From 834511d2c8f11741aeb91f6319bcadacd5e5be0e Mon Sep 17 00:00:00 2001 From: SungYune <81219515+SungYune@users.noreply.github.com> Date: Sun, 2 Oct 2022 21:53:49 +0900 Subject: [PATCH 1/2] =?UTF-8?q?=EA=B9=80=EC=84=B1=EC=9C=A4=206=EC=A3=BC?= =?UTF-8?q?=EC=B0=A8=20=EB=B3=B5=EC=8A=B5=EA=B3=BC=EC=A0=9C=20=EC=A0=9C?= =?UTF-8?q?=EC=B6=9C=ED=95=A9=EB=8B=88=EB=8B=A4.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Convolution_model_Application_v1a.ipynb" | 1 + .../Convolution_model_Step_by_Step_v2a.ipynb" | 1 + .../\352\271\200\354\204\261\354\234\244/alexnet_practice.ipynb" | 1 + 3 files changed, 3 insertions(+) create mode 100644 "6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Application_v1a.ipynb" create mode 100644 "6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Step_by_Step_v2a.ipynb" create mode 100644 "6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/alexnet_practice.ipynb" diff --git "a/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Application_v1a.ipynb" "b/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Application_v1a.ipynb" new file mode 100644 index 0000000..3074d38 --- /dev/null +++ "b/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Application_v1a.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"i67lXv5mmqpG"},"source":["# Convolutional Neural Networks: Application\n","\n","Welcome to Course 4's second assignment! In this notebook, you will:\n","\n","- Implement helper functions that you will use when implementing a TensorFlow model\n","- Implement a fully functioning ConvNet using TensorFlow \n","\n","**After this assignment you will be able to:**\n","\n","- Build and train a ConvNet in TensorFlow for a classification problem \n","\n","We assume here that you are already familiar with TensorFlow. If you are not, please refer the *TensorFlow Tutorial* of the third week of Course 2 (\"*Improving deep neural networks*\")."]},{"cell_type":"markdown","metadata":{"id":"p2PKtnmymqpJ"},"source":["### Updates to Assignment \n","\n","#### If you were working on a previous version\n","* The current notebook filename is version \"1a\". \n","* You can find your work in the file directory as version \"1\".\n","* To view the file directory, go to the menu \"File->Open\", and this will open a new tab that shows the file directory.\n","\n","#### List of Updates\n","* `initialize_parameters`: added details about tf.get_variable, `eval`. Clarified test case.\n","* Added explanations for the kernel (filter) stride values, max pooling, and flatten functions.\n","* Added details about softmax cross entropy with logits.\n","* Added instructions for creating the Adam Optimizer.\n","* Added explanation of how to evaluate tensors (optimizer and cost).\n","* `forward_propagation`: clarified instructions, use \"F\" to store \"flatten\" layer.\n","* Updated print statements and 'expected output' for easier visual comparisons.\n","* Many thanks to Kevin P. Brown (mentor for the deep learning specialization) for his suggestions on the assignments in this course!"]},{"cell_type":"markdown","metadata":{"id":"fh4iDVEWmqpK"},"source":["## 1.0 - TensorFlow model\n","\n","In the previous assignment, you built helper functions using numpy to understand the mechanics behind convolutional neural networks. Most practical applications of deep learning today are built using programming frameworks, which have many built-in functions you can simply call. \n","\n","As usual, we will start by loading in the packages. "]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ngxTfj9Vpw7M","executionInfo":{"status":"ok","timestamp":1664714039413,"user_tz":-540,"elapsed":19363,"user":{"displayName":"김성윤","userId":"09269601607968805204"}},"outputId":"a8bce8b0-eef6-4a7b-ae2c-6bbd3eeb445e"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["%cd /content/drive/MyDrive/bitamin/복습과제"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MN6dn8nzp3-u","executionInfo":{"status":"ok","timestamp":1664714339632,"user_tz":-540,"elapsed":6,"user":{"displayName":"김성윤","userId":"09269601607968805204"}},"outputId":"d59a1886-8590-4106-def2-51de70aa68eb"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/bitamin/복습과제\n"]}]},{"cell_type":"code","execution_count":11,"metadata":{"id":"RdS4AeAGmqpL","executionInfo":{"status":"ok","timestamp":1664714340026,"user_tz":-540,"elapsed":3,"user":{"displayName":"김성윤","userId":"09269601607968805204"}}},"outputs":[],"source":["import math\n","import numpy as np\n","import h5py\n","import matplotlib.pyplot as plt\n","import scipy\n","from PIL import Image\n","from scipy import ndimage\n","import tensorflow as tf\n","from tensorflow.python.framework import ops\n","from cnn_utils import *\n","\n","%matplotlib inline\n","np.random.seed(1)"]},{"cell_type":"markdown","metadata":{"id":"i7nSzVVBmqpN"},"source":["Run the next cell to load the \"SIGNS\" dataset you are going to use."]},{"cell_type":"code","source":["def load_dataset():\n"," train_dataset = h5py.File('/content/drive/MyDrive/bitamin/복습과제/datasets/train_signs.h5', \"r\")\n"," train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n"," train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n","\n"," test_dataset = h5py.File('/content/drive/MyDrive/bitamin/복습과제/datasets/test_signs.h5', \"r\")\n"," test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n"," test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n","\n"," classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n"," \n"," train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n"," test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n"," \n"," return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes"],"metadata":{"id":"6kFYixDir6lO","executionInfo":{"status":"ok","timestamp":1664714341235,"user_tz":-540,"elapsed":2,"user":{"displayName":"김성윤","userId":"09269601607968805204"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["# Loading the data (signs)\n","X_train_orig, Y_train_orig, X_test_orig, Y_test_orig, classes = load_dataset()"],"metadata":{"id":"JOAESZzXv5Z1"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"0tSPpbIImqpO"},"source":["As a reminder, the SIGNS dataset is a collection of 6 signs representing numbers from 0 to 5.\n","\n","\n","\n","The next cell will show you an example of a labelled image in the dataset. Feel free to change the value of `index` below and re-run to see different examples. "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"NHbYmCBbmqpO"},"outputs":[],"source":["# Example of a picture\n","index = 6\n","plt.imshow(X_train_orig[index])\n","print (\"y = \" + str(np.squeeze(Y_train_orig[:, index])))"]},{"cell_type":"markdown","metadata":{"id":"dgBDDv3smqpO"},"source":["In Course 2, you had built a fully-connected network for this dataset. But since this is an image dataset, it is more natural to apply a ConvNet to it.\n","\n","To get started, let's examine the shapes of your data. "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"eD5rBrU3mqpP"},"outputs":[],"source":["X_train = X_train_orig/255.\n","X_test = X_test_orig/255.\n","Y_train = convert_to_one_hot(Y_train_orig, 6).T\n","Y_test = convert_to_one_hot(Y_test_orig, 6).T\n","print (\"number of training examples = \" + str(X_train.shape[0]))\n","print (\"number of test examples = \" + str(X_test.shape[0]))\n","print (\"X_train shape: \" + str(X_train.shape))\n","print (\"Y_train shape: \" + str(Y_train.shape))\n","print (\"X_test shape: \" + str(X_test.shape))\n","print (\"Y_test shape: \" + str(Y_test.shape))\n","conv_layers = {}"]},{"cell_type":"markdown","metadata":{"id":"7DYu_dq1mqpP"},"source":["### 1.1 - Create placeholders\n","\n","TensorFlow requires that you create placeholders for the input data that will be fed into the model when running the session.\n","\n","**Exercise**: Implement the function below to create placeholders for the input image X and the output Y. You should not define the number of training examples for the moment. To do so, you could use \"None\" as the batch size, it will give you the flexibility to choose it later. Hence X should be of dimension **[None, n_H0, n_W0, n_C0]** and Y should be of dimension **[None, n_y]**. [Hint: search for the tf.placeholder documentation\"](https://www.tensorflow.org/api_docs/python/tf/placeholder)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ib0dhXD4mqpP"},"outputs":[],"source":["# GRADED FUNCTION: create_placeholders\n","\n","def create_placeholders(n_H0, n_W0, n_C0, n_y):\n"," \"\"\"\n"," Creates the placeholders for the tensorflow session.\n"," \n"," Arguments:\n"," n_H0 -- scalar, height of an input image\n"," n_W0 -- scalar, width of an input image\n"," n_C0 -- scalar, number of channels of the input\n"," n_y -- scalar, number of classes\n"," \n"," Returns:\n"," X -- placeholder for the data input, of shape [None, n_H0, n_W0, n_C0] and dtype \"float\"\n"," Y -- placeholder for the input labels, of shape [None, n_y] and dtype \"float\"\n"," \"\"\"\n","\n"," ### START CODE HERE ### (≈2 lines)\n"," X = tf.placeholder(shape=[None, n_H0, n_W0, n_C0], dtype=\"float\", name=\"X\")\n"," Y = tf.placeholder(shape=[None, n_y], dtype=\"float\", name=\"Y\")\n"," ### END CODE HERE ###\n"," \n"," return X, Y"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Iu6cwud5mqpa"},"outputs":[],"source":["X, Y = create_placeholders(64, 64, 3, 6)\n","print (\"X = \" + str(X))\n","print (\"Y = \" + str(Y))"]},{"cell_type":"markdown","metadata":{"id":"QP-aDvkymqpb"},"source":["**Expected Output**\n","\n"," \n","\n","\n","\n","\n","\n","\n","
\n"," X = Tensor(\"Placeholder:0\", shape=(?, 64, 64, 3), dtype=float32)\n","\n","
\n"," Y = Tensor(\"Placeholder_1:0\", shape=(?, 6), dtype=float32)\n","\n","
"]},{"cell_type":"markdown","metadata":{"id":"8dH6QjZEmqpb"},"source":["### 1.2 - Initialize parameters\n","\n","You will initialize weights/filters $W1$ and $W2$ using `tf.contrib.layers.xavier_initializer(seed = 0)`. You don't need to worry about bias variables as you will soon see that TensorFlow functions take care of the bias. Note also that you will only initialize the weights/filters for the conv2d functions. TensorFlow initializes the layers for the fully connected part automatically. We will talk more about that later in this assignment.\n","\n","**Exercise:** Implement initialize_parameters(). The dimensions for each group of filters are provided below. Reminder - to initialize a parameter $W$ of shape [1,2,3,4] in Tensorflow, use:\n","```python\n","W = tf.get_variable(\"W\", [1,2,3,4], initializer = ...)\n","```\n","#### tf.get_variable()\n","[Search for the tf.get_variable documentation](https://www.tensorflow.org/api_docs/python/tf/get_variable). Notice that the documentation says:\n","```\n","Gets an existing variable with these parameters or create a new one.\n","```\n","So we can use this function to create a tensorflow variable with the specified name, but if the variables already exist, it will get the existing variable with that same name.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e0RCydS4mqpb"},"outputs":[],"source":["# GRADED FUNCTION: initialize_parameters\n","\n","def initialize_parameters():\n"," \"\"\"\n"," Initializes weight parameters to build a neural network with tensorflow. The shapes are:\n"," W1 : [4, 4, 3, 8]\n"," W2 : [2, 2, 8, 16]\n"," Note that we will hard code the shape values in the function to make the grading simpler.\n"," Normally, functions should take values as inputs rather than hard coding.\n"," Returns:\n"," parameters -- a dictionary of tensors containing W1, W2\n"," \"\"\"\n"," \n"," tf.set_random_seed(1) # so that your \"random\" numbers match ours\n"," \n"," ### START CODE HERE ### (approx. 2 lines of code)\n"," W1 = tf.get_variable(\"W1\", [4, 4, 3, 8], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n"," W2 = tf.get_variable(\"W2\", [2, 2, 8, 16], initializer = tf.contrib.layers.xavier_initializer(seed = 0))\n"," ### END CODE HERE ###\n","\n"," parameters = {\"W1\": W1,\n"," \"W2\": W2}\n"," \n"," return parameters"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q1CWG04Lmqpc"},"outputs":[],"source":["tf.reset_default_graph()\n","with tf.Session() as sess_test:\n"," parameters = initialize_parameters()\n"," init = tf.global_variables_initializer()\n"," sess_test.run(init)\n"," print(\"W1[1,1,1] = \\n\" + str(parameters[\"W1\"].eval()[1,1,1]))\n"," print(\"W1.shape: \" + str(parameters[\"W1\"].shape))\n"," print(\"\\n\")\n"," print(\"W2[1,1,1] = \\n\" + str(parameters[\"W2\"].eval()[1,1,1]))\n"," print(\"W2.shape: \" + str(parameters[\"W2\"].shape))"]},{"cell_type":"markdown","metadata":{"id":"2tQt0VAQmqpc"},"source":["** Expected Output:**\n","\n","```\n","W1[1,1,1] = \n","[ 0.00131723 0.14176141 -0.04434952 0.09197326 0.14984085 -0.03514394\n"," -0.06847463 0.05245192]\n","W1.shape: (4, 4, 3, 8)\n","\n","\n","W2[1,1,1] = \n","[-0.08566415 0.17750949 0.11974221 0.16773748 -0.0830943 -0.08058\n"," -0.00577033 -0.14643836 0.24162132 -0.05857408 -0.19055021 0.1345228\n"," -0.22779644 -0.1601823 -0.16117483 -0.10286498]\n","W2.shape: (2, 2, 8, 16)\n","```"]},{"cell_type":"markdown","metadata":{"id":"_MWMZjK0mqpc"},"source":["### 1.3 - Forward propagation\n","\n","In TensorFlow, there are built-in functions that implement the convolution steps for you.\n","\n","- **tf.nn.conv2d(X,W, strides = [1,s,s,1], padding = 'SAME'):** given an input $X$ and a group of filters $W$, this function convolves $W$'s filters on X. The third parameter ([1,s,s,1]) represents the strides for each dimension of the input (m, n_H_prev, n_W_prev, n_C_prev). Normally, you'll choose a stride of 1 for the number of examples (the first value) and for the channels (the fourth value), which is why we wrote the value as `[1,s,s,1]`. You can read the full documentation on [conv2d](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d).\n","\n","- **tf.nn.max_pool(A, ksize = [1,f,f,1], strides = [1,s,s,1], padding = 'SAME'):** given an input A, this function uses a window of size (f, f) and strides of size (s, s) to carry out max pooling over each window. For max pooling, we usually operate on a single example at a time and a single channel at a time. So the first and fourth value in `[1,f,f,1]` are both 1. You can read the full documentation on [max_pool](https://www.tensorflow.org/api_docs/python/tf/nn/max_pool).\n","\n","- **tf.nn.relu(Z):** computes the elementwise ReLU of Z (which can be any shape). You can read the full documentation on [relu](https://www.tensorflow.org/api_docs/python/tf/nn/relu).\n","\n","- **tf.contrib.layers.flatten(P)**: given a tensor \"P\", this function takes each training (or test) example in the batch and flattens it into a 1D vector. \n"," * If a tensor P has the shape (m,h,w,c), where m is the number of examples (the batch size), it returns a flattened tensor with shape (batch_size, k), where $k=h \\times w \\times c$. \"k\" equals the product of all the dimension sizes other than the first dimension.\n"," * For example, given a tensor with dimensions [100,2,3,4], it flattens the tensor to be of shape [100, 24], where 24 = 2 * 3 * 4. You can read the full documentation on [flatten](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/flatten).\n","\n","- **tf.contrib.layers.fully_connected(F, num_outputs):** given the flattened input F, it returns the output computed using a fully connected layer. You can read the full documentation on [full_connected](https://www.tensorflow.org/api_docs/python/tf/contrib/layers/fully_connected).\n","\n","In the last function above (`tf.contrib.layers.fully_connected`), the fully connected layer automatically initializes weights in the graph and keeps on training them as you train the model. Hence, you did not need to initialize those weights when initializing the parameters.\n","\n","\n","#### Window, kernel, filter\n","The words \"window\", \"kernel\", and \"filter\" are used to refer to the same thing. This is why the parameter `ksize` refers to \"kernel size\", and we use `(f,f)` to refer to the filter size. Both \"kernel\" and \"filter\" refer to the \"window.\""]},{"cell_type":"markdown","metadata":{"id":"so08mpHwmqpd"},"source":["**Exercise**\n","\n","Implement the `forward_propagation` function below to build the following model: `CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED`. You should use the functions above. \n","\n","In detail, we will use the following parameters for all the steps:\n"," - Conv2D: stride 1, padding is \"SAME\"\n"," - ReLU\n"," - Max pool: Use an 8 by 8 filter size and an 8 by 8 stride, padding is \"SAME\"\n"," - Conv2D: stride 1, padding is \"SAME\"\n"," - ReLU\n"," - Max pool: Use a 4 by 4 filter size and a 4 by 4 stride, padding is \"SAME\"\n"," - Flatten the previous output.\n"," - FULLYCONNECTED (FC) layer: Apply a fully connected layer without an non-linear activation function. Do not call the softmax here. This will result in 6 neurons in the output layer, which then get passed later to a softmax. In TensorFlow, the softmax and cost function are lumped together into a single function, which you'll call in a different function when computing the cost. "]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"MPm2w1LMmqpd"},"outputs":[],"source":["# GRADED FUNCTION: forward_propagation\n","\n","def forward_propagation(X, parameters):\n"," \"\"\"\n"," Implements the forward propagation for the model:\n"," CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED\n"," \n"," Note that for simplicity and grading purposes, we'll hard-code some values\n"," such as the stride and kernel (filter) sizes. \n"," Normally, functions should take these values as function parameters.\n"," \n"," Arguments:\n"," X -- input dataset placeholder, of shape (input size, number of examples)\n"," parameters -- python dictionary containing your parameters \"W1\", \"W2\"\n"," the shapes are given in initialize_parameters\n","\n"," Returns:\n"," Z3 -- the output of the last LINEAR unit\n"," \"\"\"\n"," \n"," # Retrieve the parameters from the dictionary \"parameters\" \n"," W1 = parameters['W1']\n"," W2 = parameters['W2']\n"," \n"," ### START CODE HERE ###\n"," # CONV2D: stride of 1, padding 'SAME'\n"," s = 1\n"," Z1 = tf.nn.conv2d(X, W1, strides = [1,s,s,1], padding = 'SAME')\n"," # RELU\n"," A1 = tf.nn.relu(Z1)\n"," # MAXPOOL: window 8x8, stride 8, padding 'SAME'\n"," f = 8\n"," s = 8\n"," P1 = tf.nn.max_pool(A1, ksize = [1,f,f,1], strides = [1,s,s,1], padding = 'SAME')\n"," # CONV2D: filters W2, stride 1, padding 'SAME'\n"," s = 1\n"," Z2 = tf.nn.conv2d(P1, W2, strides = [1,s,s,1], padding = 'SAME')\n"," # RELU\n"," A2 = tf.nn.relu(Z2)\n"," # MAXPOOL: window 4x4, stride 4, padding 'SAME'\n"," f = 4\n"," s = 4\n"," P2 = tf.nn.max_pool(A2, ksize = [1,f,f,1], strides = [1,s,s,1], padding = 'SAME')\n"," # FLATTEN\n"," F = tf.contrib.layers.flatten(P2)\n"," # FULLY-CONNECTED without non-linear activation function (not not call softmax).\n"," # 6 neurons in output layer. Hint: one of the arguments should be \"activation_fn=None\" \n"," num_outputs = 6\n"," Z3 = tf.contrib.layers.fully_connected(F, num_outputs, activation_fn=None)\n"," ### END CODE HERE ###\n","\n"," return Z3"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IpgpgcYxmqpd","outputId":"5d42b8a6-87f4-49d3-f36f-5e33bb9e389b"},"outputs":[{"name":"stdout","output_type":"stream","text":["Z3 = \n","[[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064]\n"," [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]\n"]}],"source":["tf.reset_default_graph()\n","\n","with tf.Session() as sess:\n"," np.random.seed(1)\n"," X, Y = create_placeholders(64, 64, 3, 6)\n"," parameters = initialize_parameters()\n"," Z3 = forward_propagation(X, parameters)\n"," init = tf.global_variables_initializer()\n"," sess.run(init)\n"," a = sess.run(Z3, {X: np.random.randn(2,64,64,3), Y: np.random.randn(2,6)})\n"," print(\"Z3 = \\n\" + str(a))"]},{"cell_type":"markdown","metadata":{"id":"UTonDVarmqpe"},"source":["**Expected Output**:\n","\n","```\n","Z3 = \n","[[-0.44670227 -1.57208765 -1.53049231 -2.31013036 -1.29104376 0.46852064]\n"," [-0.17601591 -1.57972014 -1.4737016 -2.61672091 -1.00810647 0.5747785 ]]\n","```"]},{"cell_type":"markdown","metadata":{"id":"rcCExEkhmqpe"},"source":["### 1.4 - Compute cost\n","\n","Implement the compute cost function below. Remember that the cost function helps the neural network see how much the model's predictions differ from the correct labels. By adjusting the weights of the network to reduce the cost, the neural network can improve its predictions.\n","\n","You might find these two functions helpful: \n","\n","- **tf.nn.softmax_cross_entropy_with_logits(logits = Z, labels = Y):** computes the softmax entropy loss. This function both computes the softmax activation function as well as the resulting loss. You can check the full documentation [softmax_cross_entropy_with_logits](https://www.tensorflow.org/api_docs/python/tf/nn/softmax_cross_entropy_with_logits).\n","- **tf.reduce_mean:** computes the mean of elements across dimensions of a tensor. Use this to calculate the sum of the losses over all the examples to get the overall cost. You can check the full documentation [reduce_mean](https://www.tensorflow.org/api_docs/python/tf/reduce_mean).\n","\n","#### Details on softmax_cross_entropy_with_logits (optional reading)\n","* Softmax is used to format outputs so that they can be used for classification. It assigns a value between 0 and 1 for each category, where the sum of all prediction values (across all possible categories) equals 1.\n","* Cross Entropy is compares the model's predicted classifications with the actual labels and results in a numerical value representing the \"loss\" of the model's predictions.\n","* \"Logits\" are the result of multiplying the weights and adding the biases. Logits are passed through an activation function (such as a relu), and the result is called the \"activation.\"\n","* The function is named `softmax_cross_entropy_with_logits` takes logits as input (and not activations); then uses the model to predict using softmax, and then compares the predictions with the true labels using cross entropy. These are done with a single function to optimize the calculations.\n","\n","** Exercise**: Compute the cost below using the function above."]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"tua5OXPnmqpe"},"outputs":[],"source":["# GRADED FUNCTION: compute_cost \n","\n","def compute_cost(Z3, Y):\n"," \"\"\"\n"," Computes the cost\n"," \n"," Arguments:\n"," Z3 -- output of forward propagation (output of the last LINEAR unit), of shape (number of examples, 6)\n"," Y -- \"true\" labels vector placeholder, same shape as Z3\n"," \n"," Returns:\n"," cost - Tensor of the cost function\n"," \"\"\"\n"," \n"," ### START CODE HERE ### (1 line of code)\n"," cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = Z3, labels = Y))\n"," ### END CODE HERE ###\n"," \n"," return cost"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"obts8s-Nmqpf","outputId":"13083a02-b004-4ca3-b2b5-63040e3fb71f"},"outputs":[{"name":"stdout","output_type":"stream","text":["cost = 2.91034\n"]}],"source":["tf.reset_default_graph()\n","\n","with tf.Session() as sess:\n"," np.random.seed(1)\n"," X, Y = create_placeholders(64, 64, 3, 6)\n"," parameters = initialize_parameters()\n"," Z3 = forward_propagation(X, parameters)\n"," cost = compute_cost(Z3, Y)\n"," init = tf.global_variables_initializer()\n"," sess.run(init)\n"," a = sess.run(cost, {X: np.random.randn(4,64,64,3), Y: np.random.randn(4,6)})\n"," print(\"cost = \" + str(a))"]},{"cell_type":"markdown","metadata":{"id":"ZpdDiLEEmqpf"},"source":["**Expected Output**: \n","```\n","cost = 2.91034\n","```"]},{"cell_type":"markdown","metadata":{"id":"P28oKT-Smqpf"},"source":["## 1.5 Model \n","\n","Finally you will merge the helper functions you implemented above to build a model. You will train it on the SIGNS dataset. \n","\n","**Exercise**: Complete the function below. \n","\n","The model below should:\n","\n","- create placeholders\n","- initialize parameters\n","- forward propagate\n","- compute the cost\n","- create an optimizer\n","\n","Finally you will create a session and run a for loop for num_epochs, get the mini-batches, and then for each mini-batch you will optimize the function. [Hint for initializing the variables](https://www.tensorflow.org/api_docs/python/tf/global_variables_initializer)"]},{"cell_type":"markdown","metadata":{"id":"N7mnw2i4mqpf"},"source":["#### Adam Optimizer\n","You can use `tf.train.AdamOptimizer(learning_rate = ...)` to create the optimizer. The optimizer has a `minimize(loss=...)` function that you'll call to set the cost function that the optimizer will minimize.\n","\n","For details, check out the documentation for [Adam Optimizer](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer)"]},{"cell_type":"markdown","metadata":{"id":"rD8z2p04mqpf"},"source":["#### Random mini batches\n","If you took course 2 of the deep learning specialization, you implemented `random_mini_batches()` in the \"Optimization\" programming assignment. This function returns a list of mini-batches. It is already implemented in the `cnn_utils.py` file and imported here, so you can call it like this:\n","```Python\n","minibatches = random_mini_batches(X, Y, mini_batch_size = 64, seed = 0)\n","```\n","(You will want to choose the correct variable names when you use it in your code)."]},{"cell_type":"markdown","metadata":{"id":"776QcgZNmqpf"},"source":["#### Evaluating the optimizer and cost\n","\n","Within a loop, for each mini-batch, you'll use the `tf.Session` object (named `sess`) to feed a mini-batch of inputs and labels into the neural network and evaluate the tensors for the optimizer as well as the cost. Remember that we built a graph data structure and need to feed it inputs and labels and use `sess.run()` in order to get values for the optimizer and cost.\n","\n","You'll use this kind of syntax:\n","```\n","output_for_var1, output_for_var2 = sess.run(\n"," fetches=[var1, var2],\n"," feed_dict={var_inputs: the_batch_of_inputs,\n"," var_labels: the_batch_of_labels}\n"," )\n","```\n","* Notice that `sess.run` takes its first argument `fetches` as a list of objects that you want it to evaluate (in this case, we want to evaluate the optimizer and the cost). \n","* It also takes a dictionary for the `feed_dict` parameter. \n","* The keys are the `tf.placeholder` variables that we created in the `create_placeholders` function above. \n","* The values are the variables holding the actual numpy arrays for each mini-batch. \n","* The sess.run outputs a tuple of the evaluated tensors, in the same order as the list given to `fetches`. \n","\n","For more information on how to use sess.run, see the documentation [tf.Sesssion#run](https://www.tensorflow.org/api_docs/python/tf/Session#run) documentation."]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"jupyter":{"outputs_hidden":true},"id":"1_SU4-M3mqpg"},"outputs":[],"source":["# GRADED FUNCTION: model\n","\n","def model(X_train, Y_train, X_test, Y_test, learning_rate = 0.009,\n"," num_epochs = 100, minibatch_size = 64, print_cost = True):\n"," \"\"\"\n"," Implements a three-layer ConvNet in Tensorflow:\n"," CONV2D -> RELU -> MAXPOOL -> CONV2D -> RELU -> MAXPOOL -> FLATTEN -> FULLYCONNECTED\n"," \n"," Arguments:\n"," X_train -- training set, of shape (None, 64, 64, 3)\n"," Y_train -- test set, of shape (None, n_y = 6)\n"," X_test -- training set, of shape (None, 64, 64, 3)\n"," Y_test -- test set, of shape (None, n_y = 6)\n"," learning_rate -- learning rate of the optimization\n"," num_epochs -- number of epochs of the optimization loop\n"," minibatch_size -- size of a minibatch\n"," print_cost -- True to print the cost every 100 epochs\n"," \n"," Returns:\n"," train_accuracy -- real number, accuracy on the train set (X_train)\n"," test_accuracy -- real number, testing accuracy on the test set (X_test)\n"," parameters -- parameters learnt by the model. They can then be used to predict.\n"," \"\"\"\n"," \n"," ops.reset_default_graph() # to be able to rerun the model without overwriting tf variables\n"," tf.set_random_seed(1) # to keep results consistent (tensorflow seed)\n"," seed = 3 # to keep results consistent (numpy seed)\n"," (m, n_H0, n_W0, n_C0) = X_train.shape \n"," n_y = Y_train.shape[1] \n"," costs = [] # To keep track of the cost\n"," \n"," # Create Placeholders of the correct shape\n"," ### START CODE HERE ### (1 line)\n"," X, Y = create_placeholders(n_H0, n_W0, n_C0, n_y)\n"," ### END CODE HERE ###\n","\n"," # Initialize parameters\n"," ### START CODE HERE ### (1 line)\n"," parameters = initialize_parameters()\n"," ### END CODE HERE ###\n"," \n"," # Forward propagation: Build the forward propagation in the tensorflow graph\n"," ### START CODE HERE ### (1 line)\n"," Z3 = forward_propagation(X, parameters)\n"," ### END CODE HERE ###\n"," \n"," # Cost function: Add cost function to tensorflow graph\n"," ### START CODE HERE ### (1 line)\n"," cost = compute_cost(Z3, Y)\n"," ### END CODE HERE ###\n"," \n"," # Backpropagation: Define the tensorflow optimizer. Use an AdamOptimizer that minimizes the cost.\n"," ### START CODE HERE ### (1 line)\n"," optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(loss = cost)\n"," ### END CODE HERE ###\n"," \n"," # Initialize all the variables globally\n"," init = tf.global_variables_initializer()\n"," \n"," # Start the session to compute the tensorflow graph\n"," with tf.Session() as sess:\n"," \n"," # Run the initialization\n"," sess.run(init)\n"," \n"," # Do the training loop\n"," for epoch in range(num_epochs):\n","\n"," minibatch_cost = 0.\n"," num_minibatches = int(m / minibatch_size) # number of minibatches of size minibatch_size in the train set\n"," seed = seed + 1\n"," minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)\n","\n"," for minibatch in minibatches:\n","\n"," # Select a minibatch\n"," (minibatch_X, minibatch_Y) = minibatch\n"," \"\"\"\n"," # IMPORTANT: The line that runs the graph on a minibatch.\n"," # Run the session to execute the optimizer and the cost.\n"," # The feedict should contain a minibatch for (X,Y).\n"," \"\"\"\n"," ### START CODE HERE ### (1 line)\n"," _ , temp_cost = sess.run([optimizer, cost], feed_dict={X:minibatch_X, Y:minibatch_Y})\n"," ### END CODE HERE ###\n"," \n"," minibatch_cost += temp_cost / num_minibatches\n"," \n","\n"," # Print the cost every epoch\n"," if print_cost == True and epoch % 5 == 0:\n"," print (\"Cost after epoch %i: %f\" % (epoch, minibatch_cost))\n"," if print_cost == True and epoch % 1 == 0:\n"," costs.append(minibatch_cost)\n"," \n"," \n"," # plot the cost\n"," plt.plot(np.squeeze(costs))\n"," plt.ylabel('cost')\n"," plt.xlabel('iterations (per tens)')\n"," plt.title(\"Learning rate =\" + str(learning_rate))\n"," plt.show()\n","\n"," # Calculate the correct predictions\n"," predict_op = tf.argmax(Z3, 1)\n"," correct_prediction = tf.equal(predict_op, tf.argmax(Y, 1))\n"," \n"," # Calculate accuracy on the test set\n"," accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n"," print(accuracy)\n"," train_accuracy = accuracy.eval({X: X_train, Y: Y_train})\n"," test_accuracy = accuracy.eval({X: X_test, Y: Y_test})\n"," print(\"Train Accuracy:\", train_accuracy)\n"," print(\"Test Accuracy:\", test_accuracy)\n"," \n"," return train_accuracy, test_accuracy, parameters"]},{"cell_type":"markdown","metadata":{"id":"cwhHSImrmqpg"},"source":["Run the following cell to train your model for 100 epochs. Check if your cost after epoch 0 and 5 matches our output. If not, stop the cell and go back to your code!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"79zvo8dpmqph","outputId":"ce5ed750-f502-4c1c-93c9-e213f3686603"},"outputs":[{"name":"stdout","output_type":"stream","text":["Cost after epoch 0: 1.917929\n","Cost after epoch 5: 1.506757\n","Cost after epoch 10: 0.955359\n","Cost after epoch 15: 0.845802\n","Cost after epoch 20: 0.701174\n","Cost after epoch 25: 0.571977\n","Cost after epoch 30: 0.518435\n","Cost after epoch 35: 0.495806\n","Cost after epoch 40: 0.429827\n","Cost after epoch 45: 0.407291\n","Cost after epoch 50: 0.366394\n","Cost after epoch 55: 0.376922\n","Cost after epoch 60: 0.299491\n","Cost after epoch 65: 0.338870\n","Cost after epoch 70: 0.316400\n","Cost after epoch 75: 0.310413\n","Cost after epoch 80: 0.249549\n","Cost after epoch 85: 0.243457\n","Cost after epoch 90: 0.200031\n","Cost after epoch 95: 0.175452\n"]},{"data":{"image/png":"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","text/plain":[""]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Tensor(\"Mean_1:0\", shape=(), dtype=float32)\n","Train Accuracy: 0.940741\n","Test Accuracy: 0.783333\n"]}],"source":["_, _, parameters = model(X_train, Y_train, X_test, Y_test)"]},{"cell_type":"markdown","metadata":{"id":"NTteMtnemqph"},"source":["**Expected output**: although it may not match perfectly, your expected output should be close to ours and your cost value should decrease.\n","\n"," \n","\n"," \n","\n"," \n","\n","\n"," \n","\n"," \n","\n","\n"," \n","\n"," \n"," \n","\n","\n"," \n","\n"," \n"," \n","
\n"," **Cost after epoch 0 =**\n"," \n"," 1.917929\n","
\n"," **Cost after epoch 5 =**\n"," \n"," 1.506757\n","
\n"," **Train Accuracy =**\n"," \n"," 0.940741\n","
\n"," **Test Accuracy =**\n"," \n"," 0.783333\n","
"]},{"cell_type":"markdown","metadata":{"id":"plvFDg5Tmqpi"},"source":["Congratulations! You have finished the assignment and built a model that recognizes SIGN language with almost 80% accuracy on the test set. If you wish, feel free to play around with this dataset further. You can actually improve its accuracy by spending more time tuning the hyperparameters, or using regularization (as this model clearly has a high variance). \n","\n","Once again, here's a thumbs up for your work! "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"64oqMrkkmqpi","outputId":"313fba81-def4-4686-e868-0016afda6a5d"},"outputs":[{"data":{"text/plain":[""]},"execution_count":30,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["fname = \"images/thumbs_up.jpg\"\n","image = np.array(ndimage.imread(fname, flatten=False))\n","my_image = scipy.misc.imresize(image, size=(64,64))\n","plt.imshow(my_image)"]}],"metadata":{"coursera":{"course_slug":"convolutional-neural-networks","graded_item_id":"bwbJV","launcher_item_id":"0TkXB"},"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.9.12"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}},"colab":{"provenance":[],"collapsed_sections":[]}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git "a/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Step_by_Step_v2a.ipynb" "b/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Step_by_Step_v2a.ipynb" new file mode 100644 index 0000000..76a9121 --- /dev/null +++ "b/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Convolution_model_Step_by_Step_v2a.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"koAaOjypmrY8"},"source":["# Convolutional Neural Networks: Step by Step\n","\n","Welcome to Course 4's first assignment! In this assignment, you will implement convolutional (CONV) and pooling (POOL) layers in numpy, including both forward propagation and (optionally) backward propagation. \n","\n","**Notation**:\n","- Superscript $[l]$ denotes an object of the $l^{th}$ layer. \n"," - Example: $a^{[4]}$ is the $4^{th}$ layer activation. $W^{[5]}$ and $b^{[5]}$ are the $5^{th}$ layer parameters.\n","\n","\n","- Superscript $(i)$ denotes an object from the $i^{th}$ example. \n"," - Example: $x^{(i)}$ is the $i^{th}$ training example input.\n"," \n"," \n","- Subscript $i$ denotes the $i^{th}$ entry of a vector.\n"," - Example: $a^{[l]}_i$ denotes the $i^{th}$ entry of the activations in layer $l$, assuming this is a fully connected (FC) layer.\n"," \n"," \n","- $n_H$, $n_W$ and $n_C$ denote respectively the height, width and number of channels of a given layer. If you want to reference a specific layer $l$, you can also write $n_H^{[l]}$, $n_W^{[l]}$, $n_C^{[l]}$. \n","- $n_{H_{prev}}$, $n_{W_{prev}}$ and $n_{C_{prev}}$ denote respectively the height, width and number of channels of the previous layer. If referencing a specific layer $l$, this could also be denoted $n_H^{[l-1]}$, $n_W^{[l-1]}$, $n_C^{[l-1]}$. \n","\n","We assume that you are already familiar with `numpy` and/or have completed the previous courses of the specialization. Let's get started!"]},{"cell_type":"markdown","metadata":{"id":"FBY4aBQBmrZB"},"source":["## Updates\n","\n","#### If you were working on the notebook before this update...\n","* The current notebook is version \"v2a\".\n","* You can find your original work saved in the notebook with the previous version name (\"v2\") \n","* To view the file directory, go to the menu \"File->Open\", and this will open a new tab that shows the file directory.\n","\n","#### List of updates\n","* clarified example used for padding function. Updated starter code for padding function.\n","* `conv_forward` has additional hints to help students if they're stuck.\n","* `conv_forward` places code for `vert_start` and `vert_end` within the `for h in range(...)` loop; to avoid redundant calculations. Similarly updated `horiz_start` and `horiz_end`. **Thanks to our mentor Kevin Brown for pointing this out.**\n","* `conv_forward` breaks down the `Z[i, h, w, c]` single line calculation into 3 lines, for clarity.\n","* `conv_forward` test case checks that students don't accidentally use n_H_prev instead of n_H, use n_W_prev instead of n_W, and don't accidentally swap n_H with n_W\n","* `pool_forward` properly nests calculations of `vert_start`, `vert_end`, `horiz_start`, and `horiz_end` to avoid redundant calculations.\n","* `pool_forward' has two new test cases that check for a correct implementation of stride (the height and width of the previous layer's activations should be large enough relative to the filter dimensions so that a stride can take place). \n","* `conv_backward`: initialize `Z` and `cache` variables within unit test, to make it independent of unit testing that occurs in the `conv_forward` section of the assignment.\n","* **Many thanks to our course mentor, Paul Mielke, for proposing these test cases.**"]},{"cell_type":"markdown","metadata":{"id":"1gq1XgmHmrZD"},"source":["## 1 - Packages\n","\n","Let's first import all the packages that you will need during this assignment. \n","- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n","- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n","- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fhY4B1jfmrZE"},"outputs":[],"source":["import numpy as np\n","import h5py\n","import matplotlib.pyplot as plt\n","\n","%matplotlib inline\n","plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n","plt.rcParams['image.interpolation'] = 'nearest'\n","plt.rcParams['image.cmap'] = 'gray'\n","\n","%load_ext autoreload\n","%autoreload 2\n","\n","np.random.seed(1)"]},{"cell_type":"markdown","metadata":{"id":"uiEMf47umrZG"},"source":["## 2 - Outline of the Assignment\n","\n","You will be implementing the building blocks of a convolutional neural network! Each function you will implement will have detailed instructions that will walk you through the steps needed:\n","\n","- Convolution functions, including:\n"," - Zero Padding\n"," - Convolve window \n"," - Convolution forward\n"," - Convolution backward (optional)\n","- Pooling functions, including:\n"," - Pooling forward\n"," - Create mask \n"," - Distribute value\n"," - Pooling backward (optional)\n"," \n","This notebook will ask you to implement these functions from scratch in `numpy`. In the next notebook, you will use the TensorFlow equivalents of these functions to build the following model:\n","\n","\n","\n","**Note** that for every forward function, there is its corresponding backward equivalent. Hence, at every step of your forward module you will store some parameters in a cache. These parameters are used to compute gradients during backpropagation. "]},{"cell_type":"markdown","metadata":{"id":"6a_FFAipmrZH"},"source":["## 3 - Convolutional Neural Networks\n","\n","Although programming frameworks make convolutions easy to use, they remain one of the hardest concepts to understand in Deep Learning. A convolution layer transforms an input volume into an output volume of different size, as shown below. \n","\n","\n","\n","In this part, you will build every step of the convolution layer. You will first implement two helper functions: one for zero padding and the other for computing the convolution function itself. "]},{"cell_type":"markdown","metadata":{"id":"-w9qtZBpmrZI"},"source":["### 3.1 - Zero-Padding\n","\n","Zero-padding adds zeros around the border of an image:\n","\n","\n","
**Figure 1** : **Zero-Padding**
Image (3 channels, RGB) with a padding of 2.
\n","\n","The main benefits of padding are the following:\n","\n","- It allows you to use a CONV layer without necessarily shrinking the height and width of the volumes. This is important for building deeper networks, since otherwise the height/width would shrink as you go to deeper layers. An important special case is the \"same\" convolution, in which the height/width is exactly preserved after one layer. \n","\n","- It helps us keep more of the information at the border of an image. Without padding, very few values at the next layer would be affected by pixels as the edges of an image.\n","\n","**Exercise**: Implement the following function, which pads all the images of a batch of examples X with zeros. [Use np.pad](https://docs.scipy.org/doc/numpy/reference/generated/numpy.pad.html). Note if you want to pad the array \"a\" of shape $(5,5,5,5,5)$ with `pad = 1` for the 2nd dimension, `pad = 3` for the 4th dimension and `pad = 0` for the rest, you would do:\n","```python\n","a = np.pad(a, ((0,0), (1,1), (0,0), (3,3), (0,0)), mode='constant', constant_values = (0,0))\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"j_7gu9K5mrZJ"},"outputs":[],"source":["# GRADED FUNCTION: zero_pad\n","\n","def zero_pad(X, pad):\n"," \"\"\"\n"," Pad with zeros all images of the dataset X. The padding is applied to the height and width of an image, \n"," as illustrated in Figure 1.\n"," \n"," Argument:\n"," X -- python numpy array of shape (m, n_H, n_W, n_C) representing a batch of m images\n"," pad -- integer, amount of padding around each image on vertical and horizontal dimensions\n"," \n"," Returns:\n"," X_pad -- padded image of shape (m, n_H + 2*pad, n_W + 2*pad, n_C)\n"," \"\"\"\n"," \n"," ### START CODE HERE ### (≈ 1 line)\n"," X_pad = np.pad(X, ((0,0), (pad, pad), (pad, pad), (0,0)), mode='constant', constant_values = (0,0))\n"," ### END CODE HERE ###\n"," \n"," return X_pad"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OXBkrjSNmrZK"},"outputs":[],"source":["np.random.seed(1)\n","x = np.random.randn(4, 3, 3, 2)\n","x_pad = zero_pad(x, 2)\n","print (\"x.shape =\\n\", x.shape)\n","print (\"x_pad.shape =\\n\", x_pad.shape)\n","print (\"x[1,1] =\\n\", x[1,1])\n","print (\"x_pad[1,1] =\\n\", x_pad[1,1])\n","\n","fig, axarr = plt.subplots(1, 2)\n","axarr[0].set_title('x')\n","axarr[0].imshow(x[0,:,:,0])\n","axarr[1].set_title('x_pad')\n","axarr[1].imshow(x_pad[0,:,:,0])"]},{"cell_type":"markdown","metadata":{"id":"JxorvsZsmrZL"},"source":["**Expected Output**:\n","\n","```\n","x.shape =\n"," (4, 3, 3, 2)\n","x_pad.shape =\n"," (4, 7, 7, 2)\n","x[1,1] =\n"," [[ 0.90085595 -0.68372786]\n"," [-0.12289023 -0.93576943]\n"," [-0.26788808 0.53035547]]\n","x_pad[1,1] =\n"," [[ 0. 0.]\n"," [ 0. 0.]\n"," [ 0. 0.]\n"," [ 0. 0.]\n"," [ 0. 0.]\n"," [ 0. 0.]\n"," [ 0. 0.]]\n","```"]},{"cell_type":"markdown","metadata":{"id":"QI9eo1ckmrZM"},"source":["### 3.2 - Single step of convolution \n","\n","In this part, implement a single step of convolution, in which you apply the filter to a single position of the input. This will be used to build a convolutional unit, which: \n","\n","- Takes an input volume \n","- Applies a filter at every position of the input\n","- Outputs another volume (usually of different size)\n","\n","\n","
**Figure 2** : **Convolution operation**
with a filter of 3x3 and a stride of 1 (stride = amount you move the window each time you slide)
\n","\n","In a computer vision application, each value in the matrix on the left corresponds to a single pixel value, and we convolve a 3x3 filter with the image by multiplying its values element-wise with the original matrix, then summing them up and adding a bias. In this first step of the exercise, you will implement a single step of convolution, corresponding to applying a filter to just one of the positions to get a single real-valued output. \n","\n","Later in this notebook, you'll apply this function to multiple positions of the input to implement the full convolutional operation. \n","\n","**Exercise**: Implement conv_single_step(). [Hint](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.sum.html).\n"]},{"cell_type":"markdown","metadata":{"id":"xjbQL_9_mrZM"},"source":["**Note**: The variable b will be passed in as a numpy array. If we add a scalar (a float or integer) to a numpy array, the result is a numpy array. In the special case when a numpy array contains a single value, we can cast it as a float to convert it to a scalar."]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"UdGHqUaumrZN"},"outputs":[],"source":["# GRADED FUNCTION: conv_single_step\n","\n","def conv_single_step(a_slice_prev, W, b):\n"," \"\"\"\n"," Apply one filter defined by parameters W on a single slice (a_slice_prev) of the output activation \n"," of the previous layer.\n"," \n"," Arguments:\n"," a_slice_prev -- slice of input data of shape (f, f, n_C_prev)\n"," W -- Weight parameters contained in a window - matrix of shape (f, f, n_C_prev)\n"," b -- Bias parameters contained in a window - matrix of shape (1, 1, 1)\n"," \n"," Returns:\n"," Z -- a scalar value, the result of convolving the sliding window (W, b) on a slice x of the input data\n"," \"\"\"\n","\n"," ### START CODE HERE ### (≈ 2 lines of code)\n"," # Element-wise product between a_slice_prev and W. Do not add the bias yet.\n"," s = np.multiply(a_slice_prev, W)\n"," # Sum over all entries of the volume s.\n"," Z = np.sum(s)\n"," # Add bias b to Z. Cast b to a float() so that Z results in a scalar value.\n"," Z = Z + float(b)\n"," ### END CODE HERE ###\n","\n"," return Z"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IsCadXVEmrZO"},"outputs":[],"source":["np.random.seed(1)\n","a_slice_prev = np.random.randn(4, 4, 3)\n","W = np.random.randn(4, 4, 3)\n","b = np.random.randn(1, 1, 1)\n","\n","Z = conv_single_step(a_slice_prev, W, b)\n","print(\"Z =\", Z)"]},{"cell_type":"markdown","metadata":{"id":"Y9cXY7abmrZO"},"source":["**Expected Output**:\n","\n"," \n"," \n"," \n"," \n","\n","
\n"," **Z**\n"," \n"," -6.99908945068\n","
"]},{"cell_type":"markdown","metadata":{"id":"0-m5-eJ4mrZP"},"source":["### 3.3 - Convolutional Neural Networks - Forward pass\n","\n","In the forward pass, you will take many filters and convolve them on the input. Each 'convolution' gives you a 2D matrix output. You will then stack these outputs to get a 3D volume: \n","\n","
\n","\n","
\n","\n","**Exercise**: \n","Implement the function below to convolve the filters `W` on an input activation `A_prev`. \n","This function takes the following inputs:\n","* `A_prev`, the activations output by the previous layer (for a batch of m inputs); \n","* Weights are denoted by `W`. The filter window size is `f` by `f`.\n","* The bias vector is `b`, where each filter has its own (single) bias. \n","\n","Finally you also have access to the hyperparameters dictionary which contains the stride and the padding. \n","\n","**Hint**: \n","1. To select a 2x2 slice at the upper left corner of a matrix \"a_prev\" (shape (5,5,3)), you would do:\n","```python\n","a_slice_prev = a_prev[0:2,0:2,:]\n","```\n","Notice how this gives a 3D slice that has height 2, width 2, and depth 3. Depth is the number of channels. \n","This will be useful when you will define `a_slice_prev` below, using the `start/end` indexes you will define.\n","2. To define a_slice you will need to first define its corners `vert_start`, `vert_end`, `horiz_start` and `horiz_end`. This figure may be helpful for you to find out how each of the corner can be defined using h, w, f and s in the code below.\n","\n","\n","
**Figure 3** : **Definition of a slice using vertical and horizontal start/end (with a 2x2 filter)**
This figure shows only a single channel.
