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Morten Hjorth-Jensen b3793b828d Merge branch 'master' of https://github.com/CompPhysics/MachineLearning
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
<<<<<<< HEAD
"<!-- dom:TITLE: Week 42 Convolutional and Recurrent Neural Networks and Autoencoders -->\n",
"# Week 42 Convolutional and Recurrent Neural Networks and Autoencoders\n",
=======
"<!-- dom:TITLE: Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders -->\n",
"# Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders\n",
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Oct 15, 2020**\n",
"\n",
"Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
"\n",
"\n",
"\n",
"## Plan for week 42\n",
"\n",
<<<<<<< HEAD
"* Thursday: Convolutional Neural Networks and examples\n",
=======
"* Thursday: Convolutional Neural Networks and examples. [Video of Lecture](https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage)\n",
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"\n",
"* Friday: Recurrent Neural Networks and Autoencoders\n",
"\n",
"Reading suggestions for both days: [Aurelien Geron's chapters 13 and 14](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf). Autoencoders are discussed in chapter 15 of Geron's text.\n",
"\n",
<<<<<<< HEAD
=======
"**Excellent lectures on CNNs and RNNs.**\n",
"\n",
"* [Video on Convolutional Neural Networks from MIT](https://www.youtube.com/watch?v=iaSUYvmCekI&ab_channel=AlexanderAmini)\n",
"\n",
"* [Video on Recurrent Neural Networks from MIT](https://www.youtube.com/watch?v=SEnXr6v2ifU&ab_channel=AlexanderAmini)\n",
"\n",
"\n",
"\n",
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"\n",
"\n",
"\n",
"\n",
"## Convolutional Neural Networks (recognizing images)\n",
"\n",
"\n",
"Convolutional neural networks (CNNs) were developed during the last\n",
"decade of the previous century, with a focus on character recognition\n",
"tasks. Nowadays, CNNs are a central element in the spectacular success\n",
"of deep learning methods. The success in for example image\n",
"classifications have made them a central tool for most machine\n",
"learning practitioners.\n",
"\n",
"CNNs are very similar to ordinary Neural Networks.\n",
"They are made up of neurons that have learnable weights and\n",
"biases. Each neuron receives some inputs, performs a dot product and\n",
"optionally follows it with a non-linearity. The whole network still\n",
"expresses a single differentiable score function: from the raw image\n",
"pixels on one end to class scores at the other. And they still have a\n",
"loss function (for example Softmax) on the last (fully-connected) layer\n",
"and all the tips/tricks we developed for learning regular Neural\n",
"Networks still apply (back propagation, gradient descent etc etc).\n",
"\n",
"What is the difference? **CNN architectures make the explicit assumption that\n",
"the inputs are images, which allows us to encode certain properties\n",
"into the architecture. These then make the forward function more\n",
"efficient to implement and vastly reduce the amount of parameters in\n",
"the network.**\n",
"\n",
"Here we provide only a superficial overview, for the more interested, we recommend highly the course\n",
"[IN5400 Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html)\n",
"and the slides of [CS231](http://cs231n.github.io/convolutional-networks/).\n",
"\n",
"Another good read is the article here <https://arxiv.org/pdf/1603.07285.pdf>. \n",
"\n",
"\n",
"\n",
"\n",
"## Neural Networks vs CNNs\n",
"\n",
"Neural networks are defined as **affine transformations**, that is \n",
"a vector is received as input and is multiplied with a matrix of so-called weights (our unknown paramters) to produce an\n",
"output (to which a bias vector is usually added before passing the result\n",
"through a nonlinear activation function). This is applicable to any type of input, be it an\n",
"image, a sound clip or an unordered collection of features: whatever their\n",
"dimensionality, their representation can always be flattened into a vector\n",
"before the transformation.\n",
"\n",
"\n",
"## Why CNNS for images, sound files, medical images from CT scans etc?\n",
"\n",
"However, when we consider images, sound clips and many other similar kinds of data, these data have an intrinsic\n",
"structure. More formally, they share these important properties:\n",
"* They are stored as multi-dimensional arrays (think of the pixels of a figure) .\n",
"\n",
"* They feature one or more axes for which ordering matters (e.g., width and height axes for an image, time axis for a sound clip).\n",
"\n",
"* One axis, called the channel axis, is used to access different views of the data (e.g., the red, green and blue channels of a color image, or the left and right channels of a stereo audio track).\n",
"\n",
"These properties are not exploited when an affine transformation is applied; in\n",
"fact, all the axes are treated in the same way and the topological information\n",
"is not taken into account. Still, taking advantage of the implicit structure of\n",
"the data may prove very handy in solving some tasks, like computer vision and\n",
"speech recognition, and in these cases it would be best to preserve it. This is\n",
"where discrete convolutions come into play.\n",
"\n",
"A discrete convolution is a linear transformation that preserves this notion of\n",
"ordering. It is sparse (only a few input units contribute to a given output\n",
"unit) and reuses parameters (the same weights are applied to multiple locations\n",
"in the input).\n",
"\n",
"\n",
"\n",
"\n",
"## Regular NNs dont scale well to full images\n",
"\n",
"As an example, consider\n",
"an image of size $32\\times 32\\times 3$ (32 wide, 32 high, 3 color channels), so a\n",
"single fully-connected neuron in a first hidden layer of a regular\n",
"Neural Network would have $32\\times 32\\times 3 = 3072$ weights. This amount still\n",
"seems manageable, but clearly this fully-connected structure does not\n",
"scale to larger images. For example, an image of more respectable\n",
"size, say $200\\times 200\\times 3$, would lead to neurons that have \n",
"$200\\times 200\\times 3 = 120,000$ weights. \n",
"\n",
"We could have\n",
"several such neurons, and the parameters would add up quickly! Clearly,\n",
"this full connectivity is wasteful and the huge number of parameters\n",
"would quickly lead to possible overfitting.\n",
"\n",
"<!-- dom:FIGURE: [figslides/nn.jpeg, width=500 frac=0.6] A regular 3-layer Neural Network. -->\n",
"<!-- begin figure -->\n",
"\n",
"<p>A regular 3-layer Neural Network.</p>\n",
"<img src=\"figslides/nn.jpeg\" width=500>\n",
"\n",
"<!-- end figure -->\n",
"\n",
"\n",
"## 3D volumes of neurons\n",
"\n",
"Convolutional Neural Networks take advantage of the fact that the\n",
"input consists of images and they constrain the architecture in a more\n",
"sensible way. \n",
"\n",
"In particular, unlike a regular Neural Network, the\n",
"layers of a CNN have neurons arranged in 3 dimensions: width,\n",
"height, depth. (Note that the word depth here refers to the third\n",
"dimension of an activation volume, not to the depth of a full Neural\n",
"Network, which can refer to the total number of layers in a network.)\n",
"\n",
"To understand it better, the above example of an image \n",
"with an input volume of\n",
"activations has dimensions $32\\times 32\\times 3$ (width, height,\n",
"depth respectively). \n",
"\n",
"The neurons in a layer will\n",
"only be connected to a small region of the layer before it, instead of\n",
"all of the neurons in a fully-connected manner. Moreover, the final\n",
"output layer could for this specific image have dimensions $1\\times 1 \\times 10$, \n",
"because by the\n",
"end of the CNN architecture we will reduce the full image into a\n",
"single vector of class scores, arranged along the depth\n",
"dimension. \n",
"\n",
"<!-- dom:FIGURE: [figslides/cnn.jpeg, width=500 frac=0.6] A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels). -->\n",
"<!-- begin figure -->\n",
"\n",
"<p>A CNN arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a CNN transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).</p>\n",
"<img src=\"figslides/cnn.jpeg\" width=500>\n",
"\n",
"<!-- end figure -->\n",
"\n",
"\n",
"\n",
"\n",
"<!-- !split -->\n",
"## Layers used to build CNNs\n",
"\n",
"\n",
"A simple CNN is a sequence of layers, and every layer of a CNN\n",
"transforms one volume of activations to another through a\n",
"differentiable function. We use three main types of layers to build\n",
"CNN architectures: Convolutional Layer, Pooling Layer, and\n",
"Fully-Connected Layer (exactly as seen in regular Neural Networks). We\n",
"will stack these layers to form a full CNN architecture.\n",
"\n",
"A simple CNN for image classification could have the architecture:\n",
"\n",
"* **INPUT** ($32\\times 32 \\times 3$) will hold the raw pixel values of the image, in this case an image of width 32, height 32, and with three color channels R,G,B.\n",
"\n",
"* **CONV** (convolutional )layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as $[32\\times 32\\times 12]$ if we decided to use 12 filters.\n",
"\n",
"* **RELU** layer will apply an elementwise activation function, such as the $max(0,x)$ thresholding at zero. This leaves the size of the volume unchanged ($[32\\times 32\\times 12]$).\n",
"\n",
"* **POOL** (pooling) layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as $[16\\times 16\\times 12]$.\n",
