From 5b9376958802043ac2e011e0b9e2fc0ec92ae2a3 Mon Sep 17 00:00:00 2001 From: Emad Barsoum Date: Mon, 24 Oct 2016 20:42:43 -0700 Subject: [PATCH 1/2] Image hand on notebook --- .../CNTK_201A_CIFAR-10_DataLoader.ipynb | 339 ++++++ .../CNTK_201B_CIFAR-10_ImageHandsOn.ipynb | 998 ++++++++++++++++++ 2 files changed, 1337 insertions(+) create mode 100644 bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb create mode 100644 bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb diff --git a/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb b/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb new file mode 100644 index 000000000..b115f727f --- /dev/null +++ b/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb @@ -0,0 +1,339 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CNTK 201A Part A: CIFAR-10 Data Loader\n", + "\n", + "This tutorial will show how to prepare image data sets for use with deep learning algorithms in CNTK. The CIFAR-10 dataset (http://www.cs.toronto.edu/~kriz/cifar.html) is a popular dataset for image classification, collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. It is a labeled subset of the [80 million tiny images](http://people.csail.mit.edu/torralba/tinyimages/) dataset.\n", + "\n", + "The CIFAR-10 dataset is not included in the CNTK distribution but can be easily downloaded and converted to CNTK-supported format \n", + "\n", + "CNTK 201A tutorial is divided into two parts:\n", + "- Part A: Familiarizes you with the CIFAR-10 data and converts them into CNTK supported format. This data will be used later in the tutorial for image classification tasks.\n", + "- Part B: We will introduce image understanding tutorials.\n", + "\n", + "If you are curious about how well computers can perform on CIFAR-10 today, Rodrigo Benenson maintains a [blog](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html#43494641522d3130) on the state-of-the-art performance of various algorithms.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from __future__ import print_function\n", + "\n", + "from PIL import Image\n", + "import getopt\n", + "import numpy as np\n", + "import pickle as cp\n", + "import os\n", + "import shutil\n", + "import struct\n", + "import sys\n", + "import tarfile\n", + "import xml.etree.cElementTree as et\n", + "import xml.dom.minidom\n", + "\n", + "try: \n", + " from urllib.request import urlretrieve \n", + "except ImportError: \n", + " from urllib import urlretrieve\n", + "\n", + "# Config matplotlib for inline plotting\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data download\n", + "\n", + "The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. \n", + "There are 50,000 training images and 10,000 test images. The 10 classes are: airplane, automobile, bird, \n", + "cat, deer, dog, frog, horse, ship, and truck." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# CIFAR Image data\n", + "imgSize = 32\n", + "numFeature = imgSize * imgSize * 3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first setup a few helper functions to download the CIFAR data. The archive contains the files data_batch_1, data_batch_2, ..., data_batch_5, as well as test_batch. Each of these files is a Python \"pickled\" object produced with cPickle. To prepare the input data for use in CNTK we use three oprations:\n", + "> `readBatch`: Unpack the pickle files\n", + "\n", + "> `loadData`: Compose the data into single train and test objects\n", + "\n", + "> `saveTxt`: As the name suggests, saves the label and the features into text files for both training and testing. \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def readBatch(src):\n", + " with open(src, 'rb') as f:\n", + " if sys.version_info[0] < 3: \n", + " d = cp.load(f) \n", + " else:\n", + " d = cp.load(f, encoding='latin1')\n", + " data = d['data']\n", + " feat = data\n", + " res = np.hstack((feat, np.reshape(d['labels'], (len(d['labels']), 1))))\n", + " return res.astype(np.int)\n", + "\n", + "def loadData(src):\n", + " print ('Downloading ' + src)\n", + " fname, h = urlretrieve(src, './delete.me')\n", + " print ('Done.')\n", + " try:\n", + " print ('Extracting files...')\n", + " with tarfile.open(fname) as tar:\n", + " tar.extractall()\n", + " print ('Done.')\n", + " print ('Preparing train set...')\n", + " trn = np.empty((0, NumFeat + 1), dtype=np.int)\n", + " for i in range(5):\n", + " batchName = './cifar-10-batches-py/data_batch_{0}'.format(i + 1)\n", + " trn = np.vstack((trn, readBatch(batchName)))\n", + " print ('Done.')\n", + " print ('Preparing test set...')\n", + " tst = readBatch('./cifar-10-batches-py/test_batch')\n", + " print ('Done.')\n", + " finally:\n", + " os.remove(fname)\n", + " return (trn, tst)\n", + "\n", + "def saveTxt(filename, ndarray):\n", + " with open(filename, 'w') as f:\n", + " labels = list(map(' '.join, np.eye(10, dtype=np.uint).astype(str)))\n", + " for row in ndarray:\n", + " row_str = row.astype(str)\n", + " label_str = labels[row[-1]]\n", + " feature_str = ' '.join(row_str[:-1])\n", + " f.write('|labels {} |features {}\\n'.format(label_str, feature_str))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to saving the images in the text format, we would save the images in PNG format. In addition we also compute the mean of the image. `saveImage` and `saveMean` are two functions used for this purpose." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def saveImage(fname, data, label, mapFile, regrFile, pad, **key_parms):\n", + " # data in CIFAR-10 dataset is in CHW format.\n", + " pixData = data.reshape((3, ImgSize, ImgSize))\n", + " if ('mean' in key_parms):\n", + " key_parms['mean'] += pixData\n", + "\n", + " if pad > 0:\n", + " pixData = np.pad(pixData, ((0, 0), (pad, pad), (pad, pad)), mode='constant', constant_values=128) \n", + "\n", + " img = Image.new('RGB', (ImgSize + 2 * pad, ImgSize + 2 * pad))\n", + " pixels = img.load()\n", + " for x in range(img.size[0]):\n", + " for y in range(img.size[1]):\n", + " pixels[x, y] = (pixData[0][y][x], pixData[1][y][x], pixData[2][y][x])\n", + " img.save(fname)\n", + " mapFile.write(\"%s\\t%d\\n\" % (fname, label))\n", + " \n", + " # compute per channel mean and store for regression example\n", + " channelMean = np.mean(pixData, axis=(1,2))\n", + " regrFile.write(\"|regrLabels\\t%f\\t%f\\t%f\\n\" % (channelMean[0]/255.0, channelMean[1]/255.0, channelMean[2]/255.0))\n", + " \n", + "def saveMean(fname, data):\n", + " root = et.Element('opencv_storage')\n", + " et.SubElement(root, 'Channel').text = '3'\n", + " et.SubElement(root, 'Row').text = str(ImgSize)\n", + " et.SubElement(root, 'Col').text = str(ImgSize)\n", + " meanImg = et.SubElement(root, 'MeanImg', type_id='opencv-matrix')\n", + " et.SubElement(meanImg, 'rows').text = '1'\n", + " et.SubElement(meanImg, 'cols').text = str(ImgSize * ImgSize * 3)\n", + " et.SubElement(meanImg, 'dt').text = 'f'\n", + " et.SubElement(meanImg, 'data').text = ' '.join(['%e' % n for n in np.reshape(data, (ImgSize * ImgSize * 3))])\n", + "\n", + " tree = et.ElementTree(root)\n", + " tree.write(fname)\n", + " x = xml.dom.minidom.parse(fname)\n", + " with open(fname, 'w') as f:\n", + " f.write(x.toprettyxml(indent = ' '))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`saveTrainImages` and `saveTestImages` are simple wrapper functions to iterate through the data set that ca" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def saveTrainImages(filename, foldername):\n", + " if not os.path.exists(foldername):\n", + " os.makedirs(foldername)\n", + " data = {}\n", + " dataMean = np.zeros((3, ImgSize, ImgSize)) # mean is in CHW format.\n", + " with open('train_map.txt', 'w') as mapFile:\n", + " with open('train_regrLabels.txt', 'w') as regrFile:\n", + " for ifile in range(1, 6):\n", + " with open(os.path.join('./cifar-10-batches-py', 'data_batch_' + str(ifile)), 'rb') as f:\n", + " if sys.version_info[0] < 3: \n", + " data = cp.load(f)\n", + " else: \n", + " data = cp.load(f, encoding='latin1')\n", + " for i in range(10000):\n", + " fname = os.path.join(os.path.abspath(foldername), ('%05d.png' % (i + (ifile - 1) * 10000)))\n", + " saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, regrFile, 4, mean=dataMean)\n", + " dataMean = dataMean / (50 * 1000)\n", + " saveMean('CIFAR-10_mean.xml', dataMean)\n", + "\n", + "def saveTestImages(filename, foldername):\n", + " if not os.path.exists(foldername):\n", + " os.makedirs(foldername)\n", + " with open('test_map.txt', 'w') as mapFile:\n", + " with open('test_regrLabels.txt', 'w') as regrFile:\n", + " with open(os.path.join('./cifar-10-batches-py', 'test_batch'), 'rb') as f:\n", + " if sys.version_info[0] < 3: \n", + " data = cp.load(f)\n", + " else: \n", + " data = cp.load(f, encoding='latin1')\n", + " for i in range(10000):\n", + " fname = os.path.join(os.path.abspath(foldername), ('%05d.png' % i))\n", + " saveImage(fname, data['data'][i, :], data['labels'][i], mapFile, regrFile, 0)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# URLs for the train image and labels data\n", + "url_cifar_data = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n", + "\n", + "# Paths for saving the text files\n", + "data_dir = './data/CIFAR-10/'\n", + "train_filename = data_dir + '/Train_cntk_text.txt'\n", + "test_filename = data_dir + '/Test_cntk_text.txt'\n", + "\n", + "train_img_directory = data_dir + '/Train'\n", + "test_img_directory = data_dir + '/Test'\n", + "\n", + "root_dir = os.getcwd()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n", + "Done.\n", + "Extracting files...\n", + "Done.\n", + "Preparing train set...\n", + "Done.\n", + "Preparing test set...\n", + "Done.\n", + "Writing train text file...\n", + "Done.\n", + "Writing test text file...\n", + "Done.\n", + "Converting train data to png images...\n", + "Done.\n", + "Converting test data to png images...\n", + "Done.\n" + ] + } + ], + "source": [ + "if not os.path.exists(data_dir):\n", + " os.makedirs(data_dir)\n", + "\n", + "try:\n", + " os.chdir(data_dir) \n", + " trn, tst= loadData('http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz')\n", + " print ('Writing train text file...')\n", + " saveTxt(r'./Train_cntk_text.txt', trn)\n", + " print ('Done.')\n", + " print ('Writing test text file...')\n", + " saveTxt(r'./Test_cntk_text.txt', tst)\n", + " print ('Done.')\n", + " print ('Converting train data to png images...')\n", + " saveTrainImages(r'./Train_cntk_text.txt', 'train')\n", + " print ('Done.')\n", + " print ('Converting test data to png images...')\n", + " saveTestImages(r'./Test_cntk_text.txt', 'test')\n", + " print ('Done.')\n", + "finally:\n", + " os.chdir(\"..\\..\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb b/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb new file mode 100644 index 000000000..23d24692a --- /dev/null +++ b/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb @@ -0,0 +1,998 @@ +{ + "cells": [ + { + "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 advance feature to your network. Finally, you will implement a residual nets similar to the one that won ImageNet competition but smaller in size.\n", + "\n", + "## Introduction\n", + "\n", + "In this hands-on, you will practice the following:\n", + "\n", + "* Understanding subset of CNTK python primitive needed for image classification task.\n", + "* Write a custom convolution network to classify Cifar dataset.\n", + "* Modifying the network structure by adding:\n", + " * Drop out layer.\n", + " * Batchnormalization layer.\n", + "* Switching the above networks to CNTK layer API.\n", + "* Introduction to Residual Nets (RESNET).\n", + "* Implement and train RESNET network.\n", + "\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.\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. \n", + "\n", + "\n", + "\n", + "The stack of feature maps output are the input to the next layer.