{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "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:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "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": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "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 }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "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:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "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." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "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", "import cntk as C\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.learners import momentum_sgd, learning_rate_schedule, UnitType, momentum_as_time_constant_schedule\n", "from cntk import cross_entropy_with_softmax, classification_error, relu, input, softmax, element_times\n", "from cntk.logging import *\n", "\n", "# Use CPU in test environment.\n", "if 'TEST_DEVICE' in os.environ:\n", " if os.environ['TEST_DEVICE'] == 'cpu':\n", " C.device.set_default_device(C.device.cpu())\n", " else:\n", " C.device.set_default_device(C.device.gpu(0))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "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 }, "outputs": [], "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 }, "outputs": [], "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." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "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 = C.input_variable((num_channels, image_height, image_width))\n", " label_var = C.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", " progress_printer = ProgressPrinter(tag='Training', num_epochs=max_epochs)\n", " trainer = Trainer(z, (ce, pe), [learner], [progress_printer])\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", "\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", " batch_index += 1\n", " trainer.summarize_training_progress()\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)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training 116906 parameters in 10 parameter tensors.\n", "\n", "Finished Epoch[1 of 5]: [Training] loss = 2.063247 * 50000, metric = 0.00% * 50000 15.936s (3137.6 samples/s);\n", "Finished Epoch[2 of 5]: [Training] loss = 1.679241 * 50000, metric = 0.00% * 50000 13.449s (3717.7 samples/s);\n", "Finished Epoch[3 of 5]: [Training] loss = 1.522509 * 50000, metric = 0.00% * 50000 13.370s (3739.7 samples/s);\n", "Finished Epoch[4 of 5]: [Training] loss = 1.426424 * 50000, metric = 0.00% * 50000 13.371s (3739.4 samples/s);\n", "Finished Epoch[5 of 5]: [Training] loss = 1.342300 * 50000, metric = 0.00% * 50000 13.441s (3720.0 samples/s);\n", "\n", "Final Results: Minibatch[1-626]: errs = 42.8% * 10000\n", "\n" ] }, { "data": { "image/png": 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LSfv2sM8+8RIREZHCaPUlJ/UxaRK8+26hUyEiIlKaFJzUw4ABsNlmcNZZ8NNPhU6NiIhI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\nmtp/FzH78h+Say8yszWSfRuZ2ZNmNid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AgWY2trFlKFQ5WQD0M7MpkqrJv/GfcHeR9kULIb0MvGJmJyf3Ar4ArjSzS+qo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\nzFgAXnjBz4MGFRdZNwiCoih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"text/plain": [ "" ] }, "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 10]: [Training] loss = 2.056384 * 50000, metric = 0.00% * 50000 13.728s (3642.2 samples/s);\n", "Finished Epoch[2 of 10]: [Training] loss = 1.692674 * 50000, metric = 0.00% * 50000 13.455s (3716.1 samples/s);\n", "Finished Epoch[3 of 10]: [Training] loss = 1.542460 * 50000, metric = 0.00% * 50000 13.473s (3711.1 samples/s);\n", "Finished Epoch[4 of 10]: [Training] loss = 1.449036 * 50000, metric = 0.00% * 