels, generate images\n", "def sample(sess, noise, label, truncation=1., batch_size=8, vocab_size=vocab_size):\n", " noise = np.asarray(noise)\n", " label = np.asarray(label)\n", " num = noise.shape[0]\n", " if len(label.shape) == 0:\n", " label = np.asarray([label] * num)\n", " if label.shape[0] != num:\n", " raise ValueError('Got # noise samples ({}) != # label samples ({})'\n", " .format(noise.shape[0], label.shape[0]))\n", " label = one_hot_if_needed(label, vocab_size)\n", " ims = []\n", " for batch_start in range(0, num, batch_size):\n", " s = slice(batch_start, min(num, batch_start + batch_size))\n", " feed_dict = {input_z: noise[s], input_y: label[s], input_trunc: truncation}\n", " ims.append(sess.run(output, feed_dict=feed_dict))\n", " ims = np.concatenate(ims, axis=0)\n", " assert ims.shape[0] == num\n", " ims = np.clip(((ims + 1) / 2.0) * 256, 0, 255)\n", " ims = np.uint8(ims)\n", " return ims\n", "\n", "def interpolate(a, b, num_interps):\n", " alphas = np.linspace(0, 1, num_interps)\n", " assert a.shape == b.shape, 'A and B must have the same shape to interpolate.'\n", " return np.array([(1-x)*a + x*b for x in alphas])\n", "\n", "def interpolate_and_shape(a, b, steps):\n", " interps = interpolate(a, b, steps)\n", " return (interps.transpose(1, 0, *range(2, len(interps.shape))).reshape(steps, -1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a TensorFlow session and initialize variables" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "initializer = tf.global_variables_initializer()\n", "sess = tf.Session()\n", "sess.run(initializer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create video of interpolated BigGAN generator samples" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# category options: https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n", "category = 947 # mushroom\n", "\n", "# important parameter that controls how much variation there is\n", "truncation = 0.2 # reasonable range: [0.02, 1]\n", "\n", "seed_count = 10\n", "clip_secs = 36\n", "\n", "seed_step = int(100 / seed_count)\n", "interp_frames = int(clip_secs * 30 / seed_count) # interpolation frames\n", "\n", "cat1 = category\n", "cat2 = category\n", "all_imgs = []\n", "\n", "for i in range(seed_count):\n", " seed1 = i * seed_step # good range for seed is [0, 100]\n", " seed2 = ((i+1) % seed_count) * seed_step\n", " \n", " z1, z2 = [truncated_z_sample(truncation, seed) for seed in [seed1, seed2]]\n", " y1, y2 = [one_hot([category]) for category in [cat1, cat2]]\n", "\n", " z_interp = interpolate_and_shape(z1, z2, interp_frames)\n", " y_interp = interpolate_and_shape(y1, y2, interp_frames)\n", "\n", " imgs = sample(sess, z_interp, y_interp, truncation=truncation)\n", " \n", " all_imgs.extend(imgs[:-1])\n", "\n", "# save the video for displaying in the next cell, this is way more space efficient than the gif animation\n", "imageio.mimsave('gan.mp4', all_imgs, fps=30)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%HTML\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above code should generate a 512x512 video version of the following:\n", "\n", "![BigGAN mushroom](https://i.imgur.com/TA9uh1a.gif)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Acknowledgements\n", "\n", "The content of this tutorial is based on and inspired by the work of [TensorFlow team](https://www.tensorflow.org) (see their [Colab notebooks](https://www.tensorflow.org/tutorials/)), [Google DeepMind](https://deepmind.com/), our [MIT Human-Centered AI team](https://hcai.mit.edu), and individual pieces referenced in the [MIT Deep Learning](https://deeplearning.mit.edu) course slides.\n", "\n", "TF Colab and TF Hub content is copyrighted to The TensorFlow Authors (2018). Licensed under the Apache License, Version 2.0 (the \"License\"); http://www.apache.org/licenses/LICENSE-2.0" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" }, "colab": { "name": "tutorial_gans.ipynb", "version": "0.1.0", "provenance": [] }, "accelerator": "GPU" }, "nbformat": 4, "nbformat_minor": 1 } ================================================ FILE: tutorials_previous/1_python_perceptron.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import numpy.random as nr\n", "import matplotlib.pyplot as pl\n", "%matplotlib inline\n", "\n", "# This notebook is based on an excellent tutorial by Kostis Gourgoulias (http://kgourgou.me/)\n", "\n", "# Specify size of plot\n", "pl.rcParams['figure.figsize'] = (12.0, 10.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Playing around with the linear perceptron algorithm \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The linear perceptron algorithm can be used to classify data points according to pre-selected features they have. The idea is to find a curve (or hyperplane) that separates points with different features. Once we have the curve, we can use it to decide if future points are of feature A or B based on where they are with respect to the curve (above or below it). \n", "\n", "Now, let generate a collection of points and then paint them according to a line. If the points are above the line, they are blue, if they are below, green. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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0lvLyG/H7p+MN03GUli5l+fKxrFqlz1ORlJGpVe1Q0crco3kjChXucLFcozBa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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate some points\n", "N = 100\n", "xn = nr.rand(N,2)\n", "\n", "x = np.linspace(0,1)\n", "\n", "# Pick a line \n", "#a, b = nr.rand(), nr.rand()\n", "a, b = 0.8, 0.2\n", "f = lambda x : a*x + b\n", "\n", "fig = pl.figure()\n", "figa = pl.gca()\n", "\n", "pl.plot(xn[:,0],xn[:,1],'bo')\n", "pl.plot(x,f(x),'r')\n", "\n", "# Linearly separate the points by the line\n", "yn = np.zeros([N,1])\n", "\n", "for i in xrange(N):\n", " if(f(xn[i,0])>xn[i,1]):\n", " # Point is below line\n", " yn[i] = 1\n", " pl.plot(xn[i,0],xn[i,1],'go')\n", " else:\n", " # Point is above line\n", " yn[i] = -1\n", " \n", " \n", "pl.legend(['Above','Separator','Below'],loc=0)\n", "pl.title('Selected points with their separating line.')\n", "#figa.axes.get_xaxis().set_visible(False)\n", "#figa.axes.get_yaxis().set_visible(False)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The curve naturally separates the space into two regions, one of green points and one of blue points. Thus, if I am given a new point, I can assign it a color based on where it is with respect to the curve. It is really that simple.\n", "\n", "What is not so simple is to find the curve given the points. However, if the points are linearly separable, i.e. if a line exists that does the job, then I can just move a line around until I get it to the correct position. This is what the linear perceptron algorithm is doing." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30 0.879833009026 0.8233558434 0.0 [ 1.]\n", "19 0.174161776555 0.597111911245 1.0 [-1.]\n", "97 0.045875351594 0.423093427763 1.0 [-1.]\n", "97 0.045875351594 0.423093427763 -1.0 [-1.]\n", "21 0.840331401342 0.629225436542 -1.0 [ 1.]\n" ] } ], "source": [ "def perceptron(xn, yn, max_iter=1000, w=np.zeros(3)):\n", " '''\n", " A very simple implementation of the perceptron algorithm for two dimensional data.\n", " \n", " Given points (x,y) with x in R^{2} and y in {-1,1}, the perceptron learning algorithm searches for the best\n", " line that separates the data points according to the difference classes defined in y. \n", " \n", " Input: \n", " xn : Data points, an Nx2 vector. \n", " yn : Classification of the previous data points, an Nx1 vector. \n", " max_iter : Maximum number of iterations (optional).\n", " w : Initial vector of parameters (optional).\n", " \n", " Output: \n", " w : Parameters of the best line, y = ax+b, that linearly separates the data. \n", " \n", " Note:\n", " Convergence will be slower than expected, since this implementation picks points\n", " to update without a specific plan (randomly). This is enough for a demonstration, not \n", " so good for actual work. \n", "'''\n", " \n", " N = xn.shape[0]\n", " \n", " # Separating curve\n", " f = lambda x: np.sign(w[0]+w[1]*x[0]+w[2]*x[1])\n", "\n", " for _ in xrange(max_iter):\n", " i = nr.randint(N) # try a random sample from the dataset\n", " print i, xn[i,0], xn[i,1], f(xn[i,:]), yn[i]\n", " if(yn[i] != f(xn[i,:])): # If not classified correctly, adjust the line to account for that point.\n", " w[0] = w[0] + yn[i] # the first weight is effectively the bias\n", " w[1] = w[1] + yn[i] * xn[i,0]\n", " w[2] = w[2] + yn[i] * xn[i,1]\n", " \n", " \n", " \n", " \n", " return w\n", "\n", "w = perceptron(xn, yn, max_iter=5)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have an implementation, let's see how close it gets." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "91 0.0890732533084 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FqFSpEv3792fnzp1Zltu2bVt69eoFQNOmTQkEAvTp0weANm3a0KhRI5YsWULr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\n8MEHSx1LUjUWdEmSJBVNx44deeHFF/nWIYcwGhgOP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "w = perceptron(xn, yn)\n", "\n", "# Using weights w to compute a,b for a line y=a*x+b\n", "bnew = -w[0]/w[2];\n", "anew = -w[1]/w[2];\n", "y = lambda x: anew * x + bnew;\n", "\n", "# Computing the colors for the points\n", "sep_color = (yn+1)/2.0;\n", "\n", "pl.figure();\n", "figa = pl.gca()\n", "\n", "pl.scatter(xn[:,0],xn[:,1],c=sep_color, s=30)\n", "pl.plot(x,y(x),'b--',label='Line from perceptron implementation.')\n", "pl.plot(x,f(x),'r',label='Original line.')\n", "pl.legend()\n", "\n", "pl.title('Comparison between the linear separator and the perceptron approximation.')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not bad, right? The algorithm should have managed to converge to a good approximation of the separating line. If it didn't, try running the last piece of code again. Remember that this implementation updates randomly picked points, so in some cases convergence will be worse.\n", "\n", "Also, note that the line that separates the points is not unique, given the dataset we have available. Would it be so if we had all of the possible information? My guess is that this depends on the data. \n", "\n", "In any case, it can be proven that this process works every time, given a sufficient number of steps. This assumes that the data is linearly separable, a fact that is quite powerful on its own. We may be good at finding patterns in $\\mathbb{R}^2$ but what about $\\mathbb{R}^d$? Is there a way to show that a collection of points can be separated by \"inserting\" planes between them? We take a look at that next." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# What if the dataset is not linearly separable?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the data is not separable by a line, then, in most cases, this process will not work perfectly. Some points will be classified correctly and some will not. Then, we can think about two more questions. \n", "\n", "1. How much will it cost us if we missclassify a point? Is the cost an extra spam e-mail in our inbox or is it a patient not getting the correct medicine?\n", "2. If we don't want to take the risk with a line, which is the best curve to use instead?\n", "\n", "We are not going to answer those here. Instead, I will just show you an example where the classification can fail, if the points are not separable by a line. Then, if you download this notebook, you can try with other curves and see what happens. \n", "\n", "Remember that, in our case, given a point $x=(x_1,x_2)$, classification is done according to $\\text{sign}(f(x_1)-x_2)$, which can either be -1 or 1." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HkdYJXbp04f/+7/+YMGEC8+bNY/vtt2eDDTZIOpYkqapddx18/TWMGAF2GKiK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zr5ApgFvOt0ciZzZuYtWECmTJnsjikiIj6mYiupLjk5+cpH1/vBIhPJyZev+/m7d++mYsVKjBnz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\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Change this function to select points with respect to a different curve.\n", "f = lambda x: x**2;\n", "\n", "x = np.linspace(0,1);\n", "\n", "# Generate some data points to play with.\n", "N = 100\n", "xn = nr.rand(N,2)\n", "\n", "fig = pl.figure()\n", "figa = pl.gca();\n", "\n", "# Plot classifier \n", "pl.plot(x,f(x),'r')\n", "\n", "# Classify based on f(x)\n", "yn = np.sign(f(xn[:,0])-xn[:,1])\n", "\n", "colors = (yn+1)/2.0;\n", "\n", "pl.scatter(xn[:,0],xn[:,1],c=colors,s=30);\n", "pl.title('Classification based on f(x)')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we can see that $x^2$ colours some points as black and others as white. Let us find a linear separator now." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OHTvo1asX0dHRhIeHky9fPm699VbGjRvH2bNnczq8i9KvXz8WL17M4MGDeffd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\nQ0ZfxxERER8xTZOWLVsRFZUBj6cj1qqpYKxzR0rj8XQgNjY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+3wZwFPjMNM2P42m/A1hp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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Try percepton with that data.