\n", + "\n", + "\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:\n", + "\n", + "\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", + "### 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", + "## Computational 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": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + " \n", + "from cntk.blocks import * # non-layer like building blocks such as LSTM()\n", + "from cntk.layers import * # layer-like stuff\n", + "from cntk.models import * # higher abstraction level, e.g. entire standard models and also operators like Sequential()\n", + "from cntk.utils import *\n", + "from cntk.io import MinibatchSource, ImageDeserializer, StreamDef, StreamDefs\n", + "from cntk.initializer import glorot_uniform, he_normal\n", + "from cntk import Trainer\n", + "from cntk.learner import momentum_sgd, learning_rate_schedule\n", + "from cntk.ops import cross_entropy_with_softmax, classification_error, relu\n", + "from cntk.ops import convolution, pooling, PoolingType_Max, dropout" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we imported the needed modules, let's implement our first CNN, as shown below:\n", + "\n", + "\n", + "\n", + "To implement the above, you will first implement each of the building blocks using CNTK low level python API." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def conv_layer(input, num_filters, filter_size, init, strides=(1,1), nonlinearity=relu):\n", + " if nonlinearity is None:\n", + " nonlinearity = lambda x: x\n", + "\n", + " channel_count = input.shape[0]\n", + "\n", + " b_param = parameter(shape=(num_filters, 1, 1))\n", + " w_param = parameter(shape=(num_filters, channel_count, filter_size[0], filter_size[1]), init=init)\n", + " r = convolution(w_param, input, (channel_count, strides[0], strides[1])) + b_param\n", + " r = nonlinearity(r)\n", + "\n", + " return r\n", + "\n", + "def bn_layer(input, spatial_rank, nonlinearity=relu, bn_time_const=5000, b_value=0, sc_value=1):\n", + " if nonlinearity is None:\n", + " nonlinearity = lambda x: x\n", + "\n", + " dims = input.shape[0]\n", + "\n", + " bias_params = parameter((dims), init=b_value)\n", + " scale_params = parameter((dims), init=sc_value)\n", + " running_mean = constant(0, (dims))\n", + " running_invstd = constant(0, (dims))\n", + "\n", + " r = batch_normalization(input, scale_params, bias_params, running_mean, running_invstd, spatial_rank > 0, bn_time_const, use_cudnn_engine=True)\n", + " r = nonlinearity(r)\n", + " return r\n", + "\n", + "def conv_bn_layer(input, num_filters, filter_size, init, strides=(1,1), nonlinearity=relu, bn_time_const=5000, b_value=0, sc_value=1):\n", + " r = conv_layer(input, num_filters, filter_size, init, strides=strides, nonlinearity=None)\n", + " r = bn_layer(r, 2, nonlinearity=nonlinearity)\n", + " return r\n", + "\n", + "def dense_layer(input, num_units, init, nonlinearity=relu):\n", + " if nonlinearity is None:\n", + " nonlinearity = lambda x: x\n", + "\n", + " b_param = parameter(shape=(num_units))\n", + "\n", + " if len(input.shape) >= 3:\n", + " w_param = parameter(shape=(input.shape[0], input.shape[1], input.shape[2], num_units), init=init)\n", + " else:\n", + " w_param = parameter(shape=(input.shape[0], num_units), init=init)\n", + "\n", + " r = b_param + times(input, w_param)\n", + " r = nonlinearity(r)\n", + " return r\n", + "\n", + "def dense_bn_layer(input, num_units, init, nonlinearity=relu):\n", + " r = dense_layer(input, num_units, init, nonlinearity=None)\n", + " r = bn_layer(r, 0, nonlinearity=nonlinearity)\n", + " return r\n", + "\n", + "def max_pool_layer(input, pool_size, stride):\n", + " return pooling(input, PoolingType_Max, (1, pool_size[0], pool_size[1]), (1, stride[0], stride[1]))\n", + "\n", + "def dropout_layer(input, rate):\n", + " return dropout(input, dropout_rate=rate)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So now that we defined the basic block for each layer, we can start defining our first CNN:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def create_basic_model(input, out_dims):\n", + " net = {}\n", + "\n", + " net['conv1'] = conv_layer(input, 32, (5,5), init=glorot_uniform(scale=0.1557/256))\n", + " net['pool1'] = max_pool_layer(net['conv1'], (3,3), (2,2))\n", + "\n", + " net['conv2'] = conv_layer(net['pool1'], 32, (5,5), init=glorot_uniform(scale=0.2))\n", + " net['pool2'] = max_pool_layer(net['conv2'], (3,3), (2,2))\n", + "\n", + " net['conv3'] = conv_layer(net['pool2'], 64, (5,5), init=glorot_uniform(scale=0.2))\n", + " net['pool3'] = max_pool_layer(net['conv3'], (3,3), (2,2))\n", + "\n", + " net['fc4'] = dense_layer(net['pool3'], 64, init=glorot_uniform(scale=1.697))\n", + " net['fc5'] = dense_layer(net['fc4'], out_dims, init=glorot_uniform(scale=0.212), nonlinearity = None)\n", + "\n", + " return net" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before using the above model, we need to create a source reader in order to iterate through the labeled data." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": true + }, + "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", + " cifar_py3 = \"\" if sys.version_info.major < 3 else \"_py3\"\n", + " raise RuntimeError(\"File '%s' or '%s' does not exist. Please run CifarDownload%s.py and CifarConverter%s.py from CIFAR-10 to fetch them\" %\n", + " (map_file, mean_file, cifar_py3, cifar_py3))\n", + "\n", + " # transformation pipeline for the features has jitter/crop only when training\n", + " transforms = []\n", + " if train:\n", + " transforms += [\n", + " ImageDeserializer.crop(crop_type='Random', ratio=0.8, jitter_type='uniRatio') # train uses jitter\n", + " ]\n", + " transforms += [\n", + " ImageDeserializer.scale(width=image_width, height=image_height, channels=num_channels, interpolations='linear'),\n", + " ImageDeserializer.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's write the the training and validation loop." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "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", + "\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", + " # apply model to input\n", + " model = model_func(input_var, out_dims=10)\n", + " z = model['fc5']\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", + " # For basic model\n", + " lr_per_sample = [0.00015625]*10+[0.000046875]*10+[0.0000156]\n", + " momentum_per_sample = 0.9 ** (1.0 / minibatch_size) # BUGBUG: why does this work? Should be as time const, no?\n", + " l2_reg_weight = 0.03\n", + "\n", + " # For basic model with batch normalization\n", + " # lr_per_sample = [0.00046875]*7+[0.00015625]*10+[0.000046875]*10+[0.000015625]\n", + " # momentum_per_sample = 0\n", + " # l2_reg_weight = 0\n", + "\n", + " # trainer object\n", + " lr_schedule = learning_rate_schedule(lr_per_sample, units=epoch_size)\n", + " learner = momentum_sgd(z.parameters, lr_schedule, momentum_per_sample, \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')\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()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training 116906 parameters in 10 parameter tensors.\n", + "\n", + "Finished Epoch [1]: [Training] loss = 1.844780 * 50000, metric = 67.8% * 50000\n", + "Finished Epoch [2]: [Training] loss = 1.476277 * 50000, metric = 52.9% * 50000\n", + "Finished Epoch [3]: [Training] loss = 1.315467 * 50000, metric = 46.5% * 50000\n", + "Finished Epoch [4]: [Training] loss = 1.201646 * 50000, metric = 42.1% * 50000\n", + "Finished Epoch [5]: [Training] loss = 1.141895 * 50000, metric = 39.7% * 50000\n", + "\n", + "Final Results: Minibatch[1-626]: errs = 35.5% * 10000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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Umpx+Ouy8M1RWwjHHwOabey+gHXaAL7+E6dOz1541C777zicwlGD8EpVzIYQQ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\nnnu89ONf/1qygesVV2SXt90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gIlcBw0Skp6quKbF2TWODDeCyy+C882yWpXZtKCwM7VMcx3EcJ4dI6e0kIhvH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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "reader_train = create_reader(os.path.join('data', 'train_map.txt'), os.path.join('data', 'CIFAR-10_mean.xml'), True)\n", + "reader_test = create_reader(os.path.join('data', 'test_map.txt'), os.path.join('data', 'CIFAR-10_mean.xml'), False)\n", + "\n", + "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Adding dropout layer 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", + " net = {}\n", + "\n", + " net['conv1'] = conv_layer(input, 32, (5,5), init=glorot_uniform(scale=0.1557/256))\n", + " net['pool1'] = max_pool_layer(net['conv1'], (3,3), (2,2))\n", + "\n", + " net['conv2'] = conv_layer(net['pool1'], 32, (5,5), init=glorot_uniform(scale=0.2))\n", + " net['pool2'] = max_pool_layer(net['conv2'], (3,3), (2,2))\n", + "\n", + " net['conv3'] = conv_layer(net['pool2'], 64, (5,5), init=glorot_uniform(scale=0.2))\n", + " net['pool3'] = max_pool_layer(net['conv3'], (3,3), (2,2))\n", + "\n", + " net['fc4'] = dense_layer(net['pool3'], 64, init=glorot_uniform(scale=1.697))\n", + " net['drop4'] = dropout_layer(net['fc4'], 0.75)\n", + " net['fc5'] = dense_layer(net['drop4'], out_dims, init=glorot_uniform(scale=0.212), nonlinearity=None)\n", + "\n", + " return net" + ] + }, + { + "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]: [Training] loss = 2.079751 * 50000, metric = 79.2% * 50000\n", + "Finished Epoch [2]: [Training] loss = 1.856198 * 50000, metric = 70.3% * 50000\n", + "Finished Epoch [3]: [Training] loss = 1.757369 * 50000, metric = 65.9% * 50000\n", + "Finished Epoch [4]: [Training] loss = 1.678602 * 50000, metric = 62.8% * 50000\n", + "Finished Epoch [5]: [Training] loss = 1.643568 * 50000, metric = 61.2% * 50000\n", + "\n", + "Final Results: Minibatch[1-626]: errs = 48.4% * 10000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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sIsRshc1MYc2Oc87lu/UdhG1xFtuH5lgW57LWvr0tH3kEFixI3ta1a+J1dOTa\nunXhllsSOSlh01I0ALn00sTrGTOSz7v99rZUtWNnzYKxY+H88+HPP60nzxFHwMSJVobPP7faoBUr\nbNyWdevg4YftHOPHWw1K7dol3+u8edar6bjjSt7XOefygWg1GbhBRDoBBQUFBXTq1Kmyi+PK0Qsv\nwAknWN5HcbMlr10Ld98NF1wAG2yQ/fmXLEn0Ikp10EGWBDtxYqLX0MiRNi/QXXfB//2frRs7NjmI\ncs65OBs/fjydO3cG6Kyq5T4kZSxyTkSki4iMEpEZIlIoIt2yOOYfIvKViCwXkZki8riIlDA3rqsO\njj/eajfCwGT1ahsnJVWtWnDZZesXmIAl9IZJuQcfnLzt669trJZod+Z99oF27aBb5LfaAxPnnMss\nFsEJ0AD4CugHlFiVIyJ/A54GHgXaAccDewCPlGMZXRW12WY2Km1ZThg4dKiNfzJ6tCW37r67JdbO\nnw/bbZe871Zb2azK225rY7iEA8fNmgWnnpoY3yXVwoXw+us21H+uRBJdop1zrqqIRXCiqm+p6jWq\n+ipQQjokAHsB01T1AVX9RVU/Bh7GAhTn/nLJJTbeCMC++5bdebt2tTFS6tSxAODzzy3vBGyQuHRq\n1YLnn08k4AIMG5ac5xL16ac2qNy8eYl169YlD6G/Zo0l6bZpk+hhFFq92pa33LJ+95bODz/YfWYq\nq3POlaVYBCc5+ARoISKHA4hIU+AE4I1KLZWLnXAE2ubNoWPH8r1Wnz7Wo+eII7LbP2waCpNkU/30\nkyXxbr21vV+zxrpRR5No33jDukBPnpxoSlq+HL78MtHtOTpWS67CwOvOO9f/2A4dkse1cc65klTJ\n4CSoKTkFeF5E/gRmAQvxSQddivvus2AhHN6+PDVsaPkm2fTACd15pyXmrllTdNtPP1kPoRrBv1IR\n60G0bl1in+houF9/bfMGbbihNTOFQ/uXJjj58Ue7pxkzrPv1FluUfEzUnDnWpDVzZvnNGO2cyz9V\nMjgRkXbAYOA6bALCQ4GWWNOOc39p0sRqF/bcs7JLkl7Yjfm224pu+/lnaNUq8b5WLWvmAQtmZs+2\n2ZyHD7dB3zp2tJ5Hod9+s+WNN+ZWNlUbU+baa63pqEePot22i7NwYWLCRLDxZ5xzLhvrO85JXFwB\nfKSqdwfvvxORfsAHInK1qs7JdOCAAQNomNIPtFevXvTq1av8SutcBnvvbcuBA63nULSWY8qURDAS\nOuMMG14PnRIFAAAgAElEQVR/wQKbm+i00yyhtl49277ppracPt16IR1yiA0Ct3KlNXG1bp192aK5\nLnvtZcevWgX/+x/st58FL5kGzLv77kS36XfesS7WtVL+t1m40AKnW28tuXansNCaq4rrGu6cKxsj\nRoxgxIgRSesWLy5pmLMypqqx+gEKgW4l7PMC8GzKur2BdUCzDMd0ArSgoECdi5OzzlKtXduWRx5p\n69autXUPPJC87/vvq4Lqq6+q/vvfqo0bJ2/fckvbnqpnT1v/wAOqV12luummdo7i/PijHdO6tWph\noeq4cfb+uedUd97ZXs+ZU/S4hQttG6i2a2fHjhxp9xQV7jNqVPHlUFXt08f2/f571XvusXM65ypO\nQUGBYr1pO2kFxAKxaNYRkQYisrOIhP0Ytgvetwi23yIiT0cOeQ3oISLnikjLoGvxYOAzVZ1dwcV3\nrlQeeMAmFGzYMDEz8+jR1nSTOs/QVlvZ8vnnrYtwaqJp3742b1CqkSNtef75NkT/woU20/Eff0DK\nH0h/Wb3arvfyy1ZDctBBFk7MmJGY7yddz6QBAxKvv//ejj3hBKhZ06YcaNTIhvIPc2m6lTiqETwd\n/Ot/6ino3z95YkXnXP6JRXAC7AZMAAqwyOwuYDxwfbC9GdAi3FlVnwYuBs4HvgWeByYCPSquyM6V\njdq1bWC3HXe0HBIRmxuoTp3EvDyh7bazJppw8sPZKaH4jTfasZlEc1vefdcGiDv55PTNMx07Wt5K\nOFVAKNoCmm4Au/79LZcmOq8RWLD16KMWGI0cmQg4atWyICl1/1A4HxEkZnf+9dfM91iZKrrm27l8\nFYvgRFXfV9Uaqloz5ef0YHtfVT0o5ZgHVHUnVd1QVbdS1T6qOqty7sC50uvZM/H6oous5iI1TwMs\nv+Qf/7DX2Y5uu//+NgvzhRfCoEHw0Uc2QWE4qWCqKVMsyEg3e3KzZsVfa+ed4YsvrKYkatWqxOuC\nAqu5mTLFakGaNLE8lkWLip5PNdHzKOzV9Pjj1r35lFOSZ4COGj06/fnKy6hRsMkm2c9c7ZzLLBbB\niXMuOdnzqKOK37dtW7jySvjkk+zO/d578NBDUL++BR377GPrhw+3nky//JK8//XXw+DB6UevFYHu\n3eGss5LX33efbctUA7LRRta1GGzwObCaoDAA+uST5N5JoRo1rLy33pposho8GE4/3cofTsgYtXQp\nHHkknHde+rIUZ9my3GaADsse9pJyzuXOgxPnYmTRouyGq69Rw3JHSqrFKMnJJ9tItOFAb6ENN7Qa\nkAYN0h/3yis2I3RUmNeSKTgB2HxzqwkJa37AAqYTTrDXqUP/R11+uQUloeHDbfmf/8CHHybWr1qV\naKZ67rnM58vksstsfqZXXrGeRuFIuyXZdltrovv888S6YcOslioXr74Kf/tb8rg2zlUXHpw4FyMN\nG67/RIRlZerUxF/9Q4emb1KKUrX8lunTLSBp0cJyZMJuzdmqUcNqdcC6RhdHxEbD/fNPy9Pp1s2a\nprp0seahMWMs2Jk/P3HMihU2kFy2A/FNn27LY4+1iR333tuSlidNskDh0kvT55ZssIEFKJdearVI\nqjZ30gsvZHfdVGPHwscfw/33F63Zci7feXDinOOqq6x5ZOut7Ut1+XLLCynO2rWWG9Oypc019Ntv\nid5E66tRI2tOyaYZpnVrawpasgTefx922snWN2xoY8AATJwI//63vZ4+3QaSO+647MoSJt+GTUgT\nJljvqPvvt0DlzjsttySdMIdnxx0TAUynTtldN9Wzz9qyf/9E7yjnqgsPTpxzSZMDTpxoywtKmAwi\nOkz/++/bIG25BidgTUgili9Skrp1oXFjG7wuOvpvOOPz6NHQu7e9jg6bP3du+vOtWWPdntets+aU\nww+3vJvQ1KmWExP2Ftp44/TnGTcOmja1mpyDghT+dEP+z51rTXKpkzVG7bmnnUMkc7nzlWri99Dl\nLtOM51WBByfOOa64wpbnnpsIOrKpaZg9Gw47zLoHz5hhTTulsXy5ffEffnjJ+/7xh41C26SJlQES\nX2g//mhBQYcOyb15+va1ppZUl11m+/7rX9Y006OHjbY7dqwl3m6/vdUkNWpkicBLliRqaaIOOsg+\nk3r1EoHS5psX3W/sWEsOvvrqzPe3ZIkFe5ttlkgkrsoeeQS++Sa7fe+8E9q18+as0pgzx5oahw6t\nmknaHpw457jxRksqHTLEAozPP7euxiVp2hTefNO+SNesScy0nKswX+Wtt+yv50mTLAAp6Utq9Ghr\nZtprLxvy/623LJfl22/hwANtXJjzz7cePMOGJYbmnz/faibCGpFbb7WuwGecYe+7drVuy+efb+/b\ntk0EQjNmFF+msBfRllsm15A89lji2BdftFqnVKqWb9KwoQVf0akEqqpzzkmeqLI4n35qS689yV3Y\n/b5Pn0TCeVXiwYlzjlq1rGcIWICw++6Zmy7S+e47W67P3D3pRMdGmTvXcjfuvjvzeCwhETv2pJOs\nOadG5H+2zp0t7+T++2HffW1deL7+/W3Zpk3x5w+bZrp0sRqlCROs9uOpp2z9Sy9Zc1DUu+/a5I3X\nX2/JtWBBx1lnWTDYqJGtSzcWy+2327J2bat5qUo1JwsX2u/B448n1s0KRqA699zsJo8Me4lddVXZ\nl6+6CGvuAD77LPl9VeDBiXOu1I44wr5AO3Qo/bnCXjW33JIImMIJDUvSu7d1A86kdWsLXMLainC8\nlSFDLIE204Bue+0Fl1xi3YvBakVeesmaicCagY45JvmYFi1sv5YtrQZgzhxrigLYZRf7gURCb9SJ\nJ1qTzk03WW1UVaqWnzDBgrIzz0wEJeEX4yuvWK5Qcd3NIZFMPGECPPlk6XMnpk2DmTNLd46qJjro\nIVS9wQE9OHHOlYl0uRW5CKv+d93VEl8hUctQWvXrW0+f8C/ytm3t58ADbV3LlumPq1UL7rgjUY5w\nTqFojUumwCw8Z7Nmic9o4EBrDlu4MP3YLttuawFJ/fpWk/XRR5azUdnNHIsXl5yc+/PPiddht+ww\n2LjoIluma8qKuvhiCwZPPtlyfh59NP1+AwdajVhJttuu6DxU+e7KK2GPPWwcILDftarEgxPnXKy0\nbGn5K336JJqWmjYtu/OH0wTMn28DrGUz8WAqEQtmFi9ODJp32WXp9w1H441q3dq6LKfrkmyTqCf0\n7281SP37p0/CrUgnnmi5I8U56CBLLG7YMNFMt2iRvb7sMkvwDb8wM9l/fwsGw8EB0w2E9+GHVrN0\n4YXF16ykm4Ihji691KZ9KCvNmllzzvHHW1Bd1Xp8eXDinIudcAC4hx+2MUbKcmC6yy+35c032wSC\n4SSK66tDB+uZc8cd9j51BulQkyY2ENy0adbj54orMtfQgCXtRnsUdexoX8QtW1oibUGBBUe5jp/y\n8MNw3XW55SA0bVryl1yrVpZTs2iR/eUOFsQ1bGhNaocckjyib6qCgkSAFta8pObzgCUMg9V81a+f\n+Xx//ll8eeOgsNB6KB18cPmcv6rlLYEHJ865GNt88+QJEcvCzjtbPseee1pX3qOPzu08YZJsOB5K\ncUm19etbU02nTsljyoCNrdK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8ZLuFtFhEmgevlwCLY46wPy9EpJ+IzBGRlSIyWUR2zzB2uIiUiEhx0IbHp5Ex\np8aMWZGvfFWeGTNg2TJYsQJ++610Vt6WLeGZZ+CddxJ9V16ZfB7lm2/g7LPjt4kOPDDxevr08kru\nOI7jODmTrQJzALAoeL1/cJ56hP05IyI9gTswR+BOwDRgbFAfKY4LsfpIrYK2TSDfmJRxS4Pr4bFl\nPvJVCxo0gI02sjad/8qxx2ZvafntN3MKTlVg5syxEgZz5tj5j0HtzOXLzc/m00/JyPnnJ7auHMdx\nHCdPslJgVPVdVQ29ROcA/w36/jiA/wbX8qE/MExVR6rq58A5wArg72nkWaaqC8ID2ANoTFC9Onmo\n/hwZ+3OpxZx4GjWydunSRJ+q+cvccINtWZ1/vjn8Arz5prWZEua9/76FeU+YUDEyO47jOOsN+YRR\nzwGaxfQ3IQ8FRkTqAl2watLAH8UhxwF7ZrnM34Fxqpq637GRiMwVkW9F5HkR6RA32YmhcWNrlyxJ\n9C1caCULdtnFLD333GNh2p9/nnDmrV8//Zpdu1q7//7mO+M4juM4eZKPAiNAXOjJRsCqPNZrCtQG\nUov5/IRt+2QWRqQVcCjwUMqlWZhicxRwEvasE0WkdR4y1hymT4fLL7cSBF98kX5cuA314YeJvtde\ns3bLyE7cn/8M7dtb1WyAxx4rW4Zdd7VIpxtTcyE6juM4TnZkHUYtImFmMwWuS3GIrY3lhvm4gLJl\ny2mY8/AL0U5VnQxMDs9FZBIwEzgb87VZPxk/Hm65xV7fdVf6cSKWFG9F5GM+9VRr27QpPX7QIGs7\ndYpfb+1aaNsWjj8+ES312mtwxRU5CO84juM4Ri55YMJvJgF2BqJpXH/HHG9vz0OGhUAx0CKlvwXw\nYxbz+wIjIz46sajqWhH5CNi2rAX79+9Po9AHJKBXr1706tUrC3GqOD9H3IC22CLz2G7d4JdfSve3\nDAxjqmZJWZvxrTfq1Ek4/obbR99/n7g+a5b51YRbV47jOE6VZdSoUYwaNSqpb2nUZ7ISEM0xEZmI\nDAcuKmS+FxGZDExR1YuCcwG+Be5W1dsyzNsP853ZSVVnlnGPWsBnwCuqekmaMZ2BoqKiIjp37pzX\ns1R5Fi6EZs1g773hf//LPHbrrS0Kac0aO2/ZEs4918oWgCkw118PHTvamlttlXDqLQuRxBrR8+Li\n0iHgjuM4TpVn6tSpdOnSBaCLqk6t6Pvlk4n34rh5ItIEWJunYjMYGCEiRcD7WFRSQ4KoIhG5CWit\nqqemzDtIOc/mAAAgAElEQVQdU3xKKS8icjW2hfQVFqF0KbAF8HAe8tUcmjbNPnvuJZfAl5H6nO+9\nBw0bJs5F4Oqrk+c8+KCFc/funXntvfaCadPsdVSeQw6BmTPNMbiscgaO4zjOeks+CsxozN/kgZT+\nEzCH2cNyXVBVxwQ5X67Fto4+BrpHwp5bAptH5wQZgY/GcsLEsQnwYDB3MVAE7BmEaTvZcN55yefb\nbFP2nBdfhFdeMf+Z44/PPE7ElJio1aZhQ/juO8svk839HMdxnPWSfBSYrpgVJpV3gBti+rNCVYcC\nQ9Nc6xvT9ysW+ZRuvQHAgHzlcQKefhrefhtOPNF8YsqiXTtTYD75JLMCs+mmye2bb1p17DZt4IUX\nzFfHFRjHcRwnDfk4G9QDNojprws0KJ84TpXjq6/g/vstg282uVvCMgPLl1u7cqVZWjp2LD32t98S\nrydPhsMOM18agFdfLZ/cjuM4To0mHwXmfeCsmP5zsG0apyaxzz6J16EzbyZCv5XQEbdePWs//RRW\npaQJCq8BbBsEh4URTv/9b+6yOo7jOOsN+WwhXQWME5FdSGTPPRDYHTi4UII5VYSo5SRTlt2Qrl3h\ntNPgH/+wsOkffkhce/ZZOOmkxHndurZd1KOHlSgAs9bsu298rhnHcRzHCcjZAqOq72Ep/udhjrtH\nYpE+HVXVi9zUNBo1ggMOgIdSEx2noV49GD4cbr/dwrD33tuUkzZt4JhjSo8/6iiroh3N7vvrr/DE\nE4WR33Ecx6mR5GOBQVU/xtLzO+sDb71V9phUbo/kNBw7Fv70p/RjN0rxxW6argh5JbF8Oey2G4we\nbXWfHMdxnCpHVhaYIGT5j9eZjooT1alW/Oc/1rZvn1l5ieP112H16sLLFPLcc5b5Nw5VU6g+/zy7\nuk6O4zjOOiFbC8xiEWmlqguAJcQXcwyLPNYulHBONaZHD/Nv+etfc59bq5b5x6xcaVWvX3jBwq0b\nN4addspfpuJic0Q+9lg7j0voF0ZPAWy2Wf73chzHcSqUbBWYA4BFwev9K0gWpyYhYv4t+dK7t23h\n/PIL/O1vif5Q6Vi8GAYMgAsugGzLPpx4IjzzTOL8u+9KOwv/GJTf6tgRTjklf/kdx3GcCiWrLSRV\nfTcslhi8TntUrLjOesPo0db27Bl/fYMNYMQI2+oJWboUevWC6dMTFa9Dli9PKC+77mrt+PGl1912\nW1i0CD76KJGTBixz8CGHpC9cOX48jBxZ1lM5juM4BSJbH5iO2R75CiIi/URkjoisFJHJIrJ7hrHD\nRaRERIqDNjw+TRl3vIjMDNacJiKH5iufU8nMDMpbjRsH111n0VC/BmW2Lr4Y3n3Xtpei1bVHj7Zj\n552tsGSUnXe2dp99TDlp0ya5zhPAa69Z9t8NN0wuKKlqW2Jjx1pV7VRGjLAEfqeemrzmk0/Cxx/n\n9fiO4zhOZrINo/4Y+CjSZjpyRkR6AncAA4FOwDRgbFAfKY4LsRpHrYK2DbbFNSay5l7Ak8BDwK5Y\n/abnRaRDPjI6lUy7dtYeeihcdRUsWWLOwKowbJgpCptuCg8/bL4yYKUIQho1svann6zdZBNr337b\n2gsugN0jOvKyZZYJ+OuvSzsQRzMGjxwJRxyRfK++kUoXUYXlpJOgU6dkvxrHcRynIGSrwGwFbB20\nxwJzgPMwZaNT8Hp2cC0f+gPDVHVkUGzxHGAF8Pe4waq6TFUXhAewB1ZxekRk2IXAa6o6WFVnqeq/\ngKnA+XnK6FQ2v/ySiGYC276pVcsy+rZsaT4s06db3hlIzhq8dKkpOS1bwqhR0KIFnHBCwoJy6aWW\no+a99+z8uOMSczfayBSlp5+2Y1Hg/vXww2ZleeUVq7odMnmybS+9+mp8/aeJE8v/XjiO4zhJZOXE\nq6rfhK9F5GngQlWNFqv5RETmAdcBz+cigIjUBboAN0bupyIyDkuYlw1/B8ap6rxI356YVSfKWKBH\nLvI565AmTZLP47ZvwCpa33NPcpZfsHBsMKvJq68mW00A+vWDp56yranQeXf4cHNAVjWFB2DSJGt3\n3x1q17ZopiVLEut07WrbT6lcfbVtf4VWoFwYNMiiri6Oq5vqOI7j5FMLaWfMApPKHCCf7ZmmWOh1\n6l/5n7DtoYyISCvgUGyrKErLfNd0qjA77mjtEUeYU+7dd5s15MILLTz6ww8TFo9PA5eo2bOtjdZe\nAlMwAAYOtDU228yyDoMpMSGXXWZtq1ZQFJT7mhP3K5DCtdfCxhsnlKNcuOYa6N/fCmk6juM4pchH\ngZkJ/FNE/qhIHbz+Z3CtsjkNWIz5uDg1nTFjzAdmww1NYbnggsS1LbeELl1gjz3s/NFHrU111g0J\nlaEhQyzh3nffwRZbJK6Hjr89e8Ktt5rPzS67WDj27Nlw442m6CxdmpjzwQdw9NGJJHjHH1++uk7v\nv5//3CgLF1qEVjYVxR3HcaoB+ZQSOAd4CfhORD4J+jpiSeyOzGO9hUAx0CKlvwWQzb+ufYGRYZh3\nhB/zXbN///40Cp1AA3r16kWvXr2yEMepUDp0sCNKr17m5xI65daubXlkdtwRPvnEtomiFpU4Jk2C\n7t2T+665xqwv556bPP/GGy3R3uab23k0Yun55xPHKaeY30w+vPQSHHlk4h7pKClJvn86wpDwww+H\nPn3yk8lxHCdg1KhRjBo1KqlvafSfucpAVXM+gA2Bs4DBwXEmsGE+awXrTQbuipwLVizyH2XM2w9T\nftrHXBsNvJDS9x4wNMN6nQEtKipSpxqxZInq2Wdbm0pxservv6ef+8gjqqCa62c+Z47NM5etBEVF\nif6SktzWjLJ8ueo556guWqR6++2qX32luuOOtu4HHyTGXXKJaqdOZa93yy0299//zl8mx3GcDBQV\nFSlmzOiseeoDuRz5bCGhqstV9UFVHRAcD6lqeWJFBwNnisgpItIOeABoSBBVJCI3iUhcYZrTgSmq\nGrd1dRdwiIgMEJEdRGQQ5ix8bznkdKoijRrBAw8kQqejhGUJ0vH3v1t5gWyz+Ybceae1oX9MSJgk\nb5ttkq02jz9ulqBhw6y/f//M6zdsaP4v06bBJZdYgr3PPrNr0Urds2ZB8+bpazsBnHaaydm4sW2T\nOY7j1ADyUmBE5GQR+Z+IzBeRLYO+/iKSV4SPqo4BLgGuxXLJdAS6q2qYpawlkGRLDwpHHg3E2uhV\ndRLQG7MUfQwcA/RQ1Rn5yOjUYNJFN2Xi5JNtq+of/0jur1XLopKGDEnuf/ZZOPNMOOccO7/zTtv+\nKYtOnRKvDzvM2oYNrS0utgzAn3xieXPSRTt98IG1bdrA99+XfU/HcZxqQM4KjIici1lMXgM2IVG8\ncTGQd8ynqg5V1baq2kBV91TVDyPX+qrqASnjf1XVjVT10QxrPquq7YI1O6rq2Hzlc5wkunSxvDSb\nblr62uTJ5r8SZfvtLVleyAYbwIoVpeeuXGkOwmvW2HnUqvTEE1bMMkys9913liQvVHKuv770eq+9\nBjNmmKWpTRu3wDiOU2PIxwJzAXCmqt4ARB1nP8RCrB3HSSVUMvbc0yp0L1hgCfNGjzZFaPFi85z5\n+GPbLvrii8Tca6+1dtkymxMqPmHW35tvtjYM8Y5y663WNm9eWoFZutRy5yxeXLjndBzHqSTyUWC2\nIr5kwGrMuddxnFS23NLaevXgzTfNsrLPPhZBNWCAJe2rVStRnLJppIrG1VebcrP55jBhAjz0EHTr\nlqjS3a6dzZ80qXR+mrAkw/nnW76b5yN5Jrfbzuo1HXGE+eV8lGMlkPnzEzWrcuWhh6y8Q1gGolCs\nXWvPPH16Ydd1HKfKkY8CMwerLZTKIaybPDCOU/XZbTdro3lrwjIG4TUwJ18onYU4JPTXmTDB2v33\nNyfl1q3tfOutk518r7sO/vc/S9LXpo21IWEhzDDx3+uvmzVnxIjsnmnrrS2k/YILLOw7SlGRWXsW\nLIife9ddls14ypTs7pUtb75pDs07uzHYcWo6+eSBGQzcJyL1sXDnPUSkF5bI7oxCCuc4NYa6dc2K\nEsd++yVeT5liifgyRU5FrRahD86NN8Ijj8ALL5gC8v33ZtG55JJka05IWLCydWtThrbZBu64w+pP\ngUUuhQwZYlai8ePNahMm5gvXuPdeqxkVZhx+6y3bJgtZtap0FuSxY22dqVOTn7+8hApeNrlxHMep\n3uQTew2cBHwJlATHd8DplRH3XZEHngfGqUyi+WJuvTVxPmhQ5nl77GHjevZUnT8/+VqdOol1QHXF\niuTrn39u/X36qDZsqPr668myxOW2Sb02fnxy/047WTtnTnJ/mzbJ/emev5A8+KCtOWBAYdctFI8+\navIVF69rSRyn4FTpPDBibAE8q6rbARsBLVW1jao+UjCtynHWB4qKrMikCLRta32tWsEVV2SeF5ZA\nGDbMxkdJ3XpK9VGZOtXaxx+3CKaDD7bzm24y602Y+ffXXxNz9toreY0ffrC2e3fo2zcRvn3cccmW\nm+23t/aee+Kf45hj8gthj2PJEsvEPGGC+RbdkVrHtYowcqS18+evWzkcpwaQq51VgK8IcrKo6gpV\nTbPJ7ThORjp3hkMPtdfHH2/2iPnzM28fAcybZ74scYn7wurVF11kbRjBFHL44daGClOYbO/yy+Go\noxIFLkePTsxp1MgchsNSDXvvbe3rr1u9qXfesfOiokQNKIArr7R28OBkGcaPt0KbS5aY023XrvDN\nN5SLAw6wQp5nnpnwD6qK7L67Ffi891649NJ1LY3jVGtyUmBUtQTbOopJfuE4TqXw2mvpq2E3b27t\n/vtbe+CBydc33hiuugrmzrUCj6n07m0RQmdE3NkGDjTFKPR9WbvWil6GmYE7dDDLTmgZOvBAU8YO\nOMAsPKn1nC66yO4RKlnvv28KVe/elpQvymefWU2qsopQhv43oQxVldWr7f245Ra47bbCrOkFOp31\nlHw83S4HbhORnQotjOM4WVCnTnorzamnmlXkyCPh998tfDqV0HITFwK94YamvKxZk8gU3LUr7Ltv\norjl/PlmBdop8iegU6dEluGBAxP9Bx8MixYlzn//3UKc1641i0/UIXnUKKv2HWWnnWDQIFPaMrHT\nTladPF30VlXh11/t/Q+3ksKkhPny8sv28xB9jx1nPSEfBWYksAcwTURWisii6FFg+RzHyYU6dcwv\nJawBFVeF+7TTTMlJ9W2J0q2blUq46KJE9NTOOyeHWW+3XfKcfv3MYvKXvyT6jj4annsusca9QSmy\ncKupfv3Mz9Oli7X//W/mcTNmlJYHLBrq2Wczzw157jl7vw48MLsyD/nw669mBQv9g959t3zr3X67\ntX/6U/nWcZxqSD4KTH+svtDfgXOC8+iRFyLST0TmBErRZBHZvYzxG4jIDSIyV0RWicjXInJa5Pqp\nIlIiIsVBWyIiMbnbHWc9o2lTU3Jq104/5v33rb377tK5Wh4NqnekJosTse2kKFtvbVaYUJH6v/+z\n9rnnEmOiIc89eyZef/ABjBtnzsLTpqWXde1aCxvfdtvk/uJiC+meOtUcidOFsYeEdaLGj6+4kgtL\nl5oC0769nZ9ySvnWa9bM2rL8phynBpJzCICqjii0ECLSE7gDU4zexxShsSKyvarGbNQD8DTQDOgL\nzAZaUVohWwpsjzkfg4V3OY5TFj17WvVsSN4qAlN+XnzR6jnlyoUXWgmE7t0TfWFCvej2zzvvmB/P\nmWdapFWm5HpLlpSeDwnrxo03Wtu9e8LyEcf555t8YAn4ttiirKfJnSOPtC2kjTe280WLbLsuXwWk\npAQOOsiS902bZs7gcVY3x6mBZG2BEZFaInKpiLwnIh+IyM0i0qBAcvQHhqnqSFX9HLPsrMCsPHGy\nHAL8BThMVd9W1W9VdYpaBeooqqo/q+qC4Pg5ZjnHcVK57z5LNqdq9ZeiPPpovANwNtx1VyJqKaRJ\nk4TyccIJthU1ebKdz5plYdFg1pQHHyxtSWnSxEK5o0rRu+8mHJjDjMf//ne8TKr2TCtXJqKhsvUp\n+fFHGDo0u7FgW3JhqPnrr5u/UXmsJ889Z1audu1M6SzLV8hxahC5bCFdCdwILAO+By4C7iuvACJS\nF+gCvBX2qaoC44A900w7EiseeZmIfCcis0TktiA7cJSNgi2mb0XkeRHpELOW4zipbLppIkdMeVG1\nbL5h7pNMFoIpU0whCH07jj4aDjvMvpj/+lc4++zkHDVgW1DNm0PDhom+uXMTr8MoqLhq3WCOsKef\nbg7MzZqZxWbrrTM/02WXWcTTlVeawpWPQte9u0VjjR5tPjy5Em5zzZqV6Atz9DjOekAuCswpwHmq\neoiq/g1TIk4SkfLm7G4K1AZ+Sun/CWiZZs7WmAVmR+BvmDJ1HMkK1SzMgnMUljm4FjBRRFqXU17H\ncXJlwAA7yqJvX2jRwiKKwCwWrVolrDAQn/8mlZ494YYb4MsvE6HlUUpKbIvst98sGgqs7EKDBvDP\nf5b2p4nyyCOmYM2YYaHfYPfJl1698gv/DhW5yy9PPOPJJ+cvh+NUM3LxgdkC+MM+qarjRESB1lgp\ngcqkFlbCoLeq/gYgIgOAp0XkPFVdraqTgcnhBBGZhBWbPBsYGLPmH/Tv359GKX8ke/XqRa9evQr7\nFI6zPhBaXJ56KjlBXhwtW9p20JZbJm8VLV9u7cMPW8K6MKleOsfc+vWTMxr36JG8FfbYY/D3vycS\n/4EVvsyGaI6ccPtn3DjYM53BOANR60mutG5t7+duu5nV6OKL8/NLcpw8GDVqFKNGjUrqW7p0aeUK\nkW3NAaAYaJbStwzYqjy1DIC6wBrgqJT+EcB/0swZAXyR0tcukHGbDPcaAzyR4brXQnKciiCse/Te\ne5nH3X9/fI2kOXOs7403kusyvfyy6i67WI2nXNh+e5u/dq3quHGq11+f3bxhw0rXhgKrK5UPV19d\ndk2otWvzW9sxSkrWtQTrDVW5FpIAI0TkufAA6gMPpPTlqkCtAYqAP1KGiogE5xPTTHsPaC0ikU1v\ndiBRWLK08LbVtTPgm8SOs64oK5S5d2/LN/PRR8n9YcK3Bg1sCyfkiCMs+iZTSHgqxcXwxRe2Vu3a\n5uwblj0oi7VrrQ1LMYD56KxYYblqJqXGEUSYOBG++iq5r08f2xILQ7ijLF1qTsKPP55IfBcydKht\nddUkPvjAEhn+/nvh1nz++dL1wpwaQy4KzGPAAiw0OTweB+an9OXDYOBMETlFRNoBDwANMUsLInKT\niESKrPAk8AswXETai0g34FbgEVVdHcy5WkQOEpGtRKQT8AS2DfZwnjI6jpMvTz1lXyRhHaV0bLyx\nlRPYddfk/tq17Vq7drb1E2YDDsnksxIybZrJEG7bRLMARykuNr+SyZMTfcuWmeK01VamTM2ZY064\nw4bBJpvYmPPPN4deMOUizHkTsvfeyb48YGHdS5bYdlCUX36Bxo1N3kcfhf/9L/l6v36WVDDKmjVW\nlDNTzpyqzJVX2mefml8ojiuuSN7+S8fSpbYl6dFZNZPKMPNkcwDnAXOBlcAkYLfIteHA+JTx2wNj\ngd+AbzAFpl7k+mBgTrDefOAloGMZMvgWkuNUFxYtKnv7Jcrs2TZ23DhrGzeOH1dSYtcHDEj0de1q\nfX/9a+nx8+apjh6teuONqk2aqP7yS7xcucga3Uo77LDEdlfIX/6ietRRyXOKixNzVq7M7j5xzJ2r\nunhx7vNWry7fds1ZZ6nutFPmMatWqc6Ykf17+dRTNq5Bg/zlqi4sW6Z69932Hq0jqvIWUoWiqkNV\nta2qNlDVPVX1w8i1vqp6QMr4L1S1u6pupKpbquqlGlhfgusDVHWrYL3WqnqkqqZUinMcp9oSbit9\n/HF241sGQY1PPWVOt6nbVCGh03G0inZYNXzcuNLj27SxqKettrL8MUOGJK6NG2dWkVWr7DzbAo7R\nkPCwwOWYMWYVWrvWZN9qq+Q50YzGd98NHTvC/fdnd78obdtaGLlqQu6yWLsW6tUzGZYtK3t8cbG9\nz336JPoWL7bPaMqU9KUj9tsvOdvz998nwvPjOOYYazt3Llum6k5RkSViLKvsRg2iyigwjuM4ObH5\n5uYvkVoAMh2hUvDQQxa2HPVjSSU1C+8OO1gbFqyMI9zGCjP/giWre+QR28qAzJmA77jD/EAAttnG\n2vvuS0Rc9e5tkU6nn27KW2rZBkhEeV12GXz6abJSkw1hDajFi62IZoMGudeF2nhjSziYiVARfOKJ\nRN+iRbYd9+c/W/HQOKLbemAyplZcj1KnjoXvFxVVXH2rqsDnnyf8qHIpEKpa/oKi6xBXYBzHqb7k\nm8U29FtJxy23WLt0KZx7bsLKE7UYpBIqMA0b2vhVq6wY5ezZCevPppvGz1W1PDR77GEh3nvvbY7G\n551nZRTatbMyBJBw6I37ku/ZMzmnTNOmmZ/zppusCnhIrVoJ5e3aa6395ZfMa4ApClHiqqBHmTrV\n2vsiqbsWL7asyscdl37e7rtbOPyTT9r5ww/bl/fq1ennrF1rn0Wco3SUsWMTWZvzYeFCy2OUrdWq\nkLRvDyedZK8feCD7eVdcYYVA14XMhaAy9qmqy4H7wDhOzaZt2+x8JyZPtnFnn53wt8jGvwNUd989\ncf7669Z3xRWqdeuan0g6ttgica+ZM0tfD31zyvL/GDEiMebZZ1Ufeij92K5dS/vSFBUl36dt2/Tz\no1x1lT373nur9u6d6L/55tI+PNFnOO441WOPVd1qK9XLL1cdOlS1Th3z6Ymydq3NOeEE1S+/tNfd\nulk7f356uX75RfXJJzPLPndubj5KcVx2mc3v0yf/NX74QfWRR0r3T5igeuKJ6UPqU8P6s/VFCsd/\n9FH+MkdYb31gHMdxKpxPP83OohD6lwwbluhbs6bseRMnWmmCkLBo4+zZtt2VKdFcOBasZEIqIraF\n1a6dFZtMx6mnwquvWnviiVYQMzV8e9Uqsy5NmWKFOaPbCJ07w0svwYQJdl6vXvp7AbzxhllArrvO\nqpi3aJFcWuHyy62NFuSsVcvGATzzDDz7rEV2rV1rkVdr15Yuz/D559ZutplZu0pKLNsymPUmlcsv\nh1deMatOWUlIi4oSrzMlF1y9Or2PSbg9+Pjj9n5Hw/2z5ZRTbIsw1aJ07bW2Pfj119mtc/XVFoJf\nFmE18+HDLbpPq1e946wUGBE5KtujogV2HMfJm402Kl21Oo5mzaxcwIcfQrduVjQxmyy3e+6ZXLog\nmoW4LLbcMvH6v/9N+M1E6dPHvrTDL550HHqoKQxXX23nYcmDkLZtLUw7zCoc/QIHy6+zzz72hRYq\nDnFMnmzh5Icckuhr3jyhYP0UqRATFsoEUzhSlaqhQ23LLAwpT3XODbc5wvB0kcRW4DPPJI/98kvb\nBkzn4D13rs0PQ7YPPdT8fSCzonr99bZ193NKXeCiInuPDz/cfgaOPTY5Y3O2hKkBvktJZ3bYYdam\nC5Hv1MmU3kWLTKG84YayUwuUlNjP2Pnn2/s1fHh2Sk9VIhszDZYgLpujuDLMRhV14FtIjuMUkuJi\n1fPOU506teyxc+aoPvBAYc36L79sa9Wrl+j7+uvEPebPt/Zf/8pv/auuSqz13HPWN3CgauvWqmvW\nWP8ll1h7xhnWt2hR8hr9+iVvm337rZ2/+mrZ91+yRGND4o891vqvuirRt3q16sKFts11yikJuV94\nwTI5L12qetttmbdfwvD26JiVK61v111VTzopsW6TJmXLn0rv3jZ3zZrk/h9+yLzF1amT6u23J54z\nm+2wV16xMePHJ0LTb701d5kjVMktJFWtleWRQzpMx3GcGk6tWuao2qlT2WPbtrX/ort1s/MwEqk8\nHH64tdEtidBR9e23E9ai8eOtjYvUWbXKoprCtaIMGpR4HUZqhRaYsWMTaw8dajWb6tYtHT0VWoem\nTLG2RQuzlC1aVPbzNWpkSf7Gjk0eH24TRiPUNt0ULr3UIr2imY179LBtuY03NkfqTNXSV682J+3o\nmNBa8s9/Jlsw4qLcMm3R/PSTOSc3bw4zZyZvF7VMU9dY1Sxt48YlLFMbbJBIAZDJmiRiEV/dupkT\nMNj78/bb8VtyVZHyaD9A/crQsirrwC0wjuPUNN58M9mR97rrVJs1S5wfcYRZJMJkfZMnJ88PLSLp\n/qN/5x3Viy5KnL/wgmr79ok5d95p/WGivR13TJ6/cKH1f/hhou/MM1V//z1xvnix6sSJyX0hK1bY\n/H79kufXqZNsKYmrYRU9li2Lfz5VS2p4yCE2rnnz5Guhw/e0aaqffqp6zTWq++xj1qEo0USDcYRO\n2iL2Hh14YPL1xx9Ptiipqt5zj83p2DG5/9VXrf/bb9M/UyrR96JHj+znRahsC0w+X/K1gauB74G1\nwNZB/3XA6ZUhdIW9Ga7AOI5T0znjDNXddivdH41cirJqVeJattEtoVIR/bK+8kpN2mrKhccft7nT\np5e+Fo3OCnn00dL3Ccd06GDtOeeobrddon/BAhu3apVts0Vp3Dj5C/6HHxLKVKgszJuX+RmiGZrj\nosxUTebp002Z3GOP0tfvuSc5W/FBB+kf23NRpk2z/rffzixTlAULVF96yeY1bZr9vAhVcgsphSuB\n04BLgWjVrelAHl5Lhoj0E5E5IrJSRCaLyO5ljN9ARG4QkbkiskpEvhaR01LGHC8iM4M1p4nIofnK\n5ziOUyNYsCDZ0TiV1Bw50Sik1K2TYcPiM+82aGDXo7lXwoihxo1zlznMv7PddqWviVjkVNTJum9f\nOPro+LXeeMOcfu+/33LtXHhhslznnJPIRAyW9XjJkoQM555rkVIzZ9p5uHWVLsdPyJdfJl63bw+n\nnWYOy19+mSgCevTRlsfngAOSHaBDSkqS1wnp0SP5vEMH24YLEyOCbSd9/rk9V1iUNEqzZolaXakR\nYFWUfBSYU4CzVPUJoDjSPw1ol48QItITuAMYCHQK1horIpmyMD0N7A/0xeoi9QL+iH8Tkb2woo8P\nATkUDyAAABZtSURBVLsCLwDPi0hM+krHcZz1hAsugP79S/eHievi/HXuuQfeeSe5r08f+7JPVxRz\ns82Si1Q+9FCiP1fCIqDpIsHOPtu+lDNl2737bksGuNlmFiUUctttMGNGIiliGO4dhpYPH56QWzVR\nRDJUXH75BerXT0QxpSOMiDrvPGsfewx++MHCr/fay3xYQqWpRQtTYFassGiq0M+mSRPzwwkTAT7x\nhK1zxBHJ96pTx4pjdumS6Bs50hSngw9Or2w1bmyFVPfaK/OzVBVyNdlgxRG3DF4vI7GF1AH4LR8z\nEDAZuCtyLsB3wKVpxh8CLAIaZ1hzNPBiSt8kYGiGOb6F5DhOzaSkRPWDDzKPSY1+SUe4HZRL4reS\nEtXPPst+/DPP2KGquuGGliAvHWPGmCzffVe+gpKq5i8Eqg8+aOfhFlHo5xNGBL34op0PHKi62WZl\nr/v77/b8P/6YvB0V54fz2GN2vtde1j7wgPWHUWX5JNx7773ke6XjpJNU69fPfX2tHltIM4C/xPQf\nB6SpjpYeEakLdAHeCvtUVYFxwJ5pph0JfAhcJiLficgsEblNROpHxuwZrBFlbIY1Hcdxai6PP26p\n+N9/P/2Y1JIAqcyda1aDcDsoTFKXDSLx9ZvS8eSTVlbg/vth+fLkApephLl92rRJbAnlS9eu1p51\nlkVOhXlwwiirP/3J2nDrrFmzhIUoyiefJKxWc+dahFKHDpm3mjbayNpGjaydPdva3XazNlP9rjiW\nLYN77zXLVGiNCbcE0+X36drVPtfqUDsqV40H6AEsAS4DlgOXYNs0q4GD8livFZZDpmtK/y3ApDRz\nXsMsQS8Cu2EWmTnAI5Exq4GeKfPOBX7IIItbYBzHqXmEafjB8tLkC6jWqmVOo1A63X8hCaN+wuOl\nl9KPnT7dooPCseVJ5x86255+urWvvqo6a1bCslNSYu9BaBVJxznnWH4WVdWjj1Y99NDEtXHjEjl4\n2rdXbdlS9YknEtfHj4+3lqxdq/rXv5olLTXKKY6BAzUpwqtVK8sJBKo771z2/MWLyx4TobItMGWo\n27EKzwsiciTwr0CBuRaYChypqm/mul6e1MKUnt6q+huAiAwAnhaR81Q1Q2Wvsunfvz+NQg04oFev\nXvQqKx214zhOVaR2JEVXaGHIFQ38M0pKzJ8l9GmpKI45xqp5R++djh13tHwzO+1k53GOrtnSpImV\nE1iwwMoBtG6dXEVcxHxFyspT86c/mQPwnDnmBByWToBEBe0FC8yytOGGyXOjZSWi1K4Nb74Jn31m\nMtx3X8KnJo7Qrye0vjRtalmThw+HE07ILP9HH5lz9LvvJnITRRg1ahSjooVAgaVx2aMrkJwVGABV\nnQAcVCAZFmLOwC1S+lsA6fIa/wB8HyovATMx35k2wOxgbi5r/sGQIUPo3Llz2ZI7juNUF844A779\n1urt5IOIrfHGG4WVKx1nnGFbJmFdqrLYcUdzan3ttdwqMsex/faJLZYWqV8jWNmHaGmEOMKtpv33\nNwWlY8fSY9KVhOjSxcoKzJwZHzEUOvH265dZgVm2zCKqQpo2Nafr775LdrCOY+hQawcNsi2lgw9O\nuhz3T/3UqVPpEnUcrmDyLuYoIruJyMnBkbfEqroGKAIOjKwtwfnENNPeA1qLSHRTdAfMKhMWkZgU\nXTPgoKDfcRxn/eLBBxMWjXz58ktTgsJInIpEBA46qOyaPlF694Z//7u0RSMfwqispjHBsG3blq3A\nhNFZO+1k/i/psummo00be/5DY7J/hMpFJhYtsgimr7+GAQMs9LtuXcvE3Lx55ozDAA8/bO3bb1uh\nxypIzgqMiLQRkQnA+8BdwfGBiPxPRNrkKcdg4EwROUVE2gEPAA2BEcE9bxKRxyLjnwR+AYaLSHsR\n6QbcivnAhNtHdwGHiMgAEdlBRAZhzsL35imj4zhO9UWk7C+tsggdZD/9tPzyVHXCApdxjs3XX29h\n2S1axIekA/ztb3b9xBNNmViypHCyjRljilUmhTT6WQ8ZYkrUiSfa8+TioNu3r1U3r4LkY4F5GKgL\ntFfVJqraBGgfrPVwPkKo6hjMGfhaLJKpI9BdVcOSny2BzSPjl2PWlMbAB8C/sTwvF0XGTAJ6A2cB\nHwPHAD1UdUY+MjqO46z3HHOM5T15662yx1Z32ra1SKQ4OnQwS9SCBXDnnfFj9tjDlIZ77rHz/fcv\nnGybb24Vsbt3Tz9mk01g552tvlFI375mkYkmJ0xHvXqWN+bRR+O3v6oAomU5R6VOEFkJ7KWqH6X0\ndwEmqGqGWLeqjYh0BoqKiorcB8ZxHGd9ZtYsSyYX48AKWAHIWUHu1Ezfo/PmWTj1yScXXMSs+PJL\n8+l54w3bksqW1avNipMueWAMER+YLqo6NWdZcyQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def create_basic_model_with_batch_normalization(input, out_dims):\n", + " net = {}\n", + "\n", + " net['conv1'] = conv_bn_layer(input, 32, (5,5), init=glorot_uniform(scale=0.1557/256))\n", + " net['pool1'] = max_pool_layer(net['conv1'], (3,3), (2,2))\n", + "\n", + " net['conv2'] = conv_bn_layer(net['pool1'], 32, (5,5), init=glorot_uniform(scale=0.2))\n", + " net['pool2'] = max_pool_layer(net['conv2'], (3,3), (2,2))\n", + "\n", + " net['conv3'] = conv_bn_layer(net['pool2'], 64, (5,5), init=glorot_uniform(scale=0.2))\n", + " net['pool3'] = max_pool_layer(net['conv3'], (3,3), (2,2))\n", + "\n", + " net['fc4'] = dense_bn_layer(net['pool3'], 64, init=glorot_uniform(scale=1.697))\n", + " net['fc5'] = dense_layer(net['fc4'], out_dims, init=glorot_uniform(scale=0.212), nonlinearity=None)\n", + "\n", + " return net" + ] + }, + { + "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]: [Training] loss = 1.680356 * 50000, metric = 58.7% * 50000\n", + "Finished Epoch [2]: [Training] loss = 1.381641 * 50000, metric = 49.1% * 50000\n", + "Finished Epoch [3]: [Training] loss = 1.248170 * 50000, metric = 44.3% * 50000\n", + "Finished Epoch [4]: [Training] loss = 1.150515 * 50000, metric = 40.4% * 50000\n", + "Finished Epoch [5]: [Training] loss = 1.081821 * 50000, metric = 38.1% * 50000\n", + "\n", + "Final Results: Minibatch[1-626]: errs = 39.7% * 10000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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acRzHcZysyCQPTIdgL1hNoTWRa2swq8ndmQogInWBTsBtYZuqqoiMAbqkGHYM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\nY326dbPMwmvWmKXjjz8sR00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GjFi/4cNt++YbW5a6/HJTHNavt6WnoiJzfD79dKvsXVLI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\n5CqFjMOoVXVQ5HiMiLTFCjn+oKpTM51PROoG42+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model_with_batch_normalization)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "CNTK high layer API:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def create_basic_model_layer(input, out_dims):\n", + " net = {}\n", + "\n", + " with default_options(activation=relu):\n", + " model = Sequential([\n", + " LayerStack(3, lambda i: [\n", + " Convolution((5,5), [32,32,64][i], init=glorot_uniform(scale=[0.1557/256,0.2,0.2][i]), pad=True),\n", + " MaxPooling((3,3), strides=(2,2))\n", + " ]),\n", + " Dense(64, init=glorot_uniform(scale=1.697)),\n", + " Dense(out_dims, init=glorot_uniform(scale=0.212), activation=None)\n", + " ])\n", + "\n", + " net['fc5'] = model(input)\n", + "\n", + " return net\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training 116906 parameters in 10 parameter tensors.\n", + "\n", + "Finished Epoch [1]: [Training] loss = 1.795087 * 50000, metric = 65.6% * 50000\n", + "Finished Epoch [2]: [Training] loss = 1.454563 * 50000, metric = 52.1% * 50000\n", + "Finished Epoch [3]: [Training] loss = 1.311346 * 50000, metric = 46.3% * 50000\n", + "Finished Epoch [4]: [Training] loss = 1.227226 * 50000, metric = 43.1% * 50000\n", + "Finished Epoch [5]: [Training] loss = 1.160773 * 50000, metric = 40.3% * 50000\n", + "\n", + "Final Results: Minibatch[1-626]: errs = 39.1% * 10000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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EEFq5hjbrTK/n8RlwS1MWMLQOf/yj56SssUbhwdt22sm7LXfI+8alA8EdfHAu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\n2alTLtn19dfhggtqzvR88MF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C5AOdVXUFgIhcB7whIleq6tpylC27GDTIjj//HE0+uGEDrFkDp55q1xs3WlmN\neKZEjuM4jlO2JKWciEjtSPwSEaldVN004pwsBDYCDWLKGwBzE7SZA/wdUUwCpmDRa3cApiUarFu3\nbtQJR1wFOnXqRKdOnVIUu4LwryBHY8+eMHhw1ND2ueeidY46yoK7DR9uHkOO4zhOpWXQoEEMivyw\nDVi6dGmZypDsysliEdlOVecDS4if+C+SELBqKgKo6noRycPsVoYBiIgE148naPYVcLqIbK6qkRw/\njbHVlFlFjffII4/QvHnzVESs2Jx3Hrz+um3hDB0aLT/wQNh1V/jjD8vdA6aguHLiOI5TqYn3g338\n+PG0aNGizGRIVjk5ElgUnB9RCnL0AfoHSso4zHtnc6A/gIj0BrZX1fOD+q9hhrkviUgv4F/AA8AL\nvqUTw/HHW2C3G28smMdn333ht98saFuzZrblc//95pL8zTdw4okFvX4cx3Ecp4xISjlR1c/jnWcK\nVR0SxDS5E9vOmQC0V9UFQZWGwI6h+itF5BjgCeA74B9gMHBbpmXLCS6+2F4ATZrAlClmJFu1alQB\n2WILO/bsCV99ZZ4+r79uyQeLYsMGmDzZc/o4juM4GSNZm5Okv3lUdVI6gqhqP6BfgnsXxin7DWif\nzliVmrFjYcWKwuV77mkrJmE35gMOKL6/rl2hXz9YuLBwfiDHcRzHSYNkXYknAD+EjkW9nGymdm3Y\nfvvC5XfeCVWqwF13Qf0gMO/uuxfd12WXmWICsG5d8jIMHWorNwsWFF/XcRzHqXQkq5zsgsUW2QX4\nLzAduBILNX9AcD4tuOdURHbayVyK99jDlAZVU1YAZs+Gv/4qWH/pUnj2WTvv2BG2267o/l9+GQ4/\n3KLYnn66lT36qCkpY9IJj+M4juPkKsnanMyMnIvIG8A1qvphqMokEfkLuAt4J7MiOuXG+vUW/r57\nd7vWkJPW5pvb8eSTYeDA4vsaMwbmzrV8PxEi/bVta1tNEbuXCBs2wLJlUK9e2lNwHMdxKh7p5NbZ\nF1s5iWU60LRk4jhZRbVqUcUEYPFi+OEH+PprqF7dlIt337V6YCsv8ejRw7Il169vEWnbtrXjvfdG\n69x4Y+F2H3xgXkT5+Zmbk+M4jpP1pKOcTAFuEZFN4USD81uCe06uIAIdOtj5/vub8tG8ORxySOG6\n3brBddfF7yeihOy+u70+/zxqzxIJq98vji305pvDrFnw008lm4fjOI5ToUhHObkc85KZJSKjggR9\ns4KyyzMmKxBKAAAgAElEQVQpnJMFDBoEM2bAhAnw5pvR8nkxqZD++Qcefxy++KJwH6+8Ysfb4nh6\nV60KV10FBx1U+F7EW6hdu7REdxzHcSom6ST+G4cZx94KTApePYBdg3tOLrHVVtCokZ1vtlm0/PGY\n4L1HHmnHdu0Kb8Occ45tAe22W/wx7rwTngxyNkaMZIcNi3oNLVkCq1bFbwvWr4jZp6TK1Kme/NBx\nHCfLSGflBFVdqarPqup1wes5VV2ZaeGcLOO88+Dbb00RaNOm4L3DD4+ez5plysjUqcn1W6+ebRfN\nnGnbQ1Awmi3AiBHx2w4caCH4Ac4PAgiPG2crPcXx2GPmnbTnnsnJ6TiO45QJaSknInKuiHwpIrNF\npFFQ1k1ETimurVOBEbGcPPn5Ft4+zM47m7twhw624lG3rn3xi8DaJDMKbLll9Pzjj+04caKt3Bxz\nTPw274Scw444wgx2W7e2LaFIzqB4zJ8P114bvV64EG64wbI1O47jOOVKysqJiFyB5cIZDtQlmuhv\nMXBtonZOJeC88+D9982QNZzBUiS59ttsY0rCP//A0Udb2X77mc3LrFnmORTrEfTGG7Z6sm4dXHON\nrcBEmD4dXn21sH0MRD2MwFZafv8dHnrIFKSIka7jOI5TLqSzcnI1cImq3gOE/4t/j7kZO5WdL7+0\n42GHmZJSo0bR9cNss038uCa9e0OfPjBtWsFyETj77Khrc4RHHzVblAceME+jWOrVs/qq0KpVdJtq\n40ZLlqjxEm87juM4ZUE6yskuxA9TvxbYIk65U9lo0QI6d4bXXrNw+Zkgoiw0bpx4u0YEXnzR8gd1\n7WpB3SZNspWTv/8ufozRo+04apRv7ziO45Qj6Sgn04FmccqPw+OcOGBePa++WjCJYEl58cXo+RNP\nmI3LHnsUXuG48EKzOYnw4IN23GGHaMLDzz+3zMuxtG1rxw4dCnomxUPVYrPE2zJyHMdxSkRS4etj\n6AM8KSK1AAEOFJFOWBC2LukKIiJXAdcDDYGJwNWq+l2Cuu2Az2KKFdhOVeenK4OTxVSrBh9+aHFR\nzjrLotVC8fYs3buboSuYDco++0Q9i2IVGxHzRmpaTKDj2bNN2VGFTz+1lZrzzisY8dZxHMdJm3Ti\nnDwP3ATcDWwOvAZcAXRV1dfTEUJEOgIPAz2xRIITgREiUr8oUYA9MGWmIa6Y5D7HHw/HHhtVTK6/\nvvg2IvDRR/D667DvvvD991Z+xhnx6x94oBnFPvywtY1kT46MCfDvf0cVm/r1bcuod+/4/f35J4wc\nmdz8Ms3GjRbNd8iQ8hnfcRwnTURTMPwTEQF2BOar6hoR2RzYsqRKgYiMBb5V1a6hcf4CHlfVB+LU\nbwd8CtRV1aQib4lIcyAvLy+P5mGPDqfi0aqVKRnLllmQuFSIrLSsXBlNXhiPHXYoaKcS8SQCeP55\nuOQSc3euXz8ayTb2sxTO7Bx778UX4eKLbVto221Tm0OyrFwZdc+ePbv4zNGO4zgJGD9+PC1atABo\noarjS3u8VFdOBJiKKSio6qoMKCbVgRbAJ5EyNY1pFNAmUbtAlglBrJWRInJwSeRwKhDffGOxU1JV\nTFaG4gQWpZhANBgcwOWXmxKy335w5ZXQpYspG0cfDc2aRWO+zI/5KIwdGz2PNci9+GI7vvRScrL/\n8kvRUXLjscUWljwR4O23U2vrOI5TjqSknKhqPvA7sE0GZaiPxUqJtSych23XxGMOcBnwX+A0bJVl\ntIjEM9R1co1q1VJzT46wYgXsumvBHEGJuO46i3eybBnstBP8+iv8+CM89VThui+8AAcfbKsg+fnR\nWCwHh/Tld98t2Ob//s+OEQ+honjsMWjSBG66qfi6sZxwgh3dHsZxnApEOt46NwMPisg+mRYmWVT1\ntyBk/g+qOlZVLwa+BroV19apxKxZYwrD8ccXX1fEjG+32sqMXSPk5RWuu+225v2z5ZbWplkzC+rW\nu7eF1F+92lZcwtxzj8WB+egjGDOmaFluvNGOQ4cWL3ci/v7bkjg6juNUANLx1hmAGcJOFJF1wOrw\nTVWNE0GrSBYCG4EGMeUNgLkp9DMOOKS4St26daNOnToFyjp16kSnTp1SGMqpkDRqFM2QnAphA9ii\niLhO//QTPPts1I05gmpB76Inn7StomnTTFEBWL/eAsqFWb3alJ45c6zf1avh0EPNwDeZFaQnnjDj\n4eOOK3zvjz/Me+nkk6Fv3+L7chwn5xk0aBCDYn7MLA1H/S4DUjKIBRCRCzBPmbio6sspCxHfIPZP\nzCD2wSIbR/sYCSxT1dMT3HeDWKf0eeIJC6N/yy0Ft1IGDLCtnL/+iu/+rAqnnw5vvWVRdWvXLqjM\nnHOOxY65+2649VYrq1sXFi0qmbxhWXI9Ku7GjWagnGw6BcdxNlHWBrEpr5yoav9SkKMP0F9E8rAV\nkG7Y6kx/ABHpDWyvqucH112xYHA/A7WAS4AjgATZ4RynjIh4xMQa69aoYVsrDRrYdk/EIDZCldAO\n67PPmv3KQw+ZV9Czz9qKz4ABdj+inCxeXHg1Biw30eDBcMUVRX8R5+enPr+KzBlnQMuWUXsfx3Gy\nlqRtTkSkiojcKCJfich3InKfiBQTRjM5VHUIFoDtTiw0/n5Ae1VdEFRpSOAhFFADi4syCRiN5fQ5\nSlVHZ0Iex0mbtm0t+eB//1uwvGqQH3PBAnjuuYL3wskM27WDyy4zxQSidUVMgalSxSLTXnCBKRci\nMGFCNH4LwKmnwlVXWUbnRKxbZ30tW2aK0qBBqa2cTJliqzzxFJxXXoGvv06+r7JgwQLzWPr22/KW\nxHGcZFDVpF7AbViiv4+AdzBbkxeTbV/eL6A5oHl5eeo4Zc6SJZE0g6qrVhW+P3q0atOmqqtX23X7\n9tH6RfH334Xrde5s1/n5hevPnau6xRZ2f8GC9Ofz6KOqtWrZGPn5qiNGqI4aZef77qt61lkF67//\nfvx5lxVPPWVznjix/GRwnApMXl6eYiYdzbUMvrNT8dY5D7hSVY9T1VOBk4CzRSQdjx/HqVzUqRPN\nghwvb0+7dvDzz1Crll3PmWPxVeYWYxNePxREecUKW8l47TVzIY63pbPXXgXjvcTy/fdw883Fz+eV\nV2xsEcv+3L69xX0ZMsSyQL8eCha9YIHFgokXW0bVPJt+/bV0bV5mzjSD6P32K70xHMfJGKkoFjsB\nwyMXqjoK06K2z7RQjlPpGTrUtnYaxDqxxVCjRjSGysSJ8OWXdp5oWyXS3w03FFRswOK4tGoF999f\ncKsJzCU6stW0apW5VM+aZdfTp0frHXecGfSCKRwQrRexq1m/3raibrrJtqj23NOUpnnzLG/S6gIO\ngJnh/vvNFsdxnApBKspJNSA2j/x6oHqcuo7jlITdd7cIr8lw3HGmpIwfD98FuTKHD49f9913TTG5\n//7C98K2MAsWRM/z880YN5JAMaJ8nH22HT/4wJSpdetshejhh6188mQ7/vVXwWOjRhby/6GHLMBd\nhLPPNkPezTePRraNx5132orN++9Hxy/Kvua332xVJpKVuihWroR33oElSwqWL1hgmbDbtLHgfMnw\n55/mTt69e1RBcxwnKVLx1hHMo2ZtqKwW8LSIbFonVtXTMiWc4zhJUKOGRb79/XdzR+7TBw46KH7d\nxo3hgULpqow2bexL+ZVXTLlZsACWL4fbb4devey1dCnccYfVv+oqO0ai0EbYfXdzhf7tN7vu3t2O\nDRpY2oE5c+w6Px9OOina7tFHo9suJ55YcJtn6VKoWdMUg549rezcc20b6sQTbVUmdrUnQt26dhw4\n0BSGRo0sEN+HHxauO20a/Oc/liIh/B7utpu9FzNnRo2bi2PGjGgAvTlzbLvNcZykSGXl5GVgPrA0\n9BoIzI4pcxynrHnoIVtBadu2YF6gVOjUyXL97LSTxU+56SaLq1KjRlSJ2Hrr6MpB69bx+xExL6Cb\nbzaFYepUU56qVjUFI6LURCLeRmxx9t3X6oKtiixbZuUffWTjbr55wfxCjz1mihAU7Rb9r3/Zls7Z\nZ0e3tRKtLEXixmwTk6Fj+XI7Dh6cfJyU8PuT6zFkHCfDJL1yoqoXlqYgjuOUgA4dMtNP1aq2OtCr\nV7Rs//1h772j1w89ZJmZi+L4483deOpUC+9/zz3Re337Jo5Gu9tu9kW+enXUgDYSqVLVFI3Zs20F\npFUrs4UBePrpouWpFwSurlnTEjc+/7ytxoSjRa9aFZWrXj0br3dvOOooKxswAM48s+hxwFacnnkG\nLr3UZP/gA7joouLbOY4TpSxcgrLhhbsSO07ybNxobrfTp6uuX29lkyapfvhh6n3Fc2kujpkzoy7S\nK1aotmlj51OnFu578GDVtWvt/sknF7z/99+q69YVLPv8c6vbuXPB8pUro2MuWGD9gmrHjnacNCk6\nZqRevPfjo4/s3rRpqc87dm5Dh6pu2FCyfhwnA2SzK7HjOJWFKlXM/mPnnS0LNNi2SzJJE2NJJ1z8\nH3/YceRIMwzu39+uw8HmIn2feaYldQQYNixq+Dp+vOVFuuKKgm123dWOsTYgYVfn4cOhY0c7v/RS\neOQRmz+YPUqEWG8qVfOY2mwze+/CPPaYpSB49FFbUbn4YvNciqVLF5vXqadaML9q1azf4cPj29Us\nW2aeUREvp0WL4IsvCtcrCd9/bykZEhkDv/eebQv69pWTIVw5cRwn+4h49hx6qB332MMUprVr49ev\nXTvqRj19OoweDZYHpPBWzA47WJ14isEdd5gdS9hepF07uPba6PV770XPY/N0PfOM2emsXl0wJQHA\nZ59ZjqRu3cze5sUXzfg29gv9hRfsOGlStGzsWDM8judaXqeOuWKfe65dd+5sMi9ebIralVcmNhYO\nk58P111nhsOxnHoq3Hdf4aSUEb77zmLbVK1a8nxPjoMrJ47jZCOq0LRpNGCdiH3Bnnde4jaRFZG+\nfaP1jj3WXrFEVoSmTrW+I0Hjbr/dvJ723BO23NLKYr1z7r3XDGMjAfLOOMOMbZ95xuK3gClTsURc\nrAGOCdKAffBBQePciOHtJZfAiBF2fsEFMG6cnYdjtcyZY+7bESIGxn/+acf33oNffoGnnrK5FuVK\nvWyZzfORR0zJicgRIWLbA/DVV3Y85JCoQhRxK1eFC9080ckAZbF3lA0v3ObEcXKbsC3IN9/Y8e67\ni27Tu3e0TWxo+zlzVH/9tfhxo/5Gqp98onrrrYnr3nyz6m67FUxn8PffqrNnq65ZY2kBQPX++wu2\n27AhWn/u3Ohc99nHxgRLf7B8eUF5ZsyIni9ZUrDPjRtVFy+2888+K9ju998Ly75+ffR+ZN5VqkTv\n9+1rZbfcojp5crRu5H/umDE2z1hWrrS2P/xg80rHxmbjRrP/mTIl9balzcaNql26qP74Y3lLUiLK\n2uYk2S/2k5N9lYXQaU3UlRPHyX0OOsj+ra1cqTpyZNSYNx4LFxb8Ql65Mr0xL7kk2sfGjcm3GzNG\ntUMH1RtvjLa/6io7rl1buP5119m9sWNVly618y5dCta54w4rP+ww1csvL6jUqEYViJEjVd9+287f\neMPep2eftXFfe0111izVQw9VvfZa1dNPj/YfNlIG1eeeKzj+iBFmgNy1a8H3NjJWvFxR4XqXX666\n1VbJv4cRWrZM3H9psHRpYUPrRPzzj8l19NGlK1Mpk63KSX6Sr41pCwJXAdOxhIJjgVZJtjsEi1Q7\nvph6rpw4jhNlwgT7F3jQQSXrZ9Ei62fw4PTaRxI1gupNN6n26BG/Xn5+dHVn3Dir/+mnheu89VbB\nL87Ro22FJLyyFPE0SvSFfsMN0XutWhXsPz/flBswz6d4RJST7bZTvffeguPOnat6zDGq7dqZQhQp\n79FDtWZNLeDplJ9v5b16Ff0ehvtfulT11VdTUxRT5ZBDVFu3jl7fcIPqoEEF64RXgNq3Vz3uuNKT\npwzISuWk1IWAjlho/POAvYBngEVA/WLa1QGmYjl/XDlxHCc1pk8vbwlU582zf8XFfQGH2XVXa/Pz\nz8m3CSsn119vCkvkOnaFad266L2mTQv31auX3Vu2LLmxd9ghOs7LL0f7fuIJO3btavUi7tsDB9p1\nZCWsqBWRtWtV69VTbdLEtrUaNbL6r76qOn686oMP6qZVqdWrVV96yRSYkhCR6YEHTAmKJ2OPHqpn\nnmnnd9yhWrdu6SpMpUyFciUWkVolaR+iG/CMqg5Q1V+Ay4FVQHGRi54GXsVWWhzHcVIj1t23PNh2\nW/tqi4TlT4aIe3ZxiSFj20S+Rh980KLu9utnRsexLsLVq1uWbIC33irc1/ffm6HxVlslN/Zff9m4\n1arBLrtEy//7Xyt/9FG7PvNMc//+8ksz7B0b/Gs/+eTEfdeoYYbCkyebEXMkivDZZ8Ozz0ZzQj35\npLlDX3hh4QjAqfLJJ3a88UYzIo64us+YEa2Tlxc1Qj74YPOeirQrS665Bh5/vOzHLSEpKyciUlVE\nbhORv4EVIrJrUH6XiFycRn/VgRbApqemqgqMAtoU0e5CYBfgjlTHdBzHqdA8/bR5/JT0S/aKK+zL\nvFac35lNm5ri0Lhx4Xvvvx/NkZQqhx1m8Wv69oXttit8f/Fim9/ff5tcXbrAkCHJ9x/Okh1OLPnD\nD1EvqbAyNn++JXuMx7vvRrNrP/wwtGxp78nhh0fr7LYbHHGEnUcUr7lzLe1Cu3Z2HbkfUU5E7DVn\nDhx5pKWKSBfbGUjME09A167p919OpLNy0gO4ALgRCPmx8RPQJY3+6gNVgXkx5fOAhvEaiMgewL3A\n2apaRFINx3GcHOToo+0Lvry47rrEKQiS4ZhjojmWYtlrLzvusYfFi3nuOUs7kCxbbBHNan3EEfC/\n/1kclmbNLFbMI4/Yvd9/t+P//mfxZv7+u2DsG1VzSX/7bbu+/npbDRk3zmLYrF8Pb74Jp5xSUAma\nPz+qdO2/vx2rVrUxYoMInnCCxb9JlIwzHqqWwkHVYuJUqRJNMBmWYcUKmxPYysn69bYCFUnImeWk\no5ycB1yqqq8C4cg+EzF7kVJFRKpgWzk9VXVapLi0x3Ucx3ECHn7YkkyWBp99ZpF+69dPv48TTrAV\nlNatbeWgZcvovTPOsOM++1g8lzfesOuLL7bYLRHmzrX4L40bQ8Pgd/Ihh0QD9FWrZttSkS22yOrL\nq69G+4gEEQTLT/XLL9FoxmDKTarMmWNbX++9Z+8T2DZdZGtpwADb7vvvfy3gYGSur79ubcIrYdOn\n2xyzkKQT/4X4N2aEGksVIEH4wCJZiCk5sZunDYC5cepvBbQEmonIk6GxRUTWAceq6uhEg3Xr1o06\n4WRfQKdOnejUqVMaojuO4zgZpXbtaBbsolC1L9bly2H77QtH5E1kT7T99hY87sorzR4jwimn2GrO\nP//YdtmoUVa+114wL1jYL2qFo0MHW1Hq2NEiGTdubKs4ES6+2FYuIqsn48dHAweG+ewzUxouusjm\nuH692eAceaRl+r7/fqvXpIkpJCecYLY5Dz5oAfsiAfN++SXaZ6tWMGtW9Do/396vxo1tBezSSwuI\nMGjQIAZFEm4GLF26NPHcS4NULWiBPOCc4Hw5sGtwfjswJh2rXMyg9bHQtQB/ATfEqStA05jXk8Bk\noAmwWYIx3FvHcRynIjNwoOoBB1hMmgYNol4yK1ak19/RR1v7yZMtgBuY63XYW2ntWtVhw1SbNctM\nEsY331Tdeuuoh9TTT6tWrWqePBEX7P33V/3446gMYEHqwteR9q+9ZtcTJth15H6fPuaNFg4k+N57\ndu+pp1T/+ivq1ZQEZe2tk87KyZ3AyyLyb2zF4jQRaYxt95yYRn8AfYD+IpIHjMO8dzYH+gOISG9g\ne1U93957Jocbi8h8YI2qTklzfMdxHCebOffcaN6fAQOiqxlQcIUiFT780FZKGjaMJk6cOTNqINy6\ntXkDnXRScqs5ybDXXuYlFUmoudNOlpph5kxLDgm2LdOsWcF2++1nqy8vvAA9ekTbn3WWbbH9+992\nPWcOLFxoWzmxRGxgJk2CevXs/KCDMjOvDJOycqKq74rISdhKyUpMWRkPnKSqH6cjhKoOEZH6QV8N\ngAlAe1VdEFRpCOyYTt+O4zhODjBzZvS8Vy9zZR450pISpkv16lF7ks02M6Vgxgzb9oDScf3de297\nRWja1JI1Ajz0kBneTp1qNjfdu5sClZdn2zDPP2+vMCJRxQRsPg3j+pLAjjtaxu9Gjez699/jby1l\nAaLFuSHlCCLSHMjLy8ujeWwmUcdxHCe7+fJLMzqtXdtWFYqKfZIum21myRS/+sqUgjvvzPwYRbFs\nmWWZ7t+/YLLFLGD8+PG0sEzfLVR1fGmPl862DgAi0hKz8QCYrKp5mRHJcRzHcWI49NCC3i+lQcST\npnXr8tnuqF27+LgllYSUlRMR2QEYhOW0WRIUby0iXwNnqeqshI0dx3EcJ1v5/HOLibJxY9SmwykX\n0olz8jzmMtxEVeupaj1sBaVKcM9xHMd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dLtfpXT6bSfZaa18v9IskT+mVnMf0PR6TH/e4\nSq8gvCR5q6qt1tp7ejfORpJpesfPURZ/y/y1y/QuofvZMcniRtx11mnZtdckd0ke0tfjOcnx9+DW\nbtKf8TC9cjRNsp9ebfptHmCm/v9jDABgfSonAMBQhBMAYCjCCQAwFOEEABiKcAIADEU4AQCGIpwA\nAEMRTgCAoQgnAMBQhBMAYCjCCQAwFOEEABjKJ/ymXEGyDbuKAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model_layer)" + ] + }, + { + "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", + "| dropout0.5 |\n", + "| |\n", + "| FC-1024 |\n", + "| dropout0.5 |\n", + "| |\n", + "| FC-10 |\n" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def create_vgg9_model(input, out_dims):\n", + " net = {}\n", + " with default_options(activation=relu):\n", + " model = Sequential([\n", + " LayerStack(3, lambda i: [\n", + " Convolution((3,3), [64,96,128][i], pad=True),\n", + " Convolution((3,3), [64,96,128][i], pad=True),\n", + " MaxPooling((3,3), strides=(2,2))\n", + " ]),\n", + " LayerStack(2, lambda : [\n", + " Dense(1024),\n", + " Dropout(0.5)\n", + " ]),\n", + " Dense(out_dims, activation=None)\n", + " ])\n", + " net['fc5'] = model(input)\n", + " return net" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training 2675978 parameters in 18 parameter tensors.\n", + "\n", + "Finished Epoch [1]: [Training] loss = 1.965293 * 50000, metric = 73.2% * 50000\n", + "Finished Epoch [2]: [Training] loss = 1.616270 * 50000, metric = 59.7% * 50000\n", + "Finished Epoch [3]: [Training] loss = 1.426021 * 50000, metric = 51.6% * 50000\n", + "Finished Epoch [4]: [Training] loss = 1.277556 * 50000, metric = 45.6% * 50000\n", + "Finished Epoch [5]: [Training] loss = 1.170007 * 50000, metric = 41.4% * 50000\n", + "\n", + "Final Results: Minibatch[1-626]: errs = 42.2% * 10000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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qD6LA5Ehg3xDCzByv8TzwXQjhuEr29wNKSkpKmDq1HyefDCtWwDrVjQkSERFp4iZNmkRR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\nVq5MD/H+6CPYa6903h57zN9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RxUZEBgONVfWEUs6tiykpv2I5Be5V1WQpwhGR08i82q7jOI7jODFOV9XnKvomGVtOVDWn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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "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:\n", + "\n", + "\n", + "\n", + "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": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from cntk.ops import combine, times, element_times, AVG_POOLING\n", + "\n", + "def resnet_basic(input, num_filters):\n", + " c1 = conv_bn_layer(input, num_filters, (3,3), init=glorot_uniform())\n", + " c2 = conv_bn_layer(c1, num_filters, (3,3), init=glorot_uniform(), nonlinearity=None)\n", + " p = c2 + input\n", + " return relu(p)\n", + "\n", + "def resnet_basic_inc(input, num_filters):\n", + " c1 = conv_bn_layer(input, num_filters, (3,3), strides=(2,2), init=glorot_uniform())\n", + " c2 = conv_bn_layer(c1, num_filters, (3,3), init=glorot_uniform(), nonlinearity=None) \n", + " s = conv_bn_layer(input, num_filters, (1,1), strides=(2,2), init=glorot_uniform(), nonlinearity=None)\n", + " \n", + " p = c2 + s\n", + " return relu(p)\n", + "\n", + "def resnet_basic_stack2(input, num_filters):\n", + " r1 = resnet_basic(input, num_filters)\n", + " r2 = resnet_basic(r1, num_filters)\n", + " return r2\n", + "\n", + "def resnet_basic_stack3(input, num_filters):\n", + " r12 = resnet_basic_stack2(input, num_filters)\n", + " r3 = resnet_basic(r12, num_filters)\n", + " return r3\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's write the full model:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def create_resnet_model(input, out_dims):\n", + " net = {}\n", + " feat_scale = 1.0 / 256.0\n", + " input_norm = element_times(feat_scale, input)\n", + "\n", + " conv = conv_bn_layer(input, 16, (3,3), init=glorot_uniform(), nonlinearity=None)\n", + " r1_1 = resnet_basic_stack3(conv, 16)\n", + "\n", + " r2_1 = resnet_basic_inc(r1_1, 32)\n", + " r2_2 = resnet_basic_stack2(r2_1, 32)\n", + "\n", + " r3_1 = resnet_basic_inc(r2_2, 64)\n", + " r3_2 = resnet_basic_stack2(r3_1, 64)\n", + "\n", + " # Global average pooling\n", + " poolw = 8\n", + " poolh = 8\n", + " poolh_stride = 1\n", + " poolv_stride = 1\n", + "\n", + " pool = pooling(r3_2, AVG_POOLING, (1, poolh, poolw), (1, poolv_stride, poolh_stride))\n", + " net['fc5'] = dense_layer(pool, out_dims, init=glorot_uniform(), nonlinearity=None)\n", + " \n", + " return net" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training 273258 parameters in 86 parameter tensors.\n", + "\n", + "Finished Epoch [1]: [Training] loss = 1.479848 * 50000, metric = 54.0% * 50000\n", + "Finished Epoch [2]: [Training] loss = 1.088070 * 50000, metric = 38.8% * 50000\n", + "Finished Epoch [3]: [Training] loss = 0.913999 * 50000, metric = 32.4% * 50000\n", + "Finished Epoch [4]: [Training] loss = 0.806477 * 50000, metric = 28.2% * 50000\n", + "Finished Epoch [5]: [Training] loss = 0.733808 * 50000, metric = 25.4% * 50000\n", + "\n", + "Final Results: Minibatch[1-626]: errs = 24.8% * 10000\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+vippRXx5aP+WkaqJ2X777PWcOR76/ocfYPJkWH55T+/Z02dYpk3z6yAIgiBo\nhRSknEhaLBO/RNJi9ZUtIc7JFGA+0DsnvTfwdRHtvA5s1lChwYMH06NHj1ppgwYNYtCgQUV01QJ0\n7w733w9/+INfz5kDL77oef36+Tnjavzii7DLLi0jZxAEQdCqqaqqoqqqqlba9OnTm1WGguKcSJpP\n4g0jqYb8G/8JNxdpX7QQ0qvAa2Z2XHIvYAJwhZkVFH1M0hPA92a2ex35LR/npLGYeRTZLl3cM+fX\nv4ann3Y3Ysk9dtZaC66/3gO6Lb10S0scBEEQtAGaO85Jocs62wBTk+utm0COS4BhkkbiMyCDga7A\nMABJ5wNLmdn+yf1xwDjgXaAzcGgi13ZNIFvlIMEhh2Tve/aEc87JBmtbc02YPds3FlxmGdhtN7jn\nnsLanj4d/vlP2HNPWLftmu4EQRAElU9ByomZPZfvulyY2Z1JTJOz8eWcN4EdzOybpEgfYJlUlY54\nXJSlcJfjt4Ftzez5cstWcVx0kSsgAHfcsWB+p07ZvXjuvbe4ts8/34/MTEwQBEEQtACF2pysXWiD\nZvZ2w6Xy1rsauLqOvANz7i8CFs7NZn72s4bL7Lhj9vrpp2HbbRuuk7bDmTwZeueaAAVBEARB81Co\nK/GbwOjUub4jaGm6doWHHvLrv/2t/rIffgg33ODXI0b4+Z13/DxzJlx1ldu6BEEQBEEzUajNyQqp\n6/WAf+EzF68kaZsAJwAnlk+0oFH8/vduIJvx5MnH/ffDrrv69a67Zg1ot9vObVAysymbbgqt1Yg4\nCIIgaHUUanPyWeZa0l3AsWb2aKrI25I+B84B7i+viEHJrLGGz3q8+CJssEF2P5/HH/fZkmdS+zd2\n65bNP/PM2ss8oZgEQRAEzUgpQdjWwj1lchkHrN44cYKy88ILsOWWfn3//fD553DMMX5/ww3u/bPd\ndtC5s6fNm+fuyvPnw7nnekyVyy6DwYPDUDYIgiBoFkpRTt4HTpZ0iJnNAZDUETg5yQsqiV/9Knv9\nhz9kr9u1g4MPhl/8AjbcMJvePglTc8457qq87rqwdeI9/tVXsNRS2bKjRnn7o0bBaqs13RiCIAiC\nhYpSNv47HNgB+ELSU5KeAr5I0g4vp3BBGWjXDmbM8OtNN4W//MWvP0tW6nbcEZZYIn/d44+Hrbby\n+CkAt91WO3/oUKiuBg/MEwRBEARloaAIsQtUkrrhe+tk/i6/D9xuZj+UUbay0iYixDaW+fOzMyPF\n8NprsPHhApktAAAgAElEQVTGfp1+v2Qi1gJ8+WXtWZUgCIKgzVCpEWJrkSgh15VZlqCpKUUxAV/2\nuecen3k5+2yYNMmXg045Bf7zHxg4EDbayO1Z0syb53YqHTv6/YQJsOyyjRtDEARB0OYpZVkHSftJ\nelHSREnLJWmDJcVuc20RyUPhL7GEe/JcfTWceqrPnOy1l5dZbrnadX74ATp0cI+h1193xWW55Xzf\nnyAIgiCoh6KVE0lH4Hvh/BdYHMj8Hf8OOL58ogUVR4cOte8znjvPPgvnnZdN32ADeC7Z5eCTT3xW\nZXQSn++uu5pczCAIgqB1U8rMyTHAoWb2D2BeKn0E7mYctFUkny1Zdln47W+z6VtuCVts4dc1Ne69\n8/nnrqRk2CWZVHvyyezeQEEQBEGQh1KUkxXIH6b+R6Bb48QJWgWffQaPPJI/b/JkV1B69/blnK++\ngjlz4KCDsmUaUk6+/Rb++9/yyRsEQRC0KkpRTsYB6+ZJ35GIcxJklJa1k70i+/Tx5aAbb/RZF7OG\nNy8cPNhnZj78sGllDYIgCCqSUpSTS4AhkvYCBGwo6VTgfODCUgWRdJSkcZKqJb0qaYOGa4GkzSTN\nldTkrk1BA8yb5xFnAVZYof6yufzmNx7IzQz23tvTNt3Uz2Zw0klwxhm+tDRhQvlkDoIgCCqOol2J\nzewGSdXAuUBX4HZgInCcmf2nFCESRedi4DDgdWAw8LikVcxsSj31egC3AE8BvUvpOygj117r53PP\nrT/M/bx5MG4c9O3r4fHHjIHHHvO88eM9MBzA1Knwyiuw4orwz39m68+a5WezCKcfBEHQBilq5kTO\nssA9ZrYy0B3oY2ZLm9mNjZBjMDDUzIab2Qd4pNlZwEH1V+Na4Dbg1Ub0HZSDmTOze/acemr9Zb/4\nAlZZBRZdFO64o7anz7Rpfj7qKD/vuSd8+mk2/6KLfIblpps8/kpNTcOyzZlTWLkgCIKgIih25kTA\nJ8AawMdmNgtXIkpGUgdgAPDTL5SZWRIWf5N66h2IG+fuA5zeGBmCMtC9OwwfDsss03DZX/wie/3D\nD74E1KOHL9cstpinX3UVLLKI7+uzySbukvzGGx7wDXxfIHA35m22qbuvu++GPfaA3/0OHn64pKEF\nQRAEzUtRMydmVgN8DPQsowy98Fgpk3LSJwF98lWQtDKuzOyTyBRUAvvt53vxNEQ6XkpNDXTp4hsM\nZhSTDJddlnVBXnHFrGICMGiQn7fddsHItGn22MPPX3wBc+fCiBG1Q/A3BQ89BK/GZF4QBEGplGIQ\nexJwkaQ1yy1MIUhqhy/lnGlmmfn+MDxobXzwgUec3W8/OO00ePrp4urffjv8/e9+fcghvgHhK6/U\nLpNWWqqqPIz+Bhtk9wNqDB995PYu991XO33mTNh5Z5/tAd/PaN68BesHQRAEdVLK3jrDcUPYtyTN\nAarTmWZWxxa3dTIFmM+CBq29ga/zlF8UWB9YV9KQJK0dbhIzB9jezJ6tq7PBgwfTo0ePWmmDBg1i\nUOafeNA8rLqqxzPJUMq+P2ecARde6AayVVW+1LP33vC3v/lMTMZA97LLfOalFGpqFlRmqqqyHkV3\n3w277prNGzcuez17thv3jh4N06eX1n8QBEEzU1VVRVVVVa206c38HVb0rsSSDgDqrGRmtxQthPQq\n8JqZHZfcC5gAXGFmF+WUFdAvp4mjgK2BPwLjzaw6Jz92JW7rfP+9261kMHNvn8svdwPdjh19FmOR\nRdzz58QT8yseabbZBp55xmc+FlkEjjwShgyBm2/OBpWrrobOnbN1nnoKttvOry+/HD7+GK67Dn78\nsfxjDoIgaCYqfldiMxvWBHJcAgyTNJKsK3FXYBiApPOBpcxsf3Nt6r10ZUmTgdlmFkHgFlYWWwwO\nPzw7W2Lmy0aZpR/w2ZmMMj56NGSU1OnTF7R3AVdMIBut9uqrfRbmgAPcTubtt2srJgCbb+6u0Wuu\n6YHnBg1yb6FJkzxqbhAEQdAgBS++S2on6URJL0l6Q9IFkrqUQwgzuxP4K3A2Hhp/bWAHM/smKdIH\nKMANJFioueIKX9rZbjv3AqqPN97IXj/5pCsokisUAN8kb72ll4aXXsqWHT7cyw0cWNsFerfdPP2r\nr3wn5jffhJdfzsZs2Xnnxo+vPj79NEL+B0HQZijGMvBU3ENmBvAlcBwwpN4aRWBmV5vZ8mbWxcw2\nMbMRqbwDzaxOf1Ez+7uZxVrNwk6HDnDDDfDEE+7aXB9DUm/dRx7JKg+ZCLeZ81VXwaGHwmabQdeu\nbsibj4xh7PDhfl5nHejWDdZK9sJ8/fWswtMUDBzoIf9nNcqzPwiCoCIoZlnnT8CRZnYdgKRfA49I\nOiTceYNWx403wrvvwsYb+3LPl19m3aCfew4efNCvt9/el3BefNHLd8uzt2V1YuK0zz5upJumfXs4\n7DD4+uuG9xRqDCMSXT48g4IgaAMUM3OyLPDTvLGZPYUbxi5VbqGCoMlZf33Yf3/3GlppJdhyy+yS\nzpNPZg1eu6RWLtdYA5ZffsG2MrMsRx+dv6+hQ+GBB2rHd6mPAw+E446DiRPdZbohMi7T993ntjOj\nR2eVlXxUV7uXURAEQYVSjHKyCJC71/1coMBv3CCocA46CGbMgHPOgeuvd1fgQsgoEOvm26y7BIYN\ncwXpF7/w2ZiPP66//EWJQ1tmo8STT65tCJzLOed4cLrzz2/6gHRBEAQlUIxyItyj5t7MAXQGrs1J\nC4LWS/fubtjarh106lRYHTM/cj13GmLOHA+rv8suvsdQhiWXhL32yt6PGbNg3czyjRlceWW2HsCy\ny3qo/rRyNW+eG+1+/rnb0ACccgrcey/ceqvb07z1VnHyB0EQNBHFKCe3AJOB6anjVnxH4nRaEAR1\nMWGCuzC//bbbuTz6qNu3ZELzjxoFkyf7js3nnutp775bu40nnvAlonXW8T2HILtRInhMFvAlqcyG\nhy+95Ms+l19ee2lq9919pqa6Orx9giCoGAo2iDWzA5tSkCBYKFhuOT9ffrkvHWU4/3yf1fAgR75E\ntOGG8PjjC86c7LCDn99+G8aPX3Bp5vLL4Zpr/PrFF12JeeIJn9n55z99ZmjoUE/bcksYOdLLpnd/\nDoIgaEHKsMlIEAQFceqp2eubbvKlo8yeQCedVNubZ8MN/bzOOm4H88ADrlRMnAivvZYtt/LKC/bT\noQPcf7/He1l/ffi///OYLGuumd0m4LDD3Cj2mGN85mSLLdwN+4EH8steU5NVgt5+u+SXIAiCoBBC\nOQmC5iLjAXTwwdllmM6d3Z0ZYNFFPfz96NHZOhdc4PYjf/iD37/yiisuX34Jjz2W33sI3I7liSfc\nluT++z2tvv2Ldt/dz08+mT//7ruhVy+XZZ11fP+ipuSdd+qWJQiCNk8pG/8FQVAKK67YsHfMttvW\nvs/EVfnoI1hlFbcL2XVXWGopPwrh6KPh9NOzrtL5OOYYj+mSMarNYOaGtYMG+ezJb37j6c89V1jf\npfD997D22tn+gyBY6IiZkyBoDWSWb2680b18iuG00/xHfo016i+36qq+tPTtt/DZZx6Kf/hwn32p\nqXFbl/btXZFpaHuAfFxxhStKkybVX+6rr7LXsZtzECyUxMxJELQWunVzpaBYl+ViGDXK7VQyfPRR\n9vqcc/w8fTq89x7MnVt4YLnqag8sB15v6NC6y/7iF9nrRx/1WZsgCBYqCpo5kbRzoUdTCxwECy1j\nx7pBbFOyTM7+mssv7/FQVl0VNtjA07bf3s8TJtQuawbXXbdgOrhbcyZI3HXXZdNHj/ZlHHB7mkGD\nXJGZPx86doTFF2/0kBZgwgTf6+g//yl/20EQlAcza/AAago85hfSXh19HAWMA6qBV4EN6im7GfAi\nMAWYBbwPHN9A+/0BGzlypAVBUAc1NZmQcmbvvJO/zLhxnn/uuX7/7rtm991ntu222bpvvmn2wANm\n/fqZzZ2brfu733n+1Klmzz7r17vu6nmHHWa24oouQ1088IDXGT268eOD+vsKguAnRo4cafiWNf2t\nxN/5Yo6CZk7MrF2BRz3uAHUjaS/gYuBMYD3gLeBxSb3qqPIDcCWwObAacA5wrqRDSuk/CIIEKfvT\nveaa+cssu6yfMxFlBw50I90//zlbZt11oaoK3n/fl36qqjz93nt9WWe//bIbLR54oIfTv+4691yS\nsu3U1Ljr8vz5fp8xAr744sLHNGuWt7nYYgva68yaBd99V7wdTxAETUqjDGIllWvxezAw1MyGm9kH\nwOH4jMhB+Qqb2ZtmdoeZvW9mE8zsduBxXFkJgqApadcOXn0Vrr3Wl0emTnUvnj32cJuYn//cvYr+\n/e9sneHD/dyxo0ewfeQRv+/ZE3bcMbsR4aKL1u7rzTfddXmRRTxM//rr+zLTrbfWXh5KM2MGHH54\ndkPETJC5GTP8nLGpmTvX7XeWWKLuTRuDIGgRilZOJLWXdLqkL4GZkn6ZpJ8j6eAS2usADACezqSZ\nmQFPAZsU2MZ6Sdlni+0/CIIS2Ggj/1HfaCOPuZKZ0eja1cPv77ijKxQvvugKSa4ty2efef0PPvCZ\nlRNO8PTddqtdrl+/7PWxx/osysHJ10x6pibNXXe5wW1GOXruOZehutr3S3r1VZ8pWWSRrGfQffe5\nzUshmMHzz8POO8O0aYXVmTQpZmeCoAhKmTk5FTgAOBFIf9rGAKUsq/QC2gO5/oWTgD71VZT0uaTZ\nwOvAEDO7uYT+gyAolYx7ct+++fM328yXTnJnOZZd1l2WeyUrt//6l//ob7dd7XJdumT3/NlmG1hr\nLfjlLz0o3SqrwMyZ2RkR8DYyysvLL7sB8emnu2KQ8XJq3z7rZbT00q5kTJniBrvTp/uMUGYZKh/n\nnuth/x96KGvMWx8ffAB9+vhrNH8+rLCCu3cHQVAnpSgnfwIOM7PbgPmp9Ldw+4/m5Ff4rMvhwODE\ndiUIgubigAP8fOyxdZepLzJtIey4oysdTz+dtUfZbDP3uunZ0/clypCJpLvbbnD77dlQ+/UpAyed\nlL1edFE44gjYe+/a2wTMT33VnXFG9vqRR2C99eDjj10JyeXSS7OzP4su6grZ+PHwj3/4WNJ9pKmu\n9tmlcmDm0Xa//bY87QVBMyArMgKjpGpgNTP7TNIMYB0zGytpdeB1M+teZHsdcPuSP5rZg6n0YUAP\nM9u1wHZOBfY1s3515PcHRm6xxRb06NGjVt6gQYMYFLEUgqD18NRTvhSUUT4efdSVmHbJ/61vv/Vl\no9tug3339dmbLl3qbm/oUI8js+++MGJE1m167lwP1X/ZZX5v5stFf/oTrL46nHyyG/dmyP0+nTYt\n6w5dU+PHIqnwUg8+CDvttKA8m2/uS2I1NbUNhEth8mTo3RvuuceVtrfe8mW2JZZoXLtBm6Wqqoqq\nnNnD6dOn8/zzzwMMMLNRTS5Ese49wEhcCQCYAfwyuT4DeKEUlyHcdfjy1L2Az4G/FdHGGcDYevLD\nlTgI2hpdu2bdgqdNM/vii9ou0PPnm33zTfHtZtp86KHarsfTp3v+d9+ZzZiRdasGs6uuyt/WwIFm\nF1+cvZ81y+wXvzA76aT85W+9NdvmiBGe9sMPZnPmFD8OM7P77/e2Ro0yGzo027aZvz4NtbvPPmZV\nVaX1HbQZKtKVOIezgask/R++LLSbpOtxW5SzS1GQgEuAQyX9SdJqwLVAV2AYgKTzJd2SKSzpSEm/\nl7RSchwMnAD8O0/bQRC0VT7+OBva/6uvPLps2gW6XbusXUsxvPee24h07w79+3vaKae4QS14mP/u\n3T1I3aGHetq+++Zvq6oK/vKX7H2XLvDFF7WXo+bPz866TJ2aTb/jDj9fconbx5j5zEdmmckMtt4a\n/vlPv5892/tLL+F8/LGfV1/dl4symLkbd8eObhOUnvXJGO/++98++zRokNsFFUN1NQwY4NseBEGx\nlKLR4C67TwKT8SWZF4HtG6MlAUcC4/EgbK8A66fybgb+l7o/GngHn7n5DhiB28HU137MnARBW2X+\n/JbtPxPMbdYss2uu8ZmJ1VcvvH5mNuPcc82+/tps//3NrrjCZ0zeeMPz1lvP7Nhj/fq887zeq69m\n6773ntnf/569v+02n93p1s1szTWzfQ0e7PkTJtSeFXrvPbOddjI78ki/v/JKs+HDs/mLLGI2cqTZ\nK694O3fe6bMqdQWyS7c9Z44fM2YU/dIGlUFzz5w0eQeVcoRyEgRBk7Pnntkf5J49Gy4/b57Z5ptn\n6+y004Jlunf3vC5dzE480a9XWsnz1l8/W/eRR8y22SZ7v8kmZmed5df9+mXb+/BDT/vf/8zuvjtb\nfurU2grFMcd4+fnzzfr2Ndttt2yeWfb6ww/zjy3d1iOP1K6bj9mzza67zsu0tLIZLEBrWNYBQNL6\nkvZLjgGlthMEQdBmyAR+g/x7DOXSvj288IJfDx/uBrK5XH+9n6urs8s3n3ziyz/V1W5QO38+/Pa3\n7jV1551w4YXw+OPutgy1l2SWXx723BNWWgn++Efv/5NP3HD3mmuy5TJReNu1c5fs227L5s2a5dF+\nwfddevLJBeV+5x3fv2ixxdzlO0Nd7tedO8Nhh/l1esPJpuaKK9zoeNas5uuzVKZNyy7TtXWK1WaA\npYEX8L10piZHDb60s3RzaFSlHMTMSRAEzcHMmT4jUihjx/qMyI8/5s+fN8/zv/rK71dYwWcXHn7Y\nZ1UuuqjxMqcZMiRriJvLBRd4388847MbmdmQa6/1/JkzzTbe2Oz55/1+zhyzb7/16/RMSi5TpmTz\nOnSoXX7mzLplnTfPl9Gqq0saaq1+hg0rvY3mYtFFszNRzUzFL+sAj+HeNaum0lYFXgYeaw6hSxpo\nKCdBELQVpkxxW4+PPzabNKn5+p03z3821lnH7487rrYCkVn6efnlBevOmWO26qr5vZTmz3cbmAkT\nvI/Zs7NKQ322Oy++6GX22KN+uZ95xuz44+vOzyh8gwfnz6+pcQVxq63crsgsa7Pz3HP1910uPv+8\ntoI3f77L1a+f2Y03Nnn3rUE5qQbWy5M+AJjVHEKXNNBQToIgCBpPt25mSyzh1zU1ZhMnZq8zP5zF\nzBzVRVVVtr1nnslfJu0afeWV+cu88ELtH/SMrLfckp2tOvro2rKfcILVmuF5661s/vvv+07bmfsj\nj8zf76xZbkQ8alRJw/+Jmhofw6xZtZWTzz5zg+TMfWNmjwqgNdicfA50yJPeHphYQntBEARBa+Gz\nz2DsWL+WslsXZLYZOP74hqMCjx/vdTPHO+8sWGbgwKwNz9NPu63Fffd53XPOcTubTJ8Aw4Z5AL1M\nm337+s/25sl+sOuv7/ssSXDccbD//vDNN553xRUu96BBbseTsbcZN85tZNZZx+9//WtYbTV3v84w\nZMiCsr/1lm/RMG9e9rVKk7FzeeYZtynKF4gPXL4TTvD877932b77zgPqde1a2w7opZf8PHNmbRuf\n1kqx2gywC/AatV1918fdf//QHBpVKQcxcxIEQdB0pN2GGyLtAg2+PFMX11zjMyenn+5le/e2n7yE\nbrzRXZo/+cTstdfMLr20druTJ2evr702G5Auc9TlBr3//p6/9dZuP5NrK5O5zwTly+Wmm2rPaJx3\nnl/ffXft+qeckr2ePbvu1xTy2yStuaYvSX3//YJ1XnnF5SuTLU1FLuvgsUSmpo4f8X11fsy5ntoc\nQpc00FBOgiAImo4bb8waxhbCKqvU/wOfy3/+U/vHOmP7kWb2bLObb862PW2aL8N8/LHnp5eett++\n/v4y5TKxWdKKzMyZ2TYzPPCAu2ebecyYtEKTdhdPLwnttFP2umdPs1/9yq+//DJr3wMeUbgQvvwy\nW2fRRbNKFpg9/XRhbdRBcysnqU0e6uX4Rk7QBEEQBG2Zgw4qrvyHHxZXfpddfEnlgw9g9Oj8eyV1\n6uSbUe6yi+803b2718kg+VJLu3YN71m0557Z/Y0ydTN06+au2BlGjPA+Ad5/3zd7HDMGfv5zTzv4\n4KzL+Nixvr/SvHkeXfihhzx9iy182Qo80nF1tUf1vfBC+N3vCnqJau0O3qmT7/J9SxJcfbfd3BW5\nlVCQcmJmtzRcKgiCIAiaiM6d/Ye/EBZfPLvhYi6F7pKd2TqgEDIxX8AVEzNYY41s2n77wZtvwrvv\nepyZxx6DbbZxheeOO3zTyi5dfFftRx/1Ou3aeflikLzvN95YcFPJiy92e53Ro2HXgvbTbVGK3pW4\nVmWpM9AxnWZmdUTYaVkyuxKPHDmS/pm9MoIgCIKgscyZ4ztkH3GEG8tmjFNL4aKL3Jh2r73KI1tN\nTXa37nXXdWPdH3+sbdRbAKNGjWLAgAHQTLsSF+2tI6mbpKskTQZ+wO1R0kcQBEEQLDx07OjeQG+8\n0TjFBOBvfyufYgJZxQTg8sv9fO655Wu/iSjFlfhCYBvgCNwI9hDgTNyN+E/lEy0IgiAIgrKxxRZ+\nPuccGNXkkx+NohTlZCfgSDO7B5gHvGBm5wKnAPuUU7ggCIIgCMqEBDvv7NcXXtiysjRAKcrJEkAm\nqsz3yT343jpblCqIpKMkjZNULelVSRvUU3ZXSU9ImixpuqSXJW1fat9tjaqqqpYWoVmIcbYtYpxt\ni4VlnNDKxvrAAx74bccdW1qSeilFORkLJFtd8gGwZ3K9E1CSn5KkvYCL8eWh9YC3gMcl9aqjyhbA\nE8Bv8PglzwAPSVqnlP7bGq3qg9IIYpxtixhn22JhGSe0wrEecoi7XFcwpSgnNwMZJeAC4ChJs4FL\ngYtKlGMwMNTMhpvZB8DhwCwgr+O8mQ02s3+Z2Ugz+9TMTgU+xhWkIAiCIAhaMYUGYfsJM7s0df2U\npNXwTf8+MbO3i21PUoek/nmpdk3SU8AmBbYhYFE8em0QBEEQBK2YUmZOamFmn5nZvcBUSdeV0EQv\nfNPASTnpk4A+BbbxN6AbcGcJ/QdBEARBUEEUPXNSDz2Bg4HDythmg0jaGzgd2NnMptRTtDPA+4VG\nGGzFTJ8+nVEV7iZWDmKcbYsYZ9tiYRknLBxjTf12dm6O/hoVIbZWQ26MOsrMCowN/FO9Drh9yR/N\n7MFU+jCgh5nVGWdX0kDgBmB3M3usgX72Bm6rr0wQBEEQBPWyj5nd3tSdlHPmpCTMbK6kkcC2wIPw\nkw3JtsAVddWTNAhXTPZqSDFJeByPwzIemN1IsYMgCIJgYaIzsDz+W9rktLhyknAJMCxRUl7HvXe6\nAsMAJJ0PLGVm+yf3eyd5xwJvSOqdtFNd194+ZvYt0OTaXhAEQRC0UV5uro4KVk4k3dtAkZ+VKoSZ\n3ZnENDkb6A28CexgZt8kRfoAy6SqHIob0Q5Jjgy3UIf7cRAEQRAErYOCbU4k3VxIOTM7sFESBUEQ\nBEGwUFM2g9ggCIIgCIJy0Og4J62BYvbtqTQknSmpJud4L6fM2ZImSpol6UlJK+Xkd5I0RNIUSTMk\n3S1pyeYdyYJI2lzSg5K+TMa1c54yjR6bpMUl3Zbsw/SdpBskdWvq8aX6r3eckm7O84wfzSlT0eOU\ndLKk1yV9L2mSpPskrZKnXKt+noWMsy08z6T/wyW9lfSf2cNsx5wyrfp5Jv3XO8628jxzkXRSMpZL\nctIr45maWZs+gL1w75w/AasBQ/FIsr1aWrYC5T8TeBv4ObBkciyRyv+/ZDy/B9YE7gc+BTqmylyD\neyltie9d9DK+m3RLj21H3M5oF2A+HqsmnV+WsQH/BUYB6wObAh8Bt1bQOG8GHsl5xj1yylT0OIFH\ngf2AfsBawMOJvF3a0vMscJyt/nkm/f8uee+uCKwEnAv8CPRrK8+zwHG2ieeZI8sG+D55o4FLUukV\n80yb/UVpgYfwKnB56l7AF8CJLS1bgfKficePqSt/IjA4db8YUA3smbr/Edg1VWZVoAbYsKXHl5Kp\nhgV/tBs9NvxHpAZYL1VmB2Ae0KdCxnkzcG89dVrjOHsl8vyqjT/PfONsc88zJcO3wIFt9XnWMc42\n9TyB7sCHwDb4prlp5aRinmmbXtZRdt+epzNp5q9Uwfv2VAgry5cEPpV0q6RlACStgHsypcf3PfAa\n2fGtj3tlpct8CEyggl+DMo5tY+A7Mxudav4pwICNmkr+EtgqWSb4QNLVkpZI5Q2g9Y3zZ0nfU6FN\nP89a40zRpp6npHbyoJddgZfb6vPMHWcqqy09zyHAQ2b2v3RipT3TSolz0lTUt2/Pqs0vTkm8ChyA\na7p9gbOA5yWtib+RjPr3JeoNzLEF478Us3dRS1CusfUBJqczzWy+pKlUzvj/C9wDjMOnls8HHpW0\nSaJM96EVjVOSgMuAF80sYx/V5p5nHeOENvQ8k++ZV/AAXDPwf8wfStqENvQ86xpnkt2WnudAYF1c\nyciloj6jbV05afWYWToa3xhJrwOfAXsCH7SMVEE5MbP0hpXvSnoHX+fdCp92bW1cDawObNbSgjQx\necfZxp7nB8A6QA9gd2C4pC1aVqQmIe84zeyDtvI8JS2NK9O/NrO5LS1PQ7TpZR1gCm6A2DsnvTfw\ndfOL03jMbDpuXLQSPgZR//i+BjpKWqyeMpVIucb2NW7A9hOS2gNLUKHjN7Nx+Hs3YyXfasYp6Srg\nt8BWZvZVKqtNPc96xrkArfl5mtk8MxtrZqPN7FTgLeA42tjzrGec+cq21uc5ADfqHSVprqS5uFHr\ncZLm4LMfFfNM27RykmiHmX17gFr79jRbGN5yIqk7/qGYmHxIvqb2+BbD1/Uy4xuJGyKly6wKLItP\nY1YkZRzbK8DPJK2Xan5b/EP4WlPJ3xiSfzg9gcyPXqsYZ/KDvQuwtZlNSOe1pedZ3zjrKN8qn2cd\ntAM6taXnWQftgE75Mlrx83wK9zBbF58lWgcYAdwKrGNmY6mkZ9qcVsItceDLH7Oo7Ur8LfDzlpat\nQPkvArYAlsNdsp7ENdyeSf6JyXh2St549wMfU9v162p8vXQrXHt+icpwJe6WfEDWxa27j0/ulynn\n2LoQ2xUAAAasSURBVHD3zxG4+9xmuP3OvythnEnehfgXwHLJh3gE8D7QobWMM5HvO2Bz/F9U5uic\nKtPqn2dD42wrzzPp/7xknMvhbqXn4z9M27SV59nQONvS86xj7LneOhXzTFvsRWnmB3Ak7pddjWt1\n67e0TEXIXoW7PlfjFtG3AyvklDkLdwGbhe8YuVJOfifgSnwqcgZwF7BkBYxtS/zHen7OcVM5x4Z7\nVNwKTMd/WK4HulbCOHEDvMfwfyyz8dgD15CjPFf6OOsY33zgT+V+r1byONvK80z6vyGRvzoZzxMk\niklbeZ4NjbMtPc86xv4/UspJJT3TCF8fBEEQBEFF0aZtToIgCIIgaH2EchIEQRAEQUURykkQBEEQ\nBBVFKCdBEARBEFQUoZwEQRAEQVBRhHISBEEQBEFFEcpJEARBEAQVRSgnQRAEQRBUFKGcBEEQBEFQ\nUYRyEgStHEnPSLqkiPLLSaqRtHZyv2Vyn7vTaJMj6WZJ9zZ3v6Ui6UxJo1tajiBo64RyEgQVhqRh\nibJwdZ68IUneTankXYHTi+hiAtAHGJNKa/Q+FsUqSa2Y2PMjCJqYUE6CoPIwXIEYKOmnbduT60HA\nZ7UKm00zsx8KbtyZbGY15RI4aBySFmlpGYKgkgjlJAgqk9HA58BuqbTdcMWk1rJC7oyFpHGSTpZ0\no6TvJX0m6dBUfq1lnRS/kvSWpGpJr0haI1VnCUm3S/pC0g+S3pY0MJV/M7778nFJ2/MlLZvkrSHp\nIUnTE3mek7RCzhhOkDRR0hRJV0lqX9cLk1lakbRvMtZpkqokdct5DY7NqTda0hmp+xpJhyWy/SDp\nPUkbS1oxeU1nSnopV9ak7mGSJiT17pC0aE7+IUl71cn5iDyv/56SnpU0C9i7rvEGwcJIKCdBUJkY\ncBNwUCrtIOBmQAXU/wvwBrAucDVwjaSVc9pPI+BCYDCwPvAN8GBKSegMjAB+A6wBDAWGS1o/yT8O\neAXfGr030Bf4XNJSwHP4dvRbAeslZdIzBdsAv0zy/wQckBz1sSKwC/Bb4He4YnRSA3XycRowDFgH\neB+4HbgW+AcwAH9drsqpszKwR9LvDviYflqCk7QPvu38ycBqwCnA2ZL2y2nnfOBSoB++NX0QBAkx\nlRgElcttwAWSlsH/SGwK7AVsXUDdR8zs2uT6n5IGJ/U+TtLyKThnmdn/ACTtD3yB27PcbWYTgbQ9\nyRBJOwJ7AiPM7HtJc4BZZvZNppCko4FpwCAzm58kf5rT71TgaDMz4CNJjwDbAjfWMz4B+5vZrKSf\nfyd1irG9AbjJzO5J2rgQV7D+bmZPJWmX40pimk7Afmb2dVLmGOARSSeY2WRcMTnBzB5Iyn+WzEId\nDvw71c6lqTJBEKQI5SQIKhQzmyLpYeBA/Mf4ETObKhUyccI7OfdfA0vW1x3waqrv7yR9iP+rR1I7\n4FR8xuAXQMfkaMjWZR3ghZRiko93E8Ukw1fAmg20Oz6jmKTq1De+uki/TpOS85ictM6SupvZzCRt\nQkYxSXgFVx5XlTQTn9W5UdINqTLtcSUtzcgS5A2ChYJQToKgsrkZX1Yw4Mgi6s3NuTcat4x7InAM\nvnwzBldKLscVlPqoLqDtUmRtqE4NC84OdWigHasnrdDXrntyPgR4PScvV0Er2Ig5CBY2wuYkCCqb\nx3AFYBHgiSbsR8DGP91IiwOrAO8lSZsCD5hZlZm9A4xL8tPMwWcI0rwNbF6fgWsT8Q1u9wJAEsNl\nAcPWPBTiJryspD6p+01wxeODZFlnIrCimY3NOdJeVuGOHAT1EMpJEFQwibvvasAaOUsfTcEZkraR\ntCZuJPoNkLGJ+BjYTtImkvrhBrG9c+qPBzZKvFF6JmlXAYsBd0gaIGmlxMtmZZqW/wH7SfqVpLWS\n8cwroF6+NbPctB+BWyStLWlzfAbpjpStzZnAyZKOkbSypDWl/2/fDnEiCIIogP7WaCw34gxAgsNw\nCBIMhgNwBVaQIMmegKAw3ACBwheiF0LYDbsCkhLvJSNm0pnOtJk/NV3jeIxxvmUeYEU4geaq6v3b\nfoeNQ7ac7zKmMrtdrjO7fPaTHFbV5wv9IsljZiXnIXOPx+LHPa4yKwjPSV7HGAdV9ZbZjbOXZJnZ\n8XOa9d8yf+0ys0vobnUssr4Rd5d12nTtJcltkvvM9XhKcvY1uOom8xlPMitHyyRHmdWm3+YBVsb/\nf4wBAOxO5QQAaEU4AQBaEU4AgFaEEwCgFeEEAGhFOAEAWhFOAIBWhBMAoBXhBABoRTgBAFoRTgCA\nVoQTAKCVD1by68fkZMTdAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_resnet_model)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.4.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 56b2c6826f7a4c84f0089f3ff43a8cc1d32ce45a Mon Sep 17 00:00:00 2001 From: Emad Barsoum Date: Mon, 24 Oct 2016 23:14:01 -0700 Subject: [PATCH 2/2] Fix CIFAR data loader and images paths for ImageHandOn. --- .../CNTK_201A_CIFAR-10_DataLoader.ipynb | 30 +- .../CNTK_201B_CIFAR-10_ImageHandsOn.ipynb | 264 +++--------------- bindings/python/tutorials/img/CNN.png | Bin 0 -> 34322 bytes bindings/python/tutorials/img/Conv2D.png | Bin 0 -> 39190 bytes .../python/tutorials/img/Conv2DFeatures.png | Bin 0 -> 2878 bytes bindings/python/tutorials/img/MaxPooling.png | Bin 0 -> 9682 bytes .../python/tutorials/img/ResNetBlock2.png | Bin 0 -> 16282 bytes 7 files changed, 55 insertions(+), 239 deletions(-) create mode 100644 bindings/python/tutorials/img/CNN.png create mode 100644 bindings/python/tutorials/img/Conv2D.png create mode 100644 bindings/python/tutorials/img/Conv2DFeatures.png create mode 100644 bindings/python/tutorials/img/MaxPooling.png create mode 100644 bindings/python/tutorials/img/ResNetBlock2.png diff --git a/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb b/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb index b115f727f..b284a8c60 100644 --- a/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb +++ b/bindings/python/tutorials/CNTK_201A_CIFAR-10_DataLoader.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -114,7 +114,7 @@ " tar.extractall()\n", " print ('Done.')\n", " print ('Preparing train set...')\n", - " trn = np.empty((0, NumFeat + 1), dtype=np.int)\n", + " trn = np.empty((0, numFeature + 1), dtype=np.int)\n", " for i in range(5):\n", " batchName = './cifar-10-batches-py/data_batch_{0}'.format(i + 1)\n", " trn = np.vstack((trn, readBatch(batchName)))\n", @@ -145,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -153,14 +153,14 @@ "source": [ "def saveImage(fname, data, label, mapFile, regrFile, pad, **key_parms):\n", " # data in CIFAR-10 dataset is in CHW format.\n", - " pixData = data.reshape((3, ImgSize, ImgSize))\n", + " pixData = data.reshape((3, imgSize, imgSize))\n", " if ('mean' in key_parms):\n", " key_parms['mean'] += pixData\n", "\n", " if pad > 0:\n", " pixData = np.pad(pixData, ((0, 0), (pad, pad), (pad, pad)), mode='constant', constant_values=128) \n", "\n", - " img = Image.new('RGB', (ImgSize + 2 * pad, ImgSize + 2 * pad))\n", + " img = Image.new('RGB', (imgSize + 2 * pad, imgSize + 2 * pad))\n", " pixels = img.load()\n", " for x in range(img.size[0]):\n", " for y in range(img.size[1]):\n", @@ -175,13 +175,13 @@ "def saveMean(fname, data):\n", " root = et.Element('opencv_storage')\n", " et.SubElement(root, 'Channel').text = '3'\n", - " et.SubElement(root, 'Row').text = str(ImgSize)\n", - " et.SubElement(root, 'Col').text = str(ImgSize)\n", + " et.SubElement(root, 'Row').text = str(imgSize)\n", + " et.SubElement(root, 'Col').text = str(imgSize)\n", " meanImg = et.SubElement(root, 'MeanImg', type_id='opencv-matrix')\n", " et.SubElement(meanImg, 'rows').text = '1'\n", - " et.SubElement(meanImg, 'cols').text = str(ImgSize * ImgSize * 3)\n", + " et.SubElement(meanImg, 'cols').text = str(imgSize * imgSize * 3)\n", " et.SubElement(meanImg, 'dt').text = 'f'\n", - " et.SubElement(meanImg, 'data').text = ' '.join(['%e' % n for n in np.reshape(data, (ImgSize * ImgSize * 3))])\n", + " et.SubElement(meanImg, 'data').text = ' '.join(['%e' % n for n in np.reshape(data, (imgSize * imgSize * 3))])\n", "\n", " tree = et.ElementTree(root)\n", " tree.write(fname)\n", @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -209,7 +209,7 @@ " if not os.path.exists(foldername):\n", " os.makedirs(foldername)\n", " data = {}\n", - " dataMean = np.zeros((3, ImgSize, ImgSize)) # mean is in CHW format.\n", + " dataMean = np.zeros((3, imgSize, imgSize)) # mean is in CHW format.\n", " with open('train_map.txt', 'w') as mapFile:\n", " with open('train_regrLabels.txt', 'w') as regrFile:\n", " for ifile in range(1, 6):\n", @@ -241,7 +241,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -263,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 7, "metadata": { "collapsed": false }, diff --git a/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb b/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb index 23d24692a..834595e38 100644 --- a/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb +++ b/bindings/python/tutorials/CNTK_201B_CIFAR-10_ImageHandsOn.ipynb @@ -41,11 +41,11 @@ "\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. \n", "\n", - "\n", + "\n", "\n", "The stack of feature maps output are the input to the next layer.\n", "\n", - "\n", + "\n", "\n", "### Pooling layer\n", "\n", @@ -58,7 +58,7 @@ "\n", "Here an example of max pooling with a stride of 2:\n", "\n", - "\n", + "\n", "\n", "### Dropout layer\n", "\n", @@ -79,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -94,7 +94,7 @@ "from cntk.models import * # higher abstraction level, e.g. entire standard models and also operators like Sequential()\n", "from cntk.utils import *\n", "from cntk.io import MinibatchSource, ImageDeserializer, StreamDef, StreamDefs\n", - "from cntk.initializer import glorot_uniform, he_normal\n", + "from cntk.initializer import glorot_uniform\n", "from cntk import Trainer\n", "from cntk.learner import momentum_sgd, learning_rate_schedule\n", "from cntk.ops import cross_entropy_with_softmax, classification_error, relu\n", @@ -107,14 +107,14 @@ "source": [ "Now that we imported the needed modules, let's implement our first CNN, as shown below:\n", "\n", - "\n", + "\n", "\n", "To implement the above, you will first implement each of the building blocks using CNTK low level python API." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -189,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "metadata": { "collapsed": true }, @@ -222,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -269,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -404,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -415,21 +415,21 @@ "text": [ "Training 116906 parameters in 10 parameter tensors.\n", "\n", - "Finished Epoch [1]: [Training] loss = 1.844780 * 50000, metric = 67.8% * 50000\n", - "Finished Epoch [2]: [Training] loss = 1.476277 * 50000, metric = 52.9% * 50000\n", - "Finished Epoch [3]: [Training] loss = 1.315467 * 50000, metric = 46.5% * 50000\n", - "Finished Epoch [4]: [Training] loss = 1.201646 * 50000, metric = 42.1% * 50000\n", - "Finished Epoch [5]: [Training] loss = 1.141895 * 50000, metric = 39.7% * 50000\n", + "Finished Epoch [1]: [Training] loss = 1.854603 * 50000, metric = 68.5% * 50000\n", + "Finished Epoch [2]: [Training] loss = 1.476504 * 50000, metric = 53.1% * 50000\n", + "Finished Epoch [3]: [Training] loss = 1.315036 * 50000, metric = 46.4% * 50000\n", + "Finished Epoch [4]: [Training] loss = 1.210932 * 50000, metric = 42.3% * 50000\n", + "Finished Epoch [5]: [Training] loss = 1.137301 * 50000, metric = 39.8% * 50000\n", "\n", - "Final Results: Minibatch[1-626]: errs = 35.5% * 10000\n", + "Final Results: Minibatch[1-626]: errs = 37.1% * 10000\n", "\n" ] }, { "data": { - "image/png": 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Umpx+Ouy8M1RWwjHHwOabey+gHXaAL7+E6dOz1541C777zicwlGD8EpVzIYQQ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\nnnu89ONf/1qygesVV2SXt90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piHwhIr+LyM8i8pKIbFvNOUeLyNsiMkdEykTkUxH5c23ycdhhcOihsHhxba7inHPOudrK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\nD1Ts3xL+Uu/bN1E70LWrNZE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gIlcBw0Skp6quKbF2TWODDeCyy+C882yWpXZtKCwM7VMcx3EcJ4dI6e0kIhvH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\nGbMLeRI4JnviOY7jOE4aVKRiArZ7dJ8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C6J+TgQw1ny23NAfaZcvg6qvh8cet/J13bDdPaWy9te32KSry3D2O4zhOhZOR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\n3GMWlrffTq6kjBxpikw0em/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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -447,8 +447,9 @@ } ], "source": [ - "reader_train = create_reader(os.path.join('data', 'train_map.txt'), os.path.join('data', 'CIFAR-10_mean.xml'), True)\n", - "reader_test = create_reader(os.path.join('data', 'test_map.txt'), os.path.join('data', 'CIFAR-10_mean.xml'), False)\n", + "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", "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model)" ] @@ -462,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "collapsed": true }, @@ -489,48 +490,11 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training 116906 parameters in 10 parameter tensors.\n", - "\n", - "Finished Epoch [1]: [Training] loss = 2.079751 * 50000, metric = 79.2% * 50000\n", - "Finished Epoch [2]: [Training] loss = 1.856198 * 50000, metric = 70.3% * 50000\n", - "Finished Epoch [3]: [Training] loss = 1.757369 * 50000, metric = 65.9% * 50000\n", - "Finished Epoch [4]: [Training] loss = 1.678602 * 50000, metric = 62.8% * 50000\n", - "Finished Epoch [5]: [Training] loss = 1.643568 * 50000, metric = 61.2% * 50000\n", - "\n", - "Final Results: Minibatch[1-626]: errs = 48.4% * 10000\n", - "\n" - ] - }, - { - "data": { - "image/png": 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sIsRshc1MYc2Oc87lu/UdhG1xFtuH5lgW57LWvr0tH3kEFixI3ta1a+J1dOTa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\ncUm19etbU02nTsljyoCNrdK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8ZLuFtFhEmgevlwCLY46wPy9EpJ+IzBGRlSIyWUR2zzB2uIiUiEhx0IbHp5Ex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\n0QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model_with_dropout)" ] @@ -544,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "collapsed": true }, @@ -570,48 +534,11 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training 117290 parameters in 18 parameter tensors.\n", - "\n", - "Finished Epoch [1]: [Training] loss = 1.680356 * 50000, metric = 58.7% * 50000\n", - "Finished Epoch [2]: [Training] loss = 1.381641 * 50000, metric = 49.1% * 50000\n", - "Finished Epoch [3]: [Training] loss = 1.248170 * 50000, metric = 44.3% * 50000\n", - "Finished Epoch [4]: [Training] loss = 1.150515 * 50000, metric = 40.4% * 50000\n", - "Finished Epoch [5]: [Training] loss = 1.081821 * 50000, metric = 38.1% * 50000\n", - "\n", - "Final Results: Minibatch[1-626]: errs = 39.7% * 10000\n", - "\n" - ] - }, - { - "data": { - "image/png": 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acRzHcZysyCQPTIdgL1hNoTWRa2swq8ndmQogInWBTsBtYZuqqoiMAbqkGHYM\n8AVwhYicCizH6ihdq6qhFagLZtWJMgromamMWTNkCNSuDXXqmNKy++7W3qCBlxpwHMdxnDKQtgKj\nqgcDiMhTwMU5zPfSArPgzEton4cVaExGG8wCswo4NpjjYcwP5+ygT8sUc7Ysu8hp0rat7YcOtf2S\nJan7hpx2Gpx1lodhO47jOE4JZJOJ95Jk40SkGbCughLZ1cIqT/dR1WXB/S8FXhKR/mFBx2zJy8uj\nadOmcW3JKm9mzCabwGuvQYsUrj1FRfDss/DJJ/DTT2W7l+M4juOUE/n5+eTn58e1FRYWVqgM2Sgw\nL2AOsY8ktJ8E9AD+nuF8C4H1WBHHKFsAc1OM+R34LVReAqZhy1tbAz8GYzOZ8y8GDRpUfrWQjj22\neNvKlXDyydC/PxxyCDRvnt5cbdrAkUfCLbdAw4a2OY7jOE45k+xHfaQWUoWQTRRSZ+D9JO0fBNcy\nQlXXYpWiu4dtQU6Z7sD4FMPGAa1EpFGkbWfMKvNrcD4hOmfAYUF71eDxx61q9SefwIgRcPbZppSk\nsr4sX271lQCmToWZM+GLL0zhOeywipPbcRzHcSqZbBSY+kC9JO11gWxNAPcA54rIaSLSFrPuNAKG\nAIjI7SLydKT/UOAP4CkRaSciB2LRSk9Elo/uA44UkUtFZGcRuQFzFn4gSxlziyqce64d//vftq9f\n38Kvo5FLAJ9/bsnvmjSBhx+2tj32sP3BQWLkcePKX2bHcRzHqSJko8B8BiRLJXs+ZknJGFUdBvwH\nuAmYDLQHjlDVBUGXlkSy/KrqcsyasgnwOfAstqx1caTPBKBPIOuXwPFAT1X9NhsZc06tyEs/L/A1\nnjXLopUAnnoqdn2ffeCxx+z4P/+x/mEY9nHH2X6LxNUyx3Ecx6m5ZKPAXAOcIyIficj1wfYRlrPl\nqmwFUdWHVHU7VW2oql1U9YvItTNV9ZCE/t+r6hGq2kRVW6vq5YnOu6r6iqq2DeZsr6qjspUv5+y7\nr+2nTIFTTzULS1ERnHKKtS9bZlFLn31WvJbSzz/D/vvbcfv2VvF63jy3wjiO4zgbDBk78arqOBHp\nAlyGOe6uBKYCZ6vqjBzLV3OZMMEUllq14O4gfc4jj5hPzMqVljtmn32sFEHIllvC77/DAw/AsGFm\nsWnUCLp0gRdfhDFjoGvXSnkcx3Ecx6lIsolCQlW/BE7JsSwbHrUSDGAitm/QAK6+Oqa89OljuWR+\n+w3uuMMe10T/AAAgAElEQVTOmzWzDeCii+Ckk6BlGVPc/PgjrF8PO+1Utnkcx3Ecp5xJawlJRDaO\nHpe0lZ+oGxhHHx07fughWLfOFJz/+z9o3Tq+r4hZZ0IFKFv+9jerou04juM4VZx0LTCLRWRLVZ0P\nLCF5McewyGPtXAm3QdOlC5xwgi0JJSTVS0pRkYVW164dK1lQEvfcYxFPPSuusoLjOI7j5Ip0FZhD\ngEXB8cHlIYiIDMAikVpidZUuVNXPU/Q9iOK5aBQIlSxE5HTgqaA9NE2sUtVGVBdeein9vuvWQYeg\nXFVpdZZWr46Fbod9w/1JJ9nxwoWw2WaxMcuW2bUlS8zfZpttcBzHcZzKIi0FRlU/THacK0SkF1Z4\nsR8Wpp0HjBKRnVR1YSqxgJ2ApRHZ5if0KQz6SGRMzaReJDXPjz+adSUVCxYUb3v2Wdu3bAmXXAL3\n3w9Ll8YioB57DEaOtOPGjXMjs+M4juNkSVoKjIi0T3dCVZ2ahRx5wGBVfSa43/nAP7DQ7DtLGLeg\nlNpLGskls+Hw9tuw555wwAEW0bR6tdVhuvJK2HVXC9v+z39g+PDYmNBxt21bK2kAsNFGNrZevXjH\n3tB52HEcx3EqiXSXkL4kthRTmhUjIx8YEamLZci9LWxTVRWRMUCXkoYCX4pIA+Br4AZVTSw90ERE\nZmHOypOAq6pMIrvy4J57YKutzNH3p5/g+efhiSfgvffi+516quWS+eEH27dubXlp3noLjjjCaivN\nmRPrv9NOFv3kOI7jOFWEdBPZbQ+0Cfb/BGYC/YEOwdYfK6D4zyxkaIEpPfMS2udh/jDJ+B04L7jf\n8cBs4AMR2TPSZzpmwemBhXzXAsaLSKssZKwe5OWZn0qDBnZ+yinFlZeQPn1sv2iRWWxeeAH+/ndz\nAv7+e0uQB3DVVTBjhvV59VVzFFaNZQ92HMdxnEogXR+Yn8NjEXkJuEhV3450mSois4GbgeGJ43ON\nqn4PfB9pmigiO2BLUacHfSYCEyNyT8AqVp8HXF/eMlYq991XvLjjiSfGnIK/+sqqYqtC375mqQGr\niA3m4zJ+PBQWmkUHLPNvWLagXz8rJPnuu+X/LI7jOI6ThGwS2e2OWWASmQnsksV8C4H1QGIxny2A\nuRnM8xmQMg2tqq4TkcnA30qbKC8vj6YJocvJSodXWQ491KKFmjQxH5Zly8xv5f774frr4xPehcpL\nIo0bxzvr9u0bO27UyDICO47jOBsk+fn55Ofnx7UVFhZWqAzZKDDTgP8TkXNUdQ2AiNQD/i+4lhGq\nulZECoDuwIhgPgnO789gqj2xpaWkiEgtTPl6q7SJBg0aRMeOHTO4dRUkVMAaNbINTHEZPDi+3zbb\nwOzZcPPNyef58EOYP99KG4Q0bgzffBPfb/58+PJLOPzw3MjvOI7jVFmS/aifNGkSnTp1qjAZslFg\nzgfeAH4VkTDiqD3m3HtMlnLcAwwJFJkwjLoRMARARG4HWqnq6cH5xZjF5xugAXAulp/mr3UTEbkW\nW0L6AatafTmwLfB4ljLWTH75peTrBx5YvG1q8LZ/8w2MHQsDBpgD8Flnwdq18cqO4ziO45QD2RRz\n/ExE2mCOsW2D5heBoaq6PBshVHWYiLQAbsKWjr4EjoiEQLcEopnT6mF5Y1oBK7Bikt1V9aNIn02B\nR4Oxi4ECoIuqfpeNjE6EK66AN9+E3Xaz8512ivnK/PqrORGvXWvWnZUrrSjlgAHQu3d8VuE777QC\nlKNHV/wzOI7jONUa0dKytm5AiEhHoKCgoKD6LyGVJ7Nnw7bbxs4ffND8bnbe2aKeunc3B2FVyxBc\nt26sb/TvLazdVFRU9jpOjuM4TqUSWULqpKqTyvt+6YZRxyEip4rIJyIyR0RaB215IuKFdTYEttkG\nPvoIhgyx8xdftFwyIpYF+NBDrX38eFixIn5sqMCsXBlr++gjHMdxHCcTMlZgROQCzGdlJLZMEyau\nWwxckjvRnCrNAQfA6adbOPbXX0P9+tC8OTz+uLWDFaL88MOY4/Avv8QsLQ0bwqRAQe/WzXLPAKxf\nD9My9gV3HMdxNjCyscBcCJyrqrcC6yLtX2BRPs6GROvWlgzvl1+sAOSnn1oZg+j1c86BuXPNcrNs\nGZx3XnyyvM6dbfnppZcsv8zBB5svjeM4juOkIBsFZntgcpL21YBX+dvQuOEGOPNMc849/3xra9fO\n2rbbDnbZBWrVgi2CND9XXAGPPmoKS+3atqT08MN27bXXYll+8/PhjDNg8mQ779HDwrQdx3Ech+wU\nmJlYzpVEjiSLPDBONWeTTeDJJ02BefhhU0Bq1bK2mTPjQ6pV4Z137Piii2LtHTpYfab8fCtbAHD5\n5fD009Cxo839xhuZKTDr15sPjuM4jlMjyUaBuQd4UER6YQUV9xGRq4HbKblydImIyAARmSkiK0Vk\noojsXULfg0SkKGFbLyKbJ/Q7UUSmBXNOEZGjspXPyRE//WT7++6Lbw8jlZYsKT6mfn3YbDOz6txz\nT8nz77QTPPCAZR3u2jXmW5MtP/9svj15eWWbx3Ecx8kpGSswqvo4cAVwC5ZsbihwAXCxqr6QjRCB\nMjQQq1HUAZgCjApyw6QUBdgRy/PSEthSVedH5twvkO0xzGL0OjBcRLIpd+DkAhEYNMiWkFLRrp3t\nt4mk/ZkzBxYEKYFKSry3erVZcC68EGbNio0Fs/7Mn59yaEqOPdZ8fO69N/OxjuM4TrmRkQIjxrbA\nK6q6I9AEaKmqW6vqE2WQIw8YrKrPBInmzscS1J1VyrgFqjo/3BKuXQSMVNV7VHW6ql4HTAL+VQY5\nnbJyySVw7rnF2x95xKwzHTpYdNLnn8Mff1iV7K22gnr1rN9FF8F338GqVcXnOPVU2592moVzgzkD\nz5kT88MZnmGt0e8ieQ+rS86kFSss/47jOE4NJlMLjGCp+bcBUNUVSRSHzCYUqQt0AsaGbWrZ9cYA\nXUqR5csgF83owOISpUswR5RRpczpVBZ168L229txhw6mbDRrBr16WduMGeYj06aNWWlOOaX4HGG1\n7SFDYsUmv/0Wnojo1nvtlZlcxwTVMVIl21u/3pbDonltKpM//rBaVQ0bWjVxx3GcGkpGCoyqFgEz\ngOY5lKEFlktmXkL7PGxpKBm/A+cB/wSOB2YDH4hI1Lm4ZYZzOlWZbbeFk0+24+OOg1dftZpMTz1l\nodmhRaZHD1M0Nt3U8sv8858xK0rPnrD11pnd95lnTBlKlSl48GCzKh15ZFaPlXNCp+l16+CQQypX\nFsdxnHIkGyfeK4G7RGS3XAuTLqr6vao+pqqTVXWiqp4NjMeWopyaTmjt6NnTCkhutJE5+g4cCA89\nZNfGjIH334dOneDZZ21ZZfhwa7v++tKXg666yiw3DRpYFe+Q9evj+00OMgrUr5/ZM6jCqFG5X5aK\n1pqKRno5juPUMLIpG/wM5rw7RUTWAHG2c1VtluF8C4H1WBHHKFsAczOY5zOga+R8brZz5uXl0TT6\nRUDy0uFOJREqES+8APvua8e//gqXXpq8f61atqQC0K8f/PCDRReFpRDAlKJDDzXFBeD2222Z6uyz\nY326dbPMwmvWmKXjjz8sR00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GjFi/4cNt++YbW5a6/HJTHNavt6WnoiJzfD79dKvsXVLI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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model_with_batch_normalization)" ] @@ -625,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "collapsed": true }, @@ -651,48 +578,11 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training 116906 parameters in 10 parameter tensors.\n", - "\n", - "Finished Epoch [1]: [Training] loss = 1.795087 * 50000, metric = 65.6% * 50000\n", - "Finished Epoch [2]: [Training] loss = 1.454563 * 50000, metric = 52.1% * 50000\n", - "Finished Epoch [3]: [Training] loss = 1.311346 * 50000, metric = 46.3% * 50000\n", - "Finished Epoch [4]: [Training] loss = 1.227226 * 50000, metric = 43.1% * 50000\n", - "Finished Epoch [5]: [Training] loss = 1.160773 * 50000, metric = 40.3% * 50000\n", - "\n", - "Final Results: Minibatch[1-626]: errs = 39.1% * 10000\n", - "\n" - ] - }, - { - "data": { - "image/png": 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EEFq5hjbrTK/n8RlwS1MWMLQOf/yj56SssUbhwdt22sm7LXfI+8alA8EdfHAu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\n2alTLtn19dfhggtqzvR88MF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C5AOdVXUFgIhcB7whIleq6tpylC27GDTIjj//HE0+uGEDrFkDp55q1xs3WlmN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\n0olz8jzmMtxEVeupaj1sBaVKcM9xHMd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dLtfpXT6bSfZaa18v9IskT+mVnMf0PR6TH/e4\nSq8gvCR5q6qt1tp7ejfORpJpesfPURZ/y/y1y/QuofvZMcniRtx11mnZtdckd0ke0tfjOcnx9+DW\nbtKf8TC9cjRNsp9ebfptHmCm/v9jDABgfSonAMBQhBMAYCjCCQAwFOEEABiKcAIADEU4AQCGIpwA\nAEMRTgCAoQgnAMBQhBMAYCjCCQAwFOEEABjKJ/ymXEGyDbuKAAAAAElFTkSuQmCC\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_basic_model_layer)" ] @@ -728,7 +618,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": { "collapsed": true }, @@ -755,48 +645,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training 2675978 parameters in 18 parameter tensors.\n", - "\n", - "Finished Epoch [1]: [Training] loss = 1.965293 * 50000, metric = 73.2% * 50000\n", - "Finished Epoch [2]: [Training] loss = 1.616270 * 50000, metric = 59.7% * 50000\n", - "Finished Epoch [3]: [Training] loss = 1.426021 * 50000, metric = 51.6% * 50000\n", - "Finished Epoch [4]: [Training] loss = 1.277556 * 50000, metric = 45.6% * 50000\n", - "Finished Epoch [5]: [Training] loss = 1.170007 * 50000, metric = 41.4% * 50000\n", - "\n", - "Final Results: Minibatch[1-626]: errs = 42.2% * 10000\n", - "\n" - ] - }, - { - "data": { - "image/png": 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RxUZEBgONVfWEUs6tiykpv2I5Be5V1WQpwhGR08i82q7jOI7jODFOV9XnKvomGVtOVDWn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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_vgg9_model)" ] @@ -809,7 +662,7 @@ "\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:\n", "\n", - "\n", + "\n", "\n", "The idea of the above block is 2 folds:\n", "\n", @@ -893,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": { "collapsed": true }, @@ -927,48 +780,11 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Training 273258 parameters in 86 parameter tensors.\n", - "\n", - "Finished Epoch [1]: [Training] loss = 1.479848 * 50000, metric = 54.0% * 50000\n", - "Finished Epoch [2]: [Training] loss = 1.088070 * 50000, metric = 38.8% * 50000\n", - "Finished Epoch [3]: [Training] loss = 0.913999 * 50000, metric = 32.4% * 50000\n", - "Finished Epoch [4]: [Training] loss = 0.806477 * 50000, metric = 28.2% * 50000\n", - "Finished Epoch [5]: [Training] loss = 0.733808 * 50000, metric = 25.4% * 50000\n", - "\n", - "Final Results: Minibatch[1-626]: errs = 24.8% * 10000\n", - "\n" - ] - }, - { - "data": { - "image/png": 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+vippRXx5aP+WkaqJ2X777PWcOR76/ocfYPJkWH55T+/Z02dYpk3z6yAIgiBo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\nqXFbl/btXZFpaHuAfFxxhSt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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "train_and_evaluate(reader_train, reader_test, max_epochs=5, model_func=create_resnet_model)" ] diff --git a/bindings/python/tutorials/img/CNN.png b/bindings/python/tutorials/img/CNN.png new file mode 100644 index 0000000000000000000000000000000000000000..bf1c929d1187bc903d1446386f4301df7c536818 GIT binary patch literal 34322 zcmbSz2T)U6*S7asu%jZ<1QZYy1f)w>5im#*5hGoZUL~PJ5)}pMkVtPrK&nJKp;`cG z0YZxqiu4jfq(dO^p8&q?``-Cx{>a(7;_6~s$wAD`?XFq=6z=2bsdv_lm zIB<}9;J{z`M~?tMxnu>i1^)O8`cVDOf&8|MGr&I%S>M*Wec(Vr_=&A&hk<_|dvVVI zdf>q6@AUuv`r({qap1t)Owirik33Du)u#q7T6$5GpX5FI9aWrG&vMBAQQhqAV49C$ zX6GH9nMmmP)rVP+&%bh{U9TC%gh-b-yy=jFA^(Pt9W(Z`!CdrKckAF0NN zhix!QENo?1tu(NZh4vqGggs=1;oo#!NO)@xw=|<0kN#oF5TmA%!z)`_tDNgZ{@VND zOTGy)svcN!8$taVgZs}jOvr_<|82p)>iK!ciRMUJQT9-fh&kff%` za%f3<**f-x2?$G`c&ubSyf4ogkqIeknpdqP9@}EyVyU)$V&1c}=EUBsmo%Flm-Sgb 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