50000 13.502s (3703.2 samples/s);\n", "Finished Epoch[5 of 10]: [Training] loss = 1.372120 * 50000, metric = 0.00% * 50000 13.380s (3736.9 samples/s);\n", "Finished Epoch[6 of 10]: [Training] loss = 1.305663 * 50000, metric = 0.00% * 50000 13.409s (3728.8 samples/s);\n", "Finished Epoch[7 of 10]: [Training] loss = 1.245300 * 50000, metric = 0.00% * 50000 13.296s (3760.5 samples/s);\n", "Finished Epoch[8 of 10]: [Training] loss = 1.184832 * 50000, metric = 0.00% * 50000 13.946s (3585.3 samples/s);\n", "Finished Epoch[9 of 10]: [Training] loss = 1.140430 * 50000, metric = 0.00% * 50000 13.577s (3682.7 samples/s);\n", "Finished Epoch[10 of 10]: [Training] loss = 1.106576 * 50000, metric = 0.00% * 50000 13.371s (3739.4 samples/s);\n", "\n", "Final Results: Minibatch[1-626]: errs = 35.2% * 10000\n", "\n" ] }, { "data": { "image/png": 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1RaSdiOwTHGoR7DcLzt8uIiMjlzwD/AY8ISKtRaQzcCfwWLLAxFWML76Ivd95\nZ3jhBRg1ypp4vvkGateOBSdhYNKiReyaZ56BunXh3nuhd2+bEM4551zVkxHBCbAfMA3Iw+Y5GQJM\nBcKp8ZsCzcLEqroKOBJohE0ENxoYg3WMdWnSrp1Ndx9Wxm23Hey9d3ya1att6vzPPrOhyB9+GOsU\n+/77dv7SSy0w6d69bPP32Wc2Db9zzrnMVtqhxGVKVT+gmEBJVc9Kcuw74OjyzJcrH1ddZdtwIUIo\nvMDgl1/C8uXWPLT11pDQTYgbboAzzrDJ4jZutCahq6+GffahSAcemPxZzjnnMssmBSci8nIJSRpt\nRl6c+8vyYGq9sNlHFb7/Hj7/HPr2tWMvvWTNRb/+aqOAwpFA229f+H7z5sGFF9o1zjnnMtum1pyU\n1F03HxhVyrw4xzvvWG1J586xCeAgvrNtaMYM2zZpEn9sq62gVq3YNT//DLvsAs2bW9BTUGAz4jrn\nnMtMmxScJGteca4sde0ae69qI32OPz4+zZQpcMEF1mn2/PNtReXQAw/AEUfArbdaGoDLLrPt0Ufb\nekCTJkGXLuVbDuecc6Xnfz+6jHbssXDNNfDee/DppxZcdOhgzTvNm8PDD8Phh9uwZbA1gMDW+Wnc\n2F4vB42RZ59t20MPhcGDbTSRc865zJMRHWKdK0q1alYLEgo7tYI134CtiPz22/Dkk7Z/xhnJ7xVO\n8nbFFXBjMA4sWefY00+Hffe1dKHvvrMh0XfcAbvuCmeeGTv3zDM2Kqlt2/j7qCZvjkrFypVQr17p\nrnXOucrOa05cpRWO4GnbFmrUiB3fdVfbXhwspNCiBbzyis2hohq7rm9fayK68MJYkPLnnzYR3JVX\n2iigO++0AGP33WGbbWzm2379YH1k2cu+fS3w+egjm2guVK2aLYC4qV5+2ZYFmD9/0691zrls4MGJ\nq7SaNrXtNdfEH58+3db4Cfua3HQT9OwZO3/DDba98UYbzjxiBHz8sTUB3X57LN0339iU+6G1kfWu\nu3aF//zHgplQp06xVZpXrbJtadYNWrDAAqJko46cc64q8ODEVVrNmsGSJYWnwa9Tx/qjtGxpw4zD\noceh44+3c82bx4516gTLllnAEjbFHHWUbbcIGj9/+skmlgPrVHvHHTZEGWw0UGjhQqulOfxw2//l\nl5LLsmQJvPuuvf/1V/jb33xEkXOu6vL//lylts02xZ+PDjMOjRsHP/wQ3xQUtXAhnHUWLFpk+6tW\nWbNPs2aA3AJnAAAgAElEQVR2bN48m/ANbFZbiDUh1aljawKpxtYJatbMOt8WFFjAI2L5GjAg9swz\nzrBRRqtWWc3JDjukUnrnnMtOHpy4Kk3VgoZbbrH9adMscHj8cVs9+aijoGbN+Gt22inWWfaii2x7\n+uk2SVzYUXbZslizE8Cpp9qIo7fftv3Fi20IdLhqc9js1KWL9TXZccfkeX3hhfjmJeecy0YZEZyI\nSCcRGSsi80WkQER6bMK1B4vIehEp91USXXYSsf4jK1fGT39/8cXxI3aiGje27caNMGQIbLutdcQ9\n7zw7Pnu2bT/+OBbAHHEEnHJK/H3q17dtOAQ6L8+Ck2rV4PrrLXAKffqpBTlDhpS+rM45VxmIZsBC\nIyJyDHAQtvDfy0AvVR2bwnUNg2u+B5qoavti0rYH8vLy8mjfvshkzqUs7JsS/SeUnw+NGsUf/+MP\naNAg/ljv3vDcc3DJJTB0qB3bIsnA/rvuspFDYH1XmjUr/MxE335r6erWLTqNKqxZY81Q4f6GDUU3\ndTnnqrapU6fSoUMHgA6qWu6VARlRc6Kqb6rqDao6BtiUmSFGAE8Dn5ZPzpwr2htvWKfYqMQFCiFW\nOxI1erRNKDdsmDUb1aljtSSTJtn5O++07b/+FauF+dvfSs7TunXQurUFPsUFMM8+a8HLH3/Y/kEH\nWT6+/rrkZzjnXHnLiOCkNETkLGAX4MZ058VVTcccE1thOeqQQ2wG2qhp0+JH7dSoYXOgVKtmQcm6\ndVYTc8ghVoPxr3/F0o4bZ9c+/rgNdW7UCFavjp9TJRTOjfLGG1Z7omqv4cNjgQjYKs5Rnwbh/fPP\n2zY/3zrmOudcOlTK4EREWgG3AX1VtaCk9M5VpEmTYNCg+GP77JO8k+uGDbaNzoJbvbptw+aeSy6x\nuVz+8Q/rZPvnn1brsd9+FtCIwNKlFsTk5to1hxxigcr8+baQ4kUXxTrdhgsmgtXqqMaCmnB49d57\nJ8+vc85VhEo3fb2IVMOacgap6uzwcKrXDxw4kIYJde85OTnkJE6W4VwFGDfOtq1bFz43cKC95s+3\nZiCA/v1tnpZtt41Pm7h/zDG2HTDAVngGmDgRTjoJXn3V9s85x6b+P/RQO9ekiQ2jBpvTBSywic7h\nErV6tTUFLV9uNUHJmrScc5VPbm4uueFfOoH8/PyKzYSqZtQLKAB6FHO+YZBmHbA+eG2MHDu0iOva\nA5qXl6fOZYqw4WXWrKLTNGgQS6equmFDbP/JJ2Pvo69Vqwofu+OO2PuZM1U3blR99lnbX7BAdeJE\n1Tlz4vN1882xfIwebccWLlSdOjWWpl491SFDyu8zcs6lX15engIKtNcKiAUqY7POCqAtsA/QLniN\nAL4N3v8vfVlzbtOEU+nvtlvRacIFC596yrbVq1ufkBUr4LTT4Lrr7Hj37tZkk58fG4UDsdFDO+1k\n265dYY89rL9L7952rGlTOOywWC3JuHFWK7JkSew+l1xi25o14ye/W7nS5ngBm98l0YYNyTvnrl9v\nk8+tWWOz4mbAwEHnXIbIiOBEROqKSDsRCWeZaBHsNwvO3y4iIyH821FnRF/AYmCtqs5U1TVpKoZz\nm+zGG0v+pfzRR/Dbb/HT8DdoYP1Fate2tYN23tnmUWndOn7Ysir8/rttw74s3bvbNtpBNhwW/emn\ntjbR8cdbn5fDDoMXX7Sg5/ffLU3jxtY3pXFje1bv3rY+0Sef2LFwtlywmXZr1Ihfoyg0bJgFXHXq\n2DpC4arSzjmXKX1O9gPew6qMFAinmRoJnA00BZqlJ2vOpd/WWxd9TsTmNgkXGyxK9+42qdw//mH7\n9evbPCpbbRVL8/e/21bVOtGuXBk/FLply9jQ5smTrcYnDEYOOsi2DzwAb71lfWXWBH8qPPEEjB9v\nQ5jDmqBw5t1Zs2zV57PPthlyt98ettwyeRk2bLAFGbfcsvjaJudc5ZYRk7BVBJ+EzbmS7bmn1ZI8\n9ZTV1HTsCP8roqF07VqoVcuaZEpaQTk3N7ZAY/hfTnQSO0no0t6xo9XERC1ZElt48bzz4KGH4s8/\n/LAFaeEop9CaNdC2Lbz2mtUsFRT4oorObaoqOQmbcy4zRIcZQ2zUTujuu+H8863WolYtO9a0qf3i\nf+opqz0J+7hAbA6XxNFEic45x7aHHGLbT5NMqxhO8Q8WiIS1MnPmWKB0/vnWVJS49tDSpZamTRu7\nb/XqsXL16gWXXVZ83pxzFc+DE+fcX/r1s+3RR9t2/nzreBv2Xbn0UutfMmtW/HXHHWc1LR9/DGPH\nQrt21uFVxPqcdOwYSxvOzXLttbGZcB95xJ4RBim1allfmnBhRLBhzVFLl9q2ZUsLMsKamZtuik8X\nzqx77LFWsxLmYeNGG1Z9zz2FP4cNG2KrUjvnKp436zjnKsTHH8M//2mz5UKsWShq/XqbBXfMGJga\nVByH/0WddBLMnAmdOlnNyfjxFkSFaxJ9912sH8q++1oQ9eqrthJ048Y2omjOHDu/apW9D/u/FBTE\nNy0NHGi1RGPHxjoQV7SFC+0zOO649DzfuShv1nHOZaWDD44FHADz5hVOU6OGDa+emuS/vpdfttFB\nt99ufUYmTLBlAsLhz+vXx9JOmwYHHmhpp0yxEUHRYc516kC9evZ+xIhYYLJsmc3wG45sevTR0pZ2\n8/33v7EVrZ2rajJltI5zror48kubHn/aNBulk8yCBdbks88+8MMPNoPtE09Yx9att7YmpsQZac85\nx2pEoqsx77MPfPGF9YtZvjx2XMTuE9bK5Ofb/U49Fd5914KjIUOsn0rUxo02Ounqq2NT/ZcHVbj3\nXpuPxrmqyGtOnHMVaq+9oEOHWJ+RZLbf3jq3nnkmtGplnV179bL1hCC+OSjsMzJ+vNWIhKOLfvnF\nhj0PGGDPAxuu/Oyz9j6sSVmxwjrxjhhh88UAPPOMTes/b541C4Ujh4YMsXThhHa//Vb0aKbNEa6D\nFPaRca6q8eDEOVfhpkyJn6ytKOEqyRA/j0s0OLnkEqtpCOdrOeAA21+82CaaGzXKgpKlS+Gxx2Ij\ngsJmnXB74YXQooW9/7//s/s9+ywcdZSNQrrxxvjJ5G64wSaZ69jRmpcKgiVIx42zjsMhVRvNFF0M\nUjW26GMyY8fadvFi286cabU2UeGcL9HJ9JzLFh6cOOcyVsuWtt1vP9hhh/hzv/0WG06czJ572nbg\nQGvGadzY9sOakxdesG10zpNrr42933FHm4E3zMPgwbE8nH8+3Hyzdc4FC1zCmqAePaxJZsIE27/w\nQutUe9NNFrhs3GjPTDZrbui112zodPXqlvc2bazPTtQvv1gz16RJtq9qAd+UKUXf17nKwoMT51zG\n2m8/m+Tt888Ln9t6awseilKzpjXdDB4cfzwc1tylS/xzwGoq5s+31113WX+XX36JpevWzWpI9t3X\n9q+6KnYucSmCV16x7fTpsWPLlsVG3wwdGjv33XexYAkszUsvWd+b0GWXxd8/7PMSBieff26z8+6/\nf9KP4y8ffQR33OFrGbnMlhHBiYh0EpGxIjJfRApEpEcJ6XuJyAQRWSwi+SIyWUSOqqj8OucqTpMm\npb92q60KzwZ78MH2izk6Rf6kSTYd/7bbWu3IDjtY4FOvHvz5Z2xEUMeOVpPx2muFnzV8eGwSu/bt\nbf/4463T78CB1relf3+b2j+0zz527913t864w4bF37Nt29izc3PhhBPs/Ztv2lwyYKN68vNtdFJR\nvvzS8v3LL1bb85//WE3O5MnFfnzOpU1GBCdAXeALYAC2tk5JOgMTgGOB9ti6PONEpF255dA5l7Vq\n146tKxQVdpp9+GELaMJ1ic46y7affAKffRYLKtq2tYnsRo2yzrXjx8M771gtyW+/xe4brbWJDqm+\n/HILIsL5WMDer1tnAdPPP9uxY4+Fr7+2Z3TpAg8+WDjv69bB6adbv5RwRelrromdHzzYArXEvizL\nl1ttlXPplBHBiaq+qao3qOoYQFJIP1BV/09V81R1tqpeC3wPpGm6JOdcNvriC9tus0388RNPtGCl\nY0drRol2gL39duvvEk6RH3ay3WknuPJKq/V4//34ZpVDD42//+WXx+/XqGEjmBYujD++3XbwwQc2\ntBliHYiXLLGg4+mnLWAKF2scPdrKEp0V9557LGj54QfrXLvVVvassDOuc+mQEcHJ5hIRAeoDy0pK\n65xzqRo+3LatWxefTgRuucXeh81Qr7xizUSvvx5Lc9ddsaUBAJ580rZ33RUfrCQbgbP99jalflhL\nc8cdsSaf0E472TwvixbFT0rXLLKm+5IlVpMSLh3wzjsWUPXrB7fdFkuXWKNSFFUbBbVqlc0R8/HH\nqV3nXHGyIjgB/oU1DT1fUkLnnEvV6NE2BX7iNPvJXHut/aIO+7j07Gm1D+FQ5WTOPNOahcIOuc8/\nb80to0cXThv2kQlrVbbYwjrkHnCA7Y8ZY31YLrjARiRFJ6n7/HMYOdKGSIf+9S/L7xtv2P4ll8TW\nE3r/feuQu2RJyeWePt0mwLv9djjiCBuqPWwYrF5d9DXhuks77xwfEDkXyri1dUSkAOipqmNTTN8H\neAjooarvFZOuPZDXuXNnGiZMLZmTk0NOuGqYc85loA0brHnnyivt/e23Fz1aKZyOf8kSCzIOOii2\njlBRaUM77WR9Zdq2tSUDevUqOk9Tp8LJJ9uijHvuaf1botatszwn+v772DpIEBtenaoPPrC+OrVq\nQe/eRafbuNFmE05slnPFy83NJTc3N+5Yfn4+H374IVTQ2jqVOjgRkd7Ao8DJqvpmCWl94T/nXNaL\n1t6k8t/7xIlWO3TffbY/Zoz1gQmn83/uOTteUGBzqBQUxIZjP/tsbDXoUKdO8XOvgNX6rF1rI5/C\nBRxnz7b3L71kNUzVq1stU82axed37txYP54wX4kBVqhRIxvJtHJl/LIG5WXDhthClKk491xrmot2\nVM5UvvBfikQkB3gM6F1SYOKcc1VF2JckcX6Xohx+uE0a16WLzd/So4ctsNiyZayvSo8eFjwceGD8\nqKZkFc5Dh8beN29uo43WrrX9f//bhmGHHXTDUU+PPWaT5PXqFRuRFFq1ypqIfv7ZOhlHA5PwfNST\nT9qQ6/XrLTCBWHNV1BtvFO5gXJRwxuGS5OQUX9MU9dFHtrBkdOI/F5MRwYmI1BWRdiKyT3CoRbDf\nLDh/u4iMjKTvA4wErgA+F5EmwatBxefeOecyRzjJ20knbdp1778fvxr0zjvDjz/aTLfjxsXmVUkm\n2gl2990tGDjzTAsowhl2Ae6+O/66Vq1