"\n",
"* **FC** (i.e. fully-connected) layer will compute the class scores, resulting in volume of size $[1\\times 1\\times 10]$, where each of the 10 numbers correspond to a class score, such as among the 10 categories of the MNIST images we considered above . As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.\n",
"\n",
"## Transforming images\n",
"\n",
"CNNs transform the original image layer by layer from the original\n",
"pixel values to the final class scores. \n",
"\n",
"Observe that some layers contain\n",
"parameters and other dont. In particular, the CNN layers perform\n",
"transformations that are a function of not only the activations in the\n",
"input volume, but also of the parameters (the weights and biases of\n",
"the neurons). On the other hand, the RELU/POOL layers will implement a\n",
"fixed function. The parameters in the CONV/FC layers will be trained\n",
"with gradient descent so that the class scores that the CNN computes\n",
"are consistent with the labels in the training set for each image.\n",
"\n",
"\n",
"## CNNs in brief\n",
"\n",
"In summary:\n",
"\n",
"* A CNN architecture is in the simplest case a list of Layers that transform the image volume into an output volume (e.g. holding the class scores)\n",
"\n",
"* There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far the most popular)\n",
"\n",
"* Each Layer accepts an input 3D volume and transforms it to an output 3D volume through a differentiable function\n",
"\n",
"* Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL dont)\n",
"\n",
"* Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesnt)\n",
"\n",
"For more material on convolutional networks, we strongly recommend\n",
"the course\n",
"[IN5400 Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html)\n",
"and the slides of [CS231](http://cs231n.github.io/convolutional-networks/) which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). [Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs](http://neuralnetworksanddeeplearning.com/chap6.html).\n",
"\n",
"\n",
"\n",
"## CNNs in more detail, building convolutional neural networks in Tensorflow and Keras\n",
"\n",
"\n",
"As discussed above, CNNs are neural networks built from the assumption that the inputs\n",
"to the network are 2D images. This is important because the number of features or pixels in images\n",
"grows very fast with the image size, and an enormous number of weights and biases are needed in order to build an accurate network. \n",
"\n",
"As before, we still have our input, a hidden layer and an output. What's novel about convolutional networks\n",
"are the **convolutional** and **pooling** layers stacked in pairs between the input and the hidden layer.\n",
"In addition, the data is no longer represented as a 2D feature matrix, instead each input is a number of 2D\n",
"matrices, typically 1 for each color dimension (Red, Green, Blue). \n",
"\n",
"\n",
"## Setting it up\n",
"\n",
"It means that to represent the entire\n",
"dataset of images, we require a 4D matrix or **tensor**. This tensor has the dimensions:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"(n_{inputs},\\, n_{pixels, width},\\, n_{pixels, height},\\, depth) .\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The MNIST dataset again\n",
"\n",
"The MNIST dataset consists of grayscale images with a pixel size of\n",
"$28\\times 28$, meaning we require $28 \\times 28 = 724$ weights to each\n",
"neuron in the first hidden layer.\n",
"\n",
"If we were to analyze images of size $128\\times 128$ we would require\n",
"$128 \\times 128 = 16384$ weights to each neuron. Even worse if we were\n",
"dealing with color images, as most images are, we have an image matrix\n",
"of size $128\\times 128$ for each color dimension (Red, Green, Blue),\n",
"meaning 3 times the number of weights $= 49152$ are required for every\n",
"single neuron in the first hidden layer.\n",
"\n",
"\n",
"## Strong correlations\n",
"\n",
"Images typically have strong local correlations, meaning that a small\n",
"part of the image varies little from its neighboring regions. If for\n",
"example we have an image of a blue car, we can roughly assume that a\n",
"small blue part of the image is surrounded by other blue regions.\n",
"\n",
"Therefore, instead of connecting every single pixel to a neuron in the\n",
"first hidden layer, as we have previously done with deep neural\n",
"networks, we can instead connect each neuron to a small part of the\n",
"image (in all 3 RGB depth dimensions). The size of each small area is\n",
"fixed, and known as a [receptive](https://en.wikipedia.org/wiki/Receptive_field).\n",
"\n",
"\n",
"<!-- !split -->\n",
"## Layers of a CNN\n",
"The layers of a convolutional neural network arrange neurons in 3D: width, height and depth. \n",
"The input image is typically a square matrix of depth 3. \n",
"\n",
"A **convolution** is performed on the image which outputs\n",
"a 3D volume of neurons. The weights to the input are arranged in a number of 2D matrices, known as **filters**.\n",
"\n",
"\n",
"Each filter slides along the input image, taking the dot product\n",
"between each small part of the image and the filter, in all depth\n",
"dimensions. This is then passed through a non-linear function,\n",
"typically the **Rectified Linear (ReLu)** function, which serves as the\n",
"activation of the neurons in the first convolutional layer. This is\n",
"further passed through a **pooling layer**, which reduces the size of the\n",
"convolutional layer, e.g. by taking the maximum or average across some\n",
"small regions, and this serves as input to the next convolutional\n",
"layer.\n",
"\n",
"\n",
"## Systematic reduction\n",
"\n",
"By systematically reducing the size of the input volume, through\n",
"convolution and pooling, the network should create representations of\n",
"small parts of the input, and then from them assemble representations\n",
"of larger areas. The final pooling layer is flattened to serve as\n",
"input to a hidden layer, such that each neuron in the final pooling\n",
"layer is connected to every single neuron in the hidden layer. This\n",
"then serves as input to the output layer, e.g. a softmax output for\n",
"classification.\n",
"\n",
"\n",
"## Prerequisites: Collect and pre-process data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
<<<<<<< HEAD
"outputs": [],
=======
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"inputs = (n_inputs, pixel_width, pixel_height, depth) = (1797, 8, 8, 1)\n",
"labels = (n_inputs) = (1797,)\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 864x864 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"source": [
"%matplotlib inline\n",
"\n",
"# import necessary packages\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn import datasets\n",
"\n",
"\n",
"# ensure the same random numbers appear every time\n",
"np.random.seed(0)\n",
"\n",
"# display images in notebook\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (12,12)\n",
"\n",
"\n",
"# download MNIST dataset\n",
"digits = datasets.load_digits()\n",
"\n",
"# define inputs and labels\n",
"inputs = digits.images\n",
"labels = digits.target\n",
"\n",
"# RGB images have a depth of 3\n",
"# our images are grayscale so they should have a depth of 1\n",
"inputs = inputs[:,:,:,np.newaxis]\n",
"\n",
"print(\"inputs = (n_inputs, pixel_width, pixel_height, depth) = \" + str(inputs.shape))\n",
"print(\"labels = (n_inputs) = \" + str(labels.shape))\n",
"\n",
"\n",
"# choose some random images to display\n",
"n_inputs = len(inputs)\n",
"indices = np.arange(n_inputs)\n",
"random_indices = np.random.choice(indices, size=5)\n",
"\n",
"for i, image in enumerate(digits.images[random_indices]):\n",
" plt.subplot(1, 5, i+1)\n",
" plt.axis('off')\n",
" plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')\n",
" plt.title(\"Label: %d\" % digits.target[random_indices[i]])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing Keras and Tensorflow"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from tensorflow.keras import datasets, layers, models\n",
"from tensorflow.keras.layers import Input\n",
"from tensorflow.keras.models import Sequential #This allows appending layers to existing models\n",
"from tensorflow.keras.layers import Dense #This allows defining the characteristics of a particular layer\n",
"from tensorflow.keras import optimizers #This allows using whichever optimiser we want (sgd,adam,RMSprop)\n",
"from tensorflow.keras import regularizers #This allows using whichever regularizer we want (l1,l2,l1_l2)\n",
"from tensorflow.keras.utils import to_categorical #This allows using categorical cross entropy as the cost function\n",
<<<<<<< HEAD
"#from tensorflow.keras import Conv2D\n",
=======
"#rt Cofrom tensorflow.keras imponv2D\n",
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"#from tensorflow.keras import MaxPooling2D\n",
"#from tensorflow.keras import Flatten\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"# representation of labels\n",
"labels = to_categorical(labels)\n",
"\n",
"# split into train and test data\n",
"# one-liner from scikit-learn library\n",
"train_size = 0.8\n",
"test_size = 1 - train_size\n",
"X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,\n",
" test_size=test_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- !split -->\n",
"## Running with Keras"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
" n_filters, n_neurons_connected, n_categories,\n",