\n", "w = perceptron(xn, yn, max_iter=1000)\n", "\n", "# Re-scale the weights to construct a new representation\n", "bnew = -w[0]/w[2];\n", "anew = -w[1]/w[2];\n", "y = lambda x: anew * x + bnew;\n", "\n", "figa = pl.gca()\n", "pl.scatter(xn[:,0],xn[:,1],c=colors,s=50);\n", "pl.title('Classification based on f(x)')\n", "\n", "pl.plot(x,f(x),'r',label='Separating curve.')\n", "pl.plot(x,y(x),'b--',label = 'Curve from perceptron algorithm.')\n", "\n", "pl.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, our classifier cannot get all the cases right (white points should be above the blue line, black points below). This situation will probably become worse as we add more and more points. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: tutorials_previous/2_tensorflow_intro.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "a3bskVXPvchm" }, "source": [ "# Hello, TensorFlow\n", "## A beginner-level, getting started, basic introduction to TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Rb5rSpcZvYbX" }, "source": [ "TensorFlow is a general-purpose system for graph-based computation. A typical use is machine learning. In this notebook, we'll introduce the basic concepts of TensorFlow using some simple examples.\n", "\n", "TensorFlow gets its name from [tensors](https://en.wikipedia.org/wiki/Tensor), which are arrays of arbitrary dimensionality. A vector is a 1-d array and is known as a 1st-order tensor. A matrix is a 2-d array and a 2nd-order tensor. The \"flow\" part of the name refers to computation flowing through a graph. Training and inference in a neural network, for example, involves the propagation of matrix computations through many nodes in a computational graph.\n", "\n", "When you think of doing things in TensorFlow, you might want to think of creating tensors (like matrices), adding operations (that output other tensors), and then executing the computation (running the computational graph). In particular, it's important to realize that when you add an operation on tensors, it doesn't execute immediately. Rather, TensorFlow waits for you to define all the operations you want to perform. Then, TensorFlow optimizes the computation graph, deciding how to execute the computation, before generating the data. Because of this, a tensor in TensorFlow isn't so much holding the data as a placeholder for holding the data, waiting for the data to arrive when a computation is executed." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "E8FhiMivhcYB" }, "source": [ "## Adding two vectors in TensorFlow\n", "\n", "Let's start with something that should be simple. Let's add two length four vectors (two 1st-order tensors):\n", "\n", "$\\begin{bmatrix} 1. \u0026 1. \u0026 1. \u0026 1.\\end{bmatrix} + \\begin{bmatrix} 2. \u0026 2. \u0026 2. \u0026 2.\\end{bmatrix} = \\begin{bmatrix} 3. \u0026 3. \u0026 3. \u0026 3.\\end{bmatrix}$" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 2922, "status": "ok", "timestamp": 1474675631337, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "2iv3XQ6k3eF1", "outputId": "7dbded62-91bc-4e38-9f25-53375c4c8dd8" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "result: [ 3. 3. 3. 3.]\n" ] } ], "source": [ "from __future__ import print_function\n", "\n", "import tensorflow as tf\n", "\n", "with tf.Session():\n", " input1 = tf.constant([1.0, 1.0, 1.0, 1.0])\n", " input2 = tf.constant([2.0, 2.0, 2.0, 2.0])\n", " output = tf.add(input1, input2)\n", " result = output.eval()\n", " print(\"result: \", result)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dqLV5GXT3wLy" }, "source": [ "What we're doing is creating two vectors, [1.0, 1.0, 1.0, 1.0] and [2.0, 2.0, 2.0, 2.0], and then adding them. Here's equivalent code in raw Python and using numpy:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 214, "status": "ok", "timestamp": 1474675631563, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "7DzDJ7sW79ao", "outputId": "588b573b-95d2-4587-849e-af6f3ec1303e" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "[3.0, 3.0, 3.0, 3.0]\n" ] } ], "source": [ "print([x + y for x, y in zip([1.0] * 4, [2.0] * 4)])" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 340, "status": "ok", "timestamp": 1474675631948, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "MDWJf0lHAF4E", "outputId": "bee09475-24dd-4331-fc46-692a07dae101" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "[ 1. 1. 1. 1.] + [ 2. 2. 2. 2.] = [ 3. 3. 3. 3.]\n" ] } ], "source": [ "import numpy as np\n", "x, y = np.full(4, 1.0), np.full(4, 2.0)\n", "print(\"{} + {} = {}\".format(x, y, x + y))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "I52jQOyO8vAn" }, "source": [ "## Details of adding two vectors in TensorFlow\n", "\n", "The example above of adding two vectors involves a lot more than it seems, so let's look at it in more depth.\n", "\n", "\u003e`import tensorflow as tf`\n", "\n", "This import brings TensorFlow's public API into our IPython runtime environment.\n", "\n", "\u003e`with tf.Session():`\n", "\n", "When you run an operation in TensorFlow, you need to do it in the context of a `Session`. A session holds the computation graph, which contains the tensors and the operations. When you create tensors and operations, they are not executed immediately, but wait for other operations and tensors to be added to the graph, only executing when finally requested to produce the results of the session. Deferring the execution like this provides additional opportunities for parallelism and optimization, as TensorFlow can decide how to combine operations and where to run them after TensorFlow knows about all the operations. \n", "\n", "\u003e\u003e`input1 = tf.constant([1.0, 1.0, 1.0, 1.0])`\n", "\n", "\u003e\u003e`input2 = tf.constant([2.0, 2.0, 2.0, 2.0])`\n", "\n", "The next two lines create tensors using a convenience function called `constant`, which is similar to numpy's `array` and numpy's `full`. If you look at the code for `constant`, you can see the details of what it is doing to create the tensor. In summary, it creates a tensor of the necessary shape and applies the constant operator to it to fill it with the provided values. The values to `constant` can be Python or numpy arrays. `constant` can take an optional shape parameter, which works similarly to numpy's `fill` if provided, and an optional name parameter, which can be used to put a more human-readable label on the operation in the TensorFlow operation graph.\n", "\n", "\u003e\u003e`output = tf.add(input1, input2)`\n", "\n", "You might think `add` just adds the two vectors now, but it doesn't quite do that. What it does is put the `add` operation into the computational graph. The results of the addition aren't available yet. They've been put in the computation graph, but the computation graph hasn't been executed yet.\n", "\n", "\u003e\u003e`result = output.eval()`\n", "\n", "\u003e\u003e`print result`\n", "\n", "`eval()` is also slightly more complicated than it looks. Yes, it does get the value of the vector (tensor) that results from the addition. It returns this as a numpy array, which can then be printed. But, it's important to realize it also runs the computation graph at this point, because we demanded the output from the operation node of the graph; to produce that, it had to run the computation graph. So, this is the point where the addition is actually performed, not when `add` was called, as `add` just put the addition operation into the TensorFlow computation graph." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "H_5_2YY3ySr2" }, "source": [ "## Multiple operations\n", "\n", "To use TensorFlow, you add operations on tensors that produce tensors to the computation graph, then execute that graph to run all those operations and calculate the values of all the tensors in the graph.\n", "\n", "Here's a simple example with two operations:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 1203, "status": "ok", "timestamp": 1474675633108, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "-kQmn3U_yXX8", "outputId": "8ba14a4d-b0cd-4b90-8b95-790e77d35e70" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "[ 6. 6. 6. 6.]\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "with tf.Session():\n", " input1 = tf.constant(1.0, shape=[4])\n", " input2 = tf.constant(2.0, shape=[4])\n", " input3 = tf.constant(3.0, shape=[4])\n", " output = tf.add(tf.add(input1, input2), input3)\n", " result = output.eval()\n", " print(result)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Hod0zvsly8YT" }, "source": [ "This version uses `constant` in a way similar to numpy's `fill`, specifying the optional shape and having the values copied out across it.\n", "\n", "The `add` operator supports operator overloading, so you could try writing it inline as `input1 + input2` instead as well as experimenting with other operators." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 350, "status": "ok", "timestamp": 1474675633468, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "yS2WElRfxz53", "outputId": "2e3efae6-3990-447c-e05d-56a9d9701e87" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "[ 3. 3. 3. 3.]\n" ] } ], "source": [ "with tf.Session():\n", " input1 = tf.constant(1.0, shape=[4])\n", " input2 = tf.constant(2.0, shape=[4])\n", " output = input1 + input2\n", " print(output.eval())" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zszjoYUjkUNU" }, "source": [ "## Adding two matrices" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "EWNYBCB6kbri" }, "source": [ "Next, let's do something very similar, adding two matrices:\n", "\n", "$\\begin{bmatrix}\n", " 1. \u0026 1. \u0026 1. \\\\\n", " 1. \u0026 1. \u0026 1. \\\\\n", "\\end{bmatrix} + \n", "\\begin{bmatrix}\n", " 1. \u0026 2. \u0026 3. \\\\\n", " 4. \u0026 5. \u0026 6. \\\\\n", "\\end{bmatrix} = \n", "\\begin{bmatrix}\n", " 2. \u0026 3. \u0026 4. \\\\\n", " 5. \u0026 6. \u0026 7. \\\\\n", "\\end{bmatrix}$" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 1327, "status": "ok", "timestamp": 1474675634683, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "tmWcCxSilYkg", "outputId": "8a135ccf-e706-457c-f4bc-2187039ffd92" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "[[ 2. 3. 4.]\n", " [ 5. 6. 7.]]\n" ] } ], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "\n", "with tf.Session():\n", " input1 = tf.constant(1.0, shape=[2, 3])\n", " input2 = tf.constant(np.reshape(np.arange(1.0, 7.0, dtype=np.float32), (2, 3)))\n", " output = tf.add(input1, input2)\n", " print(output.eval())" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "JuU3Bmglq1vd" }, "source": [ "Recall that you can pass numpy or Python arrays into `constant`.\n", "\n", "In this example, the matrix with values from 1 to 6 is created in numpy and passed into `constant`, but TensorFlow also has `range`, `reshape`, and `tofloat` operators. Doing this entirely within TensorFlow could be more efficient if this was a very large matrix.\n", "\n", "Try experimenting with this code a bit -- maybe modifying some of the values, using the numpy version, doing this using, adding another operation, or doing this using TensorFlow's `range` function." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gnXnpnuLrflb" }, "source": [ "## Multiplying matrices" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Ho-QNSOorj0y" }, "source": [ "Let's move on to matrix multiplication. This time, let's use a bit vector and some random values, which is a good step toward some of what we'll need to do for regression and neural networks." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 2353, "status": "ok", "timestamp": 1474675637053, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "uNqMaFR8sIY5", "outputId": "b630554e-68b3-4904-c07d-f28a0a41bbd2" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "Input:\n", "[[ 1. 0. 0. 1.]]\n", "Weights:\n", "[[ 0.3949919 -0.83823347]\n", " [ 0.25941893 -1.58861065]\n", " [-1.11733329 -0.60435963]\n", " [ 1.04782867 0.18336453]]\n", "Output:\n", "[[ 1.44282055 -0.65486896]]\n" ] } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "import tensorflow as tf\n", "import numpy as np\n", "\n", "with tf.Session():\n", " input_features = tf.constant(np.reshape([1, 0, 0, 1], (1, 4)).astype(np.float32))\n", " weights = tf.constant(np.random.randn(4, 2).astype(np.float32))\n", " output = tf.matmul(input_features, weights)\n", " print(\"Input:\")\n", " print(input_features.eval())\n", " print(\"Weights:\")\n", " print(weights.eval())\n", " print(\"Output:\")\n", " print(output.eval())" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "JDAVTPhb22AP" }, "source": [ "Above, we're taking a 1 x 4 vector [1 0 0 1] and multiplying it by a 4 by 2 matrix full of random values from a normal distribution (mean 0, stdev 1). The output is a 1 x 2 matrix.\n", "\n", "You might try modifying this example. Running the cell multiple times will generate new random weights and a new output. Or, change the input, e.g., to \\[0 0 0 1]), and run the cell again. Or, try initializing the weights using the TensorFlow op, e.g., `random_normal`, instead of using numpy to generate the random weights.\n", "\n", "What we have here is the basics of a simple neural network already. If we are reading in the input features, along with some expected output, and change the weights based on the error with the output each time, that's a neural network." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XhnBjAUILuy8" }, "source": [ "## Use of variables\n", "\n", "Let's look at adding two small matrices in a loop, not by creating new tensors every time, but by updating the existing values and then re-running the computation graph on the new data. This happens a lot with machine learning models, where we change some parameters each time such as gradient descent on some weights and then perform the same computations over and over again." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "executionInfo": { "elapsed": 2561, "status": "ok", "timestamp": 1474675639610, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "vJ_AgZ8lLtRv", "outputId": "b8f19c28-a9b4-4fb3-9e90-6e432bf300a7" }, "outputs": [ { "metadata": {}, "name": "stdout", "output_type": "stream", "text": [ "[[ -7.29560852e-05 8.01583767e-01]] [[ -7.29560852e-05 8.01583767e-01]]\n", "[[ 0.64477301 -0.03944111]] [[ 0.64470005 0.76214266]]\n", "[[-0.07470274 -0.76814342]] [[ 0.56999731 -0.00600076]]\n", "[[-0.34230471 -0.42372179]] [[ 0.2276926 -0.42972255]]\n", "[[ 0.67873812 0.65932178]] [[ 0.90643072 0.22959924]]\n" ] } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "import tensorflow as tf\n", "import numpy as np\n", "\n", "with tf.Session() as sess:\n", " # Set up two variables, total and weights, that we'll change repeatedly.\n", " total = tf.Variable(tf.zeros([1, 2]))\n", " weights = tf.Variable(tf.random_uniform([1,2]))\n", "\n", " # Initialize the variables we defined above.\n", " tf.global_variables_initializer().run()\n", "\n", " # This only adds the operators to the graph right now. The assignment\n", " # and addition operations are not performed yet.\n", " update_weights = tf.assign(weights, tf.random_uniform([1, 2], -1.0, 1.0))\n", " update_total = tf.assign(total, tf.add(total, weights))\n", " \n", " for _ in range(5):\n", " # Actually run the operation graph, so randomly generate weights and then\n", " # add them into the total. Order does matter here. We need to update\n", " # the weights before updating the total.\n", " sess.run(update_weights)\n", " sess.run(update_total)\n", " \n", " print(weights.eval(), total.eval())" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "kSYJr89aM_n0" }, "source": [ "This is more complicated. At a high level, we create two variables and add operations over them, then, in a loop, repeatedly execute those operations. Let's walk through it step by step.\n", "\n", "Starting off, the code creates two variables, `total` and `weights`. `total` is initialized to \\[0, 0\\] and `weights` is initialized to random values between -1 and 1.\n", "\n", "Next, two assignment operators are added to the graph, one that updates weights with random values from [-1, 1], the other that updates the total with the new weights. Again, the operators are not executed here. In fact, this isn't even inside the loop. We won't execute these operations until the `eval` call inside the loop.\n", "\n", "Finally, in the for loop, we run each of the operators. In each iteration of the loop, this executes the operators we added earlier, first putting random values into the weights, then updating the totals with the new weights. This call uses `eval` on the session; the code also could have called `eval` on the operators (e.g. `update_weights.eval`).\n", "\n", "It can be a little hard to wrap your head around exactly what computation is done when. The important thing to remember is that computation is only performed on demand.\n", "\n", "Variables can be useful in cases where you have a large amount of computation and data that you want to use over and over again with just a minor change to the input each time. That happens quite a bit with neural networks, for example, where you just want to update the weights each time you go through the batches of input data, then run the same operations over again." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fL3WfAbKzqr5" }, "source": [ "## What's next?\n", "\n", "This has been a gentle introduction to TensorFlow, focused on what TensorFlow is and the very basics of doing anything in TensorFlow. If you'd like more, the next tutorial in the series is Getting Started with TensorFlow, also available in the [notebooks directory](..)." ] } ], "metadata": { "colab": { "default_view": {}, "name": "Untitled", "provenance": [], "version": "0.3.2", "views": {} } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: tutorials_previous/3_tensorflow_simple_neural_network.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6TuWv0Y0sY8n" }, "source": [ "# Getting Started in TensorFlow\n", "## A look at a very simple neural network in TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "u9J5e2mQsYsQ" }, "source": [ "This is an introduction to working with TensorFlow. It works through an example of a very simple neural network, walking through the steps of setting up the input, adding operators, setting up gradient descent, and running the computation graph. \n", "\n", "This tutorial presumes some familiarity with the TensorFlow computational model, which is introduced in the [Hello, TensorFlow](../notebooks/1_hello_tensorflow.ipynb) notebook, also available in this bundle." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Dr2Sv0vD8rT-" }, "source": [ "## A simple neural network\n", "\n", "Let's start with code. We're going to construct a very simple neural network computing a linear regression between two variables, y and x. The function it tries to compute is the best $w_1$ and $w_2$ it can find for the function $y = w_2 x + w_1$ for the data. The data we're going to give it is toy data, linear perturbed with random noise.\n", "\n", "This is what the network looks like:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 681, "status": "ok", "timestamp": 1474671827305, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "q09my4JYtKXw", "outputId": "4938066b-231d-4078-e2dd-fd223eca7c9f" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from __future__ import print_function\n", "\n", "from IPython.display import Image\n", "import base64\n", "Image(data=base64.decodestring(\"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embed=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "fBQq_R8B8rRf" }, "source": [ "Here is the TensorFlow code for this simple neural network and the results of running this code:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 7741, "status": "ok", "timestamp": 1474671834967, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "Dy8pFefa_Ho_", "outputId": "318456b0-f9de-4717-d9c7-956b5d390d05" }, "outputs": [ { "ename": "AttributeError", "evalue": "'module' object has no attribute 'subtract'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0myhat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0myerror\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubtract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myhat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 33\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0ml2_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myerror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'module' object has no attribute 'subtract'" ] } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pkg_resources import parse_version\n", "\n", "%matplotlib inline\n", "\n", "# Set up the data with a noisy linear relationship between X and Y.\n", "num_examples = 50\n", "X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])\n", "X += np.random.randn(2, num_examples)\n", "x, y = X\n", "x_with_bias = np.array([(1., a) for a in x]).astype(np.float32)\n", "\n", "losses = []\n", "training_steps = 50\n", "learning_rate = 0.002\n", "\n", "with tf.Session() as sess:\n", " # Set up all the tensors, variables, and operations.\n", " input = tf.constant(x_with_bias)\n", " target = tf.constant(np.transpose([y]).astype(np.float32))\n", " weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))\n", "\n", " if parse_version(tf.__version__) >= parse_version('0.12'):\n", " tf.global_variables_initializer().run()\n", " else:\n", " tf.initialize_all_variables().run()\n", "\n", " yhat = tf.matmul(input, weights)\n", " yerror = tf.subtract(yhat, target)\n", " loss = tf.nn.l2_loss(yerror)\n", " \n", " update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n", " \n", " for _ in range(training_steps):\n", " # Repeatedly run the operations, updating the TensorFlow variable.\n", " update_weights.run()\n", " losses.append(loss.eval())\n", "\n", " # Training is done, get the final values for the graphs\n", " betas = weights.eval()\n", " yhat = yhat.eval()\n", "\n", "# Show the fit and the loss over time.\n", "fig, (ax1, ax2) = plt.subplots(1, 2)\n", "plt.subplots_adjust(wspace=.3)\n", "fig.set_size_inches(10, 4)\n", "ax1.scatter(x, y, alpha=.7)\n", "ax1.scatter(x, np.transpose(yhat)[0], c=\"g\", alpha=.6)\n", "line_x_range = (-4, 6)\n", "ax1.plot(line_x_range, [betas[0] + a * betas[1] for a in line_x_range], \"g\", alpha=0.6)\n", "ax2.plot(range(0, training_steps), losses)\n", "ax2.set_ylabel(\"Loss\")\n", "ax2.set_xlabel(\"Training steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vNtkU8h18rOv" }, "source": [ "In the remainder of this notebook, we'll go through this example in more detail." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "r6rsv-q5gnn-" }, "source": [ "## From the beginning\n", "\n", "Let's walk through exactly what this is doing from the beginning. We'll start with what the data looks like, then we'll look at this neural network, what is executed when, what gradient descent is doing, and how it all works together." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "UgtkJKqAjuDj" }, "source": [ "## The data\n", "\n", "This is a toy data set here. We have 50 (x,y) data points. At first, the data is perfectly linear." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 271, "status": "ok", "timestamp": 1474671835304, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "-uoBWol3klhA", "outputId": "cc31ce5d-9b65-4ef6-8475-643be268569a" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "num_examples = 50\n", "X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(X[0], X[1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "AId3xHBNlcnk" }, "source": [ "Then we perturb it with noise:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 375, "status": "ok", "timestamp": 1474671835705, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "fXcGNNtjlX63", "outputId": "455c3e70-a724-4e0a-d08e-9bf6bd1aa7e9" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "X += np.random.randn(2, num_examples)\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(X[0], X[1])\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3dc1cl5imNLM" }, "source": [ "## What we want to do\n", "\n", "What we're trying to do is calculate the green line below:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 1150, "status": "ok", "timestamp": 1474671836784, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "P0m-3Mf8sQaA", "outputId": "32a8a45d-ba64-4286-acf7-0883d9184693" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "weights = np.polyfit(X[0], X[1], 1)\n", "plt.figure(figsize=(4,4))\n", "plt.scatter(X[0], X[1])\n", "line_x_range = (-3, 5)\n", "plt.plot(line_x_range, [weights[1] + a * weights[0] for a in line_x_range], \"g\", alpha=0.8)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VYUr2uPA9ah8" }, "source": [ "Remember that our simple network looks like this:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "cellView": "form", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 898, "status": "ok", "timestamp": 1474671837740, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "gt8UuSQA9frA", "outputId": "6eb7616b-25a9-4845-aeab-7472201c60f6" }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJYAAABkCAYAAABkW8nwAAAO90lEQVR4Xu2dT5Dc1J3Hv+YQT8VJ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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "execution_count": 6, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "import base64\n", "Image(data=base64.decodestring(\"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embed=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Ft95NDUZy4Rr" }, "source": [ "That's equivalent to the function $\\hat{y} = w_2 x + w_1$. What we're trying to do is find the \"best\" weights $w_1$ and $w_2$. That will give us that green regression line above.\n", "\n", "What are the best weights? They're the weights that minimize the difference between our estimate $\\hat{y}$ and the actual y. Specifically, we want to minimize the sum of the squared errors, so minimize $\\sum{(\\hat{y} - y)^2}$, which is known as the *L2 loss*. So, the best weights are the weights that minimize the L2 loss." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RHDGz_14vGNg" }, "source": [ "## Gradient descent\n", "\n", "What gradient descent does is start with random weights for $\\hat{y} = w_2 x + w_1$ and gradually moves those weights toward better values.\n", "\n", "It does that by following the downward slope of the error curves. Imagine that the possible errors we could get with different weights as a landscape. From whatever weights we have, moving in some directions will increase the error, like going uphill, and some directions will decrease the error, like going downhill. We want to roll downhill, always moving the weights toward lower error.