\n","\n","\n","**Reminder**:\n","The formulas relating the output shape of the convolution to the input shape is:\n","$$ n_H = \\lfloor \\frac{n_{H_{prev}} - f + 2 \\times pad}{stride} \\rfloor +1 $$\n","$$ n_W = \\lfloor \\frac{n_{W_{prev}} - f + 2 \\times pad}{stride} \\rfloor +1 $$\n","$$ n_C = \\text{number of filters used in the convolution}$$\n","\n","For this exercise, we won't worry about vectorization, and will just implement everything with for-loops."]},{"cell_type":"markdown","metadata":{"id":"acn8PDJimrZP"},"source":["#### Additional Hints if you're stuck\n","\n","\n","* You will want to use array slicing (e.g.`varname[0:1,:,3:5]`) for the following variables: \n"," `a_prev_pad` ,`W`, `b` \n"," Copy the starter code of the function and run it outside of the defined function, in separate cells. \n"," Check that the subset of each array is the size and dimension that you're expecting. \n","* To decide how to get the vert_start, vert_end; horiz_start, horiz_end, remember that these are indices of the previous layer. \n"," Draw an example of a previous padded layer (8 x 8, for instance), and the current (output layer) (2 x 2, for instance). \n"," The output layer's indices are denoted by `h` and `w`. \n","* Make sure that `a_slice_prev` has a height, width and depth.\n","* Remember that `a_prev_pad` is a subset of `A_prev_pad`. \n"," Think about which one should be used within the for loops."]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"zFnYge6JmrZQ"},"outputs":[],"source":["# GRADED FUNCTION: conv_forward\n","\n","def conv_forward(A_prev, W, b, hparameters):\n"," \"\"\"\n"," Implements the forward propagation for a convolution function\n"," \n"," Arguments:\n"," A_prev -- output activations of the previous layer, \n"," numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n"," W -- Weights, numpy array of shape (f, f, n_C_prev, n_C)\n"," b -- Biases, numpy array of shape (1, 1, 1, n_C)\n"," hparameters -- python dictionary containing \"stride\" and \"pad\"\n"," \n"," Returns:\n"," Z -- conv output, numpy array of shape (m, n_H, n_W, n_C)\n"," cache -- cache of values needed for the conv_backward() function\n"," \"\"\"\n"," \n"," ### START CODE HERE ###\n"," # Retrieve dimensions from A_prev's shape (≈1 line) \n"," (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape[0], A_prev.shape[1], A_prev.shape[2], A_prev.shape[3]\n"," \n"," # Retrieve dimensions from W's shape (≈1 line)\n"," (f, f, n_C_prev, n_C) = W.shape[0], W.shape[1], W.shape[2], W.shape[3]\n"," \n"," # Retrieve information from \"hparameters\" (≈2 lines)\n"," stride = hparameters[\"stride\"]\n"," pad = hparameters[\"pad\"]\n"," \n"," # Compute the dimensions of the CONV output volume using the formula given above. \n"," # Hint: use int() to apply the 'floor' operation. (≈2 lines)\n"," n_H = int(int(n_H_prev + 2*pad - f)/stride + 1)\n"," n_W = int(int(n_W_prev + 2*pad - f)/stride + 1)\n"," \n"," # Initialize the output volume Z with zeros. (≈1 line)\n"," Z = np.zeros([m, n_H, n_W, n_C])\n"," \n"," # Create A_prev_pad by padding A_prev\n"," A_prev_pad = zero_pad(A_prev, pad)\n"," \n"," for i in range(m): # loop over the batch of training examples\n"," a_prev_pad = A_prev_pad[i] # Select ith training example's padded activation\n"," for h in range(n_H): # loop over vertical axis of the output volume\n"," # Find the vertical start and end of the current \"slice\" (≈2 lines)\n"," vert_start = stride * h\n"," vert_end = vert_start + f\n"," \n"," for w in range(n_W): # loop over horizontal axis of the output volume\n"," # Find the horizontal start and end of the current \"slice\" (≈2 lines)\n"," horiz_start = stride * w\n"," horiz_end = horiz_start + f\n"," \n"," for c in range(n_C): # loop over channels (= #filters) of the output volume\n"," \n"," # Use the corners to define the (3D) slice of a_prev_pad (See Hint above the cell). (≈1 line)\n"," a_slice_prev = A_prev_pad[i, vert_start:vert_end, horiz_start:horiz_end, :]\n"," \n"," # Convolve the (3D) slice with the correct filter W and bias b, to get back one output neuron. (≈3 line)\n"," weights = W[:, :, :, c]\n"," biases = b[:, :, :, c]\n"," Z[i, h, w, c] = conv_single_step(a_slice_prev, weights, biases)\n"," \n"," ### END CODE HERE ###\n"," \n"," # Making sure your output shape is correct\n"," assert(Z.shape == (m, n_H, n_W, n_C))\n"," \n"," # Save information in \"cache\" for the backprop\n"," cache = (A_prev, W, b, hparameters)\n"," \n"," return Z, cache"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gGvqqWm9mrZR"},"outputs":[],"source":["np.random.seed(1)\n","A_prev = np.random.randn(10,5,7,4)\n","W = np.random.randn(3,3,4,8)\n","b = np.random.randn(1,1,1,8)\n","hparameters = {\"pad\" : 1,\n"," \"stride\": 2}\n","\n","Z, cache_conv = conv_forward(A_prev, W, b, hparameters)\n","print(\"Z's mean =\\n\", np.mean(Z))\n","print(\"Z[3,2,1] =\\n\", Z[3,2,1])\n","print(\"cache_conv[0][1][2][3] =\\n\", cache_conv[0][1][2][3])"]},{"cell_type":"markdown","metadata":{"id":"lRDXT9fZmrZR"},"source":["**Expected Output**:\n","```\n","Z's mean =\n"," 0.692360880758\n","Z[3,2,1] =\n"," [ -1.28912231 2.27650251 6.61941931 0.95527176 8.25132576\n"," 2.31329639 13.00689405 2.34576051]\n","cache_conv[0][1][2][3] = [-1.1191154 1.9560789 -0.3264995 -1.34267579]\n","```"]},{"cell_type":"markdown","metadata":{"id":"I9ZV2W95mrZS"},"source":["Finally, CONV layer should also contain an activation, in which case we would add the following line of code:\n","\n","```python\n","# Convolve the window to get back one output neuron\n","Z[i, h, w, c] = ...\n","# Apply activation\n","A[i, h, w, c] = activation(Z[i, h, w, c])\n","```\n","\n","You don't need to do it here. \n"]},{"cell_type":"markdown","metadata":{"id":"uYBVXD-3mrZS"},"source":["## 4 - Pooling layer \n","\n","The pooling (POOL) layer reduces the height and width of the input. It helps reduce computation, as well as helps make feature detectors more invariant to its position in the input. The two types of pooling layers are: \n","\n","- Max-pooling layer: slides an ($f, f$) window over the input and stores the max value of the window in the output.\n","\n","- Average-pooling layer: slides an ($f, f$) window over the input and stores the average value of the window in the output.\n","\n","\n","
\n","\n","\n","\n","\n","\n","\n","
\n","\n","These pooling layers have no parameters for backpropagation to train. However, they have hyperparameters such as the window size $f$. This specifies the height and width of the $f \\times f$ window you would compute a *max* or *average* over. \n","\n","### 4.1 - Forward Pooling\n","Now, you are going to implement MAX-POOL and AVG-POOL, in the same function. \n","\n","**Exercise**: Implement the forward pass of the pooling layer. Follow the hints in the comments below.\n","\n","**Reminder**:\n","As there's no padding, the formulas binding the output shape of the pooling to the input shape is:\n","\n","$$ n_H = \\lfloor \\frac{n_{H_{prev}} - f}{stride} \\rfloor +1 $$\n","\n","$$ n_W = \\lfloor \\frac{n_{W_{prev}} - f}{stride} \\rfloor +1 $$\n","\n","$$ n_C = n_{C_{prev}}$$"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"OZY7uXCSmrZS"},"outputs":[],"source":["# GRADED FUNCTION: pool_forward\n","\n","def pool_forward(A_prev, hparameters, mode = \"max\"):\n"," \"\"\"\n"," Implements the forward pass of the pooling layer\n"," \n"," Arguments:\n"," A_prev -- Input data, numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n"," hparameters -- python dictionary containing \"f\" and \"stride\"\n"," mode -- the pooling mode you would like to use, defined as a string (\"max\" or \"average\")\n"," \n"," Returns:\n"," A -- output of the pool layer, a numpy array of shape (m, n_H, n_W, n_C)\n"," cache -- cache used in the backward pass of the pooling layer, contains the input and hparameters \n"," \"\"\"\n"," \n"," # Retrieve dimensions from the input shape\n"," (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n"," \n"," # Retrieve hyperparameters from \"hparameters\"\n"," f = hparameters[\"f\"]\n"," stride = hparameters[\"stride\"]\n"," \n"," # Define the dimensions of the output\n"," n_H = int(1 + (n_H_prev - f) / stride)\n"," n_W = int(1 + (n_W_prev - f) / stride)\n"," n_C = n_C_prev\n"," \n"," # Initialize output matrix A\n"," A = np.zeros((m, n_H, n_W, n_C)) \n"," \n"," ### START CODE HERE ###\n"," for i in range(m): # loop over the training examples\n"," for h in range(n_H): # loop on the vertical axis of the output volume\n"," # Find the vertical start and end of the current \"slice\" (≈2 lines)\n"," vert_start = stride * h \n"," vert_end = vert_start + f\n"," \n"," for w in range(n_W): # loop on the horizontal axis of the output volume\n"," # Find the vertical start and end of the current \"slice\" (≈2 lines)\n"," horiz_start = stride * w\n"," horiz_end = horiz_start + f\n"," \n"," for c in range (n_C): # loop over the channels of the output volume\n"," \n"," # Use the corners to define the current slice on the ith training example of A_prev, channel c. (≈1 line)\n"," a_prev_slice = A_prev[i]\n"," \n"," # Compute the pooling operation on the slice. \n"," # Use an if statement to differentiate the modes. \n"," # Use np.max and np.mean.\n"," if mode == \"max\":\n"," A[i, h, w, c] = np.max(a_prev_slice[vert_start:vert_end, horiz_start:horiz_end, c])\n"," elif mode == \"average\":\n"," A[i, h, w, c] = np.mean(a_prev_slice[vert_start:vert_end, horiz_start:horiz_end, c])\n"," \n"," ### END CODE HERE ###\n"," \n"," # Store the input and hparameters in \"cache\" for pool_backward()\n"," cache = (A_prev, hparameters)\n"," \n"," # Making sure your output shape is correct\n"," assert(A.shape == (m, n_H, n_W, n_C))\n"," \n"," return A, cache"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"K9iX4eqQmrZT"},"outputs":[],"source":["# Case 1: stride of 1\n","np.random.seed(1)\n","A_prev = np.random.randn(2, 5, 5, 3)\n","hparameters = {\"stride\" : 1, \"f\": 3}\n","\n","A, cache = pool_forward(A_prev, hparameters)\n","print(\"mode = max\")\n","print(\"A.shape = \" + str(A.shape))\n","print(\"A =\\n\", A)\n","print()\n","A, cache = pool_forward(A_prev, hparameters, mode = \"average\")\n","print(\"mode = average\")\n","print(\"A.shape = \" + str(A.shape))\n","print(\"A =\\n\", A)"]},{"cell_type":"markdown","metadata":{"id":"kWmuWV1umrZV"},"source":["** Expected Output**\n","```\n","mode = max\n","A.shape = (2, 3, 3, 3)\n","A =\n"," [[[[ 1.74481176 0.90159072 1.65980218]\n"," [ 1.74481176 1.46210794 1.65980218]\n"," [ 1.74481176 1.6924546 1.65980218]]\n","\n"," [[ 1.14472371 0.90159072 2.10025514]\n"," [ 1.14472371 0.90159072 1.65980218]\n"," [ 1.14472371 1.6924546 1.65980218]]\n","\n"," [[ 1.13162939 1.51981682 2.18557541]\n"," [ 1.13162939 1.51981682 2.18557541]\n"," [ 1.13162939 1.6924546 2.18557541]]]\n","\n","\n"," [[[ 1.19891788 0.84616065 0.82797464]\n"," [ 0.69803203 0.84616065 1.2245077 ]\n"," [ 0.69803203 1.12141771 1.2245077 ]]\n","\n"," [[ 1.96710175 0.84616065 1.27375593]\n"," [ 1.96710175 0.84616065 1.23616403]\n"," [ 1.62765075 1.12141771 1.2245077 ]]\n","\n"," [[ 1.96710175 0.86888616 1.27375593]\n"," [ 1.96710175 0.86888616 1.23616403]\n"," [ 1.62765075 1.12141771 0.79280687]]]]\n","\n","mode = average\n","A.shape = (2, 3, 3, 3)\n","A =\n"," [[[[ -3.01046719e-02 -3.24021315e-03 -3.36298859e-01]\n"," [ 1.43310483e-01 1.93146751e-01 -4.44905196e-01]\n"," [ 1.28934436e-01 2.22428468e-01 1.25067597e-01]]\n","\n"," [[ -3.81801899e-01 1.59993515e-02 1.70562706e-01]\n"," [ 4.73707165e-02 2.59244658e-02 9.20338402e-02]\n"," [ 3.97048605e-02 1.57189094e-01 3.45302489e-01]]\n","\n"," [[ -3.82680519e-01 2.32579951e-01 6.25997903e-01]\n"," [ -2.47157416e-01 -3.48524998e-04 3.50539717e-01]\n"," [ -9.52551510e-02 2.68511000e-01 4.66056368e-01]]]\n","\n","\n"," [[[ -1.73134159e-01 3.23771981e-01 -3.43175716e-01]\n"," [ 3.80634669e-02 7.26706274e-02 -2.30268958e-01]\n"," [ 2.03009393e-02 1.41414785e-01 -1.23158476e-02]]\n","\n"," [[ 4.44976963e-01 -2.61694592e-03 -3.10403073e-01]\n"," [ 5.08114737e-01 -2.34937338e-01 -2.39611830e-01]\n"," [ 1.18726772e-01 1.72552294e-01 -2.21121966e-01]]\n","\n"," [[ 4.29449255e-01 8.44699612e-02 -2.72909051e-01]\n"," [ 6.76351685e-01 -1.20138225e-01 -2.44076712e-01]\n"," [ 1.50774518e-01 2.89111751e-01 1.23238536e-03]]]]\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WZ75jPOOmrZV"},"outputs":[],"source":["# Case 2: stride of 2\n","np.random.seed(1)\n","A_prev = np.random.randn(2, 5, 5, 3)\n","hparameters = {\"stride\" : 2, \"f\": 3}\n","\n","A, cache = pool_forward(A_prev, hparameters)\n","print(\"mode = max\")\n","print(\"A.shape = \" + str(A.shape))\n","print(\"A =\\n\", A)\n","print()\n","\n","A, cache = pool_forward(A_prev, hparameters, mode = \"average\")\n","print(\"mode = average\")\n","print(\"A.shape = \" + str(A.shape))\n","print(\"A =\\n\", A)"]},{"cell_type":"markdown","metadata":{"id":"eXDuwCnBmrZW"},"source":["**Expected Output:**\n"," \n","```\n","mode = max\n","A.shape = (2, 2, 2, 3)\n","A =\n"," [[[[ 1.74481176 0.90159072 1.65980218]\n"," [ 1.74481176 1.6924546 1.65980218]]\n","\n"," [[ 1.13162939 1.51981682 2.18557541]\n"," [ 1.13162939 1.6924546 2.18557541]]]\n","\n","\n"," [[[ 1.19891788 0.84616065 0.82797464]\n"," [ 0.69803203 1.12141771 1.2245077 ]]\n","\n"," [[ 1.96710175 0.86888616 1.27375593]\n"," [ 1.62765075 1.12141771 0.79280687]]]]\n","\n","mode = average\n","A.shape = (2, 2, 2, 3)\n","A =\n"," [[[[-0.03010467 -0.00324021 -0.33629886]\n"," [ 0.12893444 0.22242847 0.1250676 ]]\n","\n"," [[-0.38268052 0.23257995 0.6259979 ]\n"," [-0.09525515 0.268511 0.46605637]]]\n","\n","\n"," [[[-0.17313416 0.32377198 -0.34317572]\n"," [ 0.02030094 0.14141479 -0.01231585]]\n","\n"," [[ 0.42944926 0.08446996 -0.27290905]\n"," [ 0.15077452 0.28911175 0.00123239]]]]\n","```"]},{"cell_type":"markdown","metadata":{"id":"aaZAmjFqmrZW"},"source":["Congratulations! You have now implemented the forward passes of all the layers of a convolutional network. \n","\n","The remainder of this notebook is optional, and will not be graded.\n"]},{"cell_type":"markdown","metadata":{"id":"Daaj2PEBmrZW"},"source":["## 5 - Backpropagation in convolutional neural networks (OPTIONAL / UNGRADED)\n","\n","In modern deep learning frameworks, you only have to implement the forward pass, and the framework takes care of the backward pass, so most deep learning engineers don't need to bother with the details of the backward pass. The backward pass for convolutional networks is complicated. If you wish, you can work through this optional portion of the notebook to get a sense of what backprop in a convolutional network looks like. \n","\n","When in an earlier course you implemented a simple (fully connected) neural network, you used backpropagation to compute the derivatives with respect to the cost to update the parameters. Similarly, in convolutional neural networks you can calculate the derivatives with respect to the cost in order to update the parameters. The backprop equations are not trivial and we did not derive them in lecture, but we will briefly present them below.\n","\n","### 5.1 - Convolutional layer backward pass \n","\n","Let's start by implementing the backward pass for a CONV layer. \n","\n","#### 5.1.1 - Computing dA:\n","This is the formula for computing $dA$ with respect to the cost for a certain filter $W_c$ and a given training example:\n","\n","$$ dA += \\sum _{h=0} ^{n_H} \\sum_{w=0} ^{n_W} W_c \\times dZ_{hw} \\tag{1}$$\n","\n","Where $W_c$ is a filter and $dZ_{hw}$ is a scalar corresponding to the gradient of the cost with respect to the output of the conv layer Z at the hth row and wth column (corresponding to the dot product taken at the ith stride left and jth stride down). Note that at each time, we multiply the the same filter $W_c$ by a different dZ when updating dA. We do so mainly because when computing the forward propagation, each filter is dotted and summed by a different a_slice. Therefore when computing the backprop for dA, we are just adding the gradients of all the a_slices. \n","\n","In code, inside the appropriate for-loops, this formula translates into:\n","```python\n","da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += W[:,:,:,c] * dZ[i, h, w, c]\n","```\n","\n","#### 5.1.2 - Computing dW:\n","This is the formula for computing $dW_c$ ($dW_c$ is the derivative of one filter) with respect to the loss:\n","\n","$$ dW_c += \\sum _{h=0} ^{n_H} \\sum_{w=0} ^ {n_W} a_{slice} \\times dZ_{hw} \\tag{2}$$\n","\n","Where $a_{slice}$ corresponds to the slice which was used to generate the activation $Z_{ij}$. Hence, this ends up giving us the gradient for $W$ with respect to that slice. Since it is the same $W$, we will just add up all such gradients to get $dW$. \n","\n","In code, inside the appropriate for-loops, this formula translates into:\n","```python\n","dW[:,:,:,c] += a_slice * dZ[i, h, w, c]\n","```\n","\n","#### 5.1.3 - Computing db:\n","\n","This is the formula for computing $db$ with respect to the cost for a certain filter $W_c$:\n","\n","$$ db = \\sum_h \\sum_w dZ_{hw} \\tag{3}$$\n","\n","As you have previously seen in basic neural networks, db is computed by summing $dZ$. In this case, you are just summing over all the gradients of the conv output (Z) with respect to the cost. \n","\n","In code, inside the appropriate for-loops, this formula translates into:\n","```python\n","db[:,:,:,c] += dZ[i, h, w, c]\n","```\n","\n","**Exercise**: Implement the `conv_backward` function below. You should sum over all the training examples, filters, heights, and widths. You should then compute the derivatives using formulas 1, 2 and 3 above. "]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"QZpWWuKxmrZX"},"outputs":[],"source":["def conv_backward(dZ, cache):\n"," \"\"\"\n"," Implement the backward propagation for a convolution function\n"," \n"," Arguments:\n"," dZ -- gradient of the cost with respect to the output of the conv layer (Z), numpy array of shape (m, n_H, n_W, n_C)\n"," cache -- cache of values needed for the conv_backward(), output of conv_forward()\n"," \n"," Returns:\n"," dA_prev -- gradient of the cost with respect to the input of the conv layer (A_prev),\n"," numpy array of shape (m, n_H_prev, n_W_prev, n_C_prev)\n"," dW -- gradient of the cost with respect to the weights of the conv layer (W)\n"," numpy array of shape (f, f, n_C_prev, n_C)\n"," db -- gradient of the cost with respect to the biases of the conv layer (b)\n"," numpy array of shape (1, 1, 1, n_C)\n"," \"\"\"\n"," \n"," ### START CODE HERE ###\n"," # Retrieve information from \"cache\"\n"," (A_prev, W, b, hparameters) = cache\n"," \n"," # Retrieve dimensions from A_prev's shape\n"," (m, n_H_prev, n_W_prev, n_C_prev) = A_prev.shape\n"," \n"," # Retrieve dimensions from W's shape\n"," (f, f, n_C_prev, n_C) = W.shape\n"," \n"," # Retrieve information from \"hparameters\"\n"," stride = hparameters['stride']\n"," pad = hparameters['pad']\n"," \n"," # Retrieve dimensions from dZ's shape\n"," (m, n_H, n_W, n_C) = dZ.shape\n"," \n"," # Initialize dA_prev, dW, db with the correct shapes\n"," dA_prev = np.zeros(A_prev.shape) \n"," dW = np.zeros(W.shape) \n"," db = np.zeros(b.shape) \n","\n"," # Pad A_prev and dA_prev\n"," A_prev_pad = zero_pad(A_prev, pad)\n"," dA_prev_pad = zero_pad(dA_prev, pad)\n"," \n"," for i in range(dZ.shape[0]): # loop over the training examples\n"," \n"," # select ith training example from A_prev_pad and dA_prev_pad\n"," a_prev_pad = A_prev_pad[i, ...]\n"," da_prev_pad = dA_prev_pad[i, ...]\n"," \n"," for h in range(dZ.shape[1]): # loop over vertical axis of the output volume\n"," for w in range(dZ.shape[2]): # loop over horizontal axis of the output volume\n"," for c in range(dZ.shape[3]): # loop over the channels of the output volume\n"," \n"," # Find the corners of the current \"slice\"\n"," vert_start = h * stride\n"," vert_end = vert_start + f\n"," horiz_start = w * stride\n"," horiz_end = horiz_start + f\n"," \n"," # Use the corners to define the slice from a_prev_pad\n"," a_slice = a_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :]\n","\n"," # Update gradients for the window and the filter's parameters using the code formulas given above\n"," da_prev_pad[vert_start:vert_end, horiz_start:horiz_end, :] += W[:,:,:,c] * dZ[i, h, w, c]\n"," dW[:,:,:,c] += a_slice * dZ[i, h, w, c]\n"," db[:,:,:,c] += dZ[i, h, w, c]\n"," \n"," # Set the ith training example's dA_prev to the unpadded da_prev_pad (Hint: use X[pad:-pad, pad:-pad, :])\n"," dA_prev[i, :, :, :] = da_prev_pad[pad:-pad, pad:-pad, :]\n"," ### END CODE HERE ###\n"," \n"," # Making sure your output shape is correct\n"," assert(dA_prev.shape == (m, n_H_prev, n_W_prev, n_C_prev))\n"," \n"," return dA_prev, dW, db"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true,"id":"PVKsF0ZQmrZX"},"outputs":[],"source":["# We'll run conv_forward to initialize the 'Z' and 'cache_conv\",\n","# which we'll use to test the conv_backward function\n","np.random.seed(1)\n","A_prev = np.random.randn(10,4,4,3)\n","W = np.random.randn(2,2,3,8)\n","b = np.random.randn(1,1,1,8)\n","hparameters = {\"pad\" : 2,\n"," \"stride\": 2}\n","Z, cache_conv = conv_forward(A_prev, W, b, hparameters)\n","\n","# Test conv_backward\n","dA, dW, db = conv_backward(Z, cache_conv)\n","print(\"dA_mean =\", np.mean(dA))\n","print(\"dW_mean =\", np.mean(dW))\n","print(\"db_mean =\", np.mean(db))"]},{"cell_type":"markdown","metadata":{"id":"ixMpmvNGmrZY"},"source":["** Expected Output: **\n","\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","\n","
\n"," **dA_mean**\n"," \n"," 1.45243777754\n","
\n"," **dW_mean**\n"," \n"," 1.72699145831\n","
\n"," **db_mean**\n"," \n"," 7.83923256462\n","
\n"]},{"cell_type":"markdown","metadata":{"id":"4e97YCajmrZY"},"source":["## 5.2 Pooling layer - backward pass\n","\n","Next, let's implement the backward pass for the pooling layer, starting with the MAX-POOL layer. Even though a pooling layer has no parameters for backprop to update, you still need to backpropagation the gradient through the pooling layer in order to compute gradients for layers that came before the pooling layer. \n","\n","### 5.2.1 Max pooling - backward pass \n","\n","Before jumping into the backpropagation of the pooling layer, you are going to build a helper function called `create_mask_from_window()` which does the following: \n","\n","$$ X = \\begin{bmatrix}\n","1 && 3 \\\\\n","4 && 2\n","\\end{bmatrix} \\quad \\rightarrow \\quad M =\\begin{bmatrix}\n","0 && 0 \\\\\n","1 && 0\n","\\end{bmatrix}\\tag{4}$$\n","\n","As you can see, this function creates a \"mask\" matrix which keeps track of where the maximum of the matrix is. True (1) indicates the position of the maximum in X, the other entries are False (0). You'll see later that the backward pass for average pooling will be similar to this but using a different mask. \n","\n","**Exercise**: Implement `create_mask_from_window()`. This function will be helpful for pooling backward. \n","Hints:\n","- [np.max()]() may be helpful. It computes the maximum of an array.\n","- If you have a matrix X and a scalar x: `A = (X == x)` will return a matrix A of the same size as X such that:\n","```\n","A[i,j] = True if X[i,j] = x\n","A[i,j] = False if X[i,j] != x\n","```\n","- Here, you don't need to consider cases where there are several maxima in a matrix."]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"2AbW4xiMmrZY"},"outputs":[],"source":["def create_mask_from_window(x):\n"," \"\"\"\n"," Creates a mask from an input matrix x, to identify the max entry of x.\n"," \n"," Arguments:\n"," x -- Array of shape (f, f)\n"," \n"," Returns:\n"," mask -- Array of the same shape as window, contains a True at the position corresponding to the max entry of x.\n"," \"\"\"\n"," \n"," ### START CODE HERE ### (≈1 line)\n"," mask = (x == np.max(x))\n"," ### END CODE HERE ###\n"," \n"," return mask"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"scrolled":true,"id":"ZSa7Cw4AmrZZ"},"outputs":[],"source":["np.random.seed(1)\n","x = np.random.randn(2,3)\n","mask = create_mask_from_window(x)\n","print('x = ', x)\n","print(\"mask = \", mask)"]},{"cell_type":"markdown","metadata":{"id":"p84K6ghFmrZZ"},"source":["**Expected Output:** \n","\n"," \n"," \n","\n","\n","\n","\n","\n"," \n","\n","\n","\n","\n","\n","
\n","\n","**x =**\n","\n","\n","[[ 1.62434536 -0.61175641 -0.52817175]
\n"," [-1.07296862 0.86540763 -2.3015387 ]]\n","\n","
\n","**mask =**\n","\n","[[ True False False]
\n"," [False False False]]\n","
"]},{"cell_type":"markdown","metadata":{"id":"z42uYvD_mrZZ"},"source":["Why do we keep track of the position of the max? It's because this is the input value that ultimately influenced the output, and therefore the cost. Backprop is computing gradients with respect to the cost, so anything that influences the ultimate cost should have a non-zero gradient. So, backprop will \"propagate\" the gradient back to this particular input value that had influenced the cost. "]},{"cell_type":"markdown","metadata":{"id":"BlbnJ5bMmrZZ"},"source":["### 5.2.2 - Average pooling - backward pass \n","\n","In max pooling, for each input window, all the \"influence\" on the output came from a single input value--the max. In average pooling, every element of the input window has equal influence on the output. So to implement backprop, you will now implement a helper function that reflects this.\n","\n","For example if we did average pooling in the forward pass using a 2x2 filter, then the mask you'll use for the backward pass will look like: \n","$$ dZ = 1 \\quad \\rightarrow \\quad dZ =\\begin{bmatrix}\n","1/4 && 1/4 \\\\\n","1/4 && 1/4\n","\\end{bmatrix}\\tag{5}$$\n","\n","This implies that each position in the $dZ$ matrix contributes equally to output because in the forward pass, we took an average. \n","\n","**Exercise**: Implement the function below to equally distribute a value dz through a matrix of dimension shape. [Hint](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.ones.html)"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"HMuYRiJJmrZa"},"outputs":[],"source":["def distribute_value(dz, shape):\n"," \"\"\"\n"," Distributes the input value in the matrix of dimension shape\n"," \n"," Arguments:\n"," dz -- input scalar\n"," shape -- the shape (n_H, n_W) of the output matrix for which we want to distribute the value of dz\n"," \n"," Returns:\n"," a -- Array of size (n_H, n_W) for which we distributed the value of dz\n"," \"\"\"\n"," \n"," ### START CODE HERE ###\n"," # Retrieve dimensions from shape (≈1 line)\n"," (n_H, n_W) = shape\n"," \n"," # Compute the value to distribute on the matrix (≈1 line)\n"," average = dz / (n_H * n_W)\n","\n"," # Create a matrix where every entry is the \"average\" value (≈1 line)\n"," a = np.full((n_H, n_W), average)\n"," ### END CODE HERE ###\n"," \n"," return a"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"1OKSyVi0mrZa"},"outputs":[],"source":["a = distribute_value(2, (2,2))\n","print('distributed value =', a)"]},{"cell_type":"markdown","metadata":{"id":"A4LPKr21mrZa"},"source":["**Expected Output**: \n","\n"," \n"," \n","\n","\n","\n","
\n","distributed_value =\n","\n","[[ 0.5 0.5]\n"," \n","[ 0.5 0.5]]\n","
"]},{"cell_type":"markdown","metadata":{"id":"hYwIh20UmrZa"},"source":["### 5.2.3 Putting it together: Pooling backward \n","\n","You now have everything you need to compute backward propagation on a pooling layer.\n","\n","**Exercise**: Implement the `pool_backward` function in both modes (`\"max\"` and `\"average\"`). You will once again use 4 for-loops (iterating over training examples, height, width, and channels). You should use an `if/elif` statement to see if the mode is equal to `'max'` or `'average'`. If it is equal to 'average' you should use the `distribute_value()` function you implemented above to create a matrix of the same shape as `a_slice`. Otherwise, the mode is equal to '`max`', and you will create a mask with `create_mask_from_window()` and multiply it by the corresponding value of dA."]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"Ua7aOYt6mrZa"},"outputs":[],"source":["def pool_backward(dA, cache, mode = \"max\"):\n"," \"\"\"\n"," Implements the backward pass of the pooling layer\n"," \n"," Arguments:\n"," dA -- gradient of cost with respect to the output of the pooling layer, same shape as A\n"," cache -- cache output from the forward pass of the pooling layer, contains the layer's input and hparameters \n"," mode -- the pooling mode you would like to use, defined as a string (\"max\" or \"average\")\n"," \n"," Returns:\n"," dA_prev -- gradient of cost with respect to the input of the pooling layer, same shape as A_prev\n"," \"\"\"\n"," \n"," ### START CODE HERE ###\n"," \n"," # Retrieve information from cache (≈1 line)\n"," (A_prev, hparameters) = cache\n"," \n"," # Retrieve hyperparameters from \"hparameters\" (≈2 lines)\n"," stride = hparameters['stride']\n"," f = hparameters['f']\n"," \n"," # Retrieve dimensions from A_prev's shape and dA's shape (≈2 lines)\n"," m, n_H_prev, n_W_prev, n_C_prev = A_prev.shape\n"," m, n_H, n_W, n_C = dA.shape\n"," \n"," # Initialize dA_prev with zeros (≈1 line)\n"," dA_prev = np.zeros(A_prev.shape)\n"," \n"," for i in range(m): # loop over the training examples\n"," \n"," # select training example from A_prev (≈1 line)\n"," a_prev = dA_prev[i]\n"," \n"," for h in range(n_H): # loop on the vertical axis\n"," for w in range(n_W): # loop on the horizontal axis\n"," for c in range(n_C): # loop over the channels (depth)\n"," \n"," # Find the corners of the current \"slice\" (≈4 lines)\n"," vert_start = h * stride\n"," vert_end = h * stride + f\n"," horiz_start = w * stride\n"," horiz_end = w * stride + f\n"," \n"," # Compute the backward propagation in both modes.\n"," if mode == \"max\":\n"," \n"," # Use the corners and \"c\" to define the current slice from a_prev (≈1 line)\n"," a_prev_slice = a_prev[ vert_start:vert_end, horiz_start:horiz_end, c ]\n"," # Create the mask from a_prev_slice (≈1 line)\n"," mask = create_mask_from_window( a_prev_slice )\n"," # Set dA_prev to be dA_prev + (the mask multiplied by the correct entry of dA) (≈1 line)\n"," dA_prev[i, vert_start:vert_end, horiz_start:horiz_end, c] += a_prev_slice * mask\n"," \n"," elif mode == \"average\":\n"," \n"," # Get the value a from dA (≈1 line)\n"," da = dA[i, h, w, c]\n"," # Define the shape of the filter as fxf (≈1 line)\n"," shape = (f, f)\n"," # Distribute it to get the correct slice of dA_prev. i.e. Add the distributed value of da. (≈1 line)\n"," dA_prev[i, vert_start: vert_end, horiz_start: horiz_end, c] += distribute_value(da, shape)\n"," \n"," ### END CODE ###\n"," \n"," # Making sure your output shape is correct\n"," assert(dA_prev.shape == A_prev.shape)\n"," \n"," return dA_prev"]},{"cell_type":"code","execution_count":null,"metadata":{"collapsed":true,"id":"vpEPJ8fjmrZb"},"outputs":[],"source":["np.random.seed(1)\n","A_prev = np.random.randn(5, 5, 3, 2)\n","hparameters = {\"stride\" : 1, \"f\": 2}\n","A, cache = pool_forward(A_prev, hparameters)\n","dA = np.random.randn(5, 4, 2, 2)\n","\n","dA_prev = pool_backward(dA, cache, mode = \"max\")\n","print(\"mode = max\")\n","print('mean of dA = ', np.mean(dA))\n","print('dA_prev[1,1] = ', dA_prev[1,1]) \n","print()\n","dA_prev = pool_backward(dA, cache, mode = \"average\")\n","print(\"mode = average\")\n","print('mean of dA = ', np.mean(dA))\n","print('dA_prev[1,1] = ', dA_prev[1,1]) "]},{"cell_type":"markdown","metadata":{"id":"5jyPorV_mrZb"},"source":["**Expected Output**: \n","\n","mode = max:\n"," \n"," \n","\n","\n","\n","\n","\n"," \n","\n","\n","\n","
\n","\n","**mean of dA =**\n","\n","\n","0.145713902729\n","\n","
\n","**dA_prev[1,1] =** \n","\n","[[ 0. 0. ]
\n"," [ 5.05844394 -1.68282702]
\n"," [ 0. 0. ]]\n","
\n","\n","mode = average\n"," \n"," \n","\n","\n","\n","\n","\n"," \n","\n","\n","\n","
\n","\n","**mean of dA =**\n","\n","\n","0.145713902729\n","\n","
\n","**dA_prev[1,1] =** \n","\n","[[ 0.08485462 0.2787552 ]
\n"," [ 1.26461098 -0.25749373]
\n"," [ 1.17975636 -0.53624893]]\n","
"]},{"cell_type":"markdown","metadata":{"id":"AMzs7lc6mrZb"},"source":["### Congratulations !\n","\n","Congratulations on completing this assignment. You now understand how convolutional neural networks work. You have implemented all the building blocks of a neural network. In the next assignment you will implement a ConvNet using TensorFlow."]}],"metadata":{"coursera":{"course_slug":"convolutional-neural-networks","graded_item_id":"qO8ng","launcher_item_id":"7XDi8"},"kernelspec":{"display_name":"Python 3.9.12 ('base')","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":"3.9.12"},"vscode":{"interpreter":{"hash":"c6e4e9f98eb68ad3b7c296f83d20e6de614cb42e90992a65aa266555a3137d0d"}},"colab":{"provenance":[],"collapsed_sections":[]}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git "a/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/alexnet_practice.ipynb" "b/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/alexnet_practice.ipynb" new file mode 100644 index 0000000..af1aca9 --- /dev/null +++ "b/6\354\243\274\354\260\250(0928)/1\354\241\260/\352\271\200\354\204\261\354\234\244/alexnet_practice.ipynb" @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"NF2IR5u8-0xo"},"source":["# AlexNet\n","\n","In this notebook we will be implementing a modified version of [AlexNet](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf), a neural network model that uses convolutional neural network (CNN) layers and was designed for the [ImageNet challenge](http://www.image-net.org/challenges/LSVRC/). AlexNet is famous for winning the ImageNet challenge in 2012 by beating the second place competitor by over 10% accuracy and kickstarting the interest in deep learning for computer vision.\n","\n","The image below shows the architecture of AlexNet.\n","\n","![](assets/alexnet.png)\n","\n","Confusingly, there are two \"paths\" of processing through the network. This is due to the original AlexNet model being implemented on two GPUs in parallel. Almost all implementations of AlexNet are now on a single GPU and our implementation is too.\n","\n","We are now moving on from the MNIST dataset and from now on we will be using the [CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset. CIFAR10 consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. The classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. \n","\n","![](https://github.com/bentrevett/pytorch-image-classification/blob/master/assets/cifar10.png?raw=1)\n","\n","We will also show how to initialize the weights of our neural network and how to find a suitable learning rate using a modified version of the [learning rate finder](https://arxiv.org/abs/1506.01186).\n","\n","Like the previous notebooks we'll implement our model, measure its performance on the dataset, and then have a short look into seeing what the model has learned.\n","\n","Most of this notebook will be similar to the previous ones thus we will skim over code that has been shown before. We can look at the previous notebook for a refresher if needed.\n","\n","### Data Processing\n","\n","As always, we'll import the modules we need. A new import is the `_LRScheduler` which we will use to implement our learning rate finder."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"XYaxtUEgIafG"},"outputs":[],"source":["import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","import torch.optim as optim\n","from torch.optim.lr_scheduler import _LRScheduler\n","import torch.utils.data as data\n","\n","import torchvision.transforms as transforms\n","import torchvision.datasets as datasets\n","from torchvision import models\n","\n","from sklearn import decomposition\n","from sklearn import manifold\n","from sklearn.metrics import confusion_matrix\n","from sklearn.metrics import ConfusionMatrixDisplay\n","from tqdm.notebook import tqdm, trange\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","import copy\n","import random\n","import time"]},{"cell_type":"markdown","metadata":{"id":"1FcvzjWq-0xs"},"source":["We set the random seed so all of our experiments can be reproduced."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QfZxvR09IafJ"},"outputs":[],"source":["SEED = 1234\n","\n","random.seed(SEED)\n","np.random.seed(SEED)\n","torch.manual_seed(SEED)\n","torch.cuda.manual_seed(SEED)\n","torch.backends.cudnn.deterministic = True"]},{"cell_type":"markdown","metadata":{"id":"uecfsf1f-0xw"},"source":["We calculate the mean and standard deviation of our data so we can normalize it.\n","\n","Our dataset is made up of color images but three color channels (red, green and blue), compared to MNIST's black and white images with a single color channel. To normalize our data we need to calculate the means and standard deviations for each of the color channels independently. \n","\n","To do this we pass a tuple containing the axes we want to take the means and standard deviations over to the `mean` and `std` functions and we receive a list of means and standard deviations for each of the three color channels."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":123,"referenced_widgets":["10be0dcef610459c96b64976a646dd26","046385ea6710481ba439231f14413bcf","b1e7728147ec4adab28f4e780d0917a5","c7994a0bca084621879702be644d3da7","3fecbe38293a4e4cb9645b6e6c931438","159b05057e724287bdce35b4fa931cb4","d3e7ec16d9db4eb5a6e4fad38a83fd2a","78a6c401503f43cfa5a6f9d403297a84","89afddce27d84b11a8d9bb7e2b014a79","036a3ad01fd240a28b3c3e3f3e592828","0d04ca026e6a46e3952cc581b7d87513"]},"id":"IprUgI0fIafN","outputId":"e92096df-4389-4398-e17a-796f2e057b7a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to .data/cifar-10-python.tar.gz\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"10be0dcef610459c96b64976a646dd26","version_major":2,"version_minor":0},"text/plain":[" 0%| | 0/170498071 [00:00"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["N_IMAGES = 25\n","\n","images, labels = zip(*[(image, label) for image, label in\n"," [train_data[i] for i in range(N_IMAGES)]])\n","\n","classes = test_data.classes\n","\n","plot_images(images, labels, classes)"]},{"cell_type":"markdown","metadata":{"id":"UoCEaY9C-0yK"},"source":["A solution to this is to *renormalize* the images so each pixel is between $[0,1]$. This is done by clipping the pixel values between the maximum and minimum within an image and then scaling each pixel between $[0,1]$ using these maximum and minimums. \n","\n","As we can see the images below look a lot more like we were expecting, along with the rotations and cropping."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":591},"id":"-ZFsMsmajw8R","outputId":"ebd58a4c-1e06-4327-a742-f6321c0e9cc7"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plot_images(images, labels, classes, normalize=True## CODE )"]},{"cell_type":"markdown","metadata":{"id":"-MrEvgccxToO"},"source":["A solution to this is to *renormalize* the images so each pixel is between $[0,1]$. This is done by clipping the pixel values between the maximum and minimum within an image and then scaling each pixel between $[0,1]$ using these maximum and minimums. \n","\n","As we can see the images below look a lot more like we were expecting, along with the rotations and cropping."]},{"cell_type":"markdown","metadata":{"id":"bAFVQhu4-0yN"},"source":["We'll be normalizing our images by default from now on, so we'll write a function that does it for us which we can use whenever we need to renormalize an image."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gVDM7PTCswBx"},"outputs":[],"source":["def normalize_image(image):\n"," image_min = image.min()\n"," image_max = image.max()\n"," image.clamp_(min=image_min, max=image_max)\n"," image.add_(-image_min).div_(image_max - image_min + 1e-5)\n"," return image"]},{"cell_type":"markdown","metadata":{"id":"SMrfzrD0-0yQ"},"source":["As before, we'll check what images look like with Sobel filters applied to them."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"apk0CzvWoZkw"},"outputs":[],"source":["def plot_filter(images, filter, normalize=True):\n","\n"," images = torch.cat([i.unsqueeze(0) for i in images], dim=0).cpu()\n"," filter = torch.FloatTensor(filter).unsqueeze(0).unsqueeze(0).cpu()\n"," filter = filter.repeat(3, 3, 1, 1)\n","\n"," n_images = images.shape[0]\n","\n"," filtered_images = F.conv2d(images, filter)\n","\n"," images = images.permute(0, 2, 3, 1)\n"," filtered_images = filtered_images.permute(0, 2, 3, 1)\n","\n"," fig = plt.figure(figsize=(25, 5))\n","\n"," for i in range(n_images):\n","\n"," image = images[i]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax = fig.add_subplot(2, n_images, i+1)\n"," ax.imshow(image)\n"," ax.set_title('Original')\n"," ax.axis('off')\n","\n"," image = filtered_images[i]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax = fig.add_subplot(2, n_images, n_images+i+1)\n"," ax.imshow(image)\n"," ax.set_title('Filtered')\n"," ax.axis('off')"]},{"cell_type":"markdown","metadata":{"id":"X5mBc7Zy-0yS"},"source":["The filters are still 2-dimensional but they are expanded to a depth of three dimensions inside the `plot_filter` function.\n","\n","Below is a filter which detects horizontal lines."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"s37UHTS-oV2l","outputId":"1f3d98bd-01a7-439e-d2ab-baf01e6eea8b"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["N_IMAGES = 10\n","\n","images = [image for image, label in [train_data[i] for i in range(N_IMAGES)]]\n","\n","horizontal_filter = [[-1, -2, -1],\n"," [ 0, 0, 0],\n"," [ 1, 2, 1]]\n","\n","plot_filter(images, horizontal_filter)"]},{"cell_type":"markdown","metadata":{"id":"CYT28zhp-0yW"},"source":["Here's a filter that detects vertical lines."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"6i229QYDq0ai","outputId":"a4db9111-0169-40dd-b44d-89a37c073c63"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["vertical_filter = [[-1, 0, 1],\n"," [-2, 0, 2],\n"," [-1, 0, 1]]\n","\n","plot_filter(images, vertical_filter)"]},{"cell_type":"markdown","metadata":{"id":"B6SjLzwv-0yY"},"source":["We'll also do the same for subsampling/pooling."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ar1EwXXOsQi6"},"outputs":[],"source":["def plot_subsample(images, pool_type, pool_size, normalize=True):\n","\n"," images = torch.cat([i.unsqueeze(0) for i in images], dim=0).cpu()\n","\n"," if pool_type.lower() == 'max':\n"," pool = F.max_pool2d\n"," elif pool_type.lower() in ['mean', 'avg']:\n"," pool = F.avg_pool2d\n"," else:\n"," raise ValueError(f'pool_type must be either max or mean, got: {pool_type}')\n","\n"," n_images = images.shape[0]\n","\n"," pooled_images = pool(images, kernel_size=pool_size)\n","\n"," images = images.permute(0, 2, 3, 1)\n"," pooled_images = pooled_images.permute(0, 2, 3, 1)\n","\n"," fig = plt.figure(figsize=(25, 5))\n","\n"," for i in range(n_images):\n","\n"," image = images[i]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax = fig.add_subplot(2, n_images, i+1)\n"," ax.imshow(image)\n"," ax.set_title('Original')\n"," ax.axis('off')\n","\n"," image = pooled_images[i]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax = fig.add_subplot(2, n_images, n_images+i+1)\n"," ax.imshow(image)\n"," ax.set_title('Subsampled')\n"," ax.axis('off')"]},{"cell_type":"markdown","metadata":{"id":"3bSOuZcS-0yb"},"source":["As before, the higher filter sizes in the pooling layers means more information is lost, i.e. the image becomes lower resolution."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"-aP2y7Yqsc0f","outputId":"443e1c86-a37f-437f-ee02-0a26c289fa35"},"outputs":[{"data":{"image/png":"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"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# 그래프 출력 pool_type = 'max', pool_size = 2\n","plot_subsample(images, 'max', 2)## CODE"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"tD48HviKtYIZ","outputId":"8741d010-6751-46f3-9004-55578c19afb8"},"outputs":[{"data":{"image/png":"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"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# 그래프 출력 pool_type = 'max', pool_size = 3\n","plot_subsample(images, 'max', 3)## CODE"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"AK-j6-E8teSe","outputId":"55900060-36cf-46c1-f652-201548a4053a"},"outputs":[{"data":{"image/png":"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"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# 그래프 출력 pool_type = 'avg', pool_size = 2\n","plot_subsample(images, 'avg', 2)## CODE"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":215},"id":"FSl6M9cvtkB3","outputId":"1eac386c-3e55-4829-d61d-f0d178f1cdf0"},"outputs":[{"data":{"image/png":"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"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["# 그래프 출력 pool_type = 'avg', pool_size = 3\n","plot_subsample(images, 'avg', 2)## CODE"]},{"cell_type":"markdown","metadata":{"id":"BL_2X7ZF-0yl"},"source":["The final bit of the data processing is creating the iterators.\n","\n","We use a much larger batch size here than in previous models. Generally, when using a GPU, a larger batch size means our model trains faster. Our model has significantly more parameters and the images it is training on are much larger, than the previous notebook, so will generally take longer. We offset this as much as we can by using a batch size of 256 instead of 64."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o3rhhD0HIaff"},"outputs":[],"source":["BATCH_SIZE = 256\n","\n","train_iterator = data.DataLoader(train_data,\n"," shuffle=True,\n"," batch_size=BATCH_SIZE)\n","\n","valid_iterator = data.DataLoader(valid_data,\n"," batch_size=BATCH_SIZE)\n","\n","test_iterator = data.DataLoader(test_data,\n"," batch_size=BATCH_SIZE)"]},{"cell_type":"markdown","metadata":{"id":"98q8mpRN-0yp"},"source":["### Defining the Model\n","\n","Next up is defining the model.\n","\n","The actual model itself is no more difficult to understand than the previous model, LeNet. It is made up of convolutional layers, pooling layers and ReLU activation functions. See the previous notebook for a refresher on these concepts. \n","\n","There are only two new concepts introduced here, `nn.Sequential` and `nn.Dropout`.\n","\n","We can think of `Sequential` as like our transforms introduced earlier for data augmentation. We provide `Sequential` with multiple layers and when the `Sequential` module is called it will apply each layer, in order, to the input. There is no difference between using a `Sequential` and having each module defined in the `__init__` and then called in `forward` - however it makes the code significantly shorter.\n","\n","We have one `Sequential` model, `features`, for all the convolutional and pooling layers, then we flatten then data and pass it to the `classifier`, another `Sequential` model which is made up of linear layers and the second new concept, *dropout*.\n","\n","Dropout is a form of [*regularization*](https://en.wikipedia.org/wiki/Regularization_(mathematics)). As our models get larger, to perform more accurately on richer datasets, they start having a significantly higher number of parameters. The problem with lots of parameters is that our models begin to *overfit*. That is, they do not learn general image features whilst learning to classify images but instead simply memorize images within the training set. This is bad as it will cause our model to achieve poor performance on the validation/testing set. To solve this overfitting problem, we use regularization. Dropout is just one method of regularization, other common ones are *L1 regularization*, *L2 regularization* and *weight decay*.\n","\n","Dropout works by randomly setting a certain fraction, 0.5 here, of the neurons in a layer to zero. This effectively adds noise to the training of the neural network and causes neurons to learn with \"less\" data as they are only getting half of the information from a previous layer with dropout applied. It can also be thought of as causing your model to learn multiple smaller models with less parameters. \n","\n","Dropout is only applied when the model is training. It needs to be \"turned off\" when validating, testing or using the model for inference.\n","\n","As mentioned in the previous notebook, during the convolutional and pooling layers the activation function should be placed **after** the pooling layer to reduce computational cost.\n","\n","In the linear layers, dropout should be applied **after** the activation function. Although when using ReLU activation functions the same result is achieved if dropout is before or after, see [here](https://sebastianraschka.com/faq/docs/dropout-activation.html).\n","\n","One last thing to mention is that the very first convolutional layer has an `in_channel` of three. That is because we are handling color images that have three channels (red, green and blue) instead of the single channel grayscale images from the MNIST dataset. This doesn't change the way any of the convolutional filter works, it just means the first filter has a depth of three instead of a depth of one."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sQq-LE22Iafk"},"outputs":[],"source":["class AlexNet(nn.Module):\n"," def __init__(self, output_dim):\n"," super().__init__()\n","\n"," self.features = nn.Sequential(\n"," # YOUR CODE STARTS HERE\n"," nn.Conv2d(3, 64, 3, 2, 1), # in_channels, out_channels, kernel_size, stride, padding\n"," nn.MaxPool2d(2), # kernel_size\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(64, 192, 3, padding=1),\n"," nn.MaxPool2d(2),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(192, 384, 3, padding=1),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(384, 256, 3, padding=1),\n"," nn.ReLU(inplace=True),\n"," nn.Conv2d(256, 256, 3, padding=1),\n"," nn.MaxPool2d(2),\n"," nn.ReLU(inplace=True)\n"," # YOUR CODE ENDS HERE\n"," )\n","\n"," self.classifier = nn.Sequential(\n"," nn.Dropout(0.5),\n"," nn.Linear(256 * 2 * 2, 4096),\n"," nn.ReLU(inplace=True),\n"," nn.Dropout(0.5),\n"," nn.Linear(4096, 4096),\n"," nn.ReLU(inplace=True),\n"," nn.Linear(4096, output_dim),\n"," )\n","\n"," def forward(self, x):\n"," x = self.features(x)\n"," h = x.view(x.shape[0], -1)\n"," x = self.classifier(h)\n"," return x, h"]},{"cell_type":"markdown","metadata":{"id":"IBMS4TH4XVow"},"source":["We'll create an instance of our model with the desired amount of classes."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y-sDcYnBIafp"},"outputs":[],"source":["OUTPUT_DIM = 10\n","\n","model = AlexNet(OUTPUT_DIM)## CODE"]},{"cell_type":"markdown","metadata":{"id":"O9Dkc_uBXVoy"},"source":["Then we'll see how many trainable parameters our model has. \n","\n","Our LeNet architecture had ~44k, but here we have 23.2M parameters - and AlexNet is a relatively small model for computer vision."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3g-pV7t2Iafs","outputId":"7a831647-902e-4c61-aecf-f856e6622c8a"},"outputs":[{"name":"stdout","output_type":"stream","text":["The model has 23,272,266 trainable parameters\n"]}],"source":["def count_parameters(model):\n"," return sum(p.numel() for p in model.parameters() if p.requires_grad)\n","\n","\n","print(f'The model has {count_parameters(model):,} trainable parameters')"]},{"cell_type":"markdown","metadata":{"id":"n99m5MPZXVo1"},"source":["### Training the Model\n","\n","Next up, we'll initialize the parameters of our model.\n","\n","PyTorch's default initialization is usually fine however by manually trying different initialization schemes we can usually squeeze out a slight performance improvement.\n","\n","We initialize parameters in PyTorch by creating a function that takes in a PyTorch module, checking what type of module it is, and then using the `nn.init` methods to actually initialize the parameters.\n","\n","For our convolutional layers, we'll initialize the weights from a Normal distribution with a standard deviation given by:\n","\n","$$\\frac{\\text{gain}}{\\sqrt{\\text{fan mode}}}$$\n","\n","The value of $\\text{gain}$ depends on the non-linearity we will be using after the convolutional layer and we simply tell the initialization function that we are using ReLU which sets the gain to $\\sqrt{2}$. The fan mode can be either `fan_in` or `fan_out`. `fan_in` is the number of connections coming into the layer and `fan_out` is the number of connections going out of the layer. For the first convolutional layer the input is from 3x3x3 filter, so the `fan_in` is 27 and the output is 64x3x3, so the `fan_out` is 576. We leave it to the default `fan_in` mode. This initialization scheme is called *Kaiming Normal*, also known as *He Normal*. See the [paper](https://arxiv.org/abs/1502.01852) to learn more about how it was devised.\n","\n","For the linear layers we initialize with a Normal distribution but this time the standard deviation is given by:\n","\n","$$\\text{gain} \\times \\sqrt{\\frac{2}{\\text{fan_in} + \\text{fan_out}}}$$\n","\n","Confusingly, instead of just telling the initialization function which non-linearity we want to use and have it calculate the gain for us, we have to tell it what gain we want to use. Luckily, `nn.init` has a `calculate_gain` function which does that for us, and we just tell it we are using ReLUs. This type of initialize scheme is called *Xavier Normal*, also known as *Glorot Normal*. See the [paper](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf) for the theory behind it. \n","\n","For both types of layer we initialize the bias terms to zeros.\n","\n","Why do we even need to initialize our parameters this way? See [this](https://towardsdatascience.com/weight-initialization-in-neural-networks-a-journey-from-the-basics-to-kaiming-954fb9b47c79) article for a great explanation, but the gist of it is that just like how we normalized our input data to have a mean of 0 and a standard deviation of 1, we also want the outputs of each activation function (and therefore the inputs to the subsequent layer) to also have a mean of 0 and a standard deviation of 1. These initialization schemes, by taking account the number of connections in to and out of a layer as well as the non-linearity used, help achieve this normalization effect when initializing weights."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"I4NbFid9Iafw"},"outputs":[],"source":["def initialize_parameters(m):\n"," if isinstance(m, nn.Conv2d):\n"," nn.init.kaiming_normal_(m.weight.data, nonlinearity='relu')\n"," nn.init.constant_(m.bias.data, 0)\n"," elif isinstance(m, nn.Linear):\n"," nn.init.xavier_normal_(m.weight.data, gain=nn.init.calculate_gain('relu'))\n"," nn.init.constant_(m.bias.data, 0)"]},{"cell_type":"markdown","metadata":{"id":"8SkSdnytXVo3"},"source":["We apply the initialization by using the model's `apply` method. This will call the given function on every module and sub-module within the model."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Bve8IsHsIafz","outputId":"a954839a-26e7-4335-c811-421fbacbf651"},"outputs":[{"data":{"text/plain":["AlexNet(\n"," (features): Sequential(\n"," (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n"," (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n"," (2): ReLU(inplace=True)\n"," (3): Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n"," (5): ReLU(inplace=True)\n"," (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (7): ReLU(inplace=True)\n"," (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (9): ReLU(inplace=True)\n"," (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n"," (12): ReLU(inplace=True)\n"," )\n"," (classifier): Sequential(\n"," (0): Dropout(p=0.5, inplace=False)\n"," (1): Linear(in_features=1024, out_features=4096, bias=True)\n"," (2): ReLU(inplace=True)\n"," (3): Dropout(p=0.5, inplace=False)\n"," (4): Linear(in_features=4096, out_features=4096, bias=True)\n"," (5): ReLU(inplace=True)\n"," (6): Linear(in_features=4096, out_features=10, bias=True)\n"," )\n",")"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["model.apply(initialize_parameters)"]},{"cell_type":"markdown","metadata":{"id":"-gyHsCBNXVo5"},"source":["Next up is the learning rate finder. The code here is taken from a stripped down and slightly modified version of the excellent [pytorch-lr-finder](https://github.com/davidtvs/pytorch-lr-finder). \n","\n","**Note**: the learning rate finder is more of an art than a science. It is not going to find an exact learning rate to 10 decimal places which will always give us 100% accuracy - but it is usually going to be better than just picking a learning rate out of thin air. \n","\n","The most commonly used optimizer used is Adam. Adam's default learning rate is usually a fine choice but, much like how we manually initialized our parameters to potentially get some performance improvement, we can try and calculate an optimal learning rate manually.\n","\n","How does the learning rate finder work? We give the finder our model, optimizer and criterion we want to use. However we give it an optimizer with a much lower learning rate than we are expecting to use. We then train the model on the batches of data from the training set - calculating the loss and updating the parameters. After each batch we increase the learning rate exponentially from the initial, extremely low learning rate to a learning rate we know will be too high. This repeats until our loss diverges (over 5x the best loss achieved) or we reach our defined maximum learning rate. \n","\n","At each batch we are recording the learning rate and the loss achieved on that batch. By plotting them against each other we can find a suitable learning rate - but more on how to do that in a bit.\n","\n","The losses calculated are usually quite noisy so we actually save the exponentially weighted average of the loss calculated. \n","\n","We also want to use our initialized parameters, not the ones obtained by upgrading the parameters when performing the learning rate finder. Hence we save the model parameters to disk when initializing the finder and then they are reset to our desired initialized ones just before the `range_test` function returns by loading the initial values from disk."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HO-LYudEIaf2"},"outputs":[],"source":["class LRFinder:\n"," def __init__(self, model, optimizer, criterion, device):\n","\n"," self.optimizer = optimizer\n"," self.model = model\n"," self.criterion = criterion\n"," self.device = device\n","\n"," torch.save(model.state_dict(), 'init_params.pt')\n","\n"," def range_test(self, iterator, end_lr=10, num_iter=100,\n"," smooth_f=0.05, diverge_th=5):\n","\n"," lrs = []\n"," losses = []\n"," best_loss = float('inf')\n","\n"," lr_scheduler = ExponentialLR(self.optimizer, end_lr, num_iter)\n","\n"," iterator = IteratorWrapper(iterator)\n","\n"," for iteration in range(num_iter):\n","\n"," loss = self._train_batch(iterator)\n","\n"," lrs.append(lr_scheduler.get_last_lr()[0])\n","\n"," # update lr\n"," lr_scheduler.step()\n","\n"," if iteration > 0:\n"," loss = smooth_f * loss + (1 - smooth_f) * losses[-1]\n","\n"," if loss < best_loss:\n"," best_loss = loss\n","\n"," losses.append(loss)\n","\n"," if loss > diverge_th * best_loss:\n"," print(\"Stopping early, the loss has diverged\")\n"," break\n","\n"," # reset model to initial parameters\n"," model.load_state_dict(torch.load('init_params.pt'))\n","\n"," return lrs, losses\n","\n"," def _train_batch(self, iterator):\n","\n"," self.model.train()\n","\n"," self.optimizer.zero_grad()\n","\n"," x, y = iterator.get_batch()\n","\n"," x = x.to(self.device)\n"," y = y.to(self.device)\n","\n"," y_pred, _ = self.model(x)\n","\n"," loss = self.criterion(y_pred, y)\n","\n"," loss.backward()\n","\n"," self.optimizer.step()\n","\n"," return loss.item()\n","\n","\n","class ExponentialLR(_LRScheduler):\n"," def __init__(self, optimizer, end_lr, num_iter, last_epoch=-1):\n"," self.end_lr = end_lr\n"," self.num_iter = num_iter\n"," super(ExponentialLR, self).__init__(optimizer, last_epoch)\n","\n"," def get_lr(self):\n"," curr_iter = self.last_epoch\n"," r = curr_iter / self.num_iter\n"," return [base_lr * (self.end_lr / base_lr) ** r\n"," for base_lr in self.base_lrs]\n","\n","\n","class IteratorWrapper:\n"," def __init__(self, iterator):\n"," self.iterator = iterator\n"," self._iterator = iter(iterator)\n","\n"," def __next__(self):\n"," try:\n"," inputs, labels = next(self._iterator)\n"," except StopIteration:\n"," self._iterator = iter(self.iterator)\n"," inputs, labels, *_ = next(self._iterator)\n","\n"," return inputs, labels\n","\n"," def get_batch(self):\n"," return next(self)"]},{"cell_type":"markdown","metadata":{"id":"o-5vb3-dXVo8"},"source":["To prepare to use the range finder we define an initial, very low starting learning rate and then create an instance of the optimizer we want to use with that learning rate.\n","\n","We then define the loss function we want to use, the device we'll use and place our model and criterion on to our device."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5583ybdqIaf4"},"outputs":[],"source":["START_LR = 1e-7\n","\n","optimizer = optim.Adam(model.parameters(), lr=START_LR)\n","\n","device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n","\n","criterion = ## cross entropy loss function\n","\n","model = model.to(device)\n","criterion = criterion.to(device)"]},{"cell_type":"markdown","metadata":{"id":"f62J8sOuXVo-"},"source":["Next, we'll finally use the range finder.\n","\n","We first create an instance of the finder class with the model, optimizer, loss function and device. Then we use `range_test` with the training iterator, the maximum learning rate and the number of iterations we want to use. "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"BcA3kgLHIaf6","outputId":"d75b0367-af3e-4349-8c19-e6bcce4e3ea3"},"outputs":[{"name":"stdout","output_type":"stream","text":["Stopping early, the loss has diverged\n"]}],"source":["END_LR = 10\n","NUM_ITER = 100\n","\n","lr_finder = LRFinder(model, optimizer, criterion, device)\n","lrs, losses = lr_finder.range_test(train_iterator, END_LR, NUM_ITER)"]},{"cell_type":"markdown","metadata":{"id":"EPd2y04tXVpE"},"source":["Next, we can plot the learning rate against the loss. \n","\n","As our learning rate was scaled up exponentially we want to plot it on a logarithmic scale. We also do not want to plot the last few values as they are usually where the loss is very high and makes it difficult to examine the graph in detail. You can also skip the first few values as nothing interesting happens there."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4h0DrKqrIaf9"},"outputs":[],"source":["def plot_lr_finder(lrs, losses, skip_start=5, skip_end=5):\n","\n"," if skip_end == 0:\n"," lrs = lrs[skip_start:]\n"," losses = losses[skip_start:]\n"," else:\n"," lrs = lrs[skip_start:-skip_end]\n"," losses = losses[skip_start:-skip_end]\n","\n"," fig = plt.figure(figsize=(16, 8))\n"," ax = fig.add_subplot(1, 1, 1)\n"," ax.plot(lrs, losses)\n"," ax.set_xscale('log')\n"," ax.set_xlabel('Learning rate')\n"," ax.set_ylabel('Loss')\n"," ax.grid(True, 'both', 'x')\n"," plt.show()"]},{"cell_type":"markdown","metadata":{"id":"n3zLyhwJXVpH"},"source":["As we can see, the loss begins flat and then begins to decrease rapidly before reaching a minimum and starting to increase. \n","\n","How can we read this plot and get the optimal learning rate? According to [this](https://sgugger.github.io/how-do-you-find-a-good-learning-rate.html) article, we should look for the loss begins to flatten, this is around $10^{-2}$ below, and then reduce that by a factor of 10, which gives us a found learning rate of $10^{-3}$ or $0.001$."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":500},"id":"WiivDPU3IagA","outputId":"29ece72b-4f12-4c74-d6ab-71a539c75545"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plot_lr_finder(lrs, losses)"]},{"cell_type":"markdown","metadata":{"id":"WImHQVcvXVpK"},"source":["We can now create a new optimizer with our found learning rate.\n","\n","Ironically, the learning rate value we found, $0.001$ is actually Adam's default learning rate!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P6hTusQNIagD"},"outputs":[],"source":["FOUND_LR = 1e-3\n","\n","optimizer = optim.Adam(model.parameters(), lr=FOUND_LR)## CODE = Adam"]},{"cell_type":"markdown","metadata":{"id":"9HoBM4KEXVpS"},"source":["The rest of the notebook is pretty similar to the previous notebooks from these tutorials.\n","\n","We define a function to calculate accuracy..."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TJj9fQ5WIagI"},"outputs":[],"source":["def calculate_accuracy(y_pred, y):\n"," top_pred = y_pred.argmax(1, keepdim=True)\n"," correct = top_pred.eq(y.view_as(top_pred)).sum()\n"," acc = correct.float() / y.shape[0]\n"," return acc"]},{"cell_type":"markdown","metadata":{"id":"WGjesEGDXVpV"},"source":["...and a function to implement our training loop.\n","\n","As we are using dropout we need to make sure to \"turn it on\" when training by using `model.train()`."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"e65mN1cGIagK"},"outputs":[],"source":["def train(model, iterator, optimizer, criterion, device):\n","\n"," epoch_loss = 0\n"," epoch_acc = 0\n","\n"," model.train()\n","\n"," for (x, y) in tqdm(iterator, desc=\"Training\", leave=False):\n","\n"," x = x.to(device)\n"," y = y.to(device)\n","\n"," optimizer.zero_grad()\n","\n"," y_pred, _ = model(x)\n","\n"," loss = criterion(y_pred, y)\n","\n"," acc = calculate_accuracy(y_pred, y)\n","\n"," loss.backward()\n","\n"," optimizer.step()\n","\n"," epoch_loss += loss.item()\n"," epoch_acc += acc.item()\n","\n"," return epoch_loss / len(iterator), epoch_acc / len(iterator)"]},{"cell_type":"markdown","metadata":{"id":"U0usLljsXVpY"},"source":["We also define an evaluation loop, making sure to \"turn off\" dropout with `model.eval()`."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gnUhGD9MIagM"},"outputs":[],"source":["def evaluate(model, iterator, criterion, device):\n","\n"," epoch_loss = 0\n"," epoch_acc = 0\n","\n"," model.eval()\n","\n"," with torch.no_grad():\n","\n"," for (x, y) in tqdm(iterator, desc=\"Evaluating\", leave=False):\n","\n"," x = x.to(device)\n"," y = y.to(device)\n","\n"," y_pred, _ = model(x)\n","\n"," loss = criterion(y_pred, y)\n","\n"," acc = calculate_accuracy(y_pred, y)\n","\n"," epoch_loss += loss.item()\n"," epoch_acc += acc.item()\n","\n"," return epoch_loss / len(iterator), epoch_acc / len(iterator)"]},{"cell_type":"markdown","metadata":{"id":"MkkzHBcoXVpa"},"source":["Next, we define a function to tell us how long an epoch takes."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RX-Mwze-IagO"},"outputs":[],"source":["def epoch_time(start_time, end_time):\n"," elapsed_time = end_time - start_time\n"," elapsed_mins = int(elapsed_time / 60)\n"," elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n"," return elapsed_mins, elapsed_secs"]},{"cell_type":"markdown","metadata":{"id":"53srBoLAXVpe"},"source":["Then, finally, we train our model.\n","\n","We get a best validation loss of ~76% 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":0},"text/plain":["Epochs: 0%| | 0/25 [00:00"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plot_confusion_matrix(labels, pred_labels, classes)"]},{"cell_type":"markdown","metadata":{"id":"X8D82wHWXVpv"},"source":["We can then find which predictions were correct and then sort the incorrect predictions in descending order of their confidence."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"s7S4bQ8T11Bm"},"outputs":[],"source":["corrects = torch.eq(labels, pred_labels)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TRbHfNH82iXd"},"outputs":[],"source":["incorrect_examples = []\n","\n","for image, label, prob, correct in zip(images, labels, probs, corrects):\n"," if not correct:\n"," incorrect_examples.append((image, label, prob))\n","\n","incorrect_examples.sort(reverse=True,\n"," key=lambda x: torch.max(x[2], dim=0).values)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"TxAobfgH2kfF"},"outputs":[],"source":["def plot_most_incorrect(incorrect, classes, n_images, normalize=True):\n","\n"," rows = int(np.sqrt(n_images))\n"," cols = int(np.sqrt(n_images))\n","\n"," fig = plt.figure(figsize=(25, 20))\n","\n"," for i in range(rows*cols):\n","\n"," ax = fig.add_subplot(rows, cols, i+1)\n","\n"," image, true_label, probs = incorrect[i]\n"," image = image.permute(1, 2, 0)\n"," true_prob = probs[true_label]\n"," incorrect_prob, incorrect_label = torch.max(probs, dim=0)\n"," true_class = classes[true_label]\n"," incorrect_class = classes[incorrect_label]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax.imshow(image.cpu().numpy())\n"," ax.set_title(f'true label: {true_class} ({true_prob:.3f})\\n'\n"," f'pred label: {incorrect_class} ({incorrect_prob:.3f})')\n"," ax.axis('off')\n","\n"," fig.subplots_adjust(hspace=0.4)"]},{"cell_type":"markdown","metadata":{"id":"ina7RRxIXVp2"},"source":["Interestingly, the most incorrect was an example that is incorrectly labelled in the dataset itself. It is clearly a frog, which our model predicted with 100% confidence, but the label is cat.\n","\n","Truck and automobile seem to be confused a lot but even to humans these two classes are slightly ambiguous."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"qbuZs4C32vNl","outputId":"9aba07d0-6fa6-4314-9381-ececa3f09e31"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["N_IMAGES = 36\n","\n","plot_most_incorrect(incorrect_examples, classes, N_IMAGES)"]},{"cell_type":"markdown","metadata":{"id":"2QLA7SJFXVp7"},"source":["Next, we'll get the output and intermediate (after the flatten) representations."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oCAPWHbZ2yDa"},"outputs":[],"source":["def get_representations(model, iterator, device):\n","\n"," model.eval()\n","\n"," outputs = []\n"," intermediates = []\n"," labels = []\n","\n"," with torch.no_grad():\n","\n"," for (x, y) in tqdm(iterator):\n","\n"," x = x.to(device)\n","\n"," y_pred, h = model(x)\n","\n"," outputs.append(y_pred.cpu())\n"," intermediates.append(h.cpu())\n"," labels.append(y)\n","\n"," outputs = torch.cat(outputs, dim=0)\n"," intermediates = torch.cat(intermediates, dim=0)\n"," labels = torch.cat(labels, dim=0)\n","\n"," return outputs, intermediates, labels"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rzUcnw-jUHVr"},"outputs":[],"source":["outputs, intermediates, labels = get_representations(model,\n"," train_iterator,\n"," device)"]},{"cell_type":"markdown","metadata":{"id":"4KKaVGuaXVp_"},"source":["We can then perform PCA on them both and plot them."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R_EKDz3vUegC"},"outputs":[],"source":["def get_pca(data, n_components=2):\n"," pca = decomposition.PCA()\n"," pca.n_components = n_components\n"," pca_data = pca.fit_transform(data)\n"," return pca_data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MUpAtW46UhgS"},"outputs":[],"source":["def plot_representations(data, labels, classes, n_images=None):\n","\n"," if n_images is not None:\n"," data = data[:n_images]\n"," labels = labels[:n_images]\n","\n"," fig = plt.figure(figsize=(10, 10))\n"," ax = fig.add_subplot(111)\n"," scatter = ax.scatter(data[:, 0], data[:, 1], c=labels, cmap='tab10')\n"," handles, labels = scatter.legend_elements()\n"," ax.legend(handles=handles, labels=classes)"]},{"cell_type":"markdown","metadata":{"id":"4iTHBvJFXVqD"},"source":["Like previous notebooks, the classes seem more separated in the output representations than the intermediate representations."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592},"id":"ToygpaIkUjLg","outputId":"920bea5e-92cc-4a8f-ecc5-1c04068d1e95"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["output_pca_data = get_pca(outputs)\n","plot_representations(output_pca_data, labels, classes)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592},"id":"-I0mXG3dUl1m","outputId":"c5739f80-ec46-405c-d346-e21eb237d895"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAlsAAAI/CAYAAABAoBw9AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdd3xX1f348de5n5mdkB0gkLBXWAFUXK2z1rrq1jpa67bWVlv7tbVq7a+22tZqrbhXq7Zq3bZuCyggYe8RCCQhCdn7s+49vz8SAkk+n5CQTxa8n48HD/ice++57xuivHPOue+jtNYIIYQQQoi+YQx0AEIIIYQQhzNJtoQQQggh+pAkW0IIIYQQfUiSLSGEEEKIPiTJlhBCCCFEH5JkSwghhBCiD9kHOoCuJCUl6dGjRw90GEIIIYQQB7VixYoKrXVyx/ZBnWyNHj2avLy8gQ5DCCGEEOKglFK7grXLNKIQQgghRB/q9siWUupZ4Exgr9Z6amvbP4EJrafEAzVa6xlBri0A6gETCGitc3sVtRBCCCHEENGTacTngb8CL+5r0FpftO/PSqk/ArVdXP8NrXVFTwMUQgghhBjKup1saa0XKqVGBzumlFLAhcA3wxSXEEIIIbrJ7/dTVFSEx+MZ6FCOCG63mxEjRuBwOLp1frgWyB8HlGmtt4U4roGPlFIaeEJr/WSY7iuEEEIc8YqKioiJiWH06NG0jH+IvqK1prKykqKiIrKysrp1TbgWyF8CvNLF8WO11rOAbwE3KaWOD3WiUupapVSeUiqvvLw8TOEJIYQQhy+Px0NiYqIkWv1AKUViYmKPRhF7nWwppezAecA/Q52jtS5u/X0v8CYwt4tzn9Ra52qtc5OTO5WqEEIIIUQQkmj1n55+rcMxsnUysFlrXRQioCilVMy+PwOnAuvDcF8hhBBCDHJnnHEGNTU1Pbrmqquu4vXXX++bgAZAt5MtpdQrwBJgglKqSCn1g9ZDF9NhClEplaGU+qD1YyqwWCm1BvgaeF9r/d/ehy6EEEKIwe6DDz4gPj6+XZvWGsuyBiagAdDtZEtrfYnWOl1r7dBaj9BaP9PafpXWekGHc/dorc9o/fMOrfX01l9TtNa/De8jCCGEEKIn3lpVzPwHPiPrzveZ/8BnvLWqOCz9nnPOOcyePZspU6bw5JMt78KNHj2aiooKCgoKmDBhAldccQVTp06lsLCQ6OhobrvtNqZMmcJJJ51EsLXa9913H3PmzGHq1Klce+21aK0BOPHEE/n5z3/O3LlzGT9+PIsWLQLANE3uuOMO5syZQ05ODk888URYnq03pIK8EEIIcQR5a1Uxv/j3OoprmtFAcU0zv/j3urAkXM8++ywrVqwgLy+PRx55hMrKynbHt23bxo033siGDRsYNWoUjY2N5ObmsmHDBk444QTuvffeTn3efPPNLF++nPXr19Pc3Mx7773XdiwQCPD111/z8MMPt137zDPPEBcXx/Lly1m+fDlPPfUUO3fu7PWz9YYkW0IIIcQR5MEPt9DsN9u1NftNHvxwS6/7fuSRR5g+fTpHHXUUhYWFbNvWviLUqFGjOOqoo9o+G4bBRRe11Ee//PLLWbx4cac+P//8c+bNm8e0adP47LPP2LBhQ9ux8847D4DZs2dTUFAAwEcffcSLL77IjBkzmDdvHpWVlZ3i6G+DeiNqIYQQQoTXnprmHrV31xdffMEnn3zCkiVLiIyM5MQTT+xUHiEqKqrLPjq+5efxeLjxxhvJy8tj5MiR3HPPPe36dLlcANhsNgKBANCyHuzRRx/ltNNO69XzhJOMbAkhhBBHkIz4iB61d1dtbS0JCQlERkayefNmli5detBrLMtqe+vw5Zdf5thjj213fF9ilZSURENDQ7feUDzttNN4/PHH8fv9AGzdupXGxsaePk5YyciWEEIIcQS547QJ/OLf69pNJUY4bNxx2oRe9Xv66aezYMECJk2axIQJE9pNF4YSFRXF119/zf33309KSgr//Gf7kp3x8fH88Ic/ZOrUqaSlpTFnzpyD9nnNNddQUFDArFmz0FqTnJzMW2+9daiPFRZq36r+wSg3N1fn5eUNdBhCCCHEoLZp0yYmTZrU7fPfWlXMgx9uYU9NMxnxEdxx2gTOmTm8DyMMLjo6moaGhn6/bzgE+5orpVZorXM7nisjW0IIIcQR5pyZwwckuTpSyZotIYQQQgyIoTqq1VOSbAkhhBBC9CGZRhSHHcvysnfvf6muXobbPYKMjO/icqUOdFhCCCGOUJJsicNKIFBPXt75eLx7MM0mDMPFrt2PM2P6c8THd1qzKIQQQvQ5mUYUh5WCXU/S1Lwb02wCWka5TLOJDRt/wmB+81YIIcThS5ItcVjZu/d9tPZ1avf5qmhu3j0AEQkhhAjmrbfeYuPGjX16j4KCAqZOnRr02DXXXNN2/32bZfcVSbbEYcUwnCGOWBg2V7/GIoQQIrT+SLa68vTTTzN58uR+uZckW+KwMjzjUgyj45YTBlFR43C70gYkJiGEGHTW/gv+PBXuiW/5fe2/wtLtOeecw+zZs5kyZQpPPvkk0FK4dJ/XX3+dq666iq+++op33nmHO+64gxkzZpCfn8/q1as56qijyMnJ4dxzz6W6uhqAE088kdtuu43c3FwmTZrE8uXLOe+88xg3bhy//OUv2/r+05/+xNSpU5k6dSoPP/xwW3sgEOCyyy5j0qRJnH/++TQ1NbX1G6xw+t///nfmzp3LjBkzuO666zBNs9M5PSXJljisDB9+KYmJJ2AYbgwjApstCpcrhWlT/zrQoQkhxOCw9l/w7o+gthDQLb+/+6OwJFzPPvssK1asIC8vj0ceeYTKysqg5x1zzDGcddZZPPjgg6xevZoxY8ZwxRVX8Pvf/561a9cybdo07r333rbznU4neXl5XH/99Zx99tk89thjrF+/nueff57KykpWrFjBc889x7Jly1i6dClPPfUUq1atAmDLli3ceOONbNq0idjYWP72t7+FjH/Tpk3885//5Msvv2T16tXYbDb+8Y9/9PrrIm8jisOKYdjJmfYYDQ1bqK1bjcuVyrCEYzEM+VYXQggAPr0P/M3t2/zNLe05F/aq60ceeYQ333wTgMLCQrZt29at62pra6mpqeGEE04A4Morr+SCCy5oO37WWWcBMG3aNKZMmUJ6ejoA2dnZFBYWsnjxYs4991yioqIAOO+881i0aBFnnXUWI0eOZP78+QBcfvnlPPLII9x+++1B4/j0009ZsWJF2x6Mzc3NpKSk9PTL0In8CyQOS9HRE4iO7t2mqkIIcViqLepZezd98cUXfPLJJyxZsoTIyEhOPPFEPB4PSqm2czwezyH17XK1rLk1DKPtz/s+BwKBLq898P7BPh9Ia82VV17J7373u0OKMxSZRhRigAUC9dQ3bCYQqB/oUIQQR4K4ET1r76ba2loSEhKIjIxk8+bNLF26FIDU1FQ2bdqEZVlto14AMTEx1Ne3/H8vLi6OhIQEFi1aBMBLL73UNsrVHccddxxvvfUWTU1NNDY28uabb3LccccBsHv3bpYsWQLAyy+/zLHHHhuyn5NOOonXX3+dvXv3AlBVVcWuXbt68FUITpItIQaI1iZbtt7HosXzWLHiIhYtnseWLfeide8XYwohREgn3Q2ODi8SOSJa2nvh9NNPJxAIMGnSJO68806OOuooAB544AHOPPNMjjnmmLbpP4CLL76YBx98kJkzZ5Kfn88LL7zAHXfcQU5ODqtXr+buu7sfz6xZs7jqqquYO3cu8+bN45prrmHmzJkATJgwgccee4xJkyZRXV3NDTfcELKfyZMnc//993PqqaeSk5PDKaecQklJySF+RfZTg7nQY25urg72poAQh4MdO//Krl0LsKz9aycMw82oUTeQnXXzAEYmhBhqNm3axKRJk7p/wdp/tazRqi1qGdE66e5er9c60gT7miulVmitO21XImu2hBgghYXPtUu0ACzLQ2Hhc5JsCSH6Vs6Fklz1I5lGFGKABAJ1Idpr+zkSIYQQfUmSLSEGSEz0xKDt0dE9mAoQQggx6EmyJcQAGT/+1xiGG9j3GrLCMCIYP753i1SFEEIMLrJmS4gBEh+fS+7s19hZ8FcaGrYQHT2BrNE3ExPTP3t1CSGE6B+SbAkxgGJiJpMzLfTWEUIIIYY+mUYUQgghRK8VFBQwderUTu3XXHMNGzduPOj1zz//PDfffHi+iS0jW0IIIYToM08//XTQdtM0sdls/RzNwJCRLSGEEOII8/6O9zn19VPJeSGHU18/lfd3vB+WfgOBAJdddhmTJk3i/PPPp6mpiRNPPJF9Bcqjo6P56U9/yvTp01myZAnPPfcc48ePZ+7cuXz55ZdhiWEwkmRLCCGEOIK8v+N97vnqHkoaS9BoShpLuOere8KScG3ZsoUbb7yRTZs2ERsby9/+1n5NamNjI/PmzWPNmjWMGTOGX//613z55ZcsXry4W1ONQ5UkW0IIIcQR5C8r/4LH9LRr85ge/rLyL73ue+TIkcyfPx+Ayy+/nMWLF7c7brPZ+O53vwvAsmXLOPHEE0lOTsbpdHLRRRf1+v6DlSRbQgghxBGktLG0R+09oZTq8rPb7T5i1mkdSJItIYQQ4giSFpXWo/ae2L17N0uWLAHg5Zdf5thjjw157rx58/jf//5HZWUlfr+f1157rdf3H6wk2RJCCCGOILfOuhW3zd2uzW1zc+usW3vd94QJE3jssceYNGkS1dXV3HDDDSHPTU9P55577uHoo49m/vz5TJp0+G5VprTWAx1DSLm5uXrfGwxCCCGECG7Tpk09Slbe3/E+f1n5F0obS0mLSuPWWbfy7exv92GEh59gX3Ol1AqtdW7Hc6XOlhBCCHGE+Xb2tyW56kcyjSiEEEII0Yck2RJCCCGE6EOSbAkhhBBC9CFJtoQQQggh+pAkW0IIIYQQfUiSLSGEEEL0qy+++IKvvvpqoMPoN5JsCSGEEKJfSbIlhBBCiMNa7bvvsu2bJ7Fp0mS2ffMkat99Nyz9vvjii+Tk5DB9+nS+973v8e677zJv3jxmzpzJySefTFlZGQUFBSxYsIA///nPzJgxg0WLFoXl3oOZFDUVQgghjiC1775Lya/uRns8AAT27KHkV3cDEPed7xxyvxs2bOD+++/nq6++IikpiaqqKpRSLF26FKUUTz/9NH/4wx/44x//yPXXX090dDS33357WJ5psJNkSwxp9fUbKN7zTwL+WpKTTyU5+TQMQ76thRAilL1/frgt0dpHezzs/fPDvUq2PvvsMy644AKSkpIAGDZsGOvWreOiiy6ipKQEn89HVlZWr2IfqmQaUQxZRUUvk7fiQoqLX6Fs73ts2nwnq1ZfgWX5Bzo0IYQYtAIlJT1q741bbrmFm2++mXXr1vHEE0/g6ZDkHSkk2RJDkt9fx7bt92NZHsACwDSbqKtbx969/xnY4IQQYhCzp6f3qL27vvnNb/Laa69RWVkJQFVVFbW1tQwfPhyAF154oe3cmJgY6uvre3W/oUSSLTEk1dR8jVKOTu2W1UTZ3vcHICIhhBgaUm77Mcrtbtem3G5Sbvtxr/qdMmUKd911FyeccALTp0/nJz/5Cffccw8XXHABs2fPbpteBPjOd77Dm2++KQvkhRjMbLaIEEcUdntMv8YihBBDyb51WXv//DCBkhLs6emk3PbjXq3X2ufKK6/kyiuvbNd29tlndzpv/PjxrF27ttf3Gyok2RJDUnz8PAzDiWm2bzcMN8MzLh6YoIQQYoiI+853wpJcie6RaUQxJBmGnRkznsNhj8dmi8Zmi8IwXGSNvoX4+NyBDk8IIYRo0+2RLaXUs8CZwF6t9dTWtnuAHwLlraf9n9b6gyDXng78BbABT2utH+hl3EIQGzOVY49dSnX1VwQCDSQkzMPpTDr4hUIIIUQ/6sk04vPAX4EXO7T/WWv9UKiLlFI24DHgFKAIWK6UekdrvbGHsQrRiWE4SEw8YaDDEEIIIULq9jSi1nohUHUI95gLbNda79Ba+4BXgc6r5YQQQgghDkPhWLN1s1JqrVLqWaVUQpDjw4HCAz4XtbYJIYQQQhz2eptsPQ6MAWYAJcAfexuQUupapVSeUiqvvLz84BcIIYQQYtC55557eOihkKuMjii9Sra01mVaa1NrbQFP0TJl2FExMPKAzyNa20L1+aTWOldrnZucnNyb8IQQQggxhAUCgYEOISx6VWdLKZWutd63mdK5wPogpy0HximlsmhJsi4GLu3NfYUQQghx6LYuK2XJ2/k0VHmJHubi6LPHMH5eWq/7/e1vf8sLL7xASkoKI0eOZPbs2eTn53PTTTdRXl5OZGQkTz31FBMnTqS8vJzrr7+e3bt3A/Dwww8zf/587rnnHvLz89mxYweZmZm88sorvY5roPWk9MMrwIlAklKqCPg1cKJSagaggQLgutZzM2gp8XCG1jqglLoZ+JCW0g/Paq03hPMhhBBCCNE9W5eV8vk/NhPwtewr21Dl5fN/bAboVcK1YsUKXn31VVavXk0gEGDWrFnMnj2ba6+9lgULFjBu3DiWLVvGjTfeyGeffcatt97KbbfdxrHHHsvu3bs57bTT2LRpEwAbN25k8eLFRESE2i1kaOl2sqW1viRI8zMhzt0DnHHA5w+ATvW3hBBCCNG/lryd35Zo7RPwWSx5O79XydaiRYs499xziYyMBOCss87C4/Hw1VdfccEFF7Sd5/V6Afjkk0/YuHF/Fai6ujoaGhrarj1cEi2Q7XqEEEKII0pDlbdH7b1hWRbx8fGsXr066LGlS5fi7rApNkBUVFTYYxlIsl2PEEIIcQSJHubqUXt3HX/88bz11ls0NzdTX1/Pu+++S2RkJFlZWbz22msAaK1Zs2YNAKeeeiqPPvpo2/XBErLDhSRbQgghxBHk6LPHYHe2/+ff7jQ4+uwxvep31qxZXHTRRUyfPp1vfetbzJkzB4B//OMfPPPMM0yfPp0pU6bw9ttvA/DII4+Ql5dHTk4OkydPZsGCBb26/2CmtNYDHUNIubm5Oi8vb6DDEEIIIQa1TZs2MWnSpG6f31dvIx5Jgn3NlVIrtNa5Hc+VNVviiKG1RSBQh80WjWHIt74Q4sg1fl6aJFf9SP7FEUeEoqK/k7/jz5hmI4bhZFTmtYwefRNKqYEOTQghxGFOki1x2CspeYtt2x/AspoBME0/BbsWoJSd0aOvH+DohBBCHO5kgbw47O0seKQt0drHsprZtfsJBvOaRSGEEIcHSbbEYc/rLQ3aHgg0YFm+fo5GCCHEkUaSLXHYi4oaF7Td5UrGMJz9HI0QQogjjSRb4rA3dszPMYz2FYoNw83YMb+QBfJCCNFH7rnnHh566KGBDmNQkGRLHPaGDTuGGdOfJTZ2BjZbNNHRk5g69RHS0r4z0KEJIYQ4AsjbiOKIkJAwjzm5bwx0GEIIMShsWvQ5i159kfrKCmISkzju4iuYdNw3et3vb3/7W1544QVSUlIYOXIks2fPZvXq1Vx//fU0NTUxZswYnn32WRISEli+fDk/+MEPMAyDU045hf/85z+sX78+DE83+MjIlhBCCHEE2bTocz568q/UV5SD1tRXlPPRk39l06LPe9XvihUrePXVV1m9ejUffPABy5cvB+CKK67g97//PWvXrmXatGnce++9AFx99dU88cQTrF69GpvN1uvnGswk2RJCCCGOIItefZGAz9uuLeDzsujVF3vX76JFnHvuuURGRhIbG8tZZ51FY2MjNTU1nHDCCQBceeWVLFy4kJqaGurr6zn66KMBuPTSS3t178FOki0hhBDiCFJfWdGjdtF7kmwJIYQQR5CYxKQetXfX8ccfz1tvvUVzczP19fW8++67REVFkZCQwKJFiwB46aWXOOGEE4iPjycmJoZly5YB8Oqrr/bq3oOdLJAXQgghjiDHXXwFHz3513ZTiXani+MuvqJX/c6aNYuLLrqI6dOnk5KSwpw5cwB44YUX2hbIZ2dn89xzzwHwzDPP8MMf/hDDMDjhhBOIi4vr1f0HM0m2hBBCiCPIvrcO++JtxLvuuou77rqrU/vSpUs7tU2ZMoW1a9cC8MADD5Cbm9vr+w9WkmwJIYQQR5hJx30jLMlVb7z//vv87ne/IxAIMGrUKJ5//vkBjacvSbIlhBBCiH530UUXcdFFFw10GP1CFsgLIYQQQvQhGdkS/a68/GN2734Gv7+axKRvMirzhzidwwY6LCGEEKJPSLIl+tXOnX+lYNcCLKsZgKbC3ZSVvs28eR/gcMQPbHBCCCFEH5BpRNFv/P5aCnb9rS3RAtDah89fQ2HRSwMYmRBCCNF3JNkS/aa+YSNKOTu1a+2lqnLhAEQkhBAinB555BEmTZrEZZddNtChDCoyjSj6jcuZjNaBIEcULnd626eq6iXs3PEXmpoLiI6exJjs24iNzem/QIUQQhySv/3tb3zyySeMGDGirS0QCGC3H9nphoxsiX4TFTWWqKixdMzxDcNF5sjvA7B370esWXMNNbXL8fnKqapayIqVl1JTu2IAIhZCiMNT46q9lDzwNUV3LqLkga9pXLW3131ef/317Nixg29961vExcXxve99j/nz5/O9732PgoICvvnNb5KTk8NJJ53E7t27AcjPz+eoo45i2rRp/PKXvyQ6OrrXcQxGkmyJfjVj+tPExU3HMFzYbFHYbDFMnPhb4uJmoLVm67bfYFmedtdYVjPbtj0wQBELIcThpXHVXmr+vQ2zpmW7HrPGS82/t/U64VqwYAEZGRl8/vnn3HbbbWzcuJFPPvmEV155hVtuuYUrr7yStWvXctlll/GjH/0IgFtvvZVbb72VdevWtRsNO9xIsiX6ldOZRO7sf3H0UZ8we9Y/Of64r0lPOwcAy/Lg9ZYFva6hYWM/RimEEIevug8L0H6rXZv2W9R9WBDW+5x11llEREQAsGTJEi699FIAvve977F48eK29gsuuACg7fjh6MieRBUDxu3OwO3OaNfWMtrlxjQbO53vdPZuN3ohhBAt9o1odbf9UEVFRYW1v6FMRrbEoKGUwciRV2EYEe3aDSOC0aNvHKCohBDi8GKLd/WoPRyOOeYYXn31VQD+8Y9/cNxxxwFw1FFH8cYbbwC0HT8cSbIlBpXsrFsZMfxSDMONYURgs0WRNfpmMtIvHOjQhBDisBB72miUo/0//8phEHva6D6756OPPspzzz1HTk4OL730En/5y18AePjhh/nTn/5ETk4O27dvJy4urs9iGEhKaz3QMYSUm5ur8/LyBjoMMQBM04PPV4HLlYxh9N1PW0IIcTjYtGkTkyZN6vb5jav2UvdhAWaNF1u8i9jTRhM1M6UPIwyuqamJiIgIlFK8+uqrvPLKK7z99tv9HsehCPY1V0qt0FrndjxX1myJQclmcxMR0f7NFNP04vXuwelMxm4/PF8PFkKI/hA1M2VAkquOVqxYwc0334zWmvj4eJ599tmBDqlPSLIlBj2tNbt2PUnBrr+2fjZJTzuP8eN/jWE4Bjg6IYQQh+q4445jzZo1Ax1Gn5NkSwx6JaVvsrPg0XZ7KpaUvolhuBg//lcDGJkQQghxcLJAXgx6u3Y93i7RgpaaXMV7XsWyfAMUlRBCCNE9MrIlws7rLWN7/oNUVHyOzeYiI+MSRo+6DsPovAl1KKbZTGnpO9TWrsDjKQ56jtYmptnUo36FEEKI/ibJlgirQKCer5efg89XCZgEArBr1wLq69cxPefJbvXh81WyPO8cfL4aLKsJUEHPczgSsNsPz9eEhRBCHD5kGlGE1Z6SNwgE6gGzrc2yPFRVfUlD47Zu9ZG/4094veWtiRZA5/IkhuFm/LhfoVTwREwIIUT/KigoYOrUqQMdxqAkI1sirGprV3RaXwWglI2G+k1ER407aB/l5R+htT/IEYXLlUpkxGhGZ93MsISjwxCxEEKIgRYIBLDbD9+U5PB9MjEgoiLHoJQTrTsuXNed6maFEmoNllI2jpr3EXa77LclhBC9sXbtWj799FNqa2uJi4vjpJNOIicnp9f9mqbJD3/4Q7766iuGDx/O22+/zZYtW7j++utpampizJgxPPvssyQkJHDiiScyY8YMFi9ezCWXXEJmZib33nsvNpuNuLg4Fi5ciGma3HnnnXzxxRd4vV5uuukmrrvuujB8BfqXTCOKsBo+/JJOta+UchARkUls7Mxu9ZGRcSGG4e7QaiMh/ihJtIQQopfWrl3Lu+++S21tLQC1tbW8++67rF27ttd9b9u2jZtuuokNGzYQHx/PG2+8wRVXXMHvf/971q5dy7Rp07j33nvbzvf5fOTl5fHTn/6U++67jw8//JA1a9bwzjvvAPDMM88QFxfH8uXLWb58OU899RQ7d+7sdZz9TZItEVYuVyqzZv6dqKgJKGVHKQeJw45n5oyXur2+avSo64mPn9tuf8SIiJFMnvxQH0cvhBCHv08//RS/v/1SDb/fz6efftrrvrOyspgxYwYAs2fPJj8/n5qaGk444QQArrzyShYuXNh2/kUXXdT25/nz53PVVVfx1FNPYZot634/+ugjXnzxRWbMmMG8efOorKxk27burf8dTGQaUYRdbGwOR837gECgHqXs2GwRPbreMFzMnPEc9fUbqK/fiNs9nISEo1BKfjYQQoje2jei1d32nnC59u9la7PZqKmp6fL8qKj9sxULFixg2bJlvP/++8yePZsVK1agtebRRx/ltNNO63VsA0n+9RJ9xm6P6XGidaCYmClkZFzAsGHHSKIlhBBhEhcXvGROqPbe3ishIYFFixYB8NJLL7WNcnWUn5/PvHnzuO+++0hOTqawsJDTTjuNxx9/vG0kbuvWrTQ2NoY9zr4mI1tCCCHEEeSkk07i3XffbTeV6HA4OOmkk/rkfi+88ELbAvns7Gyee+65oOfdcccdbNu2Da01J510EtOnTycnJ4eCggJmzZqF1prk5GTeeuutPomzLymtO9cwGixyc3N1Xl7eQIchhBBCDGqbNm1i0qRJ3T6/r95GPJIE+5orpVZorXM7nisjW6LHtNbsKXmNXQWP4/NVEBM7hbFjf0Fc7PSDXGehtYVhyLedEEIMpJycHEmu+pEshBE9tmvXE2zdeh/Nnt2YVhM1NctZufIy6us3BD0/EKhnw8bb+fyLyXz+xSTyVlzU7WryQ4FlBWhu3t1aOV8IIYRoT5It0SOW5aVg12OdqsRblocdOx7udL7WmlWrr6Ss7P3WqvAWtbUrWJF3AT5fRf8E3Yf2lLzOosVzWLrsDBYumsv69bdimp0r6AshhDhydTvZUko9q5Taq5Raf0Dbg0qpzUqptUqpN5VS8SGuLVBKrVNKrVZKySKsIczjKQ1xRFNZtYjq6mXtWuvr19HQsLVDRXmNafko3vOvoD3V1a1jd+FzlJa9i2l6whN4H6iq+pItW+4hEKjDsprR2kd5xcds2Hj7QIcmhBBiEOnJyNbzwOkd2j4Gpmqtc4CtwC+6uP4bWusZwRaOiaHD5UpGazPoMa39rF7zfaprlre1NTXtDFrMVGsvDQ2bOrSZrF13AytWXsL27X9g8+a7WPzlMdQ3bA7vQ4RJQcHfgozweams/Ayfr2qAohJCCDHYdDvZ0lovBKo6tH2ktQ60flwKdG/zOzFk2WyRZGRcjFKuoMcty0N+/h/aPkdFjUNrq9N5huEmNqb94sziPf+isnJR2yiRaTYSCNSybu0NDMa3Zj3ePUHblXIcFlOkQgghwiOca7a+D/wnxDENfKSUWqGUujaM9xQDYNzY/yMj4+KQxxsb9i9+j46eRFTUWMB2wBkGNlsEGRkXtLtuz55/dRopAvD6ymlq2tHbsMMuPn4u7Z9rH01ExKj+DkcIIQad0aNHU1HR+YfPd955hwceeGAAIhoYYXkHXyl1FxAA/hHilGO11sVKqRTgY6XU5taRsmB9XQtcC5CZmRmO8MQh0lpTXb2EPSWvoy0faWlnkZR0MoZhZ/y4/6O09HVMs3MlX3fEiLbrN2/+PxoatgP7px6joyeSM+1xHI74DvcLEIxSKuSxgZQ1+mb27v2w9WvQMnpnGBFkZ/0Emy34yJ8QQgg466yzOOusswY6jH7T65EtpdRVwJnAZTrEXI/Wurj1973Am8DcUP1prZ/UWudqrXOTk5N7G57ohe3bf8+atddSVvY2e8v/w4aNP2Xd+lvQWmMYdjJH/gDDaL8dj2G4yc6+DYDq6iWUlr2L1u0XuTc15WO3R9FRetq5GIa7U7vNFk1U1LgwPll4RESMZO6ct0hNPROXK43YmBymTvkTmZlXD3RoQgjRpZLSt/nyy+P49LOxfPnlcZSUvt3rPhsbG/n2t7/N9OnTmTp1Kv/85z8BePTRR5k1axbTpk1j8+aWNbjPP/88N998MwBXXXUV119/Pbm5uYwfP5733nuv17EMNr1KtpRSpwM/A87SWjeFOCdKKRWz78/AqcD6YOeKwaOpaSdFxS+2m9YzzSaqKhdSU9PyxmFW1i2MHnU9NlsMStlwOlOYOPG3JCe1bPlQtvf9oNOCStmprOw8sDlixGXExEzBZosEWjakttkimTb10UG7N2Jk5GimTvkzx87/kjlz3iQ5+dSBDkkIIbpUUvo2mzff1bruVOPx7mHz5rt6nXD997//JSMjgzVr1rB+/XpOP73lnbqkpCRWrlzJDTfcwEMPPRT02oKCAr7++mvef/99rr/+ejyewfsm+qHoSemHV4AlwASlVJFS6gfAX4EYWqYGVyulFrSem6GU+qD10lRgsVJqDfA18L7W+r9hfQoRdlVVXwZtN61myis+A0Apg6ysmznh+JWccPxqjp3/Felp57Sdq5SdUN9iLcfaMwwXs2e9wtQpj5A58hrGZN/OMUd/QXy8vMAqhBDhsiP/oSBvUjezIz94ItRd06ZN4+OPP+bnP/85ixYtatvY+rzzzgNg9uzZFBQUBL32wgsvxDAMxo0bR3Z2dtsI2OGi22u2tNaXBGl+JsS5e4AzWv+8A+h6Hxcx6NhsUSjVefG3UnYc9tgObUbbaNSB0tPOpaTkdSyr/U8oWlskJgbf9V0pG0lJ3yAp6Ru9iF4IIUQoHm9Jj9q7a/z48axcuZIPPviAX/7yl20bW7tcLWtYbTYbgUDotbldfR7qBufcjBhwycknB21XykZa2tnd6iMubgajMn+IYbhQyoXNiMQw3Eyb+gh2e3Q4wxVCCNFNbld6j9q7a8+ePURGRnL55Zdzxx13sHLlym5f+9prr2FZFvn5+ezYsYMJEyb0KpbBRnYEFkHZ7TFMz3mKNWuvp6VyR8vbgpMm/p6IiJHd7ic7+8ekp59HReX/sBlukpNP6fQWohBCiP6TPeZ2Nm++q91UomFEkD2md7tfrFu3jjvuuAPDMHA4HDz++OOcf/753bo2MzOTuXPnUldXx4IFC3C7O78sNZSpwVgscp/c3Fydlye7+wwky/JSXb0Uy/KTkHB00LcIhRBCDKxNmzYxadKkbp9fUvo2O/IfwuMtwe1KJ3vM7aR3c9Yi3K666irOPPPMbidmg0Wwr7lSakWwnXJkZEt0yTBcIddXCSGEGJrS084esOTqSCTJlhBCCCEGzPPPPz/QIfQ5WSAvhBBCCNGHJNkSQgghhOhDkmyJAaG1xrL8Ax2GEEII0edkzZboV5YVYOfOv1BY9AKm2URkZBbjx/+axGHHDnRoQgghRJ+QkS3Rr7Zuu4/dhc9imo2ApqlpB2vXXkdt3ZqBDk0IIUQv1NTU8Le//S0sfX3xxReceeaZYelrMJBkS/SbQKA+6PY9luVl585HQ15nWQEGcz04IYQQoZOtUFv0HEkk2RLd0tC4jdVrfsAX/8th8ZfHsbvwObS2etSHx1MSdANq0DQ2bu/UWl39NUuXfYvPv5jI/xbmsG3bA7LOSwghwuCN0ipyv9pA+ueryf1qA2+UVvW6zzvvvJP8/HxmzJjBnDlzOO644zjrrLOYPHkyBQUFTJ06te3chx56iHvuuQeA7du3c/LJJzN9+nRmzZpFfn5+u36XL1/OzJkzO7UPJbJmSxxUc3MheXnfxTSbAI1pNpKf/0eamwuZMP7ubvfjdg9HazPIEUVMzOR2LfUNm1m95vtt20mYZhNFxS/h81cyZfKDvXgaIYQ4sr1RWsXtWwpptlpmDIq8fm7fUgjAd9OGHXK/DzzwAOvXr2f16tV88cUXfPvb32b9+vVkZWVRUFAQ8rrLLruMO++8k3PPPRePx4NlWRQWtsTz1Vdfccstt/D222+TmZl5yLENNBnZEge1a9eTmKaXfXskAlhWM3v2vIrfX93tfuz2KEaMuALDiGjXbhhusrJ+1K6toOBvWJa3XZtledi79z18vsqeP4QQQggAfrejpC3R2qfZ0vxuR0lY7zN37lyysrK6PKe+vp7i4mLOPfdcANxuN5GRkUDLdjjXXnst77777pBOtEBGtsRBaK2prFoEdJ5zV8pJU9NOoqIc7CxYwJ49r2Ka9YAiNnY648fdRWxsTrtrxo75GS5nCrt2P4nfX0NMzFTGj/s/YqIntjuvsXEr0HmaUiknHk8xTmdir57JspoxDDdKyc8bQogjS7E3+HKMUO2HKipq/166drsdy9r//3SPxxPsknbS09PxeDysWrWKjIyMsMbW3yTZEl3auvVePJ49QY9ZlpfKysWsWn1l6xTjfrW1eaxYeSm5s//VbopQKUVm5tVkZl7d5X1jYqbS2LgDaD/tqLWfiIhD/wmnrOw/bNv+W3y+vRhGBCNHXk121i0oZTvkPoUQYigZ7nJQFCSxGu5y9KrfmJgY6uvrgx5LTU1l7969VFZWEh0dzXvvvcfpp59OTEwMI0aM4K233uKcc87B6/Vimi3/34+Pj+eZZ57hlFNOISoqihNPPLFX8Q0k+bFehNTYuIM9Jf+iY8LTwo5h2CnY9bdOidY+luVhx86/HNK9R4+6AZvN1a7NMFxkpF+IwxF/SH1WVi1m46bb8XpL0NrENBvYvftptuc/dEj9CSHEUPSL7HQiDNWuLcJQ/CI7vVf9JiYmMn/+fKZOncodd9zR7pjD4eDuu+9m7ty5nHLKKUycuH8246WXXuKRRx4hJyeHY445htLS0rZjqampvPfee9x0000sW7asV/ENJDWYX6nPzc3VeXl5Ax3GEauo6O9s2/67TqUaAByOREyzodO6qo5crjSOnf/lId2/rm4d69bfjMdTBCjAIC5uFtNzFhxSwrU873zq6lZ1ajeMCI4/bkWn5E4IIYaKTZs2MWnSpG6f/0ZpFb/bUUKx189wl4NfZKf3anH8kSjY11wptUJrndvxXJlGFCHZ7bFBp9eUcqCU7aCJFkBkRNeLI7vi9ZYesBheAyZ1datZt/5HzJr5Yvf78VWgLR/NzbtDnKHx+6uw2Xr3U50QQgwV300bJslVP5JpRBFScvLJtIwotaeUDbd7xEGvV8pFTMxUtmy9jz17Xgs53RjKrt1Pt5V+2EdrP7W1eXi9ew96fXNzMcvzzuPLL49jydKTMQMNIeK043Qm9Sg2IYQQorsk2RIh2WyRzJzxHA5HAjZbFDZbNDZbFFOn/JnRo67rVMLhQA5HIobhpKj47xQVvcCWrffx1ZKT8Hi6/2qxz1cRtF0p+0FLTmhtsmLlxdTVrUNrH5blxdKdR+IMI4LsrFswjN4tDBVCCCFCkWlE0aW4uFkcO38ptbUrW6u3a2rrVuFwxBMfP4eqqoUdrnCSO/sVdu58hMqqxexbXG9ZTfh8HrZu+w0507q3d1Zi4vEUFxejdce3ZgwiI7uenqysWkQgUEvn8hEOXK4k/P5qXM4URmfdQkb6ed2KRwghBjOtNUp1no0Q4dfT9e6SbImDMgw78fGzWbv2BqprlmCazRiGK+jCefCzY+dfqTog0drPoqLi827fd/SoGygre59AoB6tfYDCMFxMGH83huHs8lqvpzTEdkJ+Eocdz6RJ/6/bcQghxGDndruprKwkMTFREq4+prWmsrISt9vd7Wsk2RLdUlr6NlXVX7WtoQqeaAFo6upWoZQRdGuenhQRdblSmDfvAwp3P0tV1WJc7nRGZf6Q+PhOL3p0Ehs7nQMr3u9js0USnzC32zEIIcRQMGLECIqKiigvLx/oUI4IbrebESMOvnZ5H0m2RLfsKXmj02L1UJzOFGJiJrB37387TQFalo9Nm+9i/LhfYbMd/KcClzOJsWN/BvysR/HGxExi2LBjqapa3JYYKuXE5UojNeVbPepLCCEGO4fDcdCtccTAkQXyoltUkLcSQ/F4ioiNmU5kxKggi+gtSkvfZP36HwW9NpymTX2MMdk/JTIyG7d7OCNHXsWc3H9jGFJPSwghRP+RkS3RLRkZF1BbtwbLal++oWXtlo/2m1Q3sW37bxk75pdUVS9sXUR/4HEvVdWLaW4uJiJieJ/FbBh2MjO/T2bm9/vsHkIIIcTByMiW6JLWmvr6TdjtcQxLOKp1pMqGYbiw2SKZMf1FDCMy2JXk7/gdfn8twdZOKeWkuXlXX4cvhBBCDDgZ2RIheX0VrF59NU1NOzGUHdPy4nDE4/NVtL72qqmrX4dlNQa9XusAERGZNDRs6LR2S1teoqLG9sNTCCGEEANLRrZESOvW3khDwxYsq5mA2VJ+wefbC1ithUKbyc9/COi8pU8Lg5Tkk1vXSO1f82UYblLTzsLlSumHpxBCCCEGliRbohPTbGbtupuorVtB51pZ7WntwW6PC3pMKTtJSd9kTu4bJCaegGFE4HSmkDX6ZiZNlDpXQgghjgwyjSg62bDxdioqPu32+Q57DDbDgde3l33rs5RyMmHCPdhsEURFjWXG9Gf6KFohhBBicJNkS7Tj9VVQWfFZkC1yQhuWeCzjxt7Jnj2vU17xCS5nEiNGXEFc3Iy+C1QIIYQYIiTZEu34vGUowwmmr5tXKEaPugGbLZKRI69g5Mgr+jQ+IYQQYqiRZEu0aWraSVHxK5hm08FPBsAgI+Mi3O70Po1LCCGEGMok2RIAVFcvY/WaH2BZfqDjBs4Kmy0Smy0W06xD6wBK2YiOmsj4cXcNRLhCCCHEkCHJlkBrzcZNPw+596FhuJgx/Xni4qZTWbmQZk8RMdGTiIubjVIK0/RQWvY2VVWLcbuHMzzjEiIjR/XzUwghhBCDkyRbAp+vAo+nOORxy/Kxu/BZcuL/SlLSN9odCwTqWb78XDzeUiyrGaUcFBX9nZxpj5OYeFxfhy6EEEIMelJnS7Cz4DE6Tx0eyKK8/D+Ul3/S6ciu3U/R7CluGxXT2o9lNbNh40/Ruqs+hRBCiCODJFtHONP0UlLyWrfO3bDxp/j9NZjm/unGvXv/i9ad31z0+6spLn6ly/4sy8f2/IdYuHA2n38xmdWrv09T086ePUAYNDZuZ/WaH/DF/3JY/OV8du1+pstEUWtNbe1qCgoep6j45db9H4UQQojgZBrxCOcPVHf7XNNsYuGiOSiliIvLZfKkP2CzBduEGsBi67b7Ac2IEZcHPWP9+luprFqIZXkAqKxayPK88zhq3ke4XMk9fJJD09xczPK872KajYDGNBvZsePPNDfvZuKEezudr7XF+vU/oqLyCyzLi2G42L79d0zPeZqEhHn9ErMQQoihRUa2jnBORxKG4ejm2RYt+yKa1NQsJ2/F+WRkXIRhRAQ9W2sfW7b+BtP0dDrW1LSLyqr/tSVarVdgWR6Kil7q8XMcqt27n2qNQbe1WVYzJSWv4fNVdjq/rOw9Kir/1zptamFZzZhmE2vX3YhlBfotbiGEEEOHJFtHOMOwk53105AJU2gWptmEzRZJWtpZXZwXoLDwhU6tjY3bUMrZuVfLR139mh7Gcuhq61ahdeckyVDOoFOaJSWvY1md65Bp7e/XuIUQQgwdkmwJRo78HpMnPUBERHaPrjPNJjzNhUya+P+Iihof8ry95f/p1BYZmRV0SyClHERHT+pRHN1hWV6Kiv5B3ooLWbnqe5Tt/QCtNVFRYwn2n4Glfbjdwzu16wNGwNpToEMdE0IIcSSTZEsAkJp6JsnJp/ToGpstkuiYlsRo+PBLQ55nWZ0X0EdFjSEublan0S3DcDByROctf5qbC9m160kKChbQ2Jjfozi1Nlm56nts2/47amtXUF39FRs3/pzNW37JqMxrMQxXhxhcDBt2fNDK+Bnp5wcdBbQsH05nYo/iOlSBQD0+X0W/3EsIIUTvSbIl2pSXf9iDs2243cNJSjwRgBHDLw26WF4pF6mpZwbtYXrOE6SnnYNhOAFFTEwOs2a9gtud0e68oqK/s3TZaeTv+BM7dv6Zr5efxY6dj3Q70oqKT2lo2NSuaKtlNVFa+iaG4WT69KeIjMhCKRuG4SIt7RymTnk4aF+pqWeSmHgchmqfoGkd4Ovl5/Q4EewJr6+ClauuYOGiOSz+8jiWLD2F2trVfXY/IYQQ4SFvI4o2TscwmpsLunm2ZnrOkyhlA6C5uQi7Pb7dvoqG4SIiYhQjR1wZtAebLZJJk37HxIm/RWsz6EJ9j6eEbdv/H5bl3X9nHWDXridIST6N6OgJB420smpRiP0eDaqrlzJ8+CUcffQnBAKNGIazyxcGlLIxdcpjLP7yaCy/94AjFqbZyPb8PzA954mDxtRTWmtWrryUpqZdQMsas6amHaxa/T2OOupj3K60sN9TCCFEeMjIlqCpaRcrV32P2rqV3b7GZoukoWEz0FIOYeWqy/F6S9udo7XF1CkPY7dHddmXUkbIBKei4lNAdWq3LD9793ZeCxaM05GMUp37V8rA4RjW9tluj+rWm5mmWUsgEKy2lqam5ut2LR5PCbt3P8POnY9SV7euW/EGU1Obh9dbwr5Eax/LCrCn+NVD7lcIIUTfk5GtI1wgUE/eiu8eUmFOuz0GgOqaZa3JR/tCoFpr9pS83m6zaq1NSkvfoaT0DRQ2MjIuICXlDJQKlfd3TrR6Kj39u+za/USnBflKOUhKOrHH/RlGJEqpoOvhHY6Etj+Xlf2HjZt+itYarQMU7FpAWto5TJxwP0r17Lk8zUVB27X20TgAhWCFEEJ0n4xsHeFKSv5NINBIsO16XK7Ob+MdyO+vxbJ8XSzWDuD1lLR90lqzdu0NbN7yK6qrl1BVvZiNm+5kw8afhrxHcvLJEOQNQMNwkJJyRpfx7RMRMZxpU/+K3R6LzRaNzRaJy5XOrJl/77Q4vjtsNhepqWd3WrdlGBFkZv4QaEliN266HcvytlbYt7AsD6Wlb1Nd/VWP7xkTOzVoVXvDiCAhfm6P+xNCCNF/JNk6wlVWLQ663Q60TKudcPxaxmT/DMNwYbNFo1TLYKhl+di46Q6+/Oo4nI7k4LWqjAgSE09o+1xT8zXV1Us6LFRvprz8I+rq1weNweVKZfy4uzEMF0q5UMqBYbgYPfomoqNDl5voKCnpGxx37NfMnPEcs2a9wvxjFhETc+glJiaMv4fEpG+0fV0Mw8XIEVcwPONiAKqqvmr7Wh3IspopKX2zx/eLjhrHsGHHYRjutjalHDgc8aSlnXPIzyGEEKLvyTTiEa6paUfIY42NWwkE6hg9+jpiY3PYtPn/8Hh2Ay3TV6bpwzQb2bLlV2SkX8SektfaEiml7Ljd6aSm7i94WlX9FWbQgqAm1dVLiI2ZGjSO4cMvJjHxePaWf4jWAZKTTiEycnSPn9UwHMTFzerxdcHYbG5ypj2G11uO11tCZGRW27Qq0OXs576XCnpq2tRH2b37WYr3vIxleUhOPpXsrFsPuiZOCCHEwJJk6wgXfKH3fs3NhTQ0bGLd+ls6bK2zX1PzDlIcZ+JyJdPcXETLlj4aj6eUhoZNxMXNAMDhiEcpZ6eRNKUcOOzxXcbhdmeQOfLq7j5Wv3G5koPu4zgs4Vi0Nju1G0bEIY9EGYaD0aOvY/To6w7peiGEEAOjR9OISqlnlVJ7lVLrD2gbppT6WCm1rfX3hBDXXtl6zjalVPBaAKLfRUWO6/J4RMQoNm66M2Sitc+uXY/h8ZSyf+2XiWU1sX7Dj9CtK8kj3CNDTFlapKSc3vPgBzG7PYqpU/+CYbgxDHfr9Keb4RkXkRB/1ECHJ4QQoh/1dM3W80DHfxXvBD7VWo8DPm393I5Sahjwa2AeMBf4daikTPSvsWN/Tqg5r6ioyXy9/Gz8/s4bMnektRk0kfL5qtpqd+3Y+XDQa93uEe2n4A4TyUknMf+YhYwb+3+Myf4Jc3LfZPz4X/X4TUQhhBBDW4+SLa31QqCqQ/PZwL6dhl8Azgly6WnAx1rrKq11NfAxnZM2MQDi4mYwdepjQTeFbmzciN9f3ss7aJSyobWmoWFT0DOCbfh8uHA6Exkx4jJGjbq2Rwv6hRBCHD7CsWYrVWu97/3+UiA1yDnDgcIDPhe1tokB1FL/yY/bnYbLlYrHU3jwi0LYVzS0Yy0rQ7lZsfJiLMuPUo6gm087HLGHfF8hhBBisAvrAnmttVZKBSn12H1KqWuBawEyMzPDEpdomearqlpMU/NuoqLGU1uzgt2FTxEI1NEyjdirvzaiosbhdCZRU7O8dWudlrVbAbOGQNs6caPTvQwjgpSUMynb+x+iosYSHdX1GjIhhBBiqAlHslWmlErXWpcopdKBvUHOKQZOPODzCOCLYJ1prZ8EngTIzc3tXQYggJYNjFesuAifrxytA63rq0z2Jz29/TIb5M5+HcNwUlDwN3YWPBq0AGdLAmZDKYVhRGCZHhyOOEpKXqO09E20DhAfP4ecaQuw2dxBrhdCCCGGnnAUNX0H2Pd24ZXA20HO+RA4VSmV0Low/tTWNtEPNm/6P5qbizDNxtaK5gF6n2C119CwEaUURcV/DzpVuJ9JauqZzJr5ImnpZ+P3V2NZHkyzAcvyUF39Ndvz/xDW2IQQQoiB1NPSD68AS4AJSqkipdQPgAeAU5RS24CTWz+jlMpVSj0NoLWuAn4DLG/9dV9rm+hjluWjsup/dNzAOMx3oWDXAor3/AufL9jA5n6G4SY6aiKxsTmUlb3fOuW4n9ZeSkpe78NYhRBCiP7Vo2lErfUlIQ6dFOTcPOCaAz4/Czzbo+hEr7Usgu/72djm5l3s2PHwQc9Tyk56+nfRWndKtPYJ1X44siw/jU35OOyxuN0ZAx2OEEKIPiAV5A8TluXH56vA4RiGzbZ/g2SbzUVc3Exqa1cQ7qnDA/l9tfj8XY1qGURHT2DypAdxOocBEB+fS03N8g5xKeLj5x1yHPX1Gymv+BSbzU1qyrcHdQJTWvouW7b+Cq0ttA4QEz2FaTmP43ImDXRoQgghwkj1x6jHocrNzdV5eXkDHcagV1j4PPk7Hm5bKzV8+KWMG3tn2x58TU07WZ53PqbZjNZeWt4ItON0xuHz1aKUCrkZdXgojp2/FJerfRLR0LCVvBUXoLUPy/JhGC4M5SQ39w2iosb06A5aa7Ztu5/iPa9iWT6UsqGUwcQJvyU9/dxwPkxY1NWtZcXKSzpU5rcTEz2BuXPfGbC4hBBCHDql1AqtdW7H9nAskBcDqKT0HbbnP4Rp1mNZHizLQ3HxK+Tn/7HtnMjILKZM+RMtbwPuK71gEQg0YBj2Pk60wDCc+P2dl+hFR4/n6KM+ZlTmdSQmfpNRmddx1NGf9DjRAqitXUHxnn+2Ji8WWvuxLC+bt9yF31/T+4cIs92FzweZLg3Q2LSDhoYtAxKTEEKIviHTiENcQcGjWFZzuzbLaqao6CWys3+CYbT8FW/f/kCHtwRNLKvzRsl9QWuNo3XqsCOXK4Xs7B/3+h6lZe8E3b9RKTuVlf8jLe3sXt8jnLyePQSb1lXKjs9XAUzo95iEEEL0DRnZGuK83rKg7Zb2Y5qNLX+2/DQ2buvPsNoo5WRYwtF9vg5JtRVMDX50sBk27DgMw9WpXWsfMTFTBiAiIYQQfUWSrSEu1D/MDkc8dnvLNjhK2TGM/i8Sahgu4uPnMGXKw2Hv2zQ9lJa+zc6df6Wi4nNSU88MkbyYJCaeGPb799aIEZfjcCS0bXMELdX0R2Vei8MRP3CBCSGECDuZRhzixo75OStXXdZuCs0w3Iwb+38o1TKio5Ri+PCLKS5+JehUW1+YMvlR4uNn4nanh73v5ubd5OVdgGk1Y5rN2GwRRERkMjzjEor3vIzWZuvLAZrJkx4clHsvOhxxzJ3zLrt2P0VFxac4HAlkZn6flOTTBjo0IYQQYSZvIx4G6urWkr/jj9TXbyIiYgTZWbeSmHhCu3Msy8fGjT+jbO+7fR5Petr5TJ78+6DHtNaYppempp3U1C6lunopWpukpZ1NasoZbW9QdiVvxYXU1q5i3/6L0DJdOWLE5QzPuISKys+wGW6SU06XMgpCCCH6Tai3EWVk6zAQG5vDzBkvdHlOINCAYYvsl3gam4KvD9u79yO2bP110Crz1dXLKCt7j5xpC9pG5IIJBBqoq1vDgYkWtKx1Kit7h/Hj7iIqKrtX8QshhBDhJMnWEcA0m1medw4eT0k/3E3hdg+nvn4jdXVrcLkyiIwcRW3dKjZv/mXIaUzLaqK6+iuqa5YyLOHoQ7pzINCAaTZh66ekUgghhOgOSbaOAKWlb+PzVdFxNKgvGIYLj6estVipbq3hpdlf3ys002ymsnJhl8mW3R5NbOx0amtXdurPsnysXvMDZs96pdfPIYQQQoSLvI14BKipzetUiyvclHJgs0WTlHgyDQ0bsCxPa7X6fQlRd9YGKkzz4Av4J096EMOICHLEoq5uHfX1G3sQuRBCCNG3JNk6AkRGjO7WwvPe0NqP1iY1tV/34o1Hiz17/smuXU91eVZk5CiGDZsf9JhSBo2N2w/x/kIIIUT4SbJ1BMjIuBDo22QLWirX+3zlvepDay87dv4Jr7erTa0hNmZaiLpaFlFRY3sVgxBCCBFOkmwdAVyuFGbOeL5f7qWUnYMndgZKOVEq2FQgKOxUVi0Meszvr6G5uZiMjItak639by4q5SQ2dhoxMZMPLXghhBCiD0iydYRISJjH+PH39vl9lLLhPEhtK6XsRESMJDXlWwT9FlQKW4eK9z5fFatWXcmixUexdNmpLM87i7FjfkFCwjGADcOIICP9u8yY/kz4HkYIIYQIA3kb8QjS1+u2oGV7nKOO+piFC6cTalG81j6amvLxeveilKN1IX27M0hM/Ea7ltVrrqahYTNaB9Daj9frYeu2+5g7522iosb0zcMIIYQQYSDJ1mHO56tgy9bfUFHxMZbl7/P7aR1gT/HLGIarGwvlTVJTvsXe8v+0JoIt5SFypi3Abo9qO6u+fhONjfloHWh3tWX5KCx6gYkT7mvX7vfXsLvwOSoqPsPpTCRz5A9ITDwuPA8ohBBC9JAkW4cxy/KxPO+7rcVMzX66q2bHzr8QGZlNQ8NmuqrtZZpeoqLGMX/cL6isWoxhuEhKPKFTUVKPd0+IUTmTpqaCdi1+fy3Lvv4OPl9Fa40vqKnJIzv7x4zKvKaXzyaEEEL0nCRbhwHLClBW9jZ7Sl4HDFJTv43blUFD42a83gr6L9HaF08zjY07iIoaR3NzYWuNr85Tijabi7i4mTidSaSnnROyP4URtE6YYbiIjh7PuvU/oq5uDZERo3G50vH7K9sSrX3x7NjxJ4ZnXITdHhOORxRCCCG6TZKtIU5rzdp1N1BdvaQtIampWcr+NwIPLdGKippKY+MGuleMtDPDMJg65RE2bvop9fWbOsWhlJPYmBzi4+eG7ENrk3Xrb6GyciFatx8hU8qOzRZFcfGrWJYXsPB4ighVqd6yLGprV8t0ohBCiH4nbyMOcTU1y6mpWRpk5MfkUBMttzsbm83BoSZa0FLvqr5+LY2NO4LGobXFxEkPdLnpdEnpm1RWLgwyMmaQkX4hUVHjW48dmIiFitlPwa7He/4gQgghRC9JsjXEVdcswzTDuxXPuHE/p75+/SFfbxgRjBxxJeXln2BZTSHPKy35d5f97NnzWtDpQ5vNzfDhl9DQ0LNteerqVtPUtKtH1wghhBC9JcnWEOdwxAetpN4b69ffgtaH9uaizRZJVtbNjBlzOzZ78KKlLQI0Ne3ssi+tQ43MKbQ2cToSexSbUk6amnb06BohhBCityTZGuLSUs9EqfD+NR64uLynIiOzGD3qepQySEv9Lko5gp6nlLvL9VoA6WnnBd1w2jBcxMRMZtToGzsdNww3UVGTg95Xa5/U5BJCCNHvJNka4hyOBKbnPI3DkYDNFsWB29cMBI+nDICKis9Yv/7GEPHYcDqHkZZ2dpd9ZWRcQETEyE7tSikCgXrS085l9KgbMIwIbLYoDMNFauqZ5Ex7rNNon2G4GDbsOCIiMg/10YQQQohDIm8jDnGW5aO+fgNORwqW9jJs2HE01G+htu7rAYnHZnPT3FzMuvW3BClqqrDbh5GaejrZWbe2K1wanKa5eU+nVp+vlt27n2XMmJ+QlXUTmZnfx+MpxulMweGIBSB39r/YsvVeamrysNkiyMi4iLFjfhqehxRCCCF6QJKtIUxrzarVV1FXt6Ytsdmzpz8LmHYWFzeLkpI3gq63stkimTTxPlJSTu9WX42N21Aq2NuFAYqKX2bMmJ+09htBVNTYdmdER09g9qyXexy/EEIIEW4yjTiEVVcvob5+XbsRJK29nba16S+G4WZU5rX4/JVBF9hrbeL3V3e7P7s9LuQWQ4FALXV16w45ViGEEKK/SLI1hNXUrgh72YdDpZSTlJRv4/WVMWzY/E5b7rTQJCQc1e0+IyJG4HKlhDiqKdv7/iHFKoQQQvQnSbaGMJczGcNwD3QYQMubfqWlb7Bu3Y1s2/YH3O6RKHXgLLVBXFwuTmdyj/odMeIKgi+y1+wt+w/l5R+h9aEXXxVCCCH6miRbQ1hq6reDbtCslHMAomlhWV48np00Nm7pMJ1pUV39JQsX5bJx089bt9hpLxBooLJyEbW1K9u250lPOwfDCP48Hm8RGzb8hG3b/19fPIoQQggRFpJsDWF2ewyzZv4dt3s4hhGBYUTgdo8kJ+dJButfrdZ+ysreY/OWe9q179nzGosWz2Pd+ptZtfoqvvzqOBoatuJ0JjJx4v/DMFxBa2eZVjPFxf/A4+n81qIQQggxGAzOf5FFt3h9FdTWriQt9TxGDL+U4RmXMHbsL1i//lba7xc4uFiWh7KytwkEGgGor9/Ilq33YFkeTLMB02zE6y1l1eor0NokPe0cjjn6c6KjJwbtTyk7NTV5/fkIQgghRLdJ6YchqrLyf6xddyNaW+0qvhcWPc9gTrT2MwgEarHboygqfhnL6ly13jSbqK5eyrBh83G5UkmIn0t9/Sag89uWDkdCP8QshBBC9JyMbA1Bpull3fofYVmeIFvrDIVECwzDidPZ8qah319F8LgV/kBd26eMjIsxjI4/HyhstkgSEo7us1iFEEKI3pBkawiqqV0+QHe2YRiR9P7bxsmwhGNZuuwUFi2eh99XjVKd90DU2k9C/Jy2z1FR2Uye/EdstujWX5FEuEcya+Y/giRhQgghxOAg/0INQWoA9j9UykFK8ulkZv6AFSsv7rAVj4HdHk3ggFGoUGy2aGJiplJR+WlbHz5fNUoplHKjdUubYUQwetQNOJ1J7a5PTTmd5KRvUle/DpsRSXT0RJQa2P0ghRBCiK5IsjUExcfn9uv9YmNnkTv71bYyE9OmPsrGTXdgWX60NolwjyQubhZ19RtoaFgftA+7PZ6MjAtIT/suy/PO7lD6wUQpN4mJ3yAQqMPhiGX48MsYFmJq0DCcxMfNDvdjDmlaa/z+Kmy2iBAFZYUQQgwUSbaGIMNwMW3aY6xdez1a67bRoL67nx2trbZkKynpmxw7fxmNTdvxNBezYeNtNJUWtG7Ro4ADi4zaiIrKZu6cdzAMJ3v3/re1hEP7OlstbyI2MWvmi336LIejqqqv2LT5F3i9ewFNcvLJTJr4O+z2mIEOTQghBLJma8hKHHYsx85fxPjxdzFq1A305V9lbe1KFi6aTUHBgrZq7YZhJyZ6IgW7HsM0Gw/YC7FjNXcTj6eY8vKPAHC7M0AHX8RfVbWIvXs/6qOnODw1Nm5nzdof4vEUobUPrf2Ul3/KmrXXDXRoQgghWkmyNYQ5HAmMGH4pY8fcTnr6+fTVX6fWAUyzkfwdf+TLr46joGABgUAjWpvU1a096PWm2UR1zdcAuFzpqJCL2S3Wrb+R5mYpUNpduwuf7bTpt9Y+6urW0NS0c4CiEkIIcSBJtg4TPl+o8gnhZOH1lrCz4BHyVnwX0/SF3ErnQIbhJsI9Asvykrfi/LZipsFptm27P3whH+aaGnegtdmpXSkHzc1FAxCREEKIjiTZOgzU1K6ksvKTfrufZXlpbi5iZ8HDxMZMD7qNTnsGqalnUbb3Q/z+aqBzcnCg2rrV4Qr1sBcXPyfoXpja8hIdPWEAIhJCCNGRLJA/DOzY8Zd+v6dlNbN797Mo5WwdWVG0fDv5O5ypMAwXX351LIbh6lAyIjinc1gfRHx4GjnySoqLXyYQCLBvZNMwIkhPOxeXK2VggxNCCAHIyNZhoalpxwDd2Wp9E9ICbAQfsdIEAtWA7laiBQaZmdeEM8jDmsuZxNw5b5OaeiYORwIREZmMHXMHEybcO9ChCSGEaCUjW0OcaXoIBOoHOgyC7Vd4qBz2uLD11V0tdaqqsdkisdnc/X7/3oiIGMHUKX8e6DCEEEKEIMnWEFJZuYit2+6nqSkfmy0KpRwEAg10nrrrSwbhWYjfsR7XPhbb8x8kKekbYbhH91RWLWbz5v9rrVOlSEk+jYkTf4vdHtVvMQghhDh8yTTiEFFds5y1666nqWk7oDHNhtbpuf5MtFpER+dAL7YMcjiGcfxxqwn17dfcXHDIffdUQ8MW1q69Do+nGK39aO1jb/mHrFt/U7/FIIQQ4vAmydYQsSP/T91c89TXLMaP/wV2ewyGcbDpNqPTm4qGEcGY7Nux26Ow22ODXuV2Dw9TrAe3a/dTWJavXZvWPmpqvqa5ubDf4hBCCHH4kmRriGhs2j7QIQCglJ242Fkcc/TnjMn+CXFxs7so/WCQkHA0bvcIWt5KdJOWdg7p6eeilCIr62YMI6L9FYabMdk/7fPn2Kfl5YLO06KGcuLxSHFVIYQQvSfJ1hARFTlmoEMAWqrJL1w0i63bfktq6lnMmP4cNluoPfgCVFV9ic9Xwb63EctK32LlqsuxLD8jR1zF2DF34HAMAxQuVyoTJ/6WlJTT++15QiWLlvYRFTW23+IQQghx+Or1Anml1ATgnwc0ZQN3a60fPuCcE4G3gX37h/xba31fb+99JNBa09SUT3LKt6itWxm0Wnh/M81GSkvfobrqS0aPvgnV5fItE8vaH7NpNVNfv4m95f8lLfU7jBx5JSNHXoll+TGMgxVHDU1rzd7y/1BU9BKm2URKyrcZOeJybLbILq/LzPwBJSWvEwiYHFinKiP9ApzOxEOORwghhNin18mW1noLMANAKWUDioE3g5y6SGt9Zm/vdyRpaNjC2nU34PGUoLXv4Bf0qwA+fzVbt/2m09587XV+69Cymigv/5i01O+0tfUm0QLYuu037NnzGpbVBEBj4zZKS99i7pw3MQxXyOvcrjTm5L5F/o6HqKr6EocjjsyRP2D48Et7FY8QQgixT7hLP5wE5Gutd4W53yOOaXpZufJS/IGagQ4lpIMlgEo5UcoIsrDfwOFICFsczc3F7NnzKpblbWuzLC+e5kLKyt4jPf27XV4fGTmKaVMfDVs8QgghxIHCvWbrYuCVEMeOVkqtUUr9Ryk1Jcz3PexUVHyC1eWI0eCWkHAss2b+I8Q0noXLmXzIfVuWn73lH1Kw6wkqKj6jpmY5SnX+ucG0mqioXHjI9xFCCCHCIWwjW6plN9yzgF8EObwSGKW1blBKnQG8BYwL0c+1wLUAmZmZ4QpvyPH5KtA6fFXZ+0aowqRgMyKIj5/FhAn3sn79LZ2O79j5MJb2kzX6xi6n+TryesvJW3E+fn81punBZnNjt8eidec4lLLjdqV2u28hhBCiL4RzZOtbwEqtdVnHA1rrOq11Q+ufPwAcSqmkYJ1orZ/UWudqrXOTkw999GOoi4/PpTeFQ/tD6JIPUFH5MeUVn1Jfv5Hgz6HZtWsBq1ZdGTRRCmXLlrvxeEoxzUbAxDQb8fnKaVnc3vE+iuTk/nuzUQghhAgmnMnWJYSYQlRKpSnV8s6aUmpu630rw3jvw05MzBSSEr/RqQ7VYHKwNVvr1/+IxobthEoatQ5Q37CB6uolnY6ZZjObt9zN519M5dPPxrFy5WU0NGylovIzOu7DuG8EMDIyi5YB1hZKGaxafTmFhc/36LmEEEKIcApLsqWUigJOAf59QNv1SqnrWz+eD6xXSq0BHgEu1j0ZzjhCTZ36FyaMv5vYmOlERU0kNfWsLmpaDT6W5aGubjWG4Qx5jmk2U1u7slP7mrU/pKTkNSyrGbCorllG3ooL0Tr0voy5s99AHVCHwrK8WJaX7fkP0tCwpVfPIoQQQhyqsKzZ0lo3Aokd2hYc8Oe/An8Nx72OJErZyMi4kIyMCwGwrACffzFxgKPqGZ+/nJEjv09h4XMEX99lw9VhXVVDwxZqa1d22EZHoy0vERGZNDfv5sCq70rZSU4+lcrKL1oXynvb9WdZfkpK32Lc2J+H67GEEEKIbpMK8kOA31/D9vw/suzrMwi1IH0wa24uxOkIVSA00GldVX3D5k77FUJLVXe3ezhOZzI2WxQANlskLlca48fdhWV5Q6z/srDMwbCvpBBCiCNRuOtsiTDz++tY9vV3Wt9OHGyFTbunsvJ/hE4SFTZb+7cRvZ6SkOc7nUlMz3mK8vL/0ti4g+jocSQnn4phOElMPJ6O67kAbLYIUlJO69UzCCGEEIdKkq1Brqj47/j9lYMu0VLKjdbdGy1SyiAyYjQNjZs7HXO7R3Ra0+UP1IbsKyIiE5vNRVra2UDLNj37tjByuVLJzv4pO3b8uXVkzMJmRJKcfBrx8fO6+WRCCCFEeEmyNchVVS5sVxl9sGhJtAwOXDsVit0ew7jxv2LNmh+0qyZvGG7Gjbur0/nRUeMwjIjWxfEccH4EcbE5AFiWj+35D1Fc/DKW5SE6agITJtzLqMxrGJZwDCWlbxII1AMGTU0FbNx0B5kjryYmRurpCiGE6F+yZmuQM2zugQ6hCwdPtJRyM3HCbxiWcBSzZv6dhIRjcDoSiYvLZXrOU6Qkn9LpmpSUM7DbYzjw21MpOy5XComJJwCwcePPKC7+R2tCpmlo3Myq1VfR0LiNmJjJZI2+iaqqLykre5va2mWUlr5N3ooLKS17L1wPL4QQQnSLJFuDXCDQONAh9ILB8IwLSW5NqAxbBGmpZzN16iPMnvUKw4YdE/Qqm83NnNx/k5T4DZSytb1tmDv7NZSy4fWWs7f8w057LlqWj127ngBg166n8PkqDjjHwrI8bNnyKyxr6G6DJIQQYuiRacRBzustHegQesGirn4dluVj7dobqK5ZChgopXA5U5g16xVcrva7BGhtUlOznECggcmTH8RujwVoVz+r2bMbw3Bhmh3XsZlt9bQqKj4Jus5Na5PGxu3ExEwK65MKIYQQoUiyNchFR4/D690z0GEcMqdjGLt2PUl1zdJ2I1FNzYVs3Hg7M2e+0NZW37CZ1auvwjSbWhe++xg58geMG/uzdn1GRoxG62Dr2GzExkwDwO6ICxqP1oG2BE4IIYToDzKNOMiNHnUTQ/WvyTAiGDnySor3vNppyg8CVNcsJRBoAFpGnFavvgqfrxzTbMSymtA6wO7dT7Ds6zPbzgNwOhNJSzu301ZGNpuLUaN+CEDmyO8H2erIRnT0JCIihof7UYUQQoiQhua/4keQ6pqvW6uiDzWK7OwfM2zY/C7eplRo3bJ+qqZmOabZFPSshoYtbN5yd7u2iRN+w+jRN+BwJKKUg/j4Ocye9SqRkVkAJCefRubIqzEMFzZbNIYRQVTUWHKmPR62JxRCCCG6Yyj+K35EKS7+x6CrsdUdhhHBqMxrAEhOPpWSktfbNozeJzJyNA5HAn5/Nbt2PxMy2QKLvXv/gzXp9xiGA2jZyihr9E1kjb4p6BVKKcaM+SkjR15Nff06nM5koqMntVv7JYQQQvQHGdka5Eyz+eAnDUJae6mtWwPAmOzbcDpT2qb1lHJhs0UxedKDmGYzXy8/l6qqRXS1FZHWZtsoWE84ncNITDyBmJjJkmgJIYQYEDKyNUgFAg00N+9iWMLR7C3/CDAHOqQe0bplNCoudjpOZxJHH/UhJaVvU1u7ksjILDIyLsTlTKJ4zz9btyLqOpGKjhqHzRbZT9ELIYQQ4SPJ1iCjtSY//yEKi55DKUfrtjMGQy3ZAgNDOdo+2WyRjBh+CSOGXwK0PGdJyb/Zvv33nSrFH0gpB4bhYOLE3/Z5xEIIIURfkGRrkCkq/ge7C59tXac1+Lbp6S7DcJCadla7Nr+/hpqa5djtMZSVfUBJ6b9DJlpKOYiOnkhC/FxGjLiCiIgR/RG2EEIIEXaSbA0iNbUr2Lr1XrqzDU7fUnS1fqozO0qBoZxoLEAzJvsnREeNaztj9+6nyd/xJ5RyAFYXi+EBDGy2SDLSLyAt7azWrXuEEEKIoUmSrUEiEKhn1aqrGPhEC7pOtGwopdDawmaLRGs/6WnnkZ39YyoqPsfSfpISv4Hbnd52RU3tCvJ3PNxaAqJ7o3WBQC3btv+O/PwHmTXrZWJiJvfukYQQQogBIm8jDhJ7SkJPqQ0uZmsJBwvTbCQl5duMHHkNJaVv4vGUEBub0y7R8vur2bDhp918NkXLt2RLwmlZzQTMetat/xFa92SkTQghhBg8ZGRrkCgt/Tc9m7obDDSlpW9SWvoWStnROsCu3U+Qnn4+E8bfg9YmeXkX4PEUdbu/YF8Dr7cEj2ePVH4XQggxJEmyNUg0Nm4f6BAOUUuCtK/wqmV5KCl5g9SUbxEI1OH17aW7SeS+hC3YPXpSI6tlFEyjlAzcCiGEGHiSbA0Sh1NiYFnNlJa9i9uVfpCF8B0plHJ2qJiviHCPxO3O6MZ9vWzPf4g9e17FNJuJiZnGxAn3Ehubg9Ya02zEZotAKVuPn0kIIYQ4VJJsDRLJyadTVvZOiJGdoUdbASKjsrHZIjHNxm5do5QNlysNn28vpunDZnOilJOpUx/t1vXrN/yEysov2ja9rq9fy8pVl5E1+lZ2Fz6N31+NYbjJzLyGrNE3HVYJrhBCiMFLkq1BYtzYX1Be/iGmeXgkW6bZzLCE41qLsnZkYBiuTovmlbIxccJvUUpRW7calyuVlOTTsNkigt7DsgI0N+/G7ohFWz4qKz/vtOm1aXrIz/8DurUorGk2sGvXE6BNsrN/HI5HFUIIIbokydYgoZTRwym3wa2y6kvyVnw36DY8Sjlap/L2v3molIMI90gSEuahlCIhYV6X/ZeVvc/mLXejtQ/LChAdNR5w0Lm0hNVpxZhlNbO78FlGj76pbWNrIYQQoq/IPMogsafkdYbe24ihWVYzzc27gx7T2kti4okMGzYfMFDKQWrKGcya9XK3FsLX1a1l46afEwjUYJpNaO2joXEzltW96cqW+PwEAvXdPl8IIYQ4VDKyNUiU7HltoEMIK4cjFp+vPMRRG1GR2WRn39pWP6snbxvu2v1027qsfVrWurUkbu0X2O8fPTuQ3RaJwxHX7XsKIYQQh0pGtgYJb8jEZLAyaClC2jlJstvjiYme0uW1MTHTqK1djdaBHiVaAB5PMcFGAW22SFJSvoXdHodSduLj5jB61A0o1f5nCsOIIDv7p/JWohBCiH4hydYgERszbaBD6BG7PZ5vnLiBqVMexeVqKctgGC5SU8/l2PlLGJl5NYYRbGG7wm6PYsPGW1m1+goWLZ5HZeXCHt17WMJ8lHJ2atfaz4Txv+aE41fyjRM343ZntG7qHWBfUuhypTJp0u8YMeLSnj6yEEIIcUhkGnGQGDP2DmpWrBgiW/aAy5Xcmlx9i9TUb2FZAQxj/7dT4rBjGZV5LQW7/ta6+bTGZovANL0EAjXt+lq77kaOPurjdtv8dGXkyCsp3vMqgUBtW6kMw4ggM/MHbVODFRWfUF7+8QFfz5aRsECgnuSkU3r17KH4fFVobeJyJfdJ/0IIIYYmGdkaJGJjppI7+1/AUJjasjN61PXtWg5MtAAqKxdRWPQiWmssy4OhnFiWDrqIXWuTktI3D3rX+vpNrQmUl3lz32N4xiVEuDOJjZnO5Em/Jzvrx23nlpS+hWkFe7vToLpmabeesruam4tYnnc+i788hi+/Op6ly06nvn5jWO8hhBBi6JKRrUFAa01x8cvs2v0UwRZzDzYuVzKpqd8Jeby5uYg1a69tt1A9YNaGPF9rHz5fRcjjfn8Nq1dfTUPjNpSyobWf1NTvMGni70IWJu2qYKkKss7sUFmWnxUrL8TrLWff311j4zZWrLyE+cf8D4cjPmz3EkIIMTTJyNYgkJ//ENu2/w6Pp5ChUP7B7UrvclF7cfGrHd4I7JrNFknisGNDHt+w8XbqGzZhWc2YZgOW5aWs7H2Kil4MeU162nkh1oxp4uOP6nZsB1NZ+QWBQAMdk2StA5SUvhW2+wghhBi6JNkaYIFAPYVFzw2ZtVoADY3bKC//hNKyd1m77iY2brqT2trVBxzf3O2+lLITHT0FrTVFRX9vfUNxf8IZCNRTVbW4U3FUy2qmsItkKzHxRNLTzsEw3CjlwDAiMIwIpk37Gzabq/sPexAez56gWyxZlidknTEhhBBHFplG7GdamxTsWkBh4QuYZj1RkeOHXAkC06xn7brrMQxn6/Y4irKy98jO/jGjMq8hPm42lZWfd6MnRULCsTQ0bGHDxtvQ2gQUcbHTmTHjWQzDhWl6CFZeAsAMhC5iqpRi4sT7GT7icqoqF2K3x5CS8q2wT+vFxuYQ7GcWmy2S+LhZYb2XEEKIoUmSrX62efOvKC17e/9myQ3rBziiQ6UP2IdQY1nNbN/+e1yudEaMuJodOx/uxqbaBlVVC+k4BVdbt4qCggVkZ9+K05mEy5WCx1PU6dr4hKMPGmVM9ERioid296F6LDZ2BnFxM6mtXdn2d6qUE5crg+TkU/vsvkIIIYYOmUbsRz5fBaVlb3aqfn74sNiw4UfkrTiX6TlPYxgu9o1KKeUgMnIsTmc6+0eqTIK9EGBZXvaUvNZ6nWLypN+3rr868NvVorz8Q/aUvNGHz3NwSilmTH+a0aNuxO0eicuVxsgRVzAn93UMo3MtMCGEEEceGdnqR41NOzGUC4vuLx4fihobt1FR+TnHH5fH3vKP8PsqiY+fQ2xsDlu33t+61srsso8D12glJBxFTs4CVq++usM5PrZs+RVuVwaFRS9QV7eGCPcIsrJuITHx+L54tKAMw0VW1k1kZd3Ub/cUQggxdEiy1Y8iIzKxtPfgJw4CSrkAq9PC9O7RlJa+yYTxd5Oedk67Iz5/FQdLtJRykJL8rXZtdbVrUMpA645v/WlWr7m6dcpS4/PtZe26G5k48f5O9x4ogUA9+fl/pLTsXcAiJeUMxo65Q8pCCCHEEUKmEfuRy5VKUtLJDP7CpYqxY+/sslbVwXRMivZJTj4Zmy0y5HU2WyRudzrZ2T9u3x9m0D619rcmhPvfYLSsZrZt+23IGPqT1hYrVlxM8Z5/EgjUEAjUUVLyBnl552NZh5LICiGEGGok2epnUyY/hNOZONBhAKA1WEHLemmKCl8gIiLrkN+UTEk5PWh7ctKpxMRMxTD2J1yG4WZYwnEMH34ZEyb8hnlz/9tp1Cc5+dQQa6CC1yUzzcbWUbSBVVW1iGZPYbu6Y1r78fr2UlHx6QBGJoQQor/INGI/MwwX8fG57N37wUCHglKhiipAs6fggE8Gbnc6Sjlpbt7Zrb5HZV5HQcETVNcsRymIi51BWto5RESMYOaMFykre5eysvew2aMZnnExw4Yd02V/MdETGTnyagoLn8OyfIDCMBzYbNH4/cGqzyvstphuxdqX6hs2Y5qdp45Ns5H6+k0hk1IhhBCHD0m2BsDIkVdRXv5Jj6qs9xWtW5Kurln4fBVMz3mKdetvxjS9aN1SXyvYyFJU1CTyVpzfWlm9ZX1WZeXn7Cx4jHHj/o+RI75Hevp5pKef16NYx465ndSUM9hb/l8UNlJSz6CxcRsbN/6sXVFYw3CTkX5hWIuXHqqIiExsNhem2b4MhmFEEhmZOUBRCSGE6E+SbA2A+LjZjB93N1u2/oqB3p5Hqe4lXFqbNDUVcPRRH1Nc/Cr19RuwO+IpLX2nNWm0UMqGYbgwDDuBQOe9ELX2sX3770hOOgm3O6PbMWqtMc1GbLYIYmImExMzue1YdNQ4fN695O/4c2tRVIv0tPMYN+7/ut1/X0pOOolt9hhMs5n9ZS4MbLYIUlLOGMjQhBBC9BNJtgbIiBGXUFj0HE1N+QMdSjdGtlqSLcNw4nQmkZV1c1t75sirKdi1gMaGzcTETCUz8zqWfR16akxr2Fv+IZkjrw55zoFKSt5ke/7v8furMQw3mZnXkDX6pnaL90eOvIrhwy/F692Lw5GA3R7VZZ/19RuprFyIzR5Fasq3cDqTuhXLoTAMJ7mzX2fTpjuprlkCQFxcLpMnPYDNFmzvRiGEEIcbSbYGiGX5aGrq3vqnwUGzZeu9NDfvJjv7trZkJzp6PFOn/Gn/WVpjGI4DqssfuvLyj9m85VdtU4Sm2UBBwWNYZjNjx/6s3bmG4SQiYkTXT6A1W7b8ipLSN9E6gFJ2tm9/gGlTHyUp6Zu9jjcUtzudmTNfaF27pbHZ3H12LyGEEIOPvI3Yj2pqVrB8+Xf538Jc/rcwl2DV0wczy2pmd+Fz7Nj5cMhzlFKkpZ4FOEIcb3kjsTt27Hy40wbdWvvZtfsJ6uu3dDfsNlVViykpfQvL8qB1AMvyYFke1m+4FdNs6nF/PWWzuSTREkKII5AkW/2kqPhlVqy8kLr61QQC1VhW6E2UBzPLaqaw8HlM009l5f/YsPFnbNp8F7W1K9vOGTful8TGTqX9wKnCMFyMHfMzIiKGd+tenubikMc2brq9x7GXlL7ZKXlrYVBVvaTH/QkhhBDdIdOI/cCyLLZsuXegwwgb0/SyYeNtVFX9r3VESFFa+jajMn9Idvat2O3R5M5+jfr6dVRVLcXrLcHtTicl5XQiIva/gefx7KGo+BWam3YQHz+X9PTzsNv3l2uIjBpLXd2qoDE0Nm7D76/D4YgNz0PpgX1RQQghxOFLRrb6QWPjFiBw0POGCpvNRUXFxwdMvWksq5ldu5+gubkIaJlOjI3NYfToaxk1+josy8+uXU9SVvY+luWntnYlS5edxu7dT7O3/L9sz3+QpctOw+trqZlVV7+exsatXUShUQes7Pd691JXt7a13ERw6WlnB69er62D1vkSQgghDpWMbPUD20HejhtKlHJgmh6C72+oqKxayIjhl7a1VFcvZfWaa9DaRGsfpWVvs7NgAZbV2G6dlGU14/P52bHjYSaMv4dVq67ENENNtdqIi52JvbWkwoYNt1FZ9T+UcqK1n1GZ15KVdWu7ZAxg2LDjSU39DqWl72BZXgzDASimTPlTl1sICSGEEL0hyVY/iIzIxG6PJRCoG+hQem1fLatglDKwGREHnGuxfsNt7dZJmWYTTU3bW/vp2HeAivKPSUs9E9MMPULldCYyefIfAdi85ZdUVi1srSrfUiR21+6niYjI7FQ0VSnFpIn/j+HDL6Wy8n/YbVGkpHwblyu5u48vhBBC9JhMI/aTWTNfRqngb+gNLaHfoNTaIjn55LbPjY3bMYNM67UUQQ02MgaGLYJAoAmtQ027KmbPepWIiOGYZhNlZR90KjPRMqX5ZMg4Y2OmkjX6JkaOvEoSLSGEEH1Okq1+EhMziRNPWMeoUTcNdCh9ZsqUP7db4G4YTnSI5Mxmi0ap9gOrhuEmI+NiCguf6eIuBg5HAgCBQH2nqcJ9fL7KngUvhBBC9JGwJVtKqQKl1Dql1GqlVF6Q40op9YhSartSaq1Sala47j1UGIaDsWN+gmEcfuuDkpJOJiW5ff2syMjRuN0jCLbdtc0WhcuVjs0Wic0WhWG4SEr6JgpFbd3qEHdRDBt2XNsbiE5nMnZ7sLcRDRISjurV8wghhBDhEu41W9/QWleEOPYtYFzrr3nA462/H1EaGrcx1IqZHpyNKZMfCnokZ9rjrFx1CT5fDQe+kenzlQGK8ePvxWGPISZmKlFR2SxZegqW5QnaV3T01HbV6pUymDD+PjZsvK11KlEDdmw2N2OyfxK2pxNCCCF6oz+nEc8GXtQtlgLxSqn0frz/gAoEGqipyaO2ZmVYtrIZTKKixrSbPmx/LJuj5n1M8ARTs2vX30hLO4uoqOyWliAL5wGUcpIz7TEcjrh27SkppzJr5kskJZ5EVNQ4MjIuYN7c94mMzOrNIwkhhBBhE86RLQ18pJTSwBNa644rlIcDhQd8LmptKwljDIPSrt1Ps2PHn1HK3lru4HAqoGkja/SPujzD5ysn1Gie11uKZQUwjJZvxbTUs9m16wks3T4hdbszcLszgvYRFzeL6dOf6HnoQgghRD8I58jWsVrrWbRMF96klDr+UDpRSl2rlMpTSuWVl5eHMbyBUVH5BTt2PIxleVrLGRxeU4hK2VrrVbVUhK+v39hp5M7lSu2yjwPfPBw16odERo3FaC0hoZQTw4hi6pSHQy6GF0IIIQazsCVbWuvi1t/3Am8CczucUgyMPODziNa2jv08qbXO1VrnJicP/dfyd+9+JsR+fIcHrX0UF79C3ooLWbL0ZFasvJiFi+awp+SNtnPs9ujWhfKdRUSMbrc5s80WydQpf8Fhj0Upe2uCZVHfsKlP4vf7q2luLkbrwysJFkIIMXiEJdlSSkUppWL2/Rk4FVjf4bR3gCta30o8CqjVWh/2U4g+X6j3BQ4ftbUrqatbg2V5Mc1GTLORTZt+xvK882lqKgBgxvTnMQxXu+sMw8XUKX9u16a1Zs3aH+D1laN1AMvyYlnNbN16L7V1a8IWs89XycpVV7Bo8dEsXXYqi788lsrKhWHrXwghhNgnXCNbqcBipdQa4Gvgfa31f5VS1yulrm895wNgB7AdeAq4MUz3HtQSE0/kcC/UHzAbgxYhratbxfK88/D7q4mKymL+MYvIGn0rw4YdT2bmtRx91CfExua0v6Z+bdA1XpblpajoxbDFvHr11dTULENrP5blwecrY+26G2lszA/bPYQQQggIUxagtd4BTA/SvuCAP2vg8K3oGcKI4Zeze/dTAx3GgDHNJgoLXyA7+8c4nYlkZ3e9mL6+bj2mGWzaVeP1hmcNX339RhqbdnRKEC3LR2HhC0yceF9Y7iOEEEKAVJDvc3X1awhW1PNQ6UH5ImNXW/j42VnwGDt3/vWgvWhtsmPnIwR7W1MpJ8lJJ3e+6BB4vKFmr02amnaE5R5CCCHEPpJs9bHt235PON9AVKol4TKtwfFmnmG4SEg4FqVcXZxlUbBrARUVn3fZV03N8pAvExiGi4yM83sR6X4x0RNDv7SgDu8pXyGEEP1Pkq0+VFu7Do+3KOz9KgWG0piDYJTLZoskZ9qjTJv2KDZbdMjzLKuZ3YXPdtlXIFBPqFHA+PhZ2Gzh2ebI768j1Ay6z1salnsIIYQQ+0iy1YfKyt7ps76VGhx/eQkJ87HbY0hOOolj539JUhdTfX5/dZd9xcfnorWvU7vNiCA15du9jrWtP5sbpWzBj9lDJ4xCCCHEoRgM/14ftsI1EjN4Gfi8e9ve4LPbo5k65ZGgG20r5SQudhZr1l7LwkW5LF12OqWlb7c7x+FIIDv7p60FTVtGuAwjgsiocaSmnhm2qCMjs4iMGEnHUTTDiGDEiMvDdh8hhBACQOnBueIagNzcXJ2XlzfQYRyyhoYtLPv6LA7cgDmcLA3GgC/daqkgP3nSg6SmngFAScmbbN7yq9YNpTWG4cZujyMQqG9dK9XyPWcYEWSNvpnRo69v12NNTR5FRX/HH6glNeUM0tLO6lSjq7eamgpYueqy1qlLjdYmaWnnMnHC/VKpXgghxCFRSq3QWud2apdkq29t2vRL9pS80id9a90yndhflHK0bhTdecG/UnZmzXyZ+PjZANTVrWV34fN4vaXEx82ismox9fXr6fimoWFEcPxxee2qyPcXrU2qqr7C5ysnLm42kZGj+j2GbmushKKvIWIYjJgDhgxKCyHEYBMq2ZJXr/pYecWHfdZ3fw/AKGUjOnpCa9LUntYBVq66nPHj7mbEiEuIjc1h6pQ/sXPnYxTsegzL8hG8pINBs6eQ6KhxYY+3sTGfHTsfoa5uFW73CLJG38SwYfPbPU9i4nFhv2/YLXwQ/vcg2J0tGXbkMPjeW5A4ZqAjE0II0Q3y43EfMk0Pfn/VQIcRNobhxmFPCHlcax/btt9PINAAtGzjU7Dr8daNqYOPoGodwOVMCXusDQ1bWZ53Dnv3foDHU0xNzTLWrL2O0tI+emmheCXkPQ9rXoX6svD1u/1TWPQnML3grQdfA9QUwj8uGKxF14QQQnQgI1t9qKzsg4EOoUtRUeNxudKJj5/Dzp2PorW3y/MDgRqqqhd1eY5SdmprV5GYeBx79rzeum6rvVriqCWO4aqKtJSTcTjievUcweTn/7G1Ev3+hMSymtm67Tekpp6JUmH6OcPvgZcvhN1LWxKifUYeBRf9HaJ7uZn610+Bv6lDo4b6UihdB+k5QS8TQggxeEiy1Qc83lLy8/9IaelbAx1Kl/z+OubkvoPN5iDgr2V34cG3FbK0Ylv1GKq9cWTF7SI1suNG2xq7PQYAs3WB/D5NRPAYP2YDOdiUiaHs3B07kindjNfrLaOs7D0CgQYSk04kOmo8WpvYg5RrqK1bSbDRNNNsxOerwOUK02jawoc6J1oAhcvg7+fBdQt7N9/bHKJchmGAt+7Q+xVCCNFvJNkKM7+/mq+/Pqu1plT4Ksf3RHcXzvt8pSxclEta6pmUlr110POrPPE8uPxman2xAFjaYHbqan4w9R8YSgMKhz2ubXPp1JQzKC//GMtqGZl5h/PYyBT8yokfQMO9+WWMjozkxGGxXd57796P2LDxNrTWaO1jZ8FfWzrAICZmMpMn/YHo6PFt5zudySGncO32ru/VI6te7JxoQUtsFdt6P/o0+WwoWQOBDhXvLRMyZh16v4eTbZ/A5/dDdQEkT4KT7oZRRw90VEII0UbWbIVZUfErmGYDA5lo9YRlNbCn5NWg030dLVh7FRWeRLymG6/pxm85WVk2nYXFJ9KStyts9hjKyz8GICnpm0RGZrXcBzibf/MwNzJa57f12Wxp/rprb5f39fvr2bDxx1iWp3WqU7f2qAGT+vr1rFh5EX5/DQBmwM/I4de01uvazzDcpKWdE943H83ORVj339AGdXt61//sK2FYFjhaa5cpA+wRcMaD4Dzc67h1w4a34F+Xw55VLaOAu7+Cv58LBYsHOjIhhGgjI1thVl39VeuC8P63b0SrL9ZN13pj2FU7Eku3r7zus1x8tvtoThzxP8CisXELGzb+lKamnSQlfYOmppbEygAiaCaCZu7kPm7ST2MqBwB7PB4KCp6gsWkrsbEzSE87t21q0OMpYXneOQf5mmosy0fh7n+y+YN6ti5djGVZZB6bybDJuzAMG1r7SUk5gwnjfx3eL8zEb8Pql0EHSa5NP2TM6F3/zij44Wew+hXY+gFEpcLcayBjZu/6PRxoDR/+H/g7jPr5m+GjX8G1Xe/FKYQQ/UWSrTAyzSbq6tYNyL0PnDrsi5IQPtPRMlUYJJHzBhwcOJJnWc3sLHgUj6cEy/J3Ot+GSQ5rWEUudmCs50N2FjyDZXnZu/dDCgoeY86ct3C70li3/hZ8voO/0WlZHjYueZ3tS6MxAy1FZHctNCjJm8AF9/6cxPRJOBxhnD7c56R7Wt4YrC9p3253w8zvQUxa7+/hiIA532/5JfYLeFpeFAhm78b+jUUIIbog04hhVFLyZtDkoj/0Zc0tpWxkxDmIcjR2OmZXfmanrgpyjZ2Gxi2A2fkYmgiasBHATRNn6lfbRq4sqxmfr4pt236H11tOff0GujMla6gIKvP9mIH2X39l97BxyT9pbNxKnxTwjU6GW1bAKb+F5AngjoPkifDtP7ZM9Ym+Y3e3jPwFE44kVwghwkRGtsKounrJQcsnDEVamwQC1Vyb80/+vPJqTMuGqe04DS+xrnrOyP4kyDV+Gho2B+3PgY9KEvkmX/Ad/TqJdBy5Mqms/BxrzO0oZRx0WlQpO4oo6guTgH1rzzTD55eROLEGk3xWr/kEpzOJWTP/jtud0eOvQZecUTD/5pZffcEMwJd/geVPga8RxnwDTvkNJAziivf9QSmYfyss+mP78hiOSDjh5wMXlxBCdCDJVhi5I0bQsrnx4VdsUmuTGcP9/L+oP/NZwSzKm5OYOGwrR6fn4bJ3XCRuw7ICQPDE047F3dyNwg3KDJpMGYYTt3sEDkcCXm9Jx6NERo4lEKjCsvwkJ59KavxVrPb8ou2MhLF1DJtQg1ObJFc0YzfrqRzWwNp1NzB3ztsMKW9eB5vf3/9G4qZ3YeciuHk5RCUNbGwD7difQMALSx9reUPT5oJv/AJmXDrQkQkhRBtJtsJoxPBL2b374LWqhiKlbAwffhFjxwwnwXlDF2ca7H9bsGuRkaOJjBxNRcWnaL1/+s9QLtLTz0cpxZTJf2T1mh+gdQCt/RhGBC5nMrmzX+1UDHXc3KPZvnwZAZ+XpKlVJNd7ydlYhwaUhjEFjRRnLKd5ShERkSMO6esQTvX1Gykv/wRlOEhL/TYREZmdT6reBZvfbUko9tFWywjX8qfhxDv7L+DByDDgm3fB8XeAp6Zl70ib/G9NCDG4yJqtMIqIyMRQEQc/sQ/0x84tqSln4vVWdnHGvm+n7pW9iImeyKSJ9xMZmY3NFoVhRGIYEcTFzSQ768cAJCTM46h5H5KZeQ0pKd9m3Li7mDfvg6BV579100+Zd96FRCUMw+mwmLapHpsFdgtsGmwWDN/TBDsH/i21rdt+S96KC9hZ8Cg7d/6FpctOp7j41c4nlm1oGa3pyPS2FE4VLexOiE6RREsIMSjJ/5nCLDp6InX1nReM97V9JR/6aqH8lMl/xuGIY3v+A12c1f3aYko5iI6eiMORwLy571Nbu4KmpgKiYyYSGzO13bkREcMZO+b2g/Zp2Gwcde5FHHXuRZQsvB42dU5eDEvj3rIIpnyv27GGW23tKoqLX2mrbaa1hdawddt9JCWfjMt5wNTgsCwI9tKF4Wgp4CmEEGLQk5GtMPP6QryK3g/Ck2i1r6OllIOxY+7C6RxGbe1KwlWsVSkbaWlntf5ZER+fS0bG+Z0SrUOVnHgyLevnOtwXUFYgLPc4VGV7PwhaRFZho7Kiw6hbyiRInwk2Z/t2mxPmXduHUQohhAgXGdkKM607lzoYSAHLwKasllrrlkGjP5J4d0PQcw0jkpEjvkdNbR6NjdtwudJxu9PZsfMhlHKiLS+WDk9pi/j4uTidnfcnbBnlMTEMR6/6t489HW24wOxQ8NIRBdMu7FlnlgXrX4e851qm73IugtlXgT3I9F43KAyCvUihIfgG2Zf+E977ccvCeG1B0jj4ziOQMPqQ7i+EEKJ/SbIVRoFAPXZ7HD5f19vP9BfTUtiUhVIt/7QbNotYo4GdNSPJii/sdL7dHk1W1s3YbC3bwBQWPs/2/Idaa2DtW6C9L1Ho3QhXVdVCliz5JuPH301S0jcwzSa2br2P0rK3sawAsbHTmDjhN8TETIHaYljyGBQtb6lldcwtLb93xRWNOmdBy5t82mqp5u6IaKn4Pv60ngX79o2w8e395QXKNsL6N+Dq/7RsydNDqWnfoaj470FGt0ySkr7Z+QJ3LJz/bMsiedMHrpge31MIIcTAUX1S6DFMcnNzdV5e3kCH0S1+fw1Llp7cugH14BBsDZfWkF8zirEJu9q1Dxt2PJMm/Q63a38xyC+/Oh6PpzhIzwZgw2ZzYJp+lNJovX9qTikndnssfn/FQWM0jAgmTryfkpLXqalZgdb7y0jYbFEcPfZxXC9e3LIFi+UHZWtZDH3ZGzB6/sG/CLVFsO518NbBuNNg5Nyezbfu3QRPfqPzRtDOKDjvqZbk7RDs2Pkou3Y9zr7NtEEzadIfSEs985D6E0IIMfCUUiu01rkd22VkK0x27352UCVaoSgFSRFV+EwHTpsfPzZe5Psc76kltXIx/kAN8XGziY2dQSBQF6IPg1kzX8YwXEREjGDduhuprVvTWoDUIjp6AmmpZ7M9/w9YVnPQPvaxrGa2bPk1MdUNTN9VS2SzSX20nR2jImmK9uH/z624vPW0TblpsyXxeu/HLXWmDiZuBBz74x59jdAamipbRsJ2fUnQumm+Rsj//JCTreysW0hL/Q4VlZ9jKAfJyafhciUfUl9CCCEGN0m2wqS84uOBDqFbtAav6SDOVd/yGYMGohje9BLrNr+OQxko5SAh4Wji4uZQWfk5ndYWaYuVqy4DIDX1DKZPf5qmpp00Nm4jMjKLmJhpaB2gvPxDauvWHDThMgP1TNlYhcunUYDL4yOxyseK6ZqI0vJO9wegagd468M/pbZzEbxzM9TtafliJYwi2EJ7bE6ISe/VrSIjR5MZeXWv+hBCCDH4SbIVJk5HIp13DhxYplbY0J1mzRIjatraHPj5EQ8DrUu2tYXWAaqrlzAq84dUV3/Ztm/hflbbdF9Z2QeYpoecaY8REzO57QylHMyc+RKVlf+juPhVKqu+aDfVeCDDAqdft6U0RsstGLejGe2MhGAjbMrWsjdeOFVsg5cvbL/1S+X24OcaNph+cXjvL4QQ4rAkpR/CxOXq3ShHX7CplhEhrcHS4DdtWFphN/YvblcH/DqQZTVTUvpvLKvrNX1a+6io+BSfr/P6LKUMkpK+wfTpT3D0UR+TlHgKHUtLoDXD9zRjBLlNbL0f5t3QMp13ILsLpl0Att69sdjJ0sch0HHroQMYDnBGt1Qpv/hlrJhkyvb+h127nqSq6ku0Dk9ZDCGEEIcXGdkKA8vysbf8PwMdRif7Rq+0Bq0NvKaDaGfn+k6heDx76N5bhxqvtwynM/Q+fRERmUyfvoDCwpfYsfOPBAIN7JsezN7VFPQaFZOO7bifQ20prHm1JckyfZB1PJzxYLefo9sqtkKI0TcAXLFw+euQlkOzr4y8r07ENBuwLC+G4SQyciyzZ/2j7W1OIYQQAmRkKyy83nIsq4sRkQGmFNgMq0eJVk/KO2htEhmZhWl6KCp6mVWrrmT9+luprum8gH3kyO9x3LF5ZGRcCthBKYrT3ZgdvhNNVyTVR3+XqpqlWGc+BD9eBxf/A276Gi57DZx9kNCMOgYMZ+jjgWYYPgtsdjZu/Ck+Xzmm2YjWAUyzicbGzezY+Wjn60w/lK6Hmt3hj1kIIcSgJyNbYeDzVQKDq5hpV7q3rU/3S4JERmYDNvJWXEBT047WBfGK8opPGZP9EzIzv9/ufMOwMyb7x1RWforfX01+lsZmQnqZB2V3U5FgZ+PEaPC+C+veRSlFzrQFJGQd372HW/c6LHkUmqpg3Clwws8hJu3g1875IXz9FDRXdT6mDMg+EYBAoIHaulV0TEYty0dp6ZuMG/vz/Y0b34Z3fgRWoOVX6hS46O8Qm3HweIQQQhwWZGQrDMorPiToG2uDlNW6hisclHKQNf4BSkr+fUCiBaCxrGbydzyE3995gbvTOYx5cz9g9KibiE2YQ81xl9J443/wXf1vNkyOwySAaTZgmg0EAvWsWftDAoH6gwf06X3w7o+gZA3UFsLKF+Hx+dB48JpfRCfDdf+DMSe1b7e5Wt56PPX+tmcLpd26rdL1LUVVPTXga4CAB/ashpfO7Z+dw4UQQgwKkmyFwWDboudgNOC3er+43Gsfzl/UL5i91uDt7f8OWuJBKQe1tSuCXu9wxJGU9E0iIkbi8RRTXr+M4uavCVVod2/5h10H1FQFSx9r/zahFWgpEbHsie49VHwmfO/f8KPVMPc6GH08HH1Ty/Rl4hgA7PaYlsr2HRJspZykHliU9OsnOi+412ZLEliyunvxiPCzzJatlx6fD4/Ohs/uB0/wmnJCCBEOMo0YBikp36Ko6KWgmwsPRnajZTPm7k0nBmcBC6yr+FpPB6BcxzAehdFp1EdjtwevhVVR8Rnr1t/Sut7Noq5uPaDaVZFv60UHDj6yVba+ZRQq0KFUhemFnV8Adx3wACZs/xQKv4a4DJj6XXDH7T8+LAvO+EPIW02e9BArVlyAafmwrCZstihcrnSys368/6SawpbkqiNlg/qy/8/eecfHUV1t+Lkzs029S5Zt2XLvNm4YjOnVYHpoofc0IJAKCSQE+AgkJAFCCC303rspptpg495ly0WWrN7btpm53x9XZVe7kuVCSZjn91vMzk65M7vaefecc9/T97k4fH28fDkUvd0tyhfeDetfhys/2+N+lw4ODg594YitvURKm+rq9/aJ0Nob8bO72FJD1yxUcHP3LQs04Cz7IZaI+wD4gGOYyZd4iBQ6AsNIITV1KrYdpq7uEwLBClJTJpOcPIENG34bdd2kDHZtF5uq08jIOKjvQSUPUMXoPREapBd2Pw/74dHjoaZIpfdcCfD+TXDhmzBgcr/OPzFxGAce+CnV1W/h95eSnDyBrKzDoxtojzgSdnwZ2+rHCqpCe4dvnpoi2PhW9HtiBVVbp3WvON5pDg4OXwuO2NpLtm//F6Wlj3Q93xvB9E0JLQBDs5DAejGd4WIbXtkIMozHk0c43NQhgvquK8qmCl2GsYSLYjGaZ+U5/JDH1f4RuN0Z7DflcQKBnSxbfiam2dphbKqRnDwB02rtZc+SSMGl6wnk5Z1CUuLIvk8qayQMmAQ7l6s+il0n64FZP+5+vugeqFqnaqigO8Lx4sXw06X9fiMMI5H8/DN6X2Hq+SqV2FKpLCtACbsZl0FSTr+O4bCPKfsq/vsbboOtnzhiy8HB4WvBEVt7gZSSHTsejEp7CbFrwbU7gmxfRbt67kcIJWWGyLX8Qt6D5s7koxmjyfG4se0QH38yJSLS1DtWx0fII/0cxoeYGHgJYqMRDjcRCJSydds/CAZriIygNTev7tVRXo3PICFhGIkJwxiQfzqZGYf070TPfhZeuhS2f65c3l0JcMLfIH9K9zqrnu0WWpE0lal6qrSC/h0rkmArrHlB1WJljYEpZ4EvHS7/BBbdC0VvgjcN9r8Sxp+itmmvh2X/gR2LIXs0zLi0oz2Qw9dGcp5K4/ZEd+/Z++7g4ODQDxyxtRdIGca0+jFDrud2Hf/pj4jymx58RnCvBVe87QWQSDu/4UZuDP2NZze/yw8yLUAiZZx0XJztOzmGt8mjHDdKQGnYSBlk9ZrumqxIpAyiaV5sG+LZZkgZxusdyMSJ9/b3FAEIaAFajvsJPvs3JOmZkD5Uia6ogfcxL6Sv13qjuQIePAwCTSpKZvjg0z/DJe+raNuRN6pHJE1l8O9Dumcpbv0Ilj4M570Gg2fs/hgc+seww8CboiJZkTNHNUNFIh0cHBy+BhyxtRds2frXuMt3JYw0ASFLx6VZu4yAGZpJ2DYwhIn2NcwdFcBAdvIgZ2JXGxTVubCs+I7uMePTkhBSIIFZfN4ltCKx7TZ6+5i5jDRcrjRa2zbGfd3v34FptmIYSWpBsFXV27TXQeEcyJvYPRZpsX7Db6iufgshXEhpkZw8jimpD2NoPQr09zsPPv6/HrVUQgmz1EH9Ovco3vsdtNZ0u8+bfiWgXr8KLu6ls8D7N4G/obuA3gqpx+s/g598uftjcOgfmg4XvQ3Pn6/qt4QGvjQ49UFIHfhtj87BweF/FEds7SG1tR9RWvrYHm0rJbh1i7CloQubsG3g1tWNumeqz61bXdt8XQX0AtCR6ISx4hWY94IuAxyZ1MiC1rS+19M9WJZJSfMgXik+nh3Ng8hJqOPSAwRnzP4Ji744jECgjJ41Yn5/CStXXcL0ac/BzmXw+MlKnFhhpNAIDZ+FdeJdJCQOZ8eO/1Bd/U5H02yV/mxuXs36Db9h0sR/Rg9o1pVQ/IHapxVUDa11N/xgz95Pit6J0+ZHQuliZf1gxHGlL/4g/kzFumIVIYucGemwb0kfCld8qqKLZhAyhn2zBZMODg7fOxyxtYfsKH2kX6m2eHR+r7t0G1uCS4+NcHWWiPfc5ruElCYntf6OFa77KApNpoAdca1dExNHs3pnO39ecikh2wA0Mn31bNixhPcXrWVC4U/ZsvVvhEKVPfYfpqVlHS3Na0l+5hwIdnshCUDf/AnFbx5JYMT++AM7Y3y+pAxTW/shluVH1yOaWRseuOB12PGFsn5IyYex82IbXvcX3YB4HwWh9Z6WdCcos9OYbVD2FQ5fP3sSxXRwcHDYAxyxtYeEw3FauuwBmoB4s/6+g9oqLllaC6+PqOGrysEQ55JomoecnGN4e8koQraqb5tbOJ8Thr2HS5hoQUnRpoUYRmLc/QuhESpZoGqbemDYkgHlzazMWdnnGG07GC221I5VL8QhB/brPPtk8tnKJNOKmFCguWD0cUqIxWPGpfDpncqGohPdrbZxefd+TA4ODg4O3xkcB/k9JCvrCIToo2nxN0hnivFbOjqJmklO48NxBaKuJzIw/0w2VKo6sDRPI/OGzcejh9E0NWjbbiccakCIWFd7KS183t5raYRUESw1szF2BF7vQAzja07JHXGj8udyJaromDsJMofBCX/vfZsDr4ZRx6oUpidZzZrMmwjz7v56x+rg4ODg8I3jRLb2kILBF1Ne/jKhUBV7YgraSc90YX8wbQ1DU8f8poxQNc2DlHZU6tQI2+RXtmGGHwctfsuizMxDMYxkspM9lNS1Mz6zCFvGanyJicBNpMmqEB5SU6eRMOwk0K+L2cbSoDLX27WHHiNG0zyMHXs74uu+QO5EuOQ9lZKsXgcZw2HoHPqc0aAb8INHoX6r6qGYPlR5hDk4ODg4/M/hRLb2EJcrjVEjr0fE8+zZDTqtO/sbmAqaOhWtebSFfdhSfGO1XCnJkykc+jM6pWFyS5jZSxrIK2tgs70Iu5f6tfb2rUgp+dlhI/C5dIKWGxk/BkZe7slkZs5BCBe6nsyggWczedID3cLElYCt6UjA1KAxxUVFbm/1TYLJkx4kPe0bslEQAgr2h+kXw7BD+hZakWQMg3En7p3Qsm3VfsjBwcHB4TuJE9naA0yznZKSf7Gz/Ln4RfJhcBcLQsMl9CPTuDt6yaVbDE4pJ2wZiH5LtL3FIDvnaHQ9ASE8SNvPhA0tGJZk/egkLE30Gl5radnA9pJ/cdq0H1HXFuJfH4UQPB2znqa5KCi4iKSkUfGHMOxQuGol1vKHqNjyIHWpgvo00VGAHqfmTWg0Ni4hI+OAvTjv7zjt9fDWdbDhDTWzcegcmPd3JeAcHBwcHL4zOGJrN6mt/YRVqy8jnhFnJ8IE7zKwPWDGue+FWrMwA6l4Uneiu3avp6LWNZNRWQ3EpBE7dcduKLjIeq94mknXPSQkFFJa+hhSBvAFbDwhm6Bboz3R6B5U3H0H2VHyAEOHXMkVhwznotmFbCtPpWLbVV0DltJk5Ijf9S60OknOxXXIDeQdcAWh0sewGhcjpU1Ly7qY5tVShgmbTf28At8dSvxB/lVazdoWP5OTE7h8cDZDfHGid7YNj86F2uLu1kTbP4OHjoSrVjjWEQ4ODg7fIRyxtRuEw82sXvMj+hJaANILzT+UqvQooijLCiVQtvDHBOoLEcJESoPMsW+SNU4ZX/a3/ipkGSzcOZPByTsZnlZC0HLh0cPYtsoL+z4S+I/of9Sruj2Ll4tPYO7Q+RSkVADd4xDCwO3OIjNjDpUVrwIC2fGajNcvOg6m1Y5th9B1L25DY3TBIYwYuIT6hs+x7SAZ6QficqX1e7xudxYjhqsarvb2bSxecnzMBAFdSyAr87B+7/O7wOqWdk5ZUUzQspneuIrDS58iFKiibuQhZB72S0gb3L3y9s+gcUd0D0hpq9mNq5+HmZd98yfg4ODg4BAXp2ZrN6ipfQ8Zz4iyp+AQdDqFRkWYypecj79uGNJyY5sJSMtN3Ya5tJTtt1uF7m7dJDuhjr8u+ynvbjuctrCX+vo0ElYIcn5nkPaagWjr376khJDtIj+xksfW/5AbF/2GTQ0jkWgIYZCZeRjTpz1PMFhNatp0hPAQ8OoEPDruoI0nuOtaIY8nC02Ljs7ouofsrCPIzZm7W0KrJwkJhQwceA6a1m3toGkJpKXPJCNj9h7v99vgt5vKaLNs5lW9z9Nrfslh9YsZ2b6d1NVPwv2zoWF798p1m+PXaYXbVZNtBwcHB4fvDE5kazewLD9CyFibhX6IJCnBcPsRwka42rDDyldKWl7qiw8hedCK3RpL2HYTsj28uvV4Dsj/Cl9KG41DvQRu9qM3gmgH2cO6SkpYUDqHt7YeTXMombzEas4c9QpjM4uYN3w+xxZ+yGdlB3DH0p9y/MQc7j1nGrYdYu3aq6lv+LzD6iIMaKydmMPU5VWM3dTKqgmpSAG2HnshNM3LiOG//VpnBI4ccQOZGXPYWf48th0kL+8kcnPmIvakz+HXhRmEQDMkZMYtnpdSsry5HV2a3Fb8DxLsbs8uQ1qqVdFHt8Op96uFOeNiez6CspAYMOVrOgkHBwcHhz3BEVu7QWbGwWxGZ1dpxN5IHz2fvGlPgoBQSy4VSy4i0DCU9BGf7NZ+AqabT8uUGachTDY1jmBG7gpEpkopWVnETe+9s+0IPthxCAkuP+2mj4q2PO5bdQlXT/03YzI249HDHDp4IZubRjMu/3SE0NlYdGNXuq+zDY6meUgZdiqtM44mraKS2S3bWa8vpbZ9ZdS1EcJg7Jj/Iy9v3h5crf4jhCAz8xCSksbQ2LgUtzuT74wtrGXC+7+HZf9RdVaeZDjmVph8VtRqQggSdY2M1mrcdpxJF9KC7Z92Py84ALJGqSiW1VGvJnS1/4mn75uxh/2w/nVo2KY8wEYe07tJq4ODg4NDrzjfnLtBQsIQCgouoaTkIeL3Z+kbT1I1QlceUp7UcgYf+le2vXsjyfmr+rW9lBC2dT7deSCra8d1LfcZgdgUZI/npi1I8zbx5zk3Y0odTUje2noUb207mpc3H8/1+/8dAF1YXDbhEcaPPw7LClJV9WZM8bltB6moeJFBA89GTDoYYbZS//n+9BShUkJr26Z+ndveIKWkeMufKSt7HCHUR9owUpi635MkJAz92o/fJ/OvhxWPdzvFtwfhzWvAlwGjjo5a9bz8TF7a3qwiWfFIyun+fyHg/NdVQ+s1z4Ntwsij4djbwZO09+NuKFHF9uF25d7vToKUgXDJfPCl7/3+HRwcHL5H7HWeRQgxWAjxkRBivRBinRDi6jjrHCqEaBJCrOx43Li3x/22GDH8F4wadQORl64/7u1C0CW0upZpJukjPwLRP1NUW2rcvuQanis6lU41ZWgWYzOKAKghm/c4lgUcRRMpUdtatsG03FW4dBOfEcSjh5hb+B4HD/ycyvbcqHEamkVJ8c+wrDZ6q4CXMsyy5ecQDNXS2roxrvs7mJSWPkI4HD0r0LKCBALlHdEyerzmp7T0MZYtP4fVa35Mff0i1cx52WPwn7nwxKkq2hJx0Wtr36es7AlsO4hltWFZbQSDlaxadSny27PWVwJr+WPRLXk6l39ye8zqvxk2gP3zBvJh5gEEe15PVwLMviZ6mTcF5v0Nrt8Jv6uCM5+A1N7d9neL134M7bXdbZJCrSrC9eGf9s3+HRwcHL5H7IvIlglcJ6VcLoRIBpYJId6XUq7vsd5nUsoT9sHxvlWCoVpKSx8n0jV+c0MhQ1JKcevmbpmMarpJ+oiPgF4sHCKfm2CUCcrbBuDT/UjArYe5Zur9GJrNG5zES5ylNpTwGJdwZvMzNJQbLK6Yjo3G1JyV/GDU6yS7VfW81whz/rgXOGjgkpixBQI72Vz8ZzyeHAKBnXHHb9thystfIDfn2F6bctt2iOItdzJ2zC1IabN129/ZseMRQKXOCgoup3DoTxFCYFkBvlp6Kn7/DmxbWWLU1X1KYW0iQ4t2qCgLqAbSWz+CE/4GpUsoXfEz7CSzx5ElgWAlbW2bd20p0V+CrfDhH2HVc2oW4Khj4ZjbIGVA/PXb63qf9dBYGrPIo2k8OKGQnQWP0frK5bh3fIow3CpqdcivYfzJ++Y8dkXYDzu+VLMbI7FCsO5lOOGub2YcDg4ODv8j7LXYklJWABUd/98ihNgADAR6iq3/egKBcpZ8dSLhcGPU8uLGYYBgVMbW3d6nZpgxQksEO2wVBIgwoINRIcj4l+Bvt97ApobheLQQozK2oAlJKYN5mTMJd/ZqFICUvLD+GIyWIDaqkPqLihkUNYzkltm34dKUOBEChqWWxB1bdfUbjB79JzZuvCGumJIySHPzSgqH/ojExFG0tKyOsxdJdfVbjB1zCyU7HmTHjkew7e5IT0nJvzGMFAoGX0BFxcv4/aVdQgvAtv1sS2tnoPTTFesJt8PKp2H8afDMGZjjDCA2siaE3hGd231s26SpaRmW7SctdQaGngCPnwSVa7obTq9/XQm/ny1TLXt6kpTbu7P7gMm9HntgSgZc8CK0VKpH1sj4+//a+I7Uuzk4ODj8j7BPa7aEEEOB/YDFcV4+QAixCigHfiGl/K+bn7512z8Ih5vpmVo7rOBzPig5mKGpO3DrPSMsfRPP8iHpDY3ERTrSDeHBEr0BXOUatlviM4JMzl4ftd0XHIRJ9Mw0rT6IaLe6hBaALQ1aQkksr5rE/gOWdy3vLfhiSYO7djRRLH/Kj/k7epyUYl3dQiwrwKSJ/2Thojm9nmfAsnl++zIa7IMZwzoGUabGZPspKbmfgsEXUFP7fpQQ6xqfLWlKMciqjxB8QoPP7wIrTG6NRVuiEXc2ZHLy+F7H1BvNzWtYueqSrjSnlCZjMi9gQM2GbqEFIE3wN8CLF8PA6SrylDWy+/WGElUUH4/Rc3c9kOQ89fimcXlhyGzY/rkqzO9Ed8OE07758Tg4ODj8l7PP5sYLIZKAl4BrpJTNPV5eDgyRUk4G7gFe7WM/lwshlgohltbU1Oyr4e0T6uo+Jd5MRK8eZHXNeAKmp1/1W52ELJ2QFat3RViACXqzwLtOU0LLkLTvH//GbaEhe7yVoiUMduxggpaXkubBMct7YgO/kzfxVNsUvhQHUUVe3OotIQQ1Ne/h9eaTnj6bnh8pIVy40o9hyqJ13GddyFNcwO+5g/v5CXZHBCUcrgPomEUY5/w0gRHucXShgb8erBCDKgL4/Baa1XF9pETDxdgx/4em9aNfUuR520FWrLyQcLgOy2rFslqx7QAbax6m1RPnCpgB2DRf1WDdfxB88c/u17Z+1PvsvfotuzWuKNrr4Z1fwV1j4e6psOheNetxX3LSvZCUrQrjhab+zRwBR/zXlls6ODg4fGvsE7ElVHX0S8BTUsqXe74upWyWUrZ2/P/bgEsIkRVvX1LKB6SU06WU07Ozs/fF8PYZLlf8FihCwDXT7idhN1rvSAm3L/k525uHxLzWcqpFuFBiuW1CBTaBkRahETYtpyqhF7b0qODaTL7ERY92NQnx2+h4tAADEit3OT4BFFKMjsWP5N/JpSpucknKMMFQFQDjxt6O252FricCAl1PxOcr4FfNp9BoWgREAiHhISQ8LOFAvkSZjiYmqmjQoEHnARpev8WQkjaGb20lrTEMSBpTeogWzYCxJ4LhQ7dhxopGRhe3kV0TZFBFmBmj7yM3tx/Rox7U1X2GlLHCxcamPLc34SZVXZUZgA9vVs7uAJ6U+F5Ymhu8abs9NgBCbfDAIfDVf6C5XIm2j25R0bV9SVoBXL0aTrwbDrsBTn8ErvzcaQPk4ODgsAfsdRpRKLfKh4ENUsq4lbNCiDygSkophRAzUSKvbm+P/U0zePAlbNz4W+LN0Evsp9CybUFzKIkUTyvlbXm8XDyPX0y7G1fETEXpgbqfm53+oSBUDZclBcGwly/Kp3HY4M+7EoTD2cIRvMeH8mjCuJSBfboLjzAJI7qiXgKbBDvEQRnLELZE9tHTECABP+fyH2awGJ34UTVb6siESQB4vfkceMDH1NTMx+8vISlpLJWemVSs3A49tg8KLwvkURykLWXkiBvU8XyF5FYFGLu5GSFBSBhUHqA2083WoUkMrXUplepJgrOfg/ShsPh+aAujS5P8qiD5dRqMPhYGHt6v96MnptkSM9aOMyXs0kAYKn3YF0XvwP5XwJi58Na1sa9rOkw6Y4/Gx+oXoK0W7AhxHfbD5vlQUwTZo/dsv/EwPE7a0MHBwWEfsC9qtmYD5wFrhBArO5ZdDxQASCnvB04HfiSEMAE/cJb8Vufk7xn5A06nouIlmpq+ilq+O612hJAkupSlwt2H/Ra/6SVoeZAyzLslR7Bw5yxsBDPzlnHCsPfw6apGSAC2bfDXpT+mtCWfnIRaJmZt7DruuTzGbD5jiZxFfdVgBpXV4zWCLDbyKQnmAoKRadu4OeHfTFxdRUlBArVZbsK6hoxT6wSwhon8kd/h7hE16zpvQBLkt6sXcsyo4Vw4KAtd92ClHcvrgXoamyxS7Vbs7S3olo2d7UMmdn/kpJHOfpMfJy11GgC2v46xm5uJdMgwbMiqC1GXnwDnPASGGwbs1+3CfvknKpq0eT4YXhg4TZl7LrgFpl4Q3U+wH6Snz4rbkkk3bbLrQpAzGmo2RfckjEKotBsog9EfvgDPnK0K5QXq35P/BemxEc1+UfJ596zMSDQddi7ft2LLwcHBwWGfsC9mI37OLqYvSSnvBe7d22N92wghmDL5IT75dCaSYNdJ9ya0pARbCuWxhVT/CnDpNlIq6wZ3RwPpO5dexbbmAsK2SlV9sONQ1tSO46ZZd6JrSn3owuSQQYtYXDmdBD3WyLSQrQxlKzXaEMIDvKSlVnFUUiOakYfbM5BkXxYNtQZFdcnotqSwxI+pwZZhiVEnIYEq8qghD60Pt3w1YVJyPg/w6+JpTHenUl79Hq/WtLKY2ewkF0wbzaNhrGzE2NyMOSwFa3gyPk1wwbCZpKV2p4rdO9diCY2ekSXDhsENCTDkgNhBpAyAU/6lCtUfOByKP1BiRHer+qkfvghD+98j0esdQMHgS9mx9V5sDRAC3ZSktJhkVbdCUp3ytKopggcPU6nDKGwYE+FwMuRA+GUxlCxUdVVDDgR3Qr/HE0PGcNA90YX6AAhIHbTn+3VwcHBw+NpwHOR3A9uW3PPOU4zymHiNviNaUoJp6+iaFVU6ZUsByKhlmxuHU9IyqEtoAZi2i1p/JqtqJjA1V1kq6Jpk/+yljG7eRIreHPf4QkBOjrJyUKFDgW1WEjArCba7QVjUZyYAJjVZ7u6NIgjh5i/8Fj8J1JNBDn1PVLDRuMi+m/J1G0CGOQnBPF7gCS5kgXEMdoYHmetDq/RjbG3GM8DHhAGpnJMfXRAvdDea7gUrOnIjgcSUCX2Ogc/uguay7tY1Vkg9Xrkcrlnb/9Bj+QqGv/UE6XYzO/O8WDrk1oTIrQmqZKy/QUWRcsepYvEPb47e/vi7Yn23dBcMO7R/x98VU8+HRfdEiy2hK5uJIf9djbcdHBwcvi98hzr1fvd5YVkpdtt8vIaK9vSWCJUSQpYby9bj1KjLmGXbmwsw7VjdG7S8bGmKTjfptknWiAa8Cf2dfdY9SClDHSmyjm07Q2098BDiIh5kOJt5hvMJ4eraS9wZiUjGsgZdBtGxMbBwE+I8/kOabABDw8r3AaBJOC7s5pX9RuDp2ZC58BC0eB9Jlw992kV9n+b617qFViRtddAY30cshvZ6eHQe1G8lozHMxI0tTFnXwoDqIFrniQ+c2r3+AT+Bn34FR90MR98CV62E/X7Yv2PtKakD4bxXIL1QpU11t+qTeOFbcRtcOzg4ODh8+ziRrd3gsUXbmZ1lYEs10a+3WEl1eyYLy2cxt/D9mNc0Qdf2nWR4G3BpJlYPGwi3FiTbVx+1zPBJhIgUUL0HbfbGmnI8a7mZ3/ZrXQMTEaeo3EZjP5byEUd1ZQYFUOjzoMcbtMsLZz4Jz56jTqrDEFTMuBwKD+57EC5f/OXSBqOX13oy/4bu9jTxEBr0FH1pBaoY/pukYH+4agW0VCjBlZDxzR7fwcHBwWG3cH4K7waW1UhibRCtI4Bi2/GjW+vrx2BoYVxabBF1wFQRr0jGZW5A13oKFhtDs5iZtyxq3Xhpw69jqoFlx5dqQkosqWGiE8YghIvVTIrx+erERkNYFiOa13Ps0Pf5wwG3MyPp55SWPYltx4nODT8MrtsIB12rDEK9aVCxUhls9sX0S1T/wKjB6sqpPTk3/jaRLH0UVj1Lb70gAXWh37xGtbL5thECUvIdoeXg4ODwX4AT2eongUA5P598E1n3tNI4DpC9+1UelL8YG4GuyajIU117GkUNI/i47EDOG/cCA5MqMW2dryqnETTdEYJFMjCpkssnPtYv766EdpNBFQG8AZu6dBeVed64buq7ooZsnuVcVjMFXZoca7/BidrraBEiULPAtTOZZwYfS1DzsIQDSKCdcfwWvcesRQ2bNUziUD7gomEPogk1ScAMQnHx7dTVfcLkSQ8geirIhhJVgxVuByS0VsK2T5Vdwkn3xb/wMy6B0sWw8c0ObysBiVnwg//s+sRtG967gfiWD5FINaZ3fwOXf7zr/f4vUbkWShapazr6uN4jiQ4ODg4OMThiq58Ub7kDWeen6QobtL7rrV0RLXs61wtbBjd+8RtsdGxb49G1ZzGyZSMf24cT1jzd24ogJ414m+MKP4obseqZNsysCzFxQzNCDYuMxhCDdwZ5b+xgUpNb+n1+TaTwO+6gjUSk0MGA1+QZVMhB/Eh0TCS1JS7Lpiw4gvnyBNpFdyTpXnkNJ/MiQ2UJhmZgIymSE/kNf2KgFtvI2rb91NcvpL7+czIz56gTK1sKm9+DtS9BuGdPQwmrn1OpxdMfjj0BTVfLazer/aTkw9A53XVMoXb44l61D81QheYzL1fF623VfacPe1K5pv/r9kWoXaU5PUn7Zn9fB7YNL18GRW+psWoudc0ueAPyJn7bo3P4b8W24KuHYenDEA7A+FNgzs8d01yH/1kcsdVPtiz7nITcMNLo/8S2SMK2wZCUnRQ1KLf08rZ8Dh7zBWdue4kX208hpLnxGQGOLfwA0zZ4aM0POW/s83iM6FRkZ9pQdDSbHlfUEuNL5Qua+NcMwntAMW+JeZzEq72akkrVtZr3OZYgXiW0OggJD19yEKfZz5EjqklvCjNuUyu6tyKmHmy52J8VcgYjzWL+w0u4x83F2PoXLCuOJ1TnsWWQVasuZmDeGYzcWIXY8CaEA4i+Unkb34D6bZBRGP/1rJHR/QnXvw4f/gnqNnedMQAL/gTFH8K5L0HdbrbO8e1G6i7sh/KV6iaSM1a9cU1l8OqPlR0EQP5+cPL9kDVi98bxTbD6OSh6W50HAB2zIJ89RznM78kfg4PDS5fCpne7PeO+/Kf6275yoarddHD4H8MRW/0g2N6G7mtHc/VdH9VXsbombNrN7tRLyPYwf8eR/Gn2LQx6u5yGqlQyhjTy0M4LaQykMCF7A249vnGmbYM0NVLCIbQ4A9IlHKat4UCe4g6u7V1oSZAdsyM3MZawiG1HIy24ad0NvJj3I0ZvaaFkUALBvDLu1H7K/8nfU8rQrpOWQqNEH8JT1SO5UJpxzUFj9o9NcM1jyKIWtF1l8UBFVqrW9i62ACklfv92rPWvkDT/DkQ4trk1YT/s+EJFwdwJar+9GpVG4EqA2Vf3Y6DAiqfg7V+oqJttKR+ss56Gx0+ClsruJs9lS+Hho1S0aMUTysOr4ACYcSkkxu8X+Y2x7D/xTVTb6qB6g7LAcHDYHao3KgEf6VFnhaC5Ata9DFPO+fbG5uDwNeGIrX6wY91qvOndvka9iarehJZtCxqDqZS2DIxa3hJKQjdgwIxq2l5PpK4ynfQxjfx6xj9I9fTs5d2NpsGa5lHMTF6H6EX8+Txt/FzcSRr1Ma95AxYjtrSR0RAmrBksyh1N7rAKNjIOS0R/JIQGM5KXUZvpxp+QRmuiDkKQSCvlDI456aDu4Ym8o5iz9Upse9diCyCnNhQVnesLGWqlfttzmBka2dlHokWkYAHa27exavUVBAI7EeEA2jQf44tMMhviCCnbhLKvYP8rISkHmmPTnVEIDWZepiwfetJSCRWrIDkP8iZB+Qp4+7qIiBBQVwyPHq/6G0YJ0Y5asAcP63DCDSshuOR+5ZCfVtC/i/N1YPUiQIWIb7Xh4LArdi7t7rIQSbgNtn3miC2H/0kcsdUPDJcbM6jj8lnYUhW+9xcpoSGYyt+XX0m0GYPNiLStACTmBcieUsu60jFcNfUBfEZPd/BYxmcWEdB02hJ0klqtqLmApgY78z2MoJgwrqjCdSNsM2N5Iy5TJRBddpg5FRtJttpZOOoQrIiPhCFD5JnlXFj4Igg3rYnhrlOw0TpSkLF4zDCesJ+AR0eKHqcdR6lKoZJ7/UlICSBt6Rt86VrBJl8K06e9iM+nRKxtmyxbfg6hUA1qBgNYusbqcSnMWtaAL9BD0eluJY40Dc55Dh47UQkIM9gjyiVUn8DzX1e2C1GDlzD/t/DVI2od21Qu78kDooUWqJonf4P6twemGebDzP3Z6hvEuLatzGlYhmaF4IM/xq9R2xu2fARf3get1TDqWJh1JfjS46876UwVwTJ7nIvh3bOareoNsOxR1d9x9HEw7iRVA+bw/SF5QHyxpXv2vI2Vg8N3HEds9YPB4yfx8TuZWKPcDEqpRKd/hqKmrbGlcShtYR9NwVTUbDcNgYVHD3P6qNcBpT3yptZSn1/Vd70S3VpF7whprR6XwtTVTbjDNhLQbKjI81KV48FLgFoy8FDfJWQ0W2Jr0cLGY5tMq97G7wv+yL3en1FDNgKYKpbzs+S30IOdwkOS1GrhCts0J9sMNbaxVQ7r+uL02EHuKPoLp9QsQBMmUgi2DPXh93mQbg9NSToefxv+hOiPXUWuh5zaYGx0SzOUeIlDZlUDO/NDrN/wS6ZNfRqAhoaFWJbqOxl1zQSU53oZXhKZDhNKMIw6Fta9omwf8qdAaoH6FwErHoPGUtVv8fDfxRcXq5+DZY8pR/dOV/fqDVDVSxG9ZiixFXFeVe4MTpjyTxpcqQQ1F27bZKh/J6+uuork4g/i72dP+eI+Va/WmRqs2QArn4QrP48vuIYdApnDVZ1cuE3dEDUdTn+kY9bnbrDqOXjjaiVopaUadi++XxmyGp5db+/wv8GwQ9VnLeyPjvB2TlxxcPgfxBFbfRAON1BZ+Tr+QAXvJQ9kx7qjuPHAv/R7e9M2+KJiBo2BNK6bdi9vbTua6vZshqVu54Rh75Gb2N0GRzMkIwdsj+vN1Um89GXQq/PFjHRSm008IZumFIOgR90ETXRSaYoSViG3xlf7pXHA0kYMq1uUBIWH/ZqL+LX3jzzBxRQxlqVyOrcFU7hE3sfQQBlT1jThDdpIoZzg/z7kRs4c/BCm1AkLD3duupN5tZ/glmaH3pGM3NaOqfsxLNg81EdOXYhVE1KxBV0Or03JBqauCvWRShwJNFoLRpKyfUNMxEtI0C2JieCTJottFZUclJGJDNXGLaqTmiDgifglrbsgcyT84FF49UrY9F505GaVG059EC77KPZNCDTDyqdh+2dqH5FFvl0H7EOMWybkjFF1WR3i7LpRv6bCk4OpqT/HsOZmc+IQbi28nNtrXux9X7tLsEW1F4o8VzMIrTWw+AE49Nfdy1sq4ZmzVH2NpitxWHgwDD8SJp+pIoK7Q6hdeZRFHjvcBlXrYOUzMP3CvTkzh/8mNB0uehteuFBZighN1Sae+qCaRezg8D+II7Z6oal5FStWnIeUFrYdoLrlahqasijfOpDBhaWIfvyo14TNoKQKNtSNpu31HK53P8jO45JJTW2Oa04aNDwIW6ITK7hUMXsvqTYhaEqNTcXoWLHrC0HIrbFsUgozV0QIMQk3+P7IUDZyJffiIUg7Cbwoz+Im/o8Va08hwW9HpSunlezk8cSLeSH1RBqsQZxSvQBXj6J43VbRNAEMKveTEJDMWNHI9gIfrYkGya0mQ3f4EcBX+6WR0RDG0jWqs7z4AuVM20FMxEsKKHPnkr3Cw5VtH1Gz+DD+POQCCqcdzsQ4jbM1S5LRZENClvLjyhkHAyYpQbH5g9gUWWdPxfQhaqZgJ6018O854G9U2+ju3muaemO/c+DoW+GT22HVs4QlfJwxE7NHWiWkuXkl90huH7oPTUsrViuhGXO+QWW5ESm2nvoBVK2PFo5lX8Ehv9l9odW5rRbn6ybcDutecsTW9420ArhsgfobNAOQNsSZ2erwP40jtuIgpWTt2qs6UlJAGE7/eCEHlq7HX2HSVgDoneuqf3t+T0gJLs1kzqBFHDxoES9ln8BL669n5itrOf6C12JUkwRWsh/JWhvj5Fq8IrpuS3QEfvo1/s5teltBCNoTDJpSDNKaTUxbsDGpkLzkbczjFbwd0/tTaOGHPE5WoAFPINYjXrNhxM5Grq16HmPIRDRhxx1j5zgSA0p0JbVbTNjY7WslNYOS8WMJpVk05ubT2laElCFaXC4WzUgnqy5EYakfT9DG0qA2w8OsTTvxSVWLNiRQzq2b/8bfzGaYOBet8V1sWwkKTfOQkDiU3Asegy8fgIX/6BBJIUjIjOPn1YEZgi//Bac+0L3s49tUrVFnPdfuFojrLjj4V8pX6+hb4OhbkLaN/GR13NUt3QMH/lQ9CQdgywLlB1Z4SP9c8XuSmN37jMvI/dUUKauMnhG6sF/Veg3dg4bX7sS4tWoAeJJ3f3+dhNqhuVwJwO+yX5lDfPZEuDs4/BfiiK04+P3bCYXqup6nPa1zYNk6XLZJ/WlhiHBI6BRZbSEvQqholkcPdS0Pml6e33QyK6onEUx280rSYTSt1zlr3KvokV4HEgpECTdyJ4eK97iIh9H6oa46o10WoqtkvT+/D6WE1gSd5EaLorYcbhh4MVfKf8aIPA9BDnF9gCl0PD1q1QRgmV6KJnjwiq3kii4N2us44yGQDM08iaEH/oJNq66lRXbXO4U8OuUDvFTmehm5uYWabA9Dt4Twymihk2AH+Nn2//CrSR9z05iDKCt7Etv2k5szj8GDz0fb8K4yNTUD3VPOWyr7ukKqHqt2M+RNBsOtart6Eyu6F6yAahGkG+oCR4ox3aNaEaVGz0h1axqz0pL4orE1yqDDwOb4xq/glrkqxRJoAmGocdlhOOS3ygRyd8geBVmjoHJdtJBy+WBWxAzLttr4USjYxTXrBdtWEUJvSqx5rO4G4VL1XONO6r/HkpTw0W3wxT0qDWVbMO1COOa23a8lc3BwcPiaccRWHITQ6QzRCD/4lqn+frYX7F5+hGua5GcL/o8TCt/juMIP8RhhwrbBrYuvpSGYhiU7LrWABeWHUtxcyPWz/o5LswiYblbVjCck3djZsME1iSAefES36uktyl5BHhsZRy3ZnC6f61c03rR13t44jtaKRHBJjh/xMknEd1FP0Fox4nhmhYVGW2YYQ+iYmBQPS2DUlra4Ng59Dsm24NO/wJf3ERoqIFuPPlkhsDVJeb6PyevbcYfCcfdnSAu9rZoBVUEGFGngyYXscaAnwKJ74tRW9cOaony5evR1FroLDrpGtQtKH6KsJBpK4M2fg79OfZTGnQTz/h5387vGDOb4ZZtpt2zabZtEYZPpr+L3G/6shFU8gfPpHSrCNHjmrs8hknOe767F0g117Y++NTpalZIPZhxRqXtg1DH9P1bdFnUNtn+mBNCww1SNmBlSUa5wmxJNG16FLe/Dh39UqaX+RDuWPKDEc+SMz+WPqSjZ4b/r/xi/TULtsP7VDr+y8R1i02mDtNuULVV/D7WbYcAUOOTXqi7SweE7hCO24uDzFeD1DqS9fStaq0R2zN4TIcAi7lVrCqagCWgzE7vuycuqJtMSTuoWWhFUtOexsnoCraFEni06FUOzAIF3fSVNk7zoOf3zqAJYyVSe51y8+DldPLfL9W0Jte0ZtFQkAgJhS0YVbO1VS1SJPNYVhhm/pRGXtNA6rBoMaWMb3dG38gE+gm6doTvaSW0x+xVh6x5UGAJNaFYSceNjQhDw6hhmfKHVsRIHl74DxRFGnEXvwIFXQXtd/E10jxJdvcx6jCZOpFEzYMhBcNhvo5fnjFXCpLVapbfcib3udYjPw+IDxvJadSNb2oJMWHQrc7c9h0f2UQ8W9itxsbtiKzlP9XWs26KuSe4EZeoK6mb10iXq5i/V57HrnHUPJGUrn7H+4G+Eh45UVhdIsGwo/khF1466WQmryjXdkcJQG4SDqu/kDx7d9f4X/j1WPIf98OX9cNgN3/36n6YyePAINWkh3KY+Hx/+UU3KcFJr/Wfz+/D8ed2iu2E7bJqvCvDzp3ybI3NwiCKO2YkDwMSJ/8TQkzDTRddVEraA9V4su4eRp+nmza1Hk+xuZUTaVgyhhNKO5kEErfhpEdN28cCaC3ly45mY0k3A8hGwvGCDtTpApZkbM7GuN/f6Q1jAfVxMGvVsYBxmH2+rlFAfSOe2pT+n1UhE6DZ5+1dj+KzIW2sXYQxSaKRhoKA6KaHLW6szXTlqaxsZDd3psrpMN8snpxJyR18jS4OqLDdlA7y0e3sfX2pj7x5j7pDdq8u8BN4ZPI9TtjwZfRMOt6um1tmj43v7uBPhV9thzi8gOZ/+JWFR6UKXT7UGOvXBXtYRqhaqD6HVSWKomXPMrfw+V+OUkuf7FloASAjuRj/HnmQOV0KtU2iF2uGRY1QRvRVSES9QYjJ3oorc9WYPEY+VzygBFfmJkmGo3aRShVXriPm0SVOJ4/7Q1ot4DrX2Uzh/y7x5LbTVdNcMhtqgpRre+dW3O67/JqRUHRoio5udEdP3/kuimw7fG5zIVi8kJY5kyqQXeOPfz5Bw0hOkvqLTPtuCcTZax/1YzRAUvLX9SL6omM7k7PXsP2CFMgGXkJtQjUcPxBFcEolAyviiQwBFO4dRMKSMkGWgC5v6QBp+00tOQi1NwVReLj6ezQ3DSfU0M7fwffQ8g0oG8k9+zu/5Pak0IpDoWLg6aq2khJXVE3hs/Vm02QlsHjaCE8e9TWZud8PqEG7C0oVXBLARuDFx0YoRthnQ1trl79WJbsPQHe3Up3cXskkhCLh1PCF13M9zx/HbUdewRYzAi5+j5LtcvfMxxm5t7hJ4EqVp82vC1GeGqMl0I/Vu4aNZNhmNIQIeDV8wVnGFXG5OStUxSppiL6gdhq0fxxZou3xw/F2qEXVTadz3olc6mzJfNB98cZrn+hvVTT8xaxf7kerGsOTBbvf4/kRlXImqee++YuObKsUXJYCkimjtfwVMPW/39le5utt3LBI7rCIPQoufxu1vRCpvonIi70lG4XffJFVKKP4g9vylCUXvfjtj+m8k7Fc+ePHYueybHYuDwy5wxFYfVG9JobViP1pPeIpwpoU52gZXd+xDCAiZOoGwF7dmckLhu1S2ZZPqbsJrhJiau5JXik8gZLnpnsvXD690W6ooF7CxfiQPrz2X1nASApuC5FIq2wcQslxINJpCqTyy9ocY6YmEvB5CePiFvIdxrCWLGsoYxA38AQ8hHt9wJl9WTCdkKQPJxdYMandmc13OvWgC1jKJu/g1CIkyeRAcy5ucxVNIv05IujCInYHn7XRm7wi9JbValAxJZtK6Bop9Azl39F0EhKpFaSeJd5hH3cAs7m+4hcyGMLYGrT6N1DYbAYzd1II1NpmGNDdCSqQQDCn1k9pssmVoAmM3taJHaAJLgy2TCqlL+Az2z2BAVYDCHe0RtWM9itWFpmo75v4FfGnwwCEdLXT62TOoc5+2CWtfVHYSnTSXw8uXq/otUG7yp9zfe0pj6SPw1cPRwqTXBpwd0tSVCEMOgLHzdmO8u6CpNNbxHlSUYFdtjOLhSuj9tZoiGDMXNr4dPeFAc8O4k/u3/2P/Dx4/Uc3S7BSILh8cd+fuj/XboDexqTnJhn5jeNQjXu/OhG+5p6iDQw+cv+w+CPlNtIFLCUsNc4ISWj3xGCaz8pdy7dR72dJUyAtFJ+HWwyqD5PZzw/53MSq9GIFNf5vSCCRj0jcBMDFrA9m+WgAkGiUtQwhGiTfV1LpBdH+5SKGxTkziE3EEJRTSLFPY2ZrHl+UzuoQWQNh2s61hCJsaRhLCzd/4JUHhJSh8hIWHsHDzHnNZY03g1aqj4vZhlEBLko7XbzF4p5+J65rJrQlgYbJkShp/H3YOYRl94ULCw2JxICsH5VMyyMunszJIbu8WOoYNU9a1cMBXDWRv8pG5Oo0hO4KkNobJrQ13HbfzsX5YKtcmXs4OkUHIo7FjoI8VE1N7n8spbSUsBk1TM9p2W2h1XsD2jnRYB7YFjxwHJYuUuLNCyqH9sRPUDL94LLon1veqJ4nZMOeXMPNymHQWnPagKnTXdFUTte5V2PiWSgXuKflT488EdCep13aXwTPp9bPuTlQRxbTBav+6W/2bWahEVH/3f/F8GH2savA9/HA47zUYeeTuj/WbRggYe4KKjEaiuWHcPoxW/q+j6TD9othJBbvTLN7B4RvCiWz1waAx6VRu2cpwo3c/JVuCaWm8sOkkLOnirDEvR/VOzE6o41cz7sUf9vCrz/5Au9lb/Y7axq2FmJm3lPpAOrYsYWntcEpaBrIrkaY3BzGztJi6JNPUeWj5OUzNWYMdZx9By8Wa2rG0Z8RJhQFB3Pyj5irkNj9/8LyKL05kywhLpq9sZM3YFMryfaofIuAJ2mzSRmDFsRFwEWZj+hBEehuaaccIuS2+QVw4/jbKvDlo0sYtTe7cdhvHV30Vs25+qc2qAfshQ25ulDcS8Gi0Jhk0ZiaTXtdCXGqLVN1PyaJ+Ci0N6JmGTIQBk7ufb/0Y2mtjIxZWGFY8qeqeeuKPbRTehe6B0x6Gcb1EsFY8BW9dq27aAhURO/NJZTGxu7TVgNVj3IYXMkeomXIf3KzSdrnjVVoxfWjf+xt1TIefWY9UoisBJpym0qs/XarSabWbIHusEky7E9kZMBnOfrb/68dDSuWn9vld6r3LGq3sI0YcsXf73RVz/6Lq41qrlCjX3coW5Jhbv97j/q9x5B8h0AJrnlfpY8uE/X8EMy79tkfm4BCFI7b6wJcaZl3jcKbmL4vbHFpKsGyNYWk7+NWMe/nPurPxGfGjC0JAqqelF7ElEUj2T13KoaMWMjJ9G1JCQMJjW+Zgx5nNGLW1JtBq/WiZHiWoOuteTBtjUzMlzQVkeBvRhYXZIzynCwvL1mgP+xDxSl2ERggveVThFbFF2wJIazFZOyaZ5hQDqXULupBbozC0lXV2rOAK42J00w4mljXjDVhRMT8TnVMn/4Nqdzqyw6q/DfjZ8D8wseFChgSirRB84TAfLb2IoYFyDGEScgvWj0qiZdwc0pctjj8TUdrwz5n02ylWCBV56ExHCl3NMpx4evc6jTviCzczAPVb4u+34EDY1EtRuBWERf9QUZCetUx1W5TQMgMQaRHy7DlwXZHytOovix+AD25SPmFdCJhwuhJW/zpApeusoBKnyx+DC95QPSN7w5cGJ9wFb/1CpQptU4nTYYfA6LlqHU1Xomx37CT2NZ/8WRnddvWK3AjP/RDOfRmGHPj1HTcxC376FRR/2CE2x+y+2HRQAuuke+Dom1UKP22IY27r8J3E+cvug9bWIlbVTMBverFsdamk7H4IAS7dxtBsXLrFZROfZE3tWOw491sJ1LT3UUcg4djaRQxN2QGofS9tM7AsH329TWaul+BheYRGpmNbmgq1hW1EUwjX6gaMsnaVLmwqQMrYyJYlDT4rO5AnPjkZe2272j4Clx3isKoveCLtj3jitBECsAXUZrq7hZaUDKjwM31lI7cX3c01JU+QaHaLUK8V4IiWzzh8TQnZdSFS2qyoM/w0fRptuq9LaAGMaC/hgMaVvJZ9eMzxvXaAEf4deGUYw5YkBGymrG0mYdOXMOEHvV472muhvaH31yMxPMoHyZ2o/n/0XDVNP3KmYWRrn0hciVBwQPzXjrq57/qmyrXKR6gnq56NP+vOCsM/JsHdU+GLf6pf+n1hmfDRLXHqXiQ07YAFt6h+kJ0RKjus0q5vXNP3fgEm/kB5jnnTwJcJU85Rtg7ftqCQUl3XLR9HC61Own5Y8A1EmDQdRh2tugSMPPLbvy7/zfjSVdTVEVoO31GcyFYPpLTYuu0eykofJWy2MDnrPG5fcjVnj3mJyVnrECI2yGAGNZpLkpBScPjAhZS25FOQUt61npQwf/thmDJe6Eji1oLMLXyf27ddwS9b72FoahkSWCwOJjBoONpmqWwnemD7dMyJ6aD3+JK2bdxLahERwqnGn4PU6e7paHcMDEHAVrU6ekU7blNiTUrF0gy8MsicmqU81HwrHml31UjF9lvs3hfAuKJWsmuDGDaAn5+VPc282o85euoDCODS8lf5Vckj6Hb8qFJ5QiZmx40nNdzC42t/y8TWTYSFQYIdiLuN0SNCZWkCvb0Z/7pH6dsmskMZC13VLIV6ad9jBdWMvTOfhBG91AXlT1GiqmRht0u95lb+VPFmDkqpBN+YebDu5fju9Lapiu0Hz4heHmrttmeIWj+s6rj8DbDgT7BjMZz5ePzxgjq+2YvdRtW6DiES532qWqu2Mzyxr4FyjX/iFGUI21l4v/Ip5YP0wxe+PR+shu3w1OnQtFOl3OMVV4NKMzs4ODjsIxyx1YOiTX+kouLlrt5654x9kZeLT2BISllcodW4NZmSBfkITXWKllLgmmIhpnevI4HpeSt5Y+vcOEcUhGwvr245AUOEuHflZZwx6lXWDJjJmqSDMRM9uCtroCWM6BExs/IT4t+0BNjZXvQqP9KtYQ5MQCa7sDM8oEtEs4V7dT2ih4WCLXW06iCeD8s5ePBGLgy+w/4tq3F3pMY6jxTZe1ECwobpK5tYPjkVT8gmpzYY5SLvtUMMClQyr/YT3s08iF9sfQBXnIbRAHZiNvrQHVgdtWf3bLyVKS0bO3ynglHHN9HQkNgItIh6qu2DfGwbktA1kzGl2c2k9c24+vKJdflUpKpsKbz+k9h0oG2B7YcXL4Vfbu7dXuDsZ1W0ZPnjSqCNOxkO/U18Z/C3f6kESG83fFDRj3gml6PnYi17nH8MOIWHBp3O3JpP+NOWe/HZETV1Yb9qMF29sXdHbV96fP8xUM2CG3uZpai5ulv6VK1T1hXNO5UQnfJDJRArVkZvG25XQrR0CRTs3/s5f11IqQRgw/Zd1+lljf5GhuTg4PD9wBFbEYTDzVRUvIhtd//ST3L7OW/sCzGaxpZgB3RKFuQjLS2qJjq0UhAc6cKTqiIVmoBsXx25CdVUted0rNUdCfJofg4v+Iz9BywnZLn5sGIOn3MAWnUbht5OeGQKImhjFDcj/FZ3ZMmtgRY/QiBdAjvJIDQzW2Uhda0r9ykzDLB6txcISxcLd4zihuQHccfJiUZGtwSgS9VcesTWNpqTe8aYOq6jHWBOwzLezp5DlTuLIaGquEcPp+UwQFZyEJ+wNjyVQxqWxhh8CsBGYAkdV4/XajLdbBuSgK0LhIRh29oZVO6P20IoilAbvHKFSgWe8A9491fxRYY0Yefy3sWC4YZDfqkefSDLVyBXPIm2q5mItqWK1Hsy9CB+O/X/eFUvZErLRk6vej9aaHUiNBVd6k1sGR7lCr/kwWjR5/LBoderoviFd0fPmNQ9MOkMJQTXvQqvXtnRgsdSrXm+vF/ZUsSLEloh2PHFtyO2di5XBem7ElouHxx+wzczJgcHh+8FTpFABIFgOSCQEkwJ1e1ZrKkZgx2n1smWgsZtyYi4fgiShs0pPdbX8OiR6RrBuOb1XLzjUS7d8ii+ZW0UbRxGfmI55496jkvtf6NXBdDL/biX1yP8FqE5uVj53RESUROIqbECQBMYTX7C49PBEN1pxgjFaKe6+ywNN4XG8xwc97VO9/ioQ0rIqwkR9GjIONG2oHBR3pyKvbSZZ/2HYsYzdNXdBKadDprGJfybk8yXsXtJNwkk3g6hpSO74lolg3zYHWaoI4vbGFzux7D7Y7ghlShZ+jDyzWuQ8YQWKMGqu1QdU8UqaO9jNmEvfNXUxgMfPondW/ouEtuC/xyrWt9EHKvBtNgelHy1+EyeXPsb9m9eE//9FAJSBsZ7RdFer2wpuuq/hLKamHe3qic6+JfKXsHwgCdFzVAccgAc92dVH/bGVUqUdv7aCPtVhKtmY/xaNMMDSbm7Pu+vA39971E8zQUI1WngzKe+3uJ4BweH7x2O2IrA487BsgIIAWHLy21LriXZ3RZl5dCJLiShkBG36BxbqGL1CKQUVLd3u4mPbdnAwfULadaTeajgQt5IP54Xa0/mqgV/Zv62w5iVt5SchGolbGyJsbUZAhYysTt9pdUGIdTjV7qU5AeruCTwFjLF1WttjDk6FfTeJYgpXWxOzWLTkCRsBItSJ/NizlFs8g7qdRshJfXpbkxd62mSgGVrLGicwITGYp4OH87c4G2EZHcBvASYdhHJU36CcCXyDOfxhOdcmozYzt/x6sY6r3bIpV7RTZv8qkBMREv2eMQ/D6t3ceZJhrUvwV9GwqPHw11j4NUfq8hOP6gMhjlr1RaqbR0rYgJA73R4gpWvVJG3Dkob6/jPml+TarXhlmZcAYzQVZqwsVTZRPRscWOZ8PBRsPr57lmWmgHeVBh/qnquu1RR+0++gtMeUi17zn9NTQyoXh+/bswKQuP2iALBCDQDxp3Yj/P+Ghg4TQnEnrh8yu7hpgb4yZKv3/bBwcHhe4cjtiJobl7TVUn0Zfk0Mrz1DEwqj2voLSwYvNBPHDcEhA4pg1WBtLRVpMzQTP485w9MyV4FSIQtWZY4nVcHnERQ8xLW3IR1N7bQeX3rXK79+Ba8kZEwIXDXt5Fbt6NrkQYYS2sg3K0oXO1+fvfl3aTIPvrmSYm+vSUiKhZ7gh49wITsjawcPJADZz7JeRNu59cjr+Xo6Q+zOmV4zBY2UJfhBiFYN1rN0AsJgzbNS7VMpcgexEvum3nWfTOLPFcxR1/D78MXAaq3d0tqIRz3ZzTNg2fkw7wvjiOoJXDdqF/RLtx0yp++2lvbQGZDGGFLPCE7boQtUpTsgY2p8oha+ogqgA+2qCLxtS/Dy5fBM2fDg4fDJ3eqdj1xeLq8DlNKXs85rNeoXVw6Ww751ezJYaUf7Nq0In2oaoT9zq/gnV/C38bBqohG5ZvehZbK6MJ8O6yWberRNiZ9iLJoyBrZvcyTHF9sgZp9eP6rKqrmSlCP9KFwwZv96hX5tZCQAYf8OjriZvggdTDs98PvfvNqBweH/1qcmq0I6us/xZIq81bRlsdpI99A16zY7+AgJC7SSF0XZkROA8W56erGKQBhoBnjaCmbhW2uxfSnkDF6AVJq3LH0KmraswDB+pQxEOmJFYXAb/kobRkYsURygvYqx02ZT8D0IoTki/JpvFFyHG2bmzDHpmGsbcSoaOP38mLa8CLqAsgsb/QxpETb2YZe4e9hDtodL3JrQQpTdjAhawM38ye2e/PJDDdzYfkrTG4p4qukiYxqKcVNGF1KTA0sQ7BpeCKaJcmuMTlpyj1Ma1rHoXVLGNm4jfHadtzC6rKPuM54kd91iK2w5sZ10dtqnFKycKcfSyaAgM/T92OHL5/h7aXoHZLLRiCJnoHo19x47BBDS9upyvYQNDREr21v1Jnu9i+NIXPUrMGeBe2mH9a/2v28cg0s/DuMPk55VY08umta/45AiJRALWdWvkOxdzBj/NsJCRc+O7Dr8QhNNZ/2pZNkthPaVe1Rp7dXpLHoG1cpD6yskVC9IX5dVahNRa3GntD3/jOGqf1UrYs2cnUlwKwrYdB0+Pk61Z5HaGrdb1vQzLlWzRpd/G+VQh13Iky76NsTgA4ODt8LHLEVQSCws6vevCCljNHpW2Lrz4OQ/JpG0scqRTKiupHslnbK05II54Dn+HYaio+iafs4mrbPBmGRPmoBH5fNpro9m7Dd0bA5Xu1Ip3lX59OI269PtDMv+20MzcSydR5bfxaraiZgo6E1hdCLm9Er/UgpaOswO3CtaSC0fw4kdrzNtkTUBzE2NPUQWgJdmFhSMCR5J4cOXsiB+YtpE0lsZQT7N63hhdXX4pLdnk0mGvcPPJ0D7C9xJTVTleVF00wGl/tZbB/I77Y+wITWzSTaAWQcTZkgglxkqOiJB4l47iwI+cE2cSXNQht6MZbQ+WH5mxQEKrpmL+odAstC0KJ5Ozo4Sso92Yzwl+EJSfZf1kjpQC8VOR4GVAWj+ihGn3XvxMy4BNUnsK0fNVqdrXrWvKD6/xXOgbOeBk3naFnJzUvOw22H8coQAeFCSMlrOUdwUt1CNCu+tQUAvgzVmgZg2KG4NLH74TkzAM+dpz5/STlKGIV7CC53ohJS/eGsp+GJk6GlQu3TDMGMy2BsR6pQiN6L878thh+uHv+N+BuUrUai0/vPweG/CUdsReDzFVAXFmQYkqm5ywnZblx6tCmkZ4sg8UsdEXGrTvWHSPXX48+2aBho4Ul5kq3v3AbCIjF3PULAV5X7dQut3uj1V7/kB6NexaWbSAl3LL2KyrYcrA5nea05jGiOTbBpYYl7URWhQ/LArWOsqseojn8zt6RBlq+Ga6beT4qnFdPWeXDdedhjBI+tux63jL4OLmzOrXiLMQe9RaG5ndfW/Zi0tgAuU/L+oEyOr/2cxA5PrN5Oa6jocIKXlio27+Akv5+7hlxIGDil5kMS7NhC8nbdx11DLqBN95EdquPqHU91veYJ24zYvhd9AokWYl3/X7MB3Ang70MQ9STcBts+g6J3YMzxHLPgp2hWW9c+vTKMjWA0rWinPQDv/kY5YffE8MKJ93RfzKyRiOkXqZl//XXB76Qz0tVYqiJuQu+OTAldFcL31uQ62ALzr4fVLyhBWXgwnPGkihIFG2HgdEj+lgrg/5dpKFENzsuXAUJZU5z6AOSO+7ZH5uDg0A+cmq0IBg48mzzNxebX3CxqsakO2oTt6CLf4AgZ995muyX+mSrM4EqsI6wHkEaQ3KlPA6pma3fI9NYxa8BXdMZVWkNJSAnLqydS057ZJbQUvcdohBCIsOxaq7fbslsLkeWtY4dWwGI5iye2nsHGiuGMaywmyYwvXJJsP9Oa1zOuoQRvq8Blqr0f2LiiS2j1hiUFSQTwax4eyTsp6rUhgQr+VHw3HiuIX8Q3zdSkzfLkMaSGGvlFyWMxYvBrIezvu5dhr9u1qaL6+2ah1xbFKe6XjK1ZCqOPV21xeiJ0mHZhbJPlY/4PMgp3fzxd2KpgfdAMZeOg6Sric+kH8c1KpYTHT4GVT6vUqbRg60fwr1nw+Anw6V9Uj0WHfYsZgkeOgbIlqsDfCkHVGjVLNbI2MNCkLDze+bWqzYv3WXJwcPhWcCJbESQkFJLXfCx5n76D/1OdzYNstDNs5KCI6Iwb6s61yXjEQGg2wgLphuA4m8CUjhSXrfOpC4K5W7jYCHLn4p9T0jyImHl0PdKGkWjC5rwxzzI0uYRnN53O2toxvL3tKAKWN+76UemuSAyBTFCC0RyahLsmEEdx2Zi6wcr9DmGlcThuQsgRgiOHv8Pk2mWEDYERR8tIBF47SLvui9rlwGA1FqIr5Rd7NDWbs114OGPSXSxLGcenGdP5Z9HNJHXYIZxX+SbH1i1kQ2IhYaHjiqgJsoE6M5mVy7Ipyj+Ka3kCXy+thPY1ca9xf1j3Cn1FoITLq+qkrDizGjv9q2I2EjD7GhUN68sYtS/0Dl+wwkM7nvfxlbDhDeW7Fe88pK2sM/5zLPxsuUpROuwbNs9XEcWeNXpWWKWqZ14GNZvgkaPVhI1wu2oR9dGtyqjXSTk6OHzrOJGtDqS0Wbf+Oqo974AFiUHIbIT1b6XT1hZ9mZqGGHw6cSBlE3y0HGFT/2OThsss0MAyNT7aeQDLvZJtbQO4fuHv2dpYwKCmWgqaq0BKDMtE620WF+ASIU4RnzP3iwpuLX2Zv3nuYWfjoA6hFXeSP1IDDBARRTxSE4THp3WeINKlId0aHb16uh4y00P7AQPA0EEIQsJDWLhZII6hOSudpfulEadbEJbQWJoygc/T9osaU3a4ETtOTZoEgi5BbbpKp1a501maMh43IaZnvkfpQFfUbTw73MDBjctxSdWoOohBCz7qtDTOL7gZO81DexlcJa/a3URa1Jh2Z9s9L+/exVEKDlS1Ur19LnQvfHEfvHmt6ovYGbXY7zwYc7xKMxq+OCMU6rXB+ysz0p5YIcifqkRWX0JLSjWjcVfnYYZg2WN9r+OwezSWxhfh4Xblhg/w2o9VlKtTdIfbVDr6wz98Q4N0cHDoCyey1UF19TtUV7+HnRim/FiNv09yUW7pYFvc7Iv+RelONAkYLtZa+fg0P4FPvciPBUn5bWhDJZ81H8BlEx9jaMoO/EUpjH26AV9QfVk2uxO5dea5FKcNihvV0gmRKtv42D+dxUzkh/qHnKgvZKC7jjNCf4g7dgkYwua+d+/kq9yxrM4aTl5bPaFML69mnaRa/YQscOmEDskjqbSKH3vvYWTaVq4vuZXmnTqeL2oYkNkEQxPYnjIYhCAovLzHXKZ7l1CV4SGvLtgVQas3kvnPqCMJ6B58lp9HB8xjULAGIW0Or1+MW2/HCMfWPrnDEq3jF/rQYCU3bPkXO4cbjGATNdluPFX5FAbKYySD3yO4I/lcNuWM4dP06ZiGC0wbrS7I+6umUuVJI4/G3XrPP8g8kDczD+JPW+4l2dp1ZKhNeNCEjO/U3nWGeyj7it+H4iOjZw52ohkq6vXhzWD6kaufpf3DW3hp3stMyRvEpNMeUi15ShergvcNr0PR2yoSMvRgmPd3SMiE+w9Stg6dx3AlwOyrlSVCTxq2K9EUbIUJp6jt/f1o2m0F1Vgd9h35U5Tpak/B5U5SMz5DbVC+gpjPnh2G9a+rWj8HB4dvFUdsdVBe8QK23Q5BuGeqi7Kwhq2BTw/h7hGkMXwWqYXNNBan4q/u9uxp2ZmEWWHw63P+gdcXxAhIcp9sRAt0SwefP8T/LXyQ84+5nvZ4DttotJBArUwHYJM5iC/scdzqeoRxYjvr5dCotVVaS3LPe3+loLWGgtYaTtvyKQAB3cW2tHxWHTkd6dNVax9bYgx00UAe9684jOYGDyNkKfe5/kFBQzWyEWo9GVwx7g+sSBlLG0m4RIjbx57P+K31TG7ZyC3DrmBl6lh0LFx2iBOqP+HvQy/sGktmqJEXV13NIKrpaWspgMxGs+v/f7LzOZZkZNGWDl/6ZvLvqT/jno23cWz9wi7B1eR18+nUfP6tXYCMbLptaNiZHqwcH58HpnB68ONdv9EdrEoaxWVjb+KgxuX9kkcSOHrqv3ly3Q3khmpJsIPYCAKam3J3FiOsBkjIgqYdu9xXr7zz6/jLbTPC4R1EqA1XOAgf3cpJo6/liMwU/j1+NHrnrL9JP1Az1pCqDquTKz5RBfUb31QCa9aPlT1FTxbdC+/9jq6b91cPwuBZqqk2u3C9dyUoAeCw7yg4AAZMVmnazgbnult5mI05oeOz0UvMVXO+4h0cvgs4f4kdyI6aoHohKA3rXcm4gIRQh/dW97rQPjgRa4uGHuHlJAAXYVqLfSRODuBbpsWdmq9Jm4PK1/DekNj+cBY6VsTb4sfLm/YsAiGDsIyWLhKwcrwcsG4ZOXFMNF12iPMTniFR/ydJspWdDOJxcTHrxGQesn6MGBIkqa6K5z03k05Lt+1FsJLnV1/LATOfYLprMUG8lGiFPDfy3O4LIISqkhLwQt4xXVG6E6s/5B9Ft6NLu9ccdc9o1/Q1tWwfmsA7BbloQvK7kddwh3kJJ9V8hJA2cmANlulCd1mYPfdqaNg5Xo7Y9BUIaNETMIVOutnSy9EV9w4+h4Dm4rO0aWj9kFsWGkc2LOHIaQ9xXsXrHFO7kGpPJo/kn8LQYBX/aH4bkvL2TmztBm5pMrfmY3418ucsqGvhucp6zhkQUZujxbn6vnQ47Lfq0RutVfBez76AEkq/iN9MOxKhq1TolB/2+zwc+oEQcN4r8PldqhOAbcLE05RBq+5Sj8I5sPWTaL8zwwNTzv72xu3g4NCFI7Y6yMk9icq6RbTZAmkaYJjKANSGN+uSaa48gaVV0zA7ZieObtnEIXyOTo/KcVsQqFNF7FoLiDgZJ7cVJi3Ym8N77C/UMAbvyAOIThMowWMWJqOvim+21HqiRc7+ZWio5oCDKOM6buc2+UeK9VHILB+HZazF3RaO8RMzpMk5VW8yetAntMkE3I0u/lh3N38a/mPMnr+WO4RWeriJfxTd3keaLRYJ3DTsp1R7MhlTuYEvtp5NccIQalwZ/HbkNdR6Mrm/9XI8JZm4R4cxcfXYgWSir4jywT5+nHU9nyXvDwiG+Hfy4IY/MK5ta9zjFicMRgqdgK5z6bibeWTd75GAxw6hY8e8CwY2gwKVtBkJ3D/4LO4ffFbXawOogBMvw75z+DdaBBkSBkjJEVUfMnbNW+DTYNKZsN+5sP1z5XQfalOu95POVE2yd8Vnf+v9teR8aCqLn+oEZQNx0r3KNLW/SKkaU9cVQ8441VKnv8anUipjVn89DJgCnqT+H/e/DZcXDrtePeJx0n1qxmJ7nSqc1w11PQ/tQ1g7ODh8YzhiqxM9jVYLZNFQzJR6zl7o4oQvTVyhMLUJydw/wYeZb6AJG0tqNLji1LkAltDxZqpQf2i4RHpA9Lg3hXWDdb1O2Y83301E/ytVWbdu27g3NbIqawRBzWB13gg8VohJtVvQXBZth9kq8xOBixCn8hx38HsQguyMNlxtsUXZPjvEyNIiHm48j0v4lP80/p5mI4Hbhl2B2cvH5qi6L7BiEoe9I4HFyRN5LfswZjWv4eXkudx14JV4rSBtencUZfy2BgY1bKMz3jgwUEVmuJGixKFIITkl+XlKUt2cz70EhOQLMYctCQUcMe1hXlv5M2Y2r4059oymtWxOGIKpufg4YyZTZ73A8bWfMLFlExdUz48RFH7NzfKUWE+jBE3jlPEHsmHLlwzSPP2o/RIqQrSnswcjxvNM3lzu2PxXTqt6v9tqo3I1fPYXaG9Q9gygarlWPAkXvqVuwrYNtZtU5CPyc1i9EUoW9n5Qd4IScksfjn1Nc8GwQ7tNV6MG26gEUWpBdBF+ez08doKqD+uMEOdNgvNe3rWje1MZPHW68p/SDCUwjvoj7H9F39vtCe31EGxW448XMfwukDIArloBxR+oa5I3QaUfv23HfgcHB8ARWwCEw81s3nANZWFB2+o0/r6+jYFN3cXA2e2NXL3iJZbmjyMkO2bSeXKoc2eQHazF6MoVCjRcbPKNwhtcT+qoFkKFkrrKTFaljcJnBpleuZ52n04q5SAL43wZ9uPLUagZibYArT6E3+XjvGNvwmuFVLG8bfH7rQ+RzjZ6toTWgEGUdj1fljUJuzT2BtIqvbzXNovENptjXYtIFEF8oSDJVhvBnrPaOtKKuow/ky6efJRAQLhociWxevFpFPsKOGbag1hCp82IrmXLaPHjkyGeXv1LbKExuWUTYc1AkzZvDptGSv52tjEMH34u4z7WyYk0izSkhDMn3knRonkxPlw/LX2GV3KPpFVoSKHT6Erh5ZwjSQs1q7Fqrq6egbbuRgoXhzZ8xTtZc7CEjil0fHaIwzMyODIjmZ98Vc+dcc++g6zRqsA5c4SK4jSWwm56g0nDS9iyMIXO8pSxvJF9KO+suDI6khj2q0ck4XbVQmjNC5A6EF66DAINSqBoLiWgDK8SUfFmvXVSWwxoalZjz+iW7lJpykhCbapJd9E7ShDpLjj2/2DKOer1t65TlgWRvRnLV6iJAMf9Oc4FkKr4Xtrw0qVQuzk6bfbBHyB3Agyd3fs57A7t9epabf9U1b65k+HEu+PXuX0X0HTVv7Ivij9U16muGNKGwBE3wpi538jwHBy+zzhiC6ipeZdN7fDFSi+/X7wOw46t4alOyEC3bLoCN0LwWt48DqpbxJi2zRjSQhgFiITDeadY48kSwbyC+fiP9PJh6SEIS6IhkQJOqHqbae2rcPssPk48pPeB9eHD1UXH67am0a51e3DdNOEK/ur6HR6ib542UEpB1/MVyWP4Uo5hFhtI6Mh5+qWLrXIAC+z9uN54Cl9HUbSG5Jbiu/n56N/g19WxhFQpt6RQCzWu1KiWPt3HFF3bgxJa6xOG80HaDK4ufxYBPDHgRMIi/sfxncw5nFv5NjNa1ncJN2+HKDh1y5dc4LuFL9InY6OTTj3jWc0XHKzqyjQXCzL259i66IhNQbCSd5ZfyS3DruDL1Mmkh5v4cemznFv5plpB605XasIgwWzjzKr5zGxay4u5R9FsJHJM41JmJ55Kw/Za3nUP4xbNRXIPvdmueUmwQ3DZh/DejbDyqd7TcLtAmAF0BBLJR2nTmNayHtlfM4pwG/LVK9V+IpdbwfiRqniYfiXa4hUiCgHjos1pefly2Py+OoYVhDDw1rWqsHvoHOXbZffwR7OCsOqZWLG1c5lqM+RvVGLLDBAz+y7cDovv33di6+kzoWJlR/sllIh98SK45H3Im7hvjvFNsvl9dQ07I541G+Cli+HEf6oaMAcHh68NR2wB4ZY6XiyGn31o4eq4j9gIXhs2m9eGz6Hd8DK+bhtmjxRZWHPzUfahrMw4lItbvGgITCSNWoC0tkaKlo1lc3oBYd0VdaXfyJ3LpTseZWLdWrZkDKU0OCT+wPYiA9COj3fbj+WYxPl4I2aQhXHzEmdGHENw/py/csHOVzm3+k18rX5esA7mYWsuFjp+1W4aT0dvwpNrPiI71MBfh1zAyuQxtOtefrzjaX6x43HCwkDD7nLxAkFQc/PEgHm8mz+D4Y2VgOCL1CmcWPMxvyx5pOsUK7zZsbVgHTyTdzw/qHwPD7EtiVzS4uKdr7Ig40AAqmQujaRFXEJJvSu163lklG2Ev5RH1/0u/gWMFAERDvqFgZ38suTR7tdWh/DU/x/WzBe5YPxtPLPmlwgkLttCCsE7mQdxWttKWPuySuXtRj1bPHQkujT5zfZH+DxtKu6eYqUP9k1CyUZFt1yqBkyiIipnPRVdq9Va0y20Ign74fO/wZDZ0VGpSHpG1wLN8PhJythzV7RW7ca5xKG5HBbcqqwz/A3ECDozpPzOTvnX3h3n2+C933cLrU7CfvjgRkdsOTh8zThiC/C/vJXKAYLhld3L7p5yGh8P2o9gR9uSxXnj0JBomNg9LluTBq8khDix3cVm3eRHK5/jsLIV3DntbMJa/Bqm7UlDyNy/kZ8P/jeakCyrmszLxfNoCycyR1vNjcbjDNfKaZYJ/Nucx3+sY5BoWGgdM/L6rh3RpM3LTafQmpjGCbxGkmyhVBTwJBexVYzsXlEITMPFwwWn81TKsSQvqaCFRKQmMEencOvAa7hV+znTmtfz5813Mb5tC7ObVrLf2o1MnvUiR9V9wbU7nsBnB7siYCaCne5cns87hvlZB7E2eRRIiy98GiC5tuRxflL6dNQZHF6/mAXp+9NuxM54W5Y6gVJvHiMCZXHPdUCwu0WMFDoB2R3h06RkVpPqu2hqoMcJylhofJk6CY8dYmrL+l6vrI1gVfJogpqb/Zo34NEEVK8n0QpyZN0XfJgxiykHvMSxtQtJM1tYkjKB06o+wK6pw3znt7h7Exd7gAubwxqX7rP9xUX3qJlvMeO2VU3VGY+riNbg/ZX4iqStWtkTxIviNZWq2q3Bs1RxfKSgETqMPDp6/fWv9W72GonhhdF7kRJrr4f753REz3pJ8UoLGrbt/r7DARXVXPui8seafolK+X2TNVV1xfGXN5WBZfZtauvg4LBXOH9dwGtb3meCX2BpoFlQ601hweBpKiLVgdQ0kBZJYT/NRlLXl6SQNiPbihnbspE6WyMlIDl450rctqnSZ/Gc1IUgdVYLBw5dgruj0fVBA79kXGYRr35xGg+6/oq3o/1MmmjnOvfzHKktoTKYzeHaCo4I/YVysvs+KVuSnhzkE+so3jZOIsFqp1339f7lLgSBpEQgEZAEp2ZCmhs6fK2WpozjpCn38N6yy8gL1XHpuD/SYiTxi5L/xPRBNJDkBut42nMMFckDO/avRKcuLX5U9hyJPZpLn1r1Af8edAYlYgABPbYl0cfp0xleURYTnQkKgwUZPSw0Oq65z/JzetX7DPPvxOx4G3puvylhCGdM+iutegInVS9gTNs2kuwev/5R8Ry/5qHMk8ufh15MlSeL27fcy4rkMbyRORvDtsgKN9DgSuH9zAMICRfH1XxKmTeH4Qe9S1Bzc3L1B9y98f8iavy+JiLqzfYK2+o9+uRvhJR8yBwe//WMYfG3FYaKaoGqf3qow8g17FceXe4kOOa26G3aqlUbmr4wvJA8AKZf3Pd6ffHVQxBq7buWTvdAYR+p/3hYYXj0eFVv1jkxYttnMOJIGHaI8iXL3697fSmhbKnqhZiUqzoE7Mp2oz+kDIDGONYkCZmO0HJw+Jr53v+FvVj0IvMnWNx9v0m7B6TU2JaSj9s2o8QWqJmGLjviRiklx1XPZ7C/rKsA29R01g/KYFpJFYftXMHy3NEEejT1DQsXI7K2dAktAEOzSXG38JMBz+Ctjb5RGlIyRW4jmLgdX1DyR9ej/CR8NSEiphp21HcpN3mLCzTJScs+YF1eCRsThzKhrZj3Bk9FJgZpJo0lzCIofMrHqlMQBi1sQxCemAEZnmhhJjSCmpu7Cs7DJU0+TZvGJTtfYmxb/F/5JjrZ60upyB3QJdgAkkw/7jipNK8M8fbyH/Hvgafz7IC57PDld58X8OjAUzi/4nXcEULFQtBsJPPvQWdG70xKpjWv46LyVzit+gMA2hN0klujb/4SOGfin6lyZyKFxntZB3Jr8d0xY5OoOGKiHeDY2s+Y2bSGA2c+xU9H/xrNNrE66rvcVpDJLUX8pOxZxrVt5bbCS3lqwAld4vG1nMP5U/E9ZJrNca9ZF7obEB3ptD1wpN8ToaUZSqRKqf4VAjKGQ/W6+OsbbmW70FNs2baasVeyMHbWpdDUjMY516nnWSPVDLqVTyshkj8VJp8JnuTofRYcqGZO9pzBaXiV5QNSRbSmXwzelOixCNH/6FHJwm7T0Hhohtr/zMv7t79O1r+mrlXk+E0/bHwDit9TP0SGzlGpWIBnzlZjsU31WXjnV3Dh29BpWrunHHo9vPXz6AkUrgQ4+Fd7t18HB4dd8r0WWzXtNdy6+FZ+/JHO9jyTm8/WOPONUYyrbiAsYtN/AptEEaDV8hE03OQHK6KEFoCBRUVqMo2+Rg4sX8uCQZtYkTOKgO4C1Be/EDYLy2cxNPVlAExbZ0tjISCZmVwMtfHHW53tYWhZgMO1lUwSW1knC5FAGJ3J2hZStHaqE7I40UhiUnAw72SfQhGteLZ/zqixX5LlXUFY6oRxcb54hDut66m0B9HsSlY3WZ+OeVguI/ylaG0WRUnRN9Kw5uK9rNn4dS+6tHlqwAkM85dxYfmrMU2nbQSlMpuz1r7KcdoSLh5/C5Zm0Gwk0mIk4gk3xb2+VZ5Myry5EQsFmrRodKXwkzE3cE/R7XikEhPtmpdj9vs3te4es+CQrEoezbWjf838zNn8ZdNfEOHYyMiqpNE0GKldYrPGnckdhRfxm20P45Ld9WGRt2oXNqnhFu5f/0dWJI/hrZxDKUpU9gkh3cPylHHkh+vxInknc07XzM2j6hbx49JnCGkuTARGXyLKNiF1iEq37eaMxd2hu35NwFF/guFHwKZ3VGRMWqqRca8b22pmZSdF76qG2A3b1Kw9M9BD9AnIHAnnPAfpETWKCRlw4E/7HmjBLBh6kPIO6xQsrgSVvjzvlVgxVbZUzXSsWKXE3bSLYPLZqhm4GVCu6wWzYrfLHAHbPo9zzYVqrD3meCVMdrex86b5qldhPDojdts/VcX9mkudZ2dtlRVSszqfPx9+umT3jtuTKWer81/wJwg0qSjiwb+MtcvYuRxWP6eOPf5Ude0dCwkHh73iey225m+fjyUtChtNfneWjt8reOqESn7zThI5tFBOOpE+6DqSsYlfUO0/FoBB/p1xZ99JAXVJPtL8QX6/5DFWZo9kwaD9+KBghnodne3Nakbg+rpR/GvVxdhS2Tno0uZh4y/M0jZG7xNo93ak4oTkRc/NFNmDeNuaSanM5i73vwHwW27uz7mKswvHd8SAPMwemUSt9CJ0iYGF0VHs/lv7FpqLx/LzMb/FFjroqo11qTePeTUfcfWOp/jJ2N91iRHNDtOmJ6AJm5PF8xzGh7iHB1mens644kYSgzZSgh83fwhfgF94eTXnCFYlT2Za01qWpY7H0lzcWng5txbfTUJEKrFd83D2xDtZnDY56rzPK3+N67c9iM8OYqOx05PDsMBOBHDB+Fsp9+USg9AwO8Y8P+sgKj3ZvLIy9obeaiREWWOkmC2UePORMnaOX1C4+DRtKhPbiskL1XFkw5cc2rCEn5U9w98LzuMfQ84DVPTz1f1+xXGr7sYjwwTxcHXJE1xX8miXKN9lrEra0LgHdUG7Sfc5Sph/PaQNhuP/ButehjUv9j1rcvBMyBqlolKL/gk167q9skLxCtmlSmEl5ezeIC0Tit5S6bQhs6GlXKXy9jsXpp4fKwJqN8Nj87pFWagNFv8bvrxPRdZsS5m9TjgVTrw3evv9r1TnE474m9ZcqlXOZR/u3rgjScpRUTG7D+Ec9qtxCS22iB0JjSXKjyx96J6PA2D6RTDtQpUudSXG+oZ9codyqjeD6v1c9SxMPB3m3e0ILgeHveA76tD3zbBgxwIGGCZ/PV7Q1lESEfa1MmRHBUe4NjNYa0TDRsMmiQCTRCmfenMJ+ZQ1fEDzYMaJgNnoiI57uCU0xtSXUJWQEfFlJcn21dEQSOGeFZfRbiYQsHwELC9tdgIXh35Fk+w2dTQ1KM33ktkYRkqoC3ppN10MFjVUkhllMlrmyeMfhUcT1AXhjsdh1vsIPfYW7xF+Xh88WwmtCAK6l9ezD+eghmX8oOq9jiFLDGmjY3MNd3AMb5NMCx4RoiFDZ/G0TMKGSmNeGPoVL9sHI6UgkJ3MhsRC6tzpTGkpAil5ZsAJXDXmeooShtCq+1iaPI5Lxt3MV6nR0+nn1XzEH7f8k3SzBa8dIsEOdAktgMmtm+KmJCMJaW7WJo1kc0JhjIDar3lD1/t3RelzrPriVO7e+H+4ia018sgw49q28PiAE9nmHdDRmsnGZwe5ZsfjDGvv8C4TsD1rCsOEn6BwkWK2RAmtjlX2mjWJI/jzkIu5e/A5lHjiCM7dpkMMPfdD5cfVl9AyvMpr6o6h8OqPoHptt9DqC01TjbB7HYJUr4c6okBmEB6dC6/8CFY8AVs/hrqtKhoz45LYonyAhf+Ire+yw0rA2qY6z3C7mh26/fPo9bJGwtnPQlqBEnS6W9VV/fCFXZ9bX0y7MMpKpFfqt0LdlvivCdG/SQL9QQiVqu0ptBpKlCFu2K+uV+e1WvMilO5lVM3B4XvO9zayZUubr6q+AjTIQX0BSTBsN1Vp7Rzz0QckzgnQ7vLSIj28J8ewccCnBOsPxg5nADqbkkZyYMPimH0LJNuTcmkhnZ1J2bwy4hDKknOj1lhfP4Y3t/rj+iSF0Lkg9EvO1D/mSO9SagZptKbC+FVtvF4+hh2t6VhSIzvJzxvZ+3Nu2ifQ8WP4jexDCEWKJynR/GF6drkBCEuDWnd8J3wNm6Du4dyKN3g+71hc0uSina/w7sCZjBXrovy7dCEJ6wYvj5qOr9jF4tA40CA8IQ0M9YW+LWEQHy69hKfy5vLQ4DN4M/tQ3sw+FID9mtbxyLrfU+dO4x8F57IieSwtRiK/2P6fqOiXunLdXF72Ak/kzyMkXH3+6jakSalvAIMDFaxOHk2S1c7E1s0k2gFuLr6XV3KO4NfbH+6zzZAE8kO1XLXjyZh3TJc2x9V+xj8LzgEJw7a9S0prGWdUvctOTw6ajF8QH8/sdVdI4L6BZ/CXwosJam6Orf2M88tfJyx0XPtitqMZjDupI3qdAMzv2T+xH0ipiuo7sW1o3K5SghWr4I1rwF+n1htzAgyaoRzxO2uM7LB6vHIF/HJL/PZDlWt6L+qPJOxXacXCOdHL04fAhNOV6Bk6W0Wlij9Q7vidUTkplaO84etfC6SskXDK/fDaT9TzUBu9xzd7WZ6YrSYdfJ0Uv0/c399hP2x8Cwpie7k6ODj0j++t2Hpl8yvddzshGFM1i/13nIDHTGDrmAD6lnc4/o03CXo8rE0bSv3QBlYUltIeHEins2lA9/Fm7nEcV/1el7mnJTQkgkBagC/zBqHZFgk0gszpEHTqy7QlmMAnO2arFF2PO66JwUo5iiKzgBvMy7EGpHNF+X0s3FyHbXYLqbLWFGa6l/LG7KO5fukTgKqViopUBW0+3zmTgQkVeIxoMdFqJuJrMRHZFrJzG2mD0HDbJgOCtdS5UkFKZjauosGVQpNI5Vl+yFzeJJtuywVDmGzLzObvWb8Cq+OGYXR/cdtorE0awZU7n+ehwWd0LT+n/HXu2Px3dGkxIFzHgxv+2HW72ZVh54BwHVfteIrbCy/F7qNNUEi42OwdxBUH3oghLSw0csINPLTu9+zwDeCi8lfx2n3PdusciVfGFp9LBFbXbEuTD0Uu8yf/k3PL32B8y+YYF/9OLLQ9mpnYZiSQHWpAlzb3brwtRpDuHbIjqrGPcSXAAT/tnlW36B7lZN5bam3jW7BlQawbfucYy5eruque5E/pn+ASQhXdR7L5A3j+PDV70A7DhteU8DR8qo7r0N+qmXuf3AEtFcpfbOIZMPfOXc8WHH8yjDpWmbM2bFdF77bZd0E+qP0KA05/9OtP4xne+MfQ9H0zG9LB4XvMPhFbQohjgX+gVMhDUsrbe7zuAR4HpgF1wJlSyu374th7gmmb3PLlLR13UMHImunM3n4qLlt9+WoykfqMsQwp/QRvMMiMqiIm1m9mZUUmf5wWfSMq8w3i4YILGOCvZGLTWgoCpXgIIxGUDx3KxjGTqfVlIcrbMUrbESjBlR+oYmTzVr7MnNHl5dWN+sLz40UL2FhbQvxr5FVcod+JL6Kew5AWYxuLeN9zepduzAvVdXiMd3xpGoLPdh7I9JxVTHAXM6q8iZQWixafwU3Bs1gtB+DLCJG/YyuHfjmf9OZ6/B4f2YNsNJdFfrCG2XVf8VXGFJagjFwXyGP4hCO4gZsYjkp7hHCxQwztuInFfmEfVbeI0W3bSbL8vLTyam4rvIx1icM4oeZzfjL2BoLCzWU7X+DAplVdwsbeRXWTBD5Jmx6TBo1Et03Gtm7hzsJLumYFatLioKqlCCSnVb1PguWPm0/vPPqubnECyYGNyxjXWsyLuUfzafp0EIKbRqg6sRNrPyHdbI7aj4nGW1lzyAg1Mad55S6OED2e60oe45odT7IqaRTGvkotdSL0/kWG+rczukadM7a7KfKG1+G9XsxkO+l0nY+HtGOFEqg0W8P2/o3f8Kjm3JHbvnJ57IxHaXcXt3/whx7HM1XKtb0ezn5618d0eVW0bOhs5bG18hk163Drx3HqtFBO+7OvUTVTCfEj0PuU0XPhrV/ELtcMVTO3ZQEMOah/0TwHB4co9lpsCSF04J/AUUAZ8JUQ4nUp5fqI1S4BGqSUI4QQZwF/hkgb82+WdXXrMCNqaGaUHtcltACQNhPXP4kubWwEbw2dxavD59Di8qHZGnaPu68UgmwGcPGqR1g8LI2Qy+CdQ05m04iJhF0dX0yJLuz8RJIWlXFqxeukhZsAyc5AHqUJgwlqcW4eqIiQXtlObnIt1Zl5DCnvUTgtJWMaNlNvpJIcamFJYwHYsrutkKERykvm2W3n8GLoJnLMEDqShDaLf2gPcv74Qkor2pj34QsYlromvqCfwFaLxZmDmZBbC7qOiY7dYdBqChcmLh6SP+L/+AU2Khq3gB5mlB2cVjmfOzf/tSsCM7tpJS+svpYzJv6Vcyb9GZDs11LE1JaNUYJEohFG1UbFwxR63AkKndcFIbCExsakYcxpWIaGZH7mbJ5c8xsOafiqawalpPeUXm9CK1IG6tLi6PrF2MDxtZ/yeP6J/HH4T0AI0sLN+DU3nfMlTTT8upc/DPsRz+bNRUNy/dYH+NHO53s5Uqzo63SRn9qyIWYW6O4TIYii/n9vdql11/x0EKxcT3jtKyRNOg3m70Jo7QpvWoflQw9WPh2/tsjwqqHouhqXlHDwr1UUrJPq9buOMsXDDMCWD6Fpp+o72V8Ss2D2z2DyWfC38XHG7IGpF8D+u2kzsTckZMAPHoUXL+zyxcMMqEkKH9yongtdzSiNF1V0cHDolX0R2ZoJFEsptwIIIZ4FTgIixdZJwB86/v9F4F4hhJCyP1W1+x4ppXKpVBMASQylRb2e0F6N3vHF+69JJ/N+wQyCnb/mpIX65rYBHUSIRMvFgWEvG0dfyPCdj7Jw3GiKRk7CNCIKpQwNmWhQkFxFRkl9V/romJoPqE0dQEnGcBYzDtkjxuKyQ5xc8gYZWxpBqtoozbbQOy5duy+RzemF7HfAC8wuW0x5KE0ZsEZgTkijwvIwSzzDxeWvcOPWf6ELSYIMcvuWv/GvsjldQqtrG6mztH4Q9vAMViaP6RJakZRRgF962CkKeJgraRQ9LRgAKblp679iUl0JdpDfbfs3J0+5h2SrnQfW3YinxzoGNkHhYoc7i8HB6jjNrAVrkkcDMKy9lIJABSuTRtPoTu1OhwiNgO7ls/RpvLLyKk6veJfDGpZE7atTYgSFgUeaWIiuwnlPL2IuUpZ0Cp5OL64Ly1/lyQHz2JJQwNU7niAr3IQAao1kfjjxDjYkjSCkqc+TBfy58BJmNy5nRPsOfDLUQ3D2LgQ15N7VarmSYPjhsPk9sAIdM+b23gxVqvhtFB7Lz6pP/sXUCadi7HZLHaEEk6arovVzno+f7lrxeGxkCtR257ygJgCYfuVQnzooeh1Xgqoh2xN0t9r37oitTpKyVbH/ske7x64Z4EmFmZft2Xj2htHHwnWb1GeieadqXWQHo1slPXW6Wsed0Pt+HBwcotgXsxEHAqURz8s6lsVdR0ppAk3AbprV7Dsag40Rdy9Jk7cm6nVbM1RPPU8y84fM7BZa0PGLzySJcsY1bOCitfO54atnWWi0cFfBYO4b80PW5ExExNORhsamgvH49e76Bw1JTlM5N/ofYgZF6D1mws2u/4KsYB1uM4TbCuOyzC7z0rDh4oM58wgZPkKah48Gz6Zo+MTYG5EQtBsJBHUPj+afxBMD5nW9NNRfTnZzddzrZEtBdqieRCv+L36J4ArxGDeJ21UKMQ4pVitpZvyeduPbtnSciyDLbIz7YQxrBj8eexM1royown8LjTezDiagubhm+2OcXfk2V5Y+S4bZFHdmXFBz8bMx15Ngxz8XAWz1DuSTtOm8knMEtxVe1iWIeqO3qJeQcFi9irCcWP1Rly/YWZPuYlXy2Jj9hjQXL+UehRFHNImIRzz8mgf/LsbZK9JUvlqd7+++cJ0HzN5+Qpkh3q9rgrReeoH2ilRjnXEpXFcEeRPir9aXWHInKp+p6RfHCi1Q5qzpQ9ijuaJWUNlg7CnH3AbH3wW5E9TYpl4AV37W/9Rh3Rb46DZ45zcqJbm3v2G9KSp1GWiOn5KVEja9u3fHcHD4nvGdK5AXQlwOXA5QUFDwtRxjZ+tOBAIpVMygZeTrZK67EG9LE8O3vkpGQxFCWmxNHYDbinaSH+wvZWLTWjKCjczesRkt5OGag68mqOvYGjRmDUMLW4gKPxT0uAlaEhm0WZE6mYPrF3UtlsCrVRP4efuz/HL0j6nTUzvML3VGt22OuQnrto0tBM+dcBEVafnoW5sRfgs73YOd66OPWnH8uo/7B53J+RVvAGAKg1y9mXpiW+RoQpJo+zmv/DX+WXBOVBsdtx0ixWztdTZjJ22al5Dmwm3FRojKParlUEhzUetKZ1CwKrbRtG2y1TeQI6Y/xDUlT3B03SJCwsWwQBlbvAMRwIODfkBIM9Btm8HBSi7f+QLtmpe3sw+m3pXWcY016o1UJrbF7w8ngRLfQC6c+H+AKnS/suwFEix/n6m6oHDFFM1bQqNN9zGppYi8kHKo3e7NpyhxaNyIjI1y549nOQF93/5XJI9mWvP6Ptbogz1Jm/UDK060rU3z8mzuMQxsDXDc8X9RXli7tdOw8qE6rI9ZkFN+GN0SpxNXIuRN2vUxfvAoPHJcR8seu/+1a1Mv2H2j00iEUEJwytm7v+2q5+CNq1X9mB2G5Y/DiCPgB4/FWjvsDlKqZtzxJjBIu39NwR0cHLrYF2JrJzA44vmgjmXx1ikTQhhAKqpQPgYp5QPAAwDTp0//etKMNsp5VEg8QnLkiFVQ8xcGLqjsmlUIkNPegBmRPpvesJTpTSu66oQ2DsikVU8irGlRaTZb6IjNbTCoh5eNlOjlASo9eV2Lyrz5fJ47hyYtif8gOFSsZPrQJSz2TuKLtMPQtvX+a73SnY3nU5WSERJkhR+5pZnQrBxw9f5F2+BSLU0k4JYhjs7ayIvtEzFl9zkYwmJW5g7yQnVcWfY8RYmFvJ95IC5pEhYGw/2lbOhwTu8LS3PxYP5pXFT2Cs+GDuUF+1AATtQXsXqwMjDVbIvkcGv8VJm0uKLsBZ7LO5ZHBp6KX/MyuXkD1426jmWpE5XTf4dVwW+2/YuLy19GSIkldP605V4uH/cH3s88EIAn1/6GzHBD3ONY6Nw87Mru58Lg5Cl38/6yS0m22nuPYsVdJvk4fTrPr7q2S6ilh5s4r/wNHht4EpaI/rPTsDmh9tPeLmGfHNS44jtnlrc8ZQxTWorQpI1XhmnVfKxIGctb+cdxe4IHcg+GM55QM/92BymVFcSg6fFfn3qeaoFTuljZK7h86rNxxuO7Fh5tdfDMOco13Q6r1KCRqCworDCsf7WXyJ+A+m1ghuIXjtsWNJeDLy22DVHUejZs+wTKVyifrzEnqIL6vgg0K6EVWVwfboPiD1XEcszxfW/fF0Vv9+75JW1lhdEbdVuUh1lChkrZxpvM4ODwPWNfiK2vgJFCiEKUqDoLOKfHOq8DFwBfAKcDC76tei2AB9Y8gM+fQ3tiBcN0iW8JpL6wk+fmaHw4RSfkgjGlkprkOtIqyqizBuMizIym5VFRJlvXcIkQI9u3sCF5bNQxBgRruGnB3xiRXEGtK43nEo/g/dpJtPvdpIcbAKh3pfF23rEERfeX0SdyCg3bkznc8xkf7T+XbQUjGb69CC0iumILwY7sobiX1iMirqKwJPgtjC3NmGPS4p67ZlvMblyh1u9YNjChhfMLl/FB5QjK2tPwGWGmZFcyMU2lF912mPs2/onrR1zNs7lzcckwGxKHdfdU3AV3DLmIl8r2p9ZKIoA617utUwmX+BA5Fo+uu4E0O347Exc2V5c+xdWlqm/c61mHcv6kP6vUWcTxZzWu4sLyV7u9sjqiTfev/yMTD3iZMa3bGd2+PW7QLyx0zp1wO1sTVXorL1DNwQ3L8OtuShJymNiyPe7YQkLn1ZzDObNqftcyCdS60nh29S8YESjrWp5qtXHDtgeY07iMi8bf2h3hkpKRbSVd78mu6Cw1pOPfkHCjY0e1F+oP1a4MXs05gmYjgUMaljK9ed0+MVsFmNZcxLwZD3BozUIyQg18mj6dTzNnkuVyMzc7Va004ojd37G0VG1Vb+guOPdl2P6ZavSclAMTTutfOu79G1V7pE5BZYVUVKepFC58E3ZcAo/OiyO4pDreF/d093zsZNVzqoWR6VdiavzJMO8fsTYKoXYV6avZAOGgElnv/houfq/3Rt+gjqsb0DP4FG5TsyT3RmwtfaT32aDjT41uudSJlPDmz2HVMx39NTUlWi94HfImxq7v4PA9Yq/FlpTSFEL8FJiPSmA9IqVcJ4S4GVgqpXwdeBh4QghRDNSjBNm3RoO/Hs3lAQklAUh8TnDXyRprhgpCLnXLWVUILgvMlEdJKD2D3AZPR+ue6NSCS5oMa9seJbbSaeZ13+/Jkk1oARgZKGVa0zoW1Q7hzdZpeDq+xFanTsTsEeUI4WKtLOSnbc9z+ptP8t4RJzFm50aEaWFKHUNYBA0P7x80D1aFYyaPCQl6pV+JrU4923Fj122TBDvADVsf6F6/c8yeID8YopoOWwj+MuQiLhp4MhftfIWflT7F/MzZvJx7NKauZiL2ipS47TAh3d31XGsIURrKQkQM1pI6oiVMQk0zhzQui90NsVEjCcxpXMafN/2VF3KPYUXKWMKawcH1S7ls54v44vhN2ULjmLqFnF3xdpcXVk9WJo7mk4yZAFy14wmuLXkcEx1dmLjtMCaxfyg2cNPwn7B/45qo5QIYHIxfAJ5oBzikYSn7tWxkRcpYkBKf5efpNb/ql9Bpw40bM2p2pk+GCAg3YaHj7mfa68OM/bl03M3YCEKai/sGn81RdYv414Y/RYn6PUJz4/Em8uiM/biufCKfNqh005z0ZP46ejCezgjTS5fu5o4FJA9QFhJ9riag8GD16MS2YPXzsOJJ9Xy/c2HSGapwvpMNr8cKKWlDySJl9FpwAPxoIdw3K9aHzAyoAvdIsbX1E3jz6mifsPWvKRH3g0ejt//sr1C1tjutG2pVqdCXL4NLP1Ttk768H4JNMGae6iXpS+9oWN7LtdL3MpoUb6IBKKE4tZeI5PrXVE/Fnunpp8+En69T/RiXPQbbPoWMQtUeKWtE/H05OPyPsU9qtqSUbwNv91h2Y8T/B4Af7Itj7Qu8QfB7gyAE+VVQnkmU0AKQmsC2JbY3hLfgCU5fk0i7KxNb7xHNkRDoYdtwqXiLNFqjvsvdms3srBJWN+QRttVlb3anYsWJtbgwaTcSyWiq5d7NtzN52Ga2NqZRFUgi29vKyORaEhdrPCZPoDpOqxapiS7rg0gOrV/CHcV3MTBYQ7PmI9EOxK1H+jRtKg8NPI0WVxJ/G3I+S1PGsc03iHbdF7Nu90ElAsktm//OmVXvccuwK3g0/2T1WrPVbXQaiSXJqI2bTUYAFe5MEqwgKVZrV5F4utnCD6rf47Tq9zFRv56DmgufFYwSc53o0uI32x7mo7QZHNC0Ouo1E8E7WXN4Ku8EAKY1reWakifw9nCS7zkjUAJrkkbyReoUbiu+O+7Ye5MsbjvMYfWLWZc0nFmNq7h5yz3khWoJCQNdWlHvR+QxTTRM3Y2FgcuKvhF6ZajfEikg3Fwx9ib8EfV37bqP9zMO4O2sORxf++meR7gSc1S/wv2vJD8pm2eyIWyrkbm0iL22VKpU1+7gSe59FmJfSAnPnQdbP+oWEOXLYeObcOaTEbNWe9mviJie4MtQMwWtOJ0GerYI+vyuWENWM6DMWh84TKVDPckw4zIVCeopUKStzFnfuk693jn2+u0qavWjhR2CMs64XT4lKPeGiWeolGbPc9AMGDgt/jbL/hNfpAUaYctH8NqPwN+kIn1bDVj5JJz1tJoR6+DwP853rdzja0dKid/dfWsKuVzszBTocUqjTEO18Al4BOXZrbisWJtNTdoU1jbgtsL4wgFcVphj5Ve4tdgogy0FOZ7udFleewVuYr+4g7j+v72zDJOjStvwfaqqbXpcoxN3DwmQBIIEdw3uizsssrC+C7ssH7bAwi4uiy8Edw0QJZ4Qd8SozAAAf+VJREFU92SSjFtrVZ3vx+mRnu6eTGQipG6uXCQtVae7urueeuV5yQ5XkG3XcUBwIZlakGE5JRzScTUjckrIcEU5XvuJ0za9R164LP71aWB1Tkt6tu8YKSMzWsdtfW6h3J2TVGhZaPyx57X0D6zisg3/44jyKfyQPYJ13o6JGwSQEq8Z5NCKaUyecQGXlbxLuh3kdyuf5LDK6arb0KOBnuSkoEOqRjobwZz0/gwY9z6dx3/FaUMfZmmaSl0IlN2CBwuPjJJpBXBhJRUJPjtMTrSGLKsO2eKEGtXcvF50LN/EolpeK5xy1mJLq4h+gTU0eNzXaz6+zxrOgrQecY5VydZjYHP7mudYM/lI7ln+CNf3u5sjR/6HG/vdyanDHuGj3LFxR0WiIo1f5+7PLf1uS7rNVNYQyZiWPTSpKA0YaTzX8ZQ2biUF9VuUEEkvaLzJVbkS148Pq+hN6RJ1Y83GbRdN0SBUpKghAlW/NPNZNUZo/ltN4mfdtHihBervK76C9TOabht8RmKkSOjQ49CmWqz0guSDoDU3DDgp/rbKNcnXaUWU2LNNCFaq9GOwIsWLkjDrpfi1W2H1Ps/+r6qFOvsV1WnpTo+ND/KqiFHLMUTbyvDzoMMw1VwAarajywenPJm6BiuaouFCaOrY1Jc31ZdJUx3Td6/b8e5JB4e9gD2uG7G9mbZpGjS7ypZGHwxrQYJRKYDLNhhfsx+TM36iMj1MtaeczKre+KwQEvDYJv03lvP3i8/lms//S+81ZRQEaijYvwrZOXHEnCYk9WbTD/rgmkUszBqEqemN42Z8hDhZ+4GlpTm43TZRDD40D+Rv5jmUkUU6Qa7V3+UM4yt02+SAmpl82OFYla5DMlpbwpMbH2ZpTXf+0usqZmU2GSb+r+goOoc281bRUZyx+VPeLziUz/LGURCp4IoNbzKmeh6m0Hlg2f30r1+NIS2imkG1kc6tfX7NDzkjMVsM1M0061j448m4WqRX/XaI69a+wg/ZI3DlSUyRGB3SgPXdivkieADjq2Y1WiQAhDQ3D3W/CDv2Jk7JHsYJI/7FD9PPpyBW85YM2ez/pjB4oPhC3ss/lK9nXRY3DBqUELtm/Wt8ka8K6GdkDeHzvHEcWx4/oDiZLAgLF/2Cq/jb4LN4MucyZT4rLbqFNvLK/NvpHC5N8qz4bRaHSnhr3s0cO/LfvFN0JINrl3BExbS4mixQw7T/3PNqlqcV85flj+K3AnFXSdskW6StZkkmwW8HU4q5Nu9n6hNw5J+VmJr6JHzxh9gAZQnf3gfjboaOQ7e9E9KOKsHW9+jE+0qXwrNHKYEVDSjx8fU9KgW3enJyERANqXRWVyW0OeIPsG6qEklmSIkWXw6c/Gj88079t6qvahi14/KruYWH3RX/uOIDlffW1lK70SAQq22Ki5gJ5SAfKIdwJPE5y7+AA2Oi6tYlsOQTNa+x94TkgnBbMdxw8YeqUH7ZZ6r+bcQFKv2XiqETY7MsW0a3RExgJmkwCFSourjs9uk8d3DYUxC7sU59q4waNUrOnDlzp27z/hn38+Lcl8CQdKruzbE/X0mZvJ1FxRarOrkwGyJSEny2h0dX/YYvs6bxav7HSKlRv/wO8gMR3HaEKiObhyffz0+9u+CJhjl80Rpclk1aXoTiw8rRjGY1SrZgcyidV9cMj1tPuSeLmR3GUqrlkkk9p1jfkrt5HWXhdCy3Qb8eYe6MXE6QpqtJD2G65lVQW5RN57J1RPKyKDUzOb/mY66vfqvxcQHNw+nDHmZ25sCmHcbSi+lmPVFhENY9IG18dpjfrnyS48u+Iytah082/cCbCMrcORyx3zPUGn7CmgchLbx2lAcX/41Ty75J+l6HhcE3OaPJi1RxXo97sefXYYYFGpIulPIv1yP00kv4W7dLyDNruLRkEmlWiBW+rvymz018nxOfrvBYYW5Y+zK3rn0x5fGVQK3m45FuF/Be/mGUeAs5sfRrHlx6f9KarvWeQkYd+Gbjvw+umMmb8+MLnZNFjgKah2NHPMkSf4+4KI1um/QOruWbmZcggHnpfXio+EIW+3swuG4Zt6x9iQH1KxsfbyH4PG8sFw++l//NuZGx1XMS9mUhWO3thI5Np9AmXM2mRm5LVAugXhgMHfse9YY/7vY0K8jjP/+FY8t/SPq8bdrP+DugvlTZECQzhfUXqujMtpLVFW5ekHj7U4fDhlnEh3M1NQC6eAzMfa3JS6w5w85RA6IbsG0lLBa+ra6Uhk6EnoclRuHqtqj6r/Ll0G2sKsJvWfResRL+PV51RTbUeDWbjRqHO0NFzeq2qHot3a3SdUf/DT67Kza4uhlCV3YTJz6U6p3aPZhheOEk2DxfrVl3q7We+bwS3aWLE5+ju5VY3BXjiBwcdgFCiJ+klAkt0/tcZCvDnYHPzCCzuoqjVlyGx7SZOMUimpbO8+cP4PvMOdjCpmeoKzeWnEfnaCFnlx1DrV7P+9nfIW0vpR7VUeUzQ3zdexB5Vhm2pjGtVyf2W7UJq1Kw4acsOo2sxtIEmpBsqUvn3Y1NoqfhJ3dRnxFsqO/OA5Egq0q/p9qqpYQc6jPSef+Is5A/B+KEFkAYD8uqOxHeryObO3Uh3Qry0ayr6BLeFPe4NDvMLWte4IIh96nXbtZx2pYvWOcp4puc/ZvsKoRGUPfxl55Xc3j5VDrI+CtpA0mHSAXfzLyYpzudzuScUXQNl3DVujcoiFSkPBF7pElYc3PG8IcRSKyD/YiAxQUbJ/Gnjf9GCLiv+BL+XXwOCMG9Pa/EkBamlvxjGdY9zM9o3TxSANOyhjI9ayg9Qhv4bPYVGLaZdNC0heCnjIFxt23y5FOn+Ui3g8ohJFaB1byyLiRczMnoz5L0nonb1AzWeTuy2N+TCiOT84fcR0hzI4XGam9nPsofT7fgemzN4Oq1r3Hhpvc5pGI6Q2qXpOwI1JD0CrV0U2l6vduCV5o8t/C3XDz4XqQEUzMwpMVJW77imBRCa5v38919rd+/PUILoYRNS8K1UDKXxLy5DWVLVNQkhSkvC9+BMdc2dcpVr1NWCpE6JRx+fg8KB8GF78a7pacXwsG3tL7c3J5w+TcqwrbmB0jvoMxC1/yYGO0yQ3DWf5U4nR5rXpFSDav2ZasoXPPnGO7d4y6/NQyPioYt/URF3tKLYPi5qnOxtgQ+vSs+6qW5oHisI7Qc9gn2ucjWhroNXPz0NVz2dV/KOh5DbsViBi98GrfLQ9qR97DOVcU6vQy/7aOP1ZF0vETtCKZmc36fuyhd+ys6RDPIEiECps6YDV9SEG1WcyEl6eEoQkqiBoyt3Iiv2iQSUqfrhZ3z2ZirfK4ihovqDodzLv3IjtaTLf5AVUYtUzIHc+N+fwIh8HxZoiwdWiAFhA/vCIaGYUc5e9PH/N+yBxIe968uE/lzr2vpU7+a9+Zch9uOcvWA33JE+TQOqZpJqSuHx4rP5ZP8g0FKnltwN8dWpD7pxq0BeC//EIK6j7M3JzpKVxoZjDjwrTgzVACfFWLSnOsYWLeCHgd/lpCaBBDSTrCWaBnZStWxGMZFVDPwykjKUTY2goDm5biRT7LU373ZBmyuW/cql61/m4e6XcDneWPxW0Eu2fg2F2x8F01oTCo4jDv63BqrAZME9Hg7ggyzjucX3M1dfW5Ska8kZEVqmDzzQjLNOtwySljz4LXDO81+IRmSxiFTVBnpfJB/KNVGOodUzmRwCrPXPQbDC9dMUSKmOZF6+Hu37XO/bxkheuYoVcfVvNvQ8MKY62DC77Z/7Q2ULoX/HJIkzaapzkgpEyOBukdF6MqXq2iX7oKTHoMBJ8Q/TkoomQM1JdBx2PaNDmpPbBs+vAXmvqKiWbYFeb3hgnfUnEgHh18ITmQrRuf0zvTZ3IfqvIMQCCzdq6Iu4Ro+NWax2RXExEbTBHOM1RwaGUigegVl0Y2MzOmNz7UUL1mqRV4Hd+cOyLXVCDt2UheCOq+bLXkdOGTRaowtIG0NV+zKe/D6Muq9bqrTvLjNKGHW4Z76Gba2iIzDy8BI486Rd6uUgxBIv4GoSXIiMZqKzk3NpYw7lz2AJVX5sxEz4Po07yAA/rn4XrLMOjQkjy++B68VwYVFt1AJj//8Vx7ofhFPdDmbOZn9ObxyWtxMwNZSSI8Wn09OpJqzmomthsd+kTsGXVpkRWs5qfRr8qJVTMkaxozMQbxdeAS50ZqYnUYzpMQlTboES9joLVJpTkBIC48d4cKS97Z6jL3ExK4w4sTWfH9v3DJKQaSSWZkDuafHFY1F900ITt/8Gcfu9yRlruxGIfjXnlfxUf54ApqHMk8eLy24k9E1KqU1K2MAN/S/izU+dYILah68ZpAlad1TrjGke9jkzqUwVn/ms8M7YwR0HC23pxoLFNlmHedv+mCbt9WeYjAlQodzXlXptmlPqRRlYX/oNELVEHUbq3y1UgwsT4m0IBCrrQtWqVRkMluHua/sHLFV0BcufA8+vFl1GjZitz5qqPtBSmiVLoa0/ET39rpSeOkUZa6qacpgdfh5cPwDO+YivzPRNDjxYRh/mxKFmZ2VKNzWRgkHh72UfU5sAXQpBzt2Eq3J7IZppFHSsQOb9BosoSNsHWF7sPQQ37p/Jr1uJRI3HasHYblNhGiowQDL6yNU2AXfpqbuo4DXz4+jj+Ws7/+C0eJHVJOSHqVVzOnWgYjhYmlxd6avWs3RMswaXydOGv4oQc3T+CNk9s3ENasc0WwzUgOzT2bcD1WGqeo6LDQui9zK0+4HsXSdDZ5CMsw6BtevaPRQ8lvBOInjt0P8evXzPN/xZF7ucAJnbfqEjqFSfEQI4VJm+0LEWSLYCP5XeASl7lzemXtjY8F7cyyhMbJmEc8t+h2alHjsMCHNww/Zw/k2ZzS2hGPLvuPzvHFEdDfnbXyPO1Y/S360iogw+CjvYG7rdxshzc24qtncu/wRss1ayoxM7uvxK9VZufZ5ojHbBF+zyFCZK4eiSLytxN19bmR6VpKxLVLiswIE9TQ6Rkr5NG8cVUZGXMQtqPuYkTWEtGg9o2sX8X/dLuaEsm85e9PH9KtfxT3LHuZXA/+MEJAXqeT4Uf9J3E8zoprBR/mHMLS+qcNuZ592VKekB5dtphwFtC3b2i0IHQaeBIFKeKBfYgqucFBMIG2HVNVc0C9m/Cnt1C/S3rH3Lo6uo+GAq+HDX4OZwsuqOdKEGU83jc2pWg2TroIv/wRH/kXNMHzrUiXEmo/WmfeaKjo3VE0m/Y9vvbgdVOQtUK4aGNx+JT7Xz4SMDtD3mOQO+dtKVuc9L+rm4LAL2DfFltaB2sgapN4XITTmDL2GQPoULN0gvbo33mARUtgIKQj412PnDMAV9WO7pif8HgshiOQU4N+8Hi0mZ5YOOJDsuhqslp5cKEHijVjYQhA13MzrN4KuVbWcFfySY4c9wxZ3bpyIsvO8REdkYyytR9RHkV4ds1cmdqem1JXPCvKrDf8DYJPMZYbsz6vW4czpN4z13g6k2UGan4ySXeuawqBvYC3r7SLWvZcNOVEysuoJBLwsretK6SkdOK38SyKaG48d4fWio/lN35v5YNY1pKcYZzOh7EcmVEwj3Wry6vHbIcZVzaFncD0F0Sr+sfQBjt2vH+MrZvDnlf9qHBTtlVFOLfuK08q+wkawLK0YDRsTnTOGPcLytK6YmouXO53E+MqZ9KlfxW1rXwCUI/wmdx45Zi1Gs8HTm9wp0hVCIIWG1w4R0HxMzR6WkPoENaexyp3N53ljQGjMyhzAg8UXUWv4ccsolmYwsnohjyz+GzlmNb8a9Bcm54xKefWub2skphVSRR/97RAx26VIC1b/CIveS97Zt2Xh9m/bNpsc2tNyoXAglMwjTrjpblUA3xqVa1Sn5JofIbsrHHRzvKlqS6KBts9dtFMIydoSeP8GFSVaNzVxhmE0oASZ7lbP/+ovaq7kuBsSt1VTAq9MbEpVWhHV0djQTam5lHP/pZ+07mjv4OCQkn1SbHU/eizfTXqKbHojpUbA34mqrL74a3PwBgsRaAipJElafRcsEUaI2I9WEqTQOL7LVVgySpqewYyCCD8X+XFFEzuxoprGlsw0qrIK+Gbscdi6izUDOvNdyShqjPREvwjAzvMRGduse0xKkBK/VY8pXJy++XPO3fg+AdzcmXMtNfkduCd0OeHCLBCCgJ7GtKyhHFA1L8GioQGfFWSzJ48bnn2WeT0HMKnL0XTZvInDFkyhyKpg7qxBjDj7fxSHSljr7UilK4viYAnD6paktAvIsWpIYjeG3w7RLbgRFzZpVpgvfvoVArtRaDUggClZQ7l6wO+pMjKIClXMHdYMFfEAql0ZvF94GH7zAG5Y9wrvFhzO7/rcgCUFDy79B0dUTMUf2+4B1fNY7y3CTuEkLxFUuzLwWSEMO5pQSxbW3HEmlyHdR0jzghCEY00MP2UN4p5eV/LUz3+iT2ANk3NHJ92XJm1OKv066X3bQ2uRp70+UVOf3JF/x5Gw5OMm+4fTnoJnj463kMjqCofemXoTFatiXYcBFYUqXwZrp8IJD8GwFIMyeh+hisXbusZURAMw/amkvxmNz20+cufre6DfcYmu7a+cCZsXxQvAuM7BsGoaeONCZabq0L5IqbpZbRPy+uw5qWCHHWKfPIrdevfFZ3qwQnMQsaiDK5CPL9AR0cLRXaCjSx8uBK5ITsJvnw2szS3CcnvJcOWgawYHVulEXC5ePvZUgu6mTsKIYWAaOgv6juSUqXMI+jKQAhbm9mF1WqdWflZbnC5jax5WMZdbpzzBBUve4QMxlhOGPs5Xgw/B7uKntkc+kWYjO27odxebPXkENE/S/WjAQeUzefL083nwvF/x6tEn8+jECzn3L49QmpPLobOmUunKYm5GfypdqhvzoKqfEmuuYjSkFVN9wHRsPs85gBv73sF93S6Ji341UOrK4bzB97HJU0BI92JpMauKJGJJIHmncAJ39r2ZGiOdepefqwb+gVv63sZXOaP5Nns/qnV/yvVqUtIzsJ40M8D5Je/HzcBU91uYyURai6iVqbn4sOBQavQ0egY34EvSCTeu4ic+/emKmDHqnkGlkcFtfW6l/9j3GTD2PX7b63rqWpsYsJcjgSgaf9oY5teL11IZNaGgH9w4D475m/IDGxEbS/PoSOVCX5akieCbvykh0rywPRpQMwKtJLYXoNJ5Y29MsTKhxhKl6MhNwPDEd0q2hhlWKcf1zcZjlS5REa22RNrKlkH1+q0/zmH7KV0Cj+8PT46D/xwKDw2ENVN296ocdgL7pNjK6eTHcA9AWpuRMZuDtEgHUsUABIL+XkGfQF+E7QJbvW0RTSfo9jCv22DWyo2YdhRb2hy1bAM5tTW8etSJ/PXS65jddyCrOnbh47GH8tnRR+NLd7G8dy8q0/0MXDKLKCEeys7Au+n39F/xAAPXfk1GoK7ZApKsSwhm5o/mof6XcnLkz1w34Lcsyu6jCucBWqQwN3oLOeCAV7m6/++SigYdm1vWvcTGgiKCXnWSDXl9VKen848LrkQk6Vqt09OIJOkkbHhkyw9XSHNzW+9beLLzmax1F/FMl9N5o8MxPNPlDNYmcah/s+iolPMMWxLVXLxdeKSqd2tYh9B4t+gILh58LxWuTD4rODhlSk8imDT3ev664p+MqPmZi9a/g9uKoEkLpGzmbLV1bATf5ozijC2f4ZJmXNH1BRsm8fLC3zAosLKVLSRbX/sRFTrHj3iC1zocQ5Urk0pXFi92PIlThv0Te++PiyVFoIac37HqKd7bsIHjf1qmRgt5M2G/i5WAmfWiivDUl6nxPk+OgycOgoeHwvs3QfUGWP19YlE9KMH10qmwOUWac8JvISuZkadUZqot04KpsCJw1D3K56thzmqS72TjtjfNg+ePhxnPqpsCFW0XdlZU/XFoH8wwPHecErXRoPoM1ZbAy6crDzaHvZp9Umxl5vnos/94pAyAXY+UFgKBSPV2iCg/Z8xlddYcjKgfX30XAnThhz5DeXvkBC5cK3ALg4/l96ysncuKtC08dv9vOe7Hb5jXpz+/u/pWHr/wMuo7FxDy+6nLyGDJoIFcNPUzov40cjb9lszqWRy/ej/GbShmzOpyzpr5OeOXzml1lEVEc2PlePD7o9j53jhn/GRYwmBy7qikwglQBeUtxIjUdJZ1786W8YX8a9GfuXvlkxQHNwLwUf7BhJO4katZgolrcdlRCqKV3NfjV4w74BW+zRmFFBoZVj1vFxxOqMW2SjwFjd2IreG2whxSMYNSd07SlIqQkj/1uKpROCUuWHLb6ufIsgIcUT6No/d7mpc7n0REMzBsE58V5Op1r9ApvAXRMgKQ4r2ck9GPbLOOSXOuZ0D9Ktx2hHSznj+v/FeCuWpbhNS2SJ62CrMGK4j38g+lxJNPtNnspIjuZpWvC9+1MJbdq/BmbfUhQtr0qV3B5kiUT8uq1Y2Repj8YLxFg7RVZ+Lm+VC1Bma/BP8+SAmjVKz+Dp6aoIw+/z1eRSpmPKsiXhtnJ/cb0z2qVqqtFI9RXlZXfAejLlEpyrE3tDKIWqqROZ/epboaOwxJHYFLwFZdhA7tw9JPYpMVWnyDpaWMeR32avbJmi2Aoy8fhivtSlZ/P52IVY9mdCIsoujSQEOj+WCZ6pwFRPRYu7UnTNRTww99x1Camc0h0z/jhJqRlLndbE6zqN08Gau6Hnfveq556xkGVq6nOjs7fudCxDYvOGLVSpYUdKawuhNeyxvbt6LP5rVszMpjeVHXlK8jorkp6halxowSdW+9WyigeSl159CxRacewCJ/YvFrulnPx3OuolPBFvylISLC4LIN73DJoL/ybc4oJox6lm9nXoyMjQvSpM03OaM4tOon0lqIirDmpsqVqYYgx5zsL9owiT+ufJyocCGFwJZNcwXHVs3hpY4nJi1WR0p0aWFIi0MqZ/KvxX/hvm6XsSKtmGiLK/vBdcu4Yv2bvNXhaL7KGY3VTNQJ26JreDNXb3gdgLt630CJJ7+xXiuiezDsKOu8nXhr3s2cM+R+St25hIULS8Q+Jy2Gfuu2SY6pIpMD61fy9U+XUubKwmVHk5qrKreu7autsps9LyxcuKWZdPZhS5pHH5f4exBMkjIMay4W+ntzaOXO9brbaehelUYLVyfepxkx5/WGdzfFJjDxWhGi0RAL64KcUJgN5StAN6A1DWKbaqiytZX0mxmEVd82/bt0iTqpZhcnH1lkhRNnNKZCaDAxNk2hoC8c/39N96UXKtf2VGORdEPNjex9BBz5J/jij8mHSMfvECpXqXSrw86nbkvyiKYZgprkpsYOew/7ZGQLQDc0jjh/CEfn9CWQV8GETIOjcwTLenyI7qklTYNOLkEodz4RdzNfGwEIm2MXzOe6D0vpFgmwsHY6qzXl12NnFeK2dMKyK18MTSfkaf2HUyA4Y9PRpEf9aGhkUstYZnIIU+hmb2TQxlVbfS0lmYUcOW0y7kiLE7m0GyMv3esshlea+M0wf+h5LYFm6TYbNX7mz72uTtj2Veteo2uopLHI3C1N0uwQjy2+B4Fko6eQPuM+5MxhD3HVwD8yeNy73NzvN40zDeOWg+DtwsNjL1yQGa3hDyv/hc+OkGnV47MjcQOcjyyfQv/6VUmjR24Z5d5lDzFz2kReXHgX6VaQa9e/SroVwIgZXBq2ycvzbmfS3Bs4qewb/vXzX5gx7RyKAxtw2xE8dpiRdYuZNOeGxn1+mn9QQmG8qbn4JP9gegY38MP088mK1tAtuJFvZlzE9GlncfHGSXQNbsQdm23nwkJKOH3oQ/ycptrt86PV2GjJ676Aje6C2Hu0bdTpaazxdODdvENidXJbT3g2CLuGx/UIbsCfxIbAa0fonsK5vvm2duT+7Sa3F1zzY2ozU9uMnbhaX4EOvLjwN/z8w0mcMO8RNSw5o6Pyqtoa0kou9FojGoBln6rBzKmItMESApS/mDcz+X0HXgVH30NKCS8lxCZhcMCVcO7r0PdY6LQfdByeIhUp4ekJSozuzQSr1CDv6U+pBoc9ha4HJC9zcPuV15rDXs0+K7YAhK4Rcgc5naEEXDVk2H5urzydCWnpHJFpMMIvqHNVJX2u6aoj6NuE1A3mFlks0TcCgu5Zwzit+CYy7HTG5lxGN6MXopXfe4nExMaQBgNZwvU8z+H8wCFM5WLe5MLA262mEhGCysxsenYpYc2Uo/hx2rkcXjYVpDrpPrbgUV75oZaXpgR4eFaQgjC8VzSBSwf9lZkZAylzZfF99kjOGPZQUg+qk8q+xSsTT2g+K0SfwNrG7rx5Gf34Im8MAT2NWpef8wbfR7mRRa2eRq2eRqWRwVEj/02VKzv2wiUB3ccif7wjePOfGgOLd+bewOXr32pKAcb+2BK+yhtDTrTpZNchUs7nMy/jnE0f0S24gT+ueIzDKmfgkhYCyLACFEbKeXLxn7lowySmTTuHD2dfQ6dIabP9J3+vG27/PmckLjvKp7OvpH9wLcXhzfx9+cNMnX4OC388iavWvsqjP/+Vb3NH80P2cE4c8TjrPYVI4K89r6RaT0/YQ0QYPNb1HABMdCYVHMaVA37Pnb1vYn56n6TracAlLR4tPo8Tyr/H1+I4tVXonFT6NV47jNbMT0q3TTLNOo4q/7HV59bofm7qczuz0vsn3V+7VXxVrFB1LW2tbUqBQHXH+u0gA+c8Aff3hNfPVzMRjSQR1Z1FqwXpWytW19QA7KPuaf1hCyeR8lPgTofOzVLEPcbDua/BFV8pV/dUw6wjAdUUsLey/At4cAB8fBt89lv414Hw5V9296oUHYdC7yOVzUYDhhfy+yoh7LBXs0+LLYBO549Ek2CEbCJCnax80oNAoKNhkKJAW0IwXYkNqWmNZxW3dGEIF8d3vZIuaf3Yz+qFBxeaTH3a6Wd1xkeUU/gMFyZGrGfOjclIaxHjt5bGEYJni0/DRqNnaANP//x7DqiehxQa+TUX07MOfDakm9A1IEDafJO7PyeMfILBY99j4rCHmJU5KOmmA1ryE46ORbChLiTmU1UULsNv1mMLnWnZwxg69m3OGXI/5w75B4PGvMOK5mNxhMDUXJw35P6Euq/mpwefHaFruAStIVUX+2PqbiZnj2x0yAeo17x0ipRxz/J/8uP08/nVxncSvKwMbAbVrWBEzc/kRioTXtcxZZMbI2ONz7GjHFM2WY0CEgaPLr4XX4vuSR0lDsfUzCOiuVmU1gOERlhz81TnMyg1sjm/5EOyzZpEASJtLip5l6jQuWDwvUzPGMzo6gWEhYuJQ/6PlzqcQDK3pbAwmJvRj/cLDkNPcoIWJPdUb7l/vx3iw9nXMLpmAbptYtgmY6vn8P6ca1OOOwIVDb2z903MyB6CW5rYbUpg7kTmvb5TC7Yb35d1U6FiGQw8VUV4GqO0e0izQHYXuOIb6DS89ceFWom6pReoTrcXTlJWFc1Jy4WzX0le9yUt5Se2KwhUJLrl7wiRgLKviAZUitkMqT9T/5X4Huwuznwejr4XOgyDwgFwyJ1wyccq7bu7iAZVFPC54+C1c2HFzrOs2ZfYZ2u2GkjvX0jFGX2IvDofKUPosXotLVYyP9DsygJjLVYzC3dDaqTjpYoW4X4BC411jLJ64dLcCCHwY3BG+EAW6uv4Wd9AWEQbT0gGGoOsrmRIL6PtCuwkJqgeGea0LV/wXQq/pgYiwk1I8+C3g6TZYW5b/Sy/7v0gnQMi7iBfvMpkRp5OqPm+pIw5aGsJYey3CycwuG4ZRrPTtonGsrRurGvRQVjqzuHg8hlMzxlGUPdhCYOZWYOb7UMmbN8SGt/mjk6IoDS8R0vSuvGXnlcnHU4dMNKYVHg4x5dPBuAvPa5kduYA5mf0xZAWs6aeQX408YQjgf1qF1Gn+8mwAo3eYxK4Z/k/mZvRnzJXDkHdgysmvOZk9OeZTqdx6ca3gcSrlO+zR3DxoHtoSNKFYsXmUU0NrZ6b3pcjqhJNcQHc2PQLrOHjvIN4fPG9eO0wfjtEnebltjXPccqwRzlmy3e8U3QE52z+CAEY0mJq9jCe7HIWH86+OqXXWRQdDYlrKwaq3UIbmTT3BoKaB4HEZ6dOo1kIFqT35v7ul/F99khmTptITrQafVfbp055nHZLVNaUwAGj4ed3wWwQnHuIPWx9qaoFa6B8Bcx+GYIV0Odo6Hu0mrU44CQoW5q8bqthXFBtCayfDhNfhj5HNN2fXph6/5mdds7rSMX6mTDpaqhcDUjocSic+uSOz1Bc8VVyT7JoEOa8AsUH7tj2dwaarhodRl2yu1eiiIbUzNDyWIckKLF10M1wyO27d217Gft8ZAugeP+uzPQKFkbrWYcaKi0QSCkZHi2mv9kZXWoYUsOQOsPN7gRFNOWFboBwo38XgBc3+1m9OC9yMEdFh9Hb6kA/qxPHRIazn9mTKBblWn2K1YlYIXbrFEbKY07xij7BtWRFJVaLNQ6vCvD3hVPIi1TjscK47Cg9g+vIj1Yl1gtIyVOdT+etwiOx0DDRqNe8bPQUcNmgxNC7IS3GVM8jN1qNq9nJ2mcF6RIqSVqPYAqdMkPVjjREb0SzP28WHpXafkDa+CzlkL7WXcgLnU9hbuYAbKET0dy8VngsEZEo0jRp898Ox3P46Od4ueMJrPB1YVZGf5b6upFl1vHVzEu4ce2LDKldihQaYd3Lem8HTt3yRVLvsFo9jQsH30ud4afOSKfO8DfWfWm2SX64giOrphPQfKz0dSaoJdbxCaA4VEKWWdtYH5duhyiIVPDY4r+SJQNcuOkDdCQVriwmDnuA3/S+mWcX/o4+wXVJ3x4BuLHihHLc20e8fBBAmh1uVWgB/LfjCZwy/FG+yBvDcWXf4bXDu15oQdtd2LeHaAA+/23M7b3F+7ct6cWUNgw7gGXGZkECC96BJ8bBj4/CT8/D/36lLCesqKrFyuqibCEg5k+X5LsUDcInd8Tf5suGAScmea0CMrsoO4z2oHoDvBgTiVZEvY6VX8MLJ8aXU0TqVe3VtmBHU+hlqfYVd5OE5V/CpGvh/Rth7bRtfCG/EOa/qXzYos3EfTQA3/2fmsnp0Gb2+chWA+f/9jDeeOQrXLWRxhorIQRRLMZYfRll9SIoIqRJNwY6q/QthEXyFIaX5D+wAkGunU6RnU0EEx2N5domZrpWYorOnJhkPltI8/Be/pEA6JYJEjRhE9WMxqs0nxXiqvWvcW/3ywnpHo4pm0xUGKxLi+CxLRrGDwtCFLpvYWLFZiZOibDRnY/HjvB/3S/lhc6nJFmwQAqDmwbcRa5ZzVEVU9HtEE/nn0ppQ+1VA1JSGC7ni/wxTJp9Pf/pOpGP8sfjs0JcsvEdcqLVXDvgd8gWwjGkeZid3o+O4VIG1y+nIFoVd39E9yQttgdwSbMx0vNcl9PQpB0nTB/ufiFHV/xAr+D6xrmQELM80HQ2ufP5Td9bQCo5J4WG36xHQ1JrpJNmBYnEhFG/+tV4ktSuAXyWNzalnYYtdD4sHM+Zxv3MyB6GLtWopuvWvsIta1+MO9wD61fGrROUF9To2oVNj5PQJbyFZxb+ns9yx+BJ0t3YnLY6y7c1QSaBNCuk0rpA5/BmPFbra9hemndoKrmj0pQW2g7PemwTqbrzzLa8XqFEzpCJMOv5nbkq1a2YlqtOgO9eGx/litbD+hkw/y0Yfg5c+R389AJM/w/UbYo/aTanfLmaAak1K5s4+TH1/4WTmpm2SljyEaz5Hq76Xs1N3Jn89Hxiatg2ld3G+hmqMeLda5QQAlXPdMrjaij51uh5WPKGCpcfBp/R9G8pVWRt0Xvq/USolPWY6+Dw327vK9s7WfJR8u+B4Vbp9gEn7vo17aU4ka0YaX4vF991HEbfbESzU49XqLoFFzqZ0tdYwzXS7Ikh498+Q2r0tzqnrvMCPLgIEyUgwrzu+ZHvXYsJiDCm9DKJk7CkmygeTFxYuAlGjyJ9s4d+JWvYb80Szp/+KX+d8zhHVEylMFxG98A6ziz5iHt7XMkTXc/m6c6nc/6Q+7ih/11ct+458vX/ENTUySpN/wxdbEITYTQkXSKlFJjV/HHlv0gzU0XWFB/HPLUEcMOGV7lq3Zt4rRB+M6CEhhCUuPOYmTGIr/MO4M5VTzNj2ll8O/Miztn0EUvSeiih1SBKZKOi5aUup3LOsAcoaynggGPLJif4UjU8/8IN79IhXEZA87AsrRtWi1RjneHnhOGPMzejb9ztbixuXvMi38+4gE6hLYBoFIH1hp9aI13totnnIKh70JKZVwIB3ZdcEEpJr8AaisLlTMkZQUj3UG+kEdR9PFZ8Li92PKnlE5JuvyUaktxoNWdv/mSbIko7I/YkgKPLf0DGopSzMwYQ1pLU9uwEJAIbeLLTGVww6G/c1fsGThn+T/oe9BHvFBzeLvtsE56MpmhRKsZcC/n91EDonY3Q1Elu7dR4cdRANKAiEqCKrRe+AzUbUwstUJ5kLbfl8sGp/wZPevztVlhFlb67f4deRlIaIloJCDWD8oUTlNCyo+rPloXw/AlQu2nr2/ZlwwkPq2id5lLbdKWpId29JzQ9bt00WPRuTGgBSPXe/fioGqOzL5FemDz1KmXrHnMOCThiqwXDJo7FjhvaLJBJTlPd7AJGmb1Bgi41dKnRz+rEAWbT3DETi3dd03nLPZV52hoiRJFINotqcmQ6p0cOYKDVhU5WDkPMYrrJ8xh9wBv8sec1/L37rzhy5H+4tvtpdC1dxmFLZzNy3TIyo/UMrVvKywt+w6ypZ/L0wt/zeqcTCOleTM1ACo2g7qPClUWHSBlh3w/8ZUiI7wp0bPePaCLxh8xrhzmoanarXY/vFhxOreFv9HW6c80zzJg6kZvWvNh44rV0N2gat/e+hfOH3McLHU/mmc6ncerwf/JI9wtjW5IYSX9M4cvcAxOK5cdUz+XYsu9Is1Q6R0gblx1lUN0y7l79FMWhEtLsMCNqfsaTZDROtSsT2aI5oaEDrXtwAy8s+E1KV/lwsxTQal8X1ng7YSWJAY2vnJF0DJAuLX6/8knqjPQ4O4ncaBWXbHiHgkhF3OM1SNi+FSf9m20buXtSd6iuzucW/ha/GWBOej+W+rsTFG30htoGFqT3ZoG/N3/veTlf5o/l+c6nMSNrKCHdy8397kiMru4KhA6jLgOjFbElNFV3tHlBap+rHaHjMNVNuHEOhOuSP6ZBDK75QQmS1qKPugcOvDb5fZWrkzch2FFY9vm2rLptFI9JLmQbuk6r1iRGp6wozHyubdsffg5cMwXG3wZjr4fz/wen/Sf+N2DxR6mF6bIv2rafXwqjLk3SKCHAkwnFY3fLkvZWnDRiCwy/m/wLBrLlJZW60dGwkbGyefWFlLH/NlOJhuDIyFDyZDpRYcWd/mwkARHh0OggCmQmEtARBESYFWITve0O7Gf2xIWBBZwwzk+pT+OZrqc0bmNpmklO1RqKaqsA0LAZyDIAdClZmN4XwzbVkORmmJqLawb+oVFAfVYEWYvyOL4sUWEL4G/LHmZpWndW+2IO0S0EiC00Stx5ZEZrCepeKlxZPNHlLF7ulCSMLARTsoczJXt4kvu0hAhUA093Pp3TtnxJjlmNz45gIYhqLh5ZfC9nZ4/kvYLD8NgRStx5XLX+jTjT1ItK3uWpLmcSFS7s2BW6souAGVlDGFm3OGF/Bja9guvoHlzPal8XkJKiSDkRzUWlKwsbDcM2G4vzLx58D+/MuZHCSDl6s0qybqFNXLPuVZ7sehYhzYMUGq5YB+P4yp+aujaBPvWreX/OtXjsaNKInY6M1a5psc9L64Xt7U2Dy3zL+MkhlTOZN+VUvs0ZzXpPAQPqdp73kgSCmoc7+9xMte4nmqTuTiD5NO8gzt/0wU7bb5vQXarLL9JKl5y0VYqlvdg4G779B3z/EEn7TV1pauQQQMm8rXdsSgtK5iamEUFFvFLZa6TlbevKt87wc+GHh9WaG/Zr+FTxforIMlZYFXC3ldyecOgdqe/3pCtT3JaiTuhtn0P5S6HjMDj+Afjo1+o9kZY67ue/7QzI3kYcsZWEUAeNV7zf09nMRRPKIbyP2YkishuFSomopBO5jIj0ZJG+ns/1ebH+RRhh9mCA1ZlZ2kqOig4jR/rjnOEH2l141zWDUq2GA82+SODzIoP6JEfDEhrLC7vQpaaUdFHPRD7Ag/oRkLgJRs5DkqxFu5lVgq2EwXOdTmFCxdQ4kdIQzHqncAKbPflNIit2h8e2iWpwcOVM5mX0477MC5m8tg8vRv7Op/njsNs4uzBuaUKLGa4SJ+o2eQq4te+tjK2ay7jq2Ri2RXFwIy5NY1zVbA6umgVAr3Ef8a/Ff43bZn60mk9mXckfel3Ldzmj8Fkhji2bzH87nchqX+ruKUvoZJr1jKhZxOOL74mN5JHMyhjA1QN/zyZPAS47ituOUObK4dO8sVxS8i4mAlMYjR5kN619mUMqZ3LVgN/TM7ieX234H8eU/4BA0iW0mbWxNdy/9AEyY3VhqVCfFHVi2Zmxq+0xLmhuftoSvx3iuFgn6M7EAm7qeweL03oSMHxg2+RHKohoLmqMjMaVpTKIbVfy+8KiSbt3RmA0AFMfj6/Vas6oS1UK870bVM3N1rzIbFMVoS/4HwydGH9fegF0H6dmQDaPSLvSYOx16ndi03wVCeo0QtXyNGfzIljxpUq9DjhJ1Zq1hjcTrvgWvvoLLPlYRblGXaqiUGXLwE4mLn3QZf/Wt7stDDkTJj+QpL5LQv8Tdt5+9hZGnAeDTlHRWk+GOs4psgEOqXHEVhLWr18PLsFqtjSe7dZoZdjCZmy0PwPsznSV+WDBj8YSluklcdYQs4yVuKVBpV5PZtQXJ7RARctOie4fs5lQ2JokKmxaxp2kgN7aJq7gDfLklph3kg+JpDz6G8aU5iRNYbltiGgxwaVpCMvk3LXvU2Zk0zWyGZq5MAQ0P//X/XJCzb1chEC3JcMqbX6/5hb2q5sHwJaoj5+srtxrn8vZGz/i38VnxY3TEdJWtU5JbB7iEBq6bWK1iFp8nTeGFWnd2eQtICxcfJo7lhyrjhfn30G3sKrLMIVBlSuTohZpuO6hjTy/8O5GcRDU3EwqnMD+1QvUe0micJAIyl1ZfDvzYtKbeWeNrlnApDk3MGb0y6SZAa5b9woXbHwPLyYl7nxu730zLmyuXP8GOdFq5mT0Y+KWz+kc3sLLC35Dmt2UPvrr8n9yxcA/EtUMRtfMb1Vo7UokykTVSJocbXpMuZFFuh1KXjvXDhjAvxf/mUojkw/zDmZs9Vy6hDcjkEzJGsa1A35Lre7fquHqTkdo6kT81VbMRLeG7k5Rl4SKnrh8ql4oVVpfc0GoJtUi4cBr1NDsUG2zwvatEA0o+4iWYgvg9GfgtXNU2rJh7WOug4KB8MgwCJTF6noEnPw4DDxJrf2jXyundttSPlGf/AbO/i/02kq9XUZRU3F+c4oGQs9DYOU3TelZzVDRt+Hntu11toXcHnDCQ/DBzbHarpg1zsQXVd3Xvojbr957h+3GEVtJyMjIaLJuENDP7MgSvYRs6SffzoilFQU2Nov1DdgtLOJNYTPPWMN+Zq+k9V7KMDX+9DaorJaOVTYDNq3BY0ZZUdCZxR264ZYaR20YRCT8OHX6W/j1/xG0x1JpXk0UAz9w39wgdwzzNU6BswVcsjLMN0UulmSqq3/dsviptiunm181mL6rtQhY5u+CISO0/DhYmqDCA0PrFzWu9kbjbaLoPGcdw9fLBX39K1mS1wuBJKK5mwrFY5ErTVoq+pVEeGWZdVS4s9VjaPL4WuvryDOdT49tR1Iq8jnwwNfpUb+W5xb+lsMqpvGvzmdxx5pn46J0YeGK6xhc5+lAx3Apx5d9iw2Uu7LxW8qHrKH27F9dJjJx06cYLa7+DWzyopWMq5qNFIIZWUN5qNtF+O0gpa7cxrV+VDAegPxwORO3fM4V699EtLj6PqpiCq/Nv40Hu16ALXT0tp4AaYpsJYrEHbfYjAgXt/T9NXeteprOzVz0W3LRoHs4Z/PHnLvpo0ah2N7XtQLINWu4YPOHcbePq5rFt9MvYK2vU5zz/y5B2vDNvZCkNrDtaDDotOSF85pLpbgqVrU+NUKa6qQfTDTlJaODSsOF69outBpJcVTTcuHST5WfV20JFA1SNWMPDohZQDRb69tXQOFAVes155Wm6FuDncjrF8BtK8C1ne78E19S6dOfnlfb7nccTPh96rFF28vwc6HfscpTSjNUAb3bv3P34bBP4YitJHTv3h2fz0c0GkVKqYSWnUY6HjLxocV+lKKttJ+HRBRv41DrrbPeXssxCzc2nswKaqsYULKGUNEYRlUYSAxqrTPRRDmaCCDwomERxWJcGXzybR3fFRhENMGYMpMMUzI1v+nwuoQLb31nkmUcCyMVRJOlZKSkKGijNfst1YTkdtcb3Gi8zc/u7tyl38Cpmz/jg4LDCDXvShMaImap4LYj2EKLKxJPs4JcvOEdVviLyYtU8n32SJamx4/uUdtpOgGs8hdz+OjnOKrsB17sdBIdImVcXPIupjBw21EimgtPLL0T1NzMyhzIR7OvxiVN7u51A691PJZLNrzDMWU/sMFbyPjKGbzc6WR+t+JfSUcSaVLSJbKFL3MPZPq0szh85NOsSitOEI6jq+fz3/nK4O+4ssmxCrx4Dqyex+vVtzUK4raKlVRpvJ0hdiyh8XHBeHrVr+Pm9S8n3eZGdwGzsgZz89qXEG2Yu9jeGEhyrTry6pbungW01tHXFnJ7qpogoSf6hBlu1e2Wat5jA9JWUauWETLDB4feCVOf3Po2WuJKgxHnt/6YvF7qD6ji+GiIhES3bcKsF6F6XXLLACFUSrK5geq2YLhVvVVrNVc7C18ODD6t/ffjsE/giK0kaJrGJZdcwuuvv05pqbp6rjIDhOxoY+rPxEJD4MFFkJaGeJBnp7NJq6aKAH2tTrhiJcYWdlz6EJQJ6hJjY1x6yWVbjKn2M6Y8SIMHvMRLrTkRQ6wFwEBvjJylm3BcSdOVrA0szlT70WxJdhQuZCyWSMNo4XzfKVLKAdXzmZI9lGizQnuvDRetDhKkCx7WxXkbWbrOR50OYUlWb5bSm6jQ0aSFlBreaIT+m9bQZ/Na1hYV8cy6u/lN35v5NH8cbjtKVLi4fP1b3Lb2+caT91uFR3DdgN9t9di47SgzMwdxy+rn+Uf3S3mw24V0DW3mgOq5/HVFU+rBZ0c4e/MngIp4vdzpRCKamxc6ncJTnc8korvZr3oBAd3L/Ix+TNzyWYKQ8NkhLl3/P0ZUL+Se7pfzj2UP8XLHE/gk/yDCsdSp247w8vw7ybTUe9rcjb759mxgg7sIC4EHkw6Rsq0Kl+2JXrXlOSp9qPG7XtcT0NO4v8elXLXhTdJkfJowiIsnup7FPcsfYULltKSXDQ3GqNp2rnd72N2Cb4c48WEVhZn1ElgtRblQBfhtEUrSBFe2ijAFY+l0KwI/Pg5prbTka0ZTDVdD6s/wQJ8jYfDpbX8dgQqSFufbUeXnlaqYHdrXjNbBYQ/FEVspyM7O5sorr6Sqqor58+fz1VdfEdKiLNTWMcjuykxjBSu1LRRYmWzQK5pqtqQaw9PP6sR3rp+xkZRpNQwyu+KWBmu1MrrZBWTQ1N68WauOdT02/QgNMDuzv9mnUaQ1YJGHJZvG5CQ3BlAnvpPWR3m3i5vedTb/mBPEAOqiZ5NlvIAQzYQTGoNrlzAjazDCjmDYEl1q/GbxQibUvcDTxWM4rsKmY6RUWV1g813OKP5VfK4qUo5FeryRMGfP+BS3GW08MWesXcN/Ms7mwaX/oHRVNjVGOj2DG8iw4gXfKVu+TC62YumUdKseUxj8evVzXLP+NSSCU8u+5m/dLqPEV0itkc6c9H4MrF+JTzaJ35Bw8WHB+EZzUpcdxdTVe/pTbJTQRndeYwqypdHn0PrlDK5f3nj7QdWz1QzDQX/j27wDOKhyVswNKvkxaNiOADpGShk8dhIeK8L06Wfj2UqaZ3sL2lujYU1SaNy++lnKXVl8njeW84b+g5fn3Y5LWrgxqdN8hDQ332WP5Hcr/50yPmuh8/ful3LxxnfpEtmyHSv+BSF01c2Xqh4LoYY/T34g0YrB8MJ5b6nZc6nMVFsSroWMjjEndVuJmPKlUO1TUa7mBfS6R0WvIs2sIqRU3XVnv6IGUQuh/KrW/KiiOj3GJ/fxAug2JnnhvcsPfY5SdWfLPkt8LbYF3Q9u2+vbmWxZDD+/p/4+8BQo6Nvqwx0cdjaO2NoK2dnZdO7cGZfLRSQSYYZrBTLSYOsQplyHCdEhzNVXU6sFybcz6Whl861rEQ32Tsv0TSzTVXG3ITWKItlkyCax5cGFT7oZF+1BsV2AjY2BnsIcNWbGtxV04LbFES5fESG32YVynXUqGvWkG5MAiS3gyeKJ9DnyNi4NmdRHTc6ZcgcD136GS5qgg1f24OBRL3BS2TectuVzoriYVDQBISUHVFjYAmbkGQzauBLDsuJOzC7bor4mg/1HvcqowEKuWvc67hYno7BwUaOnpSyq91lBxlfM5Pp1rzC0bhmVRgZZZj1dwlt4fOnf4h67wZ1PnllDWLhwSZO5Gf24o/ctaLZFhlWPqRkJNhlf5o3lwP1f4c5VT3Ns+ffo0sZrhzBi0qSl0HBJi/8uuJOhY9/Ba0fQk1zFC+CL3AP4c8+rWeHrSodIOZevf5OQ5qXKyGSDtwM9g+tbP4jbQUDzxNWxJUMAbmlSFK3gX4v/yhnDHmJK9nAOPPBVTt/8OR3CZczKHMixpZP5/KfLWx33U234uW79q2SbKfye9iXOfAECpfDBLSTtIzU8MO0JmPx/ifeZIZVeO+bv8MGNTSk63a1ETbJIke6K1W21vE/G6o2+Us+1otBlFGyY1SJqJpX4+vh2uOgDtbYfH1PbBRU1u+g9KOiXuO/sYtUl+NMLTYLK8CkRM+AktY3+x8PiD1XqVXerSNpp/9n19gnf3Q/fPaDeB4ESu4f+Bg66adeuw2GfRsjWCjF3M6NGjZIzZ87c3cvAtm3++c9/Ul1djZQSJPQyC1lllGILyQCzC/ubvXjZMzmuKzEZutS4IDy+MQUoENhIQkTw4mqs8Wq4r/0Io4tKarUcvrhyCNcUFzXd9ef8hFTG/cUXce361zCkiVta1Glefvb3JhC4h05BnbuG+cgsmUbXqsSi5bBu8OWAUZRnZeC3Qjyz8HcMrF+BLTQMaVKj+7m/+GJe7nyyekJMcDV2Nja77YCqufxuxROMrFuctKvPROOmfndS7cpgrbcjS9K6k27Vc8+yR1jq7xZLIaZwPI+JPb8ZoDBSzkezrybHTO6nJIF/dj2PpzqfwfyppyYcqW9zRnHxoHsINuvU9FkhOoY2M6xuKRHh4pGlf8eQFl47kiCSUvlbNRBFR491ETbs20SjypVJXrSqzZ8cG/gsdywXD4kXrX4zwHtzrmNQ/fb5Z+2qlOIeg2bA78uhdCn8Z3yK2i4R6zRMEbkSOhQNhuPuV8Knco3qAPMXKiuE5s9z+ZSp5Nofk+9r5EXKH6lyNfhyYekn8NFtzVzRW2D4lKCLi7gJyO4KN85T92m62pdtqbozKZWYmvG0mlU4+HTl76W7Ue3Omhqxs+xzlTodfHr7D7FuSelS+PfBieayhleZm+YmqRPdl7Btlfb1ZjkNADsJIcRPUspRLW93IlttoKGG680336SkpAQhBBtkJWnSQ4Awi/X1bNTKybLTqNLqE7oTG5HQwypMiFhpCHy448RV+wotAA+W7IDPgovKgOJmd+mJhn6/XvtC3IrS7RAjapdRG/2MevtYblkc4v5OGXSqKktwNteljenSeGXe7UwqmsAZwx5iUN1y+gVWc1j5VKrcWfy380kIIRG2jd3w/kiJzw5xxYY3+SD/UM7Y/CnXr3s1zlC0JQY2p2z5kosH30O6FeDmtS9y85oXcUuTk4Y/mlpoQaOgqzfSWK8Z3Nvjcu5f9mDyhwID6lcwvG4JIeHC16LA/p4eV8QJLUCZwbpzuG/Zg7ikyVc5BzA/vQ+9guvoV7+aofXxxoxBzYvfDiV9rcvSuvFBwXiKQyUcVT4Fjx3hq9z9eb/gMP7z859Sv8YWaECP0Ia423TbpHN4MwO3UWg11G8FcONvWcf4Syenh7KE+O4frTxItp4ilBZsmgvPHgX+AjUHsOuB0PdoNY9x8v/FunylEjWDz1Tja1ri9isndt0F+X3Ubd4sktZYNZDUs0tC7WZ4oB/UbVbdktJSIqrjMDjlCRhwgvpj2/DDI/CPHk2vsXiMGvdz+N2tvCftzOIPlDhsibSVUBx7/a5f057Cgnfg49tUOlpKJYZPeHDro6gctgtHbLWRrKwsfvWrX1FbW4tpmlhfbeaZeW/TwywiCzUzsdjK53vXYjZrVUputDhL6mj0t+Kv7BoiWNsrrhqev72RMAFUTVqOqygNV5EfYSgvITn7FbBNmjlgJGAQJk37hnrrWLoEJSsLuzOwZDXYTWJLIvEZQT5b8Cvyo9UcULuAnvVrebvoCCpdGfyx9/Vs9BYCkI7JHb07s2btfFaZMCTDx8WLHqPD6k/4zepn2/yauoU2Mn3a2XRsUYTeI7COGRmDkKnqUJoR1dx8UHBoSrFlAzMzB5Nu1SuvsBZia4Wva9Ln1es+JBo+O8JhldOZmjWUGt2fILQEStCmIi9axdNdziQsXNzU34PHCuOWUT6YfU3jYwKaB0NauLdSG5Zl1tIltIkyVzYFkQoOr5jGHaueafXTJIHpGYOoMdI5onJaY4RNAH4i7XapsMdGzAafrlzddxb1perPW5fAhD/AwTfDmGuU9YK/sCkVVzxGjeRpiNzoLnX/oFPVv8tXwJsXQ+mSVmrJWsEKK6EFTRdg0lYpyWeOghvnKhuKT+6A6U8Rlz5dOwWeOgxumr/7oiaaTtJPjBVR7vr7Kqt/gHevjo+KLnxbfY7ObOPoI4dtwhFb20hGhnKwlidncVrdBN5e+SVrLZPRdm8EgkOjA6kVIT5zzyUqrcbvuSYF2dJPocxCIrGRRDARCLy4WtmjoqHrsLmgsrGxkRjoSQVXWwWYjNhseWIeQhdkHN6VSPmvCAVPByQ+bQrZrsfQRfL0Q4N7fUiDK9doHGZ/xheMoELmAoIBLOfEyGd4m0U6ztryGf/X41LmGAMbB0DrtkmWiHBJ1w4Y3ZoaABixPzx1uJoz1wYiwmBq5lD8doijyn8g3Q5hIwhqHtLNADo2ZiuDwpuTavC0BAKaj7eLjojVPyV2j3UPbWBhep+E231WiPRYc0CaHeaCTR8wN70v9bEoFqh5iFube1gUreD76RfwfKeTmJfRj6G1S7l447sURlVnWrmRxbEjnuCteTfTMVyWsksSoEOkghnTziKoefDZ4TYX2Yc0D/vVLkp4/D4ntFxpSli0x2ilaAC++jPsd5Ga2ffZ3bEok6EE1SlPKDPSmc+q6Negk5XwC1WByIVnj1Gibacb6UolWOa9rvY389nk+whVw4K3YeQFO3n/bWTASfD1vckPzaJ3Vd1W0aBdvardz+QHEtPPZkhF++rLwd8Oo5j2cRyxtZ0IQ6PPRQcwYYrGB59+SChiUq3V86WxgJMjozk1sj9TjKVs0CoQCHpahXS28pivrcVAZ41eyiZRhVsYaFJwRmQMrphoakkyEWVhs0wroZ/dCYmknjBeXI22EtscLTNtpAk1H6+O3aAESdAeQyTSgw7uqxJq123ppc46FgCfDSdtDFDorqSf9iKrI6/iF3PJd32J1iKllGnV896c67ih/10s9PdCAKNrF/HYkL4YWoudGB64+gd1Qnk3xbDcGJbhZYueyfuFh/Bd9mgmVE7n1C2fE9HcvF50DLMyBnDf0ge5td9tySfZN8NthTl9c+KgXQnUa15W+Dozuno+OZEaavU0ck3l6N3go/WbVU9x+cA/t6jZCnLzmhfj5h2mm/Xc0P8uvij9hvNK3ic3Ws3gJOm7ZEKjMFrB7WueT7r+x7qewyZPAcePfIK/L3uIo8p/REjJsrRu9A+sSiqQWhbWJ9tnveblo/yDOWPL54yvnpV03+3FHim0QBWhr2vH2lLbhGeOVinGBhqEztqpqiOxfrOKOM14RvlcSVSxerSenS+0YkQDUDJfiRWhA0kiqLYJpYlzSXcZuT1g5IUw/T+J99mmEonHP7Dr1wUqdbfgfyoiGK5VI3EOuGrnG7Qmo3J18tt1Vyx66oitnY0jtnaQgk6FaJrGYLsrbgy62/m8bUzjXPMgJkSHsEhfT7moZYTZk/95piYU0LtsnfHRgRgpCuMbhJWORvNB2LUEybMzKNGq+M5YREhEkUi6WLmMNwe1KVrWNgxsmUvYHoxXX6DqcoQXYdvUWxMI2Qc2PlIQwhAbAA23O4NoeCRJLyk1nX7hTXw660pqdD+a4SO910HQ88LUyxh2Tmqx5c6AjkMRfY/hUjmWeVH1sf4y70C+zFPr81ghTtvyBedt/ogydzZ/73F5Y1QNACkR2BjSxm1H6RLaxC2rn4/bTRSdH7OGcvawB9mvZhGvzbuVtFhNVUvLiAkV03h08V/5c89rWOvtSH60ipvWvMBlG99p2iWQH6lkWO0S5mb05ciyXA6qSi5gtlVofJp/EBHdTamex2WD/oqIRel8VohPZ11Jn+DarW5DoI5eSPPgtSMENQ+Tc/bjjj63cGz593HjjfZprAi0Z42aGY4XWs2pWgNV64j7njWkCzcvTL3N/L5q1mCrQkxs5X5g3qtQsSLRyqJxEwZ0GBJ/m5RQs0FFBBtmJW5tvNeO0G2sGhvUsjlAWlC3G+1KPr4DZr/UVONWsQLmvwlXftf+dVNdD1CCq6XnmbSdpoF2whFbO0hxcTG5ubmYAQs3BgERAR1e0b+nr9mRIplFvQizVitNiDTpUuPEyKi4LkQbO2mhfCX15JIOgIZGDulUUc9HrlmYzQTcer2CT7U5nBwZvdNeo8RNVPbDa6xADDwZ2WkMW77OJlqThfpBjiKwyHU9hDBcWD1OgKUakjQqoreT67ov9kpsNafxwCug2ziY/TLpUUG44BxCPQ7EY0mEkeIHd/X3qRcYrYeLP0QTgndMizPnLGdWbZMQ6BlYxy1rXuC0LV8AcN2611ji78kHBYegSwtNSooiZVy37hVqjAwG1S3noKpZhIWLes2Hzw5Sr/uoMTL42d8DnxXkmUW/a7WmSgDHl03m+LLJSETSzkkBuLH4ZPZVWz0GrZEsApXZwoqhQVhaQifDStGRloRa3c99PX5FTrSG77NHMC1rKOlWgI2eQvoG1uzQuh12FqnSlymEktsPh/8WMjrBf89Qqb6kVhVeVafV2iBr21QdkalIy1W+Vg2smgyTrlJjfqStomKWqcoE3H5VbF+9QQ099mXBgdeqOYxa2yZxJKW4FU+wfsdt/3Z3hOr1auRQc5FqhtTtc1+HURe37/4PuU35jkUDTbYirjQY/+tdb82xj+CIrR1ECMGll17KT098QZdSgw52Dlu0aiwhWeoqYaksIVem47IT3+rudgEGetxIHw0NC5sqUU+uTEcgMNDJIbHA1IMLQ+pxYssWkkrqKBe15MmMnfMaiWK4NsPY6+DQu9A0jYKRFoHp6wh98zlGeBnp7s8w9C2QNwDt9AfRH1+BWR4kZB/ApvBz+PQf0fQw3uPOxjNaCcH6upFUTVoOywX8uBhpSVyd0vENziN9/45ovmbvWagq9QIbOrSEwG/ofDSqH3NqAnxQWoWO4JoZD5O1pSklqGPzr8V/5eY1L/JSp5M4qux7xlbPifPTCguDancuRUfdTWn5Wh6oy+K/WWM4fcvn3LjmZfxJu7davm8N/9/5aRyJqk+r0f3km9UJ91++4S1u8/+agN50hWzYJsPqltAhUt6mfdgIpmQN59nO8SNLTKHTObQ56Zr22FTfvkxz13jDozon+x4LZUug/wlqhmGyz6gZJO6I6t6YOGjj57nbODjjuaY5iBWr4JWJ8R2ZG2c3/T1SB3ObzYysDcI3f1Ojf467v237TEZGBxh3A0x5vGnfLh/k995943jWTY+NW2oREYwGYPnnqcVWsApWfq2e2/Ow1MLIjMCiSaobMy0P9rsEOg5tuj+3J1zxDXz5Z1Vv6C+Ag2+FIWfs+GtzSIojtnYCaWlpHHTTCZS/upiBi21+Zj2WjDa2Z3Uyc+hi59JyKHWG9CU4xIOyglijlTJFW8qx0RFxKcTmuDEYbfZmsvvnuNsFgjoR2qrY0tJdyLCFjLZS2KuBnp2Bfv5/CdbYuGoiGNleNLdO+kHdST/octWZtGUY5PWGrvsjhCD/kkGUPj0fu95EilzqrePIPKQrntHdAIiWBqh8ZzmY8fuOrqslurGOuu83UnTDCPSMmAFp8Ri1mGRX8QX9E658h2emMTwz9kMkz4cVnya03fexyrlz4t1MXziZ8s+uIztURlS4kEIQSSuk6LBfQ6SOgj6HcHzmcF5fsIp3Cw5nxoqJ+FqJam0P2ypUbGDAuA/477zbKahJ7Ko6bcsXzEvvy/OdT8FtR7HQ6BbayNML/9Dm9dgI7u9+cdztPivIZRv+11jM3xxHaG2d3SJIbRs8WaprcPDpMOBkeHx/lUJL5bvVSLPfLCuU6EyfitzecMlH8bfNeFoZi24L0QDMekGZkDakHLdG5RolNMywMnftMERF8roeCDOeglCNeh9GnKfE5+4gvZCkolUYkNUl+XPmvAIf3KwsOEA9/6yXoNfh8Y8zI/D8cbB5kTq+Qoc5r8Jx/1D1aw3k91HPd9glOKamOxmzPMiqBct55eu3G8VVz2gRB1v9WauV8Z1LCSMLm65WHoeZg3G30LwRTL52LWCjVslosxeDreKE/TQQxeQF77dxt+lSY2J4DH68KZ7V8ECBf3QR9dM2xX/vBaAJEAKjTyaBshpcFTaaoSMtibdfDq5OfszNAdxdM/CP6oAdtoisrkbzu/D0ykZoAiklkbW12PVR3N0y0f1NdWTVn6yi9rv1qTMgGvhHdSDntGYdfZ/eDVMei3+c0FUBfeGA1K9TSvjwVvVjZZsxh2wJZ70MvZsG4kY3zCG0aQF+fw7ax7erq0groh5f0J/XjnqB362ppCi4iS+mnYfX3jl1Og1F96k8tZIRRaPCnU1RpKLVx21x5TInox8dImUMqVvW5u03iILpmYP5Xa/rWJjem5xoDdeue5UrN7zpCKvtYKcLraLB4MlQ0aGWpp1J0VRjSONIoXb+7c/upiJI2cWw/xUw8xlY8vG2b8eTCRe8o1zwt8bsl9V33Y6NL9LdMPoyOPqebd9ve2Lb8M/hKmrXvOvZ5YMrJzf5ozVQvgKeGJt4nF1pcOuS+KL6WS+pqQAtPd0MH9y2XBnSOrQbqUxNHbHVDqxevZpXX32VcFiFiHWpcW74IDy4CBBmlb4FC5uoNOkpi8iUPvRYhMvEoloEmOSejhSQa6dzWuSAlPuKYPKS59vG0UCG1OhldeBgsxXxEUPP8WKHTGQwsZ7B0yeb5QPrCH24nh5mQYrRQYChRBm2rTy6EAiPTsHlQ3AVpM79V76zTIm8VtAy3XS6q8VrX/yRauWu36KiXcf+AzKKkm+gJZsXwvIv1Qlq4Mmpr5RfOBlWT44vHtU9sP/lRI78C6uCEQpL55Lz0vHbfqVO8pPuCm9nOkdK2yTgGp7fcjuy2W2OGNrz2LrYakNBelZXOOlx6LKfOmmaYZUKmvZk67VVO502rLU5rjTosj+sn5bCXb8VDA/ctCAWDWqF+jJ4aFByQXLR+20Ta7uSyjXw2jlQvlIJYN2lrDz6HZv42K//puwaWg4pd/vh+Adh2NlNt710Gqz4MnEbnkzlodXsAtNh5+M4yO9CCgsLsaxmg56FzUfuWRwZGYYHg4FWFwSCzVSRjpcwJiLmg7Rc38QsY2WjeAoRJYqVNN0okWwSVehouKSBWxoMsrowwEoRhm6OIUgbXUjtV8nn84WXVRFZuZmeVkGjEEyK2XCKB2mpKzQZsSh/YSFFt45CJOkwklJiVrY+vw9AcyfZb//j1J/toWjQ1j11IgFlEtmyS8cKw7zXcR99D/38XvAfANdMU+mYVCc5oSduh+Qn3G7hTZw38p+8XvoSrP9JRSBSdHiJFv9vfvsWI5ssK4BXtmN3nMN2kUogC81QgmLwmTD/jdQu87oHDroFeh2iisq/ugd+eHj7zEp3FKGpWqiajbRJdEUDsOZ7NTrIMhNFQyp0jxpLtLXIXagGvvpr8hmSZkhZLOxpYiunG1z9o4paReqhcKCa3pGMSF3y3xnbVs9tTirrCGmreZcOu4UdaPFwSEVaWhr7778/LldT2qxcq+M1zw+87/6JAGHWi3JKNCWUfLh5zz2D/3omM821jKhQJ2hNCgKE2aCVJ9R7NdBF5tHFyqOblc+ZkTEMtLqm9Opq+C92A7VfrQMrdb1Wd6sgrni/zUiwaiKYW5KfNAKztxBeVdXqJoRLwz92F89Rg9iPdYqTR8uxH3m91JWo4Y05VcfQ3HDEH5oG+jbffIrd2giWZA2CSz+Fq77f7jb4HLOWgJ6YPpY0uSBt7dTY/H7LiZHtVJIawOoeuOVnNSql33Hq38nQDRgcc4Z/+3I1YHl3CC1Q6bnxt6kaoLbaFNimiiaPvFDNSMzvC2NuoNV4nzRhw0x4bDQ8f0Js8HYLln+pRgrNeSX5+9GethI7g7xeqng9ldAC6HtMivdZQu8J8TeNulRF81riSVfRRYfdgiO22okjjzySY489loKCAvx+P3l5eSCgQqtjhrGCn1wrY78xynx0QnQobgwMqSOkElq2kKBBiVaFlaS4qcG29HBzMNUiMTQvkVSIOubra1mkrecN4weWaSXqTkuqqFQrOSc9qWFBGxECO5xkJhlQN6UEoqna0jUwBL6h+aQf2DH5Y9oTTzp0GkHCm6K5VeqxJUMnws0L4fiH4Oi/waWfwd0bVQTigCvjf/SEhtA9RDR33CbCwuDT/IM5rVMsHbr046RXsW05FuWeXD488M/qhzlm9xAUbkpdObxdcCRRoSccbiXEBDZqxI/V7GdhTkZ/AtpuKiLeVxCaGp+i6XDGM8pnaewNSsQ3j2MOOk0VupevUF1m7V1z1RrSUp/REx6Cw36rvO7aQsVq5Xt1y89w3QyqxtxIVLQSObctZfhphmDtNHjr0qb7zLCqT2rocEzl9YVU6ddlX0Cg9RrH7cK21Vrak+4HKSHuauhKF+q3Zez1kNM9/rE9xsPBt6hoqTtD/fEXwPlv75iFhsMO4aQR2wkhBCNHjmTkyJEAlJeX88QTT2CaJsuNTehSQ+qSQVZXNHTyZQbnhMexWiulXoRYqK8nKNRVWomW5GquYT+xH+P+ZnwUyMbme2MxK/TNjdEsicRnxZ/osYkVw8vk/qOIRqGnxFcbZzAKcHdqCllb9SptoPtdyEhyESZcGplHdcM3KB8jZyvF/e3JKU+ouW9mSP2Iu9NVvciE3yd/vD9fjVNpyRF/Uh2aPz6qrsi7j4dD70R+/gdCy78kKnQ0abPU35NXR/2ep3t0UM8zvOpqvAVbe9dtBN+Mup2zD78YBo2EqU9A2XJWVFdxbf/f0DuwjmMrfsBlxUccBbAlowdv547jq+zR2MB/Fv2BTLOO9Z4ipmYN5eKN7yKQCClxySgass1Xag2vZA+OLWwdYagoS3sg7ZjXVYzC/tDrsBazBmNu4/5C6DgkVuTePstpE1ZEjQ5a/KG6CLngbXjhpK13KlohWPBWo8XApcvKedLIoDAa/xuXtL7NjihRWrtZ2Uk8fYQydW1Lrdpnv1XixArDuJvgsLva+kpTY4ZV087sl9T7kd9Xic9uY3d82y0RAk5/GpZ/AfPfUkJq+LlQfGDyx4+/DUZepEoivFnqt6e1yJlDu+MUyO9CFi5cyKRJk4hGGwa6wv5mbwZYXRqFjEQyX1vLLNdKtNhtOhqHRAbSTRakFDphoniaucav1kr5xrUQU8T/Inuki/PCByWkB3PO7kfV28uQkRZWDJj8pK8kAx+5MoMOdnbyFTS4MmggdI2ciX1JG1KAWR6k/LXFRDequgJXURrubpnUz9icYPug+Q063n0gouXInt1BuFad3MpXQKfh0P9EMNxbfVpbsUuXsnjlT6z0dqRj99GMzExrqm+r2agKfVPMZmxO46nY8GJO+BPuMS0MUqNBuL83ROqIoONKlhh0+eCIPzF34AVMqawjz21w3GsT8FetJKi5GX7g/wjqXvIjVZS6czhz8yfcu+wRPDKaUkAp64hYY2tb35Q9Gd0NiFaiJ6loQyG57oEbZkN6EWyap/b1wU2wfkbiY11pqjPvpVPjars2eAqYl666TofXLlbHJbeX8rZqj5mNzRG6OpEPvxBmPrX1xw8+Hc54lnXBEHOfPYsjyn7AE5svKlCpa/W5SfK+udPhss9h3huqM7mttV/NcaUp4dL/+G1/bnPeuAiWfhJfT+byweVft94dvTdjhlVUtXINdBiqbCecaFkcToH8HsCgQYPo27cv69at44cffmDF8hVMN5azUt9Md6sQG5uV+maqRQAfHg6K9qdK1JMl0+gq81IKLYlsFFoNkadyUYuV5NLXxmaLqKaDzGm6URekDcmn5tPVWGa46bdZA8PrJm14B4TXRZcOfTGm1xJeXtX0XEPg6ZmNb2g+4aWV6Blu/Ad2xFWQhozabHliLnZ9tPF8E91YT7QihJHjwaoKK48vXSA0Qe7EfnuG0ALVsbjfxe22ea2gLwML+jIw2Z2ZnZTp5JIPt7odYXjh/LcR3cbiblmXUrVWXf1HlJu8O1koRHeDvwiGn8cwTxrDMmJpz4LeULUSnx3hrXm3cOHge6k20ukS2sRflz+GN8nw7QYkMCn/MKKawZnNzGRbRXeDN1utVeixNe9BF4JWBHw5YOqxbrqtrU0oa4bh58IXf2i9tqrbWGW4Oedl1d1qW6kfL6VyftfdEA1gI7ijz8280eGYRj+14lAJb867lYJQNe0utEClFE0LZj2norKtFbMLHTbMhv8cjid/EIeVTU34LEkE33SYwIQt3ySKKaFBdtdY9+V2CC1QInXqE62LLSlVR/L6mZDZGQacGG8gWlOi0v0t04dmBH54BE59cvvWtidTuUZF/CN16jvg8ilz1Es+Ur+XDq3iiK1djMvlomfPnkyePBkPBnl2BqVaDT8ZK2gYHp0rMzg0OpBs6aeY/Fa31zKt1/D3YVY3uti5fOSenTCP0RayRRW0ZNOjs8k6pjuBuWWEFpeDBE+fHHJO7U3XbC/1s7dQ9eYywg3RKA1w6+RN7Id3QC5CCNJHdYjbT3BRuUoZtjwvmTZmdVjVjQF6rpe8cwfg7pjokr/PcsKDsOrbRqEEqBOsJxPCNeqk48uGEx6G7uOSb+Pd61U7fCoMrxqHMu76RO+dQ+9UJ5tokCF1y/hp6kQWZA8mN7MQX7KBwwBCY2VaN27o82tW+zoza8rpbU8dutLg10uVQW7NejXgeMpjbTPQ3FWE61RUafGH6tiULm4l+ihh83z48k9JU8JxrPpOuYK3Bd2AZZ81nuRf7XAsbxUdRVjzEI7V1i1P68aVg//C23NubOML20lIe+sF+9KCypVQCQUlcxBJunWDupe6buOhZhaEalX6ENRn5Ji/w3cPtNFXrBVaq92KhlTkcNNc9XfDo7y7znoZeh2qHlO5WkUkW4otacGWn1tu8ZfBpKuV5U7DZz5SB6VLlMv/0ffu3rXtBThiazfh9/sJY3JwdAAZ+JBIakQQXWqk4WaNVkq2TC4+JBIbSRSTUlFDgcykQqtnibaBXJlOrkynwM4iT2YwyOzKPFf8DLsiOzthm9amABVvLiX/ksHkna9C4A1RJmnaVE1aHu80bwOmJLqpHt9ANSE+vLKa2m/XYVaF8fTKQvPoSDPJyaiZXQSAVRmi5pNV5F8yuO1v4C+djA4qXfLhrbBuihqVMuwcZc5omyrNmdExdQjfDCd6hTUnLR8u+0x1QiWj835w3lvwyZ2weRFaWi5DR09UQ4/XJvHwMTxw+O94MPskZm+u5PJ1r2FvS5VW55GqLqXLfsB+Kj2x8H8qpbqjJ9adRVoe9DhY/fn3IW1K87Zp7amOUUtcaXDIHfDTc40i9OnOZxDU47vUTM1gZno/So0MCqJVidvR9FihdeyiywypyIQVTrQR2BakrTpwW+lwbo6QVgrvMcGh3XrBQVNgyqOw4mvlqj72BnVhcV8PUkYWhQHYrR8b3aNmMKZiyuOwcVbTsWtI1750irKdOf1ZZTqaLKWsGdBpZOpt762E62DdtMT31QqrlK4jtraKI7Z2E/vvvz9Llixhsv0zR0aHoSPIkmmYWESxYrVRyS0cBIJ6gkxxLWWDVsFAswsBEWa8qZJSGgI7Vv/V1+rIPGMNGhoCOCw6GD1WRZNQ7G5Kyv/7Mx1vH43mbfpoREvqSVrbZ9oE5peSeXgx9bM2U/VOkyAzS4MIlwa6AHsrV/amJLSiGrMqjJHtdL41UjQQLv04eet6Ki+dYJUa6uvLS71dlw9umr/1gbPdD1I2FJap6jQWf6AK/VOlinoexq2ZHfikrJri0Ca8qSJgCQg44ZH4mzwZcMW3MOMZ+Pl9JS62LGrj9mLb3JlpSJcPxjWLFHXeDzbNb7tQ2hF0txLf42+HEefDzOca76rTkx9DXdpqLmYysSVRqcsDr1Ypy27jVMH5uunw/PGpo1PCFWukaeW4djtIbWerY4Bim0xym1/YaJ2GKrPio/6a+IDWTFE1TX1eU2H4VLPLgVenfsyc/6YQyVIVqH/xe2WmPOwcJTSa+6IZXjWH8RdHK9+lPbjue0/CqWzbTRQXF3PEEUewxVPLR+lzWObaxBZXDcb++XS9dhQdjh8AggR/rQZxlEkaR0eHc1H4UFbomznYHIiB3jjYuuH/GXgZahWzn9mDieExFNsqLZnKt0sGTMqeXxi/T68OKeYnyqiNtGyq3lvRIvIlkVELzW0ol/mtIAyBVd3O7dN7K231CPr2H8pv6NVz4KnDlLt0y6+47lYniUg9lMzbeiTDMuHl02DSNTD/TVj+VfITkWWCL5seaR4+G9UPq/hA6ltEXFLSewLkJBlJ5c2Eg2+GX30OiEYri0Zcfigamvg8YKfXe1mmSuE24MnYNUJL6HD7KiWOR16gPguDTmv04jq6/AdcSWqXMs06uoZSTGiQlvKm6rK/MtJc+jGs/FZZnvhykj8HQNfgsi9ad3JfO1XZnnTZX43paXnM2oAmNHh0P3j/JmWr0JIe45Nvt6BfrJEhCZ5M9bzD71YXEK29ztY+O2YYZr2oBMbxDyoBnF6kRFzPw1S0OLdHay9v78SToS4wWr7vuhuGnL571rSX4US2diMHHHAAw4YNY8OGDXi9Xjp16tTYkebumklgbimh9dUIZEpz0SpRTyc7J6l4UuOrNfY3+yTc15p9Q3RDHZGS+sYaKiPXl/L3RwZNzIpQ8uiVrewcMg7oQuCnLSAlIt2FWVKfsD1pSlyFW4m0OKRm0Xvw/UNKCDWIITuqanx0d1NBa2YnVdz70CDVXWmbqk384FtTbHeSKhJujFSkSM8ITVkVHPkneqZ56HnEpbDyBdXNubUOvnBd6/ev+Eq1+LdMYQgBFctbf+7Owo7Cx79WXWZFg2D6f3bNfosGqnq66g0w73UIlKsxVdnFUL2Bm9a+yIf546lyZRLUvRh2FJe0eGTJ31p3yZMSvvgTzHw6ZtYrVVTmuAfh87vVMWku4jQ3HPFn9dkK15EycmgG4ceH1bGK1LNdhh8Nn7V5r0NeHxh7bfz9x/wNnoqN/bHC6vOtu+Hkx+HFUxK3p7lg+Hlw7N/btv+hZzV9l5KuL6TeP01XFwMH39zml7ZX02CJEw2qY+ROV+ndw+7e3SvbK3DE1m7G6/XSq1fyupmM8Z2pe6OSEruCLnZeYyqwuVDyShf2zr6K1wRWeRBiYkuadsrfVhm10dJcSCv5GvRMN1lHdSfrqO4AmFVhNj/8EzLcVDgvXBr+cZ3QfM7HcbuZ8ljimBfbVBGQI/+s0n9Fg5VHz+L31EmqQQR9939qaHDM+yiORZPalhKyo2r2ZAOGW13l//CIstAQmioqbpmCMrzQ56jWt71pXvLUUaROpUtTjbdpZHtSikmeY4aVyBp/23Y6kgtVi1a5CipWbv2xrjQ46VFY8gm8dXFTl+LM55S/0pCzyP/ub3w782Je6XAc3+eMpFtwI5dufIdeweRjuBrJ6BDz8WoWnTND8N41aoTMkk/hu7+ryJLQ1Ov15cBr5279/Q7XNvvHDvw2RQMw9fFEsZXXC66bCTOfVe7yRYNg9OWQ1VnVDn1yZ9MaNbeKkB50U9v3O/YG1YSwYVby9bvTVLoyXKcuAjI7bSVS9gshr5eKsC6aFLN+GKKc7R3/rjbhvEt7ML4h+eSt78bCKev4wjWfXJnOSZFR6LGYFYAfL9FGZ5p42mxA2vJ5lsTo0FScr3l09FwvVnnilZ67exa634W3bw6hpZWNHYaAqtcSUP7Kz6SNLMLbLwcj20PR9SOo/nQN4RVVaH6D9PFd8O/XxmHSDskJlCe/XTdUfU7hAHUSfOPCxEhTNKCu5JOJLXcGbfaKajl7zpMBh/9W/QGY/KAaMdNwItQ9yhB29GWtbzunu4rKRZJEwCI1rT9Xc6n3oK3z+IQOBf2hep3q+myOtKG2RKXRUtUtdRim/r95fqyWJfa+udKg/wlw+lPw1q+2LraEBpkdVWTn+RPjxWa0HtZOgZ6Hgu4hM1rPVRve5KoNb2799TW8xrR81fWZjP8cpqI2LTvt3rsu6QiqNqG7oeuB6r2d9ULb/coazF5DNUoAWRE1SDm9EA69I/Hx+12k0ng/Pqqigb0PhzHXt31YPSgxddkX8PBgqNmQeL8Zho9uV+lEzVBrGjpRGZpu7/uzt+BOU3YmDtvMDoktIcT9wIlABFgBXCKlrEryuNVALeoyykxm+OWQiBCC7ON6cvzBXeg5bT7fzv+RSRUzODk6CgO9UUgdag5ijr6KEZaqFWgY47Ndcw0NgbdvDq78+HqbnFN7U/7CIhXlkijzUpdO9vFqn7ln9aP8vz8TXlWD0EWjS3xkTS1QS2hxBb6hBeSe0Rcjz0feuf23921xSEbvI9XVZktBoRnKxR7UiUvTkjuP15cm3+5+F8WiW61EM4SmxNCorYimg29R0bWpjytLin7HqUJlX3brz+t/Anxyl1pDQjfUVgSUpsNFH6g068J31MnT8KLSZj4lxEJVKoJjeJTb9un/gacmJG7L8KkreW8mDD1bFUc3t6Zw+eDov6raoHCd6mpb8D9VfD7qMhhxgXpcx6Gq2aA1WwtpKWE35dHkmbhoQNlFpBeqiGHiCydlyrfr/q1bgljh5J8RaW3dtT7F8HV0jzr+K7/dhnmOArofrKJsb1zQNLdUCBXB2v+K5E/rMV792RE0LXX9F0IJxuZpxvlvqc9FQ1eebStT2lAVdBmtZkI6bD/Lv1CDxitXK8E+4fft49TfjuyQg7wQ4ijgKymlKYS4D0BKmXC5ERNbo6SUrXzDE/mlOcjvKKElFZS/slil4FCu8TpaY6SrjiAS8OGOi35tCxmHdyXz8GKEoYSaZVlMnjyZGTNmkBYwOMDVnw6ePPw9csg4pAtGXrwos6rDhFZWUfV2C6sIVLqw4KphuDvvhMnzZlhFc/wFv/yrybZQtwWeGKcElRVGpaG8cOI/1VU3qDTU//WFQIuvodBg4Clw5nMtt6qY/BB8+zcVJWooVB94Eiz5WEVZeh6mLClye7bf66taC+9cDeumxk66IvGk3jBjr2Fgr23CiY/AsLObHhOuhaWfNaWaInVKRLm86kQ55Ez1efru/2DyA00i0/CqdNGVk1UNlRkbVzPrRbUeX47qUBt0ytZfS6ACHh2pjtXW7CMGna4iOpHaxPv6HqPqZV44Qb1WM6LW3nGYEinJUsugjl9WV5UC21Y0I3lUz4iN1+p5qPINa7lfbzbctly9p9/dnzoyKDT1nmgudRwvmATPHJn8WF/9Q/s6tX96t0obtxSHDWtsiSsNfrNeufa/dIpK3QtNPf+wu7evS3HtNPj6HihbAgUD1JihrvvYMOlF78I7V8ZHdw0fnPs69Dxk960rBakc5HfauB4hxKnAGVLK85LctxpHbO0wwcUVVLzaJLZCRPnKNZ8jokNx0TTMdXtEFqiuw85/jL9aeOedd1i0aFHTiCHA7XZzzTXXkJ2dnXQ7VR+tpO67JOF3AZlHdiPz8CSdZ23FttUVzrQnYkWqBhxyuxrIul11NL8g6suVs/aKr5TL9oHXQtfR8Y9Z8Da8e03TD5cwwO1TNgupPLdAiblV36murp6H7tTRRUmRUkWhXGnxUYFIvSqc/vS3yWvJhp4FfY9Wn5M+RySvpXntXBUtaT7rUDOU4Dzjmabbln2u3s9AOQw4CUb/KtFyw4woAZeWu22fv/IVykNt1XcxkWgnChBXmqq3++ZviWni5iNnIvXKHqNusyqe7zJapUD/r2/qIm/hglamACTF5VeC4fuHY1GuiLqteAxM+J1K9/qy4fM/qu+n0Jrqvc77HxQfoF73E2MT16V74MJ31ZzB0iUqJT32evjpBfjuH8nXM/pyOP7/tu01bAuBCvj3eBUFNIPqu2K41GcrqceWDnesVa+vai1xqXfDB+e/pexU2sqKr2I1ci2ip+fsmSKj3XhosErrt6TjMDW0fQ9jV4it94HXpZQvJ7lvFVCJ+vT9W0rZplYeR2zFY0csSv46NW5+oRWr19JSeWdtI53vOQihC2pqavj555/59NNPsVu0X2uaxv77788xxxyT8HxpSzb+eSoylOzqV5B9bA/Sx3Xe7vXx7T9UfVHzK2dXGhxzH+x34fZvd19izY8qwlC1Rp0oD75VnSj3FFZ8De9eqwSGtNUJ6rSnwR/zDqtaC4+OSjzhuf1w2lNbH8Pyl/zU0Znfbm76d6gavvwLLHxb/XvIGXDYb1N7nG0vZgQeHhLvzg0qpXnTfCU+Xj5Nrd02AQHDzlLTA1oTeDOfh0/vbMWXSo+V46XKDTar1XP51Qn+7FeUCJ73BgQrIL8/VKxQ38f+J6hjJYRKaa/8WonzvsfEe7rNfB4+uUOJTBGLUJ7+TPLj9uLJsPKb5MvrMR4uej/1698ZhGpg9stqDdnFKnX54S3KMLglOd2V4emLJya3VPHlwMUfqQ7TtvD4AWpKQUsKB8E1P27Lq9h7McNwT4fkkUTdA7/bsuvXtBW2W2wJIb4AOiS5624p5buxx9wNjAJOk0k2KIToLKXcIIQoBD4HrpdSJpWkQogrgCsAiouL91uzZjtC3b9gAgvKqHx9iTIZNePfahMLo1mEa3vocPsoJs+dyg8//ACAN2Kwv9mbznYuJhaL9PXMM9bSpbgLl12WWKMTWlJB2X9/hkiSL4cm6HjX/ujp2xkVkRL+XpxYuAzqh/Cm+du3XYc9h7Ll8O+D48W05lInqCu+bRIXn/8Rpv+76XGuNOg8Ci6cpCIMoCIQ5ctVaq3B+0hK+EtB8mL55mLLMuHJcaqQvSGNpLuVc/iVk5v2sbOoXA1vXaY6L0EVxp/+lOq0A1UDtuQjlZrqcQgUtrHmsWSemo+ZLBKju5U4eOMCkjZA9D9B1bGZYZVeHXBi/Oue+ZxKx1qmEkwun3rOaf/ZepSvvkxFDnUX9DlSCctkfPhrmJFiuPVBt8ARf2h9P+3Bxjnw3LEqOteQ0nZ5YeLL6v1548Lkv1GgBOZV329dcEkJf8oh6XERGvyhcgdfxF6ClHBft6ZGiebsob/52z2IWkp5xFY2fDFwAjAhmdCKbWND7P9bhBDvAPsDScVWLOr1H1CRra2tb18jbXA+nq4ZBOaWYgdN6r7f0FgbZcVMIFw7ILjWPzWbKdEpmKaJV7o4JTIaNy40BB5cDLd6kEs6dbleqj9bTXBeGcLQ8B/YAf/+HbFqIrQcvdiAp2fWdgktadlENwfQDAsjWUcaQO3m5Lc77F1Me1JFeppjR6FsmRIiHWPdfkf+EXqOV2mmSL2KOg0+vUkMrPkR3rq0qS4qp7uabZffB/odo+rNmke3NJdKFTaw7DOoXh9fr2NFVMRm+ZfQdyt2FdtKTne4/EuVurItSC+Iv9+T3lR7ty10HKpe78/vJ0YH0ougfJl6z5JF+joMTd7xBypl/cmd8enAaEDNjVz5tbK4aA1/Pgw/Z+vrH32ZGk/Ucn1CVxYNu4NOw+Hyr+Hb+6BkjvpMjb9dpT7Dta03bUhLRSlvTRKxao4QkJaTfIZjWivTIX5pCAHjborvYgZ1cTU+xWdzD2VHuxGPAW4HDpFSJm1XEkL4AU1KWRv7+1HAn3dkv/s6epaHjPFdAAgtqyS6XgkQDy6qqMePFxd6THop2dPWzkRRHaWLns1KfQsDzM4xJ/qmq1QXOt2sAlxLvNTWrW+MrlV/uIrwyhoyD++afMMuQdrIVpynUxCYX0rl28tUF5QtMXiUPP33GKJFDUtDBMBh76ZiRXwtVQOarlr5G8QWqBN6spN67WZ4+Yz4mq7SJSoacfMiOO4BFfEJlDeZvfoL1ZDjBjbNS241EQ2o+3a22GqgPbrWJvxBpWajQSVchaYiVic8DNVrVYSrpZjRXK2LhhVfqTq3lkTrVdfn1sRWWykcAEf+Bb74Q5NYFBqc8ZwSI9tLQ1xge+s8C/snbyjxZMS8vu5I3XVZt1l9Hgv6tb6PsTcqQddSZDQfG7UvMO4mFV2d8liTd+Chd8CIhPLwPZod9dl6DPAAn8ecz6dKKa8SQnQCnpZSHgcUAe/E7jeAV6SUn+zgfh1iZB3Tg/IXFjZGtySSn/QVFNsFBEWYxcZGvGleDq8diGjDdBHd1uhGASv1LRTZ2UnTkrquI+vMuDSmjNoEfy4nc0JXvH1zCC6tbBrxowuMbC9pQwsSttUa0U31VL6xNK6rMSqKKbPupch9ZdPvpOFLPkPNYe+j+3gVlWpZQG1G4oVWa8x5JUmkRiqxsewzNYT4+p9UdKtsqWolb2nOmNNDOWS3FFyutD2rvq0t5PWCa6Yo76m1U5UVyLgbVdQrUKG67lpiR5XtRNkSOOPZxI5fPdaZ2hKhNY4S2mmMuQYGnaqOne5WkbrtNRGt3Qwf/Vode1DbOu6BbfPh2hqjL1WNAm9dkvx+zVC2HlsVWzeoSNnUf9FYPzfmWhhz3c5b696ApsFhv4Hxv1ZpdF/uXmmkutMK5NsDp0C+bYSWVVL90SqipQH0TA/eQzuyxl1GTU0NnTt3pkePHpgb6qn+bDXRknqMXC9anpfQ7ERvJQksNjbwg7GYA6J9GGh1aRxc3UgKj0vh0sg+qRdpI4uom7qR+mklSFOSNjSfjEO7xg23bguVby+jfsamhH0JQ1LQ4WncgR/UbLcJv9v32qF/qQQr4fEDlT1Fg2BypakU2omPtP7cBj68FWY8nXi7EfPBGv2rrW8jGlRF6w1F+qCEhL9A1YkYv6CB6au+g9cvUPYSdosrMsOnoggHtRhJE65T3Y4tO0JdPlUE3nlk+655ezAj8Nh+ULOx6bOlGZDREa6ftfM7bB8ZAZVJzGs1N/x6SdujmNEg1G5Srv8NtiYOeyzbXbPlsOfj7ZOD98b4K70cusT92901g4LLhgBgB6JsfnxO0m0Jl0apUYthayzU19Hf6hQvtjSBlunCro3Gu8XH7tMz3QhdkDGuMxk70nWIGu2TtPhLN7AOfwgG7kO1C/sKvhzVzv3N32HZJ8rB/oCrYL+L276N7gfD3FcTO8IEysW8Lbh8cNnnMOlqZU5J7Lmn/OuXJbRAdfXdOAfu7514nxmEGc8mii1POpz1Erx+fpPvlLTg4F/veqEVCSg7kAbLk1GXJbcxWfJRrCauWdTTNpXAX/Khip7tTC7+UPmpNTevNbxw4DXbli52+X6Zw633MRyxtQ9S/dkarKok3UkCsk/uxeEz0ui6biVrtFKW6Bvpb3VuTCd6emeTfXwPtjw+J34eogDNq+Pps/NmhHn75hBeVd2UjowhLRt314ydth+HPYyMIjjxIeCh7Xt+/+Nhci+VImxIR7rSVNdbh8Ft305uD7j0kybR5va3/vi9GSmVaEpGqukBvSfArUtg6SfqMb2PUIOJdyWhajVeqLZErUEz1MzEiS+p492csmXJLRki9eq+HaF6vUpNCk11ZGYUQVYnuHGuKu5e9rmqMRtznWrkcNjncMTWPkhwflliVCpG2uB8NEOj58YAPaLxBe1atpv8SwYhhCD/4sGUv7YYGTSRUuIqTCPvvAEILb6Oww6bBBdVIEMmnt7ZuArSaCv+0R2o+3EjVk24sT5MuDT8YzqiZ7SzqabD3ovugks/halPwPw3VA3RqEtg5Db4sNmW8vPyZe8bQ4bTclUtWtnS+Ns1Q9U1pcKbuX1dkjuLHx+LdY3GLh5tU/1550r49bJ4q4qCvkowt6zDc/u3Xj/VGlOfgC/+iJquAHx6l5qTOPxcJbra03jVYa/BEVv7IqkacDQBQuAbVkBoWRWBuaWqCF0TCF1QcPFgYo0OeHpm0fE3+2OWhxCGhpGdmFoJr6ym7IWFICXSloAg/YAOZJ3Qs3E7raF5dIquH0Ht9xsILihD8xmkj+2Eb0j+9r92h30DdxqMv1X92VYWvAMf3QLRkBJdfY6EU57Y+Wamexon/wteOll1IVoRFQ30ZMLhv9vdK0vNoneTe4iZISUcm4/z6XecGsBthuJrttLy1X3bQ9lyJbRaNnR8cJPqyMxIZlHpsC/iiK19kLSRRdT9uCHeFFUDd7dMNI+6Esw9sy8Zh3QhvKoa3e/C2z+3cV5iA0KIhIHVDUjTpuylhY2jhWK3Uj9jE95+uXj7ti1aoPkMso7sRtaR3bbpNTo4bBfrpsO7V8e7ri/7DN68GC54e7cta5fQdTRcO12ZlZYtheKxMOLc1IajewKeFHNWbSsx7au74FdfKH+wn2PO8/1PgGPv2/75qgvfTjHnUVPDxtvSjOGwT+CIrX2QzCOKiayqJrqpvslWQQjSD4y/CnMVpuEqbHvarznhVdWQxEReRmwluNoothwcdik/PKwiWs2xIrDme5Wu2tU1SbuarC6qu3dvYf8r4IOb4+vKhK7SgtlJZrCmF8TPv9xRbLvJsysOmdjZ6bB7KJmr6vjqy1QEc8gZu6XJpW1Olw6/KDS3TtYpvYiz/bAklW8uIzA/0Q5ie1BpwxT3WUlUmIPDnkBliwHCDege1X7vsGcx9CwYdo46ebrT1Z/srnDWf3fN/gecoLy/EpDQ79hdswaH1Mx+GZ49Gma9qCKNH90GzxyVeEG1C3DE1j5KzSerE2YryqhN1fsr2Rnea54eWZBEcAm3RtqIbXeSd3DYJXQfp9zTW2JFIL/vrl+PQ+sIASc8CNfNhJP+Cee+AdfPVoJrV9BhsLJycPlURE0zlDfZ4b9LHllz2HVE6pW4igabvPKi9cqod84ru3w5ThpxHyWyrjbp7XZ9FBk0EWnbWcMQQ3Pr5EzsqxzgbQmWRLg1PL2z8Q1yCtwd9lDG3ag8m0I1yjcKYiNSbvrlF8jvzWQX7z5xc8TvYfCpsOg91f046DTV+eiwe1k/I8VIqaAaKTX60l26HEds7aPo6W7MUDDxDk0g3Ns/yLo5aUMKcHfOIDBnC3bAxNs/F0+vrDZ1Ijo47BYyOzWZqq78Rg1MHnejOoE6OKSiwxD1x2HPwZ2ROHy9AV/2Ll0KOGJrnyXjsC5UTVoRN3cQQ+DpmUVg9hY8PbIwUnQabgtGrpfMw51wusNeRHaxcop3cHDYe+k0QomqSD1xdZiuNBh92S5fjiO29lHSRhZh1USo/WodCIG0LJAQWVVDZGU1UoJ/v0KyT+ntRKIcHBwcHPYuNA3OfxtePEnN8kSo2suDboaeh+7y5Thiax9FCEHmYcVkHNSZaEWIsmcWYNdElOiKEZi9BU/vHNIcE1EHBwcHh72Ngn5w8yJY86OagVk8Rtl/7AYcsbWPI1w6RG1kKNGYT0Zs6qeXOGLLwcHBwWHvRNOhx8G7exWO9YMDaqB0ilShjDieWA4ODg4ODjuCI7YccHdOTzovUbg00kbsnpCrg4ODg4PDLwVHbDkgDI3cif0QLg10pbqEW8PVyY9/lDNI1cHBwcHBYUdwarYcAPANzKPo5v2on7kJuy6Kt28O3gF5CN3pRHRwcHBwcNgRHLHl0IiR6yXrqO67exkODg4ODg6/KJw0ooODg4ODg4NDO+KILQcHBwcHBweHdsQRWw4ODg4ODg4O7YgjthwcHBwcHBwc2hFHbDk4ODg4ODg4tCOO2HJwcHBwcHBwaEccseXg4ODg4ODg0I44YsvBwcHBwcHBoR1xxJaDg4ODg4ODQzviiC0HBwcHBwcHh3bEEVsODg4ODg4ODu2II7YcHBwcHBwcHNoRR2w5ODg4ODg4OLQjjthycHBwcHBwcGhHHLHl4ODg4ODg4NCOOGLLwcHBwcHBwaEdccSWg4ODg4ODg0M74ogtBwcHBwcHB4d2xBFbDg4ODg4ODg7tiCO2HBwcHBwcHBzaESGl3N1rSIkQohRYs7vXsYvJB8p29yIc2h3nOP/ycY7xLx/nGO8bbMtx7ialLGh54x4ttvZFhBAzpZSjdvc6HNoX5zj/8nGO8S8f5xjvG+yM4+ykER0cHBwcHBwc2hFHbDk4ODg4ODg4tCOO2Nrz+M/uXoDDLsE5zr98nGP8y8c5xvsGO3ycnZotBwcHBwcHB4d2xIlsOTg4ODg4ODi0I47Y2oMQQhwjhFgihFguhLhzd6/HYecjhFgthJgvhJgjhJi5u9fjsHMQQjwrhNgihFjQ7LZcIcTnQohlsf/n7M41OuwYKY7xH4UQG2Lf5zlCiON25xoddgwhRFchxNdCiEVCiIVCiBtjt+/wd9kRW3sIQggdeBw4FhgInCOEGLh7V+XQThwmpRzutIz/ongeOKbFbXcCX0op+wBfxv7tsPfyPInHGOCh2Pd5uJTyo128JoediwncKqUcCBwIXBs7D+/wd9kRW3sO+wPLpZQrpZQR4DXg5N28JgcHhzYgpfwOqGhx88nAC7G/vwCcsivX5LBzSXGMHX5BSClLpJSzYn+vBX4GOrMTvsuO2Npz6Aysa/bv9bHbHH5ZSOAzIcRPQogrdvdiHNqVIillSezvm4Ci3bkYh3bjOiHEvFia0UkV/0IQQnQHRgDT2AnfZUdsOTjsWg6SUo5EpYuvFUKM390Lcmh/pGr7dlq/f3k8AfQChgMlwAO7dTUOOwUhRDrwP+AmKWVN8/u297vsiK09hw1A12b/7hK7zeEXhJRyQ+z/W4B3UOljh18mm4UQHQFi/9+ym9fjsJORUm6WUlpSSht4Cuf7vNcjhHChhNZ/pZRvx27e4e+yI7b2HGYAfYQQPYQQbuBs4L3dvCaHnYgQwi+EyGj4O3AUsKD1ZznsxbwHXBT7+0XAu7txLQ7tQMMJOMapON/nvRohhACeAX6WUj7Y7K4d/i47pqZ7ELG24YcBHXhWSnnP7l2Rw85ECNETFc0CMIBXnGP8y0AI8SpwKJAPbAb+AEwC3gCKgTXARCmlU2C9l5LiGB+KSiFKYDVwZbPaHoe9DCHEQcBkYD5gx26+C1W3tUPfZUdsOTg4ODg4ODi0I04a0cHBwcHBwcGhHXHEloODg4ODg4NDO+KILQcHBwcHBweHdsQRWw4ODg4ODg4O7YgjthwcHBwcHBwc2hFHbDk4ODg4ODg4tCOO2HJwcHBwcHBwaEccseXg4ODg4ODg0I78P9MODy/GqHOIAAAAAElFTkSuQmCC\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["intermediate_pca_data = get_pca(intermediates)\n","plot_representations(intermediate_pca_data, labels, classes)"]},{"cell_type":"markdown","metadata":{"id":"tJHwIfKaXVqI"},"source":["We can do the same with the t-SNE algorithm. \n","\n","Again, we only use a subset of the data as t-SNE takes a considerable amount of time to compute.\n","\n","We also see that the classes are more well separated in the output representations compared to the intermediate representations."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D1cCXjF5XDm7"},"outputs":[],"source":["def get_tsne(data, n_components=2, n_images=None):\n","\n"," if n_images is not None:\n"," data = data[:n_images]\n","\n"," tsne = manifold.TSNE(n_components=n_components, random_state=0)\n"," tsne_data = tsne.fit_transform(data)\n"," return tsne_data"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592},"id":"eRBWfqGOXWCH","outputId":"25f02bc5-45e5-47b6-9257-ed60752414ce"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["N_IMAGES = 5_000\n","\n","output_tsne_data = get_tsne(outputs, n_images=N_IMAGES)\n","plot_representations(output_tsne_data, labels, classes, n_images=N_IMAGES)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":592},"id":"HRVE6XUCXYnI","outputId":"d6667eef-bb15-499c-cee2-7d7af94fd92b"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["intermediate_tsne_data = get_tsne(intermediates, n_images=N_IMAGES)\n","plot_representations(intermediate_tsne_data, labels, classes, n_images=N_IMAGES)"]},{"cell_type":"markdown","metadata":{"id":"Nyh_skghXVqM"},"source":["We can also imagine an image belonging to a specified class. \n","\n","Spoilers: this didn't work in the previous notebooks, won't work here, and won't work in the future. This is the last time we'll attempt to generate an image.\n","\n","If you do know a simple method to generate images that look better than pure random noise, feel free to [submit an issue](https://github.com/bentrevett/pytorch-image-classification/issues) on how it is done and it can be added to these tutorials."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VGjCxb5uXg-L"},"outputs":[],"source":["def imagine_image(model, classes, image, device, n_iterations=10_000):\n","\n"," model.eval()\n","\n"," label = classes.index(image)\n","\n"," best_prob = 0\n"," best_image = None\n","\n"," with torch.no_grad():\n","\n"," for _ in trange(n_iterations):\n","\n"," x = torch.randn(256, 3, 32, 32).to(device)\n","\n"," y_pred, _ = model(x)\n","\n"," preds = F.softmax(y_pred, dim=-1)\n","\n"," _best_prob, index = torch.max(preds[:, label], dim=0)\n","\n"," if _best_prob > best_prob:\n"," best_prob = _best_prob\n"," best_image = x[index]\n","\n"," return best_image, best_prob"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ENPavLwyXktE"},"outputs":[],"source":["IMAGE = 'frog'\n","\n","best_image, best_prob = imagine_image(model, classes, IMAGE, device)"]},{"cell_type":"markdown","metadata":{"id":"94ctq4NVXVqP"},"source":["We get an image that our model is ~100% confident is a frog, but just looks like random noise."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":33},"id":"MjfYMXltYeLy","outputId":"7ea00499-06bf-4a46-d38e-c86553d867c8"},"outputs":[{"name":"stdout","output_type":"stream","text":["Best image probability: 100.00%\n"]}],"source":["print(f'Best image probability: {best_prob.item()*100:.2f}%')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":247},"id":"7IsyTreMYMi5","outputId":"d243c2d3-cefa-4dca-bc21-ee547ef3b80d"},"outputs":[{"data":{"image/png":"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\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["best_image = normalize_image(best_image)\n","\n","plt.imshow(best_image.permute(1, 2, 0).cpu().numpy())\n","plt.axis('off');"]},{"cell_type":"markdown","metadata":{"id":"JjVyMM9xXVqR"},"source":["Next, we'll plot some images after they have been convolved with the first convolutional layer."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i-iX81gAYr1U"},"outputs":[],"source":["def plot_filtered_images(images, filters, n_filters=None, normalize=True):\n","\n"," images = torch.cat([i.unsqueeze(0) for i in images], dim=0).cpu()\n"," filters = filters.cpu()\n","\n"," if n_filters is not None:\n"," filters = filters[:n_filters]\n","\n"," n_images = images.shape[0]\n"," n_filters = filters.shape[0]\n","\n"," filtered_images = ## CODE\n","\n"," fig = plt.figure(figsize=(30, 30))\n","\n"," for i in range(n_images):\n","\n"," image = images[i]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax = fig.add_subplot(n_images, n_filters+1, i+1+(i*n_filters))\n"," ax.imshow(image.permute(1, 2, 0).numpy())\n"," ax.set_title('Original')\n"," ax.axis('off')\n","\n"," for j in range(n_filters):\n"," image = filtered_images[i][j]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax = fig.add_subplot(n_images, n_filters+1, i+1+(i*n_filters)+j+1)\n"," ax.imshow(image.numpy(), cmap='bone')\n"," ax.set_title(f'Filter {j+1}')\n"," ax.axis('off')\n","\n"," fig.subplots_adjust(hspace=-0.7)"]},{"cell_type":"markdown","metadata":{"id":"C5y92TbXXVqS"},"source":["We can see different types of edge detection and blurring that the filters have learned that are apparently decent feature extractors for this model and task."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"LjY35yaoZCmE","outputId":"fff23ebd-e69d-4820-f454-94c69aae5725"},"outputs":[{"data":{"image/png":"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"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["N_IMAGES = 5\n","N_FILTERS = 7\n","\n","images = [image for image, label in [test_data[i] for i in range(N_IMAGES)]]\n","filters = model.features[0].weight.data\n","\n","plot_filtered_images(images, filters, N_FILTERS)"]},{"cell_type":"markdown","metadata":{"id":"2YFr4T1YXVqW"},"source":["Finally, we can plot the actual filters our model has learned."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ieaJiA8D7bpT"},"outputs":[],"source":["def plot_filters(filters, normalize=True):\n","\n"," filters = filters.cpu()\n","\n"," n_filters = filters.shape[0]\n","\n"," rows = int(np.sqrt(n_filters))\n"," cols = int(np.sqrt(n_filters))\n","\n"," fig = plt.figure(figsize=(20, 10))\n","\n"," for i in range(rows*cols):\n","\n"," image = filters[i]\n","\n"," if normalize:\n"," image = normalize_image(image)\n","\n"," ax = fig.add_subplot(rows, cols, i+1)\n"," ax.imshow(image.permute(1, 2, 0))\n"," ax.axis('off')\n","\n"," fig.subplots_adjust(wspace=-0.9)"]},{"cell_type":"markdown","metadata":{"id":"qRIC4tEgXVqY"},"source":["Again, nothing really interpretable here, sadly."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":574},"id":"SU214ywn8sKJ","outputId":"d35d6b5e-2bed-4c32-ceb3-6af37abb9b3b"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAhMAAAItCAYAAACD0/j5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAblElEQVR4nO3d+9fnc73/8cfFJQothxCVU6FymFCzaVQbM2GEYuQstGkoOYRq2jZptAnlXDMip3IuoZQcI8ekXSK7khx3SJKp7Xh9/4G53rP29/lcy7LW7fbjvNZ6vK79ms+M+/qstZuRsbGxAAD8/5rvlf4BAIBXNzEBAJSICQCgREwAACViAgAoGR06HLnoy23/rx5fOvburqkkyfu2Xbtt6wMHHTwydL70zP3a3mHCF57vmkqS7L32Cm1bW//yc+O+wwEXn9z2Bjd9Zd+uqSTJ0Ye+3La10RYjg5+FI9bdp+0dfnHSbl1TSZKlfnVm29Zp008dfIcvfuD+tnfYc5FfdU0lSS784dS2rf3HXjPuO8z/4d3b3uCJ77+7aypJcsOsvjf9yF6zBj8LMw5ave0dVlh2n66pJMmV9/2gbevS2T8cfIepv5nZ9g7vuWndrqkkyYf36vszsfbI2FzfwTcTAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAEpGhw53m3Zb20VnnvCetq0k+eicqxvXDh483eMXp7Td9NCRp7ZtJcklG9/YtrX1wNljD/Xd892vfK1tK0m+9de3tm1tlPsHzxd54rK2uz54+7vbtpLk/iWHfgd7/Wnt37RtLfvHHdq2kuT13+p8h2+Pe/L7fVZqu+X931uhbStJzvn8z1v3hnzu6kPatu6ZvlvbVpJct9zRrXtDjltjUtvWvWe92LaVJPN/4/t9Y3vP/Zd9MwEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACWjQ4eb7HlY20XvOnPwqv+zjdf437ath+bxf+bRd+zbdtd/LnJ721aSPLHC51r3xvPHxb7VtnXG0xe3bSXJkjss3Dc2j4/VfJOObLtq7wW/27aVJPP91+JtW8fuMnnwfMvnH2i767GJ723bSpLDt1ijbWuPgbOZL27Wds/0Q89o20qSq763d9vW2kcNn5/3pkXb7lpv4Q3atpLkvBv/3Lo3ZKd9TmvbWvOZldu2kuTJWVu3bf1gnI+WbyYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIyMjY290j8DAPAq5psJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBkdOtxgyn+0/cMdr//rt7qmkiSrPvDXtq3jn3x2ZOj8kGt+0fYOy7xlr66pJMkCh67etvXpC84a9x1W/cIVbW9wxmbv65pKkqww/T/att5y9wmDn4WTPnlM2zvc+92nuqaSJNvdu1Lb1gcW22vwHY6bfn3bOxy4/H1dU0mSzf6l7++aH21867jvsPVtfX8vjFx2XtdUkuQjP96sbWvnn280+Fn40H5fbnuHy9/yYtdUkuSCh19o29r++C8NvsPOp/V9Hl54/NGuqSTJjkcs3ra11XOT5voOvpkAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKRocO33jPQ20XrbvUqm1bSfK5Ba9t3Ruy0K19WxMnH943luSFHTdv3RvP5Hf3/f5Numqxtq0kOeygQ9u2jpjH+cR13td213InP9y2lST/Om1W29bYJXsNni+x9i1td/39NX0/d5K8tPvVfWMPjn+0+7V/abvmlNVva9tKkpd/++7WvSFbH3dV29Ynrtm4bStJfvKPd7ZtbT+P860vOK3trifOfHvbVpKMXbJU49qkuf6qbyYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBkdOjwqamPtl105UuLtW0lybnXv6tt6zfzOF/k3O+13bXwKYe0bSXJySsd0ba1UQ4b92z5Sfu23XP5w1e0bSXJ/F/frG9st+Hjm887ve2q/7l2r7atJLn6S2u07g2Z8MMl2rbO/vihbVtJ8utlV2lcGxv35O4ZN7fdsvNlG7ZtJclOO01o3RvymlNvaNt65/wvt20lyVtGvtG4tsPg6SUbfrntps+/1PfnK0luvnODxrUd5/qrvpkAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUDIyNjb2Sv8MAMCrmG8mAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoGR06HDhSce1/cMde099sWsqSXLR80+3bf3pi/85MnT+ic9e3vYOt732jq6pJMmuD8zftnXgmYeN+w7n/rrvH3H5+p9md00lSeZf4Lq2rZ9ucv7gZ2HG5ju3vcNLOx7ZNZUkmfONg9q2Tr7xosF3GNlq47Z3mL3Tal1TSZIDvv+7tq1nv/2Tcd/hF+tt0PYGaz84uWsqSXLpixPbtj7y+NTBz8ISYye2vcOSH/lt11SS5A2rLNq2dcsxRw++w4FXb9P2Di+8tGbXVJJk2412bNt6/wKrzvUdfDMBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoGR06PDmy5dtu+ikN/+1bStJHrjqmda9IUvfdm7b1sRlJrRtJckCm6/SujeeOy7Zt21r7JxT2raSZM+Vtu4b22T4+JTP/KDtqh03XLptK0l+/sLFrXtDlp/1x7atv+z4pratJHnHAle37o3nmV1fbNu6dqEvtm0lyQkLHt629ZFMHTzf7r/uaLvrva/dpW0rSU56/d9b94aMvHlm29a/PPSVtq0kuf/uW9u23r/2qnP9dd9MAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAyejQ4Sfn/KbtopX+eVbbVpIc8+hX2rYOmcf5lO0vbLvr2Ec+07aVJLN+eHLf2K7bjnv01n9u3nbNXxa9pm0rSQ5/73vbtnaZx/m+D27VdtfPrup9hwe2OaJ1b8gxM5dq29r55ovatpLk3n+e0bo3nk3T93Mf84Yd2raS5Lmn5/VJ7vP82S+2bX1zytfbtpJkm4/f3Td26DaDx7te+HLbVWstPKttK0mOXGfBxrVd5/qrvpkAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUDIyNjb2Sv8MAMCrmG8mAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUjA4d3jRp97b/re3vLHRy11SSZIVjPte29dl1ThoZOp/4ryNt77D8TQd2TSVJRj71UNvWRcdfOO47bLvWVm1vMGHihK6pJMlKp89p29pp7LjBz8JWt/y67R0+dNSvu6aSJJ9+6c9tW/+84oDBdzhobFbbOxz3qUe6ppIk50x7sG1r5w3PHPcd7jr5l21vsOacBbumkiT77Lxk29bsNy09+FmYfPs2be+wzAnTuqaSJKP7/r5t66z1Dh18hwdm7Nn2DnfO/0LXVJLk4fes3ba135b7zfUdfDMBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoGR06PArN420XfTv8x3ftpUkNx35Yt/YJcPHv1l9k7ar3v3atdq2kuTpi3/WN3b8+Ed7rrxj2zU3LPrjtq0kOWqXP7dt7TSP8zcudFXbXf922aVtW0my1yHbt+4Nuffod7Rt3XbWDW1bSbLaeou37o1n5nJ9fwetdtKEtq0kOf7RHfrGTjh/8HjlD6/RdtWz+/X9PZMkUz4/vW/suuHjp2f+pu2qVU6+pW0rSd63yt8b1/ab66/6ZgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAEpGhw53mbZj20V/vPGktq0keeCmJ1r3hpy45TJtW3v873VtW0kyealPt+6N59Fv3tu2df+1p7VtJcmkba9v3Rvy7AlPtW1Nn902lSTZ+9LVegcHLLvKS21bVy7x+ratJDn5wxu3bZ09cPbdKQ+33XPUG3r/TCw3NqNt6+l5nM963WVtd+2+8IS2rSRZ5K7WuUETztuobesL90xp20qSP9+yRNvW6efN/dd9MwEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgZGRsbOyV/hkAgFcx30wAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAyejQ4bSjHmv7hzsuefqNXVNJki3Wv6Jt67KtthgZOv/y2Afb3mGdg1frmkqS3HPGrW1bBz51x7jvsMPYh9re4P7pC3ZNJUlGFvhg29atJ39i8LOw7MVrt73DTyYe1DWVJHn49mfatjadtvfgO2y13fS2d/jEdf/omkqS/GPDs9q2pl0wMu47jO13QdsbzL72lK6pJMnd//aJtq2T9ttp8LNw+h4ntL3DFXMe6ZpKknx2sde2ba0364uD7/CRu55se4djF92paypJ8rZt+/6Mjd1141zfwTcTAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAEpGhw6fWverbRdtd8mX27aSZP3nnmjdG/Lv832sbeu2ny7atpUkp93T14MHDpw9vtHP2u5Z9/pZbVtJ8uSNC7TuDbnvD79o2zp624+3bSXJzY+OtG1tOo/zfX+6Qttds99zU9tWkrz87IptW9Pyp3HPHjyx773f+bUN2raS5E3PL966N2SPTcd/o/+rP/zr9m1bSfLYF97Qujfkzx++rm1r5ft2a9tKkmNX+k7r3tz4ZgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAEpGhw7X/OPktou2XuqMtq0k+cOll/eNfXSPweNHD7mq7aojVn5N21aS/OWoSa1745m03CJtW7ttsVLbVpIcnkda94Zcd/W5bVtLf2GHtq0kWe+bP+0bO3T4eMrpI21X/eR3D7VtJckBG/ytdW88n57S93PvcHvvn+Mpvz+7b+zgqYPHb32p767Tlnm5bStJJk9fsm1rbB5/KHbdv++/cUcdek7bVpIsOv8trXtz45sJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACUjY2Njr/TPAAC8ivlmAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAyejQ4QVn7NX2v7W98O+26ZpKkiy4/JNtW1P23mlk6PyiOau0vcOE67/TNZUkue+h+9q2tpi+87jvsPePrmx7g3+s81zXVJLkzBk/aNsa+eZpg5+FVc79VNs7vOPCk7qmkiSLLnVL29a3T3/v4DtMGrus7R1WP27HrqkkyYpP9/1dM2PmWeO+wxXb3d32Ble8/aCuqSTJlssv1LY19eOXDn4WNljiO23vcONVz3ZNJUmOf0/ffycOGJsx+A4HbPRM2zt895re/068aeP92rZuvva5ub6DbyYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIwOHS7+7PxtF31+/9ltW0ly/lHbtu4NefPHjmvbumuHo9u2kuRne/+9bWuL6TuPe/a/Vy7bds/iV+zbtpUkJ6782rat/eZxvusFb2+7a/bTa7RtJcluqy3fuHbl4Okh+89pu2nZsZ3atpLknqmrtO6N59F112zbmv+332nbSpLN3v8vrXtDFlv/lLatw1c6v20rSR46dmrj2ozB0x9uc3LbTQ/+zzJtW0ly55cObt2bG99MAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAyejQ4S03r9p20TsXOrVtK0kOntnXQZdm+8Hzi+/bou2uTY56rm0rSd543jqte+OZfOLdbVsL7NL3nklyxcLfat0bst0/j2/b2mLqDm1bSXLbjKX7xo4ZPj7r8Yvbrlpv1lptW0ny2qMO7hvb9KBxj65Zs+/vtP123bptK0m+funObVv7bHzR4PmaGyzedtet/3l121aSzNhwk9a9IZMPOqJta8JNW7ZtJclXlvx739akuf+6byYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIyMjY290j8DAPAq5psJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBkdOhxZ+Oq2f7jj/HVO6JpKkqy9/1ptW6tuc+TI0PnFv+z7B0wev6S33166a7W2rX2v+O2473D0177a9gb37H9511SS5JP7TWjbmnji8YOfhYOnLtH2DvP9YMeuqSTJnec807Z19a5nD77D5z+7SNs7bPrshV1TSZITn9msbeuSc0bGfYe915jU9gZPPf+2rqkkyTW7Pd+29eSM8wY/C+eNHND2Dvef8LOuqSTJxBWPaduasuUHBt/hyplvbHuH6yfO7ppKklz01fe3bd3/o8Xm+g6+mQAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAEpGhw5P+szGbRet+qt/b9tKkrvOe6hta9Vths/f/Ny9bXf991pXtW0lye/X/0Xr3nhWXGqNtq0nd7+jbStJfrDVMm1bE+dx/uMl/9p21+/+NqdtK0n+MOuBvrFdh4+/fPvb2q6a9LrN27aS5LgP/6lxbflxT5Y7qe/vtFP/+FjbVpIc9ZPXte4N+cbSY21bb//C4W1bSTLjT29v3Rty24t9v4fr/O7TbVtJssQLWzWuzf332zcTAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQMjp0+K4FZ7VddMjGN7VtJcly79ijbWu7eZw/9teF2+5aZfPXtG0lySOfbxybOv7RrRde2XbNkrO2bNtKks2W26ZvbGz4eM977227atGPf7NtK0n+cP4727aWm8f5zgeu33bXYp/YvW0rSabscnXb1rMZ/++ZPba6oe2e+dZYsG0rSS7fsHVu0JemLNq2NXvCR9u2kuRjf+n783r2EsPni65zattdT52+bdtWksx55y2te3PjmwkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJSNjY2Ov9M8AALyK+WYCACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASkaHDk857ZG2f7jjUw9v0DWVJHn5fW9o2xqZfMfI0Pm3X1687x3ecUzXVJJkuy0ebdv6xrH/Me47nDd2XdsbXPalOV1TSZIDDrugbWvi2DmDn4U915za9g5vmnx/11SS5MEn39u2dcY5Zwy+w4kXTm57h5MX/XTXVJLkaxc83ba1+Zm7jvsOuy+yftsbrL7sEV1TSZLPvPGTbVsjN/734Gdh+y8e1vYO81+wQ9dUkuQDLy3UtrXXfSsOvsOPZ9/Y9g7nPn5r11SSZMqDf2vb2nX2zLm+g28mAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAiJgCAEjEBAJSMDh2u+MzNbRdteOev2raS5NTtdmvb+uQ8zr9x9cFtd936j1PbtpLkhnWPbd0b14dOa5vaa4uJbVtJcvex09q25vWTzX799La7zv5e7+/d3X/7VuPaGYOnc0auabvpsDt+0raVJE+8dUbr3njeMWejtq3dP/a3tq0k2ey0G9q2fjSP8w0vXq3troWe6vu7Nkm+/tj2bVt7ZcXB8w/mi213bXrbJm1bSbLq5LVa9+bGNxMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQIiYAgBIxAQCUiAkAoERMAAAlYgIAKBETAECJmAAASsQEAFAyOnR40uOrtF10+6bnt20lySEHrdA39sPh4w2n3Np21Qdn3tW2lSTTzniib2yH8Y8e3nL9tmtemtTbsHeuvm7b1h7zOH/df1/YdtcRM89p20qSt2ar1r0ha33/621bv/7UL9u2kuThjz3ftrXboeOfvfy1G9ru2eveXdq2kuSDb7i9cW34c3X+u1dtu+n6p//QtpUk+1zyWN/YtOHjY2dv2nbVkdNWattKkpXfcmfj2kfn+qu+mQAASsQEAFAiJgCAEjEBAJSICQCgREwAACViAgAoERMAQImYAABKxAQAUCImAIASMQEAlIgJAKBETAAAJWICACgREwBAiZgAAErEBABQMjI2NvZK/wwAwKuYbyYAgBIxAQCUiAkAoERMAAAlYgIAKBETAEDJ/wPkwEaA+ue6eQAAAABJRU5ErkJggg==\n","text/plain":["
"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plot_filters(filters)"]},{"cell_type":"markdown","metadata":{"id":"9UYRoAx1VM7t"},"source":["Can we ever learn interesting looking filters? Or are we doomed to just look at colorful blocks forever?\n","\n","Well, we can take a *pre-trained* AlexNet model and view the filters of that. This pre-trained version of AlexNet was trained by people at PyTorch and was not trained on the CIFAR10 dataset, but on the [ILSVRC](https://arxiv.org/abs/1409.0575) dataset, usually just called the ImageNet dataset. ImageNet is a dataset with over 1 million images in 1,000 classes. Torchvision provides ways of downloading different models pre-trained on ImageNet, such as AlexNet and [many others](https://pytorch.org/vision/stable/models.html). \n","\n","First, we can import the model making sure to pass `pretrained = True` to get a pre-trained version. Torchvision will then import the model, download the weights for it and load them into the new model.\n","\n","We can see that this is similar to our AlexNet model but has considerably more parameters.\n","\n","One interesting thing is that they use much larger filters in the first convolutional layer - 11x11 instead of 3x3."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":466},"id":"5I642oecVKso","outputId":"66c88aca-2a82-4c06-c64d-db33649ae163"},"outputs":[{"name":"stdout","output_type":"stream","text":["AlexNet(\n"," (features): Sequential(\n"," (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n"," (1): ReLU(inplace=True)\n"," (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n"," (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n"," (4): ReLU(inplace=True)\n"," (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n"," (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (7): ReLU(inplace=True)\n"," (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (9): ReLU(inplace=True)\n"," (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n"," (11): ReLU(inplace=True)\n"," (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n"," )\n"," (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n"," (classifier): Sequential(\n"," (0): Dropout(p=0.5, inplace=False)\n"," (1): Linear(in_features=9216, out_features=4096, bias=True)\n"," (2): ReLU(inplace=True)\n"," (3): Dropout(p=0.5, inplace=False)\n"," (4): Linear(in_features=4096, out_features=4096, bias=True)\n"," (5): ReLU(inplace=True)\n"," (6): Linear(in_features=4096, out_features=1000, bias=True)\n"," )\n",")\n"]}],"source":["model = models.alexnet(pretrained=True)\n","\n","print(model)"]},{"cell_type":"markdown","metadata":{"id":"r8E9GGKDYaEa"},"source":["We can then get the learned values of these filters the same way we did for our version of AlexNet and then plot them.\n","\n","As we can see the patterns are much more interesting, though still not really interpretable.\n","\n","So how come it learned these interesting looking filters? Is it just because the filters are bigger? Is it because models can only do well on ImageNet if they learn more interesting filters? Or something else?\n","\n","Do more interesting looking filters imply that they perform better? Or are these filters showing how the model has overfit to patterns on the images within ImageNet?\n","\n","It is difficult to answer these questions as modern computer vision architectures now seem to favour smaller filter sizes with their largest filters being 5x5 - so maybe larger filters aren't that good after all?"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":574},"id":"-KUGUuxZYOZI","outputId":"d68cbe55-9750-4bb3-b7aa-aa20d0c409d0"},"outputs":[{"data":{"image/png":"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dataset."]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"machine_shape":"hm","provenance":[]},"kernelspec":{"display_name":"Python 3 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insertions(+) create mode 100644 "8\354\243\274\354\260\250(1102)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Building_a_Recurrent_Neural_Network_Step_by_Step.ipynb" diff --git "a/8\354\243\274\354\260\250(1102)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Building_a_Recurrent_Neural_Network_Step_by_Step.ipynb" "b/8\354\243\274\354\260\250(1102)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Building_a_Recurrent_Neural_Network_Step_by_Step.ipynb" new file mode 100644 index 0000000..c4900f9 --- /dev/null +++ "b/8\354\243\274\354\260\250(1102)/1\354\241\260/\352\271\200\354\204\261\354\234\244/Building_a_Recurrent_Neural_Network_Step_by_Step.ipynb" @@ -0,0 +1,2228 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Building your Recurrent Neural Network - Step by Step\n", + "\n", + "Welcome to Course 5's first assignment! In this assignment, you will implement key components of a Recurrent Neural Network in numpy.\n", + "\n", + "Recurrent Neural Networks (RNN) are very effective for Natural Language Processing and other sequence tasks because they have \"memory\". They can read inputs $x^{\\langle t \\rangle}$ (such as words) one at a time, and remember some information/context through the hidden layer activations that get passed from one time-step to the next. This allows a unidirectional RNN to take information from the past to process later inputs. A bidirectional RNN can take context from both the past and the future. \n", + "\n", + "**Notation**:\n", + "- Superscript $[l]$ denotes an object associated with the $l^{th}$ layer. \n", + "\n", + "- Superscript $(i)$ denotes an object associated with the $i^{th}$ example. \n", + "\n", + "- Superscript $\\langle t \\rangle$ denotes an object at the $t^{th}$ time-step. \n", + " \n", + "- **Sub**script $i$ denotes the $i^{th}$ entry of a vector.\n", + "\n", + "Example: \n", + "- $a^{(2)[3]<4>}_5$ denotes the activation of the 2nd training example (2), 3rd layer [3], 4th time step <4>, and 5th entry in the vector.\n", + "\n", + "#### Pre-requisites\n", + "* We assume that you are already familiar with `numpy`. \n", + "* To refresh your knowledge of numpy, you can review course 1 of this specialization \"Neural Networks and Deep Learning\". \n", + " * Specifically, review the week 2 assignment [\"Python Basics with numpy (optional)\"](https://www.coursera.org/learn/neural-networks-deep-learning/item/Zh0CU).\n", + " \n", + " \n", + "#### Be careful when modifying the starter code\n", + "* When working on graded functions, please remember to only modify the code that is between the\n", + "```Python\n", + "#### START CODE HERE\n", + "```\n", + "and\n", + "```Python\n", + "#### END CODE HERE\n", + "```\n", + "* In particular, Be careful to not modify the first line of graded routines. These start with:\n", + "```Python\n", + "# GRADED FUNCTION: routine_name\n", + "```\n", + "* The automatic grader (autograder) needs these to locate the function.\n", + "* Even a change in spacing will cause issues with the autograder. \n", + "* It will return 'failed' if these are modified or missing.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Updates\n", + "\n", + "#### If you were working on the notebook before this update...\n", + "* The current notebook is version \"3a\".\n", + "* You can find your original work saved in the notebook with the previous version name (\"v3\") \n", + "* To view the file directory, go to the menu \"File->Open\", and this will open a new tab that shows the file directory.\n", + "\n", + "#### List of updates\n", + "* \"Forward propagation for the basic RNN\", added sections to clarify variable names and shapes:\n", + " - \"Dimensions of $x^{\\langle t \\rangle}$\"\n", + " - \"Hidden State $a$\", \n", + " - \"Dimensions of hidden state $a^{\\langle t \\rangle}$\"\n", + " - \"Dimensions of prediction $y^{\\langle t \\rangle}$\"\n", + "* `rnn_cell_forward`: \n", + " * Added additional hints.\n", + " * Updated figure 2.\n", + "* `rnn_forward`\n", + " - Set `xt` in a separate line of code to clarify what code is expected; added additional hints.\n", + " - Clarifies instructions to specify dimensions (2D or 3D), and clarifies variable names.\n", + " - Additional Hints\n", + " - Clarifies when the basic RNN works well.\n", + " - Updated figure 3.\n", + "* \"About the gates\" replaced with \"overview of gates and states\":\n", + " - Updated to include conceptual description of each gate's purpose, and an explanation of each equation. \n", + " - Added sections about the cell state, hidden state, and prediction.\n", + " - Lists variable names that are used in the code, and notes when they differ from the variables used in the equations.\n", + " - Lists shapes of the variables.\n", + " - Updated figure 4.\n", + "* `lstm_forward`\n", + " - Added instructions, noting the shapes of the variables.\n", + " - Added hints about `c` and `c_next` to help students avoid copy-by-reference mistakes.\n", + " - Set `xt` in a separate line to make this step explicit.\n", + "* Renamed global variables so that they do not conflict with local variables within the function.\n", + "* Spelling, grammar and wording corrections.\n", + "* For unit tests, updated print statements and \"expected output\" for easier comparisons.\n", + "* Many thanks to mentor Geoff Ladwig for suggested improvements and fixes in the assignments for course 5!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's first import all the packages that you will need during this assignment." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "from rnn_utils import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1 - Forward propagation for the basic Recurrent Neural Network\n", + "\n", + "Later this week, you will generate music using an RNN. The basic RNN that you will implement has the structure below. In this example, $T_x = T_y$. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "
**Figure 1**: Basic RNN model
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dimensions of input $x$\n", + "\n", + "#### Input with $n_x$ number of units\n", + "* For a single input example, $x^{(i)}$ is a one-dimensional input vector.\n", + "* Using language as an example, a language with a 5000 word vocabulary could be one-hot encoded into a vector that has 5000 units. So $x^{(i)}$ would have the shape (5000,). \n", + "* We'll use the notation $n_x$ to denote the number of units in a single training example." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Batches of size $m$\n", + "* Let's say we have mini-batches, each with 20 training examples. \n", + "* To benefit from vectorization, we'll stack 20 columns of $x^{(i)}$ examples into a 2D array (a matrix).\n", + "* For example, this tensor has the shape (5000,20). \n", + "* We'll use $m$ to denote the number of training examples. \n", + "* So the shape of a mini-batch is $(n_x,m)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Time steps of size $T_{x}$\n", + "* A recurrent neural network has multiple time steps, which we'll index with $t$.\n", + "* In the lessons, we saw a single training example $x^{(i)}$ (a vector) pass through multiple time steps $T_x$. For example, if there are 10 time steps, $T_{x} = 10$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3D Tensor of shape $(n_{x},m,T_{x})$\n", + "* The 3-dimensional tensor $x$ of shape $(n_x,m,T_x)$ represents the input $x$ that is fed into the RNN.\n", + "\n", + "#### Taking a 2D slice for each time step: $x^{\\langle t \\rangle}$\n", + "* At each time step, we'll use a mini-batches of training examples (not just a single example).\n", + "* So, for each time step $t$, we'll use a 2D slice of shape $(n_x,m)$.\n", + "* We're referring to this 2D slice as $x^{\\langle t \\rangle}$. The variable name in the code is `xt`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Definition of hidden state $a$\n", + "\n", + "* The activation $a^{\\langle t \\rangle}$ that is passed to the RNN from one time step to another is called a \"hidden state.\"\n", + "\n", + "### Dimensions of hidden state $a$\n", + "\n", + "* Similar to the input tensor $x$, the hidden state for a single training example is a vector of length $n_{a}$.\n", + "* If we include a mini-batch of $m$ training examples, the shape of a mini-batch is $(n_{a},m)$.\n", + "* When we include the time step dimension, the shape of the hidden state is $(n_{a}, m, T_x)$\n", + "* We will loop through the time steps with index $t$, and work with a 2D slice of the 3D tensor. \n", + "* We'll refer to this 2D slice as $a^{\\langle t \\rangle}$. \n", + "* In the code, the variable names we use are either `a_prev` or `a_next`, depending on the function that's being implemented.\n", + "* The shape of this 2D slice is $(n_{a}, m)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dimensions of prediction $\\hat{y}$\n", + "* Similar to the inputs and hidden states, $\\hat{y}$ is a 3D tensor of shape $(n_{y}, m, T_{y})$.\n", + " * $n_{y}$: number of units in the vector representing the prediction.\n", + " * $m$: number of examples in a mini-batch.\n", + " * $T_{y}$: number of time steps in the prediction.\n", + "* For a single time step $t$, a 2D slice $\\hat{y}^{\\langle t \\rangle}$ has shape $(n_{y}, m)$.\n", + "* In the code, the variable names are:\n", + " - `y_pred`: $\\hat{y}$ \n", + " - `yt_pred`: $\\hat{y}^{\\langle t \\rangle}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how you can implement an RNN: \n", + "\n", + "**Steps**:\n", + "1. Implement the calculations needed for one time-step of the RNN.\n", + "2. Implement a loop over $T_x$ time-steps in order to process all the inputs, one at a time. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.1 - RNN cell\n", + "\n", + "A recurrent neural network can be seen as the repeated use of a single cell. You are first going to implement the computations for a single time-step. The following figure describes the operations for a single time-step of an RNN cell. \n", + "\n", + "\n", + "
**Figure 2**: Basic RNN cell. Takes as input $x^{\\langle t \\rangle}$ (current input) and $a^{\\langle t - 1\\rangle}$ (previous hidden state containing information from the past), and outputs $a^{\\langle t \\rangle}$ which is given to the next RNN cell and also used to predict $\\hat{y}^{\\langle t \\rangle}$
\n", + "\n", + "#### rnn cell versus rnn_cell_forward\n", + "* Note that an RNN cell outputs the hidden state $a^{\\langle t \\rangle}$. \n", + " * The rnn cell is shown in the figure as the inner box which has solid lines. \n", + "* The function that we will implement, `rnn_cell_forward`, also calculates the prediction $\\hat{y}^{\\langle t \\rangle}$\n", + " * The rnn_cell_forward is shown in the figure as the outer box that has dashed lines." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Implement the RNN-cell described in Figure (2).\n", + "\n", + "**Instructions**:\n", + "1. Compute the hidden state with tanh activation: $a^{\\langle t \\rangle} = \\tanh(W_{aa} a^{\\langle t-1 \\rangle} + W_{ax} x^{\\langle t \\rangle} + b_a)$.\n", + "2. Using your new hidden state $a^{\\langle t \\rangle}$, compute the prediction $\\hat{y}^{\\langle t \\rangle} = softmax(W_{ya} a^{\\langle t \\rangle} + b_y)$. We provided the function `softmax`.\n", + "3. Store $(a^{\\langle t \\rangle}, a^{\\langle t-1 \\rangle}, x^{\\langle t \\rangle}, parameters)$ in a `cache`.\n", + "4. Return $a^{\\langle t \\rangle}$ , $\\hat{y}^{\\langle t \\rangle}$ and `cache`\n", + "\n", + "#### Additional Hints\n", + "* [numpy.tanh](https://www.google.com/search?q=numpy+tanh&rlz=1C5CHFA_enUS854US855&oq=numpy+tanh&aqs=chrome..69i57j0l5.1340j0j7&sourceid=chrome&ie=UTF-8)\n", + "* We've created a `softmax` function that you can use. It is located in the file 'rnn_utils.py' and has been imported.\n", + "* For matrix multiplication, use [numpy.dot](https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: rnn_cell_forward\n", + "\n", + "def rnn_cell_forward(xt, a_prev, parameters):\n", + " \"\"\"\n", + " Implements a single forward step of the RNN-cell as described in Figure (2)\n", + "\n", + " Arguments:\n", + " xt -- your input data at timestep \"t\", numpy array of shape (n_x, m).\n", + " a_prev -- Hidden state at timestep \"t-1\", numpy array of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)\n", + " Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)\n", + " Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " ba -- Bias, numpy array of shape (n_a, 1)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + " Returns:\n", + " a_next -- next hidden state, of shape (n_a, m)\n", + " yt_pred -- prediction at timestep \"t\", numpy array of shape (n_y, m)\n", + " cache -- tuple of values needed for the backward pass, contains (a_next, a_prev, xt, parameters)\n", + " \"\"\"\n", + " \n", + " # Retrieve parameters from \"parameters\"\n", + " Wax = parameters[\"Wax\"]\n", + " Waa = parameters[\"Waa\"]\n", + " Wya = parameters[\"Wya\"]\n", + " ba = parameters[\"ba\"]\n", + " by = parameters[\"by\"]\n", + " \n", + " ### START CODE HERE ### (≈2 lines)\n", + " # compute next activation state using the formula given above\n", + " a_next = np.tanh(np.dot(Waa,a_prev) + np.dot(Wax,xt) + ba)\n", + " ### Use np.tanh(), np.dot(), Waa, a_prev, Wax, xt, ba\n", + "\n", + " # compute output of the current cell using the formula given above\n", + " yt_pred = softmax(np.dot(Wya,a_next) + by)\n", + " ### Use softmax(), np.dot(), Wya, a_next, by\n", + "\n", + " ### END CODE HERE ###\n", + " \n", + " # store values you need for backward propagation in cache\n", + " cache = (a_next, a_prev, xt, parameters)\n", + " \n", + " return a_next, yt_pred, cache" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**이 셀 참고하시면 도움이 될 거예요!**\n", + "\n", + "$a^{\\langle t \\rangle} = \\tanh(W_{aa} a^{\\langle t-1 \\rangle} + W_{ax} x^{\\langle t \\rangle} + b_a)$\n", + "\n", + " $\\hat{y}^{\\langle t \\rangle} = softmax(W_{ya} a^{\\langle t \\rangle} + b_y)$. We provided the function `softmax`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "xt_tmp = np.random.randn(3,10)\n", + "a_prev_tmp = np.random.randn(5,10)\n", + "parameters_tmp = {}\n", + "parameters_tmp['Waa'] = np.random.randn(5,5)\n", + "parameters_tmp['Wax'] = np.random.randn(5,3)\n", + "parameters_tmp['Wya'] = np.random.randn(2,5)\n", + "parameters_tmp['ba'] = np.random.randn(5,1)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_next_tmp, yt_pred_tmp, cache_tmp = rnn_cell_forward(xt_tmp, a_prev_tmp, parameters_tmp)\n", + "print(\"a_next[4] = \\n\", a_next_tmp[4])\n", + "print(\"a_next.shape = \\n\", a_next_tmp.shape)\n", + "print(\"yt_pred[1] =\\n\", yt_pred_tmp[1])\n", + "print(\"yt_pred.shape = \\n\", yt_pred_tmp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**: \n", + "```Python\n", + "a_next[4] = \n", + " [ 0.59584544 0.18141802 0.61311866 0.99808218 0.85016201 0.99980978\n", + " -0.18887155 0.99815551 0.6531151 0.82872037]\n", + "a_next.shape = \n", + " (5, 10)\n", + "yt_pred[1] =\n", + " [ 0.9888161 0.01682021 0.21140899 0.36817467 0.98988387 0.88945212\n", + " 0.36920224 0.9966312 0.9982559 0.17746526]\n", + "yt_pred.shape = \n", + " (2, 10)\n", + "\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1.2 - RNN forward pass \n", + "\n", + "- A recurrent neural network (RNN) is a repetition of the RNN cell that you've just built. \n", + " - If your input sequence of data is 10 time steps long, then you will re-use the RNN cell 10 times. \n", + "- Each cell takes two inputs at each time step:\n", + " - $a^{\\langle t-1 \\rangle}$: The hidden state from the previous cell.\n", + " - $x^{\\langle t \\rangle}$: The current time-step's input data.\n", + "- It has two outputs at each time step:\n", + " - A hidden state ($a^{\\langle t \\rangle}$)\n", + " - A prediction ($y^{\\langle t \\rangle}$)\n", + "- The weights and biases $(W_{aa}, b_{a}, W_{ax}, b_{x})$ are re-used each time step. \n", + " - They are maintained between calls to rnn_cell_forward in the 'parameters' dictionary.\n", + "\n", + "\n", + "\n", + "
**Figure 3**: Basic RNN. The input sequence $x = (x^{\\langle 1 \\rangle}, x^{\\langle 2 \\rangle}, ..., x^{\\langle T_x \\rangle})$ is carried over $T_x$ time steps. The network outputs $y = (y^{\\langle 1 \\rangle}, y^{\\langle 2 \\rangle}, ..., y^{\\langle T_x \\rangle})$.
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise**: Code the forward propagation of the RNN described in Figure (3).\n", + "\n", + "**Instructions**:\n", + "* Create a 3D array of zeros, $a$ of shape $(n_{a}, m, T_{x})$ that will store all the hidden states computed by the RNN.\n", + "* Create a 3D array of zeros, $\\hat{y}$, of shape $(n_{y}, m, T_{x})$ that will store the predictions. \n", + " - Note that in this case, $T_{y} = T_{x}$ (the prediction and input have the same number of time steps).\n", + "* Initialize the 2D hidden state `a_next` by setting it equal to the initial hidden state, $a_{0}$.\n", + "* At each time step $t$:\n", + " - Get $x^{\\langle t \\rangle}$, which is a 2D slice of $x$ for a single time step $t$.\n", + " - $x^{\\langle t \\rangle}$ has shape $(n_{x}, m)$\n", + " - $x$ has shape $(n_{x}, m, T_{x})$\n", + " - Update the 2D hidden state $a^{\\langle t \\rangle}$ (variable name `a_next`), the prediction $\\hat{y}^{\\langle t \\rangle}$ and the cache by running `rnn_cell_forward`.\n", + " - $a^{\\langle t \\rangle}$ has shape $(n_{a}, m)$\n", + " - Store the 2D hidden state in the 3D tensor $a$, at the $t^{th}$ position.\n", + " - $a$ has shape $(n_{a}, m, T_{x})$\n", + " - Store the 2D $\\hat{y}^{\\langle t \\rangle}$ prediction (variable name `yt_pred`) in the 3D tensor $\\hat{y}_{pred}$ at the $t^{th}$ position.\n", + " - $\\hat{y}^{\\langle t \\rangle}$ has shape $(n_{y}, m)$\n", + " - $\\hat{y}$ has shape $(n_{y}, m, T_x)$\n", + " - Append the cache to the list of caches.\n", + "* Return the 3D tensor $a$ and $\\hat{y}$, as well as the list of caches.\n", + "\n", + "#### Additional Hints\n", + "- [np.zeros](https://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html)\n", + "- If you have a 3 dimensional numpy array and are indexing by its third dimension, you can use array slicing like this: `var_name[:,:,i]`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: rnn_forward\n", + "\n", + "def rnn_forward(x, a0, parameters):\n", + " \"\"\"\n", + " Implement the forward propagation of the recurrent neural network described in Figure (3).\n", + "\n", + " Arguments:\n", + " x -- Input data for every time-step, of shape (n_x, m, T_x).\n", + " a0 -- Initial hidden state, of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Waa -- Weight matrix multiplying the hidden state, numpy array of shape (n_a, n_a)\n", + " Wax -- Weight matrix multiplying the input, numpy array of shape (n_a, n_x)\n", + " Wya -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " ba -- Bias numpy array of shape (n_a, 1)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + "\n", + " Returns:\n", + " a -- Hidden states for every time-step, numpy array of shape (n_a, m, T_x)\n", + " y_pred -- Predictions for every time-step, numpy array of shape (n_y, m, T_x)\n", + " caches -- tuple of values needed for the backward pass, contains (list of caches, x)\n", + " \"\"\"\n", + " \n", + " # Initialize \"caches\" which will contain the list of all caches\n", + " caches = []\n", + " \n", + " # Retrieve dimensions from shapes of x and parameters[\"Wya\"]\n", + " n_x, m, T_x = x.shape\n", + " n_y, n_a = parameters[\"Wya\"].shape\n", + " \n", + " \n", + " # initialize \"a\" and \"y\" with zeros\n", + " a = np.zeros([n_a,m,T_x])\n", + " y_pred = np.zeros([n_y,m,T_x])\n", + " \n", + " # Initialize a_next\n", + " a_next = a0\n", + " \n", + " # loop over all time-steps\n", + " for t in range(T_x):\n", + " ### START CODE HERE ###\n", + " # Update next hidden state, compute the prediction, get the cache (≈1 line)\n", + " a_next, yt_pred, cache = rnn_cell_forward(x[:,:,t], a_next, parameters)\n", + " ### Use rnn_cell_forward(xt, a_prev, parameters), x[:,:,t], a_next, parameters\n", + " ### END CODE HERE ###\n", + "\n", + " # Save the value of the new \"next\" hidden state in a\n", + " a[:,:,t] = a_next\n", + " # Save the value of the prediction in y\n", + " y_pred[:,:,t] = yt_pred\n", + " # Append \"cache\" to \"caches\"\n", + " caches.append(cache)\n", + " \n", + " \n", + " # store values needed for backward propagation in cache\n", + " caches = (caches, x)\n", + " \n", + " return a, y_pred, caches" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "x_tmp = np.random.randn(3,10,4)\n", + "a0_tmp = np.random.randn(5,10)\n", + "parameters_tmp = {}\n", + "parameters_tmp['Waa'] = np.random.randn(5,5)\n", + "parameters_tmp['Wax'] = np.random.randn(5,3)\n", + "parameters_tmp['Wya'] = np.random.randn(2,5)\n", + "parameters_tmp['ba'] = np.random.randn(5,1)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_tmp, y_pred_tmp, caches_tmp = rnn_forward(x_tmp, a0_tmp, parameters_tmp)\n", + "print(\"a[4][1] = \\n\", a_tmp[4][1])\n", + "print(\"a.shape = \\n\", a_tmp.shape)\n", + "print(\"y_pred[1][3] =\\n\", y_pred_tmp[1][3])\n", + "print(\"y_pred.shape = \\n\", y_pred_tmp.shape)\n", + "print(\"caches[1][1][3] =\\n\", caches_tmp[1][1][3])\n", + "print(\"len(caches) = \\n\", len(caches_tmp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "```Python\n", + "a[4][1] = \n", + " [-0.99999375 0.77911235 -0.99861469 -0.99833267]\n", + "a.shape = \n", + " (5, 10, 4)\n", + "y_pred[1][3] =\n", + " [ 0.79560373 0.86224861 0.11118257 0.81515947]\n", + "y_pred.shape = \n", + " (2, 10, 4)\n", + "caches[1][1][3] =\n", + " [-1.1425182 -0.34934272 -0.20889423 0.58662319]\n", + "len(caches) = \n", + " 2\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You've successfully built the forward propagation of a recurrent neural network from scratch. \n", + "\n", + "#### Situations when this RNN will perform better:\n", + "- This will work well enough for some applications, but it suffers from the vanishing gradient problems. \n", + "- The RNN works best when each output $\\hat{y}^{\\langle t \\rangle}$ can be estimated using \"local\" context. \n", + "- \"Local\" context refers to information that is close to the prediction's time step $t$.\n", + "- More formally, local context refers to inputs $x^{\\langle t' \\rangle}$ and predictions $\\hat{y}^{\\langle t \\rangle}$ where $t'$ is close to $t$.\n", + "\n", + "In the next part, you will build a more complex LSTM model, which is better at addressing vanishing gradients. The LSTM will be better able to remember a piece of information and keep it saved for many timesteps. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2 - Long Short-Term Memory (LSTM) network\n", + "\n", + "The following figure shows the operations of an LSTM-cell.\n", + "\n", + "\n", + "
**Figure 4**: LSTM-cell. This tracks and updates a \"cell state\" or memory variable $c^{\\langle t \\rangle}$ at every time-step, which can be different from $a^{\\langle t \\rangle}$.
\n", + "\n", + "Similar to the RNN example above, you will start by implementing the LSTM cell for a single time-step. Then you can iteratively call it from inside a \"for-loop\" to have it process an input with $T_x$ time-steps. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Overview of gates and states\n", + "\n", + "#### - Forget gate $\\mathbf{\\Gamma}_{f}$\n", + "\n", + "* Let's assume we are reading words in a piece of text, and plan to use an LSTM to keep track of grammatical structures, such as whether the subject is singular (\"puppy\") or plural (\"puppies\"). \n", + "* If the subject changes its state (from a singular word to a plural word), the memory of the previous state becomes outdated, so we \"forget\" that outdated state.\n", + "* The \"forget gate\" is a tensor containing values that are between 0 and 1.\n", + " * If a unit in the forget gate has a value close to 0, the LSTM will \"forget\" the stored state in the corresponding unit of the previous cell state.\n", + " * If a unit in the forget gate has a value close to 1, the LSTM will mostly remember the corresponding value in the stored state.\n", + "\n", + "##### Equation\n", + "\n", + "$$\\mathbf{\\Gamma}_f^{\\langle t \\rangle} = \\sigma(\\mathbf{W}_f[\\mathbf{a}^{\\langle t-1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_f)\\tag{1} $$\n", + "\n", + "##### Explanation of the equation:\n", + "\n", + "* $\\mathbf{W_{f}}$ contains weights that govern the forget gate's behavior. \n", + "* The previous time step's hidden state $[a^{\\langle t-1 \\rangle}$ and current time step's input $x^{\\langle t \\rangle}]$ are concatenated together and multiplied by $\\mathbf{W_{f}}$. \n", + "* A sigmoid function is used to make each of the gate tensor's values $\\mathbf{\\Gamma}_f^{\\langle t \\rangle}$ range from 0 to 1.\n", + "* The forget gate $\\mathbf{\\Gamma}_f^{\\langle t \\rangle}$ has the same dimensions as the previous cell state $c^{\\langle t-1 \\rangle}$. \n", + "* This means that the two can be multiplied together, element-wise.\n", + "* Multiplying the tensors $\\mathbf{\\Gamma}_f^{\\langle t \\rangle} * \\mathbf{c}^{\\langle t-1 \\rangle}$ is like applying a mask over the previous cell state.\n", + "* If a single value in $\\mathbf{\\Gamma}_f^{\\langle t \\rangle}$ is 0 or close to 0, then the product is close to 0.\n", + " * This keeps the information stored in the corresponding unit in $\\mathbf{c}^{\\langle t-1 \\rangle}$ from being remembered for the next time step.\n", + "* Similarly, if one value is close to 1, the product is close to the original value in the previous cell state.\n", + " * The LSTM will keep the information from the corresponding unit of $\\mathbf{c}^{\\langle t-1 \\rangle}$, to be used in the next time step.\n", + " \n", + "##### Variable names in the code\n", + "The variable names in the code are similar to the equations, with slight differences. \n", + "* `Wf`: forget gate weight $\\mathbf{W}_{f}$\n", + "* `Wb`: forget gate bias $\\mathbf{W}_{b}$\n", + "* `ft`: forget gate $\\Gamma_f^{\\langle t \\rangle}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Candidate value $\\tilde{\\mathbf{c}}^{\\langle t \\rangle}$\n", + "* The candidate value is a tensor containing information from the current time step that **may** be stored in the current cell state $\\mathbf{c}^{\\langle t \\rangle}$.\n", + "* Which parts of the candidate value get passed on depends on the update gate.\n", + "* The candidate value is a tensor containing values that range from -1 to 1.\n", + "* The tilde \"~\" is used to differentiate the candidate $\\tilde{\\mathbf{c}}^{\\langle t \\rangle}$ from the cell state $\\mathbf{c}^{\\langle t \\rangle}$.\n", + "\n", + "##### Equation\n", + "$$\\mathbf{\\tilde{c}}^{\\langle t \\rangle} = \\tanh\\left( \\mathbf{W}_{c} [\\mathbf{a}^{\\langle t - 1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_{c} \\right) \\tag{3}$$\n", + "\n", + "##### Explanation of the equation\n", + "* The 'tanh' function produces values between -1 and +1.\n", + "\n", + "\n", + "##### Variable names in the code\n", + "* `cct`: candidate value $\\mathbf{\\tilde{c}}^{\\langle t \\rangle}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### - Update gate $\\mathbf{\\Gamma}_{i}$\n", + "\n", + "* We use the update gate to decide what aspects of the candidate $\\tilde{\\mathbf{c}}^{\\langle t \\rangle}$ to add to the cell state $c^{\\langle t \\rangle}$.\n", + "* The update gate decides what parts of a \"candidate\" tensor $\\tilde{\\mathbf{c}}^{\\langle t \\rangle}$ are passed onto the cell state $\\mathbf{c}^{\\langle t \\rangle}$.\n", + "* The update gate is a tensor containing values between 0 and 1.\n", + " * When a unit in the update gate is close to 1, it allows the value of the candidate $\\tilde{\\mathbf{c}}^{\\langle t \\rangle}$ to be passed onto the hidden state $\\mathbf{c}^{\\langle t \\rangle}$\n", + " * When a unit in the update gate is close to 0, it prevents the corresponding value in the candidate from being passed onto the hidden state.\n", + "* Notice that we use the subscript \"i\" and not \"u\", to follow the convention used in the literature.\n", + "\n", + "##### Equation\n", + "\n", + "$$\\mathbf{\\Gamma}_i^{\\langle t \\rangle} = \\sigma(\\mathbf{W}_i[a^{\\langle t-1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_i)\\tag{2} $$ \n", + "\n", + "##### Explanation of the equation\n", + "\n", + "* Similar to the forget gate, here $\\mathbf{\\Gamma}_i^{\\langle t \\rangle}$, the sigmoid produces values between 0 and 1.\n", + "* The update gate is multiplied element-wise with the candidate, and this product ($\\mathbf{\\Gamma}_{i}^{\\langle t \\rangle} * \\tilde{c}^{\\langle t \\rangle}$) is used in determining the cell state $\\mathbf{c}^{\\langle t \\rangle}$.\n", + "\n", + "##### Variable names in code (Please note that they're different than the equations)\n", + "In the code, we'll use the variable names found in the academic literature. These variables don't use \"u\" to denote \"update\".\n", + "* `Wi` is the update gate weight $\\mathbf{W}_i$ (not \"Wu\") \n", + "* `bi` is the update gate bias $\\mathbf{b}_i$ (not \"bu\")\n", + "* `it` is the forget gate $\\mathbf{\\Gamma}_i^{\\langle t \\rangle}$ (not \"ut\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### - Cell state $\\mathbf{c}^{\\langle t \\rangle}$\n", + "\n", + "* The cell state is the \"memory\" that gets passed onto future time steps.\n", + "* The new cell state $\\mathbf{c}^{\\langle t \\rangle}$ is a combination of the previous cell state and the candidate value.\n", + "\n", + "##### Equation\n", + "\n", + "$$ \\mathbf{c}^{\\langle t \\rangle} = \\mathbf{\\Gamma}_f^{\\langle t \\rangle}* \\mathbf{c}^{\\langle t-1 \\rangle} + \\mathbf{\\Gamma}_{i}^{\\langle t \\rangle} *\\mathbf{\\tilde{c}}^{\\langle t \\rangle} \\tag{4} $$\n", + "\n", + "##### Explanation of equation\n", + "* The previous cell state $\\mathbf{c}^{\\langle t-1 \\rangle}$ is adjusted (weighted) by the forget gate $\\mathbf{\\Gamma}_{f}^{\\langle t \\rangle}$\n", + "* and the candidate value $\\tilde{\\mathbf{c}}^{\\langle t \\rangle}$, adjusted (weighted) by the update gate $\\mathbf{\\Gamma}_{i}^{\\langle t \\rangle}$\n", + "\n", + "##### Variable names and shapes in the code\n", + "* `c`: cell state, including all time steps, $\\mathbf{c}$ shape $(n_{a}, m, T)$\n", + "* `c_next`: new (next) cell state, $\\mathbf{c}^{\\langle t \\rangle}$ shape $(n_{a}, m)$\n", + "* `c_prev`: previous cell state, $\\mathbf{c}^{\\langle t-1 \\rangle}$, shape $(n_{a}, m)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### - Output gate $\\mathbf{\\Gamma}_{o}$\n", + "\n", + "* The output gate decides what gets sent as the prediction (output) of the time step.\n", + "* The output gate is like the other gates. It contains values that range from 0 to 1.\n", + "\n", + "##### Equation\n", + "\n", + "$$ \\mathbf{\\Gamma}_o^{\\langle t \\rangle}= \\sigma(\\mathbf{W}_o[\\mathbf{a}^{\\langle t-1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_{o})\\tag{5}$$ \n", + "\n", + "##### Explanation of the equation\n", + "* The output gate is determined by the previous hidden state $\\mathbf{a}^{\\langle t-1 \\rangle}$ and the current input $\\mathbf{x}^{\\langle t \\rangle}$\n", + "* The sigmoid makes the gate range from 0 to 1.\n", + "\n", + "\n", + "##### Variable names in the code\n", + "* `Wo`: output gate weight, $\\mathbf{W_o}$\n", + "* `bo`: output gate bias, $\\mathbf{b_o}$\n", + "* `ot`: output gate, $\\mathbf{\\Gamma}_{o}^{\\langle t \\rangle}$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### - Hidden state $\\mathbf{a}^{\\langle t \\rangle}$\n", + "\n", + "* The hidden state gets passed to the LSTM cell's next time step.\n", + "* It is used to determine the three gates ($\\mathbf{\\Gamma}_{f}, \\mathbf{\\Gamma}_{u}, \\mathbf{\\Gamma}_{o}$) of the next time step.\n", + "* The hidden state is also used for the prediction $y^{\\langle t \\rangle}$.\n", + "\n", + "##### Equation\n", + "\n", + "$$ \\mathbf{a}^{\\langle t \\rangle} = \\mathbf{\\Gamma}_o^{\\langle t \\rangle} * \\tanh(\\mathbf{c}^{\\langle t \\rangle})\\tag{6} $$\n", + "\n", + "##### Explanation of equation\n", + "* The hidden state $\\mathbf{a}^{\\langle t \\rangle}$ is determined by the cell state $\\mathbf{c}^{\\langle t \\rangle}$ in combination with the output gate $\\mathbf{\\Gamma}_{o}$.\n", + "* The cell state state is passed through the \"tanh\" function to rescale values between -1 and +1.\n", + "* The output gate acts like a \"mask\" that either preserves the values of $\\tanh(\\mathbf{c}^{\\langle t \\rangle})$ or keeps those values from being included in the hidden state $\\mathbf{a}^{\\langle t \\rangle}$\n", + "\n", + "##### Variable names and shapes in the code\n", + "* `a`: hidden state, including time steps. $\\mathbf{a}$ has shape $(n_{a}, m, T_{x})$\n", + "* 'a_prev`: hidden state from previous time step. $\\mathbf{a}^{\\langle t-1 \\rangle}$ has shape $(n_{a}, m)$\n", + "* `a_next`: hidden state for next time step. $\\mathbf{a}^{\\langle t \\rangle}$ has shape $(n_{a}, m)$ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### - Prediction $\\mathbf{y}^{\\langle t \\rangle}_{pred}$\n", + "* The prediction in this use case is a classification, so we'll use a softmax.\n", + "\n", + "The equation is:\n", + "$$\\mathbf{y}^{\\langle t \\rangle}_{pred} = \\textrm{softmax}(\\mathbf{W}_{y} \\mathbf{a}^{\\langle t \\rangle} + \\mathbf{b}_{y})$$\n", + "\n", + "##### Variable names and shapes in the code\n", + "* `y_pred`: prediction, including all time steps. $\\mathbf{y}_{pred}$ has shape $(n_{y}, m, T_{x})$. Note that $(T_{y} = T_{x})$ for this example.\n", + "* `yt_pred`: prediction for the current time step $t$. $\\mathbf{y}^{\\langle t \\rangle}_{pred}$ has shape $(n_{y}, m)$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1 - LSTM cell\n", + "\n", + "**Exercise**: Implement the LSTM cell described in the Figure (4).\n", + "\n", + "**Instructions**:\n", + "1. Concatenate the hidden state $a^{\\langle t-1 \\rangle}$ and input $x^{\\langle t \\rangle}$ into a single matrix: \n", + "\n", + "$$concat = \\begin{bmatrix} a^{\\langle t-1 \\rangle} \\\\ x^{\\langle t \\rangle} \\end{bmatrix}$$ \n", + "\n", + "2. Compute all the formulas 1 through 6 for the gates, hidden state, and cell state.\n", + "3. Compute the prediction $y^{\\langle t \\rangle}$.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Additional Hints\n", + "* You can use [numpy.concatenate](https://docs.scipy.org/doc/numpy/reference/generated/numpy.concatenate.html). Check which value to use for the `axis` parameter.\n", + "* The functions `sigmoid()` and `softmax` are imported from `rnn_utils.py`.\n", + "* [numpy.tanh](https://docs.scipy.org/doc/numpy/reference/generated/numpy.tanh.html)\n", + "* Use [np.dot](https://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) for matrix multiplication.\n", + "* Notice that the variable names `Wi`, `bi` refer to the weights and biases of the **update** gate. There are no variables named \"Wu\" or \"bu\" in this function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: lstm_cell_forward\n", + "\n", + "def lstm_cell_forward(xt, a_prev, c_prev, parameters):\n", + " \"\"\"\n", + " Implement a single forward step of the LSTM-cell as described in Figure (4)\n", + "\n", + " Arguments:\n", + " xt -- your input data at timestep \"t\", numpy array of shape (n_x, m).\n", + " a_prev -- Hidden state at timestep \"t-1\", numpy array of shape (n_a, m)\n", + " c_prev -- Memory state at timestep \"t-1\", numpy array of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Wf -- Weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " bf -- Bias of the forget gate, numpy array of shape (n_a, 1)\n", + " Wi -- Weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " bi -- Bias of the update gate, numpy array of shape (n_a, 1)\n", + " Wc -- Weight matrix of the first \"tanh\", numpy array of shape (n_a, n_a + n_x)\n", + " bc -- Bias of the first \"tanh\", numpy array of shape (n_a, 1)\n", + " Wo -- Weight matrix of the output gate, numpy array of shape (n_a, n_a + n_x)\n", + " bo -- Bias of the output gate, numpy array of shape (n_a, 1)\n", + " Wy -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + " \n", + " Returns:\n", + " a_next -- next hidden state, of shape (n_a, m)\n", + " c_next -- next memory state, of shape (n_a, m)\n", + " yt_pred -- prediction at timestep \"t\", numpy array of shape (n_y, m)\n", + " cache -- tuple of values needed for the backward pass, contains (a_next, c_next, a_prev, c_prev, xt, parameters)\n", + " \n", + " Note: ft/it/ot stand for the forget/update/output gates, cct stands for the candidate value (c tilde),\n", + " c stands for the cell state (memory)\n", + " \"\"\"\n", + "\n", + " # Retrieve parameters from \"parameters\"\n", + " Wf = parameters[\"Wf\"] # forget gate weight\n", + " bf = parameters[\"bf\"]\n", + " Wi = parameters[\"Wi\"] # update gate weight (notice the variable name)\n", + " bi = parameters[\"bi\"] # (notice the variable name)\n", + " Wc = parameters[\"Wc\"] # candidate value weight\n", + " bc = parameters[\"bc\"]\n", + " Wo = parameters[\"Wo\"] # output gate weight\n", + " bo = parameters[\"bo\"]\n", + " Wy = parameters[\"Wy\"] # prediction weight\n", + " by = parameters[\"by\"]\n", + " \n", + " # Retrieve dimensions from shapes of xt and Wy\n", + " n_x, m = xt.shape\n", + " n_y, n_a = Wy.shape\n", + "\n", + " # Concatenate a_prev and xt\n", + " concat = np.zeros([n_x+n_a,m])\n", + " concat[: n_a, :] = a_prev\n", + " concat[n_a :, :] = xt\n", + "\n", + " ### START CODE HERE ###\n", + " # Compute values for ft, it, cct, c_next, ot, a_next using the formulas given figure (4) (≈6 lines)\n", + " # 아래 마크다운셀 참고하세요\n", + " ft = sigmoid(np.dot(Wf,concat)+bf) ### Eq(1) sigmoid)(), np.dot(), Wf, concat, bf 사용\n", + " it = sigmoid(np.dot(Wi,concat)+bi) ### Eq(2) sigmoid)(), np.dot(), Wi, concat, bi 사용\n", + " cct = np.tanh(np.dot(Wc,concat)+bc) ### Eq(3) np.tanh(), np.dot(), Wc, concat, bc 사용\n", + " c_next = ft*c_prev+it*cct ### Eq(4) ft, c_prev, it, cct 사용\n", + " ot = sigmoid(np.dot(Wo,concat)+bo) ### Eq(5) sigmoid)(), np.dot(), Wo, concat, bo 사용\n", + " a_next = ot*np.tanh(c_next) ### Eq(6) ot, np.tanh(), c_next 사용\n", + " \n", + " # Compute prediction of the LSTM cell (≈1 line)\n", + " yt_pred = softmax(np.dot(Wy,a_next)+by)\n", + " ### Eq(7), Use softmax(), np.dot(), Wy, a_next, by 사용\n", + " ### END CODE HERE ###\n", + "\n", + " # store values needed for backward propagation in cache\n", + " cache = (a_next, c_next, a_prev, c_prev, ft, it, cct, ot, xt, parameters)\n", + "\n", + " return a_next, c_next, yt_pred, cache" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "$$Eq(1) \\ \\cdot\\cdot\\cdot \\ \\mathbf{\\Gamma}_f^{\\langle t \\rangle} = \\sigma(\\mathbf{W}_f[\\mathbf{a}^{\\langle t-1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_f) \\ \\ \\ \\ \\ \\ $$\n", + "\n", + "$$Eq(2) \\ \\cdot\\cdot\\cdot \\ \\mathbf{\\Gamma}_i^{\\langle t \\rangle} = \\sigma(\\mathbf{W}_i[a^{\\langle t-1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_i) \\ \\ \\ \\ \\ \\ \\ \\ $$ \n", + "\n", + "$$Eq(3) \\ \\cdot\\cdot\\cdot \\ \\mathbf{\\tilde{c}}^{\\langle t \\rangle} = \\tanh\\left( \\mathbf{W}_{c} [\\mathbf{a}^{\\langle t - 1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_{c} \\right) $$\n", + "\n", + "$$Eq(4) \\ \\cdot\\cdot\\cdot \\ \\mathbf{c}^{\\langle t \\rangle} = \\mathbf{\\Gamma}_f^{\\langle t \\rangle}* \\mathbf{c}^{\\langle t-1 \\rangle} + \\mathbf{\\Gamma}_{i}^{\\langle t \\rangle} *\\mathbf{\\tilde{c}}^{\\langle t \\rangle} \\ \\ \\ \\ \\ \\ \\ \\ $$\n", + "\n", + "$$Eq(5) \\ \\cdot\\cdot\\cdot \\ \\mathbf{\\Gamma}_o^{\\langle t \\rangle}= \\sigma(\\mathbf{W}_o[\\mathbf{a}^{\\langle t-1 \\rangle}, \\mathbf{x}^{\\langle t \\rangle}] + \\mathbf{b}_{o}) \\ \\ \\ \\ \\ \\ $$ \n", + "\n", + "$$Eq(6) \\ \\cdot\\cdot\\cdot \\ \\mathbf{a}^{\\langle t \\rangle} = \\mathbf{\\Gamma}_o^{\\langle t \\rangle} * \\tanh(\\mathbf{c}^{\\langle t \\rangle}) \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ \\ $$\n", + "\n", + "$$Eq(7) \\ \\cdot\\cdot\\cdot \\ \\mathbf{y}^{\\langle t \\rangle}_{pred} = \\textrm{softmax}(\\mathbf{W}_{y} \\mathbf{a}^{\\langle t \\rangle} + \\mathbf{b}_{y}) \\ \\ \\ \\ \\ \\$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "xt_tmp = np.random.randn(3,10)\n", + "a_prev_tmp = np.random.randn(5,10)\n", + "c_prev_tmp = np.random.randn(5,10)\n", + "parameters_tmp = {}\n", + "parameters_tmp['Wf'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bf'] = np.random.randn(5,1)\n", + "parameters_tmp['Wi'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bi'] = np.random.randn(5,1)\n", + "parameters_tmp['Wo'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bo'] = np.random.randn(5,1)\n", + "parameters_tmp['Wc'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bc'] = np.random.randn(5,1)\n", + "parameters_tmp['Wy'] = np.random.randn(2,5)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_next_tmp, c_next_tmp, yt_tmp, cache_tmp = lstm_cell_forward(xt_tmp, a_prev_tmp, c_prev_tmp, parameters_tmp)\n", + "print(\"a_next[4] = \\n\", a_next_tmp[4])\n", + "print(\"a_next.shape = \", c_next_tmp.shape)\n", + "print(\"c_next[2] = \\n\", c_next_tmp[2])\n", + "print(\"c_next.shape = \", c_next_tmp.shape)\n", + "print(\"yt[1] =\", yt_tmp[1])\n", + "print(\"yt.shape = \", yt_tmp.shape)\n", + "print(\"cache[1][3] =\\n\", cache_tmp[1][3])\n", + "print(\"len(cache) = \", len(cache_tmp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "```Python\n", + "a_next[4] = \n", + " [-0.66408471 0.0036921 0.02088357 0.22834167 -0.85575339 0.00138482\n", + " 0.76566531 0.34631421 -0.00215674 0.43827275]\n", + "a_next.shape = (5, 10)\n", + "c_next[2] = \n", + " [ 0.63267805 1.00570849 0.35504474 0.20690913 -1.64566718 0.11832942\n", + " 0.76449811 -0.0981561 -0.74348425 -0.26810932]\n", + "c_next.shape = (5, 10)\n", + "yt[1] = [ 0.79913913 0.15986619 0.22412122 0.15606108 0.97057211 0.31146381\n", + " 0.00943007 0.12666353 0.39380172 0.07828381]\n", + "yt.shape = (2, 10)\n", + "cache[1][3] =\n", + " [-0.16263996 1.03729328 0.72938082 -0.54101719 0.02752074 -0.30821874\n", + " 0.07651101 -1.03752894 1.41219977 -0.37647422]\n", + "len(cache) = 10\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2 - Forward pass for LSTM\n", + "\n", + "Now that you have implemented one step of an LSTM, you can now iterate this over this using a for-loop to process a sequence of $T_x$ inputs. \n", + "\n", + "\n", + "
**Figure 5**: LSTM over multiple time-steps.
\n", + "\n", + "**Exercise:** Implement `lstm_forward()` to run an LSTM over $T_x$ time-steps. \n", + "\n", + "**Instructions**\n", + "* Get the dimensions $n_x, n_a, n_y, m, T_x$ from the shape of the variables: `x` and `parameters`.\n", + "* Initialize the 3D tensors $a$, $c$ and $y$.\n", + " - $a$: hidden state, shape $(n_{a}, m, T_{x})$\n", + " - $c$: cell state, shape $(n_{a}, m, T_{x})$\n", + " - $y$: prediction, shape $(n_{y}, m, T_{x})$ (Note that $T_{y} = T_{x}$ in this example).\n", + " - **Note** Setting one variable equal to the other is a \"copy by reference\". In other words, don't do `c = a', otherwise both these variables point to the same underlying variable.\n", + "* Initialize the 2D tensor $a^{\\langle t \\rangle}$ \n", + " - $a^{\\langle t \\rangle}$ stores the hidden state for time step $t$. The variable name is `a_next`.\n", + " - $a^{\\langle 0 \\rangle}$, the initial hidden state at time step 0, is passed in when calling the function. The variable name is `a0`.\n", + " - $a^{\\langle t \\rangle}$ and $a^{\\langle 0 \\rangle}$ represent a single time step, so they both have the shape $(n_{a}, m)$ \n", + " - Initialize $a^{\\langle t \\rangle}$ by setting it to the initial hidden state ($a^{\\langle 0 \\rangle}$) that is passed into the function.\n", + "* Initialize $c^{\\langle t \\rangle}$ with zeros. \n", + " - The variable name is `c_next`. \n", + " - $c^{\\langle t \\rangle}$ represents a single time step, so its shape is $(n_{a}, m)$\n", + " - **Note**: create `c_next` as its own variable with its own location in memory. Do not initialize it as a slice of the 3D tensor $c$. In other words, **don't** do `c_next = c[:,:,0]`.\n", + "* For each time step, do the following:\n", + " - From the 3D tensor $x$, get a 2D slice $x^{\\langle t \\rangle}$ at time step $t$.\n", + " - Call the `lstm_cell_forward` function that you defined previously, to get the hidden state, cell state, prediction, and cache.\n", + " - Store the hidden state, cell state and prediction (the 2D tensors) inside the 3D tensors.\n", + " - Also append the cache to the list of caches." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# GRADED FUNCTION: lstm_forward\n", + "\n", + "def lstm_forward(x, a0, parameters):\n", + " \"\"\"\n", + " Implement the forward propagation of the recurrent neural network using an LSTM-cell described in Figure (4).\n", + "\n", + " Arguments:\n", + " x -- Input data for every time-step, of shape (n_x, m, T_x).\n", + " a0 -- Initial hidden state, of shape (n_a, m)\n", + " parameters -- python dictionary containing:\n", + " Wf -- Weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " bf -- Bias of the forget gate, numpy array of shape (n_a, 1)\n", + " Wi -- Weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " bi -- Bias of the update gate, numpy array of shape (n_a, 1)\n", + " Wc -- Weight matrix of the first \"tanh\", numpy array of shape (n_a, n_a + n_x)\n", + " bc -- Bias of the first \"tanh\", numpy array of shape (n_a, 1)\n", + " Wo -- Weight matrix of the output gate, numpy array of shape (n_a, n_a + n_x)\n", + " bo -- Bias of the output gate, numpy array of shape (n_a, 1)\n", + " Wy -- Weight matrix relating the hidden-state to the output, numpy array of shape (n_y, n_a)\n", + " by -- Bias relating the hidden-state to the output, numpy array of shape (n_y, 1)\n", + " \n", + " Returns:\n", + " a -- Hidden states for every time-step, numpy array of shape (n_a, m, T_x)\n", + " y -- Predictions for every time-step, numpy array of shape (n_y, m, T_x)\n", + " c -- The value of the cell state, numpy array of shape (n_a, m, T_x)\n", + " caches -- tuple of values needed for the backward pass, contains (list of all the caches, x)\n", + " \"\"\"\n", + "\n", + " # Initialize \"caches\", which will track the list of all the caches\n", + " caches = []\n", + " \n", + " Wy = parameters['Wy'] # saving parameters['Wy'] in a local variable in case students use Wy instead of parameters['Wy']\n", + " # Retrieve dimensions from shapes of x and parameters['Wy']\n", + " n_x, m, T_x = x.shape\n", + " n_y, n_a = Wy.shape\n", + " \n", + " # initialize \"a\", \"c\" and \"y\" with zeros\n", + " a = np.zeros([n_a, m, T_x])\n", + " c = np.zeros([n_a, m, T_x])\n", + " y = np.zeros([n_y, m, T_x])\n", + " \n", + " # Initialize a_next and c_next\n", + " a_next = a0\n", + " c_next = np.zeros([n_a,m])\n", + " \n", + " # loop over all time-steps\n", + " for t in range(T_x):\n", + " ### START CODE HERE ###\n", + " # Update next hidden state, next memory state, compute the prediction, get the cache (≈1 line)\n", + " a_next, c_next, yt, cache = lstm_cell_forward(x[:,:,t], a_next, c_next, parameters)\n", + " ### Use lstm_cell_forward(xt, a_prev, c_prev, parameters), x[:,:,t], a_next, c_next, parameters\n", + " ### END CODE HERE ###\n", + "\n", + " # Save the value of the new \"next\" hidden state in a\n", + " a[:,:,t] = a_next\n", + " # Save the value of the prediction in y\n", + " y[:,:,t] = yt\n", + " # Save the value of the next cell state\n", + " c[:,:,t] = c_next\n", + " # Append the cache into caches\n", + " caches.append(cache)\n", + " \n", + " \n", + " # store values needed for backward propagation in cache\n", + " caches = (caches, x)\n", + "\n", + " return a, y, c, caches" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "x_tmp = np.random.randn(3,10,7)\n", + "a0_tmp = np.random.randn(5,10)\n", + "parameters_tmp = {}\n", + "parameters_tmp['Wf'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bf'] = np.random.randn(5,1)\n", + "parameters_tmp['Wi'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bi']= np.random.randn(5,1)\n", + "parameters_tmp['Wo'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bo'] = np.random.randn(5,1)\n", + "parameters_tmp['Wc'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bc'] = np.random.randn(5,1)\n", + "parameters_tmp['Wy'] = np.random.randn(2,5)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_tmp, y_tmp, c_tmp, caches_tmp = lstm_forward(x_tmp, a0_tmp, parameters_tmp)\n", + "print(\"a[4][3][6] = \", a_tmp[4][3][6])\n", + "print(\"a.shape = \", a_tmp.shape)\n", + "print(\"y[1][4][3] =\", y_tmp[1][4][3])\n", + "print(\"y.shape = \", y_tmp.shape)\n", + "print(\"caches[1][1][1] =\\n\", caches_tmp[1][1][1])\n", + "print(\"c[1][2][1]\", c_tmp[1][2][1])\n", + "print(\"len(caches) = \", len(caches_tmp))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "```Python\n", + "a[4][3][6] = 0.172117767533\n", + "a.shape = (5, 10, 7)\n", + "y[1][4][3] = 0.95087346185\n", + "y.shape = (2, 10, 7)\n", + "caches[1][1][1] =\n", + " [ 0.82797464 0.23009474 0.76201118 -0.22232814 -0.20075807 0.18656139\n", + " 0.41005165]\n", + "c[1][2][1] -0.855544916718\n", + "len(caches) = 2\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Congratulations! You have now implemented the forward passes for the basic RNN and the LSTM. When using a deep learning framework, implementing the forward pass is sufficient to build systems that achieve great performance. \n", + "\n", + "The rest of this notebook is optional, and will not be graded." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3 - Backpropagation in recurrent neural networks (OPTIONAL / UNGRADED)\n", + "\n", + "In modern deep learning frameworks, you only have to implement the forward pass, and the framework takes care of the backward pass, so most deep learning engineers do not need to bother with the details of the backward pass. If however you are an expert in calculus and want to see the details of backprop in RNNs, you can work through this optional portion of the notebook. \n", + "\n", + "When in an earlier course you implemented a simple (fully connected) neural network, you used backpropagation to compute the derivatives with respect to the cost to update the parameters. Similarly, in recurrent neural networks you can calculate the derivatives with respect to the cost in order to update the parameters. The backprop equations are quite complicated and we did not derive them in lecture. However, we will briefly present them below. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1 - Basic RNN backward pass\n", + "\n", + "We will start by computing the backward pass for the basic RNN-cell.\n", + "\n", + "
\n", + "**Figure 6**\n", + "\n", + "RNN-cell's backward pass. Just like in a fully-connected neural network, the derivative of the cost function $J$ backpropagates through the RNN by following the chain-rule from calculus. The chain-rule is also used to calculate $ (\\frac{\\partial J}{\\partial W_{ax}},\\frac{\\partial J}{\\partial W_{aa}},\\frac{\\partial J}{\\partial b})$ to update the parameters $(W_{ax}, W_{aa}, b_a)$.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Deriving the one step backward functions: \n", + "\n", + "To compute the `rnn_cell_backward` you need to compute the following equations. It is a good exercise to derive them by hand. \n", + "\n", + "The derivative of $\\tanh$ is $1-\\tanh(x)^2$. You can find the complete proof [here](https://www.wyzant.com/resources/lessons/math/calculus/derivative_proofs/tanx). Note that: $ \\text{sech}(x)^2 = 1 - \\tanh(x)^2$\n", + "\n", + "Similarly for $\\frac{ \\partial a^{\\langle t \\rangle} } {\\partial W_{ax}}, \\frac{ \\partial a^{\\langle t \\rangle} } {\\partial W_{aa}}, \\frac{ \\partial a^{\\langle t \\rangle} } {\\partial b}$, the derivative of $\\tanh(u)$ is $(1-\\tanh(u)^2)du$. \n", + "\n", + "The final two equations also follow the same rule and are derived using the $\\tanh$ derivative. Note that the arrangement is done in a way to get the same dimensions to match." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def rnn_cell_backward(da_next, cache):\n", + " \"\"\"\n", + " Implements the backward pass for the RNN-cell (single time-step).\n", + "\n", + " Arguments:\n", + " da_next -- Gradient of loss with respect to next hidden state\n", + " cache -- python dictionary containing useful values (output of rnn_cell_forward())\n", + "\n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dx -- Gradients of input data, of shape (n_x, m)\n", + " da_prev -- Gradients of previous hidden state, of shape (n_a, m)\n", + " dWax -- Gradients of input-to-hidden weights, of shape (n_a, n_x)\n", + " dWaa -- Gradients of hidden-to-hidden weights, of shape (n_a, n_a)\n", + " dba -- Gradients of bias vector, of shape (n_a, 1)\n", + " \"\"\"\n", + " \n", + " # Retrieve values from cache\n", + " (a_next, a_prev, xt, parameters) = cache\n", + " \n", + " # Retrieve values from parameters\n", + " Wax = parameters[\"Wax\"]\n", + " Waa = parameters[\"Waa\"]\n", + " Wya = parameters[\"Wya\"]\n", + " ba = parameters[\"ba\"]\n", + " by = parameters[\"by\"]\n", + "\n", + " # compute the gradient of tanh with respect to a_next\n", + " dtanh = (1-a_next*a_next)*da_next\n", + "\n", + " # compute the gradient of the loss with respect to Wax\n", + " dxt = np.dot(Wax.T, dtanh)\n", + " dWax = np.dot(dtanh,xt.T)\n", + "\n", + " # compute the gradient with respect to Waa \n", + " da_prev = np.dot(Waa.T, dtanh) \n", + " dWaa = np.dot( dtanh,a_prev.T)\n", + "\n", + " # compute the gradient with respect to b \n", + " dba = np.sum( dtanh,keepdims=True,axis=-1)\n", + "\n", + " \n", + " # Store the gradients in a python dictionary\n", + " gradients = {\"dxt\": dxt, \"da_prev\": da_prev, \"dWax\": dWax, \"dWaa\": dWaa, \"dba\": dba}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "xt_tmp = np.random.randn(3,10)\n", + "a_prev_tmp = np.random.randn(5,10)\n", + "parameters_tmp = {}\n", + "parameters_tmp['Wax'] = np.random.randn(5,3)\n", + "parameters_tmp['Waa'] = np.random.randn(5,5)\n", + "parameters_tmp['Wya'] = np.random.randn(2,5)\n", + "parameters_tmp['ba'] = np.random.randn(5,1)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_next_tmp, yt_tmp, cache_tmp = rnn_cell_forward(xt_tmp, a_prev_tmp, parameters_tmp)\n", + "\n", + "da_next_tmp = np.random.randn(5,10)\n", + "gradients_tmp = rnn_cell_backward(da_next_tmp, cache_tmp)\n", + "print(\"gradients[\\\"dxt\\\"][1][2] =\", gradients_tmp[\"dxt\"][1][2])\n", + "print(\"gradients[\\\"dxt\\\"].shape =\", gradients_tmp[\"dxt\"].shape)\n", + "print(\"gradients[\\\"da_prev\\\"][2][3] =\", gradients_tmp[\"da_prev\"][2][3])\n", + "print(\"gradients[\\\"da_prev\\\"].shape =\", gradients_tmp[\"da_prev\"].shape)\n", + "print(\"gradients[\\\"dWax\\\"][3][1] =\", gradients_tmp[\"dWax\"][3][1])\n", + "print(\"gradients[\\\"dWax\\\"].shape =\", gradients_tmp[\"dWax\"].shape)\n", + "print(\"gradients[\\\"dWaa\\\"][1][2] =\", gradients_tmp[\"dWaa\"][1][2])\n", + "print(\"gradients[\\\"dWaa\\\"].shape =\", gradients_tmp[\"dWaa\"].shape)\n", + "print(\"gradients[\\\"dba\\\"][4] =\", gradients_tmp[\"dba\"][4])\n", + "print(\"gradients[\\\"dba\\\"].shape =\", gradients_tmp[\"dba\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dxt\"][1][2]** =\n", + " \n", + " -1.3872130506020923\n", + "
\n", + " **gradients[\"dxt\"].shape** =\n", + " \n", + " (3, 10)\n", + "
\n", + " **gradients[\"da_prev\"][2][3]** =\n", + " \n", + " -0.15239949377395473\n", + "
\n", + " **gradients[\"da_prev\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWax\"][3][1]** =\n", + " \n", + " 0.4107728249354583\n", + "
\n", + " **gradients[\"dWax\"].shape** =\n", + " \n", + " (5, 3)\n", + "
\n", + " **gradients[\"dWaa\"][1][2]** = \n", + " \n", + " 1.1503450668497135\n", + "
\n", + " **gradients[\"dWaa\"].shape** =\n", + " \n", + " (5, 5)\n", + "
\n", + " **gradients[\"dba\"][4]** = \n", + " \n", + " [0.20023491]\n", + "
\n", + " **gradients[\"dba\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Backward pass through the RNN\n", + "\n", + "Computing the gradients of the cost with respect to $a^{\\langle t \\rangle}$ at every time-step $t$ is useful because it is what helps the gradient backpropagate to the previous RNN-cell. To do so, you need to iterate through all the time steps starting at the end, and at each step, you increment the overall $db_a$, $dW_{aa}$, $dW_{ax}$ and you store $dx$.\n", + "\n", + "**Instructions**:\n", + "\n", + "Implement the `rnn_backward` function. Initialize the return variables with zeros first and then loop through all the time steps while calling the `rnn_cell_backward` at each time timestep, update the other variables accordingly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def rnn_backward(da, caches):\n", + " \"\"\"\n", + " Implement the backward pass for a RNN over an entire sequence of input data.\n", + "\n", + " Arguments:\n", + " da -- Upstream gradients of all hidden states, of shape (n_a, m, T_x)\n", + " caches -- tuple containing information from the forward pass (rnn_forward)\n", + " \n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dx -- Gradient w.r.t. the input data, numpy-array of shape (n_x, m, T_x)\n", + " da0 -- Gradient w.r.t the initial hidden state, numpy-array of shape (n_a, m)\n", + " dWax -- Gradient w.r.t the input's weight matrix, numpy-array of shape (n_a, n_x)\n", + " dWaa -- Gradient w.r.t the hidden state's weight matrix, numpy array of shape (n_a, n_a)\n", + " dba -- Gradient w.r.t the bias, of shape (n_a, 1)\n", + " \"\"\"\n", + " \n", + " \n", + " # Retrieve values from the first cache (t=1) of caches \n", + " (caches, x) = caches\n", + " (a1, a0, x1, parameters) = caches[0]\n", + " \n", + " # Retrieve dimensions from da's and x1's shapes\n", + " n_a, m, T_x = da.shape\n", + " n_x, m = x1.shape \n", + " \n", + " # initialize the gradients with the right sizes\n", + " dx = np.zeros((n_x, m, T_x)) \n", + " dWax = np.zeros((n_a, n_x))\n", + " dWaa = np.zeros((n_a, n_a))\n", + " dba = np.zeros((n_a, 1)) \n", + " da0 = np.zeros((n_a, m))\n", + " da_prevt = np.zeros((n_a, m)) \n", + " \n", + " # Loop through all the time steps\n", + " for t in reversed(range(T_x)):\n", + " ### START CODE HERE ###\n", + " # Compute gradients at time step t. Choose wisely the \"da_next\" and the \"cache\" to use in the backward propagation step. (≈1 line)\n", + " gradients = rnn_cell_backward(da[:, :, t] + da_prevt, caches[t])\n", + " ### Use rnn_cell_backward, da[:, :, t] + da_prevt, caches[t] \n", + " ### END CODE HERE ###\n", + "\n", + " # Retrieve derivatives from gradients\n", + " dxt, da_prevt, dWaxt, dWaat, dbat = gradients[\"dxt\"], gradients[\"da_prev\"], gradients[\"dWax\"], gradients[\"dWaa\"], gradients[\"dba\"]\n", + " # Increment global derivatives w.r.t parameters by adding their derivative at time-step t \n", + " dx[:, :, t] = dxt \n", + " dWax += dWaxt \n", + " dWaa += dWaat \n", + " dba += dbat \n", + " \n", + " # Set da0 to the gradient of a which has been backpropagated through all time-steps\n", + " da0 = da_prevt\n", + "\n", + " # Store the gradients in a python dictionary\n", + " gradients = {\"dx\": dx, \"da0\": da0, \"dWax\": dWax, \"dWaa\": dWaa,\"dba\": dba}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "x_tmp = np.random.randn(3,10,4)\n", + "a0_tmp = np.random.randn(5,10)\n", + "parameters_tmp = {}\n", + "parameters_tmp['Wax'] = np.random.randn(5,3)\n", + "parameters_tmp['Waa'] = np.random.randn(5,5)\n", + "parameters_tmp['Wya'] = np.random.randn(2,5)\n", + "parameters_tmp['ba'] = np.random.randn(5,1)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_tmp, y_tmp, caches_tmp = rnn_forward(x_tmp, a0_tmp, parameters_tmp)\n", + "da_tmp = np.random.randn(5, 10, 4)\n", + "gradients_tmp = rnn_backward(da_tmp, caches_tmp)\n", + "\n", + "print(\"gradients[\\\"dx\\\"][1][2] =\", gradients_tmp[\"dx\"][1][2])\n", + "print(\"gradients[\\\"dx\\\"].shape =\", gradients_tmp[\"dx\"].shape)\n", + "print(\"gradients[\\\"da0\\\"][2][3] =\", gradients_tmp[\"da0\"][2][3])\n", + "print(\"gradients[\\\"da0\\\"].shape =\", gradients_tmp[\"da0\"].shape)\n", + "print(\"gradients[\\\"dWax\\\"][3][1] =\", gradients_tmp[\"dWax\"][3][1])\n", + "print(\"gradients[\\\"dWax\\\"].shape =\", gradients_tmp[\"dWax\"].shape)\n", + "print(\"gradients[\\\"dWaa\\\"][1][2] =\", gradients_tmp[\"dWaa\"][1][2])\n", + "print(\"gradients[\\\"dWaa\\\"].shape =\", gradients_tmp[\"dWaa\"].shape)\n", + "print(\"gradients[\\\"dba\\\"][4] =\", gradients_tmp[\"dba\"][4])\n", + "print(\"gradients[\\\"dba\\\"].shape =\", gradients_tmp[\"dba\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dx\"][1][2]** =\n", + " \n", + " [-2.07101689 -0.59255627 0.02466855 0.01483317]\n", + "
\n", + " **gradients[\"dx\"].shape** =\n", + " \n", + " (3, 10, 4)\n", + "
\n", + " **gradients[\"da0\"][2][3]** =\n", + " \n", + " -0.314942375127\n", + "
\n", + " **gradients[\"da0\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWax\"][3][1]** =\n", + " \n", + " 11.2641044965\n", + "
\n", + " **gradients[\"dWax\"].shape** =\n", + " \n", + " (5, 3)\n", + "
\n", + " **gradients[\"dWaa\"][1][2]** = \n", + " \n", + " 2.30333312658\n", + "
\n", + " **gradients[\"dWaa\"].shape** =\n", + " \n", + " (5, 5)\n", + "
\n", + " **gradients[\"dba\"][4]** = \n", + " \n", + " [-0.74747722]\n", + "
\n", + " **gradients[\"dba\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3.2 - LSTM backward pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2.1 One Step backward\n", + "\n", + "The LSTM backward pass is slightly more complicated than the forward one. We have provided you with all the equations for the LSTM backward pass below. (If you enjoy calculus exercises feel free to try deriving these from scratch yourself.) \n", + "\n", + "### 3.2.2 gate derivatives\n", + "\n", + "$$d \\Gamma_o^{\\langle t \\rangle} = da_{next}*\\tanh(c_{next}) * \\Gamma_o^{\\langle t \\rangle}*(1-\\Gamma_o^{\\langle t \\rangle})\\tag{7}$$\n", + "\n", + "$$d\\tilde c^{\\langle t \\rangle} = dc_{next}*\\Gamma_u^{\\langle t \\rangle}+ \\Gamma_o^{\\langle t \\rangle} (1-\\tanh(c_{next})^2) * i_t * da_{next} * \\tilde c^{\\langle t \\rangle} * (1-\\tanh(\\tilde c)^2) \\tag{8}$$\n", + "\n", + "$$d\\Gamma_u^{\\langle t \\rangle} = dc_{next}*\\tilde c^{\\langle t \\rangle} + \\Gamma_o^{\\langle t \\rangle} (1-\\tanh(c_{next})^2) * \\tilde c^{\\langle t \\rangle} * da_{next}*\\Gamma_u^{\\langle t \\rangle}*(1-\\Gamma_u^{\\langle t \\rangle})\\tag{9}$$\n", + "\n", + "$$d\\Gamma_f^{\\langle t \\rangle} = dc_{next}*\\tilde c_{prev} + \\Gamma_o^{\\langle t \\rangle} (1-\\tanh(c_{next})^2) * c_{prev} * da_{next}*\\Gamma_f^{\\langle t \\rangle}*(1-\\Gamma_f^{\\langle t \\rangle})\\tag{10}$$\n", + "\n", + "### 3.2.3 parameter derivatives \n", + "\n", + "$$ dW_f = d\\Gamma_f^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{11} $$\n", + "$$ dW_u = d\\Gamma_u^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{12} $$\n", + "$$ dW_c = d\\tilde c^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{13} $$\n", + "$$ dW_o = d\\Gamma_o^{\\langle t \\rangle} * \\begin{pmatrix} a_{prev} \\\\ x_t\\end{pmatrix}^T \\tag{14}$$\n", + "\n", + "To calculate $db_f, db_u, db_c, db_o$ you just need to sum across the horizontal (axis= 1) axis on $d\\Gamma_f^{\\langle t \\rangle}, d\\Gamma_u^{\\langle t \\rangle}, d\\tilde c^{\\langle t \\rangle}, d\\Gamma_o^{\\langle t \\rangle}$ respectively. Note that you should have the `keep_dims = True` option.\n", + "\n", + "Finally, you will compute the derivative with respect to the previous hidden state, previous memory state, and input.\n", + "\n", + "$$ da_{prev} = W_f^T*d\\Gamma_f^{\\langle t \\rangle} + W_u^T * d\\Gamma_u^{\\langle t \\rangle}+ W_c^T * d\\tilde c^{\\langle t \\rangle} + W_o^T * d\\Gamma_o^{\\langle t \\rangle} \\tag{15}$$\n", + "Here, the weights for equations 13 are the first n_a, (i.e. $W_f = W_f[:n_a,:]$ etc...)\n", + "\n", + "$$ dc_{prev} = dc_{next}\\Gamma_f^{\\langle t \\rangle} + \\Gamma_o^{\\langle t \\rangle} * (1- \\tanh(c_{next})^2)*\\Gamma_f^{\\langle t \\rangle}*da_{next} \\tag{16}$$\n", + "$$ dx^{\\langle t \\rangle} = W_f^T*d\\Gamma_f^{\\langle t \\rangle} + W_u^T * d\\Gamma_u^{\\langle t \\rangle}+ W_c^T * d\\tilde c_t + W_o^T * d\\Gamma_o^{\\langle t \\rangle}\\tag{17} $$\n", + "where the weights for equation 15 are from n_a to the end, (i.e. $W_f = W_f[n_a:,:]$ etc...)\n", + "\n", + "**Exercise:** Implement `lstm_cell_backward` by implementing equations $7-17$ below. Good luck! :)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def lstm_cell_backward(da_next, dc_next, cache):\n", + " \"\"\"\n", + " Implement the backward pass for the LSTM-cell (single time-step).\n", + "\n", + " Arguments:\n", + " da_next -- Gradients of next hidden state, of shape (n_a, m)\n", + " dc_next -- Gradients of next cell state, of shape (n_a, m)\n", + " cache -- cache storing information from the forward pass\n", + "\n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dxt -- Gradient of input data at time-step t, of shape (n_x, m)\n", + " da_prev -- Gradient w.r.t. the previous hidden state, numpy array of shape (n_a, m)\n", + " dc_prev -- Gradient w.r.t. the previous memory state, of shape (n_a, m, T_x)\n", + " dWf -- Gradient w.r.t. the weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWi -- Gradient w.r.t. the weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWc -- Gradient w.r.t. the weight matrix of the memory gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWo -- Gradient w.r.t. the weight matrix of the output gate, numpy array of shape (n_a, n_a + n_x)\n", + " dbf -- Gradient w.r.t. biases of the forget gate, of shape (n_a, 1)\n", + " dbi -- Gradient w.r.t. biases of the update gate, of shape (n_a, 1)\n", + " dbc -- Gradient w.r.t. biases of the memory gate, of shape (n_a, 1)\n", + " dbo -- Gradient w.r.t. biases of the output gate, of shape (n_a, 1)\n", + " \"\"\"\n", + "\n", + " # Retrieve information from \"cache\"\n", + " (a_next, c_next, a_prev, c_prev, ft, it, cct, ot, xt, parameters) = cache\n", + " \n", + " # Retrieve dimensions from xt's and a_next's shape (≈2 lines)\n", + " n_x, m = xt.shape \n", + " n_a, m = a_next.shape \n", + " \n", + " ### START CODE HERE ###\n", + " # Compute gates related derivatives, you can find their values can be found by looking carefully at equations (7) to (10) (≈4 lines)\n", + " # Code equations (7) to (10) (≈4 lines)\n", + " ### 이 파트는 하는 거 좀 무리라서 SKIP\n", + " ### 대신 위에 한 번 씩 꼭 읽어보기!!!\n", + " dit = (da_next * ot * (1 - np.tanh(c_next) ** 2) + dc_next) * cct * (1 - it) * it\n", + " dft = (da_next * ot * (1 - np.tanh(c_next) ** 2) + dc_next) * c_prev * ft * (1 - ft)\n", + " dot = da_next * np.tanh(c_next) * ot * (1 - ot)\n", + " dcct = (da_next * ot * (1 - np.tanh(c_next) ** 2) + dc_next) * it * (1 - cct ** 2)\n", + "\n", + " # Compute parameters related derivatives. Use equations (11)-(14) (≈8 lines)\n", + " dWf = np.dot(dft,np.concatenate((a_prev, xt), axis=0).T) # or use np.dot(dft, np.hstack([a_prev.T, xt.T]))\n", + " dWi = np.dot(dit,np.concatenate((a_prev, xt), axis=0).T)\n", + " dWc = np.dot(dcct,np.concatenate((a_prev, xt), axis=0).T)\n", + " dWo = np.dot(dot,np.concatenate((a_prev, xt), axis=0).T)\n", + " dbf = np.sum(dft,axis=1,keepdims=True)\n", + " dbi = np.sum(dit,axis=1,keepdims=True) \n", + " dbc = np.sum(dcct,axis=1,keepdims=True) \n", + " dbo = np.sum(dot,axis=1,keepdims=True) \n", + "\n", + " # Compute derivatives w.r.t previous hidden state, previous memory state and input. Use equations (15)-(17). (≈3 lines)\n", + " da_prev = np.dot(parameters['Wf'][:,:n_a].T,dft)+np.dot(parameters['Wi'][:,:n_a].T,dit)+np.dot(parameters['Wc'][:,:n_a].T,dcct)+np.dot(parameters['Wo'][:,:n_a].T,dot) \n", + " dc_prev = dc_next*ft+ot*(1-np.square(np.tanh(c_next)))*ft*da_next \n", + " dxt = np.dot(parameters['Wf'][:,n_a:].T,dft)+np.dot(parameters['Wi'][:,n_a:].T,dit)+np.dot(parameters['Wc'][:,n_a:].T,dcct)+np.dot(parameters['Wo'][:,n_a:].T,dot) \n", + " ### END CODE HERE ###\n", + " \n", + " # Save gradients in dictionary\n", + " gradients = {\"dxt\": dxt, \"da_prev\": da_prev, \"dc_prev\": dc_prev, \"dWf\": dWf,\"dbf\": dbf, \"dWi\": dWi,\"dbi\": dbi,\n", + " \"dWc\": dWc,\"dbc\": dbc, \"dWo\": dWo,\"dbo\": dbo}\n", + "\n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "xt_tmp = np.random.randn(3,10)\n", + "a_prev_tmp = np.random.randn(5,10)\n", + "c_prev_tmp = np.random.randn(5,10)\n", + "parameters_tmp = {}\n", + "parameters_tmp['Wf'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bf'] = np.random.randn(5,1)\n", + "parameters_tmp['Wi'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bi'] = np.random.randn(5,1)\n", + "parameters_tmp['Wo'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bo'] = np.random.randn(5,1)\n", + "parameters_tmp['Wc'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bc'] = np.random.randn(5,1)\n", + "parameters_tmp['Wy'] = np.random.randn(2,5)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_next_tmp, c_next_tmp, yt_tmp, cache_tmp = lstm_cell_forward(xt_tmp, a_prev_tmp, c_prev_tmp, parameters_tmp)\n", + "\n", + "da_next_tmp = np.random.randn(5,10)\n", + "dc_next_tmp = np.random.randn(5,10)\n", + "gradients_tmp = lstm_cell_backward(da_next_tmp, dc_next_tmp, cache_tmp)\n", + "print(\"gradients[\\\"dxt\\\"][1][2] =\", gradients_tmp[\"dxt\"][1][2])\n", + "print(\"gradients[\\\"dxt\\\"].shape =\", gradients_tmp[\"dxt\"].shape)\n", + "print(\"gradients[\\\"da_prev\\\"][2][3] =\", gradients_tmp[\"da_prev\"][2][3])\n", + "print(\"gradients[\\\"da_prev\\\"].shape =\", gradients_tmp[\"da_prev\"].shape)\n", + "print(\"gradients[\\\"dc_prev\\\"][2][3] =\", gradients_tmp[\"dc_prev\"][2][3])\n", + "print(\"gradients[\\\"dc_prev\\\"].shape =\", gradients_tmp[\"dc_prev\"].shape)\n", + "print(\"gradients[\\\"dWf\\\"][3][1] =\", gradients_tmp[\"dWf\"][3][1])\n", + "print(\"gradients[\\\"dWf\\\"].shape =\", gradients_tmp[\"dWf\"].shape)\n", + "print(\"gradients[\\\"dWi\\\"][1][2] =\", gradients_tmp[\"dWi\"][1][2])\n", + "print(\"gradients[\\\"dWi\\\"].shape =\", gradients_tmp[\"dWi\"].shape)\n", + "print(\"gradients[\\\"dWc\\\"][3][1] =\", gradients_tmp[\"dWc\"][3][1])\n", + "print(\"gradients[\\\"dWc\\\"].shape =\", gradients_tmp[\"dWc\"].shape)\n", + "print(\"gradients[\\\"dWo\\\"][1][2] =\", gradients_tmp[\"dWo\"][1][2])\n", + "print(\"gradients[\\\"dWo\\\"].shape =\", gradients_tmp[\"dWo\"].shape)\n", + "print(\"gradients[\\\"dbf\\\"][4] =\", gradients_tmp[\"dbf\"][4])\n", + "print(\"gradients[\\\"dbf\\\"].shape =\", gradients_tmp[\"dbf\"].shape)\n", + "print(\"gradients[\\\"dbi\\\"][4] =\", gradients_tmp[\"dbi\"][4])\n", + "print(\"gradients[\\\"dbi\\\"].shape =\", gradients_tmp[\"dbi\"].shape)\n", + "print(\"gradients[\\\"dbc\\\"][4] =\", gradients_tmp[\"dbc\"][4])\n", + "print(\"gradients[\\\"dbc\\\"].shape =\", gradients_tmp[\"dbc\"].shape)\n", + "print(\"gradients[\\\"dbo\\\"][4] =\", gradients_tmp[\"dbo\"][4])\n", + "print(\"gradients[\\\"dbo\\\"].shape =\", gradients_tmp[\"dbo\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dxt\"][1][2]** =\n", + " \n", + " 3.23055911511\n", + "
\n", + " **gradients[\"dxt\"].shape** =\n", + " \n", + " (3, 10)\n", + "
\n", + " **gradients[\"da_prev\"][2][3]** =\n", + " \n", + " -0.0639621419711\n", + "
\n", + " **gradients[\"da_prev\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dc_prev\"][2][3]** =\n", + " \n", + " 0.797522038797\n", + "
\n", + " **gradients[\"dc_prev\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWf\"][3][1]** = \n", + " \n", + " -0.147954838164\n", + "
\n", + " **gradients[\"dWf\"].shape** =\n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWi\"][1][2]** = \n", + " \n", + " 1.05749805523\n", + "
\n", + " **gradients[\"dWi\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWc\"][3][1]** = \n", + " \n", + " 2.30456216369\n", + "
\n", + " **gradients[\"dWc\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWo\"][1][2]** = \n", + " \n", + " 0.331311595289\n", + "
\n", + " **gradients[\"dWo\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dbf\"][4]** = \n", + " \n", + " [ 0.18864637]\n", + "
\n", + " **gradients[\"dbf\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbi\"][4]** = \n", + " \n", + " [-0.40142491]\n", + "
\n", + " **gradients[\"dbi\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbc\"][4]** = \n", + " \n", + " [ 0.25587763]\n", + "
\n", + " **gradients[\"dbc\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbo\"][4]** = \n", + " \n", + " [ 0.13893342]\n", + "
\n", + " **gradients[\"dbo\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3 Backward pass through the LSTM RNN\n", + "\n", + "This part is very similar to the `rnn_backward` function you implemented above. You will first create variables of the same dimension as your return variables. You will then iterate over all the time steps starting from the end and call the one step function you implemented for LSTM at each iteration. You will then update the parameters by summing them individually. Finally return a dictionary with the new gradients. \n", + "\n", + "**Instructions**: Implement the `lstm_backward` function. Create a for loop starting from $T_x$ and going backward. For each step call `lstm_cell_backward` and update the your old gradients by adding the new gradients to them. Note that `dxt` is not updated but is stored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def lstm_backward(da, caches):\n", + " \n", + " \"\"\"\n", + " Implement the backward pass for the RNN with LSTM-cell (over a whole sequence).\n", + "\n", + " Arguments:\n", + " da -- Gradients w.r.t the hidden states, numpy-array of shape (n_a, m, T_x)\n", + " caches -- cache storing information from the forward pass (lstm_forward)\n", + "\n", + " Returns:\n", + " gradients -- python dictionary containing:\n", + " dx -- Gradient of inputs, of shape (n_x, m, T_x)\n", + " da0 -- Gradient w.r.t. the previous hidden state, numpy array of shape (n_a, m)\n", + " dWf -- Gradient w.r.t. the weight matrix of the forget gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWi -- Gradient w.r.t. the weight matrix of the update gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWc -- Gradient w.r.t. the weight matrix of the memory gate, numpy array of shape (n_a, n_a + n_x)\n", + " dWo -- Gradient w.r.t. the weight matrix of the save gate, numpy array of shape (n_a, n_a + n_x)\n", + " dbf -- Gradient w.r.t. biases of the forget gate, of shape (n_a, 1)\n", + " dbi -- Gradient w.r.t. biases of the update gate, of shape (n_a, 1)\n", + " dbc -- Gradient w.r.t. biases of the memory gate, of shape (n_a, 1)\n", + " dbo -- Gradient w.r.t. biases of the save gate, of shape (n_a, 1)\n", + " \"\"\"\n", + "\n", + " # Retrieve values from the first cache (t=1) of caches.\n", + " (caches, x) = caches\n", + " (a1, c1, a0, c0, f1, i1, cc1, o1, x1, parameters) = caches[0]\n", + " \n", + " # Retrieve dimensions from da's and x1's shapes\n", + " n_a, m, T_x = da.shape\n", + " n_x, m = x1.shape\n", + " \n", + " # initialize the gradients with the right sizes\n", + " dx = np.zeros((n_x, m, T_x))\n", + " da0 = np.zeros((n_a, m))\n", + " da_prevt = np.zeros((n_a, m))\n", + " dc_prevt = np.zeros((n_a, m))\n", + " dWf = np.zeros((n_a, n_a + n_x))\n", + " dWi = np.zeros((n_a, n_a + n_x))\n", + " dWc = np.zeros((n_a, n_a + n_x))\n", + " dWo = np.zeros((n_a, n_a + n_x))\n", + " dbf = np.zeros((n_a, 1))\n", + " dbi = np.zeros((n_a, 1))\n", + " dbc = np.zeros((n_a, 1))\n", + " dbo = np.zeros((n_a, 1))\n", + " \n", + " # loop back over the whole sequence\n", + " for t in reversed(range(T_x)):\n", + " ### START CODE HERE ###\n", + " # Compute all gradients using lstm_cell_backward\n", + " gradients = lstm_cell_backward(da[:,:,t] + da_prevt, dc_prevt, caches[t])\n", + " ### Use lstm_cell_backward(da_next, dc_next, cache), da[:,:,t] + da_prevt, dc_prevt, caches[t]\n", + " ### END CODE HERE ###\n", + " \n", + " # Store or add the gradient to the parameters' previous step's gradient\n", + " dx[:,:,t] = gradients[\"dxt\"]\n", + " dWf += gradients[\"dWf\"]\n", + " dWi += gradients[\"dWi\"]\n", + " dWc += gradients[\"dWc\"]\n", + " dWo += gradients[\"dWo\"]\n", + " dbf += gradients[\"dbf\"]\n", + " dbi += gradients[\"dbi\"]\n", + " dbc += gradients[\"dbc\"]\n", + " dbo += gradients[\"dbo\"]\n", + " # Set the first activation's gradient to the backpropagated gradient da_prev.\n", + " da0 = gradients[\"da_prev\"]\n", + " \n", + "\n", + " # Store the gradients in a python dictionary\n", + " gradients = {\"dx\": dx, \"da0\": da0, \"dWf\": dWf,\"dbf\": dbf, \"dWi\": dWi,\"dbi\": dbi,\n", + " \"dWc\": dWc,\"dbc\": dbc, \"dWo\": dWo,\"dbo\": dbo}\n", + " \n", + " return gradients" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(1)\n", + "x_tmp = np.random.randn(3,10,7)\n", + "a0_tmp = np.random.randn(5,10)\n", + "\n", + "parameters_tmp = {}\n", + "parameters_tmp['Wf'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bf'] = np.random.randn(5,1)\n", + "parameters_tmp['Wi'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bi'] = np.random.randn(5,1)\n", + "parameters_tmp['Wo'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bo'] = np.random.randn(5,1)\n", + "parameters_tmp['Wc'] = np.random.randn(5, 5+3)\n", + "parameters_tmp['bc'] = np.random.randn(5,1)\n", + "parameters_tmp['Wy'] = np.random.randn(2,5)\n", + "parameters_tmp['by'] = np.random.randn(2,1)\n", + "\n", + "a_tmp, y_tmp, c_tmp, caches_tmp = lstm_forward(x_tmp, a0_tmp, parameters_tmp)\n", + "\n", + "da_tmp = np.random.randn(5, 10, 4)\n", + "gradients_tmp = lstm_backward(da_tmp, caches_tmp)\n", + "\n", + "print(\"gradients[\\\"dx\\\"][1][2] =\", gradients_tmp[\"dx\"][1][2])\n", + "print(\"gradients[\\\"dx\\\"].shape =\", gradients_tmp[\"dx\"].shape)\n", + "print(\"gradients[\\\"da0\\\"][2][3] =\", gradients_tmp[\"da0\"][2][3])\n", + "print(\"gradients[\\\"da0\\\"].shape =\", gradients_tmp[\"da0\"].shape)\n", + "print(\"gradients[\\\"dWf\\\"][3][1] =\", gradients_tmp[\"dWf\"][3][1])\n", + "print(\"gradients[\\\"dWf\\\"].shape =\", gradients_tmp[\"dWf\"].shape)\n", + "print(\"gradients[\\\"dWi\\\"][1][2] =\", gradients_tmp[\"dWi\"][1][2])\n", + "print(\"gradients[\\\"dWi\\\"].shape =\", gradients_tmp[\"dWi\"].shape)\n", + "print(\"gradients[\\\"dWc\\\"][3][1] =\", gradients_tmp[\"dWc\"][3][1])\n", + "print(\"gradients[\\\"dWc\\\"].shape =\", gradients_tmp[\"dWc\"].shape)\n", + "print(\"gradients[\\\"dWo\\\"][1][2] =\", gradients_tmp[\"dWo\"][1][2])\n", + "print(\"gradients[\\\"dWo\\\"].shape =\", gradients_tmp[\"dWo\"].shape)\n", + "print(\"gradients[\\\"dbf\\\"][4] =\", gradients_tmp[\"dbf\"][4])\n", + "print(\"gradients[\\\"dbf\\\"].shape =\", gradients_tmp[\"dbf\"].shape)\n", + "print(\"gradients[\\\"dbi\\\"][4] =\", gradients_tmp[\"dbi\"][4])\n", + "print(\"gradients[\\\"dbi\\\"].shape =\", gradients_tmp[\"dbi\"].shape)\n", + "print(\"gradients[\\\"dbc\\\"][4] =\", gradients_tmp[\"dbc\"][4])\n", + "print(\"gradients[\\\"dbc\\\"].shape =\", gradients_tmp[\"dbc\"].shape)\n", + "print(\"gradients[\\\"dbo\\\"][4] =\", gradients_tmp[\"dbo\"][4])\n", + "print(\"gradients[\\\"dbo\\\"].shape =\", gradients_tmp[\"dbo\"].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Expected Output**:\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " **gradients[\"dx\"][1][2]** =\n", + " \n", + " [-0.00173313 0.08287442 -0.30545663 -0.43281115]\n", + "
\n", + " **gradients[\"dx\"].shape** =\n", + " \n", + " (3, 10, 4)\n", + "
\n", + " **gradients[\"da0\"][2][3]** =\n", + " \n", + " -0.095911501954\n", + "
\n", + " **gradients[\"da0\"].shape** =\n", + " \n", + " (5, 10)\n", + "
\n", + " **gradients[\"dWf\"][3][1]** = \n", + " \n", + " -0.0698198561274\n", + "
\n", + " **gradients[\"dWf\"].shape** =\n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWi\"][1][2]** = \n", + " \n", + " 0.102371820249\n", + "
\n", + " **gradients[\"dWi\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWc\"][3][1]** = \n", + " \n", + " -0.0624983794927\n", + "
\n", + " **gradients[\"dWc\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dWo\"][1][2]** = \n", + " \n", + " 0.0484389131444\n", + "
\n", + " **gradients[\"dWo\"].shape** = \n", + " \n", + " (5, 8)\n", + "
\n", + " **gradients[\"dbf\"][4]** = \n", + " \n", + " [-0.0565788]\n", + "
\n", + " **gradients[\"dbf\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbi\"][4]** = \n", + " \n", + " [-0.06997391]\n", + "
\n", + " **gradients[\"dbi\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbc\"][4]** = \n", + " \n", + " [-0.27441821]\n", + "
\n", + " **gradients[\"dbc\"].shape** = \n", + " \n", + " (5, 1)\n", + "
\n", + " **gradients[\"dbo\"][4]** = \n", + " \n", + " [ 0.16532821]\n", + "
\n", + " **gradients[\"dbo\"].shape** = \n", + " \n", + " (5, 1)\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Congratulations !\n", + "\n", + "Congratulations on completing this assignment. You now understand how recurrent neural networks work! \n", + "\n", + "Let's go on to the next exercise, where you'll use an RNN to build a character-level language model.\n" + ] + } + ], + "metadata": { + "coursera": { + "course_slug": "nlp-sequence-models", + "graded_item_id": "xxuVc", + "launcher_item_id": "X20PE" + }, + "kernelspec": { + "display_name": "Python 3.9.12 ('base')", + "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": "3.9.12" + }, + "vscode": { + "interpreter": { + "hash": "dbc2d860e59ffcf4c7e3321cdf91ed515b575f2741e55bcb74f1a255f2f51726" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}