" eta, lmbd):\n",
" model = Sequential()\n",
" model.add(layers.Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',\n",
" activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n",
" model.add(layers.MaxPooling2D(pool_size=(2, 2)))\n",
" model.add(layers.Flatten())\n",
" model.add(layers.Dense(n_neurons_connected, activation='relu', kernel_regularizer=regularizers.l2(lmbd)))\n",
" model.add(layers.Dense(n_categories, activation='softmax', kernel_regularizer=regularizers.l2(lmbd)))\n",
" \n",
" sgd = optimizers.SGD(lr=eta)\n",
" model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n",
" \n",
" return model\n",
"\n",
"epochs = 100\n",
"batch_size = 100\n",
"input_shape = X_train.shape[1:4]\n",
"receptive_field = 3\n",
"n_filters = 10\n",
"n_neurons_connected = 50\n",
"n_categories = 10\n",
"\n",
"eta_vals = np.logspace(-5, 1, 7)\n",
"lmbd_vals = np.logspace(-5, 1, 7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Final part"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 4,
"metadata": {},
"outputs": [],
=======
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"12/12 [==============================] - 0s 2ms/step - loss: 2.8826 - accuracy: 0.2444\n",
"Learning rate = 1e-05\n",
"Lambda = 1e-05\n",
"Test accuracy: 0.244\n",
"\n",
"12/12 [==============================] - 0s 2ms/step - loss: 3.3143 - accuracy: 0.0528\n",
"Learning rate = 1e-05\n",
"Lambda = 0.0001\n",
"Test accuracy: 0.053\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 2.6159 - accuracy: 0.1611\n",
"Learning rate = 1e-05\n",
"Lambda = 0.001\n",
"Test accuracy: 0.161\n",
"\n",
"12/12 [==============================] - 0s 2ms/step - loss: 4.6617 - accuracy: 0.1278\n",
"Learning rate = 1e-05\n",
"Lambda = 0.01\n",
"Test accuracy: 0.128\n",
"\n",
"12/12 [==============================] - 0s 2ms/step - loss: 12.1948 - accuracy: 0.1139\n",
"Learning rate = 1e-05\n",
"Lambda = 0.1\n",
"Test accuracy: 0.114\n",
"\n",
"12/12 [==============================] - 0s 2ms/step - loss: 91.3207 - accuracy: 0.0917\n",
"Learning rate = 1e-05\n",
"Lambda = 1.0\n",
"Test accuracy: 0.092\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 517.6693 - accuracy: 0.1250\n",
"Learning rate = 1e-05\n",
"Lambda = 10.0\n",
"Test accuracy: 0.125\n",
"\n",
"12/12 [==============================] - 0s 2ms/step - loss: 1.3215 - accuracy: 0.6111\n",
"Learning rate = 0.0001\n",
"Lambda = 1e-05\n",
"Test accuracy: 0.611\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 1.2700 - accuracy: 0.5889\n",
"Learning rate = 0.0001\n",
"Lambda = 0.0001\n",
"Test accuracy: 0.589\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 1.4245 - accuracy: 0.5806\n",
"Learning rate = 0.0001\n",
"Lambda = 0.001\n",
"Test accuracy: 0.581\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 2.6471 - accuracy: 0.4556\n",
"Learning rate = 0.0001\n",
"Lambda = 0.01\n",
"Test accuracy: 0.456\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 10.4180 - accuracy: 0.5139\n",
"Learning rate = 0.0001\n",
"Lambda = 0.1\n",
"Test accuracy: 0.514\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 54.1625 - accuracy: 0.2583\n",
"Learning rate = 0.0001\n",
"Lambda = 1.0\n",
"Test accuracy: 0.258\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 4.5475 - accuracy: 0.0889\n",
"Learning rate = 0.0001\n",
"Lambda = 10.0\n",
"Test accuracy: 0.089\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 0.2355 - accuracy: 0.9306\n",
"Learning rate = 0.001\n",
"Lambda = 1e-05\n",
"Test accuracy: 0.931\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 0.2488 - accuracy: 0.9333\n",
"Learning rate = 0.001\n",
"Lambda = 0.0001\n",
"Test accuracy: 0.933\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 0.3576 - accuracy: 0.9194\n",
"Learning rate = 0.001\n",
"Lambda = 0.001\n",
"Test accuracy: 0.919\n",
"\n",
"12/12 [==============================] - 0s 1ms/step - loss: 1.2576 - accuracy: 0.8778\n",
"Learning rate = 0.001\n",
"Lambda = 0.01\n",
"Test accuracy: 0.878\n",
"\n",
"12/12 [==============================] - 0s 2ms/step - loss: 5.8163 - accuracy: 0.9167\n",
"Learning rate = 0.001\n",
"Lambda = 0.1\n",
"Test accuracy: 0.917\n",
"\n"
]
}
],
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"source": [
"CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)\n",
" \n",
"for i, eta in enumerate(eta_vals):\n",
" for j, lmbd in enumerate(lmbd_vals):\n",
" CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,\n",
" n_filters, n_neurons_connected, n_categories,\n",
" eta, lmbd)\n",
" CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)\n",
" scores = CNN.evaluate(X_test, Y_test)\n",
" \n",
" CNN_keras[i][j] = CNN\n",
" \n",
" print(\"Learning rate = \", eta)\n",
" print(\"Lambda = \", lmbd)\n",
" print(\"Test accuracy: %.3f\" % scores[1])\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Final visualization"
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 5,
"metadata": {},
"outputs": [],
=======
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1437/1437 [==============================] - 0s 43us/sample - loss: 3.3022 - accuracy: 0.1872\n",
"360/360 [==============================] - 0s 121us/sample - loss: 3.4180 - accuracy: 0.1778\n",
"1437/1437 [==============================] - 0s 86us/sample - loss: 3.3955 - accuracy: 0.1093\n",
"360/360 [==============================] - 0s 142us/sample - loss: 3.4203 - accuracy: 0.0917\n",
"1437/1437 [==============================] - 0s 80us/sample - loss: 2.7250 - accuracy: 0.1587\n",
"360/360 [==============================] - 0s 216us/sample - loss: 2.7661 - accuracy: 0.1556\n",
"1437/1437 [==============================] - 0s 66us/sample - loss: 3.5698 - accuracy: 0.1343\n",
"360/360 [==============================] - 0s 46us/sample - loss: 3.5947 - accuracy: 0.1167\n",
"1437/1437 [==============================] - 0s 63us/sample - loss: 12.5837 - accuracy: 0.0946\n",
"360/360 [==============================] - 0s 60us/sample - loss: 12.5511 - accuracy: 0.1111\n",
"1437/1437 [==============================] - 0s 59us/sample - loss: 91.5210 - accuracy: 0.2408\n",
"360/360 [==============================] - 0s 53us/sample - loss: 91.5551 - accuracy: 0.2222\n",
"1437/1437 [==============================] - 0s 64us/sample - loss: 518.1178 - accuracy: 0.1969\n",
"360/360 [==============================] - 0s 48us/sample - loss: 518.1064 - accuracy: 0.1889\n",
"1437/1437 [==============================] - 0s 66us/sample - loss: 1.4465 - accuracy: 0.5623\n",
"360/360 [==============================] - 0s 37us/sample - loss: 1.4667 - accuracy: 0.5444\n",
"1437/1437 [==============================] - 0s 63us/sample - loss: 1.0335 - accuracy: 0.7015\n",
"360/360 [==============================] - 0s 75us/sample - loss: 1.0560 - accuracy: 0.6806\n",
"1437/1437 [==============================] - 0s 62us/sample - loss: 1.9454 - accuracy: 0.3730\n",
"360/360 [==============================] - 0s 48us/sample - loss: 2.0023 - accuracy: 0.3611\n",
"1437/1437 [==============================] - 0s 92us/sample - loss: 2.4747 - accuracy: 0.5080\n",
"360/360 [==============================] - 0s 49us/sample - loss: 2.5625 - accuracy: 0.4722\n",
"1437/1437 [==============================] - 0s 65us/sample - loss: 10.2878 - accuracy: 0.5887\n",
"360/360 [==============================] - 0s 87us/sample - loss: 10.3306 - accuracy: 0.5694\n",
"1437/1437 [==============================] - 0s 60us/sample - loss: 53.7810 - accuracy: 0.6548\n",
"360/360 [==============================] - 0s 45us/sample - loss: 53.8126 - accuracy: 0.6194\n",
"1437/1437 [==============================] - 0s 55us/sample - loss: 4.5991 - accuracy: 0.1058\n",
"360/360 [==============================] - 0s 45us/sample - loss: 4.5992 - accuracy: 0.0889\n",
"1437/1437 [==============================] - 0s 66us/sample - loss: 0.2035 - accuracy: 0.9457\n",
"360/360 [==============================] - 0s 43us/sample - loss: 0.2762 - accuracy: 0.9194\n",
"1437/1437 [==============================] - 0s 82us/sample - loss: 0.1869 - accuracy: 0.9617\n",
"360/360 [==============================] - 0s 46us/sample - loss: 0.2421 - accuracy: 0.9417\n",
"1437/1437 [==============================] - 0s 59us/sample - loss: 0.2736 - accuracy: 0.9527\n",
"360/360 [==============================] - 0s 57us/sample - loss: 0.3346 - accuracy: 0.9278\n",
"1437/1437 [==============================] - 0s 63us/sample - loss: 1.0958 - accuracy: 0.9499\n",
"360/360 [==============================] - 0s 48us/sample - loss: 1.1583 - accuracy: 0.9194\n",
"1437/1437 [==============================] - ETA: 0s - loss: 5.7025 - accuracy: 0.95 - 0s 68us/sample - loss: 5.7254 - accuracy: 0.9506\n",
"360/360 [==============================] - 0s 226us/sample - loss: 5.7769 - accuracy: 0.9194\n",
"1437/1437 [==============================] - 0s 61us/sample - loss: 2.5815 - accuracy: 0.1886\n",
"360/360 [==============================] - 0s 56us/sample - loss: 2.5828 - accuracy: 0.1472\n",
"1437/1437 [==============================] - 0s 61us/sample - loss: 2.3024 - accuracy: 0.1044\n",
"360/360 [==============================] - 0s 39us/sample - loss: 2.3034 - accuracy: 0.0778\n",
"1437/1437 [==============================] - 0s 68us/sample - loss: 0.0132 - accuracy: 1.0000\n",
"360/360 [==============================] - 0s 59us/sample - loss: 0.0683 - accuracy: 0.9694\n",
