CNTK/Tutorials/CNTK_201B_CIFAR-10_ImageHan...

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{
"cells": [
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"cell_type": "code",
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"source": [
"from IPython.display import Image"
]
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"metadata": {},
"source": [
"# CNTK 201B: Hands On Labs Image Recognition"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This hands-on lab shows how to implement image recognition task using [convolution network][] with CNTK v2 Python API. You will start with a basic feedforward CNN architecture in order to classify Cifar dataset, then you will keep adding advanced feature to your network. Finally, you will implement a VGG net and residual net similar to the one that won ImageNet competition but smaller in size.\n",
"\n",
"[convolution network]:https://en.wikipedia.org/wiki/Convolutional_neural_network\n",
"\n",
"## Introduction\n",
"\n",
"In this hands-on, you will practice the following:\n",
"\n",
"* Understanding subset of CNTK python API needed for image classification task.\n",
"* Write a custom convolution network to classify Cifar dataset.\n",
"* Modifying the network structure by adding:\n",
" * [Dropout][] layer.\n",
" * Batchnormalization layer.\n",
"* Implement a [VGG][] style network.\n",
"* Introduction to Residual Nets (RESNET).\n",
"* Implement and train [RESNET network][].\n",
"\n",
"[RESNET network]:https://github.com/Microsoft/CNTK/wiki/Hands-On-Labs-Image-Recognition\n",
"[VGG]:http://www.robots.ox.ac.uk/~vgg/research/very_deep/\n",
"[Dropout]:https://en.wikipedia.org/wiki/Dropout_(neural_networks)\n",
"\n",
"## Prerequisites\n",
"\n",
"CNTK 201A hands-on lab, in which you will download and prepare Cifar dataset is a prerequisites for this lab. This tutorial depends on CNTK v2, so before starting this lab you will need to install CNTK v2. Furthermore, all the tutorials in this lab are done in python, therefore, you will need a basic knowledge of Python.\n",
"\n",
"CNTK 102 lab is recommended but not a prerequisites for this tutorials. However, a basic understanding of Deep Learning is needed.\n",
"\n",
"## Dataset\n",
"\n",
"You will use Cifar 10 dataset, from https://www.cs.toronto.edu/~kriz/cifar.html, during this tutorials. The dataset contains 50000 training images and 10000 test images, all images are 32x32x3. Each image is classified as one of 10 classes as shown below:"
]
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{
"data": {
"text/html": [
"<img src=\"https://cntk.ai/jup/201/cifar-10.png\" width=\"500\" height=\"500\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
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"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"# Figure 1\n",
"Image(url=\"https://cntk.ai/jup/201/cifar-10.png\", width=500, height=500)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The above image is from: https://www.cs.toronto.edu/~kriz/cifar.html\n",
"\n",
"## Convolution Neural Network (CNN)\n",
"\n",
"Convolution Neural Network (CNN) is a feedforward network comprise of a bunch of layers in such a way that the output of one layer is fed to the next layer (There are more complex architecture that skip layers, we will discuss one of those at the end of this lab). Usually, CNN start with alternating between convolution layer and pooling layer (downsample), then end up with fully connected layer for the classification part.\n",
"\n",
"### Convolution layer\n",
"\n",
"Convolution layer consist of multiple 2D convolution kernels applied on the input image or the previous layer, each convolution kernel output a feature map."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
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"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://cntk.ai/jup/201/Conv2D.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
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"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"# Figure 2\n",
"Image(url=\"https://cntk.ai/jup/201/Conv2D.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The stack of feature maps output are the input to the next layer."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://cntk.ai/jup/201/Conv2DFeatures.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"# Figure 3\n",
"Image(url=\"https://cntk.ai/jup/201/Conv2DFeatures.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, 86(11):2278-2324, November 1998\n",
"> Y. LeCun, L. Bottou, Y. Bengio and P. Haffner\n",
"\n",
"#### In CNTK:\n",
"\n",
"Here the [convolution][] layer in Python:\n",
"\n",
"```python\n",
"def Convolution(filter_shape, # e.g. (3,3)\n",
" num_filters, # e.g. 64\n",
" activation, # relu or None...etc.\n",
" init, # Random initialization\n",
" pad, # True or False\n",
" strides) # strides e.g. (1,1)\n",
"```\n",
"\n",
"[convolution]:https://www.cntk.ai/pythondocs/layerref.html#convolution\n",
"\n",
"### Pooling layer\n",
"\n",
"In most CNN vision architecture, each convolution layer is succeeded by a pooling layer, so they keep alternating until the fully connected layer. \n",
"\n",
"The purpose of the pooling layer is as follow:\n",
"\n",
"* Reduce the dimensionality of the previous layer, which speed up the network.\n",
"* Provide a limited translation invariant.\n",
"\n",
"Here an example of max pooling with a stride of 2:"
]
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
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"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://cntk.ai/jup/201/MaxPooling.png\" width=\"400\" height=\"400\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"# Figure 4\n",
"Image(url=\"https://cntk.ai/jup/201/MaxPooling.png\", width=400, height=400)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### In CNTK:\n",
"\n",
"Here the [pooling][] layer in Python:\n",
"\n",
"```python\n",
"\n",
"# Max pooling\n",
"def MaxPooling(filter_shape, # e.g. (3,3)\n",
" strides, # (2,2)\n",
" pad) # True or False\n",
"\n",
"# Average pooling\n",
"def AveragePooling(filter_shape, # e.g. (3,3)\n",
" strides, # (2,2)\n",
" pad) # True or False\n",
"```\n",
"\n",
"[pooling]:https://www.cntk.ai/pythondocs/layerref.html#maxpooling-averagepooling\n",
"\n",
"### Dropout layer\n",
"\n",
"Dropout layer takes a probability value as an input, the value is called the dropout rate. Let's say the dropu rate is 0.5, what this layer does it pick at random 50% of the nodes from the previous layer and drop them out of the nework. This behavior help regularize the network.\n",
"\n",
"> Dropout: A Simple Way to Prevent Neural Networks from Overfitting\n",
"> Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov\n",
"\n",
"\n",
"#### In CNTK:\n",
"\n",
"Dropout layer in Python:\n",
"\n",
"```python\n",
"\n",
"# Dropout\n",
"def Dropout(prob) # dropout rate e.g. 0.5\n",
"```\n",
"\n",
"### Batch normalization (BN)\n",
"\n",
"Batch normalization is a way to make the input to each layer has zero mean and unit variance. BN help the network converge faster and keep the input of each layer around zero. BN has two learnable parameters called gamma and beta, the purpose of those parameters is for the network to decide for itself if the normalized input is what is best or the raw input.\n",
"\n",
"> Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift\n",
"> Sergey Ioffe, Christian Szegedy\n",
"\n",
"#### In CNTK:\n",
"\n",
"[Batch normalization][] layer in Python:\n",
"\n",
"```python\n",
"\n",
"# Batch normalization\n",
"def BatchNormalization(map_rank) # For image map_rank=1\n",
"```\n",
"\n",
"[Batch normalization]:https://www.cntk.ai/pythondocs/layerref.html#batchnormalization-layernormalization-stabilizer\n",
"\n",
"## Microsoft Cognitive Network Toolkit (CNTK)\n",
"\n",
"CNTK is a highly flexible computation graphs, each node take inputs as tensors and produce tensors as the result of the computation. Each node is exposed in Python API, which give you the flexibility of creating any custom graphs, you can also define your own node in Python or C++ using CPU, GPU or both.\n",
"\n",
"For Deep learning, you can use the low level API directly or you can use CNTK layered API. We will start with the low level API, then switch to the layered API in this lab.\n",
"\n",
"So let's first import the needed modules for this lab."
]
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{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
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"source": [
"from __future__ import print_function\n",
"import os\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import math\n",
"\n",
"from cntk.layers import default_options, Convolution, MaxPooling, AveragePooling, Dropout, BatchNormalization, Dense, Sequential, For\n",
"from cntk.io import MinibatchSource, ImageDeserializer, StreamDef, StreamDefs\n",
"import cntk.io.transforms as xforms \n",
"from cntk.initializer import glorot_uniform, he_normal\n",
"from cntk import Trainer\n",
"from cntk.learner import momentum_sgd, learning_rate_schedule, UnitType, momentum_as_time_constant_schedule\n",
"from cntk.ops import cross_entropy_with_softmax, classification_error, relu, input_variable, softmax, element_times\n",
"from cntk.utils import *"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://cntk.ai/jup/201/CNN.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
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"metadata": {},
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"source": [
"# Figure 5\n",
"Image(url=\"https://cntk.ai/jup/201/CNN.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we imported the needed modules, let's implement our first CNN, as shown in Figure 5 above.\n",
"\n",
"Let's implement the above network using CNTK layer API:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
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"source": [
"def create_basic_model(input, out_dims):\n",
" \n",
" net = Convolution((5,5), 32, init=glorot_uniform(), activation=relu, pad=True)(input)\n",
" net = MaxPooling((3,3), strides=(2,2))(net)\n",
"\n",
" net = Convolution((5,5), 32, init=glorot_uniform(), activation=relu, pad=True)(net)\n",
" net = MaxPooling((3,3), strides=(2,2))(net)\n",
"\n",
" net = Convolution((5,5), 64, init=glorot_uniform(), activation=relu, pad=True)(net)\n",
" net = MaxPooling((3,3), strides=(2,2))(net)\n",
" \n",
" net = Dense(64, init=glorot_uniform())(net)\n",
" net = Dense(out_dims, init=glorot_uniform(), activation=None)(net)\n",
" \n",
" return net"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To train the above model we need two things:\n",
"* Read the training images and their corresponding labels.\n",
"* Define a cost function, compute the cost for each mini-batch and update the model weights according to the cost value.\n",
"\n",
"To read the data in CNTK, we will use CNTK readers which handle data augmentation and can fetch data in parallel.\n",
"\n",
"Example of a map text file:\n",
"\n",
" S:\\data\\CIFAR-10\\train\\00001.png\t9\n",
" S:\\data\\CIFAR-10\\train\\00002.png\t9\n",
" S:\\data\\CIFAR-10\\train\\00003.png\t4\n",
" S:\\data\\CIFAR-10\\train\\00004.png\t1\n",
" S:\\data\\CIFAR-10\\train\\00005.png\t1\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
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"source": [
"# model dimensions\n",
"image_height = 32\n",
"image_width = 32\n",
"num_channels = 3\n",
"num_classes = 10\n",
"\n",
"#\n",
"# Define the reader for both training and evaluation action.\n",
"#\n",
"def create_reader(map_file, mean_file, train):\n",
" if not os.path.exists(map_file) or not os.path.exists(mean_file):\n",
" raise RuntimeError(\"This tutorials depends 201A tutorials, please run 201A first.\")\n",
"\n",
" # transformation pipeline for the features has jitter/crop only when training\n",
" transforms = []\n",
" if train:\n",
" transforms += [\n",
" xforms.crop(crop_type='randomside', side_ratio=0.8) # train uses data augmentation (translation only)\n",
" ]\n",
" transforms += [\n",
" xforms.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'),\n",
" xforms.mean(mean_file)\n",
" ]\n",
" # deserializer\n",
" return MinibatchSource(ImageDeserializer(map_file, StreamDefs(\n",
" features = StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'\n",
" labels = StreamDef(field='label', shape=num_classes) # and second as 'label'\n",
" )))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let us write the the training and validation loop."
]
},
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"cell_type": "code",
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"source": [
"#\n",
"# Train and evaluate the network.\n",
"#\n",
"def train_and_evaluate(reader_train, reader_test, max_epochs, model_func):\n",
" # Input variables denoting the features and label data\n",
" input_var = input_variable((num_channels, image_height, image_width))\n",
" label_var = input_variable((num_classes))\n",
"\n",
" # Normalize the input\n",
" feature_scale = 1.0 / 256.0\n",
" input_var_norm = element_times(feature_scale, input_var)\n",
" \n",
" # apply model to input\n",
" z = model_func(input_var_norm, out_dims=10)\n",
"\n",
" #\n",
" # Training action\n",
" #\n",
"\n",
" # loss and metric\n",
" ce = cross_entropy_with_softmax(z, label_var)\n",
" pe = classification_error(z, label_var)\n",
"\n",
" # training config\n",
" epoch_size = 50000\n",
" minibatch_size = 64\n",
"\n",
" # Set training parameters\n",
" lr_per_minibatch = learning_rate_schedule([0.01]*10 + [0.003]*10 + [0.001], UnitType.minibatch, epoch_size)\n",
" momentum_time_constant = momentum_as_time_constant_schedule(-minibatch_size/np.log(0.9))\n",
" l2_reg_weight = 0.001\n",
" \n",
" # trainer object\n",
" learner = momentum_sgd(z.parameters, \n",
" lr = lr_per_minibatch, momentum = momentum_time_constant, \n",
" l2_regularization_weight=l2_reg_weight)\n",
" trainer = Trainer(z, (ce, pe), [learner])\n",
"\n",
" # define mapping from reader streams to network inputs\n",
" input_map = {\n",
" input_var: reader_train.streams.features,\n",
" label_var: reader_train.streams.labels\n",
" }\n",
"\n",
" log_number_of_parameters(z) ; print()\n",
" progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)\n",
"\n",
" # perform model training\n",
" batch_index = 0\n",
" plot_data = {'batchindex':[], 'loss':[], 'error':[]}\n",
" for epoch in range(max_epochs): # loop over epochs\n",
" sample_count = 0\n",
" while sample_count < epoch_size: # loop over minibatches in the epoch\n",
" data = reader_train.next_minibatch(min(minibatch_size, epoch_size - sample_count), input_map=input_map) # fetch minibatch.\n",
" trainer.train_minibatch(data) # update model with it\n",
"\n",
" sample_count += data[label_var].num_samples # count samples processed so far\n",
" \n",
" # For visualization... \n",
" plot_data['batchindex'].append(batch_index)\n",
" plot_data['loss'].append(trainer.previous_minibatch_loss_average)\n",
" plot_data['error'].append(trainer.previous_minibatch_evaluation_average)\n",
" \n",
" progress_printer.update_with_trainer(trainer, with_metric=True) # log progress\n",
" batch_index += 1\n",
" progress_printer.epoch_summary(with_metric=True)\n",
" \n",
" #\n",
" # Evaluation action\n",
" #\n",
" epoch_size = 10000\n",
" minibatch_size = 16\n",
"\n",
" # process minibatches and evaluate the model\n",
" metric_numer = 0\n",
" metric_denom = 0\n",
" sample_count = 0\n",
" minibatch_index = 0\n",
"\n",
" while sample_count < epoch_size:\n",
" current_minibatch = min(minibatch_size, epoch_size - sample_count)\n",
"\n",
" # Fetch next test min batch.\n",
" data = reader_test.next_minibatch(current_minibatch, input_map=input_map)\n",
"\n",
" # minibatch data to be trained with\n",
" metric_numer += trainer.test_minibatch(data) * current_minibatch\n",
" metric_denom += current_minibatch\n",
"\n",
" # Keep track of the number of samples processed so far.\n",
" sample_count += data[label_var].num_samples\n",
" minibatch_index += 1\n",
"\n",
" print(\"\")\n",
" print(\"Final Results: Minibatch[1-{}]: errs = {:0.1f}% * {}\".format(minibatch_index+1, (metric_numer*100.0)/metric_denom, metric_denom))\n",
" print(\"\")\n",
" \n",
" # Visualize training result:\n",
" window_width = 32\n",
" loss_cumsum = np.cumsum(np.insert(plot_data['loss'], 0, 0)) \n",
" error_cumsum = np.cumsum(np.insert(plot_data['error'], 0, 0)) \n",
"\n",
" # Moving average.\n",
" plot_data['batchindex'] = np.insert(plot_data['batchindex'], 0, 0)[window_width:]\n",
" plot_data['avg_loss'] = (loss_cumsum[window_width:] - loss_cumsum[:-window_width]) / window_width\n",
" plot_data['avg_error'] = (error_cumsum[window_width:] - error_cumsum[:-window_width]) / window_width\n",
" \n",
" plt.figure(1)\n",
" plt.subplot(211)\n",
" plt.plot(plot_data[\"batchindex\"], plot_data[\"avg_loss\"], 'b--')\n",
" plt.xlabel('Minibatch number')\n",
" plt.ylabel('Loss')\n",
" plt.title('Minibatch run vs. Training loss ')\n",
"\n",
" plt.show()\n",
"\n",
" plt.subplot(212)\n",
" plt.plot(plot_data[\"batchindex\"], plot_data[\"avg_error\"], 'r--')\n",
" plt.xlabel('Minibatch number')\n",
" plt.ylabel('Label Prediction Error')\n",
" plt.title('Minibatch run vs. Label Prediction Error ')\n",
" plt.show()\n",
" \n",
" return softmax(z)"
]
},
{
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"metadata": {
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 116906 parameters in 10 parameter tensors.\n",
"\n",
"Finished Epoch[1 of 300]: [Training] loss = 2.062444 * 50000, metric = 75.3% * 50000 13.316s (3754.8 samples per second);\n",
"Finished Epoch[2 of 300]: [Training] loss = 1.675133 * 50000, metric = 61.7% * 50000 13.772s (3630.5 samples per second);\n",
"Finished Epoch[3 of 300]: [Training] loss = 1.520789 * 50000, metric = 55.4% * 50000 13.674s (3656.7 samples per second);\n",
"Finished Epoch[4 of 300]: [Training] loss = 1.421881 * 50000, metric = 51.4% * 50000 13.668s (3658.2 samples per second);\n",
"Finished Epoch[5 of 300]: [Training] loss = 1.338381 * 50000, metric = 48.0% * 50000 13.675s (3656.3 samples per second);\n",
"\n",
"Final Results: Minibatch[1-626]: errs = 43.3% * 10000\n",
"\n"
]
},
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PbW3dgePuusvf777bq5qis1gIITSuLNqcSBog6SFJX0iqldRkbb+kpST9TdIE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\nNT7/3OyUU8wWLjRbbz2/1jb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"text/plain": [
"<matplotlib.figure.Figure at 0x14663f064e0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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FwKUJ5RdiFpCMUdXng5wmN2PLOZOArqr6e9CkOQnZZ4MNCI/Dcp44mSJiuxev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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x146068c5a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_path = os.path.join('data', 'CIFAR-10')\n",
"reader_train = create_reader(os.path.join(data_path, 'train_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), True)\n",
"reader_test = create_reader(os.path.join(data_path, 'test_map.txt'), os.path.join(data_path, 'CIFAR-10_mean.xml'), False)\n",
"\n",
"pred = train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Although, this model is very simple, it still has too much code, we can do better. Here the same model in more terse format:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_basic_model_terse(input, out_dims):\n",
"\n",
" with default_options(activation=relu):\n",
" model = Sequential([\n",
" For(range(3), lambda i: [\n",
" Convolution((5,5), [32,32,64][i], init=glorot_uniform(), pad=True),\n",
" MaxPooling((3,3), strides=(2,2))\n",
" ]),\n",
" Dense(64, init=glorot_uniform()),\n",
" Dense(out_dims, init=glorot_uniform(), activation=None)\n",
" ])\n",
"\n",
" return model(input)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 116906 parameters in 10 parameter tensors.\n",
"\n",
"Finished Epoch[1 of 300]: [Training] loss = 2.054147 * 50000, metric = 75.0% * 50000 13.674s (3656.6 samples per second);\n",
"Finished Epoch[2 of 300]: [Training] loss = 1.695077 * 50000, metric = 62.6% * 50000 14.271s (3503.7 samples per second);\n",
"Finished Epoch[3 of 300]: [Training] loss = 1.542115 * 50000, metric = 56.3% * 50000 13.872s (3604.3 samples per second);\n",
"Finished Epoch[4 of 300]: [Training] loss = 1.450798 * 50000, metric = 52.3% * 50000 13.823s (3617.3 samples per second);\n",
"Finished Epoch[5 of 300]: [Training] loss = 1.373555 * 50000, metric = 49.2% * 50000 13.857s (3608.4 samples per second);\n",
"Finished Epoch[6 of 300]: [Training] loss = 1.300828 * 50000, metric = 46.6% * 50000 13.965s (3580.3 samples per second);\n",
"Finished Epoch[7 of 300]: [Training] loss = 1.232516 * 50000, metric = 43.7% * 50000 13.827s (3616.0 samples per second);\n",
"Finished Epoch[8 of 300]: [Training] loss = 1.189415 * 50000, metric = 42.0% * 50000 13.885s (3600.9 samples per second);\n",
"Finished Epoch[9 of 300]: [Training] loss = 1.134052 * 50000, metric = 39.9% * 50000 13.871s (3604.6 samples per second);\n",
"Finished Epoch[10 of 300]: [Training] loss = 1.098405 * 50000, metric = 38.9% * 50000 13.961s (3581.3 samples per second);\n",
"\n",
"Final Results: Minibatch[1-626]: errs = 36.2% * 10000\n",
"\n"
]
},
{
"data": {
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wf6rAxGXW2WcnBiYAPXrY9q9/hYMPhtatLTDp2xcWLrTZabt1SwxCHn4YXnoJ\n3ngjSttmG2vuKSqCkSNtKv5DD4U//Snz5XLOOZcdORGcALsAHwFF2DwnNwPTgWHB8bbAb7/+VHU5\ncDDQEpsI7iHgaaxjrMsBl11mTTQjR1rwEtogGE91wQVR2po1sHo1dOxo+/vtZ0OR582L5lnZdVf4\n29/gwQehTRtLW7y4dvJaWmrNVdtsUzv3c845VzM5EZyo6uuq2khVGye9BgXHT1fVA5KumaWqh6jq\nxqraUVUv8lqT3HT99dH7sEYldMABsN56Ngy5VasoffFiq415803bnzs3OvZssAZ22MwDcOON8Npr\nVcuXqk3P36+fTdX/1Vc+N4tzzuWCKnWIFZEnKjml2pOvufy1xRYwa5bVeGy8MUybZpO1Jc8i261b\n2Wu7d7fzt9jC5lwB2Hlnm/Rt5Ej44x8t7aqrrPaltDT1MOZZs+Dvf7cmovXWs7SHH7bZbENbbmnT\n+jvnnMuuqtacFFfy+gZ4sDYz6PJD1672i79RI3jxxfKnty8ttdfDD8Po0bDZZrDXXtCpk81Eu3at\n3WPdOpgyJbpudVBnNnx46vtedJF1zA2biQCWL7ft2LG2nTMnmkjOOedc9lQpOAmaVyp9ZSqzLv+J\n2OvEE2Hw4MRjjRpFixWef77VdMyda4HPX/5i6ffdV/aeU6fC00/b+x12iNLXBnMcn3KKNfFMmmTP\nnjfP0hctskCpJlassFqd996DJUtqdi/nnGsocqLPiXNVNWqUBRGdOsH//mdDkLt0SZxav7jYakcu\nvDBKC4ONpUvhvPPsfbhmULNmth00yIKV1q1tVNCKFVZTUx3ffQfXXWdT+p90UvXu4ZxzDU21JmFz\nLtcceaSN8mkS+4lu3ToxqJgyxdIgdY3IQQfZdsMN4d137X2jRlHQUp0pgb75Jnr/3HPWNNW42rMA\nOedcw+A1J67eigcLItanJT6FfjwwOf10OOQQsDmEoGVLOOusxH4rYUfZSZNsfSCAiy9O/dzDD7fJ\n4ELffRc1E8VNSppKcMaMysvlnHMNnQcnrt5avtwCilGjorQVK2D8eJsPJe7++6Pj335r7++6ywKW\nuOOPt204D0uXLtGx//7Xpunfait4/nmbDC7UoUPiyJ9QGLC8+KJtX3897eI551yD5cGJq7c23tgm\ncDs/NvXe1VdbZ9owuDj3XFv/JxxefOihNtlbPKCJi3fC7djRmnUefdT299gDPv00Cm6uvTY6t107\n6/uSTBV2fINhAAAgAElEQVR22cVeAK++WvVyOudcQ+PBicsrYZ+Tn3+29X5Gjoz6mUA0l8rQoamv\n79PHhiX//vdRB9b+/S3I+PVX299sM5tN9oorLG3lSvjhB5sUbvToxPsVF0OLFtaMtHgxPPZYxfl/\n911bd6iqli2r/JxkS5bYHDLOOZdrPDhxeeWGG6L3zzyT2EEW4MorK76+SROrLSkpga23TjwW1n5c\ndhnMnGmdanfc0YKPsPnn7rttu2aNNSGFwQnAppvakOLJk8t//v/9nzVLVaSkJOqwCxYQbbKJLYZY\nFZttZsFYVa9zzrlMy4ngRET6iMgkEfleREpF5KgqXLuXiKwVkYyvkuhyn4ithjxkSOrj4eRvl19e\n/j2aNLGmm9OTZux5+mmrkQmbjB57zOZZWbsWXn7Z0ho3hqOOgqZNbZTP7rvbKKLQmDF2fMWKKG3i\nRKt9WbAA5s+3NYQ+/RTefhsmTCg7jHm99ayJKZxq/5xzbPvTT7ZVtT4u3btHaRW56abKz6nI2rVw\n882pOwQ751x15MpQ4mbAx8D9QGVT5P9GRFoA44CXgDaZyZqrbwYMsFcqTZpYALD55hXfI7nGBWwK\n/bilS6P3HTpYUCACH30Upa9cGU0QB1abAxa4rFljNSWTJ9vIobDWBazjbeigg6IOtarRKKV166w5\nKfThh9ZZ9/HH4c9/trQJE6zfTXjtjz9GAVqzZtapuLzPKl3jx9vSAFtsAQUFNbuXc85BjtScqOoU\nVb1KVZ8GUqyMUq67gEeAdys70blQu3a1M9fImWfCscdazUeycFhyfLgxWG1IqFmzqIknHpiErrjC\nti+9ZM1DIrauUKhdOwsIWre2pqOrrrKmozAwgcSJ3047za4Jg6pDD7UZdXfbLa3ilitcOiA+ssk5\n52oiJ4KT6hCR04FOwLBs58U1XP/5TzT8GKKRPOGChE89lXj+VltFQUd5zSDbb2/bffeN0j74wLaf\nfGLbeD+RI4+00UfxWpRvv7U5VVrGluIMh1eHI5cef9xmwK2pVausGSvsk+OcczVVL4MTEekK3ACc\nqKo1XP3EudrTrp0tVDhsmDWjbLVV2XOuu84WNgw9+qg13fz4o03mFs7JsmiRzcUC8Nprtn3nHbjt\ntsQmpjvvtFqRDTe0/TVroH172G67ss/u3t06zyZbvToxuFm3zpqE0pkVd8ECK3eq1aCdc646RKsz\nJ3cGiUgpcLSqTirneCOsGec+Vb0nSLsGOEpVe1Vw315A0T777EOLcPhEoKCggAJvLHd1qLjYRvpM\nm2aBRLKRI62vSKNGMHs2bLutpcf/uV51lQU6CxfC736X+jlr11rgMXOm1WxMnGiBzs8/W1AUBhT9\n+tlsthMmWPDy2mtw4402adw++9g5Tz8NBx+cOAsvRPfIsf9KnHPVVFhYSGFhYUJacXExb7zxBkBv\nVc34AJT6GJy0AJYA64j6pzQK3q8D/qiqr6W4rhdQVFRURK9e5cYwzuUkEWjVKhqhA1a78cwzFliU\nV2uxyy7w8cd2zhNPWLARNhfdc4/1TykpSd0BGKCoCHr1gvfft74pgweXncvFgxPn8t/06dPpbet/\n1ElwkiujdapiKdAjKe0cYH/g/4C5dZ0h5zKtpKRsWpMmcPTRFV/Xs6cFGPfcY/O27LFHdCwctRMG\nJi1bwi+/JF4fxvFhp9miIqtdadUqWihx4cJoZWfnnKsNOfFfiog0E5GeIrJTkNQ52G8fHB8uIuMA\n1HwRfwELgVWqOkNVV2apGM5lTKNG1QsAwiaZjh2tmWa99eDZZy39yCMTzx0+3JqHrruu/PtdfrkN\nPT74YGsiGjbMrlmyxGpQxo2z82bMiEbxgDUvZWoelBUrfI4V5/JNrtSc7AK8CmjwujlIHwcMAtoC\nKVrmnXMVadrUtsuXR2mHH26vkKoNL27eHM4+29JmzbLg5dprYf/9bebdTTe15qFQ//62vfrqqBZm\n2jRbALF7d7vulVcsvUsXG0FUUlK7tSyqNiS7QwerJZo82ZuXnMsHOVFzoqqvq2ojVW2c9BoUHD9d\nVQ+o4PphFXWGda6hOu442GEHm8+kIs2bJ+4/+KANkb7rLptn5dJLLXB55x07Pnt2dO7s2bYII1iT\nz5Il9j6+yGE438qsWRXno3Vrq4EZOzZxMrvKfPttNGdM+Px0qCaWxTmXG3IiOHHOZUaTJjYVfnXn\nM2nb1mpPnnvO9nff3X6hx+dPic+58uuv0dIAzz9vTTtbbhlN0BYuvAhWi/Liizb0ObRokW0HDYr6\nu1QkVUfgKVMqvy50xx2Wtzlz0r/GOZd5Hpw458q1wQa2TQ4Ciotte+ONUb+WQYNsG87Nstdedv38\n+alnwB01yiarGzsWbr/dpsFPZ7ba11+3ye9Cjz1ma/sA9O1rSwJUZu1aa9oKlxbw2hPnckuu9Dlx\nzuWgBQtsm9zs06lT2b4d4Sy2obCpB6xZaYcdLFjZaCObM+WBB+zYf/9rE8uBTQT3+efW4RZsuHTy\nMOdwIcXw+ccdZ9tzz42WDRCxzroffGBB0pAhtmDjqFHWR+XWW61mJ/Tdd5V9Es65uuQ1J865coW1\nIelMTf/ss/Dvf9uw5XffhdJSu/6QQ6BHDwsmPvrIFkP84x8tCIHEzrqNGtkQ5SeftP1//csCChF7\nxZtffv018fnrr2/nhE1DV19t88BceqkFR/fdB2+8YfmaP9/OOfNM244YYc1MqTrTlpSkHspdW77+\nOnP3dq6+8uDEOVeuK66wobrhqJ+KtG9vqxP/+c/WPNO4sU3FH+8DkmpK/XAF5n//O1otOpworkMH\nGDo0Onf48Oj9/fdbMBJfTBGi2XLjCxG2a2fbww+3fP3wg9XIjBxpnXgPO8z2jzrK5m2Ja9LEXsnB\nUE1NmACXXAKdOyd2HnbO5eAMsZniM8Q6lxvC/isffWRBz3bbWfNN2CST6tzOnaNak/794ayz4IBg\n/N5778Ef/lD2mo8/hp12grfesmacCRPK3j/87y+5T82iRbDZZta3Jt7594MPLGAKg6ia6NPHVpN+\n9tnEvNSG2bMtIEteasC56qrrGWK95sQ5V6cuvti2Xbva6B2R1IFJ3KefRu+HD49WfwbYddfEc+fO\ntSajnj3tF37jxlFg0iM2t/Ree0Xvw2ak0DHH2LV9+8JNNyU+q02bivOarmXLLNDJhC5dYJttMnNv\n5+qCByfOuTp10klw7LE28VtlJk+2RQibNYOnnrJmmU6dohE5LVuWrfXo2BG23z7aD1eG3mQTq2XZ\nYw+bpn/atOico49OnPPkjTdslehp0yyIevhh6zsTCvuJPPSQjUSqSq3HsmW2/fhj+OQTuPLK2gt4\n4r7/PurQ7Fx948GJc65O9ehhQ4HDfiAV6ds36n/Sr1/UBLLxxtbZ9eOPK79HmzY238rSpdbM8fbb\n1pySSqdOtt18c1soEWyelhNPTBzd07mzNUWdcopNTnfDDdExVQuoSkttf+1auPBCWLwY5s2zIOn8\n8+3YHnvYXDI//lhxp9u//Q3+97/Kyxo6+WTbhh1/natvciI4EZE+IjJJRL4XkVIROaqS848Rkaki\nslBEikXkbRH5Y13l1zmXfUc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"text/plain": [
"<matplotlib.figure.Figure at 0x14606872668>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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HpHjJW7ZMfW/DhqaAvP8+/PyzRQAde6wpLUOGWHVksAghx3Ecx6lisin8VxfY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\nYUPM+OUAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14606eb3f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pred_basic_model = train_and_evaluate(reader_train, reader_test, max_epochs=10, model_func=create_basic_model_terse)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have a trained model, let's classify the following image:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://cntk.ai/jup/201/00014.png\" width=\"64\" height=\"64\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Figure 6\n",
"Image(url=\"https://cntk.ai/jup/201/00014.png\", width=64, height=64)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import PIL\n",
"\n",
"def eval(pred_op, image_path):\n",
" label_lookup = [\"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\", \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"]\n",
" image_mean = 133.0\n",
" image_data = np.array(PIL.Image.open(image_path), dtype=np.float32)\n",
" image_data -= image_mean\n",
" image_data = np.ascontiguousarray(np.transpose(image_data, (2, 0, 1)))\n",
" \n",
" result = np.squeeze(pred_op.eval({pred_op.arguments[0]:[image_data]}))\n",
" \n",
" # Return top 3 results:\n",
" top_count = 3\n",
" result_indices = (-np.array(result)).argsort()[:top_count]\n",
"\n",
" print(\"Top 3 predictions:\")\n",
" for i in range(top_count):\n",
" print(\"\\tLabel: {:10s}, confidence: {:.2f}%\".format(label_lookup[result_indices[i]], result[result_indices[i]] * 100))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Top 3 predictions:\n",
"\tLabel: truck , confidence: 98.95%\n",
"\tLabel: ship , confidence: 0.46%\n",
"\tLabel: automobile, confidence: 0.26%\n"
]
}
],
"source": [
"eval(pred_basic_model, \"data/CIFAR-10/test/00014.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding dropout layer, with drop rate of 0.25, before the last dense layer:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_basic_model_with_dropout(input, out_dims):\n",
"\n",
" with default_options(activation=relu):\n",
" model = Sequential([\n",
" For(range(3), lambda i: [\n",
" Convolution((5,5), [32,32,64][i], init=glorot_uniform(), pad=True),\n",
" MaxPooling((3,3), strides=(2,2))\n",
" ]),\n",
" Dense(64, init=glorot_uniform()),\n",
" Dropout(0.25),\n",
" Dense(out_dims, init=glorot_uniform(), activation=None)\n",
" ])\n",
"\n",
" return model(input)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 116906 parameters in 10 parameter tensors.\n",
"\n",
"Finished Epoch[1 of 300]: [Training] loss = 2.123667 * 50000, metric = 78.7% * 50000 16.391s (3050.5 samples per second);\n",
"Finished Epoch[2 of 300]: [Training] loss = 1.817045 * 50000, metric = 67.9% * 50000 16.894s (2959.5 samples per second);\n",
"Finished Epoch[3 of 300]: [Training] loss = 1.678272 * 50000, metric = 62.2% * 50000 17.006s (2940.1 samples per second);\n",
"Finished Epoch[4 of 300]: [Training] loss = 1.583182 * 50000, metric = 58.1% * 50000 16.644s (3004.1 samples per second);\n",
"Finished Epoch[5 of 300]: [Training] loss = 1.514311 * 50000, metric = 55.3% * 50000 16.790s (2977.9 samples per second);\n",
"\n",
"Final Results: Minibatch[1-626]: errs = 49.2% * 10000\n",
"\n"
]
},
{
"data": {
"image/png": 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0tXVKQc+e8dp999hftAjatq35/C++iJoWs+bJn4iISGvSamtOalNbYALwj39Ec9Do0fDd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\nb4jApEqzQm6NhZmNN7NTzOxGM/vOzCa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"text/plain": [
"<matplotlib.figure.Figure at 0x14606e63da0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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ncth1HMdxCoZ0p3UOBMJfjgPKQxAROQ+4BGiG1em5QFU/T9F3CHAKVoRQIoe+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\nx3EcJ6/4fwTbIpgKKDU2AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14606ebaef0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pred_basic_model_dropout = train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model_with_dropout)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Add batch normalization after each convolution and before the last dense layer:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_basic_model_with_batch_normalization(input, out_dims):\n",
"\n",
" with default_options(activation=relu):\n",
" model = Sequential([\n",
" For(range(3), lambda i: [\n",
" Convolution((5,5), [32,32,64][i], init=glorot_uniform(), pad=True),\n",
" BatchNormalization(map_rank=1),\n",
" MaxPooling((3,3), strides=(2,2))\n",
" ]),\n",
" Dense(64, init=glorot_uniform()),\n",
" BatchNormalization(map_rank=1),\n",
" Dense(out_dims, init=glorot_uniform(), activation=None)\n",
" ])\n",
"\n",
" return model(input)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 117290 parameters in 18 parameter tensors.\n",
"\n",
"Finished Epoch[1 of 300]: [Training] loss = 1.512835 * 50000, metric = 54.1% * 50000 15.499s (3226.1 samples per second);\n",
"Finished Epoch[2 of 300]: [Training] loss = 1.206524 * 50000, metric = 42.8% * 50000 16.071s (3111.2 samples per second);\n",
"Finished Epoch[3 of 300]: [Training] loss = 1.087695 * 50000, metric = 38.3% * 50000 16.160s (3094.1 samples per second);\n",
"Finished Epoch[4 of 300]: [Training] loss = 1.008182 * 50000, metric = 35.4% * 50000 16.057s (3113.8 samples per second);\n",
"Finished Epoch[5 of 300]: [Training] loss = 0.953168 * 50000, metric = 33.4% * 50000 16.247s (3077.4 samples per second);\n",
"\n",
"Final Results: Minibatch[1-626]: errs = 30.8% * 10000\n",
"\n"
]
},
{
"data": {
"image/png": 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QQui8yiI4MbNnzayLmXXNeR2T7D/azHZu4Pjzzazj99U000UXwW9/m91efXVv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\n1782/fhFi2p3w+RTU5PtnjI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CkxBCCCGUlQhOQgghhFBWIjgJIYQQQlmJ4CSEEEIIZSWC\nkxBCCCGUlQhOQgghhFBWIjgJoZ2TNErSJU3Iv6akGkmbJNs7JNu5TxotOkk3Sbq3ra/bXJLOlfRm\nqcsRQkcXwUkIZUbSzUmwcFWefcOTfTemkvcDzmnCJT4BVgbGptJa/ByLpgZJ7Vg88yOEIovgJITy\nY3gAcbCk7x/bnvxcAXxcK7PZTDObVfDJ3TQzq2mtAoeWkbRYqcsQQjmJ4CSE8vQm8Cnw01TaT/HA\npFa3Qm6LhaQPJZ0l6QZJ30j6WNLPU/trdeukbCfpLUlzJL0sacPUMctJukPSZEmzJL0t6eDU/pvw\npy+flpx7kaQ1kn0bSnpQUnVSnmclrZ1Th9MlTZH0laQrJXWt74PJdK1IOiyp60xJlZJ65HwGp+Yc\n96akP6S2ayQdn5RtlqRxkn4k6YfJZ/qdpBdzy5oce7ykT5Lj7pK0TM7+45LzzUneT8zz+R8o6RlJ\ns4FD6qtvCJ1RBCchlCcDbgSOSaUdA9wEqIDjfwW8DmwGXAVcLWndnPOnCbgIGAZsAXwJjEwFCUsA\nbwB7ARsC1wC3SNoi2X8a8DL+aPTewCrAp5JWBZ7FH0e/I7B5kifdUrAz0DfZfwRwVPJqyA+BocDe\nwD54YPTbRo7J5/fAzcCmwHjgDuBfwF+AgfjncmXOMesCByTX3QOv0/ddcJIOxR87fxawPvA74I+S\nDs85zwXApUB//NH0IYRENCWGUL5uB/4mqQ/+h8S2wEHATgUc+7CZ/Sv5+UJJw5LjJiZp+QKc88zs\naQBJRwKT8fEs95jZFCA9nmS4pD2BA4E3zOwbSfOB2Wb2ZSaTpFOAmUCFmS1Kkj/Iue504BQzM+B9\nSQ8DuwA3NFA/AUea2ezkOrcmxzRl7A3AjWb2n+QcF+EB1vlm9mSSdjkeJKZ1Bw43s6lJnl8AD0s6\n3cym4YHJ6Wb2QJL/46QV6gTg1tR5Lk3lCSGkRHASQpkys68kPQQcjX8ZP2xm06VCGk54J2d7KrBS\nQ5cDXklde4akCfhf9UjqApyNtxisBnRLXo2NddkUeD4VmOTzbhKYZHwObNTIeT/KBCapYxqqX33S\nn9MXyfvYnLQlJC1tZt8laZ9kApPEy3jwuJ6k7/BWnRskXZ/K0xUP0tKqmlHeEDqFCE5CKG834d0K\nBpzUhOMW5GwbLevGPQP4Bd59MxYPSi7HA5SGzCng3M0pa2PH1FC3dWjxRs5jDaQV+tktnbwfB7yW\nsy83QCt4EHMInU2MOQmhvD2KBwCLAY8X8ToCfvT9hvQDoB8wLknaFnjAzCrN7B3gw2R/2ny8hSDt\nbWBwQwNci+RLfNwLAMkaLnUGtuZRyDThNSStnNreBg883ku6daYAPzSzSTmv9CyrmI4cQgMiOAmh\njCXTfdcHNszp+iiGP0jaWdJG+CDRL4HMmIiJwG6StpHUHx8Q2zvn+I+ArZPZKMsnaVcCywJ3SRoo\naZ1kls26FNfTwOGStpO0cVKfhQUcl6/PLDdtHjBC0iaSBuMtSHelxtqcC5wl6ReS1pW0kaSjJP2y\nkeuEEBIRnIRQ5szsu9R4h7xZGtkuJI/hs10ux2f5rAj8xMwyX+h/BkbjLTlP42M87ss5x8V4C8I4\nYJqkNcxsOj4bpwfwDD7j5zjqdsu0tgvwWUIPJq/7qDsQt5DPKV/aROBe4BH88xgDnPx9ZrMb8Doe\njbccPQMcibc2NXSdEEJCxf9jLIQQQgihcNFyEkIIIYSyEsFJCCGEEMpKBCchhBBCKCsRnIQQQgih\nrERwEkIIIYSyEsFJCCGEEMpKBCchhBBCKCsRnIQQQgihrERwEkIIIYSyEsFJCCGEEMpKBCchhBBC\nKCsRnIQQQgihrPw/wYBDgaffIXgAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x14607031ac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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jIsOABqp6SpIyTwK1VPXMSNrBWMyKrVQ1cRQGETmHzFfbdRzHcRwnxrmq+kxpN5LxyImq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\nYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x146072a3ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pred_basic_model_bn = train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model_with_batch_normalization)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's implement an inspired VGG style network, using layer API, here the architecture:\n",
"\n",
"| VGG9 |\n",
"| ------------- |\n",
"| conv3-64 |\n",
"| conv3-64 |\n",
"| max3 |\n",
"| |\n",
"| conv3-96 |\n",
"| conv3-96 |\n",
"| max3 |\n",
"| |\n",
"| conv3-128 |\n",
"| conv3-128 |\n",
"| max3 |\n",
"| |\n",
"| FC-1024 |\n",
"| FC-1024 |\n",
"| |\n",
"| FC-10 |\n"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_vgg9_model(input, out_dims):\n",
" with default_options(activation=relu):\n",
" model = Sequential([\n",
" For(range(3), lambda i: [\n",
" Convolution((3,3), [64,96,128][i], init=glorot_uniform(), pad=True),\n",
" Convolution((3,3), [64,96,128][i], init=glorot_uniform(), pad=True),\n",
" MaxPooling((3,3), strides=(2,2))\n",
" ]),\n",
" For(range(2), lambda : [\n",
" Dense(1024, init=glorot_uniform())\n",
" ]),\n",
" Dense(out_dims, init=glorot_uniform(), activation=None)\n",
" ])\n",
" \n",
" return model(input)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 2675978 parameters in 18 parameter tensors.\n",
"\n",
"Finished Epoch[1 of 300]: [Training] loss = 2.253115 * 50000, metric = 83.6% * 50000 46.007s (1086.8 samples per second);\n",
"Finished Epoch[2 of 300]: [Training] loss = 1.931100 * 50000, metric = 71.8% * 50000 46.236s (1081.4 samples per second);\n",
"Finished Epoch[3 of 300]: [Training] loss = 1.706618 * 50000, metric = 63.3% * 50000 46.271s (1080.6 samples per second);\n",
"Finished Epoch[4 of 300]: [Training] loss = 1.576171 * 50000, metric = 58.1% * 50000 46.348s (1078.8 samples per second);\n",
"Finished Epoch[5 of 300]: [Training] loss = 1.473403 * 50000, metric = 53.7% * 50000 46.386s (1077.9 samples per second);\n",
"\n",
"Final Results: Minibatch[1-626]: errs = 51.2% * 10000\n",
"\n"
]
},
{
"data": {
"image/png": 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r1azTIn1OGjpa5wpgFWAzd1/d3VcH+hOL/v2zqTInIsWxxRbR12Xx4tiuro4+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\n96STGp+PlqDgRERESl67dhE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"text/plain": [
"<matplotlib.figure.Figure at 0x146072e6860>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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mJOWmm2wKJ/QVGTkyOnbNNdC5M3z6qe0nlhbYZx9LrV9SYhltGwRvvYsvtnUY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\ntNVWmfmC1KtnVpkjj4zaXn/d2hctMqX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"text/plain": [
"<matplotlib.figure.Figure at 0x1460735c908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pred_vgg = train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_vgg9_model)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Residual Network (ResNet)\n",
"\n",
"One of the main problem of a Deep Neural Network is how to propagate the error all the way to the first layer. For a deep network, the gradient keep getting smaller until it has no effect on the network weights. [ResNet](https://arxiv.org/abs/1512.03385) was designed to overcome such problem, by defining a block with identity path, as shown below:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://cntk.ai/jup/201/ResNetBlock2.png\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Figure 7\n",
"Image(url=\"https://cntk.ai/jup/201/ResNetBlock2.png\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The idea of the above block is 2 folds:\n",
"\n",
"* During back propagation the gradient have a path that doesn't affect its magnitude.\n",
"* The network need to learn residual mapping (delta to x).\n",
"\n",
"So let's implements ResNet blocks using CNTK:\n",
"\n",
" ResNetNode ResNetNodeInc\n",
" | |\n",
" +------+------+ +---------+----------+\n",
" | | | |\n",
" V | V V\n",
" +----------+ | +--------------+ +----------------+\n",
" | Conv, BN | | | Conv x 2, BN | | SubSample, BN |\n",
" +----------+ | +--------------+ +----------------+\n",
" | | | |\n",
" V | V |\n",
" +-------+ | +-------+ |\n",
" | ReLU | | | ReLU | |\n",
" +-------+ | +-------+ |\n",
" | | | |\n",
" V | V |\n",
" +----------+ | +----------+ |\n",
" | Conv, BN | | | Conv, BN | |\n",
" +----------+ | +----------+ |\n",
" | | | |\n",
" | +---+ | | +---+ |\n",
" +--->| + |<---+ +------>+ + +<-------+\n",
" +---+ +---+\n",
" | |\n",
" V V\n",
" +-------+ +-------+\n",
" | ReLU | | ReLU |\n",
" +-------+ +-------+\n",
" | |\n",
" V V\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from cntk.ops import combine, times, element_times, AVG_POOLING\n",
"\n",
"def convolution_bn(input, filter_size, num_filters, strides=(1,1), init=he_normal(), activation=relu):\n",
" if activation is None:\n",
" activation = lambda x: x\n",
" \n",
" r = Convolution(filter_size, num_filters, strides=strides, init=init, activation=None, pad=True, bias=False)(input)\n",
" r = BatchNormalization(map_rank=1)(r)\n",
" r = activation(r)\n",
" \n",
" return r\n",
"\n",
"def resnet_basic(input, num_filters):\n",
" c1 = convolution_bn(input, (3,3), num_filters)\n",
" c2 = convolution_bn(c1, (3,3), num_filters, activation=None)\n",
" p = c2 + input\n",
" return relu(p)\n",
"\n",
"def resnet_basic_inc(input, num_filters):\n",
" c1 = convolution_bn(input, (3,3), num_filters, strides=(2,2))\n",
" c2 = convolution_bn(c1, (3,3), num_filters, activation=None)\n",
"\n",
" s = convolution_bn(input, (1,1), num_filters, strides=(2,2), activation=None)\n",
" \n",
" p = c2 + s\n",
" return relu(p)\n",
"\n",
"def resnet_basic_stack(input, num_filters, num_stack):\n",
" assert (num_stack > 0)\n",
" \n",
" r = input\n",
" for _ in range(num_stack):\n",
" r = resnet_basic(r, num_filters)\n",
" return r"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's write the full model:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def create_resnet_model(input, out_dims):\n",
" conv = convolution_bn(input, (3,3), 16)\n",
" r1_1 = resnet_basic_stack(conv, 16, 3)\n",
"\n",
" r2_1 = resnet_basic_inc(r1_1, 32)\n",
" r2_2 = resnet_basic_stack(r2_1, 32, 2)\n",
"\n",
" r3_1 = resnet_basic_inc(r2_2, 64)\n",
" r3_2 = resnet_basic_stack(r3_1, 64, 2)\n",
"\n",
" # Global average pooling\n",
" pool = AveragePooling(filter_shape=(8,8), strides=(1,1))(r3_2) \n",
" net = Dense(out_dims, init=he_normal(), activation=None)(pool)\n",
" \n",
" return net"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training 272474 parameters in 65 parameter tensors.\n",
"\n",
"Finished Epoch[1 of 300]: [Training] loss = 1.859668 * 50000, metric = 69.3% * 50000 47.499s (1052.7 samples per second);\n",
"Finished Epoch[2 of 300]: [Training] loss = 1.583096 * 50000, metric = 58.7% * 50000 48.541s (1030.0 samples per second);\n",
"Finished Epoch[3 of 300]: [Training] loss = 1.453993 * 50000, metric = 53.4% * 50000 48.982s (1020.8 samples per second);\n",
"Finished Epoch[4 of 300]: [Training] loss = 1.347815 * 50000, metric = 49.2% * 50000 48.704s (1026.6 samples per second);\n",
"Finished Epoch[5 of 300]: [Training] loss = 1.269185 * 50000, metric = 45.8% * 50000 48.155s (1038.3 samples per second);\n",
"\n",
"Final Results: Minibatch[1-626]: errs = 44.6% * 10000\n",
"\n"
]
},
{
"data": {
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XSgghhELxqyHUyjrr+Cgf8Enfcv1Jch1rCy25ZH6kUHodoHXW8ZWWC1dcBp9q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\nQw8t/byKCp95eOrUut031E0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"text/plain": [
"<matplotlib.figure.Figure at 0x14606ffbd30>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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77GH5ey67DMZkYWVu4kRbLnIcx3GcDEhJOQlCxW84LmorPVEdwJQSgGOPhW+/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\nPAbMxReb/UmPHvDGG5EL8qm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SoXFj8+L57Tc7TzYDAeY6/fXXsM8+dr5qFdx+e8F6iTmL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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x146074da400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pred_resnet = train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_resnet_model)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"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",
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