\n", "\n", "How does gradient descent know which way is downhill? It follows the partial derivatives of the L2 loss. The partial derivative is like a velocity, saying which way the error will change if we change the weight. We want to move in the direction of lower error. The partial derivative points the way.\n", "\n", "So, what gradient descent does is start with random weights and gradually walk those weights toward lower error, using the partial derivatives to know which direction to go." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "W7SgnPAWBX2M" }, "source": [ "## The code again\n", "\n", "Let's go back to the code now, walking through it with many more comments in the code this time:" ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 548, "status": "ok", "timestamp": 1474671838303, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "4qtXAPGmBWUW", "outputId": "841884bc-9a23-4627-cdec-8e7d4261a3c0" }, "outputs": [ { "data": { "image/png": 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SBYMhBykpWXjssYFX/LzNYcOKve/hrOUs7up3D/Bjb/z6mQ0FGwIwIXVym2JoS/JH/iWQ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\n8MsZ/PDLGVcSlpqgR5+4UCjknAkj78D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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "import tensorflow as tf\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# Set up the data with a noisy linear relationship between X and Y.\n", "num_examples = 50\n", "X = np.array([np.linspace(-2, 4, num_examples), np.linspace(-6, 6, num_examples)])\n", "# Add random noise (gaussian, mean 0, stdev 1)\n", "X += np.random.randn(2, num_examples)\n", "# Split into x and y\n", "x, y = X\n", "# Add the bias node which always has a value of 1\n", "x_with_bias = np.array([(1., a) for a in x]).astype(np.float32)\n", "\n", "# Keep track of the loss at each iteration so we can chart it later\n", "losses = []\n", "# How many iterations to run our training\n", "training_steps = 50\n", "# The learning rate. Also known has the step size. This changes how far\n", "# we move down the gradient toward lower error at each step. Too large\n", "# jumps risk inaccuracy, too small slow the learning.\n", "learning_rate = 0.002\n", "\n", "# In TensorFlow, we need to run everything in the context of a session.\n", "with tf.Session() as sess:\n", " # Set up all the tensors.\n", " # Our input layer is the x value and the bias node.\n", " input = tf.constant(x_with_bias)\n", " # Our target is the y values. They need to be massaged to the right shape.\n", " target = tf.constant(np.transpose([y]).astype(np.float32))\n", " # Weights are a variable. They change every time through the loop.\n", " # Weights are initialized to random values (gaussian, mean 0, stdev 0.1)\n", " weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))\n", "\n", " # Initialize all the variables defined above.\n", " tf.global_variables_initializer().run()\n", "\n", " # Set up all operations that will run in the loop.\n", " # For all x values, generate our estimate on all y given our current\n", " # weights. So, this is computing y = w2 * x + w1 * bias\n", " yhat = tf.matmul(input, weights)\n", " # Compute the error, which is just the difference between our \n", " # estimate of y and what y actually is.\n", " yerror = tf.subtract(yhat, target)\n", " # We are going to minimize the L2 loss. The L2 loss is the sum of the\n", " # squared error for all our estimates of y. This penalizes large errors\n", " # a lot, but small errors only a little.\n", " loss = tf.nn.l2_loss(yerror)\n", "\n", " # Perform gradient descent. \n", " # This essentially just updates weights, like weights -= grads * learning_rate\n", " # using the partial derivative of the loss with respect to the\n", " # weights. It's the direction we want to go to move toward lower error.\n", " update_weights = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n", "\n", " # At this point, we've defined all our tensors and run our initialization\n", " # operations. We've also set up the operations that will repeatedly be run\n", " # inside the training loop. All the training loop is going to do is \n", " # repeatedly call run, inducing the gradient descent operation, which has the effect of\n", " # repeatedly changing weights by a small amount in the direction (the\n", " # partial derivative or gradient) that will reduce the error (the L2 loss).\n", " for _ in range(training_steps):\n", " # Repeatedly run the operations, updating the TensorFlow variable.\n", " sess.run(update_weights)\n", "\n", " # Here, we're keeping a history of the losses to plot later\n", " # so we can see the change in loss as training progresses.\n", " losses.append(loss.eval())\n", "\n", " # Training is done, get the final values for the charts\n", " betas = weights.eval()\n", " yhat = yhat.eval()\n", "\n", "# Show the results.\n", "fig, (ax1, ax2) = plt.subplots(1, 2)\n", "plt.subplots_adjust(wspace=.3)\n", "fig.set_size_inches(10, 4)\n", "ax1.scatter(x, y, alpha=.7)\n", "ax1.scatter(x, np.transpose(yhat)[0], c=\"g\", alpha=.6)\n", "line_x_range = (-4, 6)\n", "ax1.plot(line_x_range, [betas[0] + a * betas[1] for a in line_x_range], \"g\", alpha=0.6)\n", "ax2.plot(range(0, training_steps), losses)\n", "ax2.set_ylabel(\"Loss\")\n", "ax2.set_xlabel(\"Training steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lSWT9YsLP1de" }, "source": [ "This version of the code has a lot more comments at each step. Read through the code and the comments.\n", "\n", "The core piece is the loop, which contains a single `run` call. `run` executes the operations necessary for the `GradientDescentOptimizer` operation. That includes several other operations, all of which are also executed each time through the loop. The `GradientDescentOptimizer` execution has a side effect of assigning to weights, so the variable weights changes each time in the loop.\n", "\n", "The result is that, in each iteration of the loop, the code processes the entire input data set, generates all the estimates $\\hat{y}$ for each $x$ given the current weights $w_i$, finds all the errors and L2 losses $(\\hat{y} - y)^2$, and then changes the weights $w_i$ by a small amount in the direction of that will reduce the L2 loss.\n", "\n", "After many iterations of the loop, the amount we are changing the weights gets smaller and smaller, and the loss gets smaller and smaller, as we narrow in on near optimal values for the weights. By the end of the loop, we should be near the lowest possible values for the L2 loss, and near the best possible weights we could have." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dFOk7ERATLk2" }, "source": [ "## The details\n", "\n", "This code works, but there are still a few black boxes that are worth diving into here. `l2_loss`? `GradientDescentOptimizer`? What exactly are those doing?\n", "\n", "One way to understand exactly what those are doing is to do the same thing without using those functions. Here is equivalent code that calculates the gradients (derivatives), L2 loss (sum squared error), and `GradientDescentOptimizer` from scratch without using those functions." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 4219, "status": "ok", "timestamp": 1474671842604, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "_geHN4sPTeRk", "outputId": "3ee8e5e5-0db0-4e6b-ef7e-f9e530fa7bee" }, "outputs": [ { "data": { "image/png": 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64YUXsNlsuFwufvOb39CrVy8+++yzQIdVL42ikJoQibW48qw+kUKIlsOviZfV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\nQcu4qzvyzN19yeqYxM5DJcx4fSPvrth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"text/plain": [ "" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "#@test {\"output\": \"ignore\"}\n", "\n", "# Use the same input data and parameters as the examples above.\n", "# We're going to build up a list of the errors over time as we train to display later.\n", "losses = []\n", "\n", "with tf.Session() as sess:\n", " # Set up all the tensors.\n", " # The input is the x values with the bias appended on to each x.\n", " input = tf.constant(x_with_bias)\n", " # We're trying to find the best fit for the target y values.\n", " target = tf.constant(np.transpose([y]).astype(np.float32))\n", " # Let's set up the weights randomly\n", " weights = tf.Variable(tf.random_normal([2, 1], 0, 0.1))\n", "\n", " tf.global_variables_initializer().run()\n", "\n", " # learning_rate is the step size, so how much we jump from the current spot\n", " learning_rate = 0.002\n", "\n", " # The operations in the operation graph.\n", " # Compute the predicted y values given our current weights\n", " yhat = tf.matmul(input, weights)\n", " # How much does this differ from the actual y?\n", " yerror = tf.subtract(yhat, target)\n", " # Change the weights by subtracting derivative with respect to that weight\n", " loss = 0.5 * tf.reduce_sum(tf.multiply(yerror, yerror))\n", " gradient = tf.reduce_sum(tf.transpose(tf.multiply(input, yerror)), 1, keep_dims=True)\n", " update_weights = tf.assign_sub(weights, learning_rate * gradient)\n", " \n", " # Repeatedly run the operation graph over the training data and weights.\n", " for _ in range(training_steps):\n", " sess.run(update_weights)\n", " \n", " # Here, we're keeping a history of the losses to plot later\n", " # so we can see the change in loss as training progresses.\n", " losses.append(loss.eval())\n", "\n", " # Training is done, compute final values for the graph.\n", " betas = weights.eval()\n", " yhat = yhat.eval()\n", "\n", "# Show the results.\n", "fig, (ax1, ax2) = plt.subplots(1, 2)\n", "plt.subplots_adjust(wspace=.3)\n", "fig.set_size_inches(10, 4)\n", "ax1.scatter(x, y, alpha=.7)\n", "ax1.scatter(x, np.transpose(yhat)[0], c=\"g\", alpha=.6)\n", "line_x_range = (-4, 6)\n", "ax1.plot(line_x_range, [betas[0] + a * betas[1] for a in line_x_range], \"g\", alpha=0.6)\n", "ax2.plot(range(0, training_steps), losses)\n", "ax2.set_ylabel(\"Loss\")\n", "ax2.set_xlabel(\"Training steps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "TzIETgHwTexL" }, "source": [ "This code looks very similar to the code above, but without using `l2_loss` or `GradientDescentOptimizer`. Let's look at exactly what it is doing instead.\n", "\n", "This code is the key difference:\n", "\n", ">`loss = 0.5 * tf.reduce_sum(tf.multiply(yerror, yerror))`\n", "\n", ">`gradient = tf.reduce_sum(tf.transpose(tf.multiply(input, yerror)), 1, keep_dims=True)`\n", "\n", ">`update_weights = tf.assign_sub(weights, learning_rate * gradient)`\n", "\n", "The first line calculates the L2 loss manually. It's the same as `l2_loss(yerror)`, which is half of the sum of the squared error, so $\\frac{1}{2} \\sum (\\hat{y} - y)^2$. With this code, you can see exactly what the `l2_loss` operation does. It's the total of all the squared differences between the target and our estimates. And minimizing the L2 loss will minimize how much our estimates of $y$ differ from the true values of $y$.\n", "\n", "The second line calculates $\\begin{bmatrix}\\sum{(\\hat{y} - y)*1} \\\\ \\sum{(\\hat{y} - y)*x_i}\\end{bmatrix}$. What is that? It's the partial derivatives of the L2 loss with respect to $w_1$ and $w_2$, the same thing as what `gradients(loss, weights)` does in the earlier code. Not sure about that? Let's look at it in more detail. The gradient calculation is going to get the partial derivatives of loss with respect to each of the weights so we can change those weights in the direction that will reduce the loss. L2 loss is $\\frac{1}{2} \\sum (\\hat{y} - y)^2$, where $\\hat{y} = w_2 x + w_1$. So, using the chain rule and substituting in for $\\hat{y}$ in the derivative, $\\frac{\\partial}{\\partial w_2} = \\sum{(\\hat{y} - y)\\, *x_i}$ and $\\frac{\\partial}{\\partial w_1} = \\sum{(\\hat{y} - y)\\, *1}$. `GradientDescentOptimizer` does these calculations automatically for you based on the graph structure.\n", "\n", "The third line is equivalent to `weights -= learning_rate * gradient`, so it subtracts a constant the gradient after scaling by the learning rate (to avoid jumping too far each time, which risks moving in the wrong direction). It's also the same thing that `GradientDescentOptimizer(learning_rate).minimize(loss)` does in the earlier code. Gradient descent updates its first parameter based on the values in the second after scaling by the third, so it's equivalent to the `assign_sub(weights, learning_rate * gradient)`.\n", "\n", "Hopefully, this other code gives you a better understanding of what the operations we used previously are actually doing. In practice, you'll want to use those high level operators most of the time rather than calculating things yourself. For this toy example and simple network, it's not too bad to compute and apply the gradients yourself from scratch, but things get more complicated with larger networks." ] }, { "cell_type": "code", "execution_count": 0, "metadata": { "cellView": null, "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [] }, "colab_type": "code", "collapsed": true, "executionInfo": { "elapsed": 164, "status": "ok", "timestamp": 1474671842705, "user": { "displayName": "", "photoUrl": "", "userId": "" }, "user_tz": 420 }, "id": "ty5-b_nYSYWR", "outputId": "311b7bff-5c8b-43ee-da0f-439a879636d1" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "default_view": {}, "name": "Untitled", "provenance": [], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: tutorials_previous/4_tensorflow_mnist.