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\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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1KTevR/Nc/Q/w8DQzcnp3Or5UjxXzf07jmqYdg9GIPYVRATarLVVlZr+1iD4fdWPWnnGEX77J/i2HKV+ntFO5Z4e0oX3/Frzb5qNM3V/EaDSQUqDWVP7Yjslsokq9Mrzf5wvibyXy5sSuvDS4FbPHrErrqqYLx35OKf5/92zZxyoVYeTCV/hu7jZ+25T8SyOzMhgNKR7n/zb+rMhgNKT4G+O2FIYMeXlZGDyiHQF5/Xhn4CKn90qVyc+oj/7D6v/tYfcvmfei514GgyHlYz+V5/45E7+n95stmfFNf65fi+TArlOUq1w0rauZIWz2jM/UuIpLMkJms5nExOS3iuLi4rBYLK6owj8KDblBrgC/pOk8ef2IvBHDrdjUfXnXfepxNi3fS2KClZioW/ywaj9BtUqmV3XTRNiFq+QukCNpOk/BXESERxEXcytVZbz9sjF32GL6VHmboS3HgcGQdAvN4mFm2ML+NHq2LgMbjOLMofOuC+wBhAbfIFfeO+n8PPn8b+//+H9Y6o7w0Ah2bjxETNQtEhOsbF21j7JVs87JMOziNXLnu2s/589B5PVobsWkLn6Ahh1r8OGKQXz53gqWTV6fHtVMN2EX7om/QE4ir0f9q/izqrArN8l9VwYjT4AfETdjibvnwiUgrx+TP++BzWZjyKtfEX3XQIJGTz7Oh58+zxczt7B0Qea9+EtJ2OUb5A6869wf6EfkzdSf+719PJk7aQN9O37KsN7zHOfBC9fuv6BkKi5pCPXt25f27dszfPhwpk6dyqeffsrw4cN55pln6Nu3ryuq8I/2/3KSspUKU6Coo19Dq2drsetfpHdP/RlMg5aO++oms5HaTcpx7PfM/eW/b/NhytUsldSJ+ek+Tdm1Zl+qyzzd50leGPUMADkC/WjZozFbl+4E4O0Fr+Ltm403Gozmyrmrrgrpge3fcZyyVYpSoFgeAFo9V5dd9/QF+Cc/f/879VtXxsPT0aiv06wiJw5dSJe6pod92/6kbPUSFCgRCEDrHg3Z9f3BVC9fq3kQ/T7swjudJqd46yyz27f1CGVr3BV/z0bsWpf6+LOyfbtPU65CQQoUdvThe7pDNXbtOO5UJpu3BxNnvsgv248xbsQKpxFhteuV5pVBLRj2+tds25T6z0xmsW/nKcoGFaZAEce5v/V/arJr29FUL9/6PzV5of+TAOTI7UOLTtVT7D4hmZtLbo21adOGmjVrsmvXLkJDQ7HZbFSvXp3XXnuNvHnzuqIK/+hmeDST313Ou1O6YbaYCLkQzsSh31Dq8YK8PqYj/TtO+8fl54xfxyvD2zJn3RvYbHYO7jrN/774yUW1fzA3wiKY2Hs2I5a+jsXDTPDpK0zoOYtSVYszaHZv+tV452/LACz9aDVvz3+FOQc+AoOBr97/lhP7zlCuVikadKrFhRPBTP5xVNL25r6zlH2bM+ftkpvXopg8ZCnvznzJsf/PXWXim0soVbEQr49/lv6tP/nH5dcu/IXs/t5MW/MGRpORU39cZO4Hq11U+4d382okk/rPY/j8vpg9zIT8FcaEfl9QqnJRBk59kVcbvv+Py/d+/xkwGBg49cWkeX/uPsWMtxand9XTxM2rkUx65UuGf/UqZg+TI/6+cylVpRgDP32JV+uPzugqppsb12OYOOY7RozrjMViIvjidSa8v4pSZfMz6J029HthDu061yAwnz9PNCzLEw3LJi37Vv+F9H7tSTDAoHfaJM0/cugC0ydm/seHgOPcP2nEcoZP6pp07p/wzv8oVb4gA9/rwKvPTP/H5ZfN/ZEhHz7DZysGYDDAwhk/cOLIJRfVPn1lhk7MrmKwp3SDNJNrWW5YRlchw1hPn8voKmQoU8GsN0Q5LdlvRmZ0FTKONes8lyk92MoVy+gqZCjj3zzbx11sOPyBS7e3/3wRl22rapGMvYOiByqKiIiIE6sbPWbQfSIVERERuYcyQiIiIuJEw+dFRERE3IAyQiIiIuLEnUaNKSMkIiIibksZIREREXFitbtPnsR9IhURERG5hzJCIiIi4sTmRnkS94lURERE5B7KCImIiIgTjRoTERERcQPKCImIiIgTjRoTERERcQNqCImIiIjb0q0xERERcWJTZ2kRERGRR58yQiIiIuLE6kZ5EveJVEREROQeygiJiIiIEw2fFxEREXEDygiJiIiIE/3oqoiIiIgbUEZIREREnFjteo6QiIiIyCNPGSERERFxoucIiYiIiLgBZYRERETEiU3PERIRERF59CkjJCIiIk7UR0hERETEDaghJCIiIm5Lt8ZERETEiR6oKCIiIuIGsmRG6Gj/PBldhQwTWNJ9Wukpsf3Pffc9QGQR993/cQUSM7oKGcrsG5/RVchQJbv/ldFVcCv60VURERERN5AlM0IiIiKSfqx6oKKIiIjIo08ZIREREXFiw336IyojJCIiIm5LGSERERFxoj5CIiIiIm5AGSERERFxoh9dFREREXEDygiJiIiIE5t+a0xERETk0aeMkIiIiDhRHyERERERN6CGkIiIiLgt3RoTERERJzY9UFFERETk0aeMkIiIiDix6kdXRURERB59ygiJiIiIE/UREhEREXEDygiJiIiIE/UREhEREXEDygiJiIiIE/UREhEREXEDygiJiIiIE6syQiIiIiKPPmWERERExIlNo8ZEREREMpc1a9bQqlUrmjVrxqJFi5K9f+TIETp16kTbtm15+eWXiYiIuO861RASERERJ1a70WWv1Lpy5QqTJ09m8eLFrFq1imXLlnHq1CmnMh988AEDBgzgu+++o3jx4nzxxRf3Xa9ujYmIiEiGiYiISDFz4+fnh5+fX9L0zp07qV27Njly5ACgefPmbNiwgf79+yeVsdlsREdHAxAbG4u/v/99t6+GkIiIiDix2V3XR2jBggVMnz492fz+/fvz2muvJU2HhoYSEBCQNB0YGMihQ4eclhk6dCg9e/Zk3LhxZMuWjW+++ea+21dDSERERDLMiy++SIcOHZLNvzsbBI5sj8Fwp4Fmt9udpuPi4nj33XeZP38+QUFBzJs3j7fffps5c+b84/bVEBIREZEMc+8tsL+TL18+9u7dmzQdFhZGYGBg0vSJEyfw9PQkKCgIgGeffZapU6fed73qLC0iIiJOrBhd9kqtunXrsmvXLsLDw4mNjWXTpk00aNAg6f2iRYty+fJlzpw5A8CWLVuoWLHifderjNBtjYsW563a9fEwmTh2LYy3t24iKiHeqcy7TzSkVcnS3LwVB8CZ69fpv2mtU5nPWrTlSnQUo3ZsdVnd08ITAWV4pXQzPIxmTkVeZuzhFURbbzmVKZk9L4PLtyG72Qub3caHR1ZxLCIYIwaGlG9LlVzFAdgZdpxPj3+fEWE8sCcqFqd/p3p4mE2cvHiVMfM3ER3nvP//07gynRo5rjQuht5g7FebuR4ZC0DnRpVoX78Cnh5mjp4LZcz8TSQkWl0ex4NqWKo4bzZ5Ag+TieOhV3nnu81Ex8enWLZpmZJMaN+Cqh/NAGBq56cpmutOh8RCOfzZc+4i/ZZ955K6p4UmhUvwVo0GeBhNHAsP460dG5J9/ofXakSr4mW48f+f/5vh9N+6hllN21LUL2dSucK+/uwOucB/N690aQwPqlH+xxgS1AgPo5ljN0MZ9ttaohKdYy/tH8Coqs3xtXhis9sZvnc9f1y/jKfJzHtVW1Apd37AwO/Xghm1fwO3rIkZE0wq1WxZmZ5ju2DxNPPX4QtM6jOHmNuf5fuVMRoNvDq1B0ENygLw2/cH+XzoYgAqNSxPn4+7YTKbiLgWyWeDF3Lm0HmXx/eoyps3L2+88QYvvPACCQkJdO7cmaCgIHr37s2AAQOoWLEiH374IQMHDsRut5M7d27GjRt33/Ua7Ha73QX1T1PFZnySpuvL5ZWNTV1fovOKJZy9eYOhderjY/FgxE9bnMqt6NSVsb/8yP7LwSmu5+UqNehTpTprTx5Pt4ZQYMlrab7OHBYfltZ7nd67Z3Mh5hr9SzfH2+zJx3/e+SLzNFpY2eBNxv6xgp1XT9AgsBz9S7fgPz9PpnXBqrQuUIX+e77EYDDwRe2+LDzzE1uu/JHmdbX9L0+arzNH9mx88/6L9Bq/lAuhN3itU328vSx8tOjOPixbNJCP+7Wh63sLiY6N5/VnGuDj5cG4hT/QuOpjvNLhCXqNX0ZkTBwf9W3DkbOXWfD9njSva2SRtO/AmNM7G+v6vUDXecs4F36DwU3r4ePhwXvfJz+Gi+bKwefPdSBPdm+qjp+R7P2KBfIytfPTPDd/GZcjotK0nnEF0ufLNZdXNjZ36kGnNYs5G3GDoTUakN3iwfCdPziVW9mmG2N3b2NfaMqff4CgPPmY1bQtndcuISQ6Mk3rafZNuWH6MHJ5erOhRR/+s2UBZ6Ou81ZQY3wsnozatyGpjJfJzLbWrzJsz1q2h5zmyQKleatSY5p9P5tBFRpSwMePIbvXYDAYmFSrHWejwpnyx09pXteS3Y+kyXr88/jy+cGPGdjoPYJPXabXuC54Z8/GtAHzUlXmqe4NeOr5+gxtOQ6D0ciUn0bzzSdr2bf5EAtPfsqYLlM4uO0IhcsUYPTyQfStOpSE+Ic/djfFL37odfwbb/3+jMu29XGlb122rZTo1hhQv0hRDoVe5uzNGwB8/cfvtCtdzqmMh9HE43kC6VulBhu6vMCsFm0okN036f3aBQrRsEgxFv3xu0vrnhZq5XmMP29e5EKMo5G1/MJuWuSv7FSmdp7HuBgbzs6rJwD4KfQo7xxcAoAJA14mDyxGMx5GMxaDiVu2zH1FeLfajxflz7OXuRDq2P//2/47LWs57/9j50Lp8O48omPj8TCbCMyRnRtRjivI1nXK8/WmfUREx2G3w7iFP7B+11GXx/Gg6pUoyuHgy5wLd8S/ZO8h2lQsm6ycl9nMhA4tGL/pxxTXYzEaGd+uOeM2bk/zRlB6alCwGIfCLnM24vbn/+hB2j1W3qmMh9FE+dyBvBxUk40dX+Kzpu0o4OPrVMZiNDKpYUve/3VbmjeC0ku9fMU5FB7C2ajrACw6tZ92RR53KlM/XwnOR11ne8hpAH4IPsFrOx3Zrt/CzjP9yC/YAZvdzp83LlPQ+/7DlTNStaeCOL73DMGnLgOwdvYPNOn6RKrLmExGvHw8sXhasHiaMXuYSYhLoOBj+Yi+GcPBbY4G24XjwcRExFKudikXRicPQg0hoEB2P0Ki7py4QqIi8fP0JLvFI2leoI8POy+dZ+Lun2mx9CsOXAnh81btHe95+zCqfmNe37weW9ZLsJHXy5/QuJtJ06FxEWS3eOFj8kyaV8QnD9duRTG8QkcW1HmF6dV7YjI6Dp+1l/YTmRDLusZDWd94GBdirvFz2DGXx/Gg8uby5Ur4nf0fej2S7N6e+Hh5OJWzWm00rFyS9RP6UKV0Idb84jjhFcmbk1y+3nw6sCNLRnenT9s6RMbEuTSGh5HP35fLN+80XC5HROLr5YmPh3P87z/9JMv2Heb4lasprqdzlQqERkbzw/HT6VrftJbfx5fguxouIdGR+Hk4f/7zemdnV8h5Ju77meYr5nMgNJi5TzmPcnm2TBBXYqLZeO6ky+r+sPJn8yMk5s7zWy7HRuDr4UV2853Yi/vmIiwuig9rtGbVUz35quFzmG9/9n++8hdno8IBKODtx0ula7L+Qua+CAgolIuwi3cy62EXw/Hx98bbN1uqymz66kcir0ez+OwMlp6fSfDpK/y6bj+XTl7Gy8eTak86+qSUrlaCouULkSt/DtcFl4ZsGF32ymguqUFwcPA/vjKawQB2kjdgrHZb0t8XIyPosXYlJ8IdH445B/ZSxN+fYv45mNasNe//vJ2wmGiX1TktGQ2GFKIHK3fiNxtMPBFQmpUXfuPFXTP55vwuplR7EYvBxH8fa8r1+GhabB3H09vH42fx5rli9VwXwEP62/httmTzfjx4miffmMWc73Yx7Y2OGAxgNhmpVb4owz5bS/cxi/D38eKVDlkp/pSPf9tdx/9z1YNItNlYfvDvb0+8WLsqs3bsTpc6pqe/3f93XdRciLrJSxuXc+K6oxE4+/AeivjloHD2O9mPXhWqMe3grvSubppyxJ7Sue/OPLPBRKP8jzauKIYAACAASURBVLH09AHab/6Sr07u5Yv6z+JhNCWVqZAzH8uavMDCk3vZFnIq2foyE4PRSErXqzarLVVlnh/RiZtXI3i2UF+eK94f35w+dBrYipjIWEZ3nkSXt9sxa++HPPl8fQ5uO0JiGtwWk/Tlks7SL7/8MmfPniUwMJB7uyQZDAa2bNnyN0u6RnBkJFXy5k+azpc9OzfiYolNvHMAl82dh3K5A1h54s7VjgEDAd4+FPbLwYh6jQAI8PbBaDDgaTYzdNsml8XwMC7H3uRx/8JJ0wGeftyMjyHOmpA0L+xWBH9FhXHk5kXAcWvs3QodKeidi8Z5H2fi0TUk2q0kJlpZF7yfpnkrsPjszy6P5UFcDo+kQvF8SdMBObJzMzqOuLtOYIUCc5Dbz5vfTzka7t/9/AfDujfFz9uLsBvRbNt/Mqlz9fpfj9K7TW3XBvEQQm5GUqngneM/r192bsTGEZtwJ/4OlR7Hy2JmVZ9uWEwmvMyOv/ssXkVoVDTl8gVgNhr57dzFjAjhoQRHR1I58K7Pv4/v7c//neO/bK4AyuUKYOWpP5PmGTCQYHN0iH88dyBmg5FfQy64ruJpIDgmgkq5CyZN583my41bscTe9dm/EhfJ6Yir/B7uOPZ/CD7BOEMrCvvk4HTkNZ4uXJ73qrVg9P6NrDmfNv140lPYhauUrVkyaTpPwVxEhEcRF3MrVWXqta/BjIELSEywkpgQy+aFO6jfsSYrpn5PXHQcQ54am7Tcl0c+Ifj0FdcElsasLnygYkZzSUZoyZIlFC9enI8//pitW7c6vTK6EQSw48JZKufNTzF/Rwqz2+OV2PyXc3rfZrczun4TCvk6nnXwfIVKHLsWxp6QS9T9ag6tli2k1bKFLPrjd9aePJ5lGkEAu6+dpEKOIhT2zg1AxyI1+SnUOb29K+wEBbLlpKxfAQCq5CyG3W4nOPY6xyMu8WQ+RzrYZDDSIKAcf9zIOl8Ivx45S4WS+Skc6Nj/nRpV4seDzle1efx9GNenNf7ZvQBoWbsspy9d42Z0HFv3neDJ6qXxtDiuKxpVeYw/z2adk9/Pp89RqWA+iuZyxN+lWhBb7rm99cwXS2jz2ULaz1lEn8WriEtMpP2cRYRGObKgNYsW4te/subomJ8unqVKYAGK+d3+/JetxKbzzvvfZrfzXp2mSRmg7uUqc+x6GJdjHLcUa+UrzM6QrBf/z5fPUCV3AYpld4x6e65kVX4IPuFU5seQ0xTyyUGFnI6LhRoBjoumC9E3aFKgFCOrNuOlH5dkiUYQwL7NhylXsxQFHnPE83Sfpuxasy/VZU4eOEuDzo4LHZPZRJ02VTn22ynsdjtjV79FqaqO0bMNn6lNfFyCRo1lAS4bNXbo0CG+/fZbxowZ89DrSutRYwCNihbn7dr1sBhNnIu4waAfNlDEz5+PmjSj1bKFALQvXY5+VWtiMhgIiY7i7a0bCY5y7hQ5sEYdcnply1KjxgDq5inNq6WbYzaauBQTzujD31IwWy7erdCB53c6Hn1eJWcxXivTkmwmD+JtiUw6upbfb5zD35KNweXbUsa3ADa7jT3hp5l67HsS7Wk/fDw9Ro2BY/j8qx3rYTEbuRh6k1FfbqBgHn+Gv/gU3d7/GoBOjYL4T+PKJFptXL0ZzUeLthB8NQKjwUCvp2vxVI0ymIwGjp0LZdzCH5INv08L6TFqDKDBY8V4s0k9LCYj56/f5O1VGyicMwdj2zxJ+znOv/Bc0N+PNf26O40aG9myMWFR0cza8Vu61A/Sb9QYQONCxR3D502Oz/8bP66niK8/H9VvQauVCwDo8Fh5+gXVxGQ0EhIdyVs/bUjqWzSm7pOExkQx7eCv6VbH9Bg1BtAof0kGBzXGYjRxPuo6g3d/RxGfnIyr0Zo2m+YCjsbP0EpN8TZbiLdaef/AJvZdvcjmln3x9/DiSuydPmb7rl5g9P6NaV7PtBo1BlCjRWV6jn0Wi4eZ4NNXmNBzFvmKBzJodm/61Xjnb8tEXo/GN1d2+k99iccqF8NqtXFw6x/MeXsRiQlWKtYvS79PXsDsYSY85AZTXpnL5b9C06TOrh419vqBri7b1tQqS1y2rZRo+HwWk14NoawivRpCWUV6NYSygvRsCGUF6dUQyirSsiGUFakhlH70QEURERFxYrNn/GguV3GfSEVERETuoYyQiIiIOLHiPrfhlRESERERt6WMkIiIiDix6TlCIiIiIo8+NYRERETEbenWmIiIiDjR8HkRERERN6CMkIiIiDixafi8iIiIyKNPGSERERFxYtXweREREZFHnzJCIiIi4kSjxkRERETcgDJCIiIi4kQ/sSEiIiLiBpQREhERESd6jpCIiIiIG1BGSERERJyoj5CIiIiIG1BGSERERJzoOUIiIiIibkANIREREXFbujUmIiIiTtRZWkRERMQNKCMkIiIiTvRARRERERE3oIyQiIiIOFEfIRERERE3oIyQiIiIOFFGSERERMQNKCMkIiIiTpQREhEREXEDWTIjtKbt5IyuQobJYbRmdBUyVFwF97lKSUmcG/0Qojh7q26njK5ChkpMTMjoKrgVZYRERERE3ECWzAiJiIhI+tGTpUVERETcgDJCIiIi4kR9hERERETcgBpCIiIi4rZ0a0xERESc6NaYiIiIiBtQRkhEREScKCMkIiIi4gaUERIREREnygiJiIiIuAFlhERERMSJXRkhERERkUefMkIiIiLiRD+6KiIiIuIGlBESERERJxo1JiIiIuIGlBESERERJxo1JiIiIuIGlBESERERJ+ojJCIiIuIG1BASERERt6VbYyIiIuJEnaVFRERE3IAyQiIiIuJEnaVFRERE3IAyQiIiIuLEbs/oGriOMkIiIiLitpQREhERESc21EdIRERE5JGnjJCIiIg40XOERERERNyAMkK37fvVxKIvPEhMgCIlbLzy5i28fZzLrF9pZsNqCx6edgoWsfPf127h6we3bsHcTz04ddyE3Q6lylr574B4PD0zJpYH8euvZr6Y60lCPJQoYePNIbH43BP/yhUWVq/ywNMTihSx8drrsfj5OZcZPTIbuXPbee31ONdVPg3s+dXEgrmeJCQYKFbCyuuD45Lt/zUrLaxd5YGHp53CRWz0GxCHrx9ER8HUiV5cvGDEboOmzRLp3DU+YwJ5QO5+/Ltz/DWalKfH0DZYPEz8dTSYKUOWEBN1K8Wyb07qxtnjwSyfvS1p3tLfP+BqyI2k6eWfbWXbqn3pXu+HUbNVVXqNew6Lp4W/Dp3jk//OIiYyNlVlRnzzJgUfy5dULl/xQA79+Ccj239EkXKFeGP2y2TL7oXdbueLYYvYu+l3V4eXJvQcITdz8wbMmOjJkFFxfDo/lrz5bSya6+FU5o+DRlYtszBqQhwTZ8dRtaaV2ZMdZ7oViyxYrfDJnFg+mRNLfLyBlUssGRHKA7lxw8DEj70YNTqW+V9Fk7+AjbmfezmVOXjAxLKlnkz4JIbZn0dTs1YikydlcyqzbKkHhw+bXFn1NHHzhoEpE7wYNjqW2QuiyZffxvy5zt9ihw6Y+N9SDz6YGMO0OTFUr5XItEmO/9HX8z3JE2Bn5hcxTJ4Zw/o1Fo4eyTofLXc//t05fv9cPgz65DnG9vmS3o3Gcfn8NXoMa5usXOHH8vLh0lep17qS0/yCJQKJvBFD/xYTkl6ZvRHkn8ePwV++wvudJ9Kz3OuE/HWFXuO7pbrMmP98Qt+qQ+hbdQiT+nxG1I1opvWfC8CAGf9lw7yt9K06hIm9ZjJ82SCMpqxzLnBX2kPA7/tMPFbaSv5CjgcnNG+TyI4tZqfnKJw+YSSoqpXcAY6ZteolsvdXEwkJUC7IRufnEzAawWSC4o/ZCLuSdVrT+/aaKF3GSqFCNgDatI1nyxaLU/wnTpioWi2RgNvx16ufwK+7zCQkON4/eNDEnt/MPN0mwdXVf2j795ooVcZGwdv7v1XbBLbfE/+pk0YqV7WS53b8desl8tuvjvj7vHqLXn0dV9Dh4QYSEkiWTcvM3P34d+f4qzYoy4nfzxN8NgyAtQt/oXH7asnKPf1iPTYu/ZUd6w46zS9fvTg2q40Jywcwc9PbPPd6c4zGzB17tWZBnNhzmkunLgOwZtYmmj5X/1+XMVvMvDW/P7PemE/YxWsAGE1GfHM6PvzevtmIj8tameG72e2ue2U0lzWEfvjhBxYuXMj58+ed5i9btsxVVfhb10KN5A68szdyB9iJiTEQG3OnTKlyNv44YEo6wW3baCYxwUBUhIHK1a0UuH0SDbtiYN0KM3UaWl0aw8MIDTUSeFf8AQF2YqINxNwVf7lyVg4cMHPlsiP+jRssJCQYiIgwcPWqgZnTvRj2bizGLNi0vhpmJE+ALWk6z+34797/pcvaOHTQROjt/b95o4XEBAOREQYMBscX4MRxXrzay4eKlawULGy7dzOZlrsf/+4cf54COQkLvnNb62rIDXz8suGd3TkjOmvEcrankOkxmYwc/PkEw5//jCGdP6Vqw7K07dEg3ev9MAIK5yHs4tWk6bCL1/Dx98bbN9u/KtOiVxOuBYfzy6rfkuZN6z+XLkM7sPj8Z3y0eSSfvvI5NmvWORe4K5d8bU2cOJGvv/6as2fP0rVrV1avXp303tKlS11RhX9ks5PiExPu/lIvX9HGMy8k8PEoT956xQuDAbL72jGb75xAT58wMmKgFy3aJVK9dtY4EcLft8jvjr9ikJUXXrjFqJHevNLXB4MRfP1sGA0wbmw2+r0SR+7cmaBp/wDsNjCkcADcHX+FICtdu8fzwchsDOznjdEAvvfs/8HvxLF4ZRSRkQaWLvRIvsJMyt2Pf3eO35G9Sf65tVpT91nesGQXs0Yu51ZsPNERsaz8fDt1WwSlcS3TltFoSPGcd3eDJTVlOg1szaIPlidNWzwtDF/6BhN6zOC5In15s+FIXv/sZQIK5U7T+ruK3W5w2SujuaSz9I8//sjKlSsxm810796dnj174uHhQcuWLbFngrxYQKCNk0fv/CvCrxrI7mvH664uMLExUD7IStOWiQBcu2pg6XzIfruz8M/bTMz91JNe/W9Rv2nWOAn+v8BAO0eP3jkYr4YZ8PW1k+2u+GNiIKhSIi1bJSSVmT/Pk5AQIyEhRj6b5egvEx5uwGaD+AQv3hycNTpMBwTaOH7szv6/lsL+j4mBCpUSaXZX/F/P88TXD/btMVGsuI3ceRz/s4aNE/hlR9boIwI6/t05/tBL1ylTpWjSdJ58/kTeiOZWbOpu6TTpWJ0zfwZz9lgw4LigSEzI3PGHnr9K2ZqlkqbzFMxFRHgUcTG3Ul2mZOVimMwmDv34Z1KZ4hUK4+ntye51+wE4uvsk545coGytUkm3ziRzcklGyG63Y7h9yV2sWDFmz57NBx98wO7du5PmZ6RK1aycPGoi5KKjLpvWmKlRN9GpTPg1A6Pe9CIm2jG9YpGFek0SMRhg7y4TX87wZPj4uCx1Evx/1aoncvSoiYsXHYfDmjUe1K3r3Nfn2lUjb77hQ/Tt+Bct8qRJ40TKP25lybIoZn8ezezPo3m6TQKNGiVmmUYQQJXqVo7/aeLS7f2/fo2F2ins/2GDvJP2/zeLPWjQJAGDAX7ebmbJVx7Y7ZAQDzt+tFCpSuK9m8m03P34d+f49/90jLJVilGgWAAArZ5/gl2b/kj18sXK5Kf74JYYjQY8vCy0eak+P605kF7VTRP7Nv1OudqlkkZ+Pd23GbtW7/lXZYIalufgNuf/06VTl/Hx96Z8ndIA5C+RlyLlC3HqwF/pGY6kAYPdBSmZ6dOns3PnToYOHUpQkCNtum/fPvr37098fDz79v27UQaHLxRK8zru321i0RcWEhMN5M1v47W3b3ElxMhnkzyYONvxpf79KjMbvrNgt0HZClZ6veYYIjvgpWxERRrIledO2rTM4zZ6D0j7jnI5jOlzot19e/h8YiLkL2Dj7aGxhIQYmTQxG7M/d5z9V6208N1qD2w2qFDRymsD4pINEV4w35OIm4Z0Gz4fl05p1D27HcPnExMhf347g4bGcjnEyKefeDFtjqOzyJpVFtattmC3GShfIZG+A27h6QlRUTBjshfnzjoaknXqJdLtxfh06S8VZ0+fa5escvynl6wQ/1t1O6Xp+v5fjcbleWno05gtJkLOXWPiG1+Tv0huXv+4C/1bTHAqO2jSc5w7HpI0fN7Ty8IrYztTtkoxTBYTO9YdZMFHa9OlnomXgtNsXTVbVqHnuOeweJgJPn2Fj1+cTv4SgQz6vB99qw752zKR16MAeG16L66FXGfxByuc1lup0eP0/uh5PLw8sCZaWfj+t+y8p5H1oDbbvk2T9aRWxe9GuWxbh9u+57JtpcQlDSGAXbt2ERgYSMmSJZPmhYSE8OWXX/Luu+/+q3WlR0Moq0ivhlBWkV4NoawivRpCkvmlV0Moq0jLhlBWpIZQ+nHZAxXr1KmTbF7+/Pn/dSNIRERE0pceqCgiIiLiBvQTGyIiIuIkEwzodhllhERERMRtKSMkIiIiTjLDgw5dRRkhERERcVvKCImIiIgTZYRERERE3IAyQiIiIuLEjQaNKSMkIiIi7ksZIREREXGiPkIiIiIibkAZIREREXHmRp2ElBESERERt6WGkIiIiLgt3RoTERERJ+osLSIiIuIGlBESERERJ3Z1lhYRERF59CkjJCIiIk7UR0hERETEDSgjJCIiIs6UERIRERF59CkjJCIiIk40akxERETEDSgjJCIiIs6UERIRERF59KkhJCIiIk7sdoPLXv/GmjVraNWqFc2aNWPRokXJ3j9z5gzdu3enbdu29OrVi5s3b953nWoIiYiISKZ35coVJk+ezOLFi1m1ahXLli3j1KlTSe/b7Xb69etH7969+e677yhXrhxz5sy573rVEBIRERFndhe+Umnnzp3Url2bHDly4O3tTfPmzdmwYUPS+0eOHMHb25sGDRoA0LdvX7p163bf9aqztIiIiGSYiIgIIiIiks338/PDz88vaTo0NJSAgICk6cDAQA4dOpQ0ff78efLkycM777zD0aNHKVGiBCNGjLjv9pUREhERkQyzYMECmjZtmuy1YMECp3I2mw2D4U6fIrvd7jSdmJjIb7/9RteuXVm5ciWFCxdm/Pjx992+MkIiIiLixJU/uvriiy/SoUOHZPPvzgYB5MuXj7179yZNh4WFERgYmDQdEBBA0aJFqVixIgBPP/00AwYMuO/2lRESERGRDOPn50ehQoWSve5tCNWtW5ddu3YRHh5ObGwsmzZtSuoPBFClShXCw8M5duwYAFu3buXxxx+/7/azZEboMbMlo6uQgdw5dvE0uO/+P50YldFVyFDWK2EZXYUMZXDr834GyIQPVMybNy9vvPEGL7zwAgkJCXTu3JmgoCB69+7NgAEDqFixIjNmzGD48OHExsaSL18+Pv744/uu12C3Z71fFIkNKZ7RVRDJEGoIua9XSzTK6CpIBtoUv9il2yv21f371qSVsy8Mddm2UpIlM0IiIiKSnlzXRyijqY+QiIiIuC1lhERERMRZlus08+CUERIRERG3pYyQiIiIOFNGSEREROTRp4yQiIiIOHPhk6UzmjJCIiIi4raUERIREREnWe9Ryw9OGSERERFxW8oIiYiIiDNlhEREREQefWoIiYiIiNvSrTERERFxpuHzIiIiIo8+ZYRERETEiUGdpUVEREQefcoIiYiIiDNlhEREREQefcoIiYiIiDONGhMRERF59CkjJCIiIs7UR0hERETk0aeMkIiIiDhTRkhERETk0aeMkIiIiDhTRkhERETk0aeMkIiIiDjTc4REREREHn1qCImIiIjb0q0xERERcWJQZ2kRERGRR58yQiIiIuLMjTJCagjd9tMuA9M+NxGfYKBUCTuj30oku49zmSUrjCxdacLTw06JonaGDbTi7weDR5o5f+lOueDLBqpVsjN1XKJrg3gIit+949++CybPgfgEKFMCxr5Nsvi/Xg6LVoKXJ5QoAiPegBx+YLXCmCmw93dHuQa1YUg/MGShQSd7fjWxYK4nCQkGipWw8vrgOLzviX/NSgtrV3ng4WmncBEb/QbE4esH0VEwdaIXFy8YsdugabNEOneNz5hAUqlmy8r0HNsFi6eZvw5fYFKfOcRExqaqjNFo4NWpPQhqUBaA374/yOdDFwNQqWF5+nzcDZPZRMS1SD4bvJAzh867PL77cff4xZlujQHhN2DUR2Ymvp/I6oUJFCpgZ+ock1OZPQcMzFtsYs4nCXzzRSL1atsZM9HRjpz4fiLffOF4jRxixTc7DBuYdb4EFb/if3c8TB0D338NhQrAJ7Ody+zeD3OXwLxJsPILR2Nn1ETHe99tgrMXYPU8WPkl7DkIG7e7PIwHdvOGgSkTvBg2OpbZC6LJl9/G/LmeTmUOHTDxv6UefDAxhmlzYqheK5Fpk7wA+Hq+J3kC7Mz8IobJM2NYv8bC0SOZ99Tqn8eXwZ+/zPvPTqFXhcGE/HWFXh90SXWZpt3qU7h0fl6u8jZ9qw0jqEE56neqhbdfNkZ+8wafD11M32pDmfbaPN5dPACLR+a63nb3+CU5l31az549y5UrVwD49ttvGTt2LOvXr3fV5v/Rrj1GHi9rp2ghx/Qzba18/4MR+12pwT+PG6hVzUbeQMd00/o2ftxlICHhTpmEBBj5oZkh/RPJF+i6+j8sxe/e8f+yByqUhWK34+/aDtb+gFP8R05AnWokxfVUA9i205FBstogNs7xd3w8JCSCp4fr43hQ+/eaKFXGRsFCjoBbtU1g+xaLU/ynThqpXNVKngDHzLr1EvntVzMJCdDn1Vv06nsLgPBwxzHh45NsM5lGtaeCOL73DMGnLgOwdvYPNOn6RKrLmExGvHw8sXhasHiaMXuYSYhLoOBj+Yi+GcPBbUcAuHA8mJiIWMrVLuXC6O7P3eOX5O7bEPryyy+pWrUqlSpVolWrVgwcOJBZs2axdetWLl68mKqNzJ8/n169etGlSxeGDRvGunXrKF68OMuXL2fGjBkPHcTDuhIK+QLunPXyBkBUtIHomDtlKpa3s+eAkWDH54LV3xtJSDBwI+JOmZXrjQTkttOkfta6uar43Tv+y6GQ/66GW0rxB5VzZIUu3Y5/5fckxd+hBfj5QqNO0KAjFCkIjZ2/VzK1q2FG8gTYkqbzBNiJiTYQe1f8pcvaOHTQROgVx/2+zRstJCYYiIwwYDCAyQQTx3nxai8fKlayUrCw7d7NZBoBhXIRdvFa0nTYxXB8/L3x9s2WqjKbvvqRyOvRLD47g6XnZxJ8+gq/rtvPpZOX8fLxpNqTFQEoXa0ERcsXIlf+HK4LLhXcPf7UMthd98po920IzZ49m48//pjNmzczZswYatWqxZUrV/j8889p37491atXp2vXrv+4juXLl7N+/Xq+/vprNmzYwOzZs+nWrRuzZs1i48aNaRbMg7LZU+7PYLrrv1M1yM7LL1oZNMLMc33MGIzg72fHclfW8+tvTfy3uzX9K5zGFL+bx/8339nGu+KvXgleeQleGw6d+zj+X/8f/4z5kNMfdqyC7f+DmxEwb5krap427LaU9//d8VcIstK1ezwfjMzGwH7eGA3g62vHbL5zFh/8ThyLV0YRGWlg6cLMmxIzGJ2znf/PZrWlqszzIzpx82oEzxbqy3PF++Ob04dOA1sRExnL6M6T6PJ2O2bt/ZAnn6/PwW1HSIzPXLeJ3T1+Se6+Ny+zZ89Oo0aNMJvNBAYGUq1aNaf3L168yMmTJ/9xHTabDQ8PDwoWLEjPnj3x9Lxz/91qzfgvjvyB8MfRO2fC0Kvg52sn250LBKJjoFolGx1aOz4sV8Jg5pcm/P0c7x87acBqheqVM0Hz9l9S/G4ef144dPTO9JWr4O9rx/ue+GtUgs6tb5cJg0+/dHSW3rwDhg8AD4vj1b4FbPwRejzr2jgeVECgjePH7pwKr101kN3Xjtdd8cfEQIVKiTRr5bgXejXMwNfzPPH1g317TBQrbiN3Hscx07BxAr/ssLg6jFQLu3CVsjVLJk3nKZiLiPAo4mJupapMvfY1mDFwAYkJVhITYtm8cAf1O9ZkxdTviYuOY8hTY5OW+/LIJwSfvuKawFLJ3eNPNf3Exh19+vTh22+//dv3CxUqROPGjf9xHc2aNeP555/HarXy2muvAXDs2DGee+45WrZs+S+rnPbq1LBx6E8D527f6fvfdyYaPeF8mRx2Ff470EJUtGN67tcmWjSxJV1J7j1ooGYVW5YaKfP/FL97x/9EDfj9Tzh7O/5l30GTe25thV6FFweSFP9nC6F1U0cmpXwp+H6bY35CImz9BSqVd139H1aV6laO/2ni0kXHzlu/xkLtus5X8eHXDAwb5E3M7fi/WexBgyYJGAzw83YzS77ywG6HhHjY8aOFSlUybxZg3+bDlKtZigKP5QPg6T5N2bVmX6rLnDxwlgadawNgMpuo06Yqx347hd1uZ+zqtyhVtTgADZ+pTXxcQqYbNeXu8UtyBrs9pQTgHVWqVCEhIYEGDRpQv359ypUrR5kyZch29+VyKuzZs4caNWokTZ85c4YLFy7QsGHDf13p2JDi/3qZ+9nxq2P4dEKCgUIF7Ix9J5GLwQbem2Dimy8cJ7WlK4wsW2XCZocqFW0Mfd2K1+3k1rgpJgJy2en9QubtG/BPFH/WiN/TkD6Zhh9/dQyfT0iAwgVh/DtwMRhGTHCMEgNYtAIWr3TcSqxaEUYMdAylv34Txk6Bo6cct5PqVIUhrziyQ2npdGJU2q7wLnt2O4bPJyZC/vx2Bg2N5XKIkU8/8WLaHEdnoTWrLKxbbcFuM1C+QiJ9B9zC0xOiomDGZC/OnXVcV9apl0i3F+Odbq2lhVdLNEqzddVoUZmeY5/F4mEm+PQVJvScRb7igQya3Zt+Nd752zKR16PxzZWd/lNf4rHKxbBabRzc+gdziKxHXAAAIABJREFU3l5EYoKVivXL0u+TFzB7mAkPucGUV+Zy+a/QNKt3WsmK8W+KX5wm60mtElMmuWxbZwYOctm2UnLfhtD58+c5fvx40uvYsWMEBwdTqFChDOvfkx4NIZGsIL0aQllBejaEsoK0bAjJ/7V373E21fsfx9/7Ombcb2NocpKmcMplnFzquBZyqYhTUnHcJ02uKeQWchANoVJuKU6oKBVRyq8yqYSpqNxzGWaYGIzLnr3X74+pzTpDRmb2ntnr9Xw85vGwtu/e6/OxvjPz8fl+194FD4VQ3rnsHqGKFSuqYsWKatasmf+xjIwM/fLLL3kaGAAACJKCt93xL/tLzduIiAjVrFkzt2MBAAAIKN7yEgAAmOSH9/cJlPz7PvAAAAB5jI4QAAAwoyMEAAAQ+iiEAACAZbE0BgAAzFgaAwAACH10hAAAgAm3zwMAAFgAHSEAAGBm2IIdQcDQEQIAAJZFRwgAAJixRwgAACD00RECAAAm3DUGAABgAXSEAACAGR0hAACA0EdHCAAAmLBHCAAAwALoCAEAADM6QgAAAKGPQggAAFgWS2MAAMCMpTEAAIDQR0cIAACYcPs8AACABVAIAQAAy6IQAgAAlsUeIQAAYMYeIQAAgNBHRwgAAJhw1xgAAIAFFMiOUNK5YEcQPFVd3mCHEFQumyPYIQTVxnPWnfzLj9ULdggIIiPTE+wQrIWOEAAAQOgrkB0hAACQh+gIAQAAhD46QgAAwIS7xgAAACyAQggAAFgWS2MAAMCMpTEAAIDQR0cIAACYsFkaAADAAugIAQAAMzpCAAAAoY+OEAAAMKMjBAAAEProCAEAABPuGgMAALAAOkIAAMCMjhAAAEDooyMEAADM6AgBAACEPjpCAADAhLvGAAAALIBCCAAAWBZLYwAAwIylMQAAgNBHRwgAAJiwWRoAAMAC6AgBAAAzOkIAAAChj44QAAAwoyMEAAAQ+ugIAQAAE1uwAwggOkIAAMCy6AgBAAAzC+0RohD63eYNNi2d65DHY9O1lQz1GJip8MLmMauX2/Xxew653YYqVDTUOd6rIsXO//3RFGlMP5fGvexR0eKBjf9qfZFo18zZLp3zSDHXGxo++JyK/E/+i99xaMlyp8LcUqW/+fRkP4+K/57/0uUOvfuhU2fPSlVu9GnEYI/c7sDn8Vf9X6JN01916JzHppjrDY1+MjNb/v99x643lzkU5jZ0/d8MDe3vVfFi0hMjnfr1wPlxBw/ZVLuGoWnjMwObxFXYtMGuJXMc8nikipUM9RiUqYiLzP/V7zrkdksVKhr69+OZ2eb/6L5ujZ91rsDN/wslbzyhHxYdltdjqPjfCukfj1aQK8JhGnNgQ7q2LkmRbDa5izhUO66CikQVnAlfp2VNdRvXUa4wp3Z/v0/P93pFGSdO52iM3W7TY9O6qnrDKpKkr1du1qtDFkmSajSqpl6THpLD6VD60RN6+YnXtSvp14Dndzl1WsWq+/hOcoW5tDtpr6b0eCl7/pcYM2LJIF1zQ5R/XFSlSCWt26qRbSeqYtVoDZjVW+FFCskwDM0ZulDfrt4S6PRwhVgak5R+THp1slOPj8zUpLkeRZY3tHiO+Qff1s02fbDEoSETPRr3cqZq1DE0b+r5OvKLNXY9O8il344WvJXV345JYya5NfGZc3p7wVldU96nGa+4TGO+3WTXgv+69OKUs1o0+6xur+vT+ClZP/jX/p9dS5Y5NXPyWS2ed1Znz9q06K2CU2OnHZNGTXRq8phMvfu6R9EVDE17xXz9v9lk07xFDr0yxaMlczL1z3qGxk7OynHymEwtmZP1NXKwV0WLSEP7F5wi6I/5329kpibP+2P+m6/f1s02rVjs1NBJHo2f5VHNOj7NuWD+f77GrnGD3AVy/l/o7PFMffviAdV74lrd9UKMCpdz6fuFh01jvGd9+nr6ftV/4lo1m1xZ5f9RRJvnJgcp4itXvExRPfFqb415YKq63/yEkncfVvdnO+Z4zB0PNdC1N5ZX71pPKa72UFVvWFUN2tdVRLFwjVwyQK8OWaS42kM0/fF5enpRX7nc+etnQfEyxfTE3D4a02GyulXtl5XbhIdyPGbs/VMUFztYcbGD9Xyvl3Xy2ClNj58tSeo7s4dWzVuruNjBmtz9RQ1fPFB2R8H8NWszAvd1JVasWKFWrVqpefPmWrhw4SXHffbZZ2ratGmOXjMoV2jChAnBOO0l/bDRrutvMhR1TdZx0zZeJa61y7jgAu3ZbtPfa/lUqmzW8T9u92nTBpsyPdJvR6WN6+0a/B9P4IPPBV9941C1m3yqGJ2VcPt7vVr1icOU/7Zf7Lq1tlflfs+/SQOvPk+0y+ORPlzt1EP3Z6p4Mclul4YOPKdWzQpOIZD4jV1/r2Lob9FZx/+6x6uVH5uv/9afbapb26dykVnHdzTwaV2iTZ4LLrnHI438j1OD4zMVFRm4+K/W9xvtqnSjT1G/X/877vZq/Sfm/Hdvt+vmWj6V/mP+/9OnTV/Zs+b/EWnjl3Y9WUDn/4UOJ51UycrhKlo+TJJUuXkp/fr5cRkX/GMYPkMyJE+GT5KUecYnh7vgFIC1m1XXz9/u0sEdhyRJ78/6WE0fvD3HYxwOuwoVDpMrzCVXmFNOt1OeMx5dc0OUTh3P0OZPf5Qk7fv5oDLST6tqvZgAZnd5tZtX1y/f7NSB33Nb8dJq3dGpwRWPcbqcenJ+vF4aMF+p+49KkuwOu4qWzGqlRhQN17kz5/I6HUs5fPiwEhIStGjRIi1fvlyLFy/Wjh07so07cuSIJk6cmOPXzfNSfejQodkeW7t2rY4fPy5J+s9//pPXIVzW0VSpVNnzP+hKlZVOZ9h0JkP+5bHKVQytXu7QkcNelSkn/d9quzI9Np1Ml0qWlvqNKji/+P/X4VSbykWezz+yrKFTp2w6lSH/8tDNVX1a/I5LyYdsKh9laMWqrGXE4+nSr/tt+u03mx5/0q0jR22qeYtPfXsXnF+Kh1OkqAuuf7my0sn/yf+Waob++45DBw95VSFKenelXR6PTcfSpbKls8Ys+9CusqUNNW1QsBbXj6ba/AWOdH7+n86Qf3mschWfVi9z6chhZc3/j7Lm/4l0qWQZqf/ogjv/L5RxxKOIMue7oeGlXco87VPmaZ9/ecwZ7lBsrwr6dPhuuYs6ZPgMNRlbKVghX7Gy0aX8v7glKXV/mgoXj1BE0XD/8tCfjVm9YJ0atK+rRXtmyuG0a+PH3+urD75TRNFwFSocptp33qKNH3+vG2tfr79Vi1ap8iUCnuOfKXttGaXuP+I/Tt1/NHv+ORhzV/emOnowTV8u/9o/bnr8bD33ySjd17+NSkQW1/gHE+Tz+gKUWS4L4I+x9PR0paenZ3u8WLFiKlbs/Pr7+vXrVa9ePZUokTWnWrRooVWrVik+Pt70vOHDhys+Pl5TpkzJ0fnzvBAqUaKEli9frri4OH9CX331lerUqZPXp84xw5BsF/kPnf2CftlNtxhq97BX055xymaTGrbwqXBRQw5X9ucVNIbv4vlf2NGtVd2nnp0zNXikW3abdHfLTBUvZsjplDIzpQ0b7Zo87pzC3NLoCS69OMelQfEFoxjyXeL6X5h/bHVDvbt4NXCEU3abdG8rn4oXM+S64DvojaUOjRhU8AqCS13/C+d/lVsMtXskUwmjXbLbpIZ3eVWkqCFnCMz/CxmX+OFvs5//Bzq+94y2Lk1V84QbVCTKre0fHlXilH2687nKsl3sHzKfsdntF83zwl/Yfzbm4RHtdfxIuh6IjpM73K3Rbw1U+/6t9PbUDzW6w/PqOuZ+9ZjQSd9//pM2f/qjMs/lr+8Ju9122fxzMqZ9/9ZK6D3Lf+wKc2n4mwP0XNeZ2vDBd6paN0Zj3huin7/ZaSoqkd1rr72mGTNmZHs8Pj5ejz/+uP84JSVFZcue/19bZGSkkpKSTM9ZsGCBqlWrpho1auT4/HleCD311FNq2LChpk6dqoEDB6pu3bp67bXX1K5du7w+dY6VLivt/On8D7DfjkiFixoKCz8/5nSGVKW6T41aZn0jpB2R3n7NoSJFAx1t7itXztAP287/1ktNtalYUUPhF+R/KkOKrenTva3PSpJSUqVZ81wqXkwqU9pQkwZef/ek5Z1ezV5QcH5Dlo+Ufth2/vqnHNFF869dw6d2rbOu/+FU6cW5Dv9m8Z+22+T1Sv+oWbC6QZJUOtK46PwvdJH53/jC+T9fITH/LxRRxqW07ec3zZ5O88hV2CFnofPfH4e2nFSZKuH+zdE3tCilLfMP6dwJr8KK5a/9MBeTuu+IqtSp7D8uc00ppaed1JmMszka88+2t2pm/9eU6fEq03Naa17/XA3uq6N3pq3UmVNnNLjZOP/z5v44RQd3mvdYBVvKr0dUpc755bqL5X+5MZVrXieH06GkdVv9YyrdfK3CIsK04YPvJEnbNmzX3h/3qUrdGAqhy+jSpctFa4ILu0GS5PP5TP/ZMAzDdPzLL79o9erVmj9/vg4dOpTj8wdkj1D9+vU1a9YsLVq0SBMnTpTX6w3EaXPslto+7dxm06Hf7/xZ+75DsfXN7cxjR6Xxg106fSrr+L1FDtVv4rvo/6QLmnr/8OqHbXb9uj8rmbdXONTwdvM1Sj1iU1z/MJ38Pf+5b7jUvKlXNpt0RyOvPv7MoTNns/5H/dmXDlWrUnDawfVv9Slpq01792cdv/WeQ41vN8efekTq0d/lz3/2Gw7d1fT89f92s011ahXM+XBLbZ92bLPr0O/X/5OLzP/fjtr07BNuZfye/7uLnCEz/y9UrkYRpW3P0InkrF94u1b/pgq3mqu9kpUKKXVrhs4cy+p0HPjmhApHugtEESRJG9d8r6p1YlTh9zuf2vS6Q4krNuZ4zPZNe9SwQz1JksPpUP27Y/XT1ztkGIbGvfukYmKzlgkb/auezp3x5Lu7xjau3qKq9WL8d361iWuuxHe/uaIx1RtV0+ZPfzA958COQypcPELV6t8oSSp/fTlVrBatHZt252U6eccI3FexYsUUHR2d7et/C6GoqCilpqb6j1NTUxUZeX5D5qpVq5Samqr27durV69eSklJUadOnS6bqs0wLtUMzhtLly7VypUrNXfu3L/8Ghv25v56/JavbVoy16FMj02RFQz1HpyplEM2zX3eoXEvZ/3AW/Nu1u3zhiHd+HefOsd75Q4zv07n5m7NXJp3tw9XdeVNEfnlV3bNfNUlT6YUXcHQ6KHndCDZpnHPubVodtYvhSXLHFq63CmfIdW82afB/TwqFCZ5vdLcN5xa86lDXp9UJcanoQM92W4/zw0um+Pyg/6Cz7/Kun3e47EpuoKhccMytf+gTc8859CSOVnX/8137Fq83CGfIdW6xach/bwq9Pv1Hz/VobKlDPXsnLcF4A+evPl23bzB/vv8lyIrGIp7MlMpyTbNft6p8bOyljj/ePsInyHddLOhLvGZ2eb/w83C9NJbZ/Nk/i8/Fpv7L3oRyd9l3T7vyzRUuJxbdeKv0ckUjza+dFDNJmd1SXasOqqdq9Jkd2bdPl+ze3kVv7ZQnsb1zT9y7/Vvvaumuo17QC63Uwd3HtZz3V5SVKVIDZzVU4/eOuySY078dkpFSxVR/LR/64aa18nr9Wnz2h/0ylMLlenx6pYGVfTolM5yup1KSz6mqX1m69DulFyJ2cjMvaX2Oi1rqdv4Tv7cJnWZofLXR2rgq48qLnbwJcec+O2kJOnxGd11NPk3LXr2HdPr1mj8d/Wc+LDchdzyZnr1+pilWv8/RdZftca3NFdeJ6dq9E0I2Lm2vDAgR+MOHz6sBx98UG+99ZbCw8PVsWNHjR07VtWrV882dv/+/ercubPWrl172dcNeCGUG/KiECoo8qoQKijyqhAqKPKqECoIAlUI5Ve5WQgVRLlZCBVEgS6Eaj4euEJo8/ScFUJS1u3zs2bNksfjUYcOHdSzZ0/17NlTffv21S233OIfdyWFUMHo5QIAAMu7++67dffdd5see/XVV7ONi46OzlERJFEIAQCA/2Wh5nPBfMtLAACAXEBHCAAAmFzpR18UZHSEAACAZdERAgAAZnSEAAAAQh8dIQAAYMIeIQAAAAugIwQAAMzoCAEAAIQ+OkIAAMCMjhAAAEDooxACAACWxdIYAAAw4fZ5AAAAC6AjBAAAzOgIAQAAhD46QgAAwMRmWKclREcIAABYFh0hAABgZp2GEB0hAABgXXSEAACACe8jBAAAYAF0hAAAgBkdIQAAgNBHRwgAAJiwRwgAAMAC6AgBAAAzOkIAAAChj0IIAABYFktjAADAhM3SAAAAFlAgO0IjYuoHOwQEiZHpCXYIQWWPiAh2CAgSIzMj2CHASugIAQAAhL4C2RECAAB5hz1CAAAAFkBHCAAAmBnWaQnREQIAAJZFRwgAAJiwRwgAAMAC6AgBAAAzOkIAAAChj44QAAAwsfmCHUHg0BECAACWRUcIAACYsUcIAAAg9FEIAQAAy2JpDAAAmPCGigAAABZARwgAAJjxoasAAAChj44QAAAwYY8QAACABdARAgAAZnSEAAAAQh8dIQAAYMIeIQAAAAugIwQAAMx4HyEAAIDQR0cIAACYsEcIAADAAugIAQAAMzpCAAAAoY9CCAAAWBZLYwAAwITN0gAAABZARwgAAJj5rNMSsnQhVKdlTXUb11GuMKd2f79Pz/d6RRknTudoTNGShfX4jG6qXONvOnPqrFa/tk7vvrhaknRj7ev16JRHVKhwmOwOu5ZMXqFPFn0ZjBT/1NXkb7fb9Ni0rqresIok6euVm/XqkEWSpBqNqqnXpIfkcDqUfvSEXn7ide1K+jXg+V1OnVax6j6+k1xhLu1O2qspPV7Knv8lxoxYMkjX3BDlHxdVKVJJ67ZqZNuJqlg1WgNm9VZ4kUIyDENzhi7Ut6u3BDq9K1KnRQ11feZfcrmd2v3jPiX0maOME2cuOvaJWT2158f9euuFlf7HCheP0OSPhun5R2dr+6Y9AYo691gtf6vPfavnDzPLLo0VL1NUT7zaW2MemKruNz+h5N2H1f3ZjjkeEzf5EZ05eUY9qw9Wv3+O1K0taqpuq1qSpJGL+2vBmLf16K3D9PTdk9R70sOqcME3Tn5wtfnf8VADXXtjefWu9ZTiag9V9YZV1aB9XUUUC9fIJQP06pBFiqs9RNMfn6enF/WVy52/au7iZYrpibl9NKbDZHWr2i8rtwkP5XjM2PunKC52sOJiB+v5Xi/r5LFTmh4/W5LUd2YPrZq3VnGxgzW5+4savnig7I78+61WvExRDXq5h8Y+NF09Yofo0O5UdRtzf7Zx195UXhM/eEoN2t5qevzW5tU17dORio7JX3M8p6yWv9XnvtXzzzEjgF9BFpArlJSU5P9zYmKiJkyYoMmTJ2vLluBVyrWbVdfP3+7SwR2HJEnvz/pYTR+8PcdjYmIr6eOFX8jnM5Tp8WrDyk1qcF8ducJcemPcO9q09gdJ0pEDaTp25ITKXlMqgNld3tXm73DYVahwmFxhLrnCnHK6nfKc8eiaG6J06niGNn/6oyRp388HlZF+WlXrxQQwu8ur3by6fvlmpw78ntuKl1brjk4NrniM0+XUk/Pj9dKA+Urdf1SSZHfYVbRkYUlSRNFwnTtzLq/TuSqxTW/Wzxt36eDOw5Kk92evVdP762cbd0+vO7Vq/jr937KvTY+3fbSZJvWYpbRDxwISb26zWv5Wn/tWzx/ZBaQQGjVqlCRp4cKFGj9+vKKiolSmTBmNHDlSb7zxRiBCyKZsdCn/5JWk1P1pKlw8QhFFw3M05qevd+jOh/4ph9OhQoXD1KBdHZWKKinPWY9Wzf/M/5xW3ZsqokghbduwPSB55dTV5r96wTqd+O2UFu2ZqTd/fVEHdx7WVx98pwPbD6lQ4TDVvvMWSVnLhH+rFq1S5UsELrkcKHttGaXuP+I/Tt1/NHv+ORhzV/emOnowTV8uP//LcXr8bHUc0k6Lfn1ZE9eM1At9XpXP68vjjP66stGldORAmv849cAf17mQadzMQa/r06VfZXv+0+2m6Jfvdud5nHnFavlbfe5bPf+cshmB+wq2gK5XLFmyRAsWLFDJkiUlSR06dFCHDh308MMPBzIMSZLNbr/oh+teOGn/bMysJxeq18SH9NI345V26Li+++R7Vat/o2ncA4PvVtv4u/T03RN17ownt1O4Kleb/8Mj2uv4kXQ9EB0nd7hbo98aqPb9W+ntqR9qdIfn1XXM/eoxoZO+//wnbf70R2Wey8zDbK6c3W67bP45GdO+f2sl9J7lP3aFuTT8zQF6rutMbfjgO1WtG6Mx7w3Rz9/sNBWV+cml8vQW0B/gV8pq+Vt97ls9f2QXkI5QZmamfD6fSpQoIbfb7X/c7XbLbg/O+mnqviMqXeF8l6LMNaWUnnZSZzLO5mhMRLFwzR66SL1qPaUhLcdLNpt/Ccnldmro6/Fq/MBt6t9wVL7cKHy1+f+z7a36aP46ZXq8ykg/rTWvf64ajarJZrPpzKkzGtxsnB79x1C9OOA1Rd9Y3r/skF+k/HpEpcuX9B9fLP/Ljalc8zo5nA4lrdvqH1Pp5msVFhGmDR98J0natmG79v64T1Xq5q+lwQul7EtT6agLrnOFkjqRdlJnM6zR1rda/laf+1bPP8cMI3BfQRaQKqREiRJq3Lixdu/erbFjx0rK2ivUsWNH3XXXXYEIIZuNa75X1Tox/k3MbXrdocQVG3M8pk2vO9V51L8kSSUii6ll1yZa++Z6SdJTrz2miKLhGtBwtA7vPaL86Grz375pjxp2qCdJcjgdqn93rH76eocMw9C4d59UTGwlSVKjf9XTuTOefFcMbly9RVXrxfjv/mgT11yJ735zRWOqN6qmzZ/+YHrOgR2HVLh4hL87WP76cqpYLVo7NuXfpZONa79XlTqVVaFyOUlS6+5NlfjBpiBHFThWy9/qc9/q+SO7gCyNvf7665KkXbt2KT09XVJWN6hv375q3LhxIELI5lhquib3nKURb/aTy+3UwZ2H9Vy3lxQTW0kDZ/XUo7cOu+QYSXpz4rt6an4fvbJpomSzacGYpfpl4y5VrRujhu3rat8vB5WwbpT/fLOHvamNa5IuFU7AXW3+Lz/xuuKn/Vtzvp8sr9enzWt/0JLJKyRJ/+k8QwNe7imn26m05GMa3eH5YKZ6UcdS0zW524sasXSQP7dJXWboxtrXa+CrjyoudvAlx/whOqa8Du1JMb3uqeMZGn3fc+oztavchdzyZno1tfcsJe/KXx2xCx1PPaEpcbM14o14Od1OJe9K0XO9XlFMres0YGY39bltZLBDzFNWy9/qc9/q+edUfti7Eyg2w8gHfakr1NzdKdghIEiMzPy11yrQ7BERwQ4BQeLLyAh2CAiiNb6lAT1fkxYTA3auTz96KmDnupj89eYuAAAg+Apci+SvK6Dv9AQAAHD16AgBAAATW8HbNfOX0RECAACWRSEEAAAsi6UxAABgFppvrH5RdIQAAIBl0RECAAAmbJYGAACwADpCAADAzDoNITpCAADAuugIAQAAM/YIAQAAhD46QgAAwMRmnYYQHSEAAGBddIQAAIAZe4QAAABCHx0hAABgYuOzxgAAAEIfHSEAAGDGHiEAAIDQR0cIAACYWachREcIAABYF4UQAACwLJbGAACAiY3N0gAAAKGPjhAAADCjIwQAABD66AgBAAAzPmIDAAAg9NERAgAAJtw1BgAAYAF0hAAAgBkdIQAAgNBXIDtCNrcr2CEgSBzFiwU7hKDynT4d7BCCxpeREewQAOugIwQAAJC/rFixQq1atVLz5s21cOHCbH//8ccf695779U999yjPn366Pjx45d9TQohAABg5gvgVw4dPnxYCQkJWrRokZYvX67Fixdrx44d/r8/efKkRo8erVdeeUXvvfeebrrpJk2fPv2yr0shBAAA8r3169erXr16KlGihCIiItSiRQutWrXK//cej0ejRo1SuXLlJEk33XSTkpOTL/u6BXKPEAAAyDuBfB+h9PR0paenZ3u8WLFiKlbs/L7QlJQUlS1b1n8cGRmppKQk/3HJkiXVrFkzSdKZM2f0yiuv6JFHHrns+SmEAABA0Lz22muaMWNGtsfj4+P1+OOP+499Pp9sNpv/2DAM0/EfTpw4occee0xVqlRRu3btLnt+CiEAABA0Xbp0uWjBcmE3SJKioqL07bff+o9TU1MVGRlpGpOSkqLu3burXr16GjZsWI7OTyEEAADMArg09r9LYJdy2223afr06UpLS1N4eLhWr16tsWPH+v/e6/UqLi5OLVu2VJ8+fXJ8fgohAACQ75UrV04DBgxQ586d5fF41KFDB1WvXl09e/ZU3759dejQIW3dulVer1cfffSRJOnmm2/Ws88++6evazOMgveuSS2KdAl2CAgSe3h4sEMIKt5QEbCmNb6lAT3fXdWHB+xcq5LGBexcF8Pt8wAAwLJYGgMAAGYFb7HoL6MjBAAALIuOEAAAMLuCj74o6OgIAQAAy6IjBAAATAL5ERvBRkcIAABYFh0hAABgRkcIAAAg9NERAgAAZj46QgAAACGPjhAAADBjjxAAAEDooxACAACWxdIYAAAwY2kMAAAg9NERAgAAZnSEAAAAQh8dIQAAYMYbKgIAAIQ+OkIAAMDM8AU7goChIwQAACyLjhAAADDjrjEAAIDQR0cIAACYWeiuMQqhC9RpUUNdn/mXXG6ndv+4Twl95ijjxJmLjn1iVk/t+XG/3nphpf+xwsUjNPmjYXr+0dnavmlPgKLOPVbO/9ZmN6vr023lCnNq99YDmtrvdWWcvHjug2Z00Z6tB/X2i2skSe5CLj02saNurHWdbDabfv5ut2Y+9abOnfEEMoWrYrVrX6dVrLqP7yRXmEu7k/ZqSo+XlHHidI7GjFgySNfcEOUfF1UpUknrtmpk24mqWDVaA2b1VnjmIUgHAAAMYElEQVSRQjIMQ3OGLtS3q7cEOr3LIn9r5w8zlsZ+V7xMUQ16uYfGPjRdPWKH6NDuVHUbc3+2cdfeVF4TP3hKDdreanr81ubVNe3TkYqOicr2nILAyvkXL11EA6d11rhur6hn/dE6tOeIuo5ol23ctTFR+s87/fXPNrGmxzsOaCm7w6E+jcapT6Oxchdy64F+dwUq/KtmtWtfvEwxPTG3j8Z0mKxuVfspefdhdZ/wUI7HjL1/iuJiBysudrCe7/WyTh47penxsyVJfWf20Kp5axUXO1iTu7+o4YsHyu7IXz9myd/a+eeYYQTuK8gCdoU+//xzpaenS5KWL1+uMWPG6O233w7U6S8rtunN+nnjLh3ceViS9P7stWp6f/1s4+7pdadWzV+n/1v2tenxto8206Qes5R26FhA4s1tVs4/tnE1/bJ5rw7uSpEkvT///9SkQ51s49p0b6yP3vhSn6/4zvT4D4nb9ebzH8owDPl8hnZ+v0+R15YKSOy5wWrXvnbz6vrlm506sOOQJGnFS6t1R6cGVzzG6XLqyfnxemnAfKXuPypJsjvsKlqysCQpomi4zp05l9fpXDHyt3b+yC4gS2PPPvustm3bpoSEBE2dOlVJSUm68847tWbNGm3btk3Dhw8PRBh/qmx0KR05kOY/Tj2QpsLFIxRRtJBpiWDmoNclSbF33Gx6/tPtpgQm0Dxi5fzLXFNSqQd+8x8fOfibChcLV0SRQqblsZeGvClJim1SzfT87z7b5v9zZHQpte3dVC8MXJjHUeceq137steWUer+I/7j1P1Hf8833L88kpMxd3VvqqMH0/Tl8vOF4fT42Xruk1G6r38blYgsrvEPJsjnzV/vx0L+1s4/x/JBpyZQAlIIrV+/Xu+9954cDofWrVunxYsXy+1264EHHlCbNm0CEcJl2e22i153b0GdxFfIyvnb7baLftN7fVeW+w3VK2rEa3FaMeczfb3m+9wKL89Z7dpfKt8Lf2HlZEz7/q2V0HuW/9gV5tLwNwfoua4zteGD71S1bozGvDdEP3+z098xyA/I39r5I7uALI0VKlRIR49mTYSoqChlZGRIkk6fPi2nM3/s107Zl6bSUSX8x2UqlNSJtJM6m2GN1qaV80/Zn6ZSF+ZevoRO/HbqinJv1PYfGv9WP80bu0yLp67KizDzjNWufcqvR1S6fEn/cZlrSik97aTOZJzN8ZjKNa+Tw+lQ0rqt/jGVbr5WYRFh2vBB1tLptg3btffHfapSNyavU7oi5G/t/JFdQAqhxx57TB06dNDEiRMVHR2tRx55ROPHj9f999+vrl27BiKEy9q49ntVqVNZFSqXkyS17t5UiR9sCnJUgWPl/L/7bJuq1K6kCtdHSpJa/buhElfl/E6Pus1vUdz4+/X0v6bps3e+yasw84zVrv3G1VtUtV6M/86fNnHNlfjuN1c0pnqjatr86Q+m5xzYcUiFi0eoWv0bJUnlry+nitWitWPT7rxM54qRv7XzzzELbZYOSDumadOmiomJ0ccff6y9e/eqZs2aKly4sCZMmKDq1asHIoTLOp56QlPiZmvEG/Fyup1K3pWi53q9opha12nAzG7qc9vIYIeYp6yc//EjJ5TQb4GentNLTrdDyXtSNfmx+YqpUVH9pj6i+CbP/unzezzTXjabTf2mPuJ/bOvXO/XiU2/mdei5wmrX/lhquiZ3e1Ejlg6Sy+3UwZ2HNanLDN1Y+3oNfPVRxcUOvuSYP0THlNehPSmm1z11PEOj73tOfaZ2lbuQW95Mr6b2nqXkXYcDneKfIn9r54/sbIaRD8qxK9SiSJdgh4AgsYeHBzuEoPKdPn35QSHK9/uSOmBFa3xLA3q+luUfC9i5VibPDNi5LqaAvsEBAADA1csfO5UBAED+UfAWi/4yOkIAAMCy6AgBAAAzOkIAAAChj44QAAAw89ERAgAACHl0hAAAgIlhhOZnDV4MHSEAAGBZdIQAAIAZe4QAAABCHx0hAABgxvsIAQAAhD4KIQAAYFksjQEAADMft88DAACEPDpCAADAjM3SAAAAoY+OEAAAMDHYIwQAABD66AgBAAAz9ggBAACEPjpCAADAjA9dBQAACH10hAAAgJnBXWMAAAAhj44QAAAwMdgjBAAAEProCAEAADP2CAEAAIQ+CiEAAGBZLI0BAAATNksDAABYAB0hAABgZqHN0jbDsNBHzAIAAFyApTEAAGBZFEIAAMCyKIQAAIBlUQgBAADLohACAACWRSEEAAAsi0IIAABYFoUQAACwLAohAABgWRRCV2DFihVq1aqVmjdvroULFwY7nIA7efKk2rRpo/379wc7lICbMWOGWrdurdatW2vSpEnBDifgpk2bplatWql169aaN29esMMJmokTJ2rIkCHBDiPgHnnkEbVu3Vr33nuv7r33Xm3ZsiXYIQXU2rVrdd9996lly5YaN25csMNBLuOzxnLo8OHDSkhI0DvvvCO3262OHTuqbt26uuGGG4IdWkBs2bJFw4cP1549e4IdSsCtX79eX3zxhZYtWyabzaYePXpozZo1atasWbBDC4ivv/5aX331ld577z1lZmaqVatWatSoka6//vpghxZQiYmJWrZsmRo3bhzsUALKMAzt2bNHn376qZxO6/3K2Ldvn0aNGqWlS5eqdOnS6tKli9atW6dGjRoFOzTkEjpCObR+/XrVq1dPJUqUUEREhFq0aKFVq1YFO6yAWbJkiUaNGqXIyMhghxJwZcuW1ZAhQ+R2u+VyuVS5cmUdPHgw2GEFTJ06dbRgwQI5nU4dPXpUXq9XERERwQ4roI4dO6aEhATFxcUFO5SA27VrlySpW7duuueee/TGG28EOaLAWrNmjVq1aqWoqCi5XC4lJCSoRo0awQ4Luch65f1flJKSorJly/qPIyMjlZSUFMSIAuvZZ58NdghBExMT4//znj17tHLlSv33v/8NYkSB53K59MILL2ju3Lm66667VK5cuWCHFFAjR47UgAEDlJycHOxQAi49PV3169fXiBEj5PF41LlzZ1WqVEm33357sEMLiL1798rlcikuLk7Jyclq3Lix+vfvH+ywkIvoCOWQz+eTzWbzHxuGYTpG6Nu+fbu6deumJ598Utddd12wwwm4vn37KjExUcnJyVqyZEmwwwmYpUuXqnz58qpfv36wQwmKWrVqadKkSSpatKhKlSqlDh06aN26dcEOK2C8Xq8SExM1fvx4LV68WElJSVq2bFmww0IuohDKoaioKKWmpvqPU1NTLblMZFUbN27Uv//9bw0aNEjt2rULdjgBtXPnTm3btk2SFB4erubNm+vnn38OclSB8+GHH+rLL7/UvffeqxdeeEFr167V+PHjgx1WwHz77bdKTEz0HxuGYam9QmXKlFH9+vVVqlQpFSpUSHfeeaelVgOsgEIoh2677TYlJiYqLS1Np0+f1urVq9WwYcNgh4UASE5O1mOPPabJkyerdevWwQ4n4Pbv36/hw4fr3LlzOnfunD755BPVrl072GEFzLx58/T+++/r3XffVd++fdW0aVMNGzYs2GEFzIkTJzRp0iSdPXtWJ0+e1LJlyyxzo4AkNWnSRF988YXS09Pl9Xr1+eef6+9//3uww0Iusk5Zf5XKlSunAQMGqHPnzvJ4POrQoYOqV68e7LAQAHPmzNHZs2c1YcIE/2MdO3bUgw8+GMSoAqdRo0ZKSkpS27Zt5XA41Lx5c0sWhFbVpEkTbdmyRW3btpXP51OnTp1Uq1atYIcVMDVq1FCPHj3UqVMneTwe3X777Wrfvn2ww0IushmGYQQ7CAAAgGBgaQwAAFgWhRAAALAsCiEAAGBZFEIAAMCyKIQAAIBlUQgBAADLohACAACWRSEEIEdatmyphg0bavv27cEOBQByDYUQgBx5//33dd111+mjjz4KdigAkGsohADkiMPhUO3atS31gasAQh+fNQYgR86cOaMPP/xQfCoPgFBCRwhAjiQkJCgyMlK//vqrTp06FexwACBXUAgBuKxNmzZp5cqVmj59uooWLcqGaQAhg0IIwJ86e/ashg0bpmeeeUYlSpRQlSpV9NNPPwU7LADIFRRCAP7UtGnTVLNmTTVp0kSSVKVKFTZMAwgZFEIALikpKUmrVq3SsGHD/I9VrVqVjhCAkGEzuAUEAABYFB0hAABgWRRCAADAsiiEAACAZVEIAQAAy6IQAgAAlkUhBAAALItCCAAAWBaFEAAAsKz/B3U5o0PL8JmoAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 720x720 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"source": [
"# visual representation of grid search\n",
"# uses seaborn heatmap, could probably do this in matplotlib\n",
"import seaborn as sns\n",
"\n",
"sns.set()\n",
"\n",
"train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
"test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))\n",
"\n",
"for i in range(len(eta_vals)):\n",
" for j in range(len(lmbd_vals)):\n",
" CNN = CNN_keras[i][j]\n",
"\n",
" train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]\n",
" test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]\n",
"\n",
" \n",
"fig, ax = plt.subplots(figsize = (10, 10))\n",
"sns.heatmap(train_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
"ax.set_title(\"Training Accuracy\")\n",
"ax.set_ylabel(\"$\\eta$\")\n",
"ax.set_xlabel(\"$\\lambda$\")\n",
"plt.show()\n",
"\n",
"fig, ax = plt.subplots(figsize = (10, 10))\n",
"sns.heatmap(test_accuracy, annot=True, ax=ax, cmap=\"viridis\")\n",
"ax.set_title(\"Test Accuracy\")\n",
"ax.set_ylabel(\"$\\eta$\")\n",
"ax.set_xlabel(\"$\\lambda$\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The CIFAR01 data set\n",
"\n",
"The CIFAR10 dataset contains 60,000 color images in 10 classes, with\n",
"6,000 images in each class. The dataset is divided into 50,000\n",
"training images and 10,000 testing images. The classes are mutually\n",
"exclusive and there is no overlap between them."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 6,
"metadata": {},
"outputs": [],
=======
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
"170500096/170498071 [==============================] - 35s 0us/step\n"
]
}
],
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"source": [
"import tensorflow as tf\n",
"\n",
"from tensorflow.keras import datasets, layers, models\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# We import the data set\n",
"(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()\n",
"\n",
"# Normalize pixel values to be between 0 and 1 by dividing by 255. \n",
"train_images, test_images = train_images / 255.0, test_images / 255.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Verifying the data set\n",
"\n",
"To verify that the dataset looks correct, let's plot the first 25 images from the training set and display the class name below each image."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
" 'dog', 'frog', 'horse', 'ship', 'truck']\n",
"\n",
=======
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x720 with 25 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n",
" 'dog', 'frog', 'horse', 'ship', 'truck']\n",
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"plt.figure(figsize=(10,10))\n",
"for i in range(25):\n",
" plt.subplot(5,5,i+1)\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
" plt.grid(False)\n",
" plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
" # The CIFAR labels happen to be arrays, \n",
" # which is why you need the extra index\n",
" plt.xlabel(class_names[train_labels[i][0]])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up the model\n",
"\n",
"The 6 lines of code below define the convolutional base using a common pattern: a stack of Conv2D and MaxPooling2D layers.\n",
"\n",
"As input, a CNN takes tensors of shape (image_height, image_width, color_channels), ignoring the batch size. If you are new to these dimensions, color_channels refers to (R,G,B). In this example, you will configure our CNN to process inputs of shape (32, 32, 3), which is the format of CIFAR images. You can do this by passing the argument input_shape to our first layer."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 8,
"metadata": {},
"outputs": [],
=======
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_49\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_49 (Conv2D) (None, 30, 30, 32) 896 \n",
"_________________________________________________________________\n",
"max_pooling2d_49 (MaxPooling (None, 15, 15, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_50 (Conv2D) (None, 13, 13, 64) 18496 \n",
"_________________________________________________________________\n",
"max_pooling2d_50 (MaxPooling (None, 6, 6, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_51 (Conv2D) (None, 4, 4, 64) 36928 \n",
"=================================================================\n",
"Total params: 56,320\n",
"Trainable params: 56,320\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"source": [
"model = models.Sequential()\n",
"model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(layers.MaxPooling2D((2, 2)))\n",
"model.add(layers.Conv2D(64, (3, 3), activation='relu'))\n",
"\n",
"# Let's display the architecture of our model so far.\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tensor of shape (height, width, channels). The width and height dimensions tend to shrink as you go deeper in the network. The number of output channels for each Conv2D layer is controlled by the first argument (e.g., 32 or 64). Typically, as the width and height shrink, you can afford (computationally) to add more output channels in each Conv2D layer.\n",
"\n",
"\n",
"\n",
"\n",
"## Add Dense layers on top\n",
"\n",
"To complete our model, you will feed the last output tensor from the\n",
"convolutional base (of shape (4, 4, 64)) into one or more Dense layers\n",
"to perform classification. Dense layers take vectors as input (which\n",
"are 1D), while the current output is a 3D tensor. First, you will\n",
"flatten (or unroll) the 3D output to 1D, then add one or more Dense\n",
"layers on top. CIFAR has 10 output classes, so you use a final Dense\n",
"layer with 10 outputs and a softmax activation."
]
},
{
"cell_type": "code",
<<<<<<< HEAD
"execution_count": 9,
"metadata": {},
"outputs": [],
=======
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_49\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"conv2d_49 (Conv2D) (None, 30, 30, 32) 896 \n",
"_________________________________________________________________\n",
"max_pooling2d_49 (MaxPooling (None, 15, 15, 32) 0 \n",
"_________________________________________________________________\n",
"conv2d_50 (Conv2D) (None, 13, 13, 64) 18496 \n",
"_________________________________________________________________\n",
"max_pooling2d_50 (MaxPooling (None, 6, 6, 64) 0 \n",
"_________________________________________________________________\n",
"conv2d_51 (Conv2D) (None, 4, 4, 64) 36928 \n",
"_________________________________________________________________\n",
"flatten_49 (Flatten) (None, 1024) 0 \n",
"_________________________________________________________________\n",
"dense_98 (Dense) (None, 64) 65600 \n",
"_________________________________________________________________\n",
"dense_99 (Dense) (None, 10) 650 \n",
"=================================================================\n",
"Total params: 122,570\n",
"Trainable params: 122,570\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"source": [
"model.add(layers.Flatten())\n",
"model.add(layers.Dense(64, activation='relu'))\n",
"model.add(layers.Dense(10))\n",
<<<<<<< HEAD
"Here's the complete architecture of our model.\n",
=======
"#Here's the complete architecture of our model.\n",
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"\n",
"model.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (1024) before going through two Dense layers.\n",
"\n",
"## Compile and train the model"
]
},
{
"cell_type": "code",
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"execution_count": 10,
"metadata": {},
"outputs": [],
=======
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train on 50000 samples, validate on 10000 samples\n",
"Epoch 1/10\n",
"50000/50000 [==============================] - 40s 793us/sample - loss: 1.5115 - accuracy: 0.4515 - val_loss: 1.2411 - val_accuracy: 0.5545\n",
"Epoch 2/10\n",
"50000/50000 [==============================] - 41s 826us/sample - loss: 1.1297 - accuracy: 0.6006 - val_loss: 1.0419 - val_accuracy: 0.6307\n",
"Epoch 3/10\n",
"50000/50000 [==============================] - 43s 870us/sample - loss: 0.9842 - accuracy: 0.6534 - val_loss: 1.0402 - val_accuracy: 0.6314\n",
"Epoch 4/10\n",
"50000/50000 [==============================] - 43s 869us/sample - loss: 0.8824 - accuracy: 0.6894 - val_loss: 0.9944 - val_accuracy: 0.6599\n",
"Epoch 5/10\n",
"50000/50000 [==============================] - 40s 803us/sample - loss: 0.8098 - accuracy: 0.7171 - val_loss: 0.9176 - val_accuracy: 0.6829\n",
"Epoch 6/10\n",
"50000/50000 [==============================] - 46s 925us/sample - loss: 0.7469 - accuracy: 0.7370 - val_loss: 0.8683 - val_accuracy: 0.7072\n",
"Epoch 7/10\n",
"50000/50000 [==============================] - 43s 857us/sample - loss: 0.6939 - accuracy: 0.7546 - val_loss: 0.8628 - val_accuracy: 0.7055\n",
"Epoch 8/10\n",
"50000/50000 [==============================] - 38s 770us/sample - loss: 0.6492 - accuracy: 0.7719 - val_loss: 0.8725 - val_accuracy: 0.7120\n",
"Epoch 9/10\n",
"50000/50000 [==============================] - 37s 743us/sample - loss: 0.6064 - accuracy: 0.7881 - val_loss: 0.8604 - val_accuracy: 0.7144\n",
"Epoch 10/10\n",
"50000/50000 [==============================] - 36s 715us/sample - loss: 0.5675 - accuracy: 0.8003 - val_loss: 0.8882 - val_accuracy: 0.7137\n"
]
}
],
>>>>>>> 9b0e2e75096cc1acee65bfac25f4eff818140252
"source": [
"model.compile(optimizer='adam',\n",
" loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
" metrics=['accuracy'])\n",
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"\n",
=======
"\n",
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"history = model.fit(train_images, train_labels, epochs=10, \n",
" validation_data=(test_images, test_labels))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Finally, evaluate the model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"plt.plot(history.history['accuracy'], label='accuracy')\n",
"plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
"plt.xlabel('Epoch')\n",
"plt.ylabel('Accuracy')\n",
"plt.ylim([0.5, 1])\n",
"plt.legend(loc='lower right')\n",
"\n",
"test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)\n",
"\n",
"print(test_acc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recurrent neural networks: Overarching view\n",
"\n",
"Till now our focus has been, including convolutional neural networks\n",
"as well, on feedforward neural networks. The output or the activations\n",
"flow only in one direction, from the input layer to the output layer.\n",
"\n",
"A recurrent neural network (RNN) looks very much like a feedforward\n",
"neural network, except that it also has connections pointing\n",
"backward. \n",
"\n",
"RNNs are used to analyze time series data such as stock prices, and\n",
"tell you when to buy or sell. In autonomous driving systems, they can\n",
"anticipate car trajectories and help avoid accidents. More generally,\n",
"they can work on sequences of arbitrary lengths, rather than on\n",
"fixed-sized inputs like all the nets we have discussed so far. For\n",
"example, they can take sentences, documents, or audio samples as\n",
"input, making them extremely useful for natural language processing\n",
"systems such as automatic translation and speech-to-text.\n",
"\n",
"\n",
"## A simple example"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Start importing packages\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"from tensorflow.keras import datasets, layers, models\n",
"from tensorflow.keras.layers import Input\n",
"from tensorflow.keras.models import Model, Sequential \n",
"from tensorflow.keras.layers import Dense, SimpleRNN, LSTM, GRU\n",
"from tensorflow.keras import optimizers \n",
"from tensorflow.keras import regularizers \n",
"from tensorflow.keras.utils import to_categorical \n",
"\n",
"\n",
"\n",
"# convert into dataset matrix\n",
"def convertToMatrix(data, step):\n",
" X, Y =[], []\n",
" for i in range(len(data)-step):\n",
" d=i+step \n",
" X.append(data[i:d,])\n",
" Y.append(data[d,])\n",
" return np.array(X), np.array(Y)\n",
"\n",
"step = 4\n",
"N = 1000 \n",
"Tp = 800 \n",
"\n",
"t=np.arange(0,N)\n",
"x=np.sin(0.02*t)+2*np.random.rand(N)\n",
"df = pd.DataFrame(x)\n",
"df.head()\n",
"\n",
"plt.plot(df)\n",
"plt.show()\n",
"\n",
"values=df.values\n",
"train,test = values[0:Tp,:], values[Tp:N,:]\n",
"\n",
"# add step elements into train and test\n",
"test = np.append(test,np.repeat(test[-1,],step))\n",
"train = np.append(train,np.repeat(train[-1,],step))\n",
" \n",
"trainX,trainY =convertToMatrix(train,step)\n",
"testX,testY =convertToMatrix(test,step)\n",
"trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
"testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n",
"\n",
"model = Sequential()\n",
"model.add(SimpleRNN(units=32, input_shape=(1,step), activation=\"relu\"))\n",
"model.add(Dense(8, activation=\"relu\")) \n",
"model.add(Dense(1))\n",
"model.compile(loss='mean_squared_error', optimizer='rmsprop')\n",
"model.summary()\n",
"\n",
"model.fit(trainX,trainY, epochs=100, batch_size=16, verbose=2)\n",
"trainPredict = model.predict(trainX)\n",
"testPredict= model.predict(testX)\n",
"predicted=np.concatenate((trainPredict,testPredict),axis=0)\n",
"\n",
"trainScore = model.evaluate(trainX, trainY, verbose=0)\n",
"print(trainScore)\n",
"\n",
"index = df.index.values\n",
"plt.plot(index,df)\n",
"plt.plot(index,predicted)\n",
"plt.axvline(df.index[Tp], c=\"r\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Set up of an RNN\n",
"\n",
"The figure here displays a simple example of an RNN, with inputs $x_t$\n",
"at a given time $t$ and outputs $y_t$. Introducing time as a variable\n",
"offers an intutitive way of understanding these networks. In addition\n",
"to the inputs $x_t$, the layer at a time $t$ receives also as input\n",
"the output from the previous layer $t-1$, that is $y_{t1}$.\n",
"\n",
"This means also that we need to have weights that link both the inputs\n",
"$x_t$ to the outputs $y_t$ as well as weights that link the output\n",
"from the previous time $y_{t-1}$ and $y_t$. The figure here shows an\n",
"example of a simple RNN.\n",
"\n",
"More material will be added here.\n",
"\n",
"\n",
"## Solving differential equations and eigenvalue problems with RNNs\n",
"\n",
"\n",
"\n",
"In our discussions of ordinary differential equations and partial\n",
"differential equations using neural networks. Here we will discuss how\n",
"we can solve say ordinary differential equations and eigenvalue\n",
"problems using RNNs. Eigenvalue problems can be solved using RNNs by\n",
"rewriting such a problems as a non-linear differential equation.\n",
"\n",
"Instead of starting with a well-known ordinary differential equation,\n",
"we start directly with an eigenvaule problem.\n",
"\n",
"\n",
"\n",
"## Long-Short Time Memory\n",
"\n",
"Discussions about dynamic unrolling through time. discuss memory cells, input and output\n",
"\n",
"\n",
"\n",
"\n",
"## Autoencoders: Overarching view\n",
"\n",
"Autoencoders are artificial neural networks capable of learning\n",
"efficient representations of the input data (these representations are called codings) without\n",
"any supervision (i.e., the training set is unlabeled). These codings\n",
"typically have a much lower dimensionality than the input data, making\n",
"autoencoders useful for dimensionality reduction. \n",
"\n",
"More importantly, autoencoders act as powerful feature detectors, and\n",
"they can be used for unsupervised pretraining of deep neural networks.\n",
"\n",
"Lastly, they are capable of randomly generating new data that looks\n",
"very similar to the training data; this is called a generative\n",
"model. For example, you could train an autoencoder on pictures of\n",
"faces, and it would then be able to generate new faces. Surprisingly,\n",
"autoencoders work by simply learning to copy their inputs to their\n",
"outputs. This may sound like a trivial task, but we will see that\n",
"constraining the network in various ways can make it rather\n",
"difficult. For example, you can limit the size of the internal\n",
"representation, or you can add noise to the inputs and train the\n",
"network to recover the original inputs. These constraints prevent the\n",
"autoencoder from trivially copying the inputs directly to the outputs,\n",
"which forces it to learn efficient ways of representing the data. In\n",
"short, the codings are byproducts of the autoencoders attempt to\n",
"learn the identity function under some constraints.\n",
"\n",
"## Simple examples of Autoencoders"
]
}
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