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Repository: zhourunlai/deep-learning-demo
Branch: master
Commit: bbe9094a7830
Files: 168
Total size: 6.5 MB

Directory structure:
gitextract_94todzum/

├── .gitignore
├── Caffe/
│   ├── 00-classification.ipynb
│   ├── 01-learning-lenet.ipynb
│   ├── 02-fine-tuning.ipynb
│   ├── CMakeLists.txt
│   ├── brewing-logreg.ipynb
│   ├── cifar10/
│   │   ├── cifar10_full.prototxt
│   │   ├── cifar10_full_sigmoid_solver.prototxt
│   │   ├── cifar10_full_sigmoid_solver_bn.prototxt
│   │   ├── cifar10_full_sigmoid_train_test.prototxt
│   │   ├── cifar10_full_sigmoid_train_test_bn.prototxt
│   │   ├── cifar10_full_solver.prototxt
│   │   ├── cifar10_full_solver_lr1.prototxt
│   │   ├── cifar10_full_solver_lr2.prototxt
│   │   ├── cifar10_full_train_test.prototxt
│   │   ├── cifar10_quick.prototxt
│   │   ├── cifar10_quick_solver.prototxt
│   │   ├── cifar10_quick_solver_lr1.prototxt
│   │   ├── cifar10_quick_train_test.prototxt
│   │   ├── convert_cifar_data.cpp
│   │   ├── create_cifar10.sh
│   │   ├── readme.md
│   │   ├── train_full.sh
│   │   ├── train_full_sigmoid.sh
│   │   ├── train_full_sigmoid_bn.sh
│   │   └── train_quick.sh
│   ├── cpp_classification/
│   │   ├── classification.cpp
│   │   └── readme.md
│   ├── detection.ipynb
│   ├── feature_extraction/
│   │   ├── imagenet_val.prototxt
│   │   └── readme.md
│   ├── finetune_flickr_style/
│   │   ├── assemble_data.py
│   │   ├── readme.md
│   │   └── style_names.txt
│   ├── finetune_pascal_detection/
│   │   ├── pascal_finetune_solver.prototxt
│   │   └── pascal_finetune_trainval_test.prototxt
│   ├── hdf5_classification/
│   │   ├── nonlinear_auto_test.prototxt
│   │   ├── nonlinear_auto_train.prototxt
│   │   ├── nonlinear_train_val.prototxt
│   │   └── train_val.prototxt
│   ├── imagenet/
│   │   ├── create_imagenet.sh
│   │   ├── make_imagenet_mean.sh
│   │   ├── readme.md
│   │   ├── resume_training.sh
│   │   └── train_caffenet.sh
│   ├── mnist/
│   │   ├── convert_mnist_data.cpp
│   │   ├── create_mnist.sh
│   │   ├── lenet.prototxt
│   │   ├── lenet_adadelta_solver.prototxt
│   │   ├── lenet_auto_solver.prototxt
│   │   ├── lenet_consolidated_solver.prototxt
│   │   ├── lenet_multistep_solver.prototxt
│   │   ├── lenet_solver.prototxt
│   │   ├── lenet_solver_adam.prototxt
│   │   ├── lenet_solver_rmsprop.prototxt
│   │   ├── lenet_train_test.prototxt
│   │   ├── mnist_autoencoder.prototxt
│   │   ├── mnist_autoencoder_solver.prototxt
│   │   ├── mnist_autoencoder_solver_adadelta.prototxt
│   │   ├── mnist_autoencoder_solver_adagrad.prototxt
│   │   ├── mnist_autoencoder_solver_nesterov.prototxt
│   │   ├── readme.md
│   │   ├── train_lenet.sh
│   │   ├── train_lenet_adam.sh
│   │   ├── train_lenet_consolidated.sh
│   │   ├── train_lenet_docker.sh
│   │   ├── train_lenet_rmsprop.sh
│   │   ├── train_mnist_autoencoder.sh
│   │   ├── train_mnist_autoencoder_adadelta.sh
│   │   ├── train_mnist_autoencoder_adagrad.sh
│   │   └── train_mnist_autoencoder_nesterov.sh
│   ├── net_surgery/
│   │   ├── bvlc_caffenet_full_conv.prototxt
│   │   └── conv.prototxt
│   ├── net_surgery.ipynb
│   ├── pascal-multilabel-with-datalayer.ipynb
│   ├── pycaffe/
│   │   ├── caffenet.py
│   │   ├── layers/
│   │   │   ├── pascal_multilabel_datalayers.py
│   │   │   └── pyloss.py
│   │   ├── linreg.prototxt
│   │   └── tools.py
│   ├── siamese/
│   │   ├── convert_mnist_siamese_data.cpp
│   │   ├── create_mnist_siamese.sh
│   │   ├── mnist_siamese.ipynb
│   │   ├── mnist_siamese.prototxt
│   │   ├── mnist_siamese_solver.prototxt
│   │   ├── mnist_siamese_train_test.prototxt
│   │   ├── readme.md
│   │   └── train_mnist_siamese.sh
│   └── web_demo/
│       ├── app.py
│       ├── exifutil.py
│       ├── readme.md
│       ├── requirements.txt
│       └── templates/
│           └── index.html
├── DLbook/
│   └── DeepLearningPapers.md
├── Keras/
│   ├── README.md
│   ├── addition_rnn.py
│   ├── antirectifier.py
│   ├── babi_memnn.py
│   ├── babi_rnn.py
│   ├── cifar10_cnn.py
│   ├── conv_filter_visualization.py
│   ├── conv_lstm.py
│   ├── deep_dream.py
│   ├── image_ocr.py
│   ├── imdb_bidirectional_lstm.py
│   ├── imdb_cnn.py
│   ├── imdb_cnn_lstm.py
│   ├── imdb_fasttext.py
│   ├── imdb_lstm.py
│   ├── lstm_benchmark.py
│   ├── lstm_text_generation.py
│   ├── mnist_acgan.py
│   ├── mnist_cnn.py
│   ├── mnist_hierarchical_rnn.py
│   ├── mnist_irnn.py
│   ├── mnist_mlp.py
│   ├── mnist_net2net.py
│   ├── mnist_siamese_graph.py
│   ├── mnist_sklearn_wrapper.py
│   ├── mnist_swwae.py
│   ├── mnist_transfer_cnn.py
│   ├── neural_doodle.py
│   ├── neural_style_transfer.py
│   ├── pretrained_word_embeddings.py
│   ├── reuters_mlp.py
│   ├── stateful_lstm.py
│   ├── variational_autoencoder.py
│   └── variational_autoencoder_deconv.py
├── MLyearning/
│   └── README.md
├── README.md
├── TensorFlow/
│   ├── LICENSE
│   ├── README.md
│   ├── examples/
│   │   ├── 1_Introduction/
│   │   │   ├── basic_operations.py
│   │   │   └── helloworld.py
│   │   ├── 2_BasicModels/
│   │   │   ├── linear_regression.py
│   │   │   ├── logistic_regression.py
│   │   │   └── nearest_neighbor.py
│   │   ├── 3_NeuralNetworks/
│   │   │   ├── autoencoder.py
│   │   │   ├── bidirectional_rnn.py
│   │   │   ├── convolutional_network.py
│   │   │   ├── dynamic_rnn.py
│   │   │   ├── multilayer_perceptron.py
│   │   │   └── recurrent_network.py
│   │   ├── 4_Utils/
│   │   │   ├── save_restore_model.py
│   │   │   ├── tensorboard_advanced.py
│   │   │   └── tensorboard_basic.py
│   │   └── 5_MultiGPU/
│   │       └── multigpu_basics.py
│   ├── input_data.py
│   ├── notebooks/
│   │   ├── 0_Prerequisite/
│   │   │   ├── ml_introduction.ipynb
│   │   │   └── mnist_dataset_intro.ipynb
│   │   ├── 1_Introduction/
│   │   │   ├── basic_operations.ipynb
│   │   │   └── helloworld.ipynb
│   │   ├── 2_BasicModels/
│   │   │   ├── linear_regression.ipynb
│   │   │   ├── logistic_regression.ipynb
│   │   │   └── nearest_neighbor.ipynb
│   │   ├── 3_NeuralNetworks/
│   │   │   ├── autoencoder.ipynb
│   │   │   ├── bidirectional_rnn.ipynb
│   │   │   ├── convolutional_network.ipynb
│   │   │   ├── multilayer_perceptron.ipynb
│   │   │   └── recurrent_network.ipynb
│   │   ├── 4_Utils/
│   │   │   ├── save_restore_model.ipynb
│   │   │   └── tensorboard_basic.ipynb
│   │   └── 5_MultiGPU/
│   │       └── multigpu_basics.ipynb
│   └── tensorflow_distributed_mnist_demo.py
└── Theano/
    ├── cnn.py
    ├── linear_regression.py
    ├── logistic_regression.p
    └── neural_networks.py

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FILE CONTENTS
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FILE: .gitignore
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.idea
*/.DS_Store


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FILE: Caffe/00-classification.ipynb
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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification: Instant Recognition with Caffe\n",
    "\n",
    "In this example we'll classify an image with the bundled CaffeNet model (which is based on the network architecture of Krizhevsky et al. for ImageNet).\n",
    "\n",
    "We'll compare CPU and GPU modes and then dig into the model to inspect features and the output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Setup\n",
    "\n",
    "* First, set up Python, `numpy`, and `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# set up Python environment: numpy for numerical routines, and matplotlib for plotting\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# display plots in this notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# set display defaults\n",
    "plt.rcParams['figure.figsize'] = (10, 10)        # large images\n",
    "plt.rcParams['image.interpolation'] = 'nearest'  # don't interpolate: show square pixels\n",
    "plt.rcParams['image.cmap'] = 'gray'  # use grayscale output rather than a (potentially misleading) color heatmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Load `caffe`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The caffe module needs to be on the Python path;\n",
    "#  we'll add it here explicitly.\n",
    "import sys\n",
    "caffe_root = '../'  # this file should be run from {caffe_root}/examples (otherwise change this line)\n",
    "sys.path.insert(0, caffe_root + 'python')\n",
    "\n",
    "import caffe\n",
    "# If you get \"No module named _caffe\", either you have not built pycaffe or you have the wrong path."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* If needed, download the reference model (\"CaffeNet\", a variant of AlexNet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CaffeNet found.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):\n",
    "    print 'CaffeNet found.'\n",
    "else:\n",
    "    print 'Downloading pre-trained CaffeNet model...'\n",
    "    !../scripts/download_model_binary.py ../models/bvlc_reference_caffenet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Load net and set up input preprocessing\n",
    "\n",
    "* Set Caffe to CPU mode and load the net from disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "caffe.set_mode_cpu()\n",
    "\n",
    "model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'\n",
    "model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'\n",
    "\n",
    "net = caffe.Net(model_def,      # defines the structure of the model\n",
    "                model_weights,  # contains the trained weights\n",
    "                caffe.TEST)     # use test mode (e.g., don't perform dropout)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Set up input preprocessing. (We'll use Caffe's `caffe.io.Transformer` to do this, but this step is independent of other parts of Caffe, so any custom preprocessing code may be used).\n",
    "\n",
    "    Our default CaffeNet is configured to take images in BGR format. Values are expected to start in the range [0, 255] and then have the mean ImageNet pixel value subtracted from them. In addition, the channel dimension is expected as the first (_outermost_) dimension.\n",
    "    \n",
    "    As matplotlib will load images with values in the range [0, 1] in RGB format with the channel as the _innermost_ dimension, we are arranging for the needed transformations here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean-subtracted values: [('B', 104.0069879317889), ('G', 116.66876761696767), ('R', 122.6789143406786)]\n"
     ]
    }
   ],
   "source": [
    "# load the mean ImageNet image (as distributed with Caffe) for subtraction\n",
    "mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')\n",
    "mu = mu.mean(1).mean(1)  # average over pixels to obtain the mean (BGR) pixel values\n",
    "print 'mean-subtracted values:', zip('BGR', mu)\n",
    "\n",
    "# create transformer for the input called 'data'\n",
    "transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})\n",
    "\n",
    "transformer.set_transpose('data', (2,0,1))  # move image channels to outermost dimension\n",
    "transformer.set_mean('data', mu)            # subtract the dataset-mean value in each channel\n",
    "transformer.set_raw_scale('data', 255)      # rescale from [0, 1] to [0, 255]\n",
    "transformer.set_channel_swap('data', (2,1,0))  # swap channels from RGB to BGR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. CPU classification\n",
    "\n",
    "* Now we're ready to perform classification. Even though we'll only classify one image, we'll set a batch size of 50 to demonstrate batching."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# set the size of the input (we can skip this if we're happy\n",
    "#  with the default; we can also change it later, e.g., for different batch sizes)\n",
    "net.blobs['data'].reshape(50,        # batch size\n",
    "                          3,         # 3-channel (BGR) images\n",
    "                          227, 227)  # image size is 227x227"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Load an image (that comes with Caffe) and perform the preprocessing we've set up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f09693a8c90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vAqVQbxvPBEJwHEd8Z1W22xNju7F/2Pn4crC/TAKKBefM4juKyeIIujtFkh+lMaFlclzN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w6H4LmXKSzzup33Bz5+8wlhecbNmlhypNnYsffcQww19/4aGX0xC9tmSOwIiMA6PXHcbHOgTy5Z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tOZ2GOkEfd/eMIXJ5/VRdu52rHkQSIIUR61t9XmNahA8ixFnJxmQfsrLSb3DM2bPJk4068z08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oxBRRmbs00dNMLX3vo6437HNKpj8/n9Gp9DwfQbQjhi24YwjEt763B9y7t/8SOeP7vGyj0C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Download .txt
gitextract_94todzum/

├── .gitignore
├── Caffe/
│   ├── 00-classification.ipynb
│   ├── 01-learning-lenet.ipynb
│   ├── 02-fine-tuning.ipynb
│   ├── CMakeLists.txt
│   ├── brewing-logreg.ipynb
│   ├── cifar10/
│   │   ├── cifar10_full.prototxt
│   │   ├── cifar10_full_sigmoid_solver.prototxt
│   │   ├── cifar10_full_sigmoid_solver_bn.prototxt
│   │   ├── cifar10_full_sigmoid_train_test.prototxt
│   │   ├── cifar10_full_sigmoid_train_test_bn.prototxt
│   │   ├── cifar10_full_solver.prototxt
│   │   ├── cifar10_full_solver_lr1.prototxt
│   │   ├── cifar10_full_solver_lr2.prototxt
│   │   ├── cifar10_full_train_test.prototxt
│   │   ├── cifar10_quick.prototxt
│   │   ├── cifar10_quick_solver.prototxt
│   │   ├── cifar10_quick_solver_lr1.prototxt
│   │   ├── cifar10_quick_train_test.prototxt
│   │   ├── convert_cifar_data.cpp
│   │   ├── create_cifar10.sh
│   │   ├── readme.md
│   │   ├── train_full.sh
│   │   ├── train_full_sigmoid.sh
│   │   ├── train_full_sigmoid_bn.sh
│   │   └── train_quick.sh
│   ├── cpp_classification/
│   │   ├── classification.cpp
│   │   └── readme.md
│   ├── detection.ipynb
│   ├── feature_extraction/
│   │   ├── imagenet_val.prototxt
│   │   └── readme.md
│   ├── finetune_flickr_style/
│   │   ├── assemble_data.py
│   │   ├── readme.md
│   │   └── style_names.txt
│   ├── finetune_pascal_detection/
│   │   ├── pascal_finetune_solver.prototxt
│   │   └── pascal_finetune_trainval_test.prototxt
│   ├── hdf5_classification/
│   │   ├── nonlinear_auto_test.prototxt
│   │   ├── nonlinear_auto_train.prototxt
│   │   ├── nonlinear_train_val.prototxt
│   │   └── train_val.prototxt
│   ├── imagenet/
│   │   ├── create_imagenet.sh
│   │   ├── make_imagenet_mean.sh
│   │   ├── readme.md
│   │   ├── resume_training.sh
│   │   └── train_caffenet.sh
│   ├── mnist/
│   │   ├── convert_mnist_data.cpp
│   │   ├── create_mnist.sh
│   │   ├── lenet.prototxt
│   │   ├── lenet_adadelta_solver.prototxt
│   │   ├── lenet_auto_solver.prototxt
│   │   ├── lenet_consolidated_solver.prototxt
│   │   ├── lenet_multistep_solver.prototxt
│   │   ├── lenet_solver.prototxt
│   │   ├── lenet_solver_adam.prototxt
│   │   ├── lenet_solver_rmsprop.prototxt
│   │   ├── lenet_train_test.prototxt
│   │   ├── mnist_autoencoder.prototxt
│   │   ├── mnist_autoencoder_solver.prototxt
│   │   ├── mnist_autoencoder_solver_adadelta.prototxt
│   │   ├── mnist_autoencoder_solver_adagrad.prototxt
│   │   ├── mnist_autoencoder_solver_nesterov.prototxt
│   │   ├── readme.md
│   │   ├── train_lenet.sh
│   │   ├── train_lenet_adam.sh
│   │   ├── train_lenet_consolidated.sh
│   │   ├── train_lenet_docker.sh
│   │   ├── train_lenet_rmsprop.sh
│   │   ├── train_mnist_autoencoder.sh
│   │   ├── train_mnist_autoencoder_adadelta.sh
│   │   ├── train_mnist_autoencoder_adagrad.sh
│   │   └── train_mnist_autoencoder_nesterov.sh
│   ├── net_surgery/
│   │   ├── bvlc_caffenet_full_conv.prototxt
│   │   └── conv.prototxt
│   ├── net_surgery.ipynb
│   ├── pascal-multilabel-with-datalayer.ipynb
│   ├── pycaffe/
│   │   ├── caffenet.py
│   │   ├── layers/
│   │   │   ├── pascal_multilabel_datalayers.py
│   │   │   └── pyloss.py
│   │   ├── linreg.prototxt
│   │   └── tools.py
│   ├── siamese/
│   │   ├── convert_mnist_siamese_data.cpp
│   │   ├── create_mnist_siamese.sh
│   │   ├── mnist_siamese.ipynb
│   │   ├── mnist_siamese.prototxt
│   │   ├── mnist_siamese_solver.prototxt
│   │   ├── mnist_siamese_train_test.prototxt
│   │   ├── readme.md
│   │   └── train_mnist_siamese.sh
│   └── web_demo/
│       ├── app.py
│       ├── exifutil.py
│       ├── readme.md
│       ├── requirements.txt
│       └── templates/
│           └── index.html
├── DLbook/
│   └── DeepLearningPapers.md
├── Keras/
│   ├── README.md
│   ├── addition_rnn.py
│   ├── antirectifier.py
│   ├── babi_memnn.py
│   ├── babi_rnn.py
│   ├── cifar10_cnn.py
│   ├── conv_filter_visualization.py
│   ├── conv_lstm.py
│   ├── deep_dream.py
│   ├── image_ocr.py
│   ├── imdb_bidirectional_lstm.py
│   ├── imdb_cnn.py
│   ├── imdb_cnn_lstm.py
│   ├── imdb_fasttext.py
│   ├── imdb_lstm.py
│   ├── lstm_benchmark.py
│   ├── lstm_text_generation.py
│   ├── mnist_acgan.py
│   ├── mnist_cnn.py
│   ├── mnist_hierarchical_rnn.py
│   ├── mnist_irnn.py
│   ├── mnist_mlp.py
│   ├── mnist_net2net.py
│   ├── mnist_siamese_graph.py
│   ├── mnist_sklearn_wrapper.py
│   ├── mnist_swwae.py
│   ├── mnist_transfer_cnn.py
│   ├── neural_doodle.py
│   ├── neural_style_transfer.py
│   ├── pretrained_word_embeddings.py
│   ├── reuters_mlp.py
│   ├── stateful_lstm.py
│   ├── variational_autoencoder.py
│   └── variational_autoencoder_deconv.py
├── MLyearning/
│   └── README.md
├── README.md
├── TensorFlow/
│   ├── LICENSE
│   ├── README.md
│   ├── examples/
│   │   ├── 1_Introduction/
│   │   │   ├── basic_operations.py
│   │   │   └── helloworld.py
│   │   ├── 2_BasicModels/
│   │   │   ├── linear_regression.py
│   │   │   ├── logistic_regression.py
│   │   │   └── nearest_neighbor.py
│   │   ├── 3_NeuralNetworks/
│   │   │   ├── autoencoder.py
│   │   │   ├── bidirectional_rnn.py
│   │   │   ├── convolutional_network.py
│   │   │   ├── dynamic_rnn.py
│   │   │   ├── multilayer_perceptron.py
│   │   │   └── recurrent_network.py
│   │   ├── 4_Utils/
│   │   │   ├── save_restore_model.py
│   │   │   ├── tensorboard_advanced.py
│   │   │   └── tensorboard_basic.py
│   │   └── 5_MultiGPU/
│   │       └── multigpu_basics.py
│   ├── input_data.py
│   ├── notebooks/
│   │   ├── 0_Prerequisite/
│   │   │   ├── ml_introduction.ipynb
│   │   │   └── mnist_dataset_intro.ipynb
│   │   ├── 1_Introduction/
│   │   │   ├── basic_operations.ipynb
│   │   │   └── helloworld.ipynb
│   │   ├── 2_BasicModels/
│   │   │   ├── linear_regression.ipynb
│   │   │   ├── logistic_regression.ipynb
│   │   │   └── nearest_neighbor.ipynb
│   │   ├── 3_NeuralNetworks/
│   │   │   ├── autoencoder.ipynb
│   │   │   ├── bidirectional_rnn.ipynb
│   │   │   ├── convolutional_network.ipynb
│   │   │   ├── multilayer_perceptron.ipynb
│   │   │   └── recurrent_network.ipynb
│   │   ├── 4_Utils/
│   │   │   ├── save_restore_model.ipynb
│   │   │   └── tensorboard_basic.ipynb
│   │   └── 5_MultiGPU/
│   │       └── multigpu_basics.ipynb
│   └── tensorflow_distributed_mnist_demo.py
└── Theano/
    ├── cnn.py
    ├── linear_regression.py
    ├── logistic_regression.p
    └── neural_networks.py
Download .txt
SYMBOL INDEX (209 symbols across 46 files)

FILE: Caffe/cifar10/convert_cifar_data.cpp
  function read_image (line 31) | void read_image(std::ifstream* file, int* label, char* buffer) {
  function convert_dataset (line 39) | void convert_dataset(const string& input_folder, const string& output_fo...
  function main (line 93) | int main(int argc, char** argv) {

FILE: Caffe/cpp_classification/classification.cpp
  class Classifier (line 21) | class Classifier {
  function PairCompare (line 86) | static bool PairCompare(const std::pair<float, int>& lhs,
  function Argmax (line 92) | static std::vector<int> Argmax(const std::vector<float>& v, int N) {
  function main (line 229) | int main(int argc, char** argv) {
  function main (line 262) | int main(int argc, char** argv) {

FILE: Caffe/finetune_flickr_style/assemble_data.py
  function download_image (line 23) | def download_image(args_tuple):

FILE: Caffe/mnist/convert_mnist_data.cpp
  function swap_endian (line 38) | uint32_t swap_endian(uint32_t val) {
  function convert_dataset (line 43) | void convert_dataset(const char* image_filename, const char* label_filen...
  function main (line 113) | int main(int argc, char** argv) {
  function main (line 143) | int main(int argc, char** argv) {

FILE: Caffe/pycaffe/caffenet.py
  function conv_relu (line 7) | def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
  function fc_relu (line 12) | def fc_relu(bottom, nout):
  function max_pool (line 16) | def max_pool(bottom, ks, stride=1):
  function caffenet (line 19) | def caffenet(lmdb, batch_size=256, include_acc=False):
  function make_net (line 47) | def make_net():

FILE: Caffe/pycaffe/layers/pascal_multilabel_datalayers.py
  class PascalMultilabelDataLayerSync (line 20) | class PascalMultilabelDataLayerSync(caffe.Layer):
    method setup (line 27) | def setup(self, bottom, top):
    method forward (line 55) | def forward(self, bottom, top):
    method reshape (line 67) | def reshape(self, bottom, top):
    method backward (line 74) | def backward(self, top, propagate_down, bottom):
  class BatchLoader (line 81) | class BatchLoader(object):
    method __init__ (line 90) | def __init__(self, params, result):
    method load_next_image (line 106) | def load_next_image(self):
  function load_pascal_annotation (line 140) | def load_pascal_annotation(index, pascal_root):
  function check_params (line 196) | def check_params(params):
  function print_info (line 208) | def print_info(name, params):

FILE: Caffe/pycaffe/layers/pyloss.py
  class EuclideanLossLayer (line 5) | class EuclideanLossLayer(caffe.Layer):
    method setup (line 11) | def setup(self, bottom, top):
    method reshape (line 16) | def reshape(self, bottom, top):
    method forward (line 25) | def forward(self, bottom, top):
    method backward (line 29) | def backward(self, top, propagate_down, bottom):

FILE: Caffe/pycaffe/tools.py
  class SimpleTransformer (line 4) | class SimpleTransformer:
    method __init__ (line 11) | def __init__(self, mean=[128, 128, 128]):
    method set_mean (line 15) | def set_mean(self, mean):
    method set_scale (line 21) | def set_scale(self, scale):
    method preprocess (line 27) | def preprocess(self, im):
    method deprocess (line 41) | def deprocess(self, im):
  class CaffeSolver (line 53) | class CaffeSolver:
    method __init__ (line 62) | def __init__(self, testnet_prototxt_path="testnet.prototxt",
    method add_from_file (line 101) | def add_from_file(self, filepath):
    method write (line 113) | def write(self, filepath):

FILE: Caffe/siamese/convert_mnist_siamese_data.cpp
  function swap_endian (line 22) | uint32_t swap_endian(uint32_t val) {
  function read_image (line 27) | void read_image(std::ifstream* image_file, std::ifstream* label_file,
  function convert_dataset (line 36) | void convert_dataset(const char* image_filename, const char* label_filen...
  function main (line 109) | int main(int argc, char** argv) {
  function main (line 126) | int main(int argc, char** argv) {

FILE: Caffe/web_demo/app.py
  function index (line 29) | def index():
  function classify_url (line 34) | def classify_url():
  function classify_upload (line 57) | def classify_upload():
  function embed_image_html (line 82) | def embed_image_html(image):
  function allowed_file (line 92) | def allowed_file(filename):
  class ImagenetClassifier (line 99) | class ImagenetClassifier(object):
    method __init__ (line 119) | def __init__(self, model_def_file, pretrained_model_file, mean_file,
    method classify_image (line 148) | def classify_image(self, image):
  function start_tornado (line 184) | def start_tornado(app, port=5000):
  function start_from_terminal (line 192) | def start_from_terminal(app):

FILE: Caffe/web_demo/exifutil.py
  function open_oriented_im (line 19) | def open_oriented_im(im_path):
  function apply_orientation (line 35) | def apply_orientation(im, orientation):

FILE: Keras/addition_rnn.py
  class CharacterTable (line 35) | class CharacterTable(object):
    method __init__ (line 41) | def __init__(self, chars):
    method encode (line 51) | def encode(self, C, num_rows):
    method decode (line 63) | def decode(self, x, calc_argmax=True):
  class colors (line 69) | class colors:

FILE: Keras/antirectifier.py
  class Antirectifier (line 21) | class Antirectifier(layers.Layer):
    method compute_output_shape (line 49) | def compute_output_shape(self, input_shape):
    method call (line 55) | def call(self, inputs):

FILE: Keras/babi_memnn.py
  function tokenize (line 29) | def tokenize(sent):
  function parse_stories (line 38) | def parse_stories(lines, only_supporting=False):
  function get_stories (line 71) | def get_stories(f, only_supporting=False, max_length=None):
  function vectorize_stories (line 85) | def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):

FILE: Keras/babi_rnn.py
  function tokenize (line 74) | def tokenize(sent):
  function parse_stories (line 83) | def parse_stories(lines, only_supporting=False):
  function get_stories (line 116) | def get_stories(f, only_supporting=False, max_length=None):
  function vectorize_stories (line 129) | def vectorize_stories(data, word_idx, story_maxlen, query_maxlen):

FILE: Keras/conv_filter_visualization.py
  function deprocess_image (line 26) | def deprocess_image(x):
  function normalize (line 56) | def normalize(x):

FILE: Keras/conv_lstm.py
  function generate_movies (line 47) | def generate_movies(n_samples=1200, n_frames=15):

FILE: Keras/deep_dream.py
  function preprocess_image (line 62) | def preprocess_image(image_path):
  function deprocess_image (line 72) | def deprocess_image(x):
  function continuity_loss (line 105) | def continuity_loss(x):
  function eval_loss_and_grads (line 153) | def eval_loss_and_grads(x):
  class Evaluator (line 164) | class Evaluator(object):
    method __init__ (line 175) | def __init__(self):
    method loss (line 179) | def loss(self, x):
    method grads (line 186) | def grads(self, x):

FILE: Keras/image_ocr.py
  function speckle (line 65) | def speckle(img):
  function paint_text (line 78) | def paint_text(text, w, h, rotate=False, ud=False, multi_fonts=False):
  function shuffle_mats_or_lists (line 122) | def shuffle_mats_or_lists(matrix_list, stop_ind=None):
  function text_to_labels (line 144) | def text_to_labels(text, num_classes):
  function is_valid_str (line 157) | def is_valid_str(in_str):
  class TextImageGenerator (line 166) | class TextImageGenerator(keras.callbacks.Callback):
    method __init__ (line 168) | def __init__(self, monogram_file, bigram_file, minibatch_size,
    method get_output_size (line 182) | def get_output_size(self):
    method build_word_list (line 187) | def build_word_list(self, num_words, max_string_len=None, mono_fractio...
    method get_batch (line 236) | def get_batch(self, index, size, train):
    method next_train (line 278) | def next_train(self):
    method next_val (line 288) | def next_val(self):
    method on_train_begin (line 296) | def on_train_begin(self, logs={}):
    method on_epoch_begin (line 301) | def on_epoch_begin(self, epoch, logs={}):
  function ctc_lambda_func (line 319) | def ctc_lambda_func(args):
  function decode_batch (line 330) | def decode_batch(test_func, word_batch):
  class VizCallback (line 347) | class VizCallback(keras.callbacks.Callback):
    method __init__ (line 349) | def __init__(self, run_name, test_func, text_img_gen, num_display_word...
    method show_edit_distance (line 358) | def show_edit_distance(self, num):
    method on_epoch_end (line 376) | def on_epoch_end(self, epoch, logs={}):
  function train (line 399) | def train(run_name, start_epoch, stop_epoch, img_w):

FILE: Keras/imdb_fasttext.py
  function create_ngram_set (line 24) | def create_ngram_set(input_list, ngram_value=2):
  function add_ngram (line 37) | def add_ngram(sequences, token_indice, ngram_range=2):

FILE: Keras/lstm_text_generation.py
  function sample (line 62) | def sample(preds, temperature=1.0):

FILE: Keras/mnist_acgan.py
  function build_generator (line 50) | def build_generator(latent_size):
  function build_discriminator (line 94) | def build_discriminator():

FILE: Keras/mnist_net2net.py
  function preprocess_input (line 74) | def preprocess_input(x):
  function preprocess_output (line 78) | def preprocess_output(y):
  function wider2net_conv2d (line 91) | def wider2net_conv2d(teacher_w1, teacher_b1, teacher_w2, new_width, init):
  function wider2net_fc (line 142) | def wider2net_fc(teacher_w1, teacher_b1, teacher_w2, new_width, init):
  function deeper2net_conv2d (line 192) | def deeper2net_conv2d(teacher_w):
  function copy_weights (line 207) | def copy_weights(teacher_model, student_model, layer_names):
  function make_teacher_model (line 217) | def make_teacher_model(train_data, validation_data, epochs=3):
  function make_wider_student_model (line 240) | def make_wider_student_model(teacher_model, train_data,
  function make_deeper_student_model (line 290) | def make_deeper_student_model(teacher_model, train_data,
  function net2wider_experiment (line 340) | def net2wider_experiment():
  function net2deeper_experiment (line 364) | def net2deeper_experiment():

FILE: Keras/mnist_siamese_graph.py
  function euclidean_distance (line 25) | def euclidean_distance(vects):
  function eucl_dist_output_shape (line 30) | def eucl_dist_output_shape(shapes):
  function contrastive_loss (line 35) | def contrastive_loss(y_true, y_pred):
  function create_pairs (line 44) | def create_pairs(x, digit_indices):
  function create_base_network (line 63) | def create_base_network(input_dim):
  function compute_accuracy (line 75) | def compute_accuracy(predictions, labels):

FILE: Keras/mnist_sklearn_wrapper.py
  function make_model (line 45) | def make_model(dense_layer_sizes, filters, kernel_size, pool_size):

FILE: Keras/mnist_swwae.py
  function convresblock (line 60) | def convresblock(x, nfeats=8, ksize=3, nskipped=2, elu=True):
  function getwhere (line 96) | def getwhere(x):

FILE: Keras/mnist_transfer_cnn.py
  function train_model (line 45) | def train_model(model, train, test, num_classes):

FILE: Keras/neural_doodle.py
  function preprocess_image (line 97) | def preprocess_image(image_path):
  function deprocess_image (line 105) | def deprocess_image(x):
  function kmeans (line 121) | def kmeans(xs, k):
  function load_mask_labels (line 132) | def load_mask_labels():
  function gram_matrix (line 218) | def gram_matrix(x):
  function region_style_loss (line 225) | def region_style_loss(style_image, target_image, style_mask, target_mask):
  function style_loss (line 246) | def style_loss(style_image, target_image, style_masks, target_masks):
  function content_loss (line 265) | def content_loss(content_image, target_image):
  function total_variation_loss (line 269) | def total_variation_loss(x):
  function eval_loss_and_grads (line 312) | def eval_loss_and_grads(x):
  class Evaluator (line 326) | class Evaluator(object):
    method __init__ (line 328) | def __init__(self):
    method loss (line 332) | def loss(self, x):
    method grads (line 339) | def grads(self, x):

FILE: Keras/neural_style_transfer.py
  function preprocess_image (line 98) | def preprocess_image(image_path):
  function deprocess_image (line 108) | def deprocess_image(x):
  function gram_matrix (line 153) | def gram_matrix(x):
  function style_loss (line 169) | def style_loss(style, combination):
  function content_loss (line 183) | def content_loss(base, combination):
  function total_variation_loss (line 190) | def total_variation_loss(x):
  function eval_loss_and_grads (line 231) | def eval_loss_and_grads(x):
  class Evaluator (line 252) | class Evaluator(object):
    method __init__ (line 254) | def __init__(self):
    method loss (line 258) | def loss(self, x):
    method grads (line 265) | def grads(self, x):

FILE: Keras/stateful_lstm.py
  function gen_cosine_amp (line 19) | def gen_cosine_amp(amp=100, period=1000, x0=0, xn=50000, step=1, k=0.0001):

FILE: Keras/variational_autoencoder.py
  function sampling (line 28) | def sampling(args):
  function vae_loss (line 44) | def vae_loss(x, x_decoded_mean):

FILE: Keras/variational_autoencoder_deconv.py
  function sampling (line 57) | def sampling(args):
  function vae_loss (line 109) | def vae_loss(x, x_decoded_mean):

FILE: TensorFlow/examples/3_NeuralNetworks/autoencoder.py
  function encoder (line 52) | def encoder(x):
  function decoder (line 63) | def decoder(x):

FILE: TensorFlow/examples/3_NeuralNetworks/bidirectional_rnn.py
  function BiRNN (line 52) | def BiRNN(x, weights, biases):

FILE: TensorFlow/examples/3_NeuralNetworks/convolutional_network.py
  function conv2d (line 36) | def conv2d(x, W, b, strides=1):
  function maxpool2d (line 43) | def maxpool2d(x, k=2):
  function conv_net (line 50) | def conv_net(x, weights, biases, dropout):

FILE: TensorFlow/examples/3_NeuralNetworks/dynamic_rnn.py
  class ToySequenceData (line 21) | class ToySequenceData(object):
    method __init__ (line 33) | def __init__(self, n_samples=1000, max_seq_len=20, min_seq_len=3,
    method next (line 63) | def next(self, batch_size):
  function dynamicRNN (line 111) | def dynamicRNN(x, seqlen, weights, biases):

FILE: TensorFlow/examples/3_NeuralNetworks/multilayer_perceptron.py
  function multilayer_perceptron (line 36) | def multilayer_perceptron(x, weights, biases):

FILE: TensorFlow/examples/3_NeuralNetworks/recurrent_network.py
  function RNN (line 50) | def RNN(x, weights, biases):

FILE: TensorFlow/examples/4_Utils/save_restore_model.py
  function multilayer_perceptron (line 36) | def multilayer_perceptron(x, weights, biases):

FILE: TensorFlow/examples/4_Utils/tensorboard_advanced.py
  function multilayer_perceptron (line 39) | def multilayer_perceptron(x, weights, biases):

FILE: TensorFlow/examples/5_MultiGPU/multigpu_basics.py
  function matpow (line 42) | def matpow(M, n):

FILE: TensorFlow/input_data.py
  function maybe_download (line 8) | def maybe_download(filename, work_directory):
  function _read32 (line 18) | def _read32(bytestream):
  function extract_images (line 21) | def extract_images(filename):
  function dense_to_one_hot (line 37) | def dense_to_one_hot(labels_dense, num_classes=10):
  function extract_labels (line 44) | def extract_labels(filename, one_hot=False):
  class DataSet (line 59) | class DataSet(object):
    method __init__ (line 60) | def __init__(self, images, labels, fake_data=False):
    method images (line 81) | def images(self):
    method labels (line 84) | def labels(self):
    method num_examples (line 87) | def num_examples(self):
    method epochs_completed (line 90) | def epochs_completed(self):
    method next_batch (line 92) | def next_batch(self, batch_size, fake_data=False):
  function read_data_sets (line 115) | def read_data_sets(train_dir, fake_data=False, one_hot=False):

FILE: TensorFlow/tensorflow_distributed_mnist_demo.py
  function main (line 35) | def main(_):

FILE: Theano/cnn.py
  function floatX (line 11) | def floatX(X):
  function init_weights (line 14) | def init_weights(shape):
  function rectify (line 17) | def rectify(X):
  function softmax (line 20) | def softmax(X):
  function dropout (line 24) | def dropout(X, p=0.):
  function RMSprop (line 31) | def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):
  function model (line 43) | def model(X, w, w2, w3, w4, p_drop_conv, p_drop_hidden):

FILE: Theano/linear_regression.py
  function model (line 11) | def model(X, w):

FILE: Theano/neural_networks.py
  function floatX (line 9) | def floatX(X):
  function init_weights (line 12) | def init_weights(shape):
  function rectify (line 15) | def rectify(X):
  function softmax (line 18) | def softmax(X):
  function RMSprop (line 22) | def RMSprop(cost, params, lr=0.001, rho=0.9, epsilon=1e-6):
  function dropout (line 34) | def dropout(X, p=0.):
  function model (line 41) | def model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):
Condensed preview — 168 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (6,987K chars).
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  {
    "path": ".gitignore",
    "chars": 18,
    "preview": ".idea\n*/.DS_Store\n"
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    "path": "Caffe/00-classification.ipynb",
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    "path": "Caffe/01-learning-lenet.ipynb",
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    "path": "Caffe/CMakeLists.txt",
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    "path": "Caffe/brewing-logreg.ipynb",
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    "path": "Caffe/cifar10/cifar10_full.prototxt",
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    "preview": "name: \"CIFAR10_full_deploy\"\n# N.B. input image must be in CIFAR-10 format\n# as described at http://www.cs.toronto.edu/~k"
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    "path": "Caffe/cifar10/cifar10_full_sigmoid_solver.prototxt",
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    "path": "Caffe/cifar10/cifar10_full_sigmoid_train_test.prototxt",
    "chars": 2879,
    "preview": "name: \"CIFAR10_full\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  "
  },
  {
    "path": "Caffe/cifar10/cifar10_full_sigmoid_train_test_bn.prototxt",
    "chars": 3192,
    "preview": "name: \"CIFAR10_full\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  "
  },
  {
    "path": "Caffe/cifar10/cifar10_full_solver.prototxt",
    "chars": 944,
    "preview": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs ("
  },
  {
    "path": "Caffe/cifar10/cifar10_full_solver_lr1.prototxt",
    "chars": 944,
    "preview": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs ("
  },
  {
    "path": "Caffe/cifar10/cifar10_full_solver_lr2.prototxt",
    "chars": 945,
    "preview": "# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10\n# then another factor of 10 after 10 more epochs ("
  },
  {
    "path": "Caffe/cifar10/cifar10_full_train_test.prototxt",
    "chars": 3122,
    "preview": "name: \"CIFAR10_full\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  "
  },
  {
    "path": "Caffe/cifar10/cifar10_quick.prototxt",
    "chars": 1921,
    "preview": "name: \"CIFAR10_quick_test\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param { shape: { dim: 1 dim: 3 d"
  },
  {
    "path": "Caffe/cifar10/cifar10_quick_solver.prototxt",
    "chars": 881,
    "preview": "# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10\n\n# The train/test net protocol buffer definitio"
  },
  {
    "path": "Caffe/cifar10/cifar10_quick_solver_lr1.prototxt",
    "chars": 882,
    "preview": "# reduce the learning rate after 8 epochs (4000 iters) by a factor of 10\n\n# The train/test net protocol buffer definitio"
  },
  {
    "path": "Caffe/cifar10/cifar10_quick_train_test.prototxt",
    "chars": 3088,
    "preview": "name: \"CIFAR10_quick\"\nlayer {\n  name: \"cifar\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n "
  },
  {
    "path": "Caffe/cifar10/convert_cifar_data.cpp",
    "chars": 3677,
    "preview": "//\n// This script converts the CIFAR dataset to the leveldb format used\n// by caffe to perform classification.\n// Usage:"
  },
  {
    "path": "Caffe/cifar10/create_cifar10.sh",
    "chars": 467,
    "preview": "#!/usr/bin/env sh\n# This script converts the cifar data into leveldb format.\nset -e\n\nEXAMPLE=examples/cifar10\nDATA=data/"
  },
  {
    "path": "Caffe/cifar10/readme.md",
    "chars": 5243,
    "preview": "---\ntitle: CIFAR-10 tutorial\ncategory: example\ndescription: Train and test Caffe on CIFAR-10 data.\ninclude_in_docs: true"
  },
  {
    "path": "Caffe/cifar10/train_full.sh",
    "chars": 530,
    "preview": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_solver.pr"
  },
  {
    "path": "Caffe/cifar10/train_full_sigmoid.sh",
    "chars": 139,
    "preview": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_sigmoid_s"
  },
  {
    "path": "Caffe/cifar10/train_full_sigmoid_bn.sh",
    "chars": 142,
    "preview": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n    --solver=examples/cifar10/cifar10_full_sigmoid_s"
  },
  {
    "path": "Caffe/cifar10/train_quick.sh",
    "chars": 341,
    "preview": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train \\\n  --solver=examples/cifar10/cifar10_quick_solver.pro"
  },
  {
    "path": "Caffe/cpp_classification/classification.cpp",
    "chars": 8661,
    "preview": "#include <caffe/caffe.hpp>\n#ifdef USE_OPENCV\n#include <opencv2/core/core.hpp>\n#include <opencv2/highgui/highgui.hpp>\n#in"
  },
  {
    "path": "Caffe/cpp_classification/readme.md",
    "chars": 2837,
    "preview": "---\ntitle: CaffeNet C++ Classification example\ndescription: A simple example performing image classification using the l"
  },
  {
    "path": "Caffe/detection.ipynb",
    "chars": 702457,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"[R-CNN](https://github.com/rbgirshi"
  },
  {
    "path": "Caffe/feature_extraction/imagenet_val.prototxt",
    "chars": 3279,
    "preview": "name: \"CaffeNet\"\nlayer {\n  name: \"data\"\n  type: \"ImageData\"\n  top: \"data\"\n  top: \"label\"\n  transform_param {\n    mirror:"
  },
  {
    "path": "Caffe/feature_extraction/readme.md",
    "chars": 3087,
    "preview": "---\ntitle: Feature extraction with Caffe C++ code.\ndescription: Extract CaffeNet / AlexNet features using the Caffe util"
  },
  {
    "path": "Caffe/finetune_flickr_style/assemble_data.py",
    "chars": 3636,
    "preview": "#!/usr/bin/env python\n\"\"\"\nForm a subset of the Flickr Style data, download images to dirname, and write\nCaffe ImagesData"
  },
  {
    "path": "Caffe/finetune_flickr_style/readme.md",
    "chars": 10483,
    "preview": "---\ntitle: Fine-tuning for style recognition\ndescription: Fine-tune the ImageNet-trained CaffeNet on the \"Flickr Style\" "
  },
  {
    "path": "Caffe/finetune_flickr_style/style_names.txt",
    "chars": 173,
    "preview": "Detailed\nPastel\nMelancholy\nNoir\nHDR\nVintage\nLong Exposure\nHorror\nSunny\nBright\nHazy\nBokeh\nSerene\nTexture\nEthereal\nMacro\nD"
  },
  {
    "path": "Caffe/finetune_pascal_detection/pascal_finetune_solver.prototxt",
    "chars": 330,
    "preview": "net: \"examples/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt\"\ntest_iter: 100\ntest_interval: 1000\nbase"
  },
  {
    "path": "Caffe/finetune_pascal_detection/pascal_finetune_trainval_test.prototxt",
    "chars": 5614,
    "preview": "name: \"CaffeNet\"\nlayer {\n  name: \"data\"\n  type: \"WindowData\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n "
  },
  {
    "path": "Caffe/hdf5_classification/nonlinear_auto_test.prototxt",
    "chars": 782,
    "preview": "layer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  hdf5_data_param {\n    source: \"examples/hdf5_cl"
  },
  {
    "path": "Caffe/hdf5_classification/nonlinear_auto_train.prototxt",
    "chars": 783,
    "preview": "layer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  hdf5_data_param {\n    source: \"examples/hdf5_cl"
  },
  {
    "path": "Caffe/hdf5_classification/nonlinear_train_val.prototxt",
    "chars": 1395,
    "preview": "name: \"LogisticRegressionNet\"\nlayer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    pha"
  },
  {
    "path": "Caffe/hdf5_classification/train_val.prototxt",
    "chars": 999,
    "preview": "name: \"LogisticRegressionNet\"\nlayer {\n  name: \"data\"\n  type: \"HDF5Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    pha"
  },
  {
    "path": "Caffe/imagenet/create_imagenet.sh",
    "chars": 1503,
    "preview": "#!/usr/bin/env sh\n# Create the imagenet lmdb inputs\n# N.B. set the path to the imagenet train + val data dirs\nset -e\n\nEX"
  },
  {
    "path": "Caffe/imagenet/make_imagenet_mean.sh",
    "chars": 287,
    "preview": "#!/usr/bin/env sh\n# Compute the mean image from the imagenet training lmdb\n# N.B. this is available in data/ilsvrc12\n\nEX"
  },
  {
    "path": "Caffe/imagenet/readme.md",
    "chars": 7634,
    "preview": "---\ntitle: ImageNet tutorial\ndescription: Train and test \"CaffeNet\" on ImageNet data.\ncategory: example\ninclude_in_docs:"
  },
  {
    "path": "Caffe/imagenet/resume_training.sh",
    "chars": 207,
    "preview": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n    --solver=models/bvlc_reference_caffenet/solver.prototxt \\\n    "
  },
  {
    "path": "Caffe/imagenet/train_caffenet.sh",
    "chars": 117,
    "preview": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n    --solver=models/bvlc_reference_caffenet/solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/convert_mnist_data.cpp",
    "chars": 4520,
    "preview": "// This script converts the MNIST dataset to a lmdb (default) or\n// leveldb (--backend=leveldb) format used by caffe to "
  },
  {
    "path": "Caffe/mnist/create_mnist.sh",
    "chars": 634,
    "preview": "#!/usr/bin/env sh\n# This script converts the mnist data into lmdb/leveldb format,\n# depending on the value assigned to $"
  },
  {
    "path": "Caffe/mnist/lenet.prototxt",
    "chars": 1738,
    "preview": "name: \"LeNet\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param { shape: { dim: 64 dim: 1 dim: 28 dim: "
  },
  {
    "path": "Caffe/mnist/lenet_adadelta_solver.prototxt",
    "chars": 777,
    "preview": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies ho"
  },
  {
    "path": "Caffe/mnist/lenet_auto_solver.prototxt",
    "chars": 778,
    "preview": "# The train/test net protocol buffer definition\ntrain_net: \"mnist/lenet_auto_train.prototxt\"\ntest_net: \"mnist/lenet_auto"
  },
  {
    "path": "Caffe/mnist/lenet_consolidated_solver.prototxt",
    "chars": 6003,
    "preview": "# lenet_consolidated_solver.prototxt consolidates the lenet_solver, lenet_train,\n# and lenet_test prototxts into a singl"
  },
  {
    "path": "Caffe/mnist/lenet_multistep_solver.prototxt",
    "chars": 871,
    "preview": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies ho"
  },
  {
    "path": "Caffe/mnist/lenet_solver.prototxt",
    "chars": 790,
    "preview": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies ho"
  },
  {
    "path": "Caffe/mnist/lenet_solver_adam.prototxt",
    "chars": 886,
    "preview": "# The train/test net protocol buffer definition\n# this follows \"ADAM: A METHOD FOR STOCHASTIC OPTIMIZATION\"\nnet: \"exampl"
  },
  {
    "path": "Caffe/mnist/lenet_solver_rmsprop.prototxt",
    "chars": 830,
    "preview": "# The train/test net protocol buffer definition\nnet: \"examples/mnist/lenet_train_test.prototxt\"\n# test_iter specifies ho"
  },
  {
    "path": "Caffe/mnist/lenet_train_test.prototxt",
    "chars": 2282,
    "preview": "name: \"LeNet\"\nlayer {\n  name: \"mnist\"\n  type: \"Data\"\n  top: \"data\"\n  top: \"label\"\n  include {\n    phase: TRAIN\n  }\n  tra"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder.prototxt",
    "chars": 4814,
    "preview": "name: \"MNISTAutoencoder\"\nlayer {\n  name: \"data\"\n  type: \"Data\"\n  top: \"data\"\n  include {\n    phase: TRAIN\n  }\n  transfor"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver.prototxt",
    "chars": 433,
    "preview": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { sta"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver_adadelta.prototxt",
    "chars": 451,
    "preview": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { sta"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver_adagrad.prototxt",
    "chars": 423,
    "preview": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { sta"
  },
  {
    "path": "Caffe/mnist/mnist_autoencoder_solver_nesterov.prototxt",
    "chars": 466,
    "preview": "net: \"examples/mnist/mnist_autoencoder.prototxt\"\ntest_state: { stage: 'test-on-train' }\ntest_iter: 500\ntest_state: { sta"
  },
  {
    "path": "Caffe/mnist/readme.md",
    "chars": 11948,
    "preview": "---\ntitle: LeNet MNIST Tutorial\ndescription: Train and test \"LeNet\" on the MNIST handwritten digit data.\ncategory: examp"
  },
  {
    "path": "Caffe/mnist/train_lenet.sh",
    "chars": 101,
    "preview": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_lenet_adam.sh",
    "chars": 106,
    "preview": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train --solver=examples/mnist/lenet_solver_adam.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_lenet_consolidated.sh",
    "chars": 118,
    "preview": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/lenet_consolidated_solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_lenet_docker.sh",
    "chars": 4518,
    "preview": "#!/usr/bin/env sh\nset -e\n# The following example allows for the MNIST example (using LeNet) to be\n# trained using the ca"
  },
  {
    "path": "Caffe/mnist/train_lenet_rmsprop.sh",
    "chars": 115,
    "preview": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n    --solver=examples/mnist/lenet_solver_rmsprop.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder.sh",
    "chars": 117,
    "preview": "#!/usr/bin/env sh\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder_adadelta.sh",
    "chars": 120,
    "preview": "#!/bin/bash\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver_adadelta.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder_adagrad.sh",
    "chars": 119,
    "preview": "#!/bin/bash\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver_adagrad.prototxt $@\n"
  },
  {
    "path": "Caffe/mnist/train_mnist_autoencoder_nesterov.sh",
    "chars": 120,
    "preview": "#!/bin/bash\nset -e\n\n./build/tools/caffe train \\\n  --solver=examples/mnist/mnist_autoencoder_solver_nesterov.prototxt $@\n"
  },
  {
    "path": "Caffe/net_surgery/bvlc_caffenet_full_conv.prototxt",
    "chars": 3180,
    "preview": "# Fully convolutional network version of CaffeNet.\nname: \"CaffeNetConv\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"d"
  },
  {
    "path": "Caffe/net_surgery/conv.prototxt",
    "chars": 486,
    "preview": "# Simple single-layer network to showcase editing model parameters.\nname: \"convolution\"\nlayer {\n  name: \"data\"\n  type: \""
  },
  {
    "path": "Caffe/net_surgery.ipynb",
    "chars": 583251,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Net Surgery\\n\",\n    \"\\n\",\n    \"Ca"
  },
  {
    "path": "Caffe/pascal-multilabel-with-datalayer.ipynb",
    "chars": 1539559,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Multilabel classification on PASC"
  },
  {
    "path": "Caffe/pycaffe/caffenet.py",
    "chars": 2112,
    "preview": "from __future__ import print_function\nfrom caffe import layers as L, params as P, to_proto\nfrom caffe.proto import caffe"
  },
  {
    "path": "Caffe/pycaffe/layers/pascal_multilabel_datalayers.py",
    "chars": 6846,
    "preview": "# imports\nimport json\nimport time\nimport pickle\nimport scipy.misc\nimport skimage.io\nimport caffe\n\nimport numpy as np\nimp"
  },
  {
    "path": "Caffe/pycaffe/layers/pyloss.py",
    "chars": 1223,
    "preview": "import caffe\nimport numpy as np\n\n\nclass EuclideanLossLayer(caffe.Layer):\n    \"\"\"\n    Compute the Euclidean Loss in the s"
  },
  {
    "path": "Caffe/pycaffe/linreg.prototxt",
    "chars": 1302,
    "preview": "name: 'LinearRegressionExample'\n# define a simple network for linear regression on dummy data\n# that computes the loss b"
  },
  {
    "path": "Caffe/pycaffe/tools.py",
    "chars": 3457,
    "preview": "import numpy as np\n\n\nclass SimpleTransformer:\n\n    \"\"\"\n    SimpleTransformer is a simple class for preprocessing and dep"
  },
  {
    "path": "Caffe/siamese/convert_mnist_siamese_data.cpp",
    "chars": 4368,
    "preview": "//\n// This script converts the MNIST dataset to the leveldb format used\n// by caffe to train siamese network.\n// Usage:\n"
  },
  {
    "path": "Caffe/siamese/create_mnist_siamese.sh",
    "chars": 617,
    "preview": "#!/usr/bin/env sh\n# This script converts the mnist data into leveldb format.\nset -e\n\nEXAMPLES=./build/examples/siamese\nD"
  },
  {
    "path": "Caffe/siamese/mnist_siamese.ipynb",
    "chars": 158921,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Setup\\n\",\n    \"\\n\",\n    \"Import"
  },
  {
    "path": "Caffe/siamese/mnist_siamese.prototxt",
    "chars": 1488,
    "preview": "name: \"mnist_siamese\"\nlayer {\n  name: \"data\"\n  type: \"Input\"\n  top: \"data\"\n  input_param {\n    shape: { dim: 10000 dim: "
  },
  {
    "path": "Caffe/siamese/mnist_siamese_solver.prototxt",
    "chars": 810,
    "preview": "# The train/test net protocol buffer definition\nnet: \"examples/siamese/mnist_siamese_train_test.prototxt\"\n# test_iter sp"
  },
  {
    "path": "Caffe/siamese/mnist_siamese_train_test.prototxt",
    "chars": 4907,
    "preview": "name: \"mnist_siamese_train_test\"\nlayer {\n  name: \"pair_data\"\n  type: \"Data\"\n  top: \"pair_data\"\n  top: \"sim\"\n  include {\n"
  },
  {
    "path": "Caffe/siamese/readme.md",
    "chars": 5949,
    "preview": "---\ntitle: Siamese Network Tutorial\ndescription: Train and test a siamese network on MNIST data.\ncategory: example\ninclu"
  },
  {
    "path": "Caffe/siamese/train_mnist_siamese.sh",
    "chars": 125,
    "preview": "#!/usr/bin/env sh\nset -e\n\nTOOLS=./build/tools\n\n$TOOLS/caffe train --solver=examples/siamese/mnist_siamese_solver.prototx"
  },
  {
    "path": "Caffe/web_demo/app.py",
    "chars": 7793,
    "preview": "import os\nimport time\nimport cPickle\nimport datetime\nimport logging\nimport flask\nimport werkzeug\nimport optparse\nimport "
  },
  {
    "path": "Caffe/web_demo/exifutil.py",
    "chars": 1046,
    "preview": "\"\"\"\nThis script handles the skimage exif problem.\n\"\"\"\n\nfrom PIL import Image\nimport numpy as np\n\nORIENTATIONS = {   # us"
  },
  {
    "path": "Caffe/web_demo/readme.md",
    "chars": 1876,
    "preview": "---\ntitle: Web demo\ndescription: Image classification demo running as a Flask web server.\ncategory: example\ninclude_in_d"
  },
  {
    "path": "Caffe/web_demo/requirements.txt",
    "chars": 50,
    "preview": "werkzeug\nflask\ntornado\nnumpy\npandas\npillow\npyyaml\n"
  },
  {
    "path": "Caffe/web_demo/templates/index.html",
    "chars": 4981,
    "preview": "<!DOCTYPE html>\n<html lang=\"en\">\n  <head>\n    <meta charset=\"utf-8\">\n    <meta name=\"viewport\" content=\"width=device-wid"
  },
  {
    "path": "DLbook/DeepLearningPapers.md",
    "chars": 14115,
    "preview": "\n### Survey Review\n- Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton [[pdf]](https://www.cs.toronto.edu/~hinton/"
  },
  {
    "path": "Keras/README.md",
    "chars": 4867,
    "preview": "# Keras 例子\naddition_rnn.py 用RNN拟合加法运算\n\nantirectifier.py 为Keras编写自定义图层\n\nbabi_memnn.py 在[bAbI](https://research.facebook.c"
  },
  {
    "path": "Keras/addition_rnn.py",
    "chars": 7033,
    "preview": "# -*- coding: utf-8 -*-\n'''An implementation of sequence to sequence learning for performing addition\nInput: \"535+61\"\nOu"
  },
  {
    "path": "Keras/antirectifier.py",
    "chars": 3364,
    "preview": "'''The example demonstrates how to write custom layers for Keras.\n\nWe build a custom activation layer called 'Antirectif"
  },
  {
    "path": "Keras/babi_memnn.py",
    "chars": 8873,
    "preview": "'''Trains a memory network on the bAbI dataset.\n\nReferences:\n- Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov"
  },
  {
    "path": "Keras/babi_rnn.py",
    "chars": 8953,
    "preview": "'''Trains two recurrent neural networks based upon a story and a question.\nThe resulting merged vector is then queried t"
  },
  {
    "path": "Keras/cifar10_cnn.py",
    "chars": 3745,
    "preview": "'''Train a simple deep CNN on the CIFAR10 small images dataset.\n\nGPU run command with Theano backend (with TensorFlow, t"
  },
  {
    "path": "Keras/conv_filter_visualization.py",
    "chars": 4307,
    "preview": "'''Visualization of the filters of VGG16, via gradient ascent in input space.\n\nThis script can run on CPU in a few minut"
  },
  {
    "path": "Keras/conv_lstm.py",
    "chars": 5070,
    "preview": "\"\"\" This script demonstrates the use of a convolutional LSTM network.\nThis network is used to predict the next frame of "
  },
  {
    "path": "Keras/deep_dream.py",
    "chars": 7395,
    "preview": "'''Deep Dreaming in Keras.\n\nRun the script with:\n```\npython deep_dream.py path_to_your_base_image.jpg prefix_for_results"
  },
  {
    "path": "Keras/image_ocr.py",
    "chars": 20934,
    "preview": "'''This example uses a convolutional stack followed by a recurrent stack\nand a CTC logloss function to perform optical c"
  },
  {
    "path": "Keras/imdb_bidirectional_lstm.py",
    "chars": 1456,
    "preview": "'''Train a Bidirectional LSTM on the IMDB sentiment classification task.\n\nOutput after 4 epochs on CPU: ~0.8146\nTime per"
  },
  {
    "path": "Keras/imdb_cnn.py",
    "chars": 2122,
    "preview": "'''This example demonstrates the use of Convolution1D for text classification.\n\nGets to 0.89 test accuracy after 2 epoch"
  },
  {
    "path": "Keras/imdb_cnn_lstm.py",
    "chars": 1991,
    "preview": "'''Train a recurrent convolutional network on the IMDB sentiment\nclassification task.\n\nGets to 0.8498 test accuracy afte"
  },
  {
    "path": "Keras/imdb_fasttext.py",
    "chars": 4909,
    "preview": "'''This example demonstrates the use of fasttext for text classification\n\nBased on Joulin et al's paper:\n\nBags of Tricks"
  },
  {
    "path": "Keras/imdb_lstm.py",
    "chars": 1869,
    "preview": "'''Trains a LSTM on the IMDB sentiment classification task.\nThe dataset is actually too small for LSTM to be of any adva"
  },
  {
    "path": "Keras/lstm_benchmark.py",
    "chars": 3097,
    "preview": "'''Compare LSTM implementations on the IMDB sentiment classification task.\n\nimplementation=0 preprocesses input to the L"
  },
  {
    "path": "Keras/lstm_text_generation.py",
    "chars": 3326,
    "preview": "'''Example script to generate text from Nietzsche's writings.\n\nAt least 20 epochs are required before the generated text"
  },
  {
    "path": "Keras/mnist_acgan.py",
    "chars": 11293,
    "preview": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nTrain an Auxiliary Classifier Generative Adversarial Network (ACGAN) o"
  },
  {
    "path": "Keras/mnist_cnn.py",
    "chars": 2270,
    "preview": "'''Trains a simple convnet on the MNIST dataset.\n\nGets to 99.25% test accuracy after 12 epochs\n(there is still a lot of "
  },
  {
    "path": "Keras/mnist_hierarchical_rnn.py",
    "chars": 3352,
    "preview": "\"\"\"This is an example of using Hierarchical RNN (HRNN) to classify MNIST digits.\n\nHRNNs can learn across multiple levels"
  },
  {
    "path": "Keras/mnist_irnn.py",
    "chars": 2295,
    "preview": "'''This is a reproduction of the IRNN experiment\nwith pixel-by-pixel sequential MNIST in\n\"A Simple Way to Initialize Rec"
  },
  {
    "path": "Keras/mnist_mlp.py",
    "chars": 1636,
    "preview": "'''Trains a simple deep NN on the MNIST dataset.\n\nGets to 98.40% test accuracy after 20 epochs\n(there is *a lot* of marg"
  },
  {
    "path": "Keras/mnist_net2net.py",
    "chars": 16438,
    "preview": "'''This is an implementation of Net2Net experiment with MNIST in\n'Net2Net: Accelerating Learning via Knowledge Transfer'"
  },
  {
    "path": "Keras/mnist_siamese_graph.py",
    "chars": 4165,
    "preview": "'''Train a Siamese MLP on pairs of digits from the MNIST dataset.\n\nIt follows Hadsell-et-al.'06 [1] by computing the Euc"
  },
  {
    "path": "Keras/mnist_sklearn_wrapper.py",
    "chars": 3690,
    "preview": "'''Example of how to use sklearn wrapper\n\nBuilds simple CNN models on MNIST and uses sklearn's GridSearchCV to find best"
  },
  {
    "path": "Keras/mnist_swwae.py",
    "chars": 7761,
    "preview": "'''Trains a stacked what-where autoencoder built on residual blocks on the\r\nMNIST dataset.  It exemplifies two influenti"
  },
  {
    "path": "Keras/mnist_transfer_cnn.py",
    "chars": 3728,
    "preview": "'''Transfer learning toy example:\n\n1- Train a simple convnet on the MNIST dataset the first 5 digits [0..4].\n2- Freeze c"
  },
  {
    "path": "Keras/neural_doodle.py",
    "chars": 14079,
    "preview": "'''Neural doodle with Keras\r\n\r\nScript Usage:\r\n    # Arguments:\r\n    ```\r\n    --nlabels:              # of regions (color"
  },
  {
    "path": "Keras/neural_style_transfer.py",
    "chars": 10638,
    "preview": "'''Neural style transfer with Keras.\n\nRun the script with:\n```\npython neural_style_transfer.py path_to_your_base_image.j"
  },
  {
    "path": "Keras/pretrained_word_embeddings.py",
    "chars": 4894,
    "preview": "'''This script loads pre-trained word embeddings (GloVe embeddings)\ninto a frozen Keras Embedding layer, and uses it to\n"
  },
  {
    "path": "Keras/reuters_mlp.py",
    "chars": 1955,
    "preview": "'''Trains and evaluate a simple MLP\non the Reuters newswire topic classification task.\n'''\nfrom __future__ import print_"
  },
  {
    "path": "Keras/stateful_lstm.py",
    "chars": 2633,
    "preview": "'''Example script showing how to use stateful RNNs\nto model long sequences efficiently.\n'''\nfrom __future__ import print"
  },
  {
    "path": "Keras/variational_autoencoder.py",
    "chars": 3482,
    "preview": "'''This script demonstrates how to build a variational autoencoder with Keras.\n\nReference: \"Auto-Encoding Variational Ba"
  },
  {
    "path": "Keras/variational_autoencoder_deconv.py",
    "chars": 6745,
    "preview": "'''This script demonstrates how to build a variational autoencoder\nwith Keras and deconvolution layers.\n\nReference: \"Aut"
  },
  {
    "path": "MLyearning/README.md",
    "chars": 12526,
    "preview": "#***Andrew Ng*** 机器学习新书\n\n>小润  \n>2016-12-05  \n\n第一周发布了前12章,这一周目前发布了第13章和14章,接下来几周将会发布更多  \n邮件订阅前往:[http://www.mlyearning.or"
  },
  {
    "path": "README.md",
    "chars": 7430,
    "preview": "# deeplearningDemo\n\n## Wunderlist\n\n- [ ] 1. [Machine Learning Yearning](https://github.com/zhourunlai/deep-learning-demo"
  },
  {
    "path": "TensorFlow/LICENSE",
    "chars": 1386,
    "preview": "The MIT License (MIT)\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and"
  },
  {
    "path": "TensorFlow/README.md",
    "chars": 12370,
    "preview": "# TensorFlow Examples\nTensorFlow Tutorial with popular machine learning algorithms implementation. This tutorial was des"
  },
  {
    "path": "TensorFlow/examples/1_Introduction/basic_operations.py",
    "chars": 2350,
    "preview": "'''\nBasic Operations example using TensorFlow library.\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien"
  },
  {
    "path": "TensorFlow/examples/1_Introduction/helloworld.py",
    "chars": 522,
    "preview": "'''\nHelloWorld example using TensorFlow library.\n\nAuthor: Aymeric Damien\nProject: https://github.com/aymericdamien/Tenso"
  },
  {
    "path": "TensorFlow/examples/2_BasicModels/linear_regression.py",
    "chars": 2825,
    "preview": "'''\nA linear regression learning algorithm example using TensorFlow library.\n\nAuthor: Aymeric Damien\nProject: https://gi"
  },
  {
    "path": "TensorFlow/examples/2_BasicModels/logistic_regression.py",
    "chars": 2303,
    "preview": "'''\nA logistic regression learning algorithm example using TensorFlow library.\nThis example is using the MNIST database "
  },
  {
    "path": "TensorFlow/examples/2_BasicModels/nearest_neighbor.py",
    "chars": 1682,
    "preview": "'''\nA nearest neighbor learning algorithm example using TensorFlow library.\nThis example is using the MNIST database of "
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/autoencoder.py",
    "chars": 3991,
    "preview": "# -*- coding: utf-8 -*-\n\n\"\"\" Auto Encoder Example.\nUsing an auto encoder on MNIST handwritten digits.\nReferences:\n    Y."
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/bidirectional_rnn.py",
    "chars": 4474,
    "preview": "'''\nA Bidirectional Recurrent Neural Network (LSTM) implementation example using TensorFlow library.\nThis example is usi"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/convolutional_network.py",
    "chars": 4555,
    "preview": "'''\nA Convolutional Network implementation example using TensorFlow library.\nThis example is using the MNIST database of"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/dynamic_rnn.py",
    "chars": 7561,
    "preview": "'''\nA Dynamic Recurrent Neural Network (LSTM) implementation example using\nTensorFlow library. This example is using a t"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/multilayer_perceptron.py",
    "chars": 3243,
    "preview": "'''\nA Multilayer Perceptron implementation example using TensorFlow library.\nThis example is using the MNIST database of"
  },
  {
    "path": "TensorFlow/examples/3_NeuralNetworks/recurrent_network.py",
    "chars": 3904,
    "preview": "'''\nA Recurrent Neural Network (LSTM) implementation example using TensorFlow library.\nThis example is using the MNIST d"
  },
  {
    "path": "TensorFlow/examples/4_Utils/save_restore_model.py",
    "chars": 4827,
    "preview": "'''\nSave and Restore a model using TensorFlow.\nThis example is using the MNIST database of handwritten digits\n(http://ya"
  },
  {
    "path": "TensorFlow/examples/4_Utils/tensorboard_advanced.py",
    "chars": 5080,
    "preview": "'''\nGraph and Loss visualization using Tensorboard.\nThis example is using the MNIST database of handwritten digits\n(http"
  },
  {
    "path": "TensorFlow/examples/4_Utils/tensorboard_basic.py",
    "chars": 3286,
    "preview": "'''\nGraph and Loss visualization using Tensorboard.\nThis example is using the MNIST database of handwritten digits\n(http"
  },
  {
    "path": "TensorFlow/examples/5_MultiGPU/multigpu_basics.py",
    "chars": 2356,
    "preview": "from __future__ import print_function\n'''\nBasic Multi GPU computation example using TensorFlow library.\n\nAuthor: Aymeric"
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  {
    "path": "TensorFlow/input_data.py",
    "chars": 5709,
    "preview": "\"\"\"Functions for downloading and reading MNIST data.\"\"\"\nfrom __future__ import print_function\nimport gzip\nimport os\nimpo"
  },
  {
    "path": "TensorFlow/notebooks/0_Prerequisite/ml_introduction.ipynb",
    "chars": 1914,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Machine Learning\\n\",\n    \"\\n\",\n  "
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    "path": "TensorFlow/notebooks/0_Prerequisite/mnist_dataset_intro.ipynb",
    "chars": 2558,
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    "path": "TensorFlow/notebooks/1_Introduction/helloworld.ipynb",
    "chars": 1592,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"out"
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    "path": "TensorFlow/notebooks/2_BasicModels/linear_regression.ipynb",
    "chars": 61960,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"out"
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    "path": "TensorFlow/notebooks/2_BasicModels/logistic_regression.ipynb",
    "chars": 5086,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outp"
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  {
    "path": "TensorFlow/notebooks/2_BasicModels/nearest_neighbor.ipynb",
    "chars": 13159,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outp"
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    "path": "TensorFlow/notebooks/3_NeuralNetworks/autoencoder.ipynb",
    "chars": 392323,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \""
  },
  {
    "path": "TensorFlow/notebooks/3_NeuralNetworks/bidirectional_rnn.ipynb",
    "chars": 12900,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"'''\\n\",\n "
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    "path": "TensorFlow/notebooks/3_NeuralNetworks/convolutional_network.ipynb",
    "chars": 19488,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"o"
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  {
    "path": "TensorFlow/notebooks/3_NeuralNetworks/multilayer_perceptron.ipynb",
    "chars": 6516,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"o"
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    "path": "TensorFlow/notebooks/3_NeuralNetworks/recurrent_network.ipynb",
    "chars": 12270,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"source\": [\n    \"'''\\n\",\n "
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    "path": "TensorFlow/notebooks/4_Utils/save_restore_model.ipynb",
    "chars": 8664,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"o"
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    "path": "TensorFlow/notebooks/4_Utils/tensorboard_basic.ipynb",
    "chars": 626695,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"o"
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    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outp"
  },
  {
    "path": "TensorFlow/tensorflow_distributed_mnist_demo.py",
    "chars": 4750,
    "preview": "'''\n@authou:zhourunlai\n@time:20161122\n1. For ps:\n    python tensorflow_distributed_mnist_demo.py --job_name=\"ps\" --task_"
  },
  {
    "path": "Theano/cnn.py",
    "chars": 2789,
    "preview": "import theano\nfrom theano import tensor as T\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\nimpor"
  },
  {
    "path": "Theano/linear_regression.py",
    "chars": 614,
    "preview": "import theano\nfrom theano import tensor as T\nimport numpy as np\n\ntrX = np.linspace(-1, 1, 101)\ntrY = 2 * trX + np.random"
  },
  {
    "path": "Theano/logistic_regression.p",
    "chars": 982,
    "preview": "import theano\nfrom theano import tensor as T\nimport numpy as np\nfrom load import mnist\n\ndef floatX(X):\n    return np.asa"
  },
  {
    "path": "Theano/neural_networks.py",
    "chars": 2247,
    "preview": "import theano\nfrom theano import tensor as T\nfrom theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams\nimpor"
  }
]

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