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9yupXUk1DKOe" }, "source": [ "# MNIST from scratch\n", "\n", "This notebook walks through an example of training a TensorFlow model to do digit classification using the [MNIST data set](http://yann.lecun.com/exdb/mnist/). MNIST is a labeled set of images of handwritten digits.\n", "\n", "An example follows." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:20.863031", "start_time": "2016-09-16T14:49:20.818734" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "collapsed": false, "id": "sbUKaF8_uDI_", "outputId": "67a51332-3aea-4c29-8c3d-4752db08ccb3" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from __future__ import print_function\n", "\n", "from IPython.display import Image\n", "import base64\n", "Image(data=base64.decodestring(\"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\".encode('utf-8')), embed=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "J0QZYD_HuDJF" }, "source": [ "We're going to be building a model that recognizes these digits as 5, 0, and 4.\n", "\n", "# Imports and input data\n", "\n", "We'll proceed in steps, beginning with importing and inspecting the MNIST data. This doesn't have anything to do with TensorFlow in particular -- we're just downloading the data archive." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:20.958307", "start_time": "2016-09-16T14:49:20.864840" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 110, "status": "ok", "timestamp": 1446749124399, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "w5vKZqr6CDz9", "outputId": "794eac6d-a918-4888-e8cf-a8628474d7f1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n" ] } ], "source": [ "import os\n", "from six.moves.urllib.request import urlretrieve\n", "\n", "SOURCE_URL = 'http://yann.lecun.com/exdb/mnist/'\n", "WORK_DIRECTORY = \"/tmp/mnist-data\"\n", "\n", "def maybe_download(filename):\n", " \"\"\"A helper to download the data files if not present.\"\"\"\n", " if not os.path.exists(WORK_DIRECTORY):\n", " os.mkdir(WORK_DIRECTORY)\n", " filepath = os.path.join(WORK_DIRECTORY, filename)\n", " if not os.path.exists(filepath):\n", " filepath, _ = urlretrieve(SOURCE_URL + filename, filepath)\n", " statinfo = os.stat(filepath)\n", " print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')\n", " else:\n", " print('Already downloaded', filename)\n", " return filepath\n", "\n", "train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\n", "train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\n", "test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\n", "test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gCtMhpIoC84F" }, "source": [ "## Working with the images\n", "\n", "Now we have the files, but the format requires a bit of pre-processing before we can work with it. The data is gzipped, requiring us to decompress it. And, each of the images are grayscale-encoded with values from [0, 255]; we'll normalize these to [-0.5, 0.5].\n", "\n", "Let's try to unpack the data using the documented format:\n", "\n", " [offset] [type] [value] [description] \n", " 0000 32 bit integer 0x00000803(2051) magic number \n", " 0004 32 bit integer 60000 number of images \n", " 0008 32 bit integer 28 number of rows \n", " 0012 32 bit integer 28 number of columns \n", " 0016 unsigned byte ?? pixel \n", " 0017 unsigned byte ?? pixel \n", " ........ \n", " xxxx unsigned byte ?? pixel\n", " \n", "Pixels are organized row-wise. Pixel values are 0 to 255. 0 means background (white), 255 means foreground (black).\n", "\n", "We'll start by reading the first image from the test data as a sanity check." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:22.112407", "start_time": "2016-09-16T14:49:20.960204" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 57, "status": "ok", "timestamp": 1446749125010, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "P_3Fm5BpFMDF", "outputId": "c8e777e0-d891-4eb1-a178-9809f293cc28" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "magic number 2051\n", "image count 10000\n", "rows 28\n", "columns 28\n", "First 10 pixels: [0 0 0 0 0 0 0 0 0 0]\n" ] } ], "source": [ "import gzip, binascii, struct, numpy\n", "import matplotlib.pyplot as plt\n", "\n", "with gzip.open(test_data_filename) as f:\n", " # Print the header fields.\n", " for field in ['magic number', 'image count', 'rows', 'columns']:\n", " # struct.unpack reads the binary data provided by f.read.\n", " # The format string '>i' decodes a big-endian integer, which\n", " # is the encoding of the data.\n", " print(field, struct.unpack('>i', f.read(4))[0])\n", " \n", " # Read the first 28x28 set of pixel values. \n", " # Each pixel is one byte, [0, 255], a uint8.\n", " buf = f.read(28 * 28)\n", " image = numpy.frombuffer(buf, dtype=numpy.uint8)\n", " \n", " # Print the first few values of image.\n", " print('First 10 pixels:', image[:10])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "7NXKCQENNRQT" }, "source": [ "The first 10 pixels are all 0 values. Not very interesting, but also unsurprising. We'd expect most of the pixel values to be the background color, 0.\n", "\n", "We could print all 28 * 28 values, but what we really need to do to make sure we're reading our data properly is look at an image." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:22.525418", "start_time": "2016-09-16T14:49:22.114324" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 887, "status": "ok", "timestamp": 1446749126640, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "F_5w-cOoNLaG", "outputId": "77dabc81-e3ee-4fcf-ac72-88038494fb6c" }, "outputs": [ { "data": { "image/png": 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ARYUDEZHnKSRI3S1VSUh3Nxw9epTp6ekT9m3wO0T6BZT81Mes7oZwvYRwx8dK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\n/BEunlQoFNixY0eyDoIPCL6CoEAgIrJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "# We'll show the image and its pixel value histogram side-by-side.\n", "_, (ax1, ax2) = plt.subplots(1, 2)\n", "\n", "# To interpret the values as a 28x28 image, we need to reshape\n", "# the numpy array, which is one dimensional.\n", "ax1.imshow(image.reshape(28, 28), cmap=plt.cm.Greys);\n", "\n", "ax2.hist(image, bins=20, range=[0,255]);" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "weVoVR-nN0cN" }, "source": [ "The large number of 0 values correspond to the background of the image, another large mass of value 255 is black, and a mix of grayscale transition values in between.\n", "\n", "Both the image and histogram look sensible. But, it's good practice when training image models to normalize values to be centered around 0.\n", "\n", "We'll do that next. The normalization code is fairly short, and it may be tempting to assume we haven't made mistakes, but we'll double-check by looking at the rendered input and histogram again. Malformed inputs are a surprisingly common source of errors when developing new models." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:22.895369", "start_time": "2016-09-16T14:49:22.527595" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 531, "status": "ok", "timestamp": 1446749126656, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "jc1xCZXHNKVp", "outputId": "bd45b3dd-438b-41db-ea8f-d202d4a09e63" }, "outputs": [ { "data": { "image/png": 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TJ5mdnV2xr4LfsdEvfOSnMOZ1K4TrHYQ7MJYLBAoLIrJTKBxI3fgQkD2Wl5dT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\nxUBBQERk51E42CLCykGhUMA5l0xR9GV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Let's convert the uint8 image to 32 bit floats and rescale \n", "# the values to be centered around 0, between [-0.5, 0.5]. \n", "# \n", "# We again plot the image and histogram to check that we \n", "# haven't mangled the data.\n", "scaled = image.astype(numpy.float32)\n", "scaled = (scaled - (255 / 2.0)) / 255\n", "_, (ax1, ax2) = plt.subplots(1, 2)\n", "ax1.imshow(scaled.reshape(28, 28), cmap=plt.cm.Greys);\n", "ax2.hist(scaled, bins=20, range=[-0.5, 0.5]);" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PlqlwkX-O0Hd" }, "source": [ "Great -- we've retained the correct image data while properly rescaling to the range [-0.5, 0.5].\n", "\n", "## Reading the labels\n", "\n", "Let's next unpack the test label data. The format here is similar: a magic number followed by a count followed by the labels as `uint8` values. In more detail:\n", "\n", " [offset] [type] [value] [description] \n", " 0000 32 bit integer 0x00000801(2049) magic number (MSB first) \n", " 0004 32 bit integer 10000 number of items \n", " 0008 unsigned byte ?? label \n", " 0009 unsigned byte ?? label \n", " ........ \n", " xxxx unsigned byte ?? label\n", "\n", "As with the image data, let's read the first test set value to sanity check our input path. We'll expect a 7." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:22.925176", "start_time": "2016-09-16T14:49:22.897739" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 90, "status": "ok", "timestamp": 1446749126903, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "d8zv9yZzQOnV", "outputId": "ad203b2c-f095-4035-e0cd-7869c078da3d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "magic number 2049\n", "label count 10000\n", "First label: 7\n" ] } ], "source": [ "with gzip.open(test_labels_filename) as f:\n", " # Print the header fields.\n", " for field in ['magic number', 'label count']:\n", " print(field, struct.unpack('>i', f.read(4))[0])\n", "\n", " print('First label:', struct.unpack('B', f.read(1))[0])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zAGrQSXCQtIm" }, "source": [ "Indeed, the first label of the test set is 7.\n", "\n", "## Forming the training, testing, and validation data sets\n", "\n", "Now that we understand how to read a single element, we can read a much larger set that we'll use for training, testing, and validation.\n", "\n", "### Image data\n", "\n", "The code below is a generalization of our prototyping above that reads the entire test and training data set." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:23.525119", "start_time": "2016-09-16T14:49:22.928289" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {} ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 734, "status": "ok", "timestamp": 1446749128718, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "ofFZ5oJeRMDA", "outputId": "ff2de90b-aed9-4ce5-db8c-9123496186b1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting /tmp/mnist-data/train-images-idx3-ubyte.gz\n", "Extracting /tmp/mnist-data/t10k-images-idx3-ubyte.gz\n" ] } ], "source": [ "IMAGE_SIZE = 28\n", "PIXEL_DEPTH = 255\n", "\n", "def extract_data(filename, num_images):\n", " \"\"\"Extract the images into a 4D tensor [image index, y, x, channels].\n", " \n", " For MNIST data, the number of channels is always 1.\n", "\n", " Values are rescaled from [0, 255] down to [-0.5, 0.5].\n", " \"\"\"\n", " print('Extracting', filename)\n", " with gzip.open(filename) as bytestream:\n", " # Skip the magic number and dimensions; we know these values.\n", " bytestream.read(16)\n", "\n", " buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images)\n", " data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)\n", " data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH\n", " data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, 1)\n", " return data\n", "\n", "train_data = extract_data(train_data_filename, 60000)\n", "test_data = extract_data(test_data_filename, 10000)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "0x4rwXxUR96O" }, "source": [ "A crucial difference here is how we `reshape` the array of pixel values. Instead of one image that's 28x28, we now have a set of 60,000 images, each one being 28x28. We also include a number of channels, which for grayscale images as we have here is 1.\n", "\n", "Let's make sure we've got the reshaping parameters right by inspecting the dimensions and the first two images. (Again, mangled input is a very common source of errors.)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:23.829853", "start_time": "2016-09-16T14:49:23.527283" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ {}, {} ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 400, "status": "ok", "timestamp": 1446749129657, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "0AwSo8mlSja_", "outputId": "11490c39-7c67-4fe5-982c-ca8278294d96" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training data shape (60000, 28, 28, 1)\n" ] }, { "data": { "image/png": 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rqzg/P0+a81dXV9HpdPLcfhUwCn6MbHlTq69OhZwCvrGxkRMvI+bVl64Er981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\nH8CH5E/tLMueDj27MYCEr5o9TfiMlNeWt1p6d9io2VSVvV7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print('Training data shape', train_data.shape)\n", "_, (ax1, ax2) = plt.subplots(1, 2)\n", "ax1.imshow(train_data[0].reshape(28, 28), cmap=plt.cm.Greys);\n", "ax2.imshow(train_data[1].reshape(28, 28), cmap=plt.cm.Greys);" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "cwBhQ3ouTQcW" }, "source": [ "Looks good. Now we know how to index our full set of training and test images." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "PBCB9aYxRvBi" }, "source": [ "### Label data\n", "\n", "Let's move on to loading the full set of labels. As is typical in classification problems, we'll convert our input labels into a [1-hot](https://en.wikipedia.org/wiki/One-hot) encoding over a length 10 vector corresponding to 10 digits. The vector [0, 1, 0, 0, 0, 0, 0, 0, 0, 0], for example, would correspond to the digit 1." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:23.854577", "start_time": "2016-09-16T14:49:23.831545" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 191, "status": "ok", "timestamp": 1446749131421, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "9pK1j2WlRwY9", "outputId": "1ca31655-e14f-405a-b266-6a6c78827af5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting /tmp/mnist-data/train-labels-idx1-ubyte.gz\n", "Extracting /tmp/mnist-data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "NUM_LABELS = 10\n", "\n", "def extract_labels(filename, num_images):\n", " \"\"\"Extract the labels into a 1-hot matrix [image index, label index].\"\"\"\n", " print('Extracting', filename)\n", " with gzip.open(filename) as bytestream:\n", " # Skip the magic number and count; we know these values.\n", " bytestream.read(8)\n", " buf = bytestream.read(1 * num_images)\n", " labels = numpy.frombuffer(buf, dtype=numpy.uint8)\n", " # Convert to dense 1-hot representation.\n", " return (numpy.arange(NUM_LABELS) == labels[:, None]).astype(numpy.float32)\n", "\n", "train_labels = extract_labels(train_labels_filename, 60000)\n", "test_labels = extract_labels(test_labels_filename, 10000)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "hb3Vaq72UUxW" }, "source": [ "As with our image data, we'll double-check that our 1-hot encoding of the first few values matches our expectations." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:23.864350", "start_time": "2016-09-16T14:49:23.857177" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 127, "status": "ok", "timestamp": 1446749132853, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "uEBID71nUVj1", "outputId": "3f318310-18dd-49ed-9943-47b4aae7ee69" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training labels shape (60000, 10)\n", "First label vector [ 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n", "Second label vector [ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n" ] } ], "source": [ "print('Training labels shape', train_labels.shape)\n", "print('First label vector', train_labels[0])\n", "print('Second label vector', train_labels[1])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5EwtEhxRUneF" }, "source": [ "The 1-hot encoding looks reasonable.\n", "\n", "### Segmenting data into training, test, and validation\n", "\n", "The final step in preparing our data is to split it into three sets: training, test, and validation. This isn't the format of the original data set, so we'll take a small slice of the training data and treat that as our validation set." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:23.874014", "start_time": "2016-09-16T14:49:23.866161" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 176, "status": "ok", "timestamp": 1446749134110, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "e7aBYBtIVxHE", "outputId": "bdeae1a8-daff-4743-e594-f1d2229c0f4e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Validation shape (5000, 28, 28, 1)\n", "Train size 55000\n" ] } ], "source": [ "VALIDATION_SIZE = 5000\n", "\n", "validation_data = train_data[:VALIDATION_SIZE, :, :, :]\n", "validation_labels = train_labels[:VALIDATION_SIZE]\n", "train_data = train_data[VALIDATION_SIZE:, :, :, :]\n", "train_labels = train_labels[VALIDATION_SIZE:]\n", "\n", "train_size = train_labels.shape[0]\n", "\n", "print('Validation shape', validation_data.shape)\n", "print('Train size', train_size)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1JFhEH8EVj4O" }, "source": [ "# Defining the model\n", "\n", "Now that we've prepared our data, we're ready to define our model.\n", "\n", "The comments describe the architecture, which fairly typical of models that process image data. The raw input passes through several [convolution](https://en.wikipedia.org/wiki/Convolutional_neural_network#Convolutional_layer) and [max pooling](https://en.wikipedia.org/wiki/Convolutional_neural_network#Pooling_layer) layers with [rectified linear](https://en.wikipedia.org/wiki/Convolutional_neural_network#ReLU_layer) activations before several fully connected layers and a [softmax](https://en.wikipedia.org/wiki/Convolutional_neural_network#Loss_layer) loss for predicting the output class. During training, we use [dropout](https://en.wikipedia.org/wiki/Convolutional_neural_network#Dropout_method).\n", "\n", "We'll separate our model definition into three steps:\n", "\n", "1. Defining the variables that will hold the trainable weights.\n", "1. Defining the basic model graph structure described above. And,\n", "1. Stamping out several copies of the model graph for training, testing, and validation.\n", "\n", "We'll start with the variables." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:28.803525", "start_time": "2016-09-16T14:49:23.875999" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 2081, "status": "ok", "timestamp": 1446749138298, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "Q1VfiAzjzuK8", "outputId": "f53a39c9-3a52-47ca-d7a3-9f9d84eccf63" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done\n" ] } ], "source": [ "import tensorflow as tf\n", "\n", "# We'll bundle groups of examples during training for efficiency.\n", "# This defines the size of the batch.\n", "BATCH_SIZE = 60\n", "# We have only one channel in our grayscale images.\n", "NUM_CHANNELS = 1\n", "# The random seed that defines initialization.\n", "SEED = 42\n", "\n", "# This is where training samples and labels are fed to the graph.\n", "# These placeholder nodes will be fed a batch of training data at each\n", "# training step, which we'll write once we define the graph structure.\n", "train_data_node = tf.placeholder(\n", " tf.float32,\n", " shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))\n", "train_labels_node = tf.placeholder(tf.float32,\n", " shape=(BATCH_SIZE, NUM_LABELS))\n", "\n", "# For the validation and test data, we'll just hold the entire dataset in\n", "# one constant node.\n", "validation_data_node = tf.constant(validation_data)\n", "test_data_node = tf.constant(test_data)\n", "\n", "# The variables below hold all the trainable weights. For each, the\n", "# parameter defines how the variables will be initialized.\n", "conv1_weights = tf.Variable(\n", " tf.truncated_normal([5, 5, NUM_CHANNELS, 32], # 5x5 filter, depth 32.\n", " stddev=0.1,\n", " seed=SEED))\n", "conv1_biases = tf.Variable(tf.zeros([32]))\n", "conv2_weights = tf.Variable(\n", " tf.truncated_normal([5, 5, 32, 64],\n", " stddev=0.1,\n", " seed=SEED))\n", "conv2_biases = tf.Variable(tf.constant(0.1, shape=[64]))\n", "fc1_weights = tf.Variable( # fully connected, depth 512.\n", " tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],\n", " stddev=0.1,\n", " seed=SEED))\n", "fc1_biases = tf.Variable(tf.constant(0.1, shape=[512]))\n", "fc2_weights = tf.Variable(\n", " tf.truncated_normal([512, NUM_LABELS],\n", " stddev=0.1,\n", " seed=SEED))\n", "fc2_biases = tf.Variable(tf.constant(0.1, shape=[NUM_LABELS]))\n", "\n", "print('Done')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QHB_u04Z4HO6" }, "source": [ "Now that we've defined the variables to be trained, we're ready to wire them together into a TensorFlow graph.\n", "\n", "We'll define a helper to do this, `model`, which will return copies of the graph suitable for training and testing. Note the `train` argument, which controls whether or not dropout is used in the hidden layer. (We want to use dropout only during training.)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:28.834326", "start_time": "2016-09-16T14:49:28.805723" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 772, "status": "ok", "timestamp": 1446749138306, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "V85_B9QF3uBp", "outputId": "457d3e49-73ad-4451-c196-421dd4681efc" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done\n" ] } ], "source": [ "def model(data, train=False):\n", " \"\"\"The Model definition.\"\"\"\n", " # 2D convolution, with 'SAME' padding (i.e. the output feature map has\n", " # the same size as the input). Note that {strides} is a 4D array whose\n", " # shape matches the data layout: [image index, y, x, depth].\n", " conv = tf.nn.conv2d(data,\n", " conv1_weights,\n", " strides=[1, 1, 1, 1],\n", " padding='SAME')\n", "\n", " # Bias and rectified linear non-linearity.\n", " relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))\n", "\n", " # Max pooling. The kernel size spec ksize also follows the layout of\n", " # the data. Here we have a pooling window of 2, and a stride of 2.\n", " pool = tf.nn.max_pool(relu,\n", " ksize=[1, 2, 2, 1],\n", " strides=[1, 2, 2, 1],\n", " padding='SAME')\n", " conv = tf.nn.conv2d(pool,\n", " conv2_weights,\n", " strides=[1, 1, 1, 1],\n", " padding='SAME')\n", " relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))\n", " pool = tf.nn.max_pool(relu,\n", " ksize=[1, 2, 2, 1],\n", " strides=[1, 2, 2, 1],\n", " padding='SAME')\n", "\n", " # Reshape the feature map cuboid into a 2D matrix to feed it to the\n", " # fully connected layers.\n", " pool_shape = pool.get_shape().as_list()\n", " reshape = tf.reshape(\n", " pool,\n", " [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])\n", " \n", " # Fully connected layer. Note that the '+' operation automatically\n", " # broadcasts the biases.\n", " hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)\n", "\n", " # Add a 50% dropout during training only. Dropout also scales\n", " # activations such that no rescaling is needed at evaluation time.\n", " if train:\n", " hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)\n", " return tf.matmul(hidden, fc2_weights) + fc2_biases\n", "\n", "print('Done')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "7bvEtt8C4fLC" }, "source": [ "Having defined the basic structure of the graph, we're ready to stamp out multiple copies for training, testing, and validation.\n", "\n", "Here, we'll do some customizations depending on which graph we're constructing. `train_prediction` holds the training graph, for which we use cross-entropy loss and weight regularization. We'll adjust the learning rate during training -- that's handled by the `exponential_decay` operation, which is itself an argument to the `MomentumOptimizer` that performs the actual training.\n", "\n", "The vaildation and prediction graphs are much simpler the generate -- we need only create copies of the model with the validation and test inputs and a softmax classifier as the output." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.058141", "start_time": "2016-09-16T14:49:28.836169" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 269, "status": "ok", "timestamp": 1446749139596, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "9pR1EBNT3sCv", "outputId": "570681b1-f33e-4618-b742-48e12aa58132" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done\n" ] } ], "source": [ "# Training computation: logits + cross-entropy loss.\n", "logits = model(train_data_node, True)\n", "loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n", " labels=train_labels_node, logits=logits))\n", "\n", "# L2 regularization for the fully connected parameters.\n", "regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +\n", " tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))\n", "# Add the regularization term to the loss.\n", "loss += 5e-4 * regularizers\n", "\n", "# Optimizer: set up a variable that's incremented once per batch and\n", "# controls the learning rate decay.\n", "batch = tf.Variable(0)\n", "# Decay once per epoch, using an exponential schedule starting at 0.01.\n", "learning_rate = tf.train.exponential_decay(\n", " 0.01, # Base learning rate.\n", " batch * BATCH_SIZE, # Current index into the dataset.\n", " train_size, # Decay step.\n", " 0.95, # Decay rate.\n", " staircase=True)\n", "# Use simple momentum for the optimization.\n", "optimizer = tf.train.MomentumOptimizer(learning_rate,\n", " 0.9).minimize(loss,\n", " global_step=batch)\n", "\n", "# Predictions for the minibatch, validation set and test set.\n", "train_prediction = tf.nn.softmax(logits)\n", "# We'll compute them only once in a while by calling their {eval()} method.\n", "validation_prediction = tf.nn.softmax(model(validation_data_node))\n", "test_prediction = tf.nn.softmax(model(test_data_node))\n", "\n", "print('Done')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4T21uZJq5UfH" }, "source": [ "# Training and visualizing results\n", "\n", "Now that we have the training, test, and validation graphs, we're ready to actually go through the training loop and periodically evaluate loss and error.\n", "\n", "All of these operations take place in the context of a session. In Python, we'd write something like:\n", "\n", " with tf.Session() as s:\n", " ...training / test / evaluation loop...\n", " \n", "But, here, we'll want to keep the session open so we can poke at values as we work out the details of training. The TensorFlow API includes a function for this, `InteractiveSession`.\n", "\n", "We'll start by creating a session and initializing the varibles we defined above." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.357483", "start_time": "2016-09-16T14:49:29.059952" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": false, "id": "z6Kc5iql6qxV" }, "outputs": [], "source": [ "from pkg_resources import parse_version\n", "\n", "# Create a new interactive session that we'll use in\n", "# subsequent code cells.\n", "s = tf.InteractiveSession()\n", "\n", "# Use our newly created session as the default for \n", "# subsequent operations.\n", "s.as_default()\n", "\n", "# Initialize all the variables we defined above. \n", "if parse_version(tf.__version__) >= parse_version('0.12'):\n", " tf.global_variables_initializer().run() # for tf version >=0.12\n", "else:\n", " tf.initialize_all_variables().run() # for tf version < 0.12" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "hcG8H-Ka6_mw" }, "source": [ "Now we're ready to perform operations on the graph. Let's start with one round of training. We're going to organize our training steps into batches for efficiency; i.e., training using a small set of examples at each step rather than a single example." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.584699", "start_time": "2016-09-16T14:49:29.359107" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 386, "status": "ok", "timestamp": 1446749389138, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "LYVxeEox71Pg", "outputId": "9184b5df-009a-4b1b-e312-5be94351351f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done\n" ] } ], "source": [ "BATCH_SIZE = 60\n", "\n", "# Grab the first BATCH_SIZE examples and labels.\n", "batch_data = train_data[:BATCH_SIZE, :, :, :]\n", "batch_labels = train_labels[:BATCH_SIZE]\n", "\n", "# This dictionary maps the batch data (as a numpy array) to the\n", "# node in the graph it should be fed to.\n", "feed_dict = {train_data_node: batch_data,\n", " train_labels_node: batch_labels}\n", "\n", "# Run the graph and fetch some of the nodes.\n", "_, l, lr, predictions = s.run(\n", " [optimizer, loss, learning_rate, train_prediction],\n", " feed_dict=feed_dict)\n", "\n", "print('Done')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "7bL4-RNm_K-B" }, "source": [ "Let's take a look at the predictions. How did we do? Recall that the output will be probabilities over the possible classes, so let's look at those probabilities." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.593985", "start_time": "2016-09-16T14:49:29.586233" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 160, "status": "ok", "timestamp": 1446749519023, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "2eNitV_4_ZUL", "outputId": "f1340dd1-255b-4523-bf62-7e3ebb361333" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.25392316e-04 4.76219466e-05 1.66868209e-03 5.67827410e-05\n", " 6.03432059e-01 4.34970036e-02 2.19317553e-05 1.41285273e-04\n", " 1.54903228e-05 3.50893855e-01]\n" ] } ], "source": [ "print(predictions[0])" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "X5MgraJb_eQZ" }, "source": [ "As expected without training, the predictions are all noise. Let's write a scoring function that picks the class with the maximum probability and compares with the example's label. We'll start by converting the probability vectors returned by the softmax into predictions we can match against the labels." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.606284", "start_time": "2016-09-16T14:49:29.597095" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 220, "status": "ok", "timestamp": 1446750411574, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "wMMlUf5rCKgT", "outputId": "2c10e96d-52b6-47b0-b6eb-969ad462d46b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First prediction 4\n", "(60, 10)\n", "All predictions [4 4 2 7 7 7 7 7 7 7 7 7 0 8 9 0 7 7 0 7 4 0 5 0 9 9 7 0 7 4 7 7 7 0 7 7 9\n", " 7 9 9 0 7 7 7 2 7 0 7 2 9 9 9 9 9 0 7 9 4 8 7]\n" ] } ], "source": [ "# The highest probability in the first entry.\n", "print('First prediction', numpy.argmax(predictions[0]))\n", "\n", "# But, predictions is actually a list of BATCH_SIZE probability vectors.\n", "print(predictions.shape)\n", "\n", "# So, we'll take the highest probability for each vector.\n", "print('All predictions', numpy.argmax(predictions, 1))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "8pMCIZ3_C2ni" }, "source": [ "Next, we can do the same thing for our labels -- using `argmax` to convert our 1-hot encoding into a digit class." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.615484", "start_time": "2016-09-16T14:49:29.609168" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 232, "status": "ok", "timestamp": 1446750498351, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "kZWp4T0JDDUe", "outputId": "47b588cd-bc82-45c3-a5d0-8d84dc27a3be" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch labels [7 3 4 6 1 8 1 0 9 8 0 3 1 2 7 0 2 9 6 0 1 6 7 1 9 7 6 5 5 8 8 3 4 4 8 7 3\n", " 6 4 6 6 3 8 8 9 9 4 4 0 7 8 1 0 0 1 8 5 7 1 7]\n" ] } ], "source": [ "print('Batch labels', numpy.argmax(batch_labels, 1))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "bi5Z6whtDiht" }, "source": [ "Now we can compare the predicted and label classes to compute the error rate and confusion matrix for this batch." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.841313", "start_time": "2016-09-16T14:49:29.618274" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 }, { "item_id": 2 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 330, "status": "ok", "timestamp": 1446751307304, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "U4hrLW4CDtQB", "outputId": "720494a3-cbf9-4687-9d94-e64a33fdd78f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0666666666667\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "correct = numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(batch_labels, 1))\n", "total = predictions.shape[0]\n", "\n", "print(float(correct) / float(total))\n", "\n", "confusions = numpy.zeros([10, 10], numpy.float32)\n", "bundled = zip(numpy.argmax(predictions, 1), numpy.argmax(batch_labels, 1))\n", "for predicted, actual in bundled:\n", " confusions[predicted, actual] += 1\n", "\n", "plt.grid(False)\n", "plt.xticks(numpy.arange(NUM_LABELS))\n", "plt.yticks(numpy.arange(NUM_LABELS))\n", "plt.imshow(confusions, cmap=plt.cm.jet, interpolation='nearest');" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "iZmx_9DiDXQ3" }, "source": [ "Now let's wrap this up into our scoring function." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:49:29.857607", "start_time": "2016-09-16T14:49:29.843904" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 178, "status": "ok", "timestamp": 1446751995007, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "DPJie7bPDaLa", "outputId": "a06c64ed-f95f-416f-a621-44cccdaba0f8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Done\n" ] } ], "source": [ "def error_rate(predictions, labels):\n", " \"\"\"Return the error rate and confusions.\"\"\"\n", " correct = numpy.sum(numpy.argmax(predictions, 1) == numpy.argmax(labels, 1))\n", " total = predictions.shape[0]\n", "\n", " error = 100.0 - (100 * float(correct) / float(total))\n", "\n", " confusions = numpy.zeros([10, 10], numpy.float32)\n", " bundled = zip(numpy.argmax(predictions, 1), numpy.argmax(labels, 1))\n", " for predicted, actual in bundled:\n", " confusions[predicted, actual] += 1\n", " \n", " return error, confusions\n", "\n", "print('Done')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "sLv22cjeB5Rd" }, "source": [ "We'll need to train for some time to actually see useful predicted values. Let's define a loop that will go through our data. We'll print the loss and error periodically.\n", "\n", "Here, we want to iterate over the entire data set rather than just the first batch, so we'll need to slice the data to that end.\n", "\n", "(One pass through our training set will take some time on a CPU, so be patient if you are executing this notebook.)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:53:26.998313", "start_time": "2016-09-16T14:49:29.860079" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, "colab_type": "code", "collapsed": false, "id": "4cgKJrS1_vej" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step 0 of 916\n", "Mini-batch loss: 7.71263 Error: 91.66667 Learning rate: 0.01000\n", "Validation error: 88.9%\n", "Step 100 of 916\n", "Mini-batch loss: 3.32480 Error: 10.00000 Learning rate: 0.01000\n", "Validation error: 6.0%\n", "Step 200 of 916\n", "Mini-batch loss: 3.30628 Error: 11.66667 Learning rate: 0.01000\n", "Validation error: 3.7%\n", "Step 300 of 916\n", "Mini-batch loss: 3.16925 Error: 5.00000 Learning rate: 0.01000\n", "Validation error: 3.3%\n", "Step 400 of 916\n", "Mini-batch loss: 3.06869 Error: 3.33333 Learning rate: 0.01000\n", "Validation error: 2.5%\n", "Step 500 of 916\n", "Mini-batch loss: 2.99121 Error: 0.00000 Learning rate: 0.01000\n", "Validation error: 2.6%\n", "Step 600 of 916\n", "Mini-batch loss: 3.07648 Error: 5.00000 Learning rate: 0.01000\n", "Validation error: 2.1%\n", "Step 700 of 916\n", "Mini-batch loss: 3.15329 Error: 8.33333 Learning rate: 0.01000\n", "Validation error: 2.1%\n", "Step 800 of 916\n", "Mini-batch loss: 3.12467 Error: 6.66667 Learning rate: 0.01000\n", "Validation error: 1.8%\n", "Step 900 of 916\n", "Mini-batch loss: 2.85096 Error: 0.00000 Learning rate: 0.01000\n", "Validation error: 1.8%\n" ] } ], "source": [ "# Train over the first 1/4th of our training set.\n", "steps = train_size // BATCH_SIZE\n", "for step in range(steps):\n", " # Compute the offset of the current minibatch in the data.\n", " # Note that we could use better randomization across epochs.\n", " offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)\n", " batch_data = train_data[offset:(offset + BATCH_SIZE), :, :, :]\n", " batch_labels = train_labels[offset:(offset + BATCH_SIZE)]\n", " # This dictionary maps the batch data (as a numpy array) to the\n", " # node in the graph it should be fed to.\n", " feed_dict = {train_data_node: batch_data,\n", " train_labels_node: batch_labels}\n", " # Run the graph and fetch some of the nodes.\n", " _, l, lr, predictions = s.run(\n", " [optimizer, loss, learning_rate, train_prediction],\n", " feed_dict=feed_dict)\n", " \n", " # Print out the loss periodically.\n", " if step % 100 == 0:\n", " error, _ = error_rate(predictions, batch_labels)\n", " print('Step %d of %d' % (step, steps))\n", " print('Mini-batch loss: %.5f Error: %.5f Learning rate: %.5f' % (l, error, lr))\n", " print('Validation error: %.1f%%' % error_rate(\n", " validation_prediction.eval(), validation_labels)[0])\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "J4LskgGXIDAm" }, "source": [ "The error seems to have gone down. Let's evaluate the results using the test set.\n", "\n", "To help identify rare mispredictions, we'll include the raw count of each (prediction, label) pair in the confusion matrix." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:55:10.942063", "start_time": "2016-09-16T14:53:26.999971" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 }, { "item_id": 2 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 436, "status": "ok", "timestamp": 1446752934104, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "6Yh1jGFuIKc_", "outputId": "4e411de4-0fe2-451b-e4ca-8a4854f0db89" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test error: 2.0%\n" ] }, { "data": { "image/png": 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V8zZseJaMjNc5c2YQaWmDOXNmEJUrl3BLHmbsb098vm/E9CPt/JRSCvgA2Ky13uWKNpOS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\nQNVbNm6xKGbN6sTAgaux2bzhb5Tn+fv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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_error, confusions = error_rate(test_prediction.eval(), test_labels)\n", "print('Test error: %.1f%%' % test_error)\n", "\n", "plt.xlabel('Actual')\n", "plt.ylabel('Predicted')\n", "plt.grid(False)\n", "plt.xticks(numpy.arange(NUM_LABELS))\n", "plt.yticks(numpy.arange(NUM_LABELS))\n", "plt.imshow(confusions, cmap=plt.cm.jet, interpolation='nearest');\n", "\n", "for i, cas in enumerate(confusions):\n", " for j, count in enumerate(cas):\n", " if count > 0:\n", " xoff = .07 * len(str(count))\n", " plt.text(j-xoff, i+.2, int(count), fontsize=9, color='white')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "yLnS4dGiMwI1" }, "source": [ "We can see here that we're mostly accurate, with some errors you might expect, e.g., '9' is often confused as '4'.\n", "\n", "Let's do another sanity check to make sure this matches roughly the distribution of our test set, e.g., it seems like we have fewer '5' values." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2016-09-16T14:55:18.083458", "start_time": "2016-09-16T14:55:17.830485" }, "cellView": "both", "colab": { "autoexec": { "startup": false, "wait_interval": 0 }, "output_extras": [ { "item_id": 1 } ] }, "colab_type": "code", "collapsed": false, "executionInfo": { "elapsed": 352, "status": "ok", "timestamp": 1446753006584, "user": { "color": "#1FA15D", "displayName": "Michael Piatek", "isAnonymous": false, "isMe": true, "permissionId": "00327059602783983041", "photoUrl": "//lh6.googleusercontent.com/-wKJwK_OPl34/AAAAAAAAAAI/AAAAAAAAAlk/Rh3u6O2Z7ns/s50-c-k-no/photo.jpg", "sessionId": "716a6ad5e180d821", "userId": "106975671469698476657" }, "user_tz": 480 }, "id": "x5KOv1AJMgzV", "outputId": "2acdf737-bab6-408f-8b3c-05fa66d04fe6" }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xticks(numpy.arange(NUM_LABELS))\n", "plt.hist(numpy.argmax(test_labels, 1));" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "E6DzLSK5M1ju" }, "source": [ "Indeed, we appear to have fewer 5 labels in the test set. So, on the whole, it seems like our model is learning and our early results are sensible.\n", "\n", "But, we've only done one round of training. We can greatly improve accuracy by training for longer. To try this out, just re-execute the training cell above." ] } ], "metadata": { "anaconda-cloud": {}, "colab": { "default_view": {}, "name": "Untitled", "provenance": [], "version": "0.3.2", "views": {} }, "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 } ================================================ FILE: tutorials_previous/5_tensorflow_traffic_light_classification.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, we'll create a framework for image classification in Tensorflow.\n", "\n", "This code should be replicable to any image task with a few changes.\n", "\n", "Our specific task will be to classify images of traffic lights as red, yellow, or green." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#!/usr/bin/python2\n", "\n", "%matplotlib inline\n", "\n", "import sys, os, time\n", "import itertools\n", "import math, random\n", "import glob\n", "import tensorflow as tf\n", "import numpy as np\n", "import cv2\n", "from sklearn.metrics import confusion_matrix\n", "import matplotlib.pyplot as plt\n", "from IPython.display import Image, display\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we set some basic parameters:\n", "\n", " base_image_path: the path where our training data is stored\n", " \n", " image_types: subdirectories in the images folder, each one representing a different class\n", " \n", " input_img_x/y: width and height of the images\n", " \n", " train_test_split_ratio: the ratio of training images to testing images\n", " \n", " batch_size: the minibatch size\n", " \n", " checkpoint_name: where we will save our best model\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Basic parameters\n", "\n", "max_epochs = 25\n", "base_image_path = \"5_tensorflow_traffic_light_images/\"\n", "image_types = [\"red\", \"green\", \"yellow\"]\n", "input_img_x = 32\n", "input_img_y = 32\n", "train_test_split_ratio = 0.9\n", "batch_size = 32\n", "checkpoint_name = \"model.ckpt\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Helper layer functions\n", "def weight_variable(shape):\n", " initial = tf.truncated_normal(shape, stddev=0.1)\n", " return tf.Variable(initial)\n", "\n", "def bias_variable(shape):\n", " initial = tf.constant(0.1, shape=shape)\n", " return tf.Variable(initial)\n", "\n", "def conv2d(x, W, stride):\n", " return tf.nn.conv2d(x, W, strides=[1, stride, stride, 1], padding='SAME')\n", "\n", "def max_pool_2x2(x):\n", " return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],\n", " strides=[1, 2, 2, 1], padding='SAME')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we initialize our input and output neurons.\n", "\n", "Our input neurons will be the shape of the image which is (32 x 32 x 3)\n", "\n", "Because our data will be one-hot encoded, we have as many output neurons as we have classes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Model\n", "\n", "x = tf.placeholder(tf.float32, shape=[None, input_img_x, input_img_y, 3])\n", "y_ = tf.placeholder(tf.float32, shape=[None, len(image_types)])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is where we specify our first convolutional layers.\n", "\n", "We specify the number of weights in the first line:\n", "\n", " W_conv1 = weight_variable([3, 3, 3, 16])\n", " \n", "This line is for specifying the number of bias variables, or the variables that will be added to weights after multiplying them by the activation.\n", "\n", " b_conv1 = bias_variable([16])\n", " \n", "Next, we specify the activation:\n", "\n", " h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1, 1) + b_conv1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_image = x\n", "\n", "# Our first three convolutional layers, of 16 3x3 filters\n", "W_conv1 = weight_variable([3, 3, 3, 16])\n", "b_conv1 = bias_variable([16])\n", "h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1, 1) + b_conv1)\n", "\n", "W_conv2 = weight_variable([3, 3, 16, 16])\n", "b_conv2 = bias_variable([16])\n", "h_conv2 = tf.nn.relu(conv2d(h_conv1, W_conv2, 1) + b_conv2)\n", "\n", "W_conv3 = weight_variable([3, 3, 16, 16])\n", "b_conv3 = bias_variable([16])\n", "h_conv3 = tf.nn.relu(conv2d(h_conv2, W_conv3, 1) + b_conv3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Our pooling layer\n", "\n", "h_pool4 = max_pool_2x2(h_conv3)\n", "\n", "n1, n2, n3, n4 = h_pool4.get_shape().as_list()\n", "\n", "W_fc1 = weight_variable([n2*n3*n4, 3])\n", "b_fc1 = bias_variable([3])\n", "\n", "# We flatten our pool layer into a fully connected layer\n", "\n", "h_pool4_flat = tf.reshape(h_pool4, [-1, n2*n3*n4])\n", "\n", "y = tf.matmul(h_pool4_flat, W_fc1) + b_fc1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess = tf.InteractiveSession()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our loss function is defined as computing softmax, and then cross entropy.\n", "\n", "We also specify our optimizer, which takes a learning rate, and a loss function.\n", "\n", "Finally, we initialize all of our variables which will tell us if our model is valid." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Our loss function and optimizer\n", "\n", "loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels = y_, logits = y))\n", "train_step = tf.train.AdamOptimizer(1e-4).minimize(loss)\n", "sess.run(tf.initialize_all_variables())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to load in our images. We do so using OpenCV's imread function. After loading in each image, we resize it to our input size.\n", "\n", "With each loaded image, we also specify the expected output. For this, we use a one-hot encoding, creating an array of zeros represnting each class, and setting the index of the expected class number to 1.\n", "\n", "For example, if we have three classes, and we expect an order of: [red neuron, green neuron, yellow neuron]\n", "\n", "We initialize an array to [0, 0, 0] and if we loaded a yellow light, we change the last value to 1: [0, 0, 1]\n", "\n", "Finally, we shuffle our dataset. (It's generally useful to seed our random generator with 0 at the start of the program)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "saver = tf.train.Saver()\n", "time_start = time.time()\n", "\n", "v_loss = least_loss = 99999999\n", "\n", "# Load data\n", "\n", "full_set = []\n", "\n", "for im_type in image_types:\n", " for ex in glob.glob(os.path.join(base_image_path, im_type, \"*\")):\n", " im = cv2.imread(ex)\n", " if not im is None:\n", " im = cv2.resize(im, (32, 32))\n", "\n", " # Create an array representing our classes and set it\n", " one_hot_array = [0] * len(image_types)\n", " one_hot_array[image_types.index(im_type)] = 1\n", " assert(im.shape == (32, 32, 3))\n", "\n", " full_set.append((im, one_hot_array, ex))\n", "\n", "random.shuffle(full_set)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using our train_test_split_ratio we create two lists of examples: testing and training." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# We split our data into a training and test set here\n", "\n", "split_index = int(math.floor(len(full_set) * train_test_split_ratio))\n", "train_set = full_set[:split_index]\n", "test_set = full_set[split_index:]\n", "\n", "# We ensure that our training and test sets are a multiple of batch size\n", "train_set_offset = len(train_set) % batch_size\n", "test_set_offset = len(test_set) % batch_size\n", "train_set = train_set[: len(train_set) - train_set_offset]\n", "test_set = test_set[: len(test_set) - test_set_offset]\n", "\n", "train_x, train_y, train_z = zip(*train_set)\n", "test_x, test_y, test_z = zip(*test_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every time we iterate over all of our training examples, we have completed one epoch. Generally, we should start with one epoch while debugging, and in practice many datasets will converge with less than 100 epochs. It's something that needs to be explored with each dataset.\n", "\n", "We split our training set into batches, which we train on in order.\n", "\n", "We then use our entire datset to calculate training and validation loss. These are the values we want to minimize, but it's important to pay attention to the interaction between them. If training loss is going down, but validation is staying the same, it means we are overfitting our dataset: our network is becoming increasingly good at correctly classifying our training examples, but our network isn't generalizing to other examples outside the training set.\n", "\n", "We save our model if the current model has the lowest validation seen in this training run." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print(\"Starting training... [{} training examples]\".format(len(train_x)))\n", "\n", "v_loss = 9999999\n", "train_loss = []\n", "val_loss = []\n", "\n", "for i in range(0, max_epochs):\n", "\n", " # Iterate over our training set\n", " for tt in range(0, (len(train_x) // batch_size)):\n", " start_batch = batch_size * tt\n", " end_batch = batch_size * (tt + 1)\n", " train_step.run(feed_dict={x: train_x[start_batch:end_batch], y_: train_y[start_batch:end_batch]})\n", " ex_seen = \"Current epoch, examples seen: {:20} / {} \\r\".format(tt * batch_size, len(train_x))\n", " sys.stdout.write(ex_seen.format(tt * batch_size))\n", " sys.stdout.flush()\n", "\n", " ex_seen = \"Current epoch, examples seen: {:20} / {} \\r\".format((tt + 1) * batch_size, len(train_x))\n", " sys.stdout.write(ex_seen.format(tt * batch_size))\n", " sys.stdout.flush()\n", "\n", " t_loss = loss.eval(feed_dict={x: train_x, y_: train_y})\n", " v_loss = loss.eval(feed_dict={x: test_x, y_: test_y})\n", " \n", " train_loss.append(t_loss)\n", " val_loss.append(v_loss)\n", "\n", " sys.stdout.write(\"Epoch {:5}: loss: {:15.10f}, val. loss: {:15.10f}\".format(i + 1, t_loss, v_loss))\n", "\n", " if v_loss < least_loss:\n", " sys.stdout.write(\", saving new best model to {}\".format(checkpoint_name))\n", " least_loss = v_loss\n", " filename = saver.save(sess, checkpoint_name)\n", "\n", " sys.stdout.write(\"\\n\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.figure()\n", "plt.xticks(np.arange(0, len(train_loss), 1.0))\n", "plt.ylabel(\"Loss\")\n", "plt.xlabel(\"Epochs\")\n", "train_line = plt.plot(range(0, len(train_loss)), train_loss, 'r', label=\"Train loss\")\n", "val_line = plt.plot(range(0, len(val_loss)), val_loss, 'g', label=\"Validation loss\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's print the examples from our test set that were wrong:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zipped_x_y = zip(test_x, test_y)\n", "conf_true = []\n", "conf_pred = []\n", "for tt in range(0, len(zipped_x_y)):\n", " q = zipped_x_y[tt]\n", " sfmax = list(sess.run(tf.nn.softmax(y.eval(feed_dict={x: [q[0]]})))[0])\n", " sf_ind = sfmax.index(max(sfmax))\n", " \n", " predicted_label = image_types[sf_ind]\n", " actual_label = image_types[q[1].index(max(q[1]))]\n", " \n", " conf_true.append(actual_label)\n", " conf_pred.append(predicted_label)\n", " \n", " if predicted_label != actual_label:\n", " print(\"Actual: {}, predicted: {}\".format(actual_label, predicted_label))\n", " img_path = test_z[tt] \n", " ex_img = Image(filename=img_path)\n", " display(ex_img)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# From sklearn docs\n", "def plot_confusion_matrix(cm, classes,\n", " normalize=False,\n", " title='Confusion matrix',\n", " cmap=plt.cm.Blues):\n", " \"\"\"\n", " This function prints and plots the confusion matrix.\n", " Normalization can be applied by setting `normalize=True`.\n", " \"\"\"\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", " tick_marks = np.arange(len(classes))\n", " plt.xticks(tick_marks, classes, rotation=45)\n", " plt.yticks(tick_marks, classes)\n", "\n", " cm2 = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "\n", " cm2 = np.around(cm2, 2)\n", "\n", " thresh = cm.max() / 2.\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " plt.text(j, i, str(cm[i, j]) + \" / \" + str(cm2[i, j]),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", " plt.tight_layout()\n", " plt.ylabel('True label')\n", " plt.xlabel('Predicted label')\n", "\n", "cnf_matrix = confusion_matrix(conf_true, conf_pred)\n", "plt.figure()\n", "plot_confusion_matrix(cnf_matrix, classes=image_types, normalize=False,\n", " title='Normalized confusion matrix')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }