{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "

TensorFlow

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Lesson 6

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CIFAR-10

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Flowchart
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Preparation and Pre-Processing
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Helper-Function for Creating Pre-Processing
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Helper-Function for Creating Main Processing
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Helper-Function for Creating Neural Network
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Create Training Neural Network
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Create Testing Neural Network
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Helper-Function for Getting a Random Batch
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Helper-Function for Performing Optimizations
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Examples of Distorted Images
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Results and Analysis
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Summary
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Challenge
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***Original Tutorial by Magnus Erik Hvass Pedersen:***
https://github.com/Hvass-Labs/TensorFlow-Tutorials
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "OVERVIEW\n", "
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\n", "This tutorial shows how to make a Convolutional Neural Network for classifying images in the CIFAR-10 data-set. It also shows how to use different networks during training and testing.\n", "

\n", "This builds on the previous tutorials, so you should have a basic understanding of TensorFlow and the add-on package Pretty Tensor. A lot of the source-code and text in this tutorial is similar to the previous tutorials and may be read quickly if you have recently read the previous tutorials.\n", "
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[Click here to follow along with the video on YouTube](https://www.youtube.com/watch?v=3BXfw_1_TF4&list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ&t=0s&index=9)
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\n", "FLOWCHART\n", "
\n", "\n", "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below. First the network has a pre-processing layer which distorts the input images so as to artificially inflate the training-set. Then the network has two convolutional layers, two fully-connected layers and finally a softmax classification layer. The weights and outputs of the convolutional layers are shown in more detail in the larger plots further below, and Tutorial #02 goes into more detail on how convolution works.\n", "\n", "In this case the image is mis-classified. The image actually shows a dog but the neural network is confused whether it is a dog or a cat and thinks the image is most likely a cat.\n", "\n", "![Flowchart](data/images/06_network_flowchart.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "PREPARATION AND PRE-PROCESSING\n", "
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Imports

\n", "\n", "As portions of this code are modified specifically for the content in this lesson we have not provided a shared_code.py file for this lesson." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Reasonable\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", " from ._conv import register_converters as _register_converters\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "import time\n", "from datetime import timedelta\n", "import math\n", "import os\n", "\n", "# Use PrettyTensor to simplify Neural Network construction.\n", "import prettytensor as pt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "'1.6.0'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PrettyTensor version:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'0.7.4'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pt.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Load Data

" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "from data import cifar10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set the path for storing the data-set on your computer." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "cifar10.data_path = \"data/CIFAR-10/\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CIFAR-10 data-set is about 163 MB and will be downloaded automatically if it is not located in the given path.\n", "\n", "> We have not included it with the git repo due to file size constraints with basic accounts" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data has apparently already been downloaded and unpacked.\n" ] } ], "source": [ "cifar10.maybe_download_and_extract()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the class-names." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading data: data/CIFAR-10/cifar-10-batches-py/batches.meta\n" ] }, { "data": { "text/plain": [ "['airplane',\n", " 'automobile',\n", " 'bird',\n", " 'cat',\n", " 'deer',\n", " 'dog',\n", " 'frog',\n", " 'horse',\n", " 'ship',\n", " 'truck']" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_names = cifar10.load_class_names()\n", "class_names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the training-set. This returns the images, the class-numbers as integers, and the class-numbers as One-Hot encoded arrays called labels." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_1\n", "Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_2\n", "Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_3\n", "Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_4\n", "Loading data: data/CIFAR-10/cifar-10-batches-py/data_batch_5\n" ] } ], "source": [ "images_train, cls_train, labels_train = cifar10.load_training_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load the test-set." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loading data: data/CIFAR-10/cifar-10-batches-py/test_batch\n" ] } ], "source": [ "images_test, cls_test, labels_test = cifar10.load_test_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The CIFAR-10 data-set has now been loaded and consists of 60,000 images and associated labels (i.e. classifications of the images). The data-set is split into 2 mutually exclusive sub-sets, the training-set and the test-set." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Size of:\n", "- Training-set:\t\t50000\n", "- Test-set:\t\t10000\n" ] } ], "source": [ "print(\"Size of:\")\n", "print(\"- Training-set:\\t\\t{}\".format(len(images_train)))\n", "print(\"- Test-set:\\t\\t{}\".format(len(images_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Data Dimensions

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data dimensions are used in several places in the source-code below. They have already been defined in the cifar10 module, so we just need to import them." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from data.cifar10 import img_size, num_channels, num_classes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The images are 32 x 32 pixels, but we will crop the images to 24 x 24 pixels." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "img_size_cropped = 24" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Helper-Function for plotting Images

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None, smooth=True):\n", "\n", " assert len(images) == len(cls_true) == 9\n", "\n", " # Create figure with sub-plots.\n", " fig, axes = plt.subplots(3, 3)\n", "\n", " # Adjust vertical spacing if we need to print ensemble and best-net.\n", " if cls_pred is None:\n", " hspace = 0.3\n", " else:\n", " hspace = 0.6\n", " fig.subplots_adjust(hspace=hspace, wspace=0.3)\n", "\n", " for i, ax in enumerate(axes.flat):\n", " # Interpolation type.\n", " if smooth:\n", " interpolation = 'spline16'\n", " else:\n", " interpolation = 'nearest'\n", "\n", " # Plot image.\n", " ax.imshow(images[i, :, :, :],\n", " interpolation=interpolation)\n", " \n", " # Name of the true class.\n", " cls_true_name = class_names[cls_true[i]]\n", "\n", " # Show true and predicted classes.\n", " if cls_pred is None:\n", " xlabel = \"True: {0}\".format(cls_true_name)\n", " else:\n", " # Name of the predicted class.\n", " cls_pred_name = class_names[cls_pred[i]]\n", "\n", " xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name, cls_pred_name)\n", "\n", " # Show the classes as the label on the x-axis.\n", " ax.set_xlabel(xlabel)\n", " \n", " # Remove ticks from the plot.\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " \n", " # Ensure the plot is shown correctly with multiple plots\n", " # in a single Notebook cell.\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Plot a few Images to See if Data is Correct

" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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vT7AVrk9FhOpRXDanU85yyHJzy9RlbzI9djQaoadNEffEAw0Y8dff8KUvf/lY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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get the first images from the test-set.\n", "images = images_test[0:9]\n", "\n", "# Get the true classes for those images.\n", "cls_true = cls_test[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true, smooth=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pixelated images above are what the neural network will get as input. The images might be a bit easier for the human eye to recognize if we smoothen the pixels." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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wphK6jutuRy4zD8c70hQpRQnesttuiFk5HEdyzqgq682GV69e8/qTT9htt+wP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\nA8uyNDHEhWmeOOxN7Kw2MutaK7v9jq9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shUNO7OeJm8Oe69sbbqc9+zQzaSZ5QYZIt1kx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\nHBL/B2Jz2NCjD8TeAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_images(images=images, cls_true=cls_true, smooth=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

TensorFlow Graph

\n", "\n", "The entire purpose of TensorFlow is to have a so-called computational graph that can be executed much more efficiently than if the same calculations were to be performed directly in Python. TensorFlow can be more efficient than NumPy because TensorFlow knows the entire computation graph that must be executed, while NumPy only knows the computation of a single mathematical operation at a time.\n", "\n", "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n", "\n", "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n", "\n", "A TensorFlow graph consists of the following parts which will be detailed below:\n", "\n", "* Placeholder variables used for inputting data to the graph.\n", "* Variables that are going to be optimized so as to make the convolutional network perform better.\n", "* The mathematical formulas for the convolutional network.\n", "* A loss measure that can be used to guide the optimization of the variables.\n", "* An optimization method which updates the variables.\n", "\n", "In addition, the TensorFlow graph may also contain various debugging statements e.g. for logging data to be displayed using TensorBoard, which is not covered in this tutorial." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Placeholder Variables

\n", "\n", "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n", "\n", "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size, img_size, num_channels]`, where `None` means that the tensor may hold an arbitrary number of images with each image being `img_size` pixels high and `img_size` pixels wide and with `num_channels` colour channels." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, num_channels], name='x')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From :1: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use the `axis` argument instead\n" ] } ], "source": [ "y_true_cls = tf.argmax(y_true, dimension=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "HELPER FUNCTION FOR CREATING PRE-PROCESSING\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following helper-functions create the part of the TensorFlow computational graph that pre-processes the input images. Nothing is actually calculated at this point, the function merely adds nodes to the computational graph for TensorFlow.\n", "\n", "The pre-processing is different for training and testing of the neural network:\n", "* For training, the input images are randomly cropped, randomly flipped horizontally, and the hue, contrast and saturation is adjusted with random values. This artificially inflates the size of the training-set by creating random variations of the original input images. Examples of distorted images are shown further below.\n", "\n", "* For testing, the input images are cropped around the centre and nothing else is adjusted." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def pre_process_image(image, training):\n", " # This function takes a single image as input,\n", " # and a boolean whether to build the training or testing graph.\n", " \n", " if training:\n", " # For training, add the following to the TensorFlow graph.\n", "\n", " # Randomly crop the input image.\n", " image = tf.random_crop(image, size=[img_size_cropped, img_size_cropped, num_channels])\n", "\n", " # Randomly flip the image horizontally.\n", " image = tf.image.random_flip_left_right(image)\n", " \n", " # Randomly adjust hue, contrast and saturation.\n", " image = tf.image.random_hue(image, max_delta=0.05)\n", " image = tf.image.random_contrast(image, lower=0.3, upper=1.0)\n", " image = tf.image.random_brightness(image, max_delta=0.2)\n", " image = tf.image.random_saturation(image, lower=0.0, upper=2.0)\n", "\n", " # Some of these functions may overflow and result in pixel\n", " # values beyond the [0, 1] range. It is unclear from the\n", " # documentation of TensorFlow 0.10.0rc0 whether this is\n", " # intended. A simple solution is to limit the range.\n", "\n", " # Limit the image pixels between [0, 1] in case of overflow.\n", " image = tf.minimum(image, 1.0)\n", " image = tf.maximum(image, 0.0)\n", " else:\n", " # For training, add the following to the TensorFlow graph.\n", "\n", " # Crop the input image around the centre so it is the same\n", " # size as images that are randomly cropped during training.\n", " image = tf.image.resize_image_with_crop_or_pad(image,\n", " target_height=img_size_cropped,\n", " target_width=img_size_cropped)\n", "\n", " return image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function above is called for each image in the input batch using the following function." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def pre_process(images, training):\n", " # Use TensorFlow to loop over all the input images and call\n", " # the function above which takes a single image as input.\n", " images = tf.map_fn(lambda image: pre_process_image(image, training), images)\n", "\n", " return images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to plot the distorted images, we create the pre-processing graph for TensorFlow, so we may execute it later." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "distorted_images = pre_process(images=x, training=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "HELPER FUNCTION FOR CREATING MAIN PROCESSING\n", "
\n", "\n", "The following helper-function creates the main part of the convolutional neural network. It uses Pretty Tensor which was described in the previous tutorials." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "def main_network(images, training):\n", " # Wrap the input images as a Pretty Tensor object.\n", " x_pretty = pt.wrap(images)\n", "\n", " # Pretty Tensor uses special numbers to distinguish between\n", " # the training and testing phases.\n", " if training:\n", " phase = pt.Phase.train\n", " else:\n", " phase = pt.Phase.infer\n", "\n", " # Create the convolutional neural network using Pretty Tensor.\n", " # It is very similar to the previous tutorials, except\n", " # the use of so-called batch-normalization in the first layer.\n", " with pt.defaults_scope(activation_fn=tf.nn.relu, phase=phase):\n", " y_pred, loss = x_pretty.\\\n", " conv2d(kernel=5, depth=64, name='layer_conv1', batch_normalize=True).\\\n", " max_pool(kernel=2, stride=2).\\\n", " conv2d(kernel=5, depth=64, name='layer_conv2').\\\n", " max_pool(kernel=2, stride=2).\\\n", " flatten().\\\n", " fully_connected(size=256, name='layer_fc1').\\\n", " fully_connected(size=128, name='layer_fc2').\\\n", " softmax_classifier(num_classes=num_classes, labels=y_true)\n", "\n", " return y_pred, loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "HELPER FUNCTION FOR CREATING NEURAL NETWORK\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following helper-function creates the full neural network, which consists of the pre-processing and main-processing defined above.\n", "\n", "Note that the neural network is enclosed in the variable-scope named 'network'. This is because we are actually creating two neural networks in the TensorFlow graph. By assigning a variable-scope like this, we can re-use the variables for the two neural networks, so the variables that are optimized for the training-network are re-used for the other network that is used for testing." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "def create_network(training):\n", " # Wrap the neural network in the scope named 'network'.\n", " # Create new variables during training, and re-use during testing.\n", " with tf.variable_scope('network', reuse=not training):\n", " # Just rename the input placeholder variable for convenience.\n", " images = x\n", "\n", " # Create TensorFlow graph for pre-processing.\n", " images = pre_process(images=images, training=training)\n", "\n", " # Create TensorFlow graph for the main processing.\n", " y_pred, loss = main_network(images=images, training=training)\n", "\n", " return y_pred, loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "CREATE NEURAL NETWORK FOR TRAINING PHASE\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First create a TensorFlow variable that keeps track of the number of optimization iterations performed so far. In the previous tutorials this was a Python variable, but in this tutorial we want to save this variable with all the other TensorFlow variables in the checkpoints.\n", "\n", "Note that `trainable=False` which means that TensorFlow will not try to optimize this variable." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "global_step = tf.Variable(initial_value=0,\n", " name='global_step', trainable=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the neural network to be used for training. The `create_network()` function returns both `y_pred` and `loss`, but we only need the `loss`-function during training." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:tensorflow:From C:\\Users\\Reasonable\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\nn\\python\\ops\\cross_entropy.py:68: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "\n", "Future major versions of TensorFlow will allow gradients to flow\n", "into the labels input on backprop by default.\n", "\n", "See tf.nn.softmax_cross_entropy_with_logits_v2.\n", "\n" ] } ], "source": [ "_, loss = create_network(training=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create an optimizer which will minimize the `loss`-function. Also pass the `global_step` variable to the optimizer so it will be increased by one after each iteration." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss, global_step=global_step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "CREATE NEURAL NETWORK FOR TEST PHASE / INFERENCE\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now create the neural network for the test-phase. Once again the `create_network()` function returns the predicted class-labels `y_pred` for the input images, as well as the `loss`-function to be used during optimization. During testing we only need `y_pred`." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "y_pred, _ = create_network(training=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then calculate the predicted class number as an integer. The output of the network `y_pred` is an array with 10 elements. The class number is the index of the largest element in the array." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "y_pred_cls = tf.argmax(y_pred, dimension=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we create a vector of booleans telling us whether the predicted class equals the true class of each image." ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Saver

\n", "\n", "In order to save the variables of the neural network, so they can be reloaded quickly without having to train the network again, we now create a so-called Saver-object which is used for storing and retrieving all the variables of the TensorFlow graph. Nothing is actually saved at this point, which will be done further below." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "saver = tf.train.Saver()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Getting the Weights

\n", "\n", "Further below, we want to plot the weights of the neural network. When the network is constructed using Pretty Tensor, all the variables of the layers are created indirectly by Pretty Tensor. We therefore have to retrieve the variables from TensorFlow.\n", "\n", "We used the names `layer_conv1` and `layer_conv2` for the two convolutional layers. These are also called variable scopes. Pretty Tensor automatically gives names to the variables it creates for each layer, so we can retrieve the weights for a layer using the layer's scope-name and the variable-name.\n", "\n", "The implementation is somewhat awkward because we have to use the TensorFlow function `get_variable()` which was designed for another purpose; either creating a new variable or re-using an existing variable. The easiest thing is to make the following helper-function." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "def get_weights_variable(layer_name):\n", " # Retrieve an existing variable named 'weights' in the scope\n", " # with the given layer_name.\n", " # This is awkward because the TensorFlow function was\n", " # really intended for another purpose.\n", "\n", " with tf.variable_scope(\"network/\" + layer_name, reuse=True):\n", " variable = tf.get_variable('weights')\n", "\n", " return variable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this helper-function we can retrieve the variables. These are TensorFlow objects. In order to get the contents of the variables, you must do something like: `contents = session.run(weights_conv1)` as demonstrated further below." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "weights_conv1 = get_weights_variable(layer_name='layer_conv1')\n", "weights_conv2 = get_weights_variable(layer_name='layer_conv2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Getting the Layer Outputs

" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Similarly we also need to retrieve the outputs of the convolutional layers. The function for doing this is slightly different than the function above for getting the weights. Here we instead retrieve the last tensor that is output by the convolutional layer." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def get_layer_output(layer_name):\n", " # The name of the last operation of the convolutional layer.\n", " # This assumes you are using Relu as the activation-function.\n", " tensor_name = \"network/\" + layer_name + \"/Relu:0\"\n", "\n", " # Get the tensor with this name.\n", " tensor = tf.get_default_graph().get_tensor_by_name(tensor_name)\n", "\n", " return tensor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the output of the convoluational layers so we can plot them later." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "output_conv1 = get_layer_output(layer_name='layer_conv1')\n", "output_conv2 = get_layer_output(layer_name='layer_conv2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Create TensorFlow Session

\n", "\n", "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "session = tf.Session()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Restore or Initialize Variables

\n", "\n", "Training this neural network may take a long time, especially if you do not have a GPU. We therefore save checkpoints during training so we can continue training at another time (e.g. during the night), and also for performing analysis later without having to train the neural network every time we want to use it.\n", "\n", "If you want to restart the training of the neural network, you have to delete the checkpoints first.\n", "\n", "This is the directory used for the checkpoints." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "save_dir = 'data/checkpoints/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the directory if it does not exist." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "if not os.path.exists(save_dir):\n", " os.makedirs(save_dir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is the base-filename for the checkpoints, TensorFlow will append the iteration number, etc." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "save_path = os.path.join(save_dir, 'data/cifar10_cnn')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First try to restore the latest checkpoint. This may fail and raise an exception e.g. if such a checkpoint does not exist, or if you have changed the TensorFlow graph." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Trying to restore last checkpoint ...\n", "INFO:tensorflow:Restoring parameters from data/checkpoints/best_validation\n", "Failed to restore checkpoint. Initializing variables instead.\n" ] } ], "source": [ "try:\n", " print(\"Trying to restore last checkpoint ...\")\n", "\n", " # Use TensorFlow to find the latest checkpoint - if any.\n", " last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=save_dir)\n", "\n", " # Try and load the data in the checkpoint.\n", " saver.restore(session, save_path=last_chk_path)\n", "\n", " # If we get to this point, the checkpoint was successfully loaded.\n", " print(\"Restored checkpoint from:\", last_chk_path)\n", "except:\n", " # If the above failed for some reason, simply\n", " # initialize all the variables for the TensorFlow graph.\n", " print(\"Failed to restore checkpoint. Initializing variables instead.\")\n", " session.run(tf.global_variables_initializer())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "HELPER FUNCTION FOR GETTING RANDOM TRAINING BATCH\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 50,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", "\n", "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to perform more optimization iterations." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "train_batch_size = 64" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for selecting a random batch of images from the training-set." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def random_batch():\n", " # Number of images in the training-set.\n", " num_images = len(images_train)\n", "\n", " # Create a random index.\n", " idx = np.random.choice(num_images,\n", " size=train_batch_size,\n", " replace=False)\n", "\n", " # Use the random index to select random images and labels.\n", " x_batch = images_train[idx, :, :, :]\n", " y_batch = labels_train[idx, :]\n", "\n", " return x_batch, y_batch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "HELPER FUNCTION TO PERFORM OPTIMIZATION\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function performs a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations. A checkpoint is saved every 1000 iterations and also after the last iteration." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "def optimize(num_iterations):\n", " # Start-time used for printing time-usage below.\n", " start_time = time.time()\n", "\n", " for i in range(num_iterations):\n", " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", " x_batch, y_true_batch = random_batch()\n", "\n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", " feed_dict_train = {x: x_batch,\n", " y_true: y_true_batch}\n", "\n", " # Run the optimizer using this batch of training data.\n", " # TensorFlow assigns the variables in feed_dict_train\n", " # to the placeholder variables and then runs the optimizer.\n", " # We also want to retrieve the global_step counter.\n", " i_global, _ = session.run([global_step, optimizer],\n", " feed_dict=feed_dict_train)\n", "\n", " # Print status to screen every 100 iterations (and last).\n", " if (i_global % 100 == 0) or (i == num_iterations - 1):\n", " # Calculate the accuracy on the training-batch.\n", " batch_acc = session.run(accuracy,\n", " feed_dict=feed_dict_train)\n", "\n", " # Print status.\n", " msg = \"Global Step: {0:>6}, Training Batch Accuracy: {1:>6.1%}\"\n", " print(msg.format(i_global, batch_acc))\n", "\n", " # Save a checkpoint to disk every 1000 iterations (and last).\n", " if (i_global % 1000 == 0) or (i == num_iterations - 1):\n", " # Save all variables of the TensorFlow graph to a\n", " # checkpoint. Append the global_step counter\n", " # to the filename so we save the last several checkpoints.\n", " saver.save(session,\n", " save_path=save_path,\n", " global_step=global_step)\n", "\n", " print(\"Saved checkpoint.\")\n", "\n", " # Ending time.\n", " end_time = time.time()\n", "\n", " # Difference between start and end-times.\n", " time_dif = end_time - start_time\n", "\n", " # Print the time-usage.\n", " print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Helper-Function for plotting Example Errors

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for plotting examples of images from the test-set that have been mis-classified." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", " # This function is called from print_test_accuracy() below.\n", "\n", " # cls_pred is an array of the predicted class-number for\n", " # all images in the test-set.\n", "\n", " # correct is a boolean array whether the predicted class\n", " # is equal to the true class for each image in the test-set.\n", "\n", " # Negate the boolean array.\n", " incorrect = (correct == False)\n", " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", " images = images_test[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", " cls_true = cls_test[incorrect]\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", " cls_true=cls_true[0:9],\n", " cls_pred=cls_pred[0:9])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Helper-Function to plot Confusion Matrix

" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "def plot_confusion_matrix(cls_pred):\n", " # This is called from print_test_accuracy() below.\n", "\n", " # cls_pred is an array of the predicted class-number for\n", " # all images in the test-set.\n", "\n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_test, # True class for test-set.\n", " y_pred=cls_pred) # Predicted class.\n", "\n", " # Print the confusion matrix as text.\n", " for i in range(num_classes):\n", " # Append the class-name to each line.\n", " class_name = \"({}) {}\".format(i, class_names[i])\n", " print(cm[i, :], class_name)\n", "\n", " # Print the class-numbers for easy reference.\n", " class_numbers = [\" ({0})\".format(i) for i in range(num_classes)]\n", " print(\"\".join(class_numbers))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Helper-Function for Calculating Classifications

\n", "\n", "This function calculates the predicted classes of images and also returns a boolean array whether the classification of each image is correct.\n", "\n", "The calculation is done in batches because it might use too much RAM otherwise. If your computer crashes then you can try and lower the batch-size." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "# Split the data-set in batches of this size to limit RAM usage.\n", "batch_size = 256\n", "\n", "def predict_cls(images, labels, cls_true):\n", " # Number of images.\n", " num_images = len(images)\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", " cls_pred = np.zeros(shape=num_images, dtype=np.int)\n", "\n", " # Now calculate the predicted classes for the batches.\n", " # We will just iterate through all the batches.\n", " # There might be a more clever and Pythonic way of doing this.\n", "\n", " # The starting index for the next batch is denoted i.\n", " i = 0\n", "\n", " while i < num_images:\n", " # The ending index for the next batch is denoted j.\n", " j = min(i + batch_size, num_images)\n", "\n", " # Create a feed-dict with the images and labels\n", " # between index i and j.\n", " feed_dict = {x: images[i:j, :],\n", " y_true: labels[i:j, :]}\n", "\n", " # Calculate the predicted class using TensorFlow.\n", " cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n", "\n", " # Set the start-index for the next batch to the\n", " # end-index of the current batch.\n", " i = j\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", "\n", " return correct, cls_pred" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the predicted class for the test-set." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "def predict_cls_test():\n", " return predict_cls(images = images_test,\n", " labels = labels_test,\n", " cls_true = cls_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Helper-Function for Classification Accuracy

\n", "\n", "This function calculates the classification accuracy given a boolean array whether each image was correctly classified. E.g. `classification_accuracy([True, True, False, False, False]) = 2/5 = 0.4`. The function also returns the number of correct classifications." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def classification_accuracy(correct):\n", " # When averaging a boolean array, False means 0 and True means 1.\n", " # So we are calculating: number of True / len(correct) which is\n", " # the same as the classification accuracy.\n", " \n", " # Return the classification accuracy\n", " # and the number of correct classifications.\n", " return correct.mean(), correct.sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Helper-Function for Showing Performance

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for printing the classification accuracy on the test-set.\n", "\n", "It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "def print_test_accuracy(show_example_errors=False,\n", " show_confusion_matrix=False):\n", "\n", " # For all the images in the test-set,\n", " # calculate the predicted classes and whether they are correct.\n", " correct, cls_pred = predict_cls_test()\n", " \n", " # Classification accuracy and the number of correct classifications.\n", " acc, num_correct = classification_accuracy(correct)\n", " \n", " # Number of images being classified.\n", " num_images = len(correct)\n", "\n", " # Print the accuracy.\n", " msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n", " print(msg.format(acc, num_correct, num_images))\n", "\n", " # Plot some examples of mis-classifications, if desired.\n", " if show_example_errors:\n", " print(\"Example errors:\")\n", " plot_example_errors(cls_pred=cls_pred, correct=correct)\n", "\n", " # Plot the confusion matrix, if desired.\n", " if show_confusion_matrix:\n", " print(\"Confusion Matrix:\")\n", " plot_confusion_matrix(cls_pred=cls_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Helper-Function for plotting Convolutional Weights

" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def plot_conv_weights(weights, input_channel=0):\n", " # Assume weights are TensorFlow ops for 4-dim variables\n", " # e.g. weights_conv1 or weights_conv2.\n", "\n", " # Retrieve the values of the weight-variables from TensorFlow.\n", " # A feed-dict is not necessary because nothing is calculated.\n", " w = session.run(weights)\n", "\n", " # Print statistics for the weights.\n", " print(\"Min: {0:.5f}, Max: {1:.5f}\".format(w.min(), w.max()))\n", " print(\"Mean: {0:.5f}, Stdev: {1:.5f}\".format(w.mean(), w.std()))\n", " \n", " # Get the lowest and highest values for the weights.\n", " # This is used to correct the colour intensity across\n", " # the images so they can be compared with each other.\n", " w_min = np.min(w)\n", " w_max = np.max(w)\n", " abs_max = max(abs(w_min), abs(w_max))\n", "\n", " # Number of filters used in the conv. layer.\n", " num_filters = w.shape[3]\n", "\n", " # Number of grids to plot.\n", " # Rounded-up, square-root of the number of filters.\n", " num_grids = math.ceil(math.sqrt(num_filters))\n", " \n", " # Create figure with a grid of sub-plots.\n", " fig, axes = plt.subplots(num_grids, num_grids)\n", "\n", " # Plot all the filter-weights.\n", " for i, ax in enumerate(axes.flat):\n", " # Only plot the valid filter-weights.\n", " if iHelper-Function for plotting Output of Convolutional Layers" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "def plot_layer_output(layer_output, image):\n", " # Assume layer_output is a 4-dim tensor\n", " # e.g. output_conv1 or output_conv2.\n", "\n", " # Create a feed-dict which holds the single input image.\n", " # Note that TensorFlow needs a list of images,\n", " # so we just create a list with this one image.\n", " feed_dict = {x: [image]}\n", " \n", " # Retrieve the output of the layer after inputting this image.\n", " values = session.run(layer_output, feed_dict=feed_dict)\n", "\n", " # Get the lowest and highest values.\n", " # This is used to correct the colour intensity across\n", " # the images so they can be compared with each other.\n", " values_min = np.min(values)\n", " values_max = np.max(values)\n", "\n", " # Number of image channels output by the conv. layer.\n", " num_images = values.shape[3]\n", "\n", " # Number of grid-cells to plot.\n", " # Rounded-up, square-root of the number of filters.\n", " num_grids = math.ceil(math.sqrt(num_images))\n", " \n", " # Create figure with a grid of sub-plots.\n", " fig, axes = plt.subplots(num_grids, num_grids)\n", "\n", " # Plot all the filter-weights.\n", " for i, ax in enumerate(axes.flat):\n", " # Only plot the valid image-channels.\n", " if i\n", "EXAMPLES OF DISTORTED IMAGES\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to artificially inflate the number of images available for training, the neural network uses pre-processing with random distortions of the input images. This should hopefully make the neural network more flexible at recognizing and classifying images.\n", "\n", "This is a helper-function for plotting distorted input images." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "def plot_distorted_image(image, cls_true):\n", " # Repeat the input image 9 times.\n", " image_duplicates = np.repeat(image[np.newaxis, :, :, :], 9, axis=0)\n", "\n", " # Create a feed-dict for TensorFlow.\n", " feed_dict = {x: image_duplicates}\n", "\n", " # Calculate only the pre-processing of the TensorFlow graph\n", " # which distorts the images in the feed-dict.\n", " result = session.run(distorted_images, feed_dict=feed_dict)\n", "\n", " # Plot the images.\n", " plot_images(images=result, cls_true=np.repeat(cls_true, 9))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Helper-function for getting an image and its class-number from the test-set." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "def get_test_image(i):\n", " return images_test[i, :, :, :], cls_test[i]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get an image and its true class from the test-set." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "img, cls = get_test_image(16)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot 9 random distortions of the image. If you re-run this code you will get slightly different results." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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wPMAiv7gInhh1iWtPlRw/p+8vwLsi8NaA6ze/W5UoKqOSRqdpOPCMmaLkfSIl9IeAEDxS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\ndQ6igW03/mWmwz/UmEElATmhx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_distorted_image(img, cls)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Perform Optimizations

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My laptop computer is a Quad-Core with 2 GHz per core. It has a GPU but it is not fast enough for TensorFlow so it only uses the CPU. It takes about 1 hour to perform 10,000 optimization iterations using the CPU on this PC. For this tutorial I performed 150,000 optimization iterations so that took about 15 hours. I let it run during the night and at various points during the day.\n", "\n", "Because we are saving the checkpoints during optimization, and because we are restoring the latest checkpoint when restarting the code, we can stop and continue the optimization later." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "scrolled": true }, "outputs": [], "source": [ "if False:\n", " optimize(num_iterations=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "RESULTS AND ANALYSIS\n", "
\n", "\n", "After 150,000 optimization iterations, the classification accuracy is about 79-80% on the test-set. Examples of mis-classifications are plotted below. Some of these are difficult to recognize even for humans and others are reasonable mistakes e.g. between a large car and a truck, or between a cat and a dog, while other mistakes seem a bit strange." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test-Set: 10.3% (1031 / 10000)\n", "Example errors:\n" ] }, { "data": { "image/png": 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U707dh9jfzw2qmZGqZgnEYuzyrOtYmG9vCnhF/El+LQWWoRSOYGYAKWL6LeuD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\nbZ9Ds/LwQ48SpoyrqiL4J4DH+5rLu3ssl4dMhXEr5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix:\n", "[ 0 0 278 0 0 0 105 390 227 0] (0) airplane\n", "[ 0 0 119 0 0 0 152 576 153 0] (1) automobile\n", "[ 0 0 298 0 0 0 65 481 156 0] (2) bird\n", "[ 0 0 321 0 0 0 67 474 138 0] (3) cat\n", "[ 0 0 303 0 0 0 77 485 135 0] (4) deer\n", "[ 0 0 276 0 0 0 61 549 114 0] (5) dog\n", "[ 0 0 303 0 0 0 59 524 114 0] (6) frog\n", "[ 0 0 296 0 0 0 158 386 160 0] (7) horse\n", "[ 0 0 110 0 0 0 153 449 288 0] (8) ship\n", "[ 0 0 160 0 0 0 276 324 240 0] (9) truck\n", " (0) (1) (2) (3) (4) (5) (6) (7) (8) (9)\n" ] } ], "source": [ "print_test_accuracy(show_example_errors=True,\n", " show_confusion_matrix=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Convolutional Weights

\n", "\n", "The following shows some of the weights (or filters) for the first convolutional layer. There are 3 input channels so there are 3 of these sets, which you may plot by changing the `input_channel`.\n", "\n", "Note that positive weights are red and negative weights are blue." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min: -0.58472, Max: 0.65258\n", "Mean: 0.00126, Stdev: 0.16380\n" ] }, { "data": { "image/png": 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EklWM+qjev3sms7M//FCKwkBzu8nzaz6Zr3TMTKEbMLIPsz/bJs9qq/EvX45+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\nR9Cj4/gi0UmDjezF//7vzO7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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_conv_weights(weights=weights_conv1, input_channel=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot some of the weights (or filters) for the second convolutional layer. These are apparently closer to zero than the weights for the first convolutional layers, see the lower standard deviation." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Min: -0.14185, Max: 0.14978\n", "Mean: -0.00008, Stdev: 0.03539\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_conv_weights(weights=weights_conv2, input_channel=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Output of Convolutional Layers

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Helper-function for plotting an image." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "def plot_image(image):\n", " # Create figure with sub-plots.\n", " fig, axes = plt.subplots(1, 2)\n", "\n", " # References to the sub-plots.\n", " ax0 = axes.flat[0]\n", " ax1 = axes.flat[1]\n", "\n", " # Show raw and smoothened images in sub-plots.\n", " ax0.imshow(image, interpolation='nearest')\n", " ax1.imshow(image, interpolation='spline16')\n", "\n", " # Set labels.\n", " ax0.set_xlabel('Raw')\n", " ax1.set_xlabel('Smooth')\n", " \n", " # Ensure the plot is shown correctly with multiple plots\n", " # in a single Notebook cell.\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot an image from the test-set. The raw pixelated image is used as input to the neural network." ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "image/png": 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CpQ4aLc6goTbMKEIJWPl8No98njGp9Q11xRpitFgGS9LmnDGjmu3YXMF7nQdg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\nbIYAdYMAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img, cls = get_test_image(16)\n", "plot_image(img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the raw image as input to the neural network and plot the output of the first convolutional layer." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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RRupvpmmiVqu5OjeNQP9+RVHQaDTQ7/cRDodRLBaxurrqqtTlWUA6AjTxdxKQ\n9Ge73Ua73WZGA/Ag5TVp9OKIcS0UClhfX+e8FfBggxD9SVEUdDodprUQZYOS8E4bV6KJkBtPerOa\npiGZTCKTyUDXdezv7+Po6MgVybNpkM/ncfnyZaRSKYiiiGAwOFIQiEajzG5wc3zL40BRFH7mgiDA\n4/FwgY6KACSyTeLpJBBOXuN5gCTmgAc5uFgsxnlMksH0+/0Ih8NIJpNIpVIcCrs5ApxgGMaIbq9t\n20ilUpAkiddFFxJdBJR2c/sZDhsZwzBQq9Xg9/uxsLAwkq6imV4fBbz33nt4//33pwvnj9WBMpkM\nFhYWEIlEeBzURN8z9WqP4ejoiCv/kiSxhqKmacjlctB1nb0EURQhyzI8Hg8ajQYymQyCwaDjOTfi\n3ZE2JlVW0+k0ywfW63UoisIpCbfV0B8GKrx5PB6sra1BFMUToRV53KIofuQ8A4Isy1xpJ1COsF6v\nsxdG43/C4TDn44cNntuo1+uIRqMj+XZZlpFKpTgkJDFlyr/qus5pJbcjBxqWSYjFYjw6O5lMstMS\nCoXY2FF9o16vI51Ou7ZH+v0+AoEA4vE4G3V6dsOfOTw8PPGMLwq//e1vEY/HT9Qqtre30W63x9Kq\nPB4PotEoVFVlXedkMolOpzNVlPVYb2Fra2vEEPT7fbRaLdTrdVZsD4VCuHLlClOMKBFuGAbnQJwO\ntbxeLzKZDJaXlzE3N4fNzU2YpomlpSWuphI9iAzvtPPLnYLH40GlUgHwwKtaXV09UeSjnBGxBsZJ\nv100VldXma5CBoFyV6FQCLFYjIsW3W4XyWQSy8vLqFQqbDCcxjANBwAXNY97/zQRQVVVyLIMn8+H\nVqsF4MHe7Pf70HWdaVtOYngiB9UkyDhSQTYejyOXy7HjQhNWqY5B0p/0zB81A+5x11qpVPCpT32K\nc5KFQmHEuNKZorN/0c7A0tISotHoSL5VVVXcunXrofQ1enYkAm6aJrLZ7Mj04klw5n95o9FAvV7H\npUuX+Pe63S7rHdbrdSbrU7Gj2WzyvCSaXkpjX5yE1+vlsR6SJOHy5cuoVqsjF0EoFOLOJsuyUK1W\nXRm+dhqGXxbpTx7vagmHw6x/O250xnmDxmak02mmt9Bkh2azyfQlCqFJaJg2LRmFpaUlpFIpfO97\n33Ml57q9vc0NIoZhQFVVeL1e2LY9QsvRdR2apvGlRaOtKQ9LY8zr9TrK5bKja/V6vXjnnXf48slm\ns2xoyTDRhF2i2JmmiXK5zM0iNObcNE3s7u7C7/fjySefdGyNhH6/j3K5jMFggHQ6DY/Hc4LPOhgM\nkEwmPxKGFQC+/OUvY29vb2Q47ThDAAAgAElEQVTMz/vvv4/BYICFhYWxf0fXdXQ6HT6LFFnNz89j\nbm7OXePa6XRQLpdPUBtowF86nWZDRXQNmltPubdut4tqtQpJknDz5s2zLOOhGBbGBcCFi2FQmoLY\nC27n0h4GQRA457uysjI2pyrLMnK53LkVVE6DqqrY2dmBpmlYW1uDruvcNDBMti8WixgMBmi1WvzO\nqcpdLpeRTqfZo3CD4tTpdFAqlVCv19Hr9ZhHTEMc6efato1YLMZ5bSoWkdElD5IEmJ02rrVaDfF4\nHLIsQ1EU3L17F/F4HJcvX+bPkfEnz1aWZcRiMWY60J+TZ+uGcRVFEaurqzzjaxy1iRyWi25kIbTb\n7ZHBlADYQRnHeyWjmk6n2Ymhz1mWhaeffnpi2tiZjCtRq4ZHc1SrVVSrVc5tDs+n0jRtpA+ZDlqn\n08H9+/fRbDbPsoyHwjAM7O7uYmlp6aGfCQQCyOfzPNr5PDpcxkEQBL79H5Wj8ng8POmTOnbc4gae\nBvKYqOLf6/VQLpc5l0phIo0tHh6jMXzplUolLCwscB7eaVDOjNJQuq5DEAS0Wq2RohrlisljIYeA\nNAfImPr9flQqFUcjLcqVPvfcczznibr16JnQeaGJxlR8o2mlzWYTtm0z+8KtUdU+nw/5fP6RfNpw\nOMxTVT8KnivN+huOsD0eDw9yPF5redQZpHzzpA7Omf71RAuybRuVSoXFRHq9Hra3t9FqtXDlyhWu\nvlJIFggEOAw2TZP7pJ1OCxDN6jSkUil+mBfpEU4y/TIej6PdbuPo6IgvA0qpON0+fBpKpRL6/T6y\n2SwXfZrNJo94pgo3eamDwYANFXnfXq93pDvKjZlfZJBEUYQkSej3+3yZUjhNHiyNJSEjRfRBQq/X\nQ7vdhqIojueH6/U6qtUq88OpK4vqAIFAAIVCAeFwmOlBxMCh5+fxeGDbNnRdd41RQs/oNMiyjG63\n+5EwrqFQiPcoedPZbJYLqZTvngS0J1xtf7UsC7quo9lsYmdnB7lcDoVCAYlEAqIoYnNzE2+99Ray\n2SzW19c5N0fhYSAQYE+BqvZOYng2zmnweDysgUAdO07PM3oUjk+XfBQWFxcBPODa1Wo1ng923hhW\n45JlGX6/H9FoFJVKBd1ul70XGmYYDAYRi8XY86bJqrTpqULvBoiyRLSvwWCAarXKFz09P6/Xi2Aw\nyBV4KmoND7M7nm5yCpTTJZaCqqpot9vodDoj7azxeJwNPkWAVMii/yhSdAPHL5xH4bwv/IeBusqG\n007FYpEbQ+7cuYNYLPbQ/OtxEAd6EpzJuJLxIm+Fkr6hUAiXLl1CsVjE9vY2bt26hW63i9XVVSST\nSYiiiEajwfQs2sxO37TU/zwpaEOSwSCKyXncvNOuFXjAK240Gjw1d7haex4gEnu/32cO7traGlKp\nFH7zm99AVVVkMhkYhoFWq8Vc0kgkgmAwyB1bgiBweO5GWoC6aSi/FgwGRwoS1J01bMCofzwUCvFa\nqRDm8/kQCoUcXSsVKYPBIEd1ZJi2t7chSRJWVlbYs5ckCZlMhpsyOp3OSBg+zeE/C6a9BMmrvqgU\nliAITPkbxtraGnZ2dnBwcIBKpYJ79+7h+eefP9VZmYbDO5X1oC81DAOKojCFJpvNQlVVlEolKIqC\n+fl5PPHEE5BlmavH1CudSqW4BZJCGqdb5qbxXAVBQDabxdbWFprNJgu72LYNSZLOxYs9/uInQSKR\nQCQSQavVwu7uLsLhMCKRiGsjtYehqio6nQ4qlQri8Ti34xYKBWiahhs3bsCyLORyOczPz0MURXQ6\nHQ5Z8/k8VFVlzQFZll1lQJDhiUaj6Pf7iMViXEgk40+Hny77YDDIOhn1eh31eh2apjlOcxqeRCrL\nMkKhEIes3W4X9XodgiBgZWWF/04oFOILldqNg8EgFEVhLeCPCrxeLxqNBgKBwIVEWeTZN5tN5uOX\ny2Xs7e3h2rVrKBaLLOBzeHjIMoMPA0VAk+BMxpVUj7LZLBdWqAunVqtxOFgsFjlVQAdpuPoaj8dh\nmqbjxSRqWhjG8EjlcZ+XZRntdpu9MmqBI/K4WzlZn8/3SK2AdrvNDRkAsLGxwbkjajiIx+Oo1Woo\nlUoQBIF5m26BQtZQKIRoNMrGybIsXLp0iWXwhhsfEokEOp0OC6RQqC7LMtPznEYwGIRt28y2OB7S\nyrLM+dXjGI6saD+1223Hi28UueRyOd4HtA9XVlZw5cqVsX+POKbtdpvlEomr6+ZY9bN4xXSW6Fyd\nJ5WQ9COGozuqFTQaDeRyOSwtLWFpaYlt02mpD1cLWp1OB4lEAr1eD5VKBRsbG7AsC6lUCsVikf8h\n+/v7AB54NMlkko0oeVtUAHGDX3r8Bfp8PjQaDUiSNDYfRERxEhehHnnyEMmLcRr9fn+sIaRiRjQa\nHXmZiUSCD76iKNje3kYikeBbGXhwM7tpXHVdR6lU4udBnmy5XEYmk8Ha2hrm5uawsbGBWq3G7zcS\niXA0s7OzwwbarRQMdVwFAoGHHpjTfi55KiT8kc1mHfdck8nkyAVLqZZJEI1G4ff72TDE4/GRLjkn\ncZojMA5UvKa0Cnnp55UmsCyLdQVs28bi4iIGgwF3bOm6joODA967xCp4VKPOpNHhVDuawtc//uM/\nRj6fx8HBAVRVxdLS0tgfWCgUYBjGid+PxWK8AZrNJhthp0AFiWEcHR1hb2+PXzBxBYnrSt4UVWwp\nNLMsC7VaDYFAAC+++KKj66S1HmcLHO8WGw6nSP1d0zSmhVQqFaiqinQ6jVAo5HozRLFYxKVLl1g/\nNJVKIZ1OI51Oj/CFh3maBI/Hw9HM1tbWCOXJafj9fiwvL7MAzllBrBdFUSBJkqM5Tb/fj5deeunM\nf58EZSi1RobMDQwGg6mMKwmNU9eWz+dDNpuFJEnnppFhWRbm5+dRKpWg6zrzg3Vdx71797C/v49m\ns4l6vY5UKsVe7nC7LLFMqBllUk72VMaV+qqp24E6NB5lyU97iPF43HGP0Ov1cksp4fDwELu7uxAE\ngb0RKmgQ/5I6oaiSSMUFEspwC8PG9VFtuIPBgPPclmWhXq+j3W5zfrlarSIej7ve0724uMjc0OFi\n1DQ/NxQK4emnn8bh4SHTj5xGJpPB+vq6I99FaaxEIuHoWoPB4GMVJCVJQqlUQqlUYg/bLa9wnLA9\nsRYo1zsYDNDr9bgQSNB1HZVKBaVSiffPeRRiJUnCm2++iXv37qFYLGJjY4MnT1QqFfT7fa6tUHpr\nf38f5XIZzz//PACwMtnR0RG63a47Ba3jeUwqDEwD6nKZn5/nF+X0rCfqBx9GOBzG0tISRFFEIBA4\n8RkST/F6vYhGo1hcXEQoFHKVlD38s4EHsn1USCNGBf0Z6YoahsG5zEAgwKNIlpeXubuHhIvdwqQz\nhCZBLpebeiTIpLh27Zrj3/nFL37RceP1uM7F/Pw8dz66Sd4nxgfh6OiIo06fz8cRHxnZ4VFLVNdQ\nFAW3b99GLBbjaCeTcWR+4ljcunWL04G5XA6pVAqJRAKapmFlZQWyLCMcDrNNINogqaANp5Mo9TZp\nZDjVWyAvtNvtciiaz+d5jMtpsG0bGxsb+OCDD9Dv91Gv13Hr1q2p5kNNgl6vh2q1ygaKWAnErTzO\nVyQCNg3VI2EM6p9227hSSoL63kulErrd7kjll7wl8qBpdE08Hke1WsUHH3yAZ555BvF4HKqqXljH\n2WkYPvy//vWvsb29jU9/+tMXvKrJUSqVLnoJY0F5eDf5pURpsywLe3t7aDQaJ6QOyUANy48CH0Zk\nly9fRjQa5fwwFWfdoo/94he/QLPZZAEXoqqJosj6B9TpRrrDmqYhn88/lHE0KdfXM80/yuPxVAA4\nNk3yGJacGgHs8jqBj89aHVsnMFvrED4u7x/4+Kz1d+79T2VcZ5hhhhlmmAwfLVHQGWaYYYbfEcyM\n6wwzzDCDC5iqoJVOpwfLy8tT/QBd13l2lt/v5+IRCRZTcvjevXuo1WqOlGHj8fhgfn6ev5voKZSw\nBqYTTDmOd955p+pUfuj4M6U0zXC6hniLw4wBor/4fL6RfxPplQ4GA+zv76PRaDhW2p72/ff7fWZh\n0LwnosIdx/b2NqrV6oWtdRo4udZ0Oj04PiCRiixU+Jtk8gQpj5FwNvCgmr+7u+vaXnUSv4vvfyrj\nury8jLfffnviz+u6js3NTbRaLQQCARSLRW4zbTQaEAQBCwsLGAwGj0WkPo5MJoNvf/vbWFpa4gqq\nIAg8yptg2zZUVZ2668Tj8TiWKJ/2mbbbbbz33ntoNptIJpOYn59HLpfjGfbLy8tMiyGe3nmutdPp\noNVqQZIkbG9vc3Wd6GOFQoGV9judDorFIvL5vON0vGmf6zRwcq3ZbBZf+9rXWLPg1q1bmJ+fh6qq\nKBaLsG0bS0tLePbZZ1lF7u7du/jpT3+Ku3fvYmFhAU8++SSCwSBu3ryJdrvNeg57e3v4xje+ce57\ndTAY4PXXX8fdu3chyzLS6TTy+TySyeRDW50v4v1Xq1UMBoOpqWCTrtVV2adWq8WtpETFiMViPFaF\nPC+aY+UUTNPE0dERTxqNRqPcKCAIAgtwkJ7otMb1IkFUJmrHLRaLPKTu+OiSi1AiIk+ayNm5XI4l\n8NLpNE/ftSwLt27dujC1pGH0ej2m6p03RFHEl770JWiaxp1BkiQhHo+f6CyTJAmSJKFYLOKzn/3s\nie/SdR3lchmxWAw7OzvY2dk5r38Go9vt4gc/+AHK5TLm5uaQTCZ5CoSu67h58yaCwSCKxeIIFctp\n2LaNcrk8lpNaq9VY8J+Ex4edrp2dHVSrVVy+fHkireWHwTXj2ul0UK1WoaoqK6XTQWo0Gtje3obP\n50MqleJ+Y6dBCvSJRIK5qxSqttttBINBVwnMTkPTNOzu7qLZbLK4h2EYiEQiqFar6HQ68Hg8WFhY\nQKvVupDxxoIgQFEUtNtt1hdVFAXdbndkLHG3231oiuA8sLe3h2w2C13XsbOzw5cwibQIgnAukpNE\n/q/X67yGcaNJJgF1GxJv+iKchp2dHezv7yOVSiGfz7MG7XFdgfn5eVfX8TDtWU3TWFc6Eomw/SF1\nNOBBByJpJz8OXNk9pONJ3iLl2xRFwf7+PqtoUVjuhnGlF2oYBhsh27a5bTQWi13IQMLHQbPZ5FHc\npmlCVVU0Gg0oioKjoyOYpon5+Xm0220Oec4LdGgodKVW3UajwZqjAFgPlfRzz1OL1rIsTg8ZhoFa\nrYatra2Rhg1qzPB6vdzB4yZI3d+yLE6nTNNieRyGYeDg4ACmabq+9uOo1Wosfzk3Nwev14t+vw9R\nFNFsNrG7uwvbtlEoFM5lgvG476fJ1JSfJrU+mvHnZCTlinEVBIFlxoaHwdGkVUoBkCfpxugMGuXR\narVYgs2yLO4PphlEH4VRFJNgMBhwWEiydz6fj6UeKe1CY0DIezkv0KbsdDojo6FJrJpSFySGQ5dD\nq9U6t+iBvGTDMCDLMn7961/j6OgIgUAAmqaxJq5hGAgEAiNC726BWkNFUYRhGAiFQsjlcmfuCsxm\ns9jY2GDdjPPCnTt38Nvf/haBQABPPfUUe9B08dNY8Gw2i1wuNzLyxy0c71bTdR3b29s8kLLb7fJA\nx2g0yp4s8OH8tcdZnyuWxefzcesoKTiRWtLwuAzqne90Oo4aV9IIIIFeCkF1XcfW1hbP7VJV1TV5\nNqfh8XhYBYue5bAoOB0kkk4kBfjzBolMk5wgRS20TtM0WWUskUhcyNRd6nFXVRWVSgU+n4/3DBlg\nErFuNBquGlcS4aHco2maoCo3jZ6fBuFwmAuG5xm51Ot19Pt9PPnkk4jFYrh//0EdzbZtKIrClz/p\nH9B/booMHReNajabMAwD0WgU+XweGxsbuHfvHs/5o+iFHBav1/tYF5SrblsymUS32+Vwlm5p8mwp\ndHTDcx32CMjIK4oCv9+PWCzG3p2qqsjlcucq4Ps4iEQimJubYzoWGdjhEdU0+eG8c67DKv+dTgf1\nep1DbvJcKVVDMnnBYNCVsdqngUa2+Hw+dLtdHjxHo36GhwS6Dbp4hmlUAPCTn/wEX/jCF6bKnfr9\nfoRCIe6bPw/cvXuXmR9EKyMlOa/Xy7KjRMWkQrcoiq4Z1+OORbVahaZpyGQyrIlM6SrScBZFEbIs\n8yy9x4WrT5+KA8eFHILBIHw+H5rNJhqNhuOGgA4xAD4g9XodwWCQFZ2IoUCFF7c4cW4gkUigUqkw\nX5hy1iSa7PP5UKvVzt240gVFh5vSFJQOojQGpQvoAJCxPU9kMhlcv36dmSuBQID3qaZpCAaDODo6\nQigUwvz8vGv5y+G5XMNavKTXe5boIxKJYHd31+mljoWiKNjd3UU6ncbVq1fh9/thWRai0SiPxqFL\ng/RcyfC6+c57vd6IqhVNwVheXkY4HEaj0UA4HIbX60Wn0xmZ5XY82jorHDOuNEH1+Dws4jdSEQMA\nq9KQERiuIjsBQRAQi8UwPz8PTdPQaDTg9/uRTCZ5FtHW1hZX3gFgYWHhQsdrdzqdE7f4w+TjRFHk\ngXtkpAKBAIfYhmGg2Wy66hEeHBxAlmVeM6V5hvOrqVSKCwZ0EVC6otfrcZGLqFDnjWKxiGq1ilgs\nBtM0oSgKc7JpdpUgCKjX664WhwRBQDKZHKElSZKE559/HuVyeerC38LCAl577TXXKvJ7e3sQBIHD\nbFEUsby8zJc7sYSoMEeTdn0+H/L5PERR5L3gFmjoKBnXbDaLo6MjTgMGg0GOAomaSXq1gUCAja6m\naZzinBZnMq6bm5tYXFwcSRhXKhVWmR9GOBzmMJxCQNu2EQwGkUgkUCqVcP/+fccNQT6fZ7lAEvAd\n3mzxeJxvd5pke95TVIfx5ptv4sqVK8xtPDw8RLvdxsLCwti8TzKZRDgc5pArGAyiXq/zJAW6qd3A\n4eEh53XJuNLspnQ6zRu61+shHA4jmUwiGAyyh0qVbNM00W63R0bUnDc+8YlP4Pbt29je3ubuKOLo\nkpTmsAylG6Dx48dB0nzTpqzi8TjC4TCL2zsNTdPQbDbx5ptvIpvN4umnn+bUBUUp5XIZfr8f8/Pz\nI15qtVrlfREIBHhqtBsYzldLkjQSnUajUczNzcHv98MwDGaQ1Ot1NvwUgRFfflqcybjeunULqqri\n+vXrAB7cZNVqlQsUw/kKug3I5TYMg2dV0Y1dr9cdNa7Hx03TOJdhLzCdTvN6Scv1InH//n2Ew2G+\n4Xd3d9Htdpk0fhxzc3NQFIXHpFBOm/QqO52Oa8Wit956C4Ig4MqVK8wbJl7zcIqFQqvhGVR04Mlr\nqdfraLVajq9x0v3k8Xg4FKfmE0VRkEql+AJpNpuo1WrIZDKu8HIfZbRDoRBfXNPkXl944QVsbGw4\nsTwGRZeLi4us3u/xePhyVBQF5XIZ7Xab9WWJldPv99Fut5l/7fP5kEgk0Gq1XDGuxznU49rdaQwV\nNTm1220cHR2h3+8jlUrxfLKzethnMq7k5lerVXi9XhwdHXGBhWgsw4hGo0w1oTElxO2jSaVOpgVU\nVcX+/j4WFhb498aF18vLy9y1dZ60lXEIh8OwLAuqqnJY7/P50Ol0xn6einXDQxWB0VHCbnmuRF+h\nPJWu62i329B1HV6vF81mE/F4HLIs85/R5ygt0Gq1kEwmMRgMuODpJGh20927d5FMJh85/iaRSCAc\nDnOFW5IkqKrKk0DL5TIAuJ6bp4LrcJWb2rOn9V6vXr2KO3fuOLo+Suvt7e2h2WyiWCyiUCiwEVMU\nBQcHBwDA9LZutzvCEhjm9C4tLcHn87kiQK5pGur1OjM9ms0mYrHYyHMko0r7lqiOgiAglUox66Ld\nbp+p8HYm40odLJRTMU0Tuq5zlfr4HPphcRHKaQDgkIcOmVMwDAMffPDBiHEdh3g8juXlZa66E8n8\nIkCHn54JdTrVajVUKpWxXFCv1ztCH6JKsW3bPA3UDRDP9vDwEKlUir0SEuOhERnksVKhiMJByrkS\nBS8ajTpelR8eNlmv13Hz5k1cvXr1xJ4gqiCFujQxllojLcuCYRi4f/8+arWaq8ZVkqQTRlTXdSST\nyTOxWZxOc5EH1+l0sL+/D5/Ph3g8jnq9jnq9Dq/Xy+OFKKXi9/txdHTEThXl5ckQE6/YaRiGgZs3\nb+Izn/kM2yWiMw5jYWGBjSydJdM00Wg00G63uYYwbKgnxZmMK1X76UGJoojDw0MOY2m6J4GoLeSx\n0v9S4pjCC6cwGAwmfmFUHKLqIiXgzxuU0wkGg7wpqcGiXC6PTapTaEXJdzJePp+PqUVugGh0FOZT\n+sHn8/E8eJ/PB1EUIUnSCDtgeCYYTdJMpVKuUOFofXSwt7e3YZomVlZW+OfRSKBarcbqafTvOT6V\n9ujoyPE1DmPcMzBNE/fv38fKysojh1eOw2nOxbQYNq6pVIpplrRXicY0GAwgyzJ3m/n9ft6XpmnC\nNE1omob9/X2Ew+EztfqehsFggFqtxgJBAMZe4JIkIZ1Oo1wu86gX4uBSFEEFTlVVp3qmZ9rRw11W\nNBUznU5zB0S1Wj35g7xeyLKMRCLB8+spb0NsAicxiddGxotGABM1zK1CwKNAXmggEIAsy4hGo5Ak\nCYFAALZtj32mAFgMJxaLMYGcckVur5VaGAVBgKqq0HUd0WgU8XgcnU4HtVoNHo+HVZHIKFP3nCRJ\nIxe1G+ukMev9fh+qqqJer2Nzc5Mv33A4jPX1dSwvL7OkIxVbAHC7MVHdzhvBYJCjl+Hc3yR5QLea\nHyiHapomut0uRFHk6ISaWOr1OkqlEheSU6kUG2Fit/R6PRZYchrkJRO3lih241JlyWQSkUgEqqqi\nVqvxEFDaM5QrftgZfBjOtFsof+LxeHiOO80jb7Va2N/fRzqdHjtWm14EUXQoR+v04TrutY279cnr\nbrVaXBTy+/2cIjhv4Qvi50YiEXS7Xd4IxAscFpcYhs/nY4OqKAp0XXfNYAEPIhHqGSdJR4/Hg4OD\nA1QqFSwsLCCbzeLw8BCKonDKgxgN1WqV//5x4rzToFnzoihC0zTOa25tbY1Mh6VGkq2tLf4MXRzj\nJoG6gXH99sViET6fj8PUtbU1AOO93ONwqwGC9peqqtA0DYVCAel0Gr1eD5VKBZ1OB+VyGYZhoFAo\nYGlpCfF4nBteNE3jJp9hjWU3QNEx8KFIDp0TAHyeSF3u/fff545SirjJk532/Z/JuGqaxrcNeU5E\nxI1Go+h0OtjZ2cHy8vLYBVHuQxRFKIpypoT9o0DpimFQK+HxF0m5oXK5zB4r/fssy3qo/qTToGIg\niYek02lOtluWxTlU27bHeiQUGZCBHpe/cwp0GVLYFIlE2BM9ODjA1tYWrly5gnw+P/L3QqEQTwuu\n1WrcQOLWWqljjCaNUoGQqIAbGxu4fPkyf5463/b39zk1QFEa0fnchGma6HQ6J/Lr+Xwe4XAYP//5\nz1EqlfD7v//7AE4XfKcClNOgFACdl3g8zqygXC7Hbefb29ssmiTLMoujtNttaJrG6Ro3OMTUCQo8\nCP2Jg3t0dIRcLodQKHTCNuVyObTbbezt7XFBkyKf4xOjJ8GZc670v+Sdrq6ucv4lkUgwAfdR1l4U\nRYiiiLm5OUdvr3Gbrt/vo1arIZFInChaybKM/f19LrBQw4OiKOh0Oux5uXnD0iEmjykUCmF5eZlp\nTu12mxsHHgUycplMxrX1Uj5VFEXuuAEeGIH19XU0m82Hhq10CRBVR1GUsZfh44Lyp3RZUc43HA6j\nUqmwIdvb28P8/DxWVlYQDAaRzWY5emg0GqjVami1Wmi1Wq4b152dHWxtbeGP/uiP+GLd2NiA3+/n\nwuGbb74Jj8eDxcVF7n6khpzj79tpKh4ZF3qWyWQSc3NzJ+oryWQSgiCMRAZUy4hGo8hkMqhUKmg0\nGpBl2bV9SnuK0mudTgdHR0ewLAtPPPHEyGfv3bvHDJx8Ps95eF3XR8T2p8GZjSsRbsmLogYCotuE\nQqGJb6SlpSVHQy4qmA2DcoC1Wg3AAwNRKBTYMPj9fvR6PaZlkOAEHaxYLOY6DWdciEybddr82fr6\numuk93g8znSr4+Rqv9+PdDr9yL9PDAKqMANw5YCR4clkMmi32wiHw+j3+0in06hWq8hkMrh06RL6\n/f6Iw1AoFJDL5dDpdLi7bG9vz9WW0larhd3dXRiGgTt37nDjTaVSQaVSwdzcHDKZDKrVKvb391nV\nKRgMYm5ujlNZZOjc4DnThbm0tAS/34/l5eWxZ3wc44bSAFTQJk/XTcM6vDepWSCRSECSJBweHiKX\ny/Gfk5ATdTpSZEZCLsFgcOrneSbjGo1GmRlwHNTVMI3aVDgcdrRYMM64kifa6/XYc7p37x4ymQzS\n6TRCoRCSySQfKPJ4yIu1bdtV4xoOhzl0cgLz8/OuGdf19XU0Go3HEt0IBALIZDLMcXSa2eD1etFu\nt9Hv99HtdpFKpUYO02kgmlEsFoOu65ibm3Olqk343//9X3Q6HaTTady4cQMrKysc+c3NzUGSJOTz\nedi2zboR1P1GBrXVavGeJVqfG1hbWzthvCYFCfcQD9ktUKstgVIQFIE2Gg0cHBxgeXkZiUQC6+vr\nAB6kDw8ODlimlGbSJZPJqdMsZ7JoVIU9zUOZFJZlORoWEs9zGNQoQNVpKmZpmoZarcYFFuqQ0nWd\nSfKkkOQmstksYrGYY0U0qui7AQqfH7dgRjQY4KSK0ePC6/UiGo2i0WggnU6fib9M1W8qvE1jnKeF\nz+dDJpNhoxiNRjn0NwyDhY8oDB8MBiyKQ8Y1Fouh0WhwC/pxTufjgp7h4ypZ0Sy1bDaLX/7yl67w\nXCVJYtqUYRjcQUo2IJPJcOpoGOFwGJcuXUIsFuMCbaVSgWVZU+sOn8likLKUU8jn864WjSiXOUwX\nEQSBwxTiiFL3CFE2NE1jz8BtWbxPfOITjn4fCVG4BaeModMGgEAdd61W68zG1ePx4NatWzSZGIqi\n4Pnnn3d0L6iqCgDMoCVCg/AAACAASURBVNnc3EShUIDf78fOzg5zSeln0uVPVCZVVTE3N4fPfe5z\n6PV6ePPNNxEKhdButx3fs07vJ7/fj8985jOOfufwd1OI3+/3USgUJrYxpDERj8eRzWZhGAYajcb5\nsAWcBEl/OQlBEHhKajKZhKIoaDQanCogSshw4Ws4Wd9qtXD37l20Wi0UCgWsrq6yitOTTz7p6Frd\nwuLioqtCxB8XPI4j4PF4cO3aNaTTaezu7sLn8+G5555ztIuP6HZ+vx/NZpNbgQ8PD0ek74YLVvRr\nmpm1ubmJRqOBbDaLzc1N3L17F7VaDZ/73OccWycAx8+pm6BnFQgEpjaKNEWBKKbAgzzztG26nmly\nXR6PpwLAsVG9x7Dk1Hx1l9cJfHzW6tg6gdlah/Bxef/Ax2etv3PvfyrjOsMMM8www2T4eMw2mWGG\nGWb4mGFmXGeYYYYZXMBUBa10Oj1wi+v5/wm+OFKCjkQig4WFBSblE4H5eAHrrBXvd955p+pUfkiS\npAEpcxG8Xi+vj1R6qLGBurf8fj/ruNJsKlJ0sm0boVCIhGgc4zhN8/6pbZdmU532rJ18/wAQjUYH\nhUKBhWJIbGS4cysQCHCjSDAY5HlrpMsgCALrOtDfp9FETq01FosNisUity0P6/KS1vC0oO4uALh9\n+7Zje1WW5UEymWTFNWojJgYDsW1IrnG4S5DahzVNg2VZ8Hg8zDenkfe1Ws2x959IJAaTjig/OjqC\naZpj9VCo7ZUEYIAHAxnr9fqpa53KuC4vL+Ptt9+e6LPlchmlUglPP/30RBSOT37yk9Ms5ZHIZDL4\n1re+xboAHo8H0WgU9+/fhyAIkGUZuVzuTD3NP/vZz/D5z3/esUR5Pp/HP/7jP6JQKDB9iMa3UF92\nPB5nWb6rV68+8vuazSY2NzcRDAbxla98xallAjj9/ZPi0dbWFn7+858jkUjg+eefRz6fP7Vi6+T7\nBx40Ufz3f/830uk0q4WR+IqiKPzuv/Wtb6FareJP//RPMT8/PxHDwsm15nI5fPe730W1WkU2m0W5\nXEYgEEAkEkEmk5maqra7u4tqtcpi66+88opje3Vubg7//M//jGeffZadFkmSMDc3B9M08frrr7M2\nwqc+9ampvtvp97+4uIgf/vCHKBQKj/zcd77zHbzzzjv4sz/7Mzz77LMTffdpZ5DgWlpgZ2cH8Xjc\nFYLwJFAUhekrHo8H9Xqdb1gaoXsWuCEyQVMIhidO0tz5WCyGSCQCr9c7UdMGCeJchGzi5uYm3n//\nfbz22msol8s8bob2gKqqKJVK2NvbO5f1DAYDVKtV9jjIwNN6fvnLX2Jrawvr6+vcvTOMGzduYHt7\nm3/thsoUeXQ0IJE8ZkEQzsQBXlhY4MkeboyrB8DeZ6/XQ7lcZjFymqM3rWF1A9QO/Kg///73v4/t\n7W18+tOfxqVLl0b+XNM03Lhxgzu1CM1mc+J94Ipx3djY4LDh4OCAJ6yeF4ZFmUm6j3hrsVjssTpt\nnN6wg8GAx1DTmokcTiI4Pp+PRTLGoVarjbxwtxsIHraGUqmEGzdu8ISCSCSCdruNer2ORqOBu3fv\n4s6dOyiVSkyedwtkCI6rGdG8tg8++ACvvfYarl69ivX19RPaDffu3cPdu3dH9q4b2gIk7E4hMxHd\nH0eLldTf3LgMhrWXqaX0xo0buH//Pvr9/ohYy0WC0g4Pw//8z//gjTfewPr6Ol544QXWDjAMgy8M\nURR5xA8hHo9PnKpx9AQOBgO8/vrrLOrRbDZZpINu4Xq9ju3tbVy/ft3Vriyv18tzvYAHaQpJkpDN\nZh/L8Lgxl8rj8fB4ZzIGkUiE0xrhcPiRF8LxcR4kVn3eIM80mUwilUpxd8ywXKJt2ygWi+dCSKfQ\nVdd1CILAEok3b97EnTt38KlPfYqbLY5PLU6n03jyySextLTEv+fGqGrK/w6PGYpGo4/VAELf54ae\nLz1DXdchyzJ6vR7u37+PZrOJF154gUfXfxTwqOjt+9//PlZWVvAnf/InI7/f6XQ4wn1Y6nBSzQ5H\nrdu///u/Y3t7mzU0W60WNE1Do9HA7u4uh4gko+cWqAOLDpVlWahWq/D5fI8tZuK0GMZgMICmaVxQ\nURQFoVCIVa38fv/UgiGkn3Ce6HQ6sCwLy8vLuHTpEoLBIDRNg6qqME0TXq8XiUQCVGh0G1TIisVi\nsG2bR7T4/X788Ic/hNfrxcsvv4zFxcUThhV44P2vr6+P5IrPY/yPIAiYn58fGTczLWi8tRst26Qc\nRwLtFJnmcjm8/PLLAD4cYHnReJiN+dGPfoRisYi//uu/PvFnkUgE6XSatQfGYdJLyzHPdXNzEx6P\nB0tLS+h2u7Asa2T+N4XmVJWbRjXrLKAZTZSe8Hq9jvxMp5WmyHOhEdWkmp9MJrG3t3emNdMAyfMC\nXaTPPPMMa2FubW3xHlAUhedpBYPBc5m0S8ZVFEVUKhWuAr/66quo1+t49tlnL2RsyzgQE8S2baRS\nKZ70QI4KtWBOilgsdmrO8aygIZTRaBT1eh3NZhORSAQvvPACf2Z3dxeBQAD5fH7sxXUeIAfrON54\n4w00Gg383d/93UPXRqJHD9sfk47adsyF6Ha7ePnll7G2toZwOMyharVa5TCl3W7zOBU3QcK8i4uL\niEajPFv9qaee4s90Oh1UKpWpc6hOXwokbUf6B2QUPR4P8vn8mUPR8zSulUoFsixjbW0NkiQhl8th\nbm6Oc78+nw+apqHVavG8Mrfh9/vR7XZRr9dhmiai0Sh2d3exsbGBv/zLv2SJuYtGMBhkFaZ4PM7V\nbdM0eTheo9GY6juHZ1o5Ca/Xi42NDWQyGczPz7Pz9NJLL43IccqyDEVRpp455SQCgQDm5+fx3e9+\nFz/+8Y9Rr9dRqVSgqiq+9KUvjTWsm5ubaLVap373pPbLsas7n8+zQc3lcnj33XdxcHAA27bRaDTQ\n7Xb5sLmdbyNeGuXLSqXSiJGi1IQoirBteyoPxumhb8RvpFHTuVyOtS5pzb1eD+12G4lEYuKQxA3R\nlnGHtdfroVarjRQyAoEAG1RBEHjCLtHiBoMBut0u/54bCAaD6HQ6PPqmXq/jZz/7GRYWFvDSSy+N\nfLbVasGyLMckNKddZzQaxbVr13isCPDAQOZyObRaLZimiVgsNtWzEkXR8aIh8YDJk85ms9jb2xsR\nMyItVBr5cpFIJpNc7D04OIBlWVhcXDzxnt99912Ew2Gsra2h3++j0+k88vxM+h4c2dlUHR7+4TRx\n0+fzoVqtQtM0RKNRJBIJJBIJaJrm+M1KCAQCI+s57v1RGAZML6PmdJgzGAzQbDZRKBRQKBSQz+fR\nbrdHBirSILhpWBdOz6wHHtBQ/uu//mvk9/b29sZShprNJle/qQkiGo0ytWzcMD4nYRgGKpUKTzu4\nc+cOKpUKnnvuOf6MZVm4efMm3nvvPVZJuyiEw+ETURFdUtFodOpob2Vl5aHskrPCMAx88Ytf5F/7\n/X586UtfGvkMRUz0ni8KpmniN7/5Db7yla/gr/7qr/DKK69gY2PjxJ5799138etf/xr379/nVMBp\n657UZkztud64cWPES1EUZayRjEajKBQK6Ha7mJ+fR6FQYLoDzS/q9/uuqPtHIpFH6qOSCv5HAb1e\nD81mE9euXUMymeTuKrr1afIk4DwNbFqoqor//M//RC6Xw3PPPYfDw0O8++67+OxnPzvyORqiKEkS\nz3eicdXNZpMbJGRZPpXkfVa0Wi1IksSarD/60Y+wtrY2Uv2v1+v44IMPIAgC03DOIx88DcLhMFRV\nnTrNk8vlHM+50ggnwrgUWTqdZi3Vi0SlUsHf//3f49VXXwXwwKH68z//8xMSlGtra6zXvLOzg8XF\nRQDgCRbD1LNpMbVx/Y//+I8R4yrL8li6QiKRQLFYhKZpSCQSGAwG2N/fRyAQQKFQgG3bqFQqZ1r0\naXBrpDTgjoEbpn50Op2RoW+macLn8zk6Auas6PV6WFhYwNWrV9FsNrG3t8cRyjDq9TpkWWYqGc0v\nKpVKXEXudDqIRqM86dZpdLtdfPKTn4Qoiuh2u4jH4yeI4pIkoVgswjAMvtTGgYqMF4FgMDgyG2sa\nON3wIggCdnZ28OlPf/qRn3NLAH0ayLKM5eVlbGxsQNd1zM/Pj1yswAMnIBKJjAwrtG0b1WoVlmVx\n3pqK2OSxTlozmMq4GoaBX/3qVzymGsBD8xOiKPLIEpqZQ6F4s9nEYDCAruu4devWiUmMH2U4fcgk\nSeLWPxpGePzn9Xq9sWPBzxvUOx6Px3mQY7FYPHGIKc8OgLvkqCVTlmWepqlpGtrttivG1efzodVq\nQRRFXL9+HUtLSyee32AwwNraGlqt1ohhNQyDB+gBzr/zaXHWn++0Fy4IwokL6qMKv9+PZ555BsVi\nkS/yYVSrVVSrVYTD4ZFxMO12Gz6fj/PKqqqi2+2yvdvZ2XEn5+r1epHNZqHrOlRVhaqqPP7iOCiU\noRwrjVTRdR31ep2HrzUaDfzmN7+ZZhmOw41OlmnwqBQF5SopbL1IUDGqXC7Dtm0OPQ8PD0eeYS6X\nQzKZ5Flluq6zl0DdcySEcbwDxinQ5U1IJBInDhhxNmm8c7lc5n+LW/WAs4DO2bQD8py+FI4/09PQ\n7XZxcHBwYawBRVEQDAa5QWd47el0GrIs48aNG7h9+zYAMAWS6iDAg7QMRT/9fh+53P9j70ti5Lqu\ns7+qV1WvxldzVVd39cRuihQpirIGy45lyMkmdhADcbxxAHsVINlklU0WCbLNxrsACRAkhuNNggQI\nENtBjCBIbEseZJEWRcmcms2eax7eVPVqrn/B/xy+6oGs1/1et+j0BwgkWz3cfu/ec8899zvfNzP1\nCdJScKXj9nA45MWu6zp2d3cP1HfIXoE4kETBIg4sZT6hUAh7e3tnGuAEQYAsy1PdrpI1t52YZiek\nFtmzBh3zyW54MBig0Wgc6MGenZ3Fiy++yEFtdnaWbY0B8M34aTQUAId31iUSCcRiMbhcLjZdJCWl\nsy7BmBGNRuHz+dBoNFAsFm23zLaCaTadSqXCtt/ZbBaj0QjVavVU17jL5UIgEMB4PEav14OiKCgU\nChMXg/l8Hl/60pfw7rvv4l/+5V+YZZROpzEcDlEul9Hv97n5iJTUpoXl4Eo1RzL/kmUZ9+/fP9B3\nHQwGmeZEWSsdK+lCgwJtv98/9cuaarU60cERi8VQr9ext7f31K9zgqM77e9+mtzVo0AyeD6fD6FQ\nCLFYjJWmDjvBZDIZRKNRLgV4PB7eKKgl1imYg9BRpHJSn3JCkMdORCIR+P1+nqOnrdcBPGnMeBYy\nmQy/V7fbjXA4jEgkgk6nc+qCQh6PB5IkcRZ/2Cb7h3/4hxgOh/j2t7/NnXzJZBLZbJa7DAHrHXqW\nL7Qo4/D7/ajVajAMA81mExsbGxNtmoIgIBgMwjAMtgIeDodotVoYDoec2Xq9XmYOnCbS6TRP0lwu\nxy+BhBsikQhnWWY4ITjidFOFXTAHKJfLxbXXbrfLdatqtQq32z1BBUskEhMbM4no0GbrRKvk/kDQ\nbDaRSCQOdNgNh0M8fPgQ2Wx24iLGTIU7TbTb7QPzjvQZCJqmodfrQVVVJBKJU90YnlbHVVWVSy/m\n34ESLzrFUHnLyVo2tb8TKJOl2un+S8o/+IM/AADcvHkTiqJwk8lJmoYsBVfyTne5XKjX69A0jbUn\ndV3Hd7/7XayurmJ2dpYl02KxGHZ2djAajSBJEtxuN7fCEtlY07RTyVyLxSLG4zEkSWIa0Mcff4yf\n//znCIfDyGazvABbrRb7x5thtVvmWaCs/mk4q4V+GEaj0cRiTqfTaLVaLDqSTqdRq9VQqVQmeJZU\ne282mzAMA5qmQZZlGIbh2GnA/MySyeShmYcgCLh06RJ2dnZQqVTwwgsv8MdPGyTdl0qlkMlk4PP5\nYBgGdnZ20Gw2eYMKh8O8Fnu9HsLhMCRJmghoREWzE6PR6ND6qSzLKJVKWFtbQyqVwquvvsraErIs\n8+VlqVRCvV6HJEkIhUJ8E+/Esx6Px2g0Guh2u9jc3MTFixcnSlB+vx/vvPMO3njjjYkg+9prr/Hd\nEG0k5uYOK7D0FaQ92el0UK/XEY1GkUgkWPmqWCzixz/+MV588UW8/fbb/HXz8/N8iZXNZllIpVKp\nQFVVxzNXRVGwsbHBHFuqG2qaxoXqarUKwzBQqVRYPeswDu5hR9+T4lmdLEdNvm63y+2lpwE64pkn\nqcfjObC7P+32n4Is8Jg3Wa/XHevYM3/fZx3p5ufnsbm5iY2NDSQSCce1L/aj1Wpha2uLLbUbjQYG\ngwH6/T7a7TbXhVVVhWEYCAaDCIVCSKVSnI3TqSoYDKJardre8CIIwoFj/YMHD6BpGmKxGN544w3O\nGEulEh49eoR+v4/5+XnWmSVVvEgkgqWlJcfmriAIiEQiXCc1z1kKlp///OeP/Fpzhl6pVFAoFA6l\n8z0NloLrcDhEu93Gz372M1y8eJEJt9QW9+lPfxrtdhvb29sHon0ikeCbOODxxPd4PAgGg2i3246J\naGxvb+PBgwfY2dnB3NwcK08NBgM0m01+aJlMBoFAgCXqbt++jWazyUo/BLsvOqatYxFGoxGazSZL\nyrXbbQQCgQMBzYlaJilb2YVAIIB8Pm/b9zODSOBWQJspkeVP67INAG7fvo1isQhd11loqNvtslAK\nzTtVVdHr9TAzMwOPxwNVVSckEweDAVwuF7ca24lerzfR6go8Psklk0m2e3G73SzYQ3ZEiqJA0zSs\nrKwglUohlUpB0zTbRZDMMG9A+2UQnxZr6ALMPLbZ2Vnu8iQxqGlgKaL1ej0UCgXIsjzR6WA+/gWD\nQVy6dOnIF0sfpwyGbuOcKAv89Kc/xQ9+8AP0ej1ks1k+OnU6HVQqlQlF9X6/D13XIUkSZmZmsLq6\nir29Pbz33nu4du0aH7GmtXiwgsMmWa/XY18sAKyapWka98ILgoBms4ler4cLFy5MtPk6wWoIBALI\n5XIn/j57e3vo9/tYXFx0rOFjNBod61adfL+I3ULaCE6BatZ0jPd4PKhUKlxSEwSBg2yv12NqkNmb\nSlVVXoMUOEiJzk643W5cunSJ//3RRx8hlUrxpSY9b2osCoVC6Ha7aLfbqFaruHHjBlZWVrC6uup4\na6yu6/j444+n/nyzv5ooirhx4waWlpYOJC1+v3/qU7al4GoYBqLR6MQDJnQ6HXi9XrZVOQx0pAkE\nAqhWq6hWq2g2m/jRj37Et3R2YDQaYXd3F++++y7u3r0LURSRSCRYe5KK2ZFIhF+yrutQVRV7e3so\nl8u4cOECFhcXoSgKfvjDH0IQBPzGb/wG/vIv/9K2cQKPF/POzs7EcYPUo0gDod1us+o78ISYTwts\nb2+P2Re5XM7SBLCCcDiML3/5yyf+PqPRCA8ePMD777+PQCAwtXeRFfT7fcs6uMDjjF+WZSiKAq/X\nO9HV40SnlizL+PGPf4yHDx+i2+2iXq9jPB4jnU7D5/NhNBpxizmVgMbjMcbjMctoBoNBfPTRRzyX\nB4MBfvKTn+C1116zdaxerxfD4RB37tzBz372M7TbbSwvLyMYDMLr9XLW1+/30ev1OLj7/X643W58\n9NFH+Md//Ed0Oh0sLS1hZmYGkiQdqqt6Ung8nonS5LOw/5Ty+uuv4+7du7hz5w4ATLR4T3vStBRc\nvV4vPvvZz/LNsBnTTDpJkrhpgAQ/Ll68iHw+b2uXTrfbxf/8z//gu9/9LotN12o1eDwevvEml8rD\nlJl0XUe/34csy5AkCYqioFwuo9fr4fvf/75t4wQeP1Pz0bjZbEJRFKaxud1uVpWi4ErtstFoFCsr\nK1hZWUGn05no03fiJHCYuMhxQDU4J3FY+UJRFKiqysdXer70/slplza0YrGIhw8f4tVXX0U8HsfH\nH3+Mhw8f2jpOoibquo69vT3ouo5MJsO8YUpUqPmCslgaK3VIkvcWGROqqnqAe3xSEG+0UqlAkiSk\nUin0+33U63UWa6KxulwuzqJpza2urvJ8Jt2HRqOBQqFg6ziBx3X/r3/96wAed2MpisI1ePM6otOh\nWcNDFEUkk0msrq7i5s2b+NWvfoWbN2/iC1/4gqVym8vKInS5XFUAtrlJ7sOiXRbADo8TeH7Gats4\ngfOxmvC8vH/g+Rnrr937txRcz3GOc5zjHNPh9K5Dz3GOc5zj/xDOg+s5znGOcziA8+B6jnOc4xwO\nwBJbIJVKjZeWltDr9fjG0uVyMWWEbgj3U7H2NxRQZxTx9RRFQb1eh2EYtpAeaZxHgbiBJEJCHWZH\nkcb3c05v3rxZs6v4/qyxAo9vsVutFgRBgM/nm7rhYnNzE7VazTYiaSgUGsfjcaaBud1ufu/AE2NI\n0kJ1u91MG6Lfgz6PxHqopbNQKEDTNNvGOs1zpbZiEhIKBAIHmkTMHX2E9fV1NJtNW8YaiUTGJHRi\n5lpSWzTxbs2sluFwyHOXLLhJe4A66GRZJv6so3OV3i1pRXQ6HWZcUJyg38ntdsPn8/Hv53K52EzR\n7rk6zfs/LqYdq6XgurS0hBs3bkBVVfT7fVSrVQSDQe7UOgr1eh2RSIQnrizLeP/99xGPxxEOh/Gz\nn/0Mf/7nf25lKFONcz+Ip3j//n385Cc/QTAYRDKZRC6Xw+rqKqsOjcfjCWpYr9ebWHQul8u2W8ij\nxgoA9+/fx3A4xL1797C5uYm5uTlucsjlchiPx9je3kalUsHs7CzzGknMnES47UIqlcI3v/lNhEIh\nBINBJBIJ3qRIqyGVSlnqbGq1WigUCvjiF79o61if9lwB4MMPP4Tf74eqqrh16xYEQUAul0MwGGRb\nokAgwBJ/5oVqduI4KZLJJP7hH/4BqVQKw+GQTTNTqRRqtRpKpRIuXrzI4kLT4pe//CXK5TJ+53d+\n51TmKuH999/H9vY2qtUqRqMR8vk8EokEPB4PUqkUFhYWeC2RHgUA2+fqNGM1o1gsolqtotPpIBwO\nIxqNwuPxIBaLHaBrmn3YnoZj9Zy63W7W85wGmqZhe3sbkiQhkUig0+mwX5SiKBPZj5OoVqtwuVy4\nc+cO2u02QqEQc9xIaejhw4fY29vD/Pw8e7F3u91T1/c0DAPVahWyLDP3kTIrl8vF+rONRgMej2ei\nS446eewGaUu4XC50u13oug6Px4N+v8/B1mrLaCgUOtU2U+Ax75HanEulEmfhlUqFhVJyuRzC4TBa\nrRZbhhDsZtiQDCN1a1HG53K5EIlE2JcKADfhyLKM0Wh0pBtxNBo9dVnCUqnEjhPNZpO5rL1eD9Vq\nFVtbW3jw4AEWFhbYmvuT4GVXr9dRrVa5DT+ZTCKTyWAwGPC7MOsQT4tjBVeajNMKONPuRUeWUqkE\nAExyPg3nzU6ng1KphG63C8MwuDvL7XZD0zSUy2V4vV4W8TarYZ2FOHGz2WQVr4WFBVbzd7vd6PV6\nTICm7N+cadst2GEGHeep95063Sg7mRbm3n2ndCWOQiqV4kAwHo950xoOhwiFQshkMkilUlBVFZVK\nBeVyGZ/+9Kf56+3eDLrdLpdaSLUtmUxywCURESpdAAd9qu7fv492u40rV66wItVpblokLm3uwkyn\n06zX4fF40Gg0IMsyVFVFuVxGKpXi/v+zgqZp2N3dhaqqLIUpCAKGwyE/R9JAoc93pP2VYBaTOGrn\nnPgh/18rlTAzMwNVVVEsFllgw+ng2mq1sLu7i3Q6jVQqhXa7ze18breba0X0O5kX/GkrJPV6PbRa\nLbjdbszOziIUCrF8GnXpCILAtc3TzqqpDkhavfl83vIzMi/8s+Ba9/t9jEYjzM/Ps4jzYDDgsZDw\nx/Ly8oHFb6eS03g8RqvVgq7rLCYuiiLrHZuf69NOivtb0sn147QwGo3g8/lYTjAUCsHr9bIYPpUA\nJEnC6uqqY66/VrG2tgZN0/geSBAELg8Mh0Pous6JYSQSmWhDfxYsB1cS7qW6xHERi8UgiiILZDgt\nlk2uA6IoQhRFBINB1iEFwEGW5MbMWpinnVmR3u3i4iLm5uYwGAwgiiJr6PZ6Pc4eKZM1ZzhOgtoE\nKZOmVsZPgkuCFczMzCCVSiESiaBUKvFpgE4EVH7x+/0HWnXtzAjNMp6kLme2TDpuuSyZTDoi3nMU\n6BRz6dIldk3VdZ0DF7Vwk1IWrcHTKAceBXIjppMzuaXQxRyVvHRdZ/U5K6fYYwXX0WjEYgzHBd0e\nkkSZ09mLYRhYWlriY3S9Xkez2eQAQdkzLS4K/LTITgvtdhvlchl+v59dcev1OmRZ5hdLmSotTLLb\noRqck8fB4XDIGyLVS0/67s5igZnVvagmqGkaAoEAZ5CGYbB8HgCeH3aOd7/qGWXUhzkSWMHs7Owz\nLYvsgmEYMAwDLpcLCwsLnNnTZSCZkdIpi36/QCBw6vV2QrVaRbfbZQF30j+g34PeOdkZjcfjCQfr\naWA5uJJFw0kfCk1gRVG41uEUHj58CI/Hg2vXrrG6kKZpXAsi9SmzqAs53EqS5GhZYP9OSMpdV69e\n5Y+RuAdl34FAAH6/f4LyRLJ1TthUE3w+H8LhMDqdDgRBYKHmk2atp73AKEgSotHoxIKiDYuoQ91u\nlxX47X6+dJkbDofh8Xi4Ft3tdk8UXKlm6DQo0yYRJHPJJBKJQFEU1nr1+/2cwVIJhtbYaWM0GrEO\nLpXdaO1T4memCpI0KcWJaWA5uPb7fVQqlQPq+ZqmYTAYTH2ZEovFkEwm0Wg0UKlUHDOqU1UV1WoV\nFy5cmDjeBwIBiKLItVbSeiUNz16vx4pDTkJRFACPLT50Xcf9+/eRz+cnjvj9fh+KoqDT6bBVjlmY\nmHiYTntxCYKA+fl59Pt9NJtNxONxttem08xxQA6sdmNjYwOhUAjpdJq//9raGmZmZibeq6ZpCAaD\n8Pl8HNDIUI+ePZVBUqmUrWM1DAP37t3DxYsXMTMzg/F4jGq1ikqlwpKCVkGnC6dU/m/dugXgyV0K\nBR1z+YQ4z9FoldZ08AAAIABJREFUlLM+Ap0I3G73oVZKp4FsNst/j8VibAsDgEsDoijy6ZBONVZK\nmJaDq8vlQqvVmvBI6nQ62Nvbw2AwQCQSmbpGmc/n2UtrvzX3SbG2toZ8Po/19fUJ3VYaL1GYyLaY\nakEulwvBYJCPLu12+8DNrJ2ggKiqKnZ3d1Eulw8IUlOGnU6nIUkSvF4v77Kko0sXMqIoIp/PO1In\nJs+jubk5vPDCC3C73YhEImxN3uv1jrVQIpGI7TVbwzDw3//933jzzTc5IJIjhdfrPbBp+nw+xONx\nNtP0eDxoNpvw+XzQNA26rnOAsPN+oNvtolgsYnd3F8DjNSFJEkqlEusnWwWxDpy40CqVStjd3eWA\nSnNSkiTmg7fbbfb4IvcKuoylExgZk35SzDnpgpgaYzweD1usu91utiOyUgazvAJjsRjrXZpBuzzR\nnKb9XgsLC7z72YlGo4FWq4W7d+8e8OohLcxQKARJkhAIBCYEfsfjMQzDgKIoKJVKEwr/doMy9qWl\nJYxGI8iyzAucuJjEI6UxEbG53W5zTYusKWj3daI7RRRFpFIpDk7mQBoMBvnG9TjHUbs3g+FwiH/7\nt39DKBTChQsX+Oh3WIdbPB5Hq9VCq9WCYRhsn6LrOpsB1ut1FlO3MxEIhUIIh8N49OgRQqEQer0e\n4vE4lyqOi16vZ6sAPWFtbQ23b9+eYAD5/X74fD4Wee90Ony6Ii45lb+GwyGvdaJFniZUVYXP5zv0\nlCVJEm+ctNlTWaDT6aBarVo6yR5rRsdisQkHUAq0fr/fcpBMp9OOHL3n5uYmSNS06Kl+Eg6HOYDR\nTk8ZCu1gXq8Xuq6jUCgcIOrbBdoFa7UahsMhFhcXEQgEoGkalwDcbjeCwSAzA4i6Y25uoIs3Mo90\nIrj6/f4DfkQEOgKe1QXFfpBlSjabRb1eR6vVQrvd5oyLapt0mer3+5nj3Ov1uNW41WrxRYemaWy0\naRcCgQBeeeUVXLx4kYn1Ozs7CIVCJxIUd+qSMJvNYnZ2FuVymS1caGOi9dLr9eD3+xGNRhEKhZg+\nSFkridEbhuE4jZC889xuN4+VLrHN8Pv9kCRpIus3DIPrw8Dj01Cv13OW57q//c+cCVg9Mnk8Hiwu\nLtq+KCuVCrfbiaLIth3A46O43+9Hr9dDp9OZyP4IdPQeDAbY3t5mcrndoJ281Wqh2+2i0Wjwy5ck\niS1qxuPxhCcR8DiA0CUcUUnI4fYscNqUtadhNBphbm4OKysrCAaD7EAQDochyzJzMcnNl2p/xBLQ\nNI2ZAm63G7lcDvF4HJFIxNbj9ng8PuDuMRqNkE6nJ9aE1RMBuQTYifF4jFgshk9/+tMoFApoNpsI\nBAKIRqPs7+bxeBAOh3mzp81LEAS2gBEEYaJs4CTW1tb4ORDTg4758Xicfz5trnSpSLVVsk+iU62Z\nvvks2LIaqOYiy/KxXqgTFrtU3wEeLxxFURCNRlksJhaLTZDzSUCCzAqpPEA3t+aCvN2gGg8V+A3D\ngKqqiEajCAQCiEQiCIfD6Ha7UFWVj4+tVgudTocv5ejrz5I7eFzYvchoYSuKAr/fj0AgwMR2URSZ\nzxwIBCa6xYhW5PF4EIlEmLNJpZD91uJ2jHP/ae8wR9zjlFrs7nwyNzwQ/5e62yiAUnCl50TW4LSJ\nxWIx5mXTn06C2prpvVNrOLlAJxIJ7s4jGxtq6KBSIZXuqD4+bfJiW6oRj8fh9XpZYcjKZNhP4bAD\n1KILPPHwoc4yURSZ/B6JRFCr1aCqKt8S0k5lpmU4VXinjJMWMLEYAoEAhsMhtwgHg0GmsTQaDbb4\npdY8qnmbvYDOEvvpTs+C3RsClXuoIYCebavVYmdVgjlYkt13s9nkQGuuEY5GI9vnqlPZmxO38GZ+\nOrXsUjmQ1hZdClEGCzzukNQ0jSmOgiBwcuAkzHoYXq+XNTFIYEjTNK4Li6KIfr/PFEfyraO44PP5\nJnjPz4JtwdXv90PXdfh8PlSrVWSz2alvgJ/mGHtc0K0/1Sqpi4hS/eFwyLtrJpOBx+NBvV7nNj16\nEeRh72Q2SJOSOkLIxI24rCTQ4vP5eDLPzMyg2Wyi2WwCeBx8qbzxSQiuVp+X3TxnykbNx9SFhQWU\ny2XU63UIgnCkmhtxeHVd5/qbud3Y7rnwtOBqzqqtwu4yDb0jUoki3vPMzAzcbjdfFtEJ0PycqAzT\narVYjIiSMSdBp0+yWicJzHa7zSdD4jQHAgGEw2HemKncZv7TysZ67KdPWpgE4mXu7OwwKd9sS/w0\nOFHUJtIy3QCSQybpd1L5gnq4qbOpVqvxC6eOEgrGTmEwGHBnECke0QR1u908MQnFYpHrydlsFpVK\nBYqiQBAE7iw7ByZaWoHHzQKkFrW9vQ1d17G4uHjk8ZmOt5qmQVEUvmW2O9N82txyu91oNptwuVyW\nKYF2H7mp0YKSpkAgAEmS+HKINDuIuXJYckVz2TAMNJtNboxxEvR8qVTk9XqZfUMZuK7rHFwpuXK7\n3VAUhbP1Xq9nacM6VnBttVpMGTFjfn4ebrcbW1tb2Nvbw3A4nMo7nrhkdsLtdiORSPDumE6n+bYw\nFAoxQ2E4HPLHYrEYIpEI9/AbhsH1ObsvBwiUidIE9fv9z1xExIOlI8z8/DxmZmbQaDQmLu7OCpqm\nodVq8YZ7mAj1ftitLUEBNZ1OH3ge+8sCzwLxpKnZwO6a67MWbDweh6qq2NjYYAnCaWD3CYaoVaFQ\nCIIg8IWrGdN2lVG92+maa7vdhiRJ6HQ6bJmt6zqzglqtFjeV0Bwl/RHqhJRlGZqmcRY87cnlWMG1\nUCggmUxOiN0S5ubmMDc3x73703Q5pdNp2+tYRBUzNxAc9uIFQZjIXARBQCaT4cVEl0V20m/sAtVf\ny+UywuEw0uk0QqGQ7X71ViEIAmq1Gu/0Xq+X2RZHdXHZXRagS0s7G0BoHp12cAWeXBpTs8lhl177\nYTdrhEpt+9fMSeC02JCmaXz6kySJRckVRUGz2cRoNOIM/LCx0YVyuVxm1a9p3/+xgmsgEECz2cRg\nMDiSnpRMJgE8EZp+2gWXExdaJ+F5Ev0CAIs2OHXpcFKObyKRwGAwQKVSYQEKAI42PhBI+Ibqa0QW\nV1WVbUboeNVut1Gv15HJZA6Vm3Oi7GLWYLUTdm4ELpfLUoChIEvNIrFY7Mi5OY0cqFWcRavqSTAe\nj6EoCvL5PJaWlnhzp/bXaQRyQqEQkskkvF6vpUTwWMHV4/EwidhcDyQOJh1HyDuLjq9HDWp9fd32\nY+FJivnEOzUT9Z0SlrFDXCMQCPClwnA4xPb2tg0jOxyqqiIQCKBaraJQKAB40m5JTBGi2ZhlEak1\nmpSa9gdYJy4MnRLcsXMumEtC+0HMlf2LnzRFRVHk0xVtcuZLOqe0BZ4nkGj3yy+/fOj/n7aMQW3y\nyWTS2eBK+obUQ0x6AmYJPCLhejweriOaj2jVahW1Wg1erxcfffTRJ4qATnzIdDqNvb09rK2twev1\n4sUXX7T9Z51E+YiwXzvBKeuMTqeD7e1taJqGYrHIFDKip1D2af470XUURcH8/DxmZ2dZ/Z2OtuVy\n2ZHM9a233rLl++zXyrUzeyOaGJlmUqmnWq3C6/Uyo0VVVaiqim63y92DqVQKL7zwAqLRKDY3N9Fs\nNlGpVHDx4kV0u11mkvxfxurqKkt3nhT1eh3r6+tTf/6xIprZhI4K/MQHo9t3c2vpYUTpdDrNQeCt\nt96aaKf9JICK28vLy0e2fP5fw2AwwO3bt9Hv95kXTDVpM6ghwgxRFLkjJ5lMQpZldDodRCIR3L9/\nn0nmdsKutmon64LECAGeZNrU1UT0n9FoBEmS+AL5ME3ZpaWlA6Ww03bQ+CTi2rVrtn2vXC6H69ev\nT81uclk54rhcrioA29wk92HRLgtgh8cJPD9jtW2cwPlYTXhe3j/w/Iz11+79Wwqu5zjHOc5xjunw\nyZAwOsc5znGOXzOcB9dznOMc53AA58H1HOc4xzkcgCW2QCqVGk9LzifuHfFX6QbZfMvp9Xr535ub\nm6jVaraQHZPJ5HhxcZG5ltRT7HK5WKvRbNdAfyeVKeCJkhapT1Hvv2EY2NraqtlVfA+Hw2OSQiTV\nc9KU9Hg86HQ63BlDQiT7b+LpOZN5Gj3X9fV1qKpqG4F0mvdvtlE241kCJHa+fwAIBoPjfD7P750Y\nK8RR9Pl83HlHc5D+H80Pan4hqb3RaIRgMIhqtQpZlm0Z67RrihpbyK+M5gE9U1J12v/cb968adtc\nDYVCYyLRE/WS1gn9fFo/ZlU6Gj8ZWwYCAbhcLtYhCAQCKBaLaDQatr3/aDQ6TqVSfLNPqnMAmA9O\n3YNPA0mPEheZPP90XX/mWC0F16WlJdy4ceOZn7e5ucn9uO12G5FIBMlkEuFwGIFAAIIgHPC6f/31\n160M5alYXl7GjRs3uHNIEAQ8evQIoigypzKRSMAwDCwuLiISiaBYLKJcLvNkocYIauNMJBJwuVx4\n9OgRfv/3f9+2W8h4PI4//uM/xiuvvAJJkhAOh1lHMpvN4sGDB4hGo7hy5Qr7ET0NZkEds4OsHTjq\n/e/s7MDn8+HWrVtsl0zeY3Nzc2i1Wvjggw8gyzKuXLmCt9566wCVyM73DwAzMzP4p3/6J4RCIQQC\nASQSCbTbbaTTaaaEFQoFNp4zW0Ifhq2tLZRKJbz55pu2jnWaNUUmm+VyGbIsIx6PY2Fhge1+yB0h\nm80eaEt1uVy2zdVsNou///u/h8vlwtzcHHOczR1QVrGzswOXy4UvfvGLdg0TwOMmlb/7u7/D4uIi\nOp0OyuUy0uk0b5i9Xm+q8RaLRVSrVSwvLyMSieC//uu/8I1vfGOqMdjO3C8UCqhUKmi326hWq7y7\nAWAzPUEQkEwmHbWBBialDEkZ3ePxIJfLQRRFJt+3223UajUUCgWWGEwmk/D7/eh0OuxpRdmh3SAp\nRDKkUxSF+8IXFhaQSCS4nXia3xkAZ2xOg0zmFEVBt9tFNpuFx+PhbFpRFIxGI97MMpnMqYl5j0Yj\n1Go15HI5zla63S4rMVHmmkgkDgRWVVUxGo248YX0SM0NEqcBRVGws7ODVquFcrnMNjXktExJCukp\nOw16r6qqspvHeDye2vV5P/L5PNuW2w0Suqbu0E6ng1gsNuH8+izkcjnkcjnmcpNN+DSwNbh2Oh0U\ni0Xoug5ZltFut+F2uyHLMlqtFgc4URSh6zqGw6GlX9QqSMAXeOKpFA6HD3TYkHADyaf5/X6Ew2F2\npqUXREdEu0GlAHIVoOAUDAaRSqWO1WAxHA5PJYjVajUUi0XMzMzg8uXL6PV6rCpG0o2JRAIXL17E\n8vKyo+/bDJLoK5VKAB4HWp/Ph729PTSbTSwtLcHr9SKTyRywiQcedxA2Gg3ugEomk1AUhTfZ00K5\nXIau62g2m+z/ZBgGf4x0h2OxGGZnZ7G4uGhLS/VRoLkpyzLbDx3WJDQtKOFxYl2RVxeV2/r9/rHt\n36ncQU4mU33NsX7SEZBleaJ7Yb9vDtVbqBYzGAw4M3QCZtUuUvI/DC6XC/Pz8wgGg+xf5Xa7USqV\nWM+VBHWd0Eo1K9yTD1E2m0UkEkG/3z+W3q0Tos5mkG5EtVqFz+fDhQsXoOs6bt26hb29PW7RJK93\nEp0ul8tsUew0yG2Cnm80GsW9e/cgyzJSqRRmZ2ePDEQrKytYWVnhf2ezWUcDwWGg9RQIBNjehwRx\nKIOiDb/dbvM9h5Mg2UEyG8xkMggGg9B1ferT1X7QXYHdICcJmgfmZMsqyDb8sO64o2BrcI1EIuwB\nRYV3+pMeHgURUlByUjWf1Mf7/f4zJdLcbjfS6fTETuz3+7mGqWkaK/3bDTIWJHtiURSxvLyMdrt9\n7CzE6eA6Ho9RrVYhCAIuXLjAiy0ajSKTyaBarXKpY3d3l2Xq/H4/tre3HXGnPQpUe6/Vami32/D5\nfNA0zfKzdcKg8mnw+/0s0+j3+1Gv19lUczgcsiASBQ+a606Csj+v14vhcIhQKIRgMIjRaMS6yFbh\n1GY1HA7RbrdZNtCq2LUZrVaLY9u047U1uIZCIQ5G1EtOCkkEWvD9fp8Dr1OgxT0YDKZ+6TQ+Cvyk\n9kRjbrVato+TsnqqAZLHF9WIjgsng6vb7cajR4+4551siIHHR+pQKMSuoOTtRQuSDCCdPL4CT3y8\nRFFk6/JkMjnBEDju9z0N0EmL7Ip6vR68Xi8fTem4C4CZME67UNBpk+Zsv99HKpXCeDxGoVBAqVRC\nIBCwJHnplL02JUndbheJRIK1jo9z10OJo5Vk0PYLLapdjsdjFuegOhVZKlD9g+gZTmlEPnjwAH6/\n/1gqUR6Ph51B6SXpum57cHW5XCiXyygUClwnjMVi6HQ6vDk4LSh8HJRKJaiqyoyEYrEIRVH4WCqK\nIh/JSUCF5gDpvDoJKkHRHBwOh1heXmZniZME9tO80CJIksSODmQ7QhkkZWMej4cvcZyQGxRFEZcv\nX2bHZFLqB56UC8hZwEq5z4ky0Wg0QrFY5AvWZDJpyRZ7P8wnwTMpC5jh8/nYRpduWIkHR7xB4PHu\nbLd6T6/Xw/b2Nm7fvo10Og2/38/Fd+LfPesBkfka2b2Q5JvdMm4ejwfNZhOKoqDT6WBxcRE+nw+1\nWg0LCwsIhUKo1+sQRfHMlcOGwyHq9ToMw8C9e/f44g8AW2lUq1X2JyLWRa/Xg6ZpEAQB1WqVHUKd\nBG2qu7u7AMB6sisrK+wycdxjrN2nLapfPwv0vDVNYzdTMtLr9XrMhPD7/Y4wcQKBAKvEFQoF+Hy+\niZ8jSRIHLyvv9zDu9klB62pmZoZr63QPcFwQX95RJwIz6Oi8nzQsiiJzDOno0uv1uP7a7Xbhcrn4\ncsFO9Ho9KIqCarWKeDzOVCvyJG+327h48eJTFxbJJ1KDAenUVqtVW8cqCAJmZ2fx2muvoVgsIhqN\nIhgM8jOiY3W/37ccXJ2wTgmFQrh58yYURcEbb7wBWZYRi8WQSqUwGAywu7uLZDKJaDTKLgSNRgOt\nVot9zDRNw71792zT2TwM9LtHo1FsbW1hMBhA0zQOjMR9HgwGWFxcPDaVyA5Uq1X2RQPAG9F+rd9Y\nLIZ4PM6/A5VYADCTwNwgY7eur3l9H3bsp3uL45gO2l0a8Pv9uHTpEq5cucIfo8BKpT6rJ0KXy2Xp\n5Gp5uxgOh7hz5w729vawvb2Ner0OWZYP+DaRc2okEoEkSfyLUfcRdT/V63WUy2Wrw3gqgsEgLl26\nhH6/z+r44/GYRZ5LpRIqlcpTv4fL5YLP50M0GkU4HObSht2cPLJxvnz5Mt566y3Mz88jm81yhgU8\nPjaFw2HLNsROHL39fj9ef/11hEIhptPRUTAajSKXy6Hb7TIdS5ZlDgayLPNmXC6XHalfE0gfNp/P\nI51OI5VKMYmcLoXo88gyZVrYfeT+1a9+BVmW2dq91WqhWq0eqO/RpWcikeDNy+fzseU31exVVXXE\njWLazZrE888aR5mjmjV0rYDq946VBer1OrWqsdUw1VvG4zEHIqJfUXZIdrtkA0J1K+LG2nnLSa2L\nr7766oTXvKZpUFV1qtoUPXxJkhCLxZi3afeFAdGY5ufn+WNmXx/K9ukC4ZMAasTQdZ1rwuPxGD6f\nDwsLC+j1ehywOp0OBzqPx4NQKARRFNFsNvHxxx/j1VdfdWQToAVw6dIl5HI57OzsYH5+nuvohmEg\nGAxyoLICuy/ibt68CeBxt14sFuPNqlarTXBwXS4XJEmCoijcnGFmCPT7faZqOVGnt2J4eBpUu2fh\nKGrYSebbNMaQBMvBlXrzaaF3Oh3ufgHAPFEAfDtMtrZ0a0zHFwIR9e3GtWvXEAqFuJBNNSsqTdAG\n8DSIooh8Ps+XCXa6iQKPs4H9xHrzcZB64D8JmUC73WbyeiQSQalUgtvt5glHPe9zc3Pw+Xx8qZHP\n5yeYGzQHZFnGnTt3cP36ddvHSsElHA4jHA5PHGPdbjcMw4CmafD5fMyBnPYZ2x04IpEIO3nUajUe\nl9vthiiKXLLw+XxMfxIEgUny7XZ7wnKnXC7b5sJgxllbtluFEwaN0WjUOSqWmfBLx3sq8oqiCMMw\nIAgC9/BSACOmgLnLiRaZU7evyWSSu7JIY0AQBAyHw4lmhmchEAggn8+j0+nY7qo6TXfLJyGwAk9K\nJYqioFwus3Op2YiSDCsjkQgqlQqfWEhrotvtcm//eDxGqVRyJLg+awH4/X6USiVuI7XLKvo4EEUR\noihyF5kgCDAMA4qiMCMgHA5DFEVmkVCphSiPxL+mrJo2srOAqqpwuVyOBPizBj3vaWB51ZKjp3ny\nUs82XQIR/87n8yEej0MURZ40+zscqPXTCU4mdVoRyLOceqGtBq3D1J5OE2edwdIlGxHXB4MB1/wA\n4M6dO/B4PHjllVcQCoWQzWaxs7OD3d1dZmzous4MDNronMCz5lM+n4ckSbh79y7K5fKR9bnTgFmz\ngpSu6M4CANe2zQJItDFQFisIAit3me81zgKSJKFQKKBarXLZ5bT0JMxw4meaL+ifBcvnG/rG9PLp\nqE0kZ+qNDwQCCAQCXNfKZDLIZrM8Oaizw+/3c0C2G/u/pyiKfGNdLBYtX6jQBYLdmPZ313UdiqLY\n/vOtgE4C0WiUSyX0HEkt6r333sPW1haGwyGWlpa4bEC1QJJYPE2+6GE1fUmScP36dWiahtu3b59Z\npgc8aSShurTX60UoFEIqlUIkEmGNDuDxPF5cXMT8/DyT46lpgzJcs2iRXbDy/Yj+JMsySqXSmc/b\nw3CcOxQrrbrHSoOIJ+r3+xEKhThTJQaALMv8gunzI5EId5eEQiHmdlI54TSCKwCWl9vY2MDDhw+x\nsrIydRNDMBh0hGs6bYZhd733OBgMBpAkCR6PB7u7u3wk7ff7iMVieOWVVwAAe3t72NvbQz6fZwZE\nvV5HvV7nC0f6OiewfwEcVQLy+/1YXFzEvXv38OGHH+K1115zZDxPA81TURQhSRIymQxqtRq63S5a\nrRZSqdShJ6ZYLAZJkphp0Gg0IAgCvF4vK9A5Mc79oDLP/v/v9XqhqirTGBVFYTGi08DTTnnD4ZAt\nzamTbxpYSQgsB1cKpIFAAOl0GpFIhOktpEBz1PGZeqQDgQB38NDt/WneLoZCIbz00ksArHFBiVZm\nJ4gMbhVmUZrTgiAIXEejQE+nkP0Xg3Nzc8xlpguXZDLJJ4dGowG32+1YcN2/YR12cUllllgshs98\n5jPY2dmBqqqOdQweBSoFJBIJpNNpbhhptVrMCz7qcoa+LhqNIhaLoVarQVEURzQw9sMwDN4s3W43\n0+suX76MRCKB0WiEdrvNeg7U399sNhEMBpka5xSetrZJl2E4HHLjRSAQgKIoEAThyCSKSi/TwHJw\npaN8PB7H3Nwcf9yKslUwGOQ6LB0tz4pmZDVjtlu8YzweW8pIx+MxdnZ2MB6PEYlEnqr2ZTf2TyrK\ntAj79QKIehcOhyHLMtbX15k7nMvlkMlksLe358hYD3Nr2D/H9mc28/Pzp6YbYAYlK3NzczwfSU9g\nWpBGsiRJ0DTN9k5C4OD7LxQKLNQtSRKCwSCKxSK+//3vY3FxEeFwmDNEVVWhaRry+Tzi8Ti63S42\nNjbg8/kcqw0TdYz4wQB4vC6XC8PhEHfv3sX29jYikQhWV1cRi8UgiiIURWGngng8zu9FlmXnLrT8\nfj/m5+dPrGpE5HkqKzwvsML1mxbLy8sHPrb/8qrRaKDRaEAURaiqykIkkiThwoULSCaTXDN06tJr\nP3fS7/ejVqvxMU8URaa90YZB3W2iKGJmZgb1eh27u7uWpNuOg/0LYNrNW5ZlzmJOCysrK1hdXT3x\n8yBGTjwed+QkSHN/PB6jXq8za4ROUIZhwO12o9fr4eHDh6zoFQgEkEql0Gq1mJZJnZOUyTpRfyfd\nDkVRWNCd7oVIIEnTNObuU7ceCfkPh0Ooqop+v890yUaj4Wzmaqdc3GmXBE6CUqmEer1u6/ccDAYo\nl8tMZO/3+ygUClxzo2YHcisAntC3BoMBtra2UK1Wcf36dczNzU0Ef6ezsFAoxGMyKzKZyxWUVdPv\nQjq13W4Xqqpa7jqbFlYvp1RVRa/XgyzLqFar8Hq9tlvPHIXPfe5ztpV4aGN1Yk11Oh3s7u4iEAig\nVCrB5XLxSY42hkgkguXlZb689Hq9GAwGLOw9Ho+ZPUDZ5DRaH8dBt9vFzs4OHj16hG63y5d+lJGS\nqBTpjtA9gNfrhSRJyOfzGAwGqFaraDabiMfjlrrPzpxA+TwRk2dmZvDVr34Vf/qnf2rr9zXvhJVK\nBYqicBYQDAaZskR17fF4DFEUkc1mEQ6HuQMKmOzRPo0TAdVg3W73U7M9IvMDp3Mxd9jvvru7i1ar\nxZYfpNlAXGuiQAHARx99hBs3buCNN97AK6+8AkEQsLm56YgOrRNrwIlnrOs6vv3tb/Om6vV6+ecY\nhsFt5qQkRwwI4rmTyeLHH3+Mq1evIhwOo9vt4n//938dqREXCgXcvn0blUoFwWAQsViMPeooORFF\nEblcjoM9bQbEzKESFuHNN9+cukPvzIPr8wa7BT50XcdHH30En8/H+gflcpl3VbrJJJk5ulAcj8do\nNptIpVLI5XLcdWYOKnbXsuzIhOv1+rEV661gfxlgb28Pu7u7fBFIQdTMMaU/vV4vLl68iEePHrFm\n7dLS0qmM+5MMt9uNarWKR48eIRKJIBAIoFAoTMwLmqs0DynQUhu5qqq4d+8eHj16xE0+tVptIkGw\nA6PRCM1mE++99x4URWFDxd3d3YkNAHjC3Sf6miAIvKbq9Tozm4LBIB48eHBAR+UouKwsGJfLVQVg\nm5vkPizaZQHs8DiB52esto0TOB+rCc/L+ween7H+2r1/S8H1HOc4xznOMR2ej5ukc5zjHOd4znAe\nXM9xjnOcwwFYutCSJGmcTCbZB8ss4EJ/J28f4jcSqFBMHzd/nWEYKBaL6HQ6tlxvJxKJMTm57v+P\nxkLdZEQ/6QwoAAAgAElEQVRrok4h+jxzgZu+htp019fXa3bVh6LR6Hh5eXkqeodVU7/NzU3UajXb\nKAOpVGrslGvrWY/VPDeeBTvHGo1Gx7lcjuebeQzECqE1Mw2rgKQLaT7dvHnTtrkaCoXGsViMWSHm\nS0NaM+aPkzre/vGRlxrwmHrY6/XIg+0TP1cNw0C5XJ7q/VsKrvF4HH/0R3+EWCzGtAtSRiL/nNnZ\nWWSzWdy5cwelUomFk8k3nqT+2u02stksMpkMbt68ib/92789/m+8D3Nzc/jrv/5r5laS1B3ZKfd6\nPbRaLfZ9evDgAXq9HoLBIEsk5nI50EZC3L7XX38dlUoF3/jGN2wrlC8vL+PWrVsTXSQEcn3IZrPY\n3d1FNpud6Ip7FuzmaS4tLeHGjRtTfa6qqrwZTNNOehZj7ff7zF1WVZU7d/x+P+tkuFwuxONxLCws\nIB6PYzAY4Atf+IJt48zn87hx48ahgcgOuFwu2+ZqPB7Hn/zJn+DNN9+E1+tFJBJBOBxm51+iXOVy\nORbRnwa6ruOzn/2sXcMEACwsLODf//3feb2YA/pJ8PDhQ3zta1+b6nMtBddAIIDXXnsNCwsLzBEj\nEzpyIKBd7Y033jjy+5TLZezs7GBlZQXxeByCIOBb3/qWlaE8FaIoHlgApCdKmqNmgeput3tAmWsw\nGKBUKqHZbEIQBMRiMTx8+NB23U/KMKrVKgs3q6oKWZZx69YtVpyXJAmvvvoqgMf9zffv34fH48Hd\nu3dx9+5d1Ot1LC8v43Of+xySySRWV1dtHee0IBUkshtxu92IxWKIRqMQBAHtdhvj8Rj5fP5MNVSB\nx1mUYRhot9uskBSJRJBIJBCJRBCLxVilyinO8GAwwM2bN5FIJFggO5/Po1AoYHZ2Fj6fz5KOBHlp\nSZKE999/39axEn2p1WqxEL4gCJBlGclkEoZhcPvotM/LLJFoJ7rdLm7fvo179+5haWkJsizj+vXr\nrJn7LHQ6HXz00UdoNBqYm5uDKIoolUr46U9/6kyHFh05ut0ulwFIp9PKrpBIJDAcDpkz6kRL6X6Q\n6ywJxhAMw0C/38f29jZarRYymQzcbjf3QjcaDeRyOfT7fSiK4pjQSCKR4OcaCASwtrYGTdMwMzOD\n8XiMr3zlK/y59Xod1WoVOzs7SCaT+MpXvoKLFy/y71UoFBwZ47PQ6XS437zT6XBZyOVy8SKiDJ06\nZs4S1WoViqJwCyRxhUejEVqtFlRVxczMDILBoGPB1eVyTbSVCoKAer3OUpyAtSYDRVG4a8qJLi1q\ntiBOMDUT0Bhp/UwLKiPa/XyJ8x0MBll9j4RapoHf78cbb7yBvb09zM3NcatvpVJxNrj2+30+Xu8X\npJ4GjUaDfYIA+1L2p8HtdrNOplkhnYRjVFXFw4cPsbm5yZmLLMtsD+PxeLjM4AQCgQAr9JA+ai6X\nQzqdPiCDFwgEkMvlYBgGlpeXJ5T8O52O7a6f0+L/180xGAzQ6XTQ6XTYwFDXdRbrCYfDZ96Z1+/3\noaoqFEVBs9lEu91mNwVq4KBW2Pn5eSwsLDgmkEP22GSZRHcSZi+1aWEWgXdCEIWCK/B4Y1BVlUt9\n1FF4HDix/lutFrLZLCdQgHVzSSorRKNRfO5zn8P3vve9qb/2WB5a5l3gOGZt5v5c8uNyOrg+TWVI\nEATMz8+z2PDMzAw72prb8p5maGgHQqEQCoUCdnd3kUqlIEkSLl68OBGIisUie3lduXJlomuoXq/j\ngw8+wMsvv2y7etezUK/X0ev1+NKSat10DKNWU8okSCvBqjmgXSBtYY/HwxqotBEMh0O0Wi34fD5H\nJfEIFFCpZDUajbC9vY1kMmk5WJlbkO020yTQmiVJQbp0O8m7dGr9U5srJVQkg3mc90r192nHavk3\nIg8fOsIcR/iW+nyBx7uLU8INhGnEQTKZDFZXV9ltMxaLcS8y+UT1ej3HJoGu62i1WtA0jTVSr169\nOnF0vnfvHisLmfUxaRGRTuqzbMOdAEkJUn82tevSSYfUiCibbbVaE6eXswDVVSVJ4iRhMBhwn3y3\n20W/3+e6LGC/GA7p+eq6Dp/Ph1Qqhc3NTRQKhWMJs5sDqt3B1eVycXnN3EJKTIaTeGY5sf7J2SGR\nSCCRSECWZU6OjivARJof08By5kqLhPzTj5vJUYbT7XZ5MToBK7YMZpBeKmUVrVYL3W7XscyVhCLI\nhE7TtImM9d69e7xYDMNg37HRaIRKpYJ2u42NjQ243W6+nT/N7rtIJIJQKMQeQ5qmQdM0fl7kFU8l\nA0EQmHFyVpKT8Xgcuq6j3W5zQCUbczMVjwIu4EwQIGeGWCzGGf/Vq1f5/ZfLZQQCgalYF+aTpBNZ\nN9210AnWLM5ykrKJU04kJDyeTCZZ4rDX62F3d/dYWhFW7GospWEkMNvr9eD1evnoedwshLhx+w0P\n7cJgMDh2PZeOsYPBgO1InHCqpd+71Wpx4b1UKk0o8ezt7UHTNL7dpuPqcDhEOBxGIpFgyhvRhoDT\nUcUyw+12IxqNsj8aMTAoi6X/qK5Jk/2sIAgC5ubmEI/H+YRCPmmiKLJjBlnZODFeSjCi0Sjcbjcq\nlQouXLiAT33qUwAev/tyuYytrS3LwuJ2WxLRRmh+v3QhZebpkteXFdh9IjS7TJPf33A4hN/v5wTx\nOL5ejtm8kBlds9lk6shwOESpVIIoimxEZxVUZrAb6+vrGI1GePHFFy1/LdUHKRBQBmw3W6BWq7Fr\nqtfrRaPRYDNHAjl9DodDLC8vc5G+Xq9zdpNIJHgnPs5FiJ2IRqOIRqNotVpcSiG3CVpE3W6XywTH\nqdvbBa/Xy2N1u91otVoYDAacSJD4N9WO7X6uoihidXWVN9O1tTVcvnyZ/7+maRgOh2z0aQV229XQ\nhdWlS5fQ6/XYvkWSJC5f0SZERpZnBbpHIYpYv99nK5p2u42lpSUOsPl8furgbiXbtRRcRVGE3+9H\nJpPh2mSpVEK1WuV/WwHRc5zIXAuFAj788EPMzs7yx8jOYRrZQNqdO50OdnZ24PF4mGRuJ+jG+rOf\n/SwGgwFu3bo1wQ64ceMG8vn8hB4qACa7r6+vo9VqQRRF9Pt97O3todfr4Utf+pKt4zwMvV4Po9Ho\nwHGQjBxjsRi8Xu+EfiaJahPdrVqtnholi4K5JEkTiymZTPKm6XK50Ol00Ov1uCRAtvFOWMB7vd6J\nU8rFixcn/n8wGGSSvlV3BLtLWOTqTElUv9+HrusH2B8kn3nWoHVETAxJklAsFjEej5HNZjEcDrGz\ns4NCoTB1YmglgbQUXEkR//Lly0z3GY1GCIfDTGOxQrEhc7iTFMKPwvr6OgDwbl8oFLC9vc3Gb9M8\nJOLJUacOHcvtRDQa5e4Uj8eD69ev8wIuFAp48OABIpHIgc1LVVUUCgWsr68z13hpaQnRaBQ3b948\nsEjtwt7eHrLZLE9YygrM75Dqh3TRQUZ71NFHXXBOM0VKpRIajQYikQi3XxM9ySwmTfV0omDtr7kC\njzcSXdcd83s6CgsLC8jlcscqR9mtPezxeCZOGeQvZQa5OZ8Gy2JaUMY9HA6xsLAw0aa7tLQEXdfR\n6XSmqhlbycYtBVeanOaFND8/j2AweKJJ58ROR35NAJg3KooidF2Hx+N5ZnClWm0qlUI6nUaj0WCq\njp0gtsWNGzfgcrkmslZVVZFKpQ6UIgqFAtbW1lAsFjE3N4eFhQUMh0Nks1lcuXIFu7u7KJVKto6T\nUK1WOasjOh7xMoPBIH+MapcEOmaTkwL9vdlsOhaw2u02Njc3+Sjv8Xjg9/uRTqeZimXOvrPZLFRV\nha7rrD9BjAFqnjmLEoaZpG8FdtPcXC7XRLnqMFDzwyfBuqnVak2cio7K5D0eDxRFmegwPQpW1pVl\ntsDKysqBCB+NRnlBHQfEh7QTFy9eRL/fx4ULF5iu5Pf7OSsxDOOpD5ImhyiKmJ2d5Vtupzq02u02\nFEXBwsIC0uk0NjY2EAgE8PnPf/7ApJidncVwOMSFCxcwPz8P4HHQI/+f69evO2JSOB6Poes6ZFnm\nSzUaW6fTQSaTYbYAdcMFAgHO/IkhQgGZLoqcCq7khUTvW9d15jtTQkB0HVEU+dabFhuVB4bDIZ9c\nrBjU/bphNBpNtbl8EgIrANy5c+epbfgEv98/NdPBCoXL8go8TDjkpCaDZssFO3HlyhUAjx8eXUzR\nAtJ1feoa1oULFxCPx/HLX/4S5XLZ9nECwNWrV9FqtVAul9FoNNDtdjE3N3fkGCmoAuAefurwmp2d\ndcynigINEcipq8jlciESicDr9UIURc4Y6Djr8/k4wFFApf+smglOi16vxy665o2AKGKpVIq5uW63\nG5qmcV14PB5z0KUAS0yZT0I98SxAzSHPC57F86V6sRXQKXga2JbenCS4noaNcTKZRLfb5aOC1ZvU\neDzuqMc6ZU7hcBjBYBDlchmbm5swDGOihEENEbTTKooCXdcnaE7dbtdRE0CzPB8F2m63C8Mw2FBR\nEISJi6PRaMQUPuolJ56rU1bg5I9lpoQR37bb7aJYLGI4HCKRSECSJAQCATSbTQBPuKe0mOjUchZZ\nq6Zp6PV6TOCnUyLVkk8TZ8VJPg6etQmGw2Gsr69D0zRcuHBhqphgxa34E2FQ6HSHFiGVSmEwGCAY\nDB6rdubkcYduYim7yufzKBaL+M///E/k83m+/ff7/Wg0GtzJRfVEXdc563IqAJg1canxg5xpicYk\nCAKy2Sy/T8oM9r9f+rdT1tr0MwRBgCiKfLqizJN0JsxNJsTdjsViqNfr3NFD/1EmfNoBJhKJYHt7\nG36/H8PhkE9gmqYhkUicSnJCsJq1G4aB0Wh0JiI903Sorays4Ec/+hF+8IMf4MUXX8S1a9ds+/mW\ng6sTR6LTmqzUokn8Rasv3AkqDmF/zUcQBLz00kvY2NjA5uYmVFXlnTWRSEw0R5AXOwVYp0DNAFT4\np6M23bJTbdMwDB4rOX/6fD74/X4Eg0F206Rs0ilBFLPecDAYhNfr5S4xwzAmShlm+Hw+5HI5DrKN\nRgNer5cz7dOqKZq1LBYWFrC1tcUMDappt9ttxOPxY7WhWwXVna2ApPo6nQ4SicSpbkzm90SZ/2Hv\n7u2338Y777yDzc1NyLKMubk5LC0tHfq5VjYyy8F1modDxz8rD/K0Hno0GkWn04Esy+h0OpZIwcTz\nPS2Ew2F87Wtfw9raGhRFmTi27H/xoVCIW2ft7iIjUIeO1+tFMplENBrl4z2xAPx+/6HHK7o0CAQC\nzIMlWUenGh78fj/i8Tg3WFh9d8TplCSJM1knywJUM+92u2g2mxBFEd1uF5lMhksXd+/e5TZdr9eL\ncDiMZrOJUqmEdDqNVCrFAZlKHHZiWu4sbQydToepeO12m/UcTgPlchmapiESiWBnZweiKKLRaMDl\ncmF5eZlb7yVJwuc//3ncv38fjUYDtVoNqqoimUxO3G0AsHTitRxcn3WMMwwDzWYTkiRZKhaf5o7m\n9/sxMzNj2Ss9GAye6hEMePxcXnjhBQDA+++/D1VVcenSJeTzeSZBC4LALZzE4wXs18mlLJPExqlW\nauUEQKLq1PdNm4ITGI/H8Hq9iMViJ9oUA4EAZmZmHK9xNhoNvPfeezAMA51Oh7vHCoUCNE2bcHcI\nhUIYDAao1Wos5t5ut7l7MpPJ2B5czWUhMwqFAtf8Q6EQut0uarUab0Q0Dp/PB13XoWkaUqmU42uJ\nBNs9Hg+CwSBKpRJ0XUcqlUKxWGS6XbvdxszMDJLJJKrVKgs2dbtdrK2twe/3Y3Z2lrnZ08JycO12\nu6jX6xOBhsQQqD+bOjeoNkQ77FFwSryDaoFHfW+rN4WpVAqqqtoxtCPRbrextbUFSZK4xuf3+1Eu\nl/Gv//qvuHv3Lt5++218+ctfRiwWgyzLePToESRJwksvvYRkMjlxiWQnXC4XMpkM0un0iS6hgsEg\nZ1+ANTFoK1hYWGCtg5PA6/XyonKS51qtViHLMsrlMo9dURS02220Wi0UCgWmu7lcLhZzIVobnQ5I\nwcsJdbT9VMTd3V1sbm7yu5QkibWPSYvBnMXSv71eL7d8O4F2uw1d1xEKhbC1tcWskQcPHmBtbQ0+\nn4/r78FgEHNzc8hkMhiPx9jY2EA6ncbc3BzS6TQURUGpVEIsFkOxWJx6DJZXiCzLuHDhwsSuQ502\n9BBpcdMNa7/fRzgcnmhFNaPb7TpSKyQeo10wGxk6ga2tLXznO99BJpPBZz7zGYxGIzQaDTSbTUQi\nEbz55pt4+eWXsbi4iHA4jHg8jtFoxC19dAQbDocsYGw3FhcXT/w9BEFANBpFrVabEF92AnYR6Smg\naZpmy/fbj3v37uGf//mfMR6PMTc3x626mqah0+kgFAoxbSwcDmMwGKDRaMDtduPq1avsaEC1ZeBw\n2uRJMBqNWJRlOByiXq9jfX0dhmFwgxE1lFDgom48aiohMXWaA04F136/j93dXbjdbmSzWWxubrJQ\nC50A4vE4IpEIN0U1Gg0IgoB8Ps9uFLOzs1zP1jQN29vbU4/BUnAlug0RxYmcTxOAdiXzrSrtUJqm\nsWXCfjh1k2j3i2u326jVarZ+T8La2hr+5m/+Bp1OB7/927/N7gKkHkUeVPuRy+UmetPN+CS1IO7H\n/p765wF+vx/Ly8u2f9+bN2/iF7/4BTqdDtej6XgdCAQQj8c5aNIlIFGx6vU67t69i8uXLyMSiaBQ\nKGA8HjtS16Tgube3B13XWSbTrA2yX/fWrKQHgOl5+0sMdp+yZFnGzZs38R//8R/I5/OIRqPQdZ0p\ngqR8RgySRqPBm8FwOGSGxre+9S381m/9FlKpFMrlMq5du4af//znU43BUnCl48fGxsaEbBdRHug2\nkR4U9WZTTcjj8WBnZ4cDxtzcHFKpFOr1+qnXMo+DaDSKt99+25Hvff/+fSQSCfze7/3eBB3ESvfI\nOZ4/KIqC73znO3jvvfeQSCRw+fJlPHz4kMsuRBUzy12a3R4GgwGT4fP5PPb29rC3t4e/+Iu/wDe/\n+U1bx0rZ8jvvvAPDMLh5Zf+mT+uf/iRZxfF4zApaw+EQv/jFL5BKpZDP548lU/g0UEBXFIV96Khp\nhMZEf1LHIPDE7406IEVRRLFYxKVLl/CpT30Ksizj3XffnWoMLivUCpfLVQVgm1XvPiza5a/u8DiB\n52esto0TOB+rCc/L+ween7H+2r1/S8H1HOc4xznOMR0+GQoL5zjHOc7xa4bz4HqOc5zjHA7A0oVW\nKBQaJ5NJvnwi2hWJbxAVy+ot/Wg0wvr6OhRFsYXsGo1Gx9lslgU7gMe301QCOS49iy7uPv7445pd\n9aFUKjVeWlo6UGCnXnYSY2m328zZpdZHes50gww8KeR7PB5UKhXUajXbCMSJRGI8NzfnCNdzc3PT\n1rHSc50W+y9i6WOHtbvaOVar47SKmzdv2j5XnwbStzAzVeiCaH9coDnt9/uxsbGBer1+Zu/fCqZ9\n/5aCazqdxje/+U3Mz8/DMAyEw2FW66fOILP/z7R477338NWvftXy1z1tnH/1V3+F+fl5CIIARVFw\n6dIliKIIURSP5Xygqip3zSQSCdsK5UtLS7hx48bEx0jQ+9GjRxAEAR988AEGgwGuXLnCVJdIJMJc\nQ9IpjUQirEP64osv4utf/7pdwwTwmEbzZ3/2Z0yuXl5exqNHj6DrOnK5HGq1GkKhEARBQCKRgMfj\nwWg0QrlcRqVSwXg8xqVLl5DL5dBsNvHuu+/iN3/zNxEOh/H666/bOtbDnut+jMdj1Go1bG1toVKp\nMPeRaG/9fp+F1c1t0naOdZpxAmA9Bk3TUKvV0Ov1EI/HEQgEJnRp6UbctPHaPldJIrRarSKTyUyI\niG9tbSGXy00E142NDbzzzjuQJAkvv/wyMyDq9Tqy2SwuXLhgu3PG0tIS3nvvvQOJlKIoiEajaLfb\nWFtb44YWt9uNlZUViKKIZrPJ3F0SBzc3Ok37/i03ERAdhLyGiFMnCMKx+apOEN6pSyyVSjHXzufz\nHdtShnyCTkNyjvruu90uXC4XwuEw8/HcbjdGoxH29vbQaDTQarVYUzUej0NVVQyHw2ObRT4NFDSB\nx115ZOeysbGBTCaD69evQ5ZlGIaBSCTCCy6RSCAUCqHf70NVVSQSCYTDYXzqU586U31QRVHQarWg\n6zoajQbLJQaDQQ4azWbTUTGcaTAej1GpVNDr9fh9U0NDt9tFMBhEr9dDJpOB1+tlbQ8nQR1iwGTX\n2mFNJtQB1Wq1UK/X0e128ZOf/ASNRgNf+9rXAFhzVbUyxv1QFIVbYYnTSqpd9HvQpmV+hsfpID2W\ncAv1bJu7LSKRyLH5mKIo2t7+SjxcenBmkzKrIO+s8Xh8asR8URQxGo2QSqWQyWTQaDTg9/vh9Xoh\nyzK7DpDiE2Wu4XAYhmE4ZgNu1kOlJhJRFLG0tMTB97CyC9l9m+HEBjAtut0uyuUyZFlGsVhkIRQq\nsZByWrPZZCdgCr6nDVmW+XnLsgxVVeHz+dg8keaA2+12tKWUUKlUWGthGut6l8vFbcgLCwvIZrN4\n8cUX8b3vfW+CC2s39o+r0+lAVVUIgsCtwWTz0+122SyTNtOT8suPFVwpEJr7rU9iMmg2grMLRAbW\ndR3pdBr9fh+apiGTyRzre1Hr62kF1+FwCFEUOROYnZ3lIE/Sf4lEgsUyPB4PC2TMzs4iGAzaPmFd\nLhc7kQ6HQzQaDdy5c4etdOhZPw+o1+vQNI0zU3LP1TSN7VxokyLt17MIrAC4zbXVavGJhqyi6TSV\nTqe5PXVubs6yDbcV9Pt97OzsYH5+nttun7YuJEnC4uLixMmHLMVJ1es0ToT1eh2j0YiFccz+aCTK\nEgwG2R9uf/ZqFZaC63g8Rrvd5sF4PB5Wzz8JSJneLrhcLrbu6PV66Pf7iMVifKQ+TgkiGo2ycv1p\nQNd1tqkhUH2bDPZo16U6J5knOhUI3G43YrEY943fv3+f2zN3dnZYzPmsgpAVUGLgdrsRDoe5a5Ay\nfjo50IWtLMtIJpOOZ4WHjZPUpMx6slROofk9Go2QzWb5jsHJ4EqZHgCW73xaKzMZQZrnxebmJp8u\nnWop3w+zni9JIJJ7MV0Q06bl8/lQr9eRSCSOnVBZjjL0MoEnAhwntca2u72TJiDdtANPbF42NzdZ\ne9RqZkfOlk6j3W4jFAoduQmQJiqJ/5LAR7vd5lp4oVBwJBugLE7TNBiGgRdeeIHbDOnI9TzA7GQs\nSRJn4/1+n8sdNHeo1mnFP8kueDwehEIh7oWnU4p5/tKpStd19jhzEoPBgBMqt9s9lUbEftYFqepp\nmsYZpdOg0140GmWNA1mW2deNlPxo4x2PxycS6rEUXMkyg4SSY7GYLeIbkiTZvijz+TyuXLnCbqqU\nkXS7Xc4CrF5UkHusE6BMwDAM9Hq9Z14OiqLIdUAyJySxHF3XUalUbM+yqY+chHtWVlbQarWwubnJ\n9e319XVsbm7a+nOdQiaTwczMDNvr+P1+9jIjO2u65Oj3+2cSXIHHzz0YDCKZTCIcDrPtDB1fyRFC\n0zT0+32uGTuFDz/8EA8ePMDDhw/5+ei6jrt37x4ZJKPR6ERwzWazWFxcRK1WQ6vVcjS4DgYDdm3N\nZDKQJAmzs7Pwer0cC3q9HpeBgCeiR1QmOg4slQXcbjfS6TReeumlY/2wo2C3zzmJ25ovUTqdDlZX\nV1EqlVCtV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_layer_output(output_conv1, image=img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the same image as input to the neural network, now plot the output of the second convolutional layer." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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EjtJ5gvngxkHjz3r33XclxSfdxXbnTUI6nda+ffvKVscffvihpLjD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\nbHxff54BXDfmAc+sfM8u5oNbX5ifa2t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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_layer_output(output_conv2, image=img)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Predicted Class Labels

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the predicted class-label and class-number for this image." ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "label_pred, cls_pred = session.run([y_pred, y_pred_cls],\n", " feed_dict={x: [img]})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the predicted class-label." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.042 0.098 0.132 0.053 0.04 0.008 0.099 0.38 0.119 0.029]\n" ] } ], "source": [ "# Set the rounding options for numpy.\n", "np.set_printoptions(precision=3, suppress=True)\n", "\n", "# Print the predicted label.\n", "print(label_pred[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The predicted class-label is an array of length 10, with each element indicating how confident the neural network is that the image is the given class.\n", "\n", "In this case the element with index 3 has a value of 0.493, while the element with index 5 has a value of 0.490. This means the neural network believes the image either shows a class 3 or class 5, which is a cat or a dog, respectively." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'cat'" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_names[3]" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'dog'" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "class_names[5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "

Close TensorFlow Session

" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now done using TensorFlow, so we close the session to release its resources." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "# This can be commented out in case you want to modify and experiment\n", "# with the Notebook without having to restart it.\n", "session.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "SUMMARY\n", "
\n", "\n", "This tutorial showed how to make a Convolutional Neural Network for classifying images in the CIFAR-10 data-set. The classification accuracy was about 79-80% on the test-set.\n", "\n", "The output of the convolutional layers was also plotted, but it was difficult to see how the neural network recognizes and classifies the input images. Better visualization techniques are needed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "CHALLENGE\n", "
\n", "\n", "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", "\n", "You may want to backup this Notebook before making any changes.\n", "\n", "* Run the optimization for 10,000 iterations and see what the classification accuracy is. This will create a checkpoint that saves all the variables of the TensorFlow graph.\n", "* Continue running the optimization for another 100,000 iterations and see if the classification accuracy has improved. Then try another 100,000 iterations. Does the accuracy improve and do you think it is worth the extra computational time?\n", "* Try changing the image distortions in the pre-processing.\n", "* Try changing the structure of the neural network. You can try making \n", "the neural network both smaller or bigger. How does it affect the training time and the classification accuracy? Note that the checkpoints cannot be reloaded when you change the structure of the neural network.\n", "* Try using batch-normalization for the 2nd convolutional layer as well. Also try removing it from both layers.\n", "* Research some of the [better neural networks](http://rodrigob.github.io/are_we_there_yet/build/classification_datasets_results.html) for CIFAR-10 and try to implement them.\n", "* Explain to a friend how the program works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "LICENSE (MIT)\n", "
\n", "\n", "Copyright (c) 2016 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 1 }