sG442ev11C2h++CEW0LRqZfPDNG8eW9MI7P4Qm6huzhwb\nGv3CCzb9f3TRxOrVbQ2kaE1St27xnZOL07OndXROtlp21I8/2uSAqQgDp/KyerU1aVVWGRGcAPsB\n04A8bJ6TIcBU4MbgfFMg0t+cc4HqwAPAgsgr4UffOeeqlrZt7Zdw27abd5+ddrKaivAX+OGHx86J\nWCfbGjWsOeigg+yZqlC/vg1xfuQRS1u3LpxxRnwtxRdf2Cva7ySqVatYB+CFC+Nnzw0NG2a1OOPG\n2fDnESNsUrlwdFQ4V8zChRZodewITz1lx8LOut98Y0sghIFQUcK1jubOjR175RV7dtSPP8YWjyzJ\nypW2nTYttfSbqm7d1AOlTJQRwYmqfqCq1VS1esLr7OD8Wap6eCT9YUnS/pXeOefc5lm50kYShbp1\nsxqEcOr+Aw6wX37JJq8D6zty2GHW7DJqVGyI9TXXWC1MWBMzd671UalRIxYEhKLNRo8/Hnu/dq09\ne/Jk6yC8fLkNx46qX9+CpWin4TDQmj/fttWq2RpKYQ3OzJk2iilRuE7T8uWx2pcTT7TmrtCMGdb0\ns912yT+PRMuX2/OLq5FK9MsvcP31sUUmwZ756qvx6cLAp1JT1SrxwmaS1by8PHXOOVe8oUOtLmT1\natUxY1QLCuz40qVhHYnq5Zer/vFH0fcI023YoLpxo+ppp6lOnVr8c8eMUT388Ni14Su83957x6c/\n+mg7vvvusbSffab6wgt2vqAgdnzCBNv272/bunXj73/AAfZ+xQrVUaOsbHfdFbsufJ1ySnzZVFW3\n2872R41SHTFCddWq4su5YYPqsmWqr76qetttxacNnXuuPeO77wp/xj/9FDv22mux4wsWpHbvkuTl\n5SnWstFeK+J3dkU8JBNeHpw451zqCgpUV65Mfi78xbduXfH3mDNH9Z13Svf8hg1VGzeO/fJXVZ01\nSzU/Pz7dmjWWl6uvVn3lFdW33y58r+XL7Zd0NMBo00Z1xozCwQ9YwACqf/ubba+9VrVmzcIBE6i+\n9ZZde9RRtn/rrfGfz/77q15zjernnycvZ/fulnbjxsLnPvhA9amnYueuuMLSvvtuLE00L/Pmxcrb\npo3qQQclv29pVHRwkhHNOs455zKLSNHDb999FwYNSj6HSdQuu0DXrqV7/vLlsHSpdWYN7babjSSK\nql3b+pXcfLM1Ox1xROF7NWxoM+yGnn7a+puEKzt37Ro/HLlnT9uGK1LPn2/DoKP9ScJRRYsX2/Ev\nv7ShwUdFlqBdtsw60d52W9GrRYd9a+6917YXXWR5mTjRRlCdfnosbbiKdeJ8MqGvvoqV95tvrKPy\n8uVWjt9+gxdftI64BQV2/3ChykzkwYlzzrlNcvjhqQ9VrghNm8YHMUV56SUbat2nj+3XrWu/pEta\nnfmCC2wbzgUTTuy2xx7WEffHHy0wueee2Gy/YEFAdBmA336z5QKuuCJ2TIM+LAMH2hpN4ZIJ0aDu\nssssyHnpJdsPF3JcscK2AwbY9uuv4/O9ZIkN0W7WzPqlnHKKzf1Svbrdf+RIMpYHJ84556qEE08s\n3OH1sMNiw50PPth+wf/tb7b/wAO23XVX2x5/vAUTjRpZjc3MmbFh1IcdFqsF+e47m8E2HCJ9/fW2\n3WYbW8l66FCrHVG19ZdCYe0JxGpyJk60oKhDB3vWNdfY8gaPPmq1ILNmwf33W9pBg2z0j03kaoFQ\n6JxzCn8embwO0ibMZeecc85ln+nT4a23rEkmnBNl/nyrBTnpJKt9KMmBB8beh6N/wpqJCy+0Ziew\nWp7Q+vW2gGOXLhYIffihLUtw9dXxPVtC334Lt95qo5XCQCg8v/XWNoIqnABdNfa+KB9/bOmKmmE3\nnbzmxDnnXJVWrRoce2ysaWiHHayPiEhsCHRRwgCiTp3C584OJrfYbjs4/3wLOsJJ8sCGW9erZ8sE\nbLWVHatVy2pH3n/f9qOBQ9hkFPYtiVqzBt6MzJW+caPV+PTrF+tb0rOnDc++9FIrI8CvvxZfvnTJ\nuLV1youvreOcc64irV9vnXXDjrfFmTzZmpWWLCm8UKGq9S8J16ytX9/mMvn4Y5sAD6yJ5/PP4cgj\nbX/Dhvh+OBs3WqATrrv09de2GOSkSbaSdEl8bR3nnHMuC9SokVpgArFZdpOtoCwSC0zAJp6D+BFA\nDRvGOsh+9VXhDsLVq8ev/ByuURSuc5RpMiI4EZFOIjJWROaLSIGI9EjhmkNFJE9E1orIdyLSryLy\nWhkkLnWdrbyc2cXLmV2qSjmh4ss6aJAFMolDuXv0gPHjYzPaFqdOHRtlFB2qnEkyIjgB6gJfAAOw\nSV6KJSI7A68B7wLtgHuAR0XkyPLLYuVRVf5T8HJmFy9ndqkq5YTMKesWW9gyA6l2cG3ePLUh2OmQ\nEaN1VPVN4E0AkZQ+1guBOap6VbA/S0QOAQYCb5dPLp1zzjlXETKl5mRTdQTeSTj2FlDEElTOOeec\nqywqa3DSFFiUcGwR0EBEaqUhP84555wrIxnRrFNBagPMnDkz3fkod/n5+UydWu4jvdLOy5ldvJzZ\npaqUE6pGWSO/O2tXxPMybp4TESkAeqrq2GLSfADkqerlkWP9gWGqulUR1/QBni7j7DrnnHNVSV9V\nfaa8H1JZa04+AY5NOHZUcLwobwF9gXnA2vLJlnPOOZeVagM7Y79Ly11G1JyISF1gV0CAqcDlwHvA\nMlX9WURuB3ZQ1X5B+p2Br4DhwONAV+BuoJuqJnaUdc4551wlkinBSRcsGEnMzEhVPVtEngB2UtXD\nI9d0BoYBbYBfgJtUdXRF5dk555xz5SMjghPnnHPOuVBlHUrsnHPOuSxVJYITEblIROaKyBoR+VRE\n9i/5qvRJZa0hEblJRBaIyGoReVtEdk04X0tEHhCRpSLyh4i8KCLbJaTZSkSeFpF8EfldRB4N+v+U\nOxG5WkQ+E5EVIrJIRF4Rkd2SpKvs5bxARKYHz84Xkckickw2lTEZEflP8LM7NOF4pS+riAwKyhZ9\nzUhIU+nLGeRhBxEZHeRzdfCz3D4hTaUuq9jvhsTvs0BE7suWMgbPryYiN4vInKAcP4jIdUnSZUZZ\nVTWrX8Bp2OicM4E9gIeAZcA26c5bMXk+BrgJOAHYCPRIOP/voAzHA22BV4HZQM1ImgexkUldgH2B\nycCkhPu8gXVA3g84CPgOeKqCyvg6cAb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l+3jl4vXXhxkzLONs8+a2rFOvnllUwmWf2bNhyRLo1AnWWMMUlOnTS4/fpUvp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\nXG+8UTX8Dgnz0nTtaucrVqgedZTqc8+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"text/plain": [ "" ] }, "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": [ "" ], "text/plain": [ "" ] }, "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: 97.17%\n", "\tLabel: ship , confidence: 1.82%\n", "\tLabel: automobile, confidence: 0.51%\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 5]: [Training] loss = 2.132150 * 50000, metric = 0.00% * 50000 13.891s (3599.5 samples/s);\n", "Finished Epoch[2 of 5]: [Training] loss = 1.828141 * 50000, metric = 0.00% * 50000 13.548s (3690.6 samples/s);\n", "Finished Epoch[3 of 5]: [Training] loss = 1.676076 * 50000, metric = 0.00% * 50000 13.398s (3731.9 samples/s);\n", "Finished Epoch[4 of 5]: [Training] loss = 1.587138 * 50000, metric = 0.00% * 50000 13.441s (3720.0 samples/s);\n", "Finished Epoch[5 of 5]: [Training] loss = 1.516182 * 50000, metric = 0.00% * 50000 13.424s (3724.7 samples/s);\n", "\n", "Final Results: Minibatch[1-626]: errs = 48.0% * 10000\n", "\n" ] }, { "data": { "image/png": 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VPf2tt2CjjbLbCxfWv9bPGWdEoANw221wyCFNm1cRkeYwbtw4+vbtC9C3JdbR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\n6TXgM2DvVNreRGBSrVkht8bCzCaZ2Zl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w6qt2vNNOsO22yft++imsXQu5uWa9ef55a9+wofTldBzHcZwEZGKBQVWnAudn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\n5apff21lHFRVX31Vdd481Ztvtn4rVhS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"text/plain": [ "" ] }, "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 5]: [Training] loss = 1.510581 * 50000, metric = 0.00% * 50000 13.826s (3616.4 samples/s);\n", "Finished Epoch[2 of 5]: [Training] loss = 1.192194 * 50000, metric = 0.00% * 50000 13.504s (3702.6 samples/s);\n", "Finished Epoch[3 of 5]: [Training] loss = 1.077411 * 50000, metric = 0.00% * 50000 13.573s (3683.8 samples/s);\n", "Finished Epoch[4 of 5]: [Training] loss = 1.012250 * 50000, metric = 0.00% * 50000 13.409s (3728.8 samples/s);\n", "Finished Epoch[5 of 5]: [Training] loss = 0.941758 * 50000, metric = 0.00% * 50000 13.409s (3728.8 samples/s);\n", "\n", "Final Results: Minibatch[1-626]: errs = 30.0% * 10000\n", "\n" ] }, { "data": { "image/png": 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eFn5UGUIIoYm1y8c6oXlcckk+MHnlFe8ge+CB+VaRf/8bllvO50YBX+OnY0c4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\n2Vdf1X3eqio/7uqra7aQfP2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kYJSINE7RTIHWQLPgCPOwVF+23hquvRa2CjZ3btMGJk6E94MtjO64A7bZBpYs\ngY4dzYm2QwdTYj75BB54oOpkdxzHcZwckY1y0gfoBvwoIqNFZDTwY1DWJ0s5+gODVHWoqk4P+lkL\nXJi6GYtVdVF4ZDl2fqIKs2bZeb9+sfI6dWDSJPjiC7seNMisKgceWPkyOo7jOE4FkLFyoqoTMefY\nm7EN/6YANwG7BfcyQkTqAJ2J7XCMqiowGjg4VVPgCxGZJyLvisghKepWPwYNstedd4YddoBRo+z6\n669hxoxYvT7Z6oOO4ziOk59ku/HfGuDxHMnQGAs/XhhXvhBok6TNfOAPwGdAPeASYIyIHBhsSlj9\nOe00WLgQbrrJro89NuYU2707/POf8OmnULdurM2KFfDOO9CtG2y3XeXL7DiO4zg5IKsc6iJyjoj8\nL7BatArK+ovIKbkVLzGqOlNVn1DVz1V1gqpeBIzHlocKgx13hAEDYIsE+uMuu1gIcfPmpcsXLoRe\nvSy5m+M4juNUUzK2nIjIZcCtwP3Y0k6YdG05Fk78WoZdLgGKgaZx5U2BBRn0MxE4tLxK/fv3p2Fc\nBtZevXrRq1evDIbKU1q1MifZWbMs94njOI7jZMiwYcMYNmxYqbKVK1dWqgyiyfJnJGsg8jVwo6q+\nKiKrgA6qOltE2gNjVDVVhE2yPicAn6jq1cG1AHOBB1X1rjT7eBf4WVVPT3K/E1BUVFREpzBUtxBp\n1AhWroQNG8x51nEcx3E2k0mTJtG5c2eAzqo6qaLHy2ZZZ1fg8wTl64Gts5TjXuASETlXRPYCHgPq\nA0MAROQOEXk6rCwiV4vIySKyu4i0E5H7sd2SH85y/MIh1G5Hj65aORzHcRwnS7JRTuYA+yUoP44s\n85yo6gjgWmy56HNgX6Cbqi4OqjQDWkSa1MXyokwBxmCJ4Y5W1THZjF9QhInYli0re2/+/FgUkOM4\njuPkKdlE69wLPCIiW2LhvAeKSC8sCdvF2QqiqgOBgUnuXRB3fReQ1nJPjaNPH7jtNvjll9Lln38e\nyzzbo0fqaJ5Nm6B2bfNfiWfxYli1CnbdNfF9x3Ecx9lMsslz8m/gT8Dt2NLL88BlwNWq+p/ciudk\nRVER9O4NCxaY7wnAt9/G7r/9tr1On26bDkaZOdN8Vc47L3Hfzz4L7dpZVloRuPfe3MvvOI7j1Ggy\nUk7EaAm8pKqtgQZAM1XdWVWfrBAJncxp1gzq1bNQ40aNYP16OOUU2HJLi+Lp0sXq3Xef5UqJ0iZI\nLfPMM4mytVtAAAAgAElEQVT7HjUKDjnErCcA11xTMXNwHMdxaiyZWk4E+JbA/0NV1xZc2vhCY906\nW8apW9fOx4yBFi1sA8HHH7flm+JiaNIELrvMjkSomjVl1ChL8jZ/fqVOw3Ecx6k5ZKScqGoJ8A2w\nQ8WI4+SUdu3stVGj0uUlJbaBINjSzpNPmi/JY4/BwIGxrLRffhlr88MPMHSonZ9xRkw5mT7dLDPL\nl9v1hg223CNiVpVnn62YuTmO4zgFSzbROjcAdwV5TZx85oMP7PWcc5LXefZZePllO28a5MHr2xfO\nOgv22CNWr2XL2Pmuu8If/mDnrVqZJWX77c26ErWofPwx3OV+y47jOE5mZBOtMxRzhJ0sIhuAddGb\nqrp9LgRzckCTJrH9eKLUqgU//QRbbWVRO2GdBUFC3p12gueeK9vuvfdiETrPP2+7I2+5JXz0kZVN\nnVpaOdl3XwtdXrfOxnIcx3GcNMhGOekPZJZW1sk/dtopdv7nP8ONN5bf5re/jZ0feKAdAC++CKef\nDhdfDFdcYWVFRRb1M2gQTJyYOp3+ggWmSNXKaqsnx3Ecp8DIWDlR1SEVIIdTlRxxROLyK66ARx6x\npZvPP0+eG+W00+z1009tx+RPP7WcKmEiuKVLk4+9bJlFFf3rX3DddVlPwXEcxykc0v6pKiK1ROR6\nERknIp+KyJ0i4rb6Quapp+z1++9h7NjUdbt1s9dGjWD//e083GAxUbbakCeDCPSxY215aVpWSYZL\n8+9/m8K1fv3m9+U4juNUOpnY0W8C/gGsAn4CrgYeqQihnDyhR4/Y+caNqeu+844pF9GssbWDDasv\nucQihKIsWWJt/hPk7Xv9dXj1Vdh7bysL+zrjjORjrl8fSy43axZcfbWN849/mB/MxInpzdNxHMfJ\nKzJRTs4F+qrqcap6KtAdOFtE3FGgUHniCcsmu2YNnHpqdn106GDLQlHl5MMPYccd4fjjY2HOZ55p\n5QC9esWUjhdfTNzv7NnmjNu6teVsmTjRrDArV8aWkcaPz05mx3Ecp0rJRLFoCbwdXqjqaMwxdqek\nLZzqTZ06cNxxUL9+zAqSKV98Ad99B2vXwuTJVnbUUbH7p58ODz0Ew4bBlVfGyj/+OHW/u+8eO//q\nK/jb3yyZXL9+8PPPVv7qq9nJ7DiO41QpmSgnWwBxu8mxEaiTO3GcguXcc2G/YDPrMAvtXnvZeRjh\n07p1rH50b5/Vq2PnU6fCP/8Zu/7qK/jf/2DGDNvsMIwKWrIklufFcRzHqVZkopwIMEREXg4PYEvg\nsbgyxynL8cfbq6ploV2zxiKA4pk+3TLVjh1rFpbJk2HuXEsCN2WKhSa/+aYt3SxbZj4qW29tbY88\nEi680MbYYQcLTX7ttcS5XsrjhRdy45zrOI7jZEwmocRPJyjz3OROeoSRO++8Y4pK/fqJ67VpE9t8\n8P337fXDDy19/po1Ftp86KGWkTakc2d7vfvu0n2NH2++MsOHm2Nt//7mp3LnnallXbo05gycjWLj\nOI7jbBZpKyeqekFFCuLUEE44wZxjo1E9qZg5M+ajEoY2//RT6ToHHACbNpX1izngAHvt2dMigF55\nxa7vuCP1+J06xc7jI5BSsXSpZdE988z06mfCpk3mAzRypOWScRzHKWA80sapHDp2tNdu3dL/sgdL\n0Bby73/ba7gBYZREDrsNGsTOQysMlK88rFhhrzfdZEpBIhYvts0O16+37LZvvw2NG5eONMoFK1ea\nX84TT9j1ySfnrm/HcZw8xZUTp3Jo08asEO+8k1m7cPdksCyyTZpAly7pt58500KZP/4Yxo2zshEj\n4K23zCfll4iPd7iE066dpfS//XazViSiSRPYc09L0794sSkRIVHH3s3lmWfMB6dv39z16TiOk+fk\njXIiIpeLyBwRWSciE0TkgDTbHSoiG0VkUkXL6FQRqnZcdx0sXJhZ29atLZR5771tueb44+Gbb+DE\nE63PN96wehMnwkEHWUjy+PGWyK08liwx/xeAtm1hzBg7D/uMZ/VquPXW8hPaRVm0yF4nTIgpasXF\n6bd3HMephmSz8V/OEZGewD3ApcBEbHPBUSKyp6ouSdGuIeaoOxpoWhmyOtWYLbc0iwnYks/q1eYo\n+/XXppiA7Qs0YECszbBh0KyZRQKFRK0kIfvua8tViRxo//pXs8Z8/7313aAB/PGP6cn85ZdwzDEm\n38KFZu2ZPdusNjfcYP4zjuM4BUa+WE76A4NUdaiqTgf6AGuBC8tp9xjwHDChguVzCo2oBWbOnNh5\nr16l6511ljnkRndxDn1fwCwsy5en9qO57Tbzcwkjlq65pqwSs3gxPP+8WVU2bIiVT5tmVhmArbaC\nevVMMYHyo44qkqlT4e9/twiq+K0JHMdxNpO0LCcikrYXnqqOzEQAEakDdMb27Qn7UBEZDRycot0F\nwK7A2cBfMhnTcahf3/xf6tSxxG3XXWfWjWjyNzBLRUkJzJ8fK7vmGthlF7OmREOaQ375xfb66dMH\nbr45Vt6qVey8f39bPpo4EX7/e4skOuMMs4T8+KMpPGvX2hJUv36l+3/2Wejd2843bYItcmQAzSQy\n6dRTzYJz883mTzR9em5kcBzHIf1lnXTzgCuQaZ7zxkGbeGeChUCbRA1EpDWmzBymqiWSSfSH44SE\nOymDOdsm4sEHLYNt166WAK5PH0uLf9ppyfvdKrJZ9znn2Outt9pWABdcANttB/fea+Vz58ZCnOvW\nteWbxYvtesoU8y8Jd3kOOftsOPhgc8aNWi0WLzY/mNDSki5r11o23e7dTanabbfy2zzxBBx9tJ2v\nWpXZeI7jOOWQlnKiqvmy/EOw0eBzwABVnRUWp9u+f//+NAzN6wG9evWiV7w533HAomROPRX+7/9i\nVoVTTzXrSL16idvstVfMktC7N9x3n/mtQCxXy88/2/JQq1Yx/5ff/MZypSxaZMslX31llp327cuO\nsdtuZZWIJk3s9YcfYOedy5/bmjV29OhhuziD7Vm03XYmR7zSP2aMWYseeMCWu8DCukMFzHGcgmDY\nsGEMGzasVNnKRL52FYjoZmTAFJEtVTV+v51M+6iD+ZecFl0SEpEhQENV/V1c/YbAcmATMaWkVnC+\nCThWVcckGKcTUFRUVESnaJItx0mX996DY4+181R/N6qxKJ7//Q8++8yy2EZzsWzYEFNunn7alpOW\nLrUw58suMyXggw9sOSma6yUVoTKxcWN6Sz3t25sC9N575nQbZc4cW7pK1H84R8dxagyTJk2is2Xj\n7qyqFR4dm/FitYjUBm7EnFabBhE1s0XkNuA7VX0yk/5UdaOIFAFHAyODMSS4fjBBk5+B+J+SlwNH\nAqcB32UyvuOkzTHHwB57wOGHp64nYv4kIQceWLZO3brw7rvmJHvggeZ30qBBbHPEDz+011SKyaZN\nZvlo2DAWQbTNNqaYLF9uFpBUfPVVbIyLLrJootGjreydd2wJKxEVpdwvWwaNGpmfD1ikkirss0/5\nbV980d6zpk1hxx0tkd6f/1wxcjqOU/GoakYH8FdgFuaIuhbYLSjvCXycaX9B2x5BX+cCewGDgKXA\njsH9O4CnU7QfAEwqZ4xOgBYVFanj5DUHHGCZXcrj/vut3ty5qitW2LmI6rPP2vnSpcnbvvNOmD2m\ndPnGjYnL162zsiFDEvf35puq3bqplpSklnnmzFj/kyap/vCDlRcXW9nNN8fqJpIjSnFx2bqg2q+f\n6u67p5bDcZyMKCoqUsyvtJNm8T2f6ZGNL8m5wKWq+hwQzQY1OVAsMkZVRwDXArcCnwP7At1UNfAM\npBnQIpu+Hafa8d57ZjUoj++/t9eWLe1r+aSTLJJn112t/OCkwW6x6KOwbsgWW9heRAMH2tLTtGkw\nY4ZZen74wfxtEnHiiTBqlDnkgi1l3X576TqqsWUxML+bFi0szHr9eit79dXYeSo++MCWyWbMsOuo\n03DHjubYu2ZN+f04jpOfZKrNAOuAVsH5KmKWk72B1ZWhUWVz4JYTp9CYPj1mLZgyJVYeWlG22cau\nN2xQnTatbPtnnlFdtSp5//36xfofMSK1LGedZfXuv1911KjS7UD18cdLW2X+9jfVPn1i1489prrb\nbnZ+1lmqRxxh5x06mPxr19o4n36q2rWr6meflbaWqKo2bqz68MOq775rZd26lZXzySdVX3st9VxU\nVX/6SXXCBDs/7DDV3r3Lb1PdWbOmqiVw8pjKtpxk8yVfBPTWssrJX4GxlSF0VhN15cQpRMaOtT/j\nDz8sXd67t5X//LPqjTfGzlXLX3oJiX75n3FG6rpr18bqXn117Pzoo0srEG3aqB5+uJ0PGhS7N368\narNmdj5xYqz8iy9Ut9jCzn/4QbVePTufPbuschLy+uuJy6NzKo/u3VXr17f3Kt021Zlx42yOfftW\ntSROnlLZykk22ZtuBZ4Wkf/DomR+LyJtsOWek7Loz3GcbDn0UFsCateudHnPnrbE8+WXsSWir76y\nFP4dO8LkyZZyPxVDh1pYc/v2Fuacimhul6OOstDrBQvglFNghx0sCmn1apMhjPq59FLL/bJxo4Uw\nd+hgbTp0sGihL7+083Bn6KOPji35PPCARTeNH196bIADgm25PvqodJK6mTPttX17SyB30knm+Nuy\npW1hsOOOdoAtaR11lG1nEL6fuWLFCoveuv9+Cwf/5z9tPuU5MFcUn30Wiy7bHGfnBQssKWHdumXv\nrVhhjs7bbpt9/07NIhuNBugCvAcswhxZ/4eF8Fa5hSSFzG45cWoOa9bYL+HBg2NLLiNHqrZta+f/\n+lfuxxw61JZn4vnwQxvzpptSt1+6VPWNN8qWL15c2koCqt9+W748U6ZY3XB5Jmw7bpzqNdfYeZMm\nqps22fkee1i9Sy6x6xtvtGUgUF2/Ptb+hRfKHzueBQtUr73WlqguuyzW10UX2Ws2/5dKSmxpb889\nbYlM1Zb0FizIrJ/QsTqRdWjyZFtCS0eWVBa2unXNEuVUW6qDQyyqOlZVj1HVJqpaX1UPU9V3c6Es\nOY6TA+rXt1/Ep51m1o/TT7eEb9Om2f3QmTaXnHMO/OEPZcvD8Oi//z11++23N8faeBo3tv2J3nrL\nnFzXrzdLS3m0bGmvJ58Mb78dKz/kELMegb03b75p599+a69PPGGvDRqYNaBx49LWgBdeKD1Ojx52\npOKzz+Duu62fyy+Pld94o7127mzz2rgRxo6NWYtCJkwovcnjihXw+uuW8G/mTGsTJt8LE/6lS5jh\nd8qUsvfGji3/uUHs8/TCC/DII2Xvb9hgmYgdJ02yzvwqIvuLyDnB0TmXQjmOkwM6d7a8J6NH25JB\n+IW+777pfeHkikaNLFPuJZdk38fNN8Pxx5vSlWjZIBENG9qy0aJFcMIJcP75sXmffXas3tixiduf\ncgrMmxfLNfOPYPuv+MzAL7xQVmGJ5/jjY+fz5lkG4REjSkdLNW1qS1SHH26bQEb55BNTZETgllvs\neZ5ySuz+iy+aMrZ6tV0XF5OQ006zLRmiNGpkG16G+WT697f3adMmU4JeeSWmRCVj4sTY+X33Ja93\n9tk2h2TyOU5IpqYWYGdgLFACLAuOEmxpZ+fKMPdkc+DLOk5NZPVqM7dffrldR3OD1ATC5Yrzzkte\n54QTrE7LlrbEs2KF6ldf2b2DDrIjpHt31ZNOil1/911sjHXrEve/zTbmCBzW69699P2HHtJf876E\ndeIjjR5/vOzS1qWXWl6bsWNjeWKiS0/169tS2dSpqh99pPrJJ8mXb6L89rexJaeXXoq1CZ2u338/\n8Vx//tnGDd+7KBdfXFq+f/wjtQxO3lEdonXeASYAbSJlbYDxwDuVIXRWE3XlxKmpLFliX7o1kS++\nsH9ziUKpo6xcqfrxx2XLL7lE9Z57YtdFRaZEbNxofS9apNq5s42RyDfjggtiX8irVqm+955FGiUj\nGoYdJYxOioZfJ2LhQvP76NXL6rz6almlpjzlpH37WL0ff4yd//KL+fqA6hVXWN10w4+XLDEFZf78\n9GRQNdknT06vf6fCqQ7KyTqgY4LyzsDayhA6q4m6cuI4Tq6IftFv2GCvDRvavZIS1X33Vb3hhlid\nL79Mv++//93Cp1XNIjJxYiwMXNVCpaN5bRKxyy6JlRIo/wt/wACr16WLXU+fbsrYp5/G+mjd2hS1\nUGFKxbJlpa/DPoqLVb/+WnXXXVVPOaVsu513NqfkVLz5plmF0qGkJD3nXich1cEh9gegToLy2sC8\nLPpzHMepPkT9Jbp2tZ2jL7nE/CmaNLGQ2SlT4M47rc6555YN9U7FFVeYIy6YE+yBB8b2WgILgS5v\nv6Hozuvhfklgu2mXF0I+YIBl+n3xRbtu08bCsVesiNW5777Y/lGPPRZztI5n7lxzdB4xIlb2l7/Y\n64svwt57W9j4a6+ZyhKyYQP8+GPZnbejTJ9uDtRdu8b2lkrEDz+YU/Trr1sm4URO2+ny3Xe2s/i8\n4KvuppvMKdnJPZlqM8ApwCfA/pGy/YGPgVMrQ6PK5sAtJ47j5IqnnrJf/8uXly6PWigmTLAlkvnz\nsx8n7GvixMzalZTYkkuYAbi4uLS/Ubg8NGNG+n2GifbCfYuic03mbxMuq73/fqwstLiES0Th8fLL\nqqNHx8Lgw1D4MBT97rtL992lS6ze+ecnlzvTZa1UhO332Uf1gw/Kn38BkZeWExFZLiLLRGQZMBjY\nD/hERNaLyPpAWekEPJVTzclxHCcfueAC+1pq1Kh0+WWXmaVj4kQ46CCYOjXz0N6QWbNi5+mETkcR\nscimBg3sulat2G7PYNYOMKtIumy1le1l9Pnndj1oEFx/PZSUWHK/RIThw02bxso6dbL3bvfdzfJw\nxx1mRXn1VejXr3Q01BlnwJFH2vnGjdbumWcsad6BB9ocd9jBotLuuSdmcUrEb38bi1Q66KD0552I\nGTNi+zoBjBmTXru1a6F79+TRXfHWo9NOS7/vAiPdDLH9KlQKx3GcQmDgwNz11by5ZdHt2dOWRnLJ\nTz9ZFl+w8ONQiSmPPfeMnV96aeI6Rx5pisxbb9nmjpA8M2x0c8gxYywrcDTU+aOPYud//KMpIeGG\njosXW+4YsKWh3XaDa6+15adttjEF5K67TFE7/3xbjgmXoiZOtCWaXXaxTMMTJiTOsRMyenQsezCY\n4rjzztb33Lk25+eesyWz8pbc3njDjqgiArZM1batZUW+7jqbw8svm+J3xBGp+yxEKsM8kw8Hvqzj\nOI4To18/C6PONdGlk+HDEy9/JeKee8ouu4RZfo85xkKio0sz0Qi0aCj1Pfeozplj5wcfbPMM/+9v\n2mQh2G3aWGTTYYfF2hUXW2h4Itq2Vb3ySltyGjcu+bw7dChbXlJie12FS1sHHWR1n3uudL3oRp6g\neuedsfNE+2E9+GD5G3LmkLyP1inVGLYEto0elSF0lrK6cuI4jlPRPPKIRRtt2KDarl3yL9d4wm0E\n4n1Cli41n47iYtVbbrFtGN57r2z777+3KKUuXVTfftv6GTky8VglJaXHA9Wbb7bXp58uXS8MB3/o\nodTyh/lyvv++dHm4SzioXnVV6fDumTNL1w3LR41SffbZ2HW8b1C4BULz5qllirJuneWt+eKL9Df/\njJCXPidRRGRrEXlYRBYBa4DlcYfjOI5TU2nXzrLLHnKIbfQIsc0eU1G7tvmVhEtBIdtvb0sbtWpZ\nJFH37uY/Ek/LlrZZ49ixUFRk9U84IfFYIjZeyLp1sSWm6FLS1KkweLCdH3JIavlD/6NWrUpHNoWb\nVYZzPPlky8jbqBHssYf5n4Rjjx0L770Hxx5r0V833WTlO+8c62PtWnj0UTuPz/abiiFDzIdlv/1i\ny4+jRsW2L8gzRFXLrxVtIPIIcCTwF+AZ4HLg/4A/ADeo6nO5FjIXiEgnoKioqIhOm7PzpuM4jpOc\nZcvMSRVsT6MGDeCwwypn7OnTze+kY0d4/HFYuDB1/Q0bTDFp2DCmQF18sfmf7LgjHHxwTIkpKUmt\nZG3cGNtaIb7u6afDSy/BuHGllZyvv46FmX/zjSkrUYqLzZ9mjz1sTpddZmV9+9r9TL6/o/J8841t\nS3D99ba1wltvldt80qRJdO7cGaCzqk5Kf+DsSNchNkp34FxVHSMig4GxqvqtiHwPnA3kpXLiOI7j\nVAKh8+7xx9veRpXJXnuZs2mPHqYglEfdunaomnXippvgootiSka4sJIOdepYHpkVK8oqMZdcArNn\n235XUW69NXY+b15Z5aR27VjZqlVw1VWWr6VtWxg61GS78kpTrrbayuRPpkBdcYUpi++/b9ad66+3\n8o8/Tm9+lUw2Sdi2B2YH5z8H12B76xyerSAicrmIzBGRdSIyQUQOSFH3UBH5n4gsEZG1IjJNRDyi\nKGDYsGFVLUKl4PMsLHyeBYQqw845p+rGf+EFSySXLiKWrK1PH0s4FxK/xJSEX59pvXoWNr1xI7zz\njkUCAXTrBpMmld00cvBgOO88C48+vJy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46eOWE8dxHMdx8gpXThzHcRzHyStcOXEcx3Ec\nJ69w5cRxHMdxnLzClRPHcRzHcfIKV04cx3Ecx8krXDlxHMf5/3brWAAAAABgkL/1NHYURcCKnAAA\nK3ICAKzICQCwIicAwIqcAAArAabcSfZxrobCAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "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 5]: [Training] loss = 2.252732 * 50000, metric = 0.00% * 50000 14.907s (3354.1 samples/s);\n", "Finished Epoch[2 of 5]: [Training] loss = 1.924135 * 50000, metric = 0.00% * 50000 13.797s (3624.0 samples/s);\n", "Finished Epoch[3 of 5]: [Training] loss = 1.711592 * 50000, metric = 0.00% * 50000 13.737s (3639.8 samples/s);\n", "Finished Epoch[4 of 5]: [Training] loss = 1.580235 * 50000, metric = 0.00% * 50000 13.677s (3655.8 samples/s);\n", "Finished Epoch[5 of 5]: [Training] loss = 1.476467 * 50000, metric = 0.00% * 50000 13.582s (3681.3 samples/s);\n", "\n", "Final Results: Minibatch[1-626]: errs = 49.7% * 10000\n", "\n" ] }, { "data": { "image/png": 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pac3J34DewEbuvry7Lw8MJhb9a+FyZiJSTFsGJgCXXw5/+EP+cy5IOffc2E6a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\ncweZ2QnAp0CNuy9Kkt8suO8s4AR3d+A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PjzzS9nl58MYbcOGF0LIlDBxo5S+9VLht8+a2f/pp2z/5JCxebLMytWtbVuUj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\nlk8/NXuYV14xe5fhwy1c//Ll0dD6kbg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"text/plain": [ "" ] }, "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": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 23, "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": 24, "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": 25, "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": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training 272474 parameters in 65 parameter tensors.\n", "\n", "Finished Epoch[1 of 5]: [Training] loss = 1.860499 * 50000, metric = 0.00% * 50000 17.333s (2884.7 samples/s);\n", "Finished Epoch[2 of 5]: [Training] loss = 1.577205 * 50000, metric = 0.00% * 50000 16.166s (3092.9 samples/s);\n", "Finished Epoch[3 of 5]: [Training] loss = 1.450118 * 50000, metric = 0.00% * 50000 16.113s (3103.1 samples/s);\n", "Finished Epoch[4 of 5]: [Training] loss = 1.342510 * 50000, metric = 0.00% * 50000 16.078s (3109.8 samples/s);\n", "Finished Epoch[5 of 5]: [Training] loss = 1.257909 * 50000, metric = 0.00% * 50000 16.147s (3096.6 samples/s);\n", "\n", "Final Results: Minibatch[1-626]: errs = 45.8% * 10000\n", "\n" ] }, { "data": { "image/png": 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I8+Z5wPL003DssbDHHr5v0KD8MZLXyFx2GfznP94EdPbZsPHGXktiBjfd5McP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\ngeHAtsAXwKhUkNAVeBXYB9gauAb4t6R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PPgi9epnNiuM4juNUEmkpJ6r6frJrpwbxa7AB9Pnnww47xNL328/irEydCsuW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\nRJIkdcVwIkmSumI4kSRJXTGcSJKkrvwD/hunXoWXpIAAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "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": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }