Datasets
Models
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"machine_shape": "hm",
"gpuType": "V28"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "TPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "8XnVMPBXmtRa"
},
"source": [
"# TensorNetworks in Neural Networks.\n",
"\n",
"Here, we have a small toy example of how to use a TN inside of a fully connected neural network.\n",
"\n",
"First off, let's install tensornetwork"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7HGRsYNAFxME"
},
"source": [
"# !pip install tensornetwork\n",
"\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import tensorflow as tf\n",
"# Import tensornetwork\n",
"import tensornetwork as tn\n",
"import random\n",
"import time\n",
"import pandas as pd\n",
"# Set the backend to tesorflow\n",
"# (default is numpy)\n",
"tn.set_default_backend(\"tensorflow\")\n",
"np.random.seed(42)\n",
"random.seed(42)\n",
"tf.random.set_seed(42)\n",
"# Explainability code assistance aided by ChatGPT3.5\n",
"# 2021 Kelly, D. TensorFlow Explainable AI tutorial https://www.youtube.com/watch?v=6xePkn3-LME"
],
"execution_count": 90,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "g1OMCo5XmrYu"
},
"source": [
"# TensorNetwork layer definition\n",
"\n",
"Here, we define the TensorNetwork layer we wish to use to replace the fully connected layer. Here, we simply use a 2 node Matrix Product Operator network to replace the normal dense weight matrix.\n",
"\n",
"We TensorNetwork's NCon API to keep the code short."
]
},
{
"cell_type": "code",
"metadata": {
"id": "wvSMKtPufnLp"
},
"source": [
"class TNLayer(tf.keras.layers.Layer):\n",
"\n",
" def __init__(self):\n",
" super(TNLayer, self).__init__()\n",
" # Create the variables for the layer.\n",
" self.a_var = tf.Variable(tf.random.normal(shape=(32, 32, 2),\n",
" stddev=1.0/32.0),\n",
" name=\"a\", trainable=True)\n",
" self.b_var = tf.Variable(tf.random.normal(shape=(32, 32, 2),\n",
" stddev=1.0/32.0),\n",
" name=\"b\", trainable=True)\n",
" self.bias = tf.Variable(tf.zeros(shape=(32, 32)),\n",
" name=\"bias\", trainable=True)\n",
"\n",
" def call(self, inputs):\n",
" # Define the contraction.\n",
" # We break it out so we can parallelize a batch using\n",
" # tf.vectorized_map (see below).\n",
" def f(input_vec, a_var, b_var, bias_var):\n",
" # Reshape to a matrix instead of a vector.\n",
" input_vec = tf.reshape(input_vec, (32, 32))\n",
"\n",
" # Now we create the network.\n",
" a = tn.Node(a_var)\n",
" b = tn.Node(b_var)\n",
" x_node = tn.Node(input_vec)\n",
" a[1] ^ x_node[0]\n",
" b[1] ^ x_node[1]\n",
" a[2] ^ b[2]\n",
"\n",
" # The TN should now look like this\n",
" # | |\n",
" # a --- b\n",
" # \\ /\n",
" # x\n",
"\n",
" # Now we begin the contraction.\n",
" c = a @ x_node\n",
" result = (c @ b).tensor\n",
"\n",
" # To make the code shorter, we also could've used Ncon.\n",
" # The above few lines of code is the same as this:\n",
" # result = tn.ncon([x, a_var, b_var], [[1, 2], [-1, 1, 3], [-2, 2, 3]])\n",
"\n",
" # Finally, add bias.\n",
" return result + bias_var\n",
"\n",
" # To deal with a batch of items, we can use the tf.vectorized_map\n",
" # function.\n",
" # https://www.tensorflow.org/api_docs/python/tf/vectorized_map\n",
" result = tf.vectorized_map(\n",
" lambda vec: f(vec, self.a_var, self.b_var, self.bias), inputs)\n",
" return tf.nn.relu(tf.reshape(result, (-1, 1024)))"
],
"execution_count": 91,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "V-CVqIhPnhY_"
},
"source": [
"# Smaller model\n",
"These two models are effectively the same, but notice how the TN layer has nearly 10x fewer parameters."
]
},
{
"cell_type": "code",
"metadata": {
"id": "bbKsmK8wIFTp",
"outputId": "4146e343-d5ff-4f17-faf5-2a8bc7b74c8c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"Dense = tf.keras.layers.Dense\n",
"tn_model = tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(2,)),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" # Start Modified Layers\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" TNLayer(),\n",
" TNLayer(),\n",
" # Finish Modified Layers\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 92,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_8\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_30 (Dense) (None, 1024) 3072 \n",
" \n",
" dense_31 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_32 (Dense) (None, 1024) 1049600 \n",
" \n",
" tn_layer_9 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_10 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_33 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 2113537 (8.06 MB)\n",
"Trainable params: 2113537 (8.06 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GWwoYp0WnsLA"
},
"source": [
"# Training a model\n",
"\n",
"You can train the TN model just as you would a normal neural network model! Here, we give an example of how to do it in Keras."
]
},
{
"cell_type": "code",
"metadata": {
"id": "qDFzOC7sDBJ-"
},
"source": [
"X = np.concatenate([np.random.randn(120, 2) + np.array([3, 3]),\n",
" np.random.randn(120, 2) + np.array([-3, -3]),\n",
" np.random.randn(120, 2) + np.array([-3, 3]),\n",
" np.random.randn(120, 2) + np.array([3, -3])])\n",
"\n",
"Y = np.concatenate([np.ones((240)), -np.ones((240))])"
],
"execution_count": 93,
"outputs": []
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "19TWP-1eKURB",
"outputId": "676af897-2ef8-45bf-8e5f-d1338ef2fe9b"
},
"execution_count": 94,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712560083.9302807\n",
"Mon Apr 8 07:08:03 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "7a7a4363-88c0-44c0-e572-fd6af9f22775",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"tn_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n",
"tn_model.fit(X, Y, epochs=300, verbose=2)"
],
"execution_count": 95,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"15/15 - 2s - loss: 0.9375 - 2s/epoch - 103ms/step\n",
"Epoch 2/300\n",
"15/15 - 0s - loss: 0.2116 - 174ms/epoch - 12ms/step\n",
"Epoch 3/300\n",
"15/15 - 0s - loss: 0.0701 - 171ms/epoch - 11ms/step\n",
"Epoch 4/300\n",
"15/15 - 0s - loss: 0.0407 - 166ms/epoch - 11ms/step\n",
"Epoch 5/300\n",
"15/15 - 0s - loss: 0.0273 - 162ms/epoch - 11ms/step\n",
"Epoch 6/300\n",
"15/15 - 0s - loss: 0.0219 - 163ms/epoch - 11ms/step\n",
"Epoch 7/300\n",
"15/15 - 0s - loss: 0.0176 - 170ms/epoch - 11ms/step\n",
"Epoch 8/300\n",
"15/15 - 0s - loss: 0.0134 - 164ms/epoch - 11ms/step\n",
"Epoch 9/300\n",
"15/15 - 0s - loss: 0.0097 - 161ms/epoch - 11ms/step\n",
"Epoch 10/300\n",
"15/15 - 0s - loss: 0.0070 - 157ms/epoch - 10ms/step\n",
"Epoch 11/300\n",
"15/15 - 0s - loss: 0.0078 - 157ms/epoch - 10ms/step\n",
"Epoch 12/300\n",
"15/15 - 0s - loss: 0.0031 - 158ms/epoch - 11ms/step\n",
"Epoch 13/300\n",
"15/15 - 0s - loss: 0.0022 - 158ms/epoch - 11ms/step\n",
"Epoch 14/300\n",
"15/15 - 0s - loss: 0.0010 - 161ms/epoch - 11ms/step\n",
"Epoch 15/300\n",
"15/15 - 0s - loss: 3.7597e-04 - 160ms/epoch - 11ms/step\n",
"Epoch 16/300\n",
"15/15 - 0s - loss: 2.3599e-04 - 162ms/epoch - 11ms/step\n",
"Epoch 17/300\n",
"15/15 - 0s - loss: 1.7035e-04 - 164ms/epoch - 11ms/step\n",
"Epoch 18/300\n",
"15/15 - 0s - loss: 1.1514e-04 - 172ms/epoch - 11ms/step\n",
"Epoch 19/300\n",
"15/15 - 0s - loss: 1.2201e-04 - 166ms/epoch - 11ms/step\n",
"Epoch 20/300\n",
"15/15 - 0s - loss: 9.0416e-05 - 165ms/epoch - 11ms/step\n",
"Epoch 21/300\n",
"15/15 - 0s - loss: 3.3621e-05 - 162ms/epoch - 11ms/step\n",
"Epoch 22/300\n",
"15/15 - 0s - loss: 3.5003e-05 - 159ms/epoch - 11ms/step\n",
"Epoch 23/300\n",
"15/15 - 0s - loss: 2.5957e-05 - 169ms/epoch - 11ms/step\n",
"Epoch 24/300\n",
"15/15 - 0s - loss: 1.8330e-05 - 165ms/epoch - 11ms/step\n",
"Epoch 25/300\n",
"15/15 - 0s - loss: 2.0533e-05 - 165ms/epoch - 11ms/step\n",
"Epoch 26/300\n",
"15/15 - 0s - loss: 2.0419e-05 - 164ms/epoch - 11ms/step\n",
"Epoch 27/300\n",
"15/15 - 0s - loss: 1.7338e-05 - 167ms/epoch - 11ms/step\n",
"Epoch 28/300\n",
"15/15 - 0s - loss: 1.1321e-05 - 162ms/epoch - 11ms/step\n",
"Epoch 29/300\n",
"15/15 - 0s - loss: 1.0546e-05 - 162ms/epoch - 11ms/step\n",
"Epoch 30/300\n",
"15/15 - 0s - loss: 1.0944e-05 - 169ms/epoch - 11ms/step\n",
"Epoch 31/300\n",
"15/15 - 0s - loss: 1.1172e-05 - 167ms/epoch - 11ms/step\n",
"Epoch 32/300\n",
"15/15 - 0s - loss: 6.0298e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 33/300\n",
"15/15 - 0s - loss: 6.9388e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 34/300\n",
"15/15 - 0s - loss: 6.2688e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 35/300\n",
"15/15 - 0s - loss: 4.5486e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 36/300\n",
"15/15 - 0s - loss: 4.6380e-06 - 164ms/epoch - 11ms/step\n",
"Epoch 37/300\n",
"15/15 - 0s - loss: 4.7684e-06 - 169ms/epoch - 11ms/step\n",
"Epoch 38/300\n",
"15/15 - 0s - loss: 1.5904e-05 - 161ms/epoch - 11ms/step\n",
"Epoch 39/300\n",
"15/15 - 0s - loss: 1.5738e-05 - 159ms/epoch - 11ms/step\n",
"Epoch 40/300\n",
"15/15 - 0s - loss: 2.6713e-06 - 161ms/epoch - 11ms/step\n",
"Epoch 41/300\n",
"15/15 - 0s - loss: 2.3722e-06 - 166ms/epoch - 11ms/step\n",
"Epoch 42/300\n",
"15/15 - 0s - loss: 3.4382e-06 - 170ms/epoch - 11ms/step\n",
"Epoch 43/300\n",
"15/15 - 0s - loss: 2.8751e-06 - 161ms/epoch - 11ms/step\n",
"Epoch 44/300\n",
"15/15 - 0s - loss: 3.6055e-06 - 157ms/epoch - 10ms/step\n",
"Epoch 45/300\n",
"15/15 - 0s - loss: 2.0925e-06 - 152ms/epoch - 10ms/step\n",
"Epoch 46/300\n",
"15/15 - 0s - loss: 3.7457e-06 - 151ms/epoch - 10ms/step\n",
"Epoch 47/300\n",
"15/15 - 0s - loss: 8.4038e-06 - 158ms/epoch - 11ms/step\n",
"Epoch 48/300\n",
"15/15 - 0s - loss: 1.5027e-06 - 158ms/epoch - 11ms/step\n",
"Epoch 49/300\n",
"15/15 - 0s - loss: 1.9858e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 50/300\n",
"15/15 - 0s - loss: 7.2850e-06 - 163ms/epoch - 11ms/step\n",
"Epoch 51/300\n",
"15/15 - 0s - loss: 1.0350e-05 - 159ms/epoch - 11ms/step\n",
"Epoch 52/300\n",
"15/15 - 0s - loss: 9.2933e-05 - 152ms/epoch - 10ms/step\n",
"Epoch 53/300\n",
"15/15 - 0s - loss: 5.7264e-04 - 153ms/epoch - 10ms/step\n",
"Epoch 54/300\n",
"15/15 - 0s - loss: 7.0577e-04 - 156ms/epoch - 10ms/step\n",
"Epoch 55/300\n",
"15/15 - 0s - loss: 2.0676e-04 - 154ms/epoch - 10ms/step\n",
"Epoch 56/300\n",
"15/15 - 0s - loss: 6.8527e-05 - 150ms/epoch - 10ms/step\n",
"Epoch 57/300\n",
"15/15 - 0s - loss: 4.0665e-05 - 157ms/epoch - 10ms/step\n",
"Epoch 58/300\n",
"15/15 - 0s - loss: 7.2068e-06 - 151ms/epoch - 10ms/step\n",
"Epoch 59/300\n",
"15/15 - 0s - loss: 3.5091e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 60/300\n",
"15/15 - 0s - loss: 3.4957e-06 - 157ms/epoch - 10ms/step\n",
"Epoch 61/300\n",
"15/15 - 0s - loss: 8.3725e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 62/300\n",
"15/15 - 0s - loss: 1.6718e-05 - 158ms/epoch - 11ms/step\n",
"Epoch 63/300\n",
"15/15 - 0s - loss: 1.9949e-05 - 154ms/epoch - 10ms/step\n",
"Epoch 64/300\n",
"15/15 - 0s - loss: 9.0646e-06 - 155ms/epoch - 10ms/step\n",
"Epoch 65/300\n",
"15/15 - 0s - loss: 1.2163e-05 - 156ms/epoch - 10ms/step\n",
"Epoch 66/300\n",
"15/15 - 0s - loss: 1.5129e-05 - 152ms/epoch - 10ms/step\n",
"Epoch 67/300\n",
"15/15 - 0s - loss: 9.2843e-06 - 155ms/epoch - 10ms/step\n",
"Epoch 68/300\n",
"15/15 - 0s - loss: 1.3217e-05 - 158ms/epoch - 11ms/step\n",
"Epoch 69/300\n",
"15/15 - 0s - loss: 1.7184e-05 - 163ms/epoch - 11ms/step\n",
"Epoch 70/300\n",
"15/15 - 0s - loss: 3.9230e-06 - 150ms/epoch - 10ms/step\n",
"Epoch 71/300\n",
"15/15 - 0s - loss: 9.9230e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 72/300\n",
"15/15 - 0s - loss: 1.0119e-05 - 159ms/epoch - 11ms/step\n",
"Epoch 73/300\n",
"15/15 - 0s - loss: 8.1462e-06 - 151ms/epoch - 10ms/step\n",
"Epoch 74/300\n",
"15/15 - 0s - loss: 5.1228e-06 - 155ms/epoch - 10ms/step\n",
"Epoch 75/300\n",
"15/15 - 0s - loss: 5.1433e-06 - 150ms/epoch - 10ms/step\n",
"Epoch 76/300\n",
"15/15 - 0s - loss: 2.0605e-06 - 162ms/epoch - 11ms/step\n",
"Epoch 77/300\n",
"15/15 - 0s - loss: 7.6334e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 78/300\n",
"15/15 - 0s - loss: 9.4166e-07 - 160ms/epoch - 11ms/step\n",
"Epoch 79/300\n",
"15/15 - 0s - loss: 1.7487e-06 - 152ms/epoch - 10ms/step\n",
"Epoch 80/300\n",
"15/15 - 0s - loss: 1.0295e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 81/300\n",
"15/15 - 0s - loss: 2.8929e-06 - 158ms/epoch - 11ms/step\n",
"Epoch 82/300\n",
"15/15 - 0s - loss: 7.4247e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 83/300\n",
"15/15 - 0s - loss: 4.7313e-06 - 159ms/epoch - 11ms/step\n",
"Epoch 84/300\n",
"15/15 - 0s - loss: 1.3355e-06 - 156ms/epoch - 10ms/step\n",
"Epoch 85/300\n",
"15/15 - 0s - loss: 1.2078e-05 - 153ms/epoch - 10ms/step\n",
"Epoch 86/300\n",
"15/15 - 0s - loss: 4.9421e-05 - 152ms/epoch - 10ms/step\n",
"Epoch 87/300\n",
"15/15 - 0s - loss: 1.7619e-04 - 152ms/epoch - 10ms/step\n",
"Epoch 88/300\n",
"15/15 - 0s - loss: 2.8511e-05 - 162ms/epoch - 11ms/step\n",
"Epoch 89/300\n",
"15/15 - 0s - loss: 9.1337e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 90/300\n",
"15/15 - 0s - loss: 1.2788e-05 - 150ms/epoch - 10ms/step\n",
"Epoch 91/300\n",
"15/15 - 0s - loss: 1.7800e-05 - 154ms/epoch - 10ms/step\n",
"Epoch 92/300\n",
"15/15 - 0s - loss: 4.3808e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 93/300\n",
"15/15 - 0s - loss: 2.5492e-06 - 159ms/epoch - 11ms/step\n",
"Epoch 94/300\n",
"15/15 - 0s - loss: 2.7812e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 95/300\n",
"15/15 - 0s - loss: 2.8194e-06 - 154ms/epoch - 10ms/step\n",
"Epoch 96/300\n",
"15/15 - 0s - loss: 1.4666e-06 - 158ms/epoch - 11ms/step\n",
"Epoch 97/300\n",
"15/15 - 0s - loss: 1.1745e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 98/300\n",
"15/15 - 0s - loss: 4.2295e-07 - 162ms/epoch - 11ms/step\n",
"Epoch 99/300\n",
"15/15 - 0s - loss: 3.2026e-07 - 153ms/epoch - 10ms/step\n",
"Epoch 100/300\n",
"15/15 - 0s - loss: 2.3621e-07 - 148ms/epoch - 10ms/step\n",
"Epoch 101/300\n",
"15/15 - 0s - loss: 4.3640e-07 - 157ms/epoch - 10ms/step\n",
"Epoch 102/300\n",
"15/15 - 0s - loss: 2.1619e-07 - 156ms/epoch - 10ms/step\n",
"Epoch 103/300\n",
"15/15 - 0s - loss: 3.2122e-07 - 154ms/epoch - 10ms/step\n",
"Epoch 104/300\n",
"15/15 - 0s - loss: 3.5772e-07 - 155ms/epoch - 10ms/step\n",
"Epoch 105/300\n",
"15/15 - 0s - loss: 1.9384e-07 - 151ms/epoch - 10ms/step\n",
"Epoch 106/300\n",
"15/15 - 0s - loss: 1.1047e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 107/300\n",
"15/15 - 0s - loss: 1.2855e-07 - 162ms/epoch - 11ms/step\n",
"Epoch 108/300\n",
"15/15 - 0s - loss: 1.0713e-07 - 156ms/epoch - 10ms/step\n",
"Epoch 109/300\n",
"15/15 - 0s - loss: 1.3824e-07 - 166ms/epoch - 11ms/step\n",
"Epoch 110/300\n",
"15/15 - 0s - loss: 1.7921e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 111/300\n",
"15/15 - 0s - loss: 5.4620e-07 - 157ms/epoch - 10ms/step\n",
"Epoch 112/300\n",
"15/15 - 0s - loss: 3.5711e-06 - 159ms/epoch - 11ms/step\n",
"Epoch 113/300\n",
"15/15 - 0s - loss: 2.8771e-06 - 152ms/epoch - 10ms/step\n",
"Epoch 114/300\n",
"15/15 - 0s - loss: 1.8036e-06 - 155ms/epoch - 10ms/step\n",
"Epoch 115/300\n",
"15/15 - 0s - loss: 7.9089e-07 - 160ms/epoch - 11ms/step\n",
"Epoch 116/300\n",
"15/15 - 0s - loss: 7.1739e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 117/300\n",
"15/15 - 0s - loss: 4.0318e-07 - 165ms/epoch - 11ms/step\n",
"Epoch 118/300\n",
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"Epoch 119/300\n",
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"Epoch 120/300\n",
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"Epoch 121/300\n",
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"Epoch 122/300\n",
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"Epoch 123/300\n",
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"Epoch 126/300\n",
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"Epoch 127/300\n",
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"Epoch 128/300\n",
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"Epoch 130/300\n",
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"Epoch 131/300\n",
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"Epoch 132/300\n",
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"Epoch 133/300\n",
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"Epoch 134/300\n",
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"Epoch 135/300\n",
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"Epoch 136/300\n",
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"Epoch 137/300\n",
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"Epoch 138/300\n",
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"Epoch 139/300\n",
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"Epoch 140/300\n",
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"Epoch 141/300\n",
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"Epoch 142/300\n",
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"Epoch 143/300\n",
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"Epoch 144/300\n",
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"Epoch 145/300\n",
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"Epoch 146/300\n",
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"Epoch 147/300\n",
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"Epoch 148/300\n",
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"Epoch 149/300\n",
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"Epoch 150/300\n",
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"Epoch 151/300\n",
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"Epoch 152/300\n",
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"Epoch 153/300\n",
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"Epoch 154/300\n",
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"Epoch 155/300\n",
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"Epoch 156/300\n",
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"Epoch 157/300\n",
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"Epoch 158/300\n",
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"Epoch 159/300\n",
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"Epoch 160/300\n",
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"Epoch 161/300\n",
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"Epoch 162/300\n",
"15/15 - 0s - loss: 7.8941e-05 - 160ms/epoch - 11ms/step\n",
"Epoch 163/300\n",
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"Epoch 164/300\n",
"15/15 - 0s - loss: 5.7652e-05 - 165ms/epoch - 11ms/step\n",
"Epoch 165/300\n",
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"Epoch 166/300\n",
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"Epoch 167/300\n",
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"Epoch 168/300\n",
"15/15 - 0s - loss: 5.3796e-06 - 163ms/epoch - 11ms/step\n",
"Epoch 169/300\n",
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"Epoch 170/300\n",
"15/15 - 0s - loss: 5.9178e-06 - 163ms/epoch - 11ms/step\n",
"Epoch 171/300\n",
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"Epoch 172/300\n",
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"Epoch 173/300\n",
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"Epoch 174/300\n",
"15/15 - 0s - loss: 2.0013e-06 - 159ms/epoch - 11ms/step\n",
"Epoch 175/300\n",
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"Epoch 176/300\n",
"15/15 - 0s - loss: 6.4147e-07 - 162ms/epoch - 11ms/step\n",
"Epoch 177/300\n",
"15/15 - 0s - loss: 1.6583e-06 - 159ms/epoch - 11ms/step\n",
"Epoch 178/300\n",
"15/15 - 0s - loss: 3.5972e-06 - 171ms/epoch - 11ms/step\n",
"Epoch 179/300\n",
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"Epoch 180/300\n",
"15/15 - 0s - loss: 1.0166e-06 - 160ms/epoch - 11ms/step\n",
"Epoch 181/300\n",
"15/15 - 0s - loss: 1.2895e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 182/300\n",
"15/15 - 0s - loss: 1.5877e-07 - 162ms/epoch - 11ms/step\n",
"Epoch 183/300\n",
"15/15 - 0s - loss: 9.0683e-07 - 157ms/epoch - 10ms/step\n",
"Epoch 184/300\n",
"15/15 - 0s - loss: 3.4370e-07 - 156ms/epoch - 10ms/step\n",
"Epoch 185/300\n",
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"Epoch 186/300\n",
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"Epoch 187/300\n",
"15/15 - 0s - loss: 6.3974e-07 - 163ms/epoch - 11ms/step\n",
"Epoch 188/300\n",
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"Epoch 189/300\n",
"15/15 - 0s - loss: 6.2188e-06 - 157ms/epoch - 10ms/step\n",
"Epoch 190/300\n",
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"Epoch 191/300\n",
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"Epoch 192/300\n",
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"Epoch 193/300\n",
"15/15 - 0s - loss: 2.0316e-04 - 166ms/epoch - 11ms/step\n",
"Epoch 194/300\n",
"15/15 - 0s - loss: 2.0298e-04 - 164ms/epoch - 11ms/step\n",
"Epoch 195/300\n",
"15/15 - 0s - loss: 4.8672e-04 - 162ms/epoch - 11ms/step\n",
"Epoch 196/300\n",
"15/15 - 0s - loss: 0.0535 - 168ms/epoch - 11ms/step\n",
"Epoch 197/300\n",
"15/15 - 0s - loss: 0.0445 - 160ms/epoch - 11ms/step\n",
"Epoch 198/300\n",
"15/15 - 0s - loss: 0.0185 - 166ms/epoch - 11ms/step\n",
"Epoch 199/300\n",
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"Epoch 200/300\n",
"15/15 - 0s - loss: 0.0048 - 158ms/epoch - 11ms/step\n",
"Epoch 201/300\n",
"15/15 - 0s - loss: 9.8256e-04 - 165ms/epoch - 11ms/step\n",
"Epoch 202/300\n",
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"Epoch 203/300\n",
"15/15 - 0s - loss: 8.6200e-05 - 165ms/epoch - 11ms/step\n",
"Epoch 204/300\n",
"15/15 - 0s - loss: 3.2787e-05 - 159ms/epoch - 11ms/step\n",
"Epoch 205/300\n",
"15/15 - 0s - loss: 1.8895e-05 - 165ms/epoch - 11ms/step\n",
"Epoch 206/300\n",
"15/15 - 0s - loss: 1.3025e-05 - 159ms/epoch - 11ms/step\n",
"Epoch 207/300\n",
"15/15 - 0s - loss: 1.0598e-05 - 157ms/epoch - 10ms/step\n",
"Epoch 208/300\n",
"15/15 - 0s - loss: 7.2356e-06 - 154ms/epoch - 10ms/step\n",
"Epoch 209/300\n",
"15/15 - 0s - loss: 5.1274e-06 - 153ms/epoch - 10ms/step\n",
"Epoch 210/300\n",
"15/15 - 0s - loss: 4.4314e-06 - 168ms/epoch - 11ms/step\n",
"Epoch 211/300\n",
"15/15 - 0s - loss: 3.8059e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 212/300\n",
"15/15 - 0s - loss: 3.0771e-06 - 154ms/epoch - 10ms/step\n",
"Epoch 213/300\n",
"15/15 - 0s - loss: 2.6971e-06 - 156ms/epoch - 10ms/step\n",
"Epoch 214/300\n",
"15/15 - 0s - loss: 2.6234e-06 - 152ms/epoch - 10ms/step\n",
"Epoch 215/300\n",
"15/15 - 0s - loss: 3.0180e-06 - 158ms/epoch - 11ms/step\n",
"Epoch 216/300\n",
"15/15 - 0s - loss: 3.4726e-06 - 157ms/epoch - 10ms/step\n",
"Epoch 217/300\n",
"15/15 - 0s - loss: 2.3030e-06 - 164ms/epoch - 11ms/step\n",
"Epoch 218/300\n",
"15/15 - 0s - loss: 1.9915e-06 - 172ms/epoch - 11ms/step\n",
"Epoch 219/300\n",
"15/15 - 0s - loss: 1.6561e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 220/300\n",
"15/15 - 0s - loss: 1.4225e-06 - 157ms/epoch - 10ms/step\n",
"Epoch 221/300\n",
"15/15 - 0s - loss: 1.2966e-06 - 160ms/epoch - 11ms/step\n",
"Epoch 222/300\n",
"15/15 - 0s - loss: 1.2228e-06 - 166ms/epoch - 11ms/step\n",
"Epoch 223/300\n",
"15/15 - 0s - loss: 1.1352e-06 - 156ms/epoch - 10ms/step\n",
"Epoch 224/300\n",
"15/15 - 0s - loss: 1.1424e-06 - 164ms/epoch - 11ms/step\n",
"Epoch 225/300\n",
"15/15 - 0s - loss: 1.1585e-06 - 166ms/epoch - 11ms/step\n",
"Epoch 226/300\n",
"15/15 - 0s - loss: 1.2208e-06 - 157ms/epoch - 10ms/step\n",
"Epoch 227/300\n",
"15/15 - 0s - loss: 1.0862e-06 - 163ms/epoch - 11ms/step\n",
"Epoch 228/300\n",
"15/15 - 0s - loss: 1.1594e-06 - 158ms/epoch - 11ms/step\n",
"Epoch 229/300\n",
"15/15 - 0s - loss: 1.5344e-06 - 159ms/epoch - 11ms/step\n",
"Epoch 230/300\n",
"15/15 - 0s - loss: 1.7111e-06 - 158ms/epoch - 11ms/step\n",
"Epoch 231/300\n",
"15/15 - 0s - loss: 1.3852e-06 - 164ms/epoch - 11ms/step\n",
"Epoch 232/300\n",
"15/15 - 0s - loss: 8.7603e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 233/300\n",
"15/15 - 0s - loss: 6.9070e-07 - 173ms/epoch - 12ms/step\n",
"Epoch 234/300\n",
"15/15 - 0s - loss: 6.5760e-07 - 162ms/epoch - 11ms/step\n",
"Epoch 235/300\n",
"15/15 - 0s - loss: 6.0191e-07 - 164ms/epoch - 11ms/step\n",
"Epoch 236/300\n",
"15/15 - 0s - loss: 6.3585e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 237/300\n",
"15/15 - 0s - loss: 5.3993e-07 - 166ms/epoch - 11ms/step\n",
"Epoch 238/300\n",
"15/15 - 0s - loss: 5.6463e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 239/300\n",
"15/15 - 0s - loss: 6.5308e-07 - 167ms/epoch - 11ms/step\n",
"Epoch 240/300\n",
"15/15 - 0s - loss: 6.8948e-07 - 165ms/epoch - 11ms/step\n",
"Epoch 241/300\n",
"15/15 - 0s - loss: 4.7476e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 242/300\n",
"15/15 - 0s - loss: 4.8182e-07 - 163ms/epoch - 11ms/step\n",
"Epoch 243/300\n",
"15/15 - 0s - loss: 4.8436e-07 - 165ms/epoch - 11ms/step\n",
"Epoch 244/300\n",
"15/15 - 0s - loss: 4.2307e-07 - 165ms/epoch - 11ms/step\n",
"Epoch 245/300\n",
"15/15 - 0s - loss: 4.2961e-07 - 170ms/epoch - 11ms/step\n",
"Epoch 246/300\n",
"15/15 - 0s - loss: 4.5584e-07 - 164ms/epoch - 11ms/step\n",
"Epoch 247/300\n",
"15/15 - 0s - loss: 3.9954e-07 - 154ms/epoch - 10ms/step\n",
"Epoch 248/300\n",
"15/15 - 0s - loss: 3.5126e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 249/300\n",
"15/15 - 0s - loss: 3.8651e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 250/300\n",
"15/15 - 0s - loss: 3.5794e-07 - 166ms/epoch - 11ms/step\n",
"Epoch 251/300\n",
"15/15 - 0s - loss: 3.8071e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 252/300\n",
"15/15 - 0s - loss: 3.4994e-07 - 167ms/epoch - 11ms/step\n",
"Epoch 253/300\n",
"15/15 - 0s - loss: 3.3035e-07 - 157ms/epoch - 10ms/step\n",
"Epoch 254/300\n",
"15/15 - 0s - loss: 3.4228e-07 - 166ms/epoch - 11ms/step\n",
"Epoch 255/300\n",
"15/15 - 0s - loss: 2.8867e-07 - 155ms/epoch - 10ms/step\n",
"Epoch 256/300\n",
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"Epoch 257/300\n",
"15/15 - 0s - loss: 3.3378e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 258/300\n",
"15/15 - 0s - loss: 2.8491e-07 - 163ms/epoch - 11ms/step\n",
"Epoch 259/300\n",
"15/15 - 0s - loss: 2.9364e-07 - 165ms/epoch - 11ms/step\n",
"Epoch 260/300\n",
"15/15 - 0s - loss: 4.6714e-07 - 161ms/epoch - 11ms/step\n",
"Epoch 261/300\n",
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"Epoch 262/300\n",
"15/15 - 0s - loss: 2.1951e-07 - 163ms/epoch - 11ms/step\n",
"Epoch 263/300\n",
"15/15 - 0s - loss: 2.5106e-07 - 161ms/epoch - 11ms/step\n",
"Epoch 264/300\n",
"15/15 - 0s - loss: 4.2915e-07 - 166ms/epoch - 11ms/step\n",
"Epoch 265/300\n",
"15/15 - 0s - loss: 3.9619e-07 - 157ms/epoch - 10ms/step\n",
"Epoch 266/300\n",
"15/15 - 0s - loss: 2.4926e-07 - 170ms/epoch - 11ms/step\n",
"Epoch 267/300\n",
"15/15 - 0s - loss: 2.6463e-07 - 162ms/epoch - 11ms/step\n",
"Epoch 268/300\n",
"15/15 - 0s - loss: 2.2773e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 269/300\n",
"15/15 - 0s - loss: 2.3046e-07 - 165ms/epoch - 11ms/step\n",
"Epoch 270/300\n",
"15/15 - 0s - loss: 1.8284e-07 - 160ms/epoch - 11ms/step\n",
"Epoch 271/300\n",
"15/15 - 0s - loss: 1.7507e-07 - 161ms/epoch - 11ms/step\n",
"Epoch 272/300\n",
"15/15 - 0s - loss: 2.1668e-07 - 163ms/epoch - 11ms/step\n",
"Epoch 273/300\n",
"15/15 - 0s - loss: 1.6727e-07 - 154ms/epoch - 10ms/step\n",
"Epoch 274/300\n",
"15/15 - 0s - loss: 1.6174e-07 - 152ms/epoch - 10ms/step\n",
"Epoch 275/300\n",
"15/15 - 0s - loss: 1.9149e-07 - 157ms/epoch - 10ms/step\n",
"Epoch 276/300\n",
"15/15 - 0s - loss: 2.4064e-07 - 161ms/epoch - 11ms/step\n",
"Epoch 277/300\n",
"15/15 - 0s - loss: 2.9025e-07 - 166ms/epoch - 11ms/step\n",
"Epoch 278/300\n",
"15/15 - 0s - loss: 1.5687e-07 - 160ms/epoch - 11ms/step\n",
"Epoch 279/300\n",
"15/15 - 0s - loss: 1.3006e-07 - 169ms/epoch - 11ms/step\n",
"Epoch 280/300\n",
"15/15 - 0s - loss: 1.2471e-07 - 156ms/epoch - 10ms/step\n",
"Epoch 281/300\n",
"15/15 - 0s - loss: 1.1829e-07 - 157ms/epoch - 10ms/step\n",
"Epoch 282/300\n",
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"Epoch 283/300\n",
"15/15 - 0s - loss: 1.5758e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 284/300\n",
"15/15 - 0s - loss: 1.5391e-07 - 160ms/epoch - 11ms/step\n",
"Epoch 285/300\n",
"15/15 - 0s - loss: 1.5173e-07 - 152ms/epoch - 10ms/step\n",
"Epoch 286/300\n",
"15/15 - 0s - loss: 2.4573e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 287/300\n",
"15/15 - 0s - loss: 2.2439e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 288/300\n",
"15/15 - 0s - loss: 2.5811e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 289/300\n",
"15/15 - 0s - loss: 3.3408e-07 - 162ms/epoch - 11ms/step\n",
"Epoch 290/300\n",
"15/15 - 0s - loss: 2.6625e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 291/300\n",
"15/15 - 0s - loss: 1.8685e-07 - 154ms/epoch - 10ms/step\n",
"Epoch 292/300\n",
"15/15 - 0s - loss: 7.9143e-07 - 159ms/epoch - 11ms/step\n",
"Epoch 293/300\n",
"15/15 - 0s - loss: 3.9901e-06 - 157ms/epoch - 10ms/step\n",
"Epoch 294/300\n",
"15/15 - 0s - loss: 6.7444e-07 - 158ms/epoch - 11ms/step\n",
"Epoch 295/300\n",
"15/15 - 0s - loss: 9.3395e-07 - 175ms/epoch - 12ms/step\n",
"Epoch 296/300\n",
"15/15 - 0s - loss: 1.7670e-06 - 174ms/epoch - 12ms/step\n",
"Epoch 297/300\n",
"15/15 - 0s - loss: 2.1740e-06 - 155ms/epoch - 10ms/step\n",
"Epoch 298/300\n",
"15/15 - 0s - loss: 1.3254e-06 - 165ms/epoch - 11ms/step\n",
"Epoch 299/300\n",
"15/15 - 0s - loss: 1.2474e-06 - 169ms/epoch - 11ms/step\n",
"Epoch 300/300\n",
"15/15 - 0s - loss: 1.0169e-06 - 160ms/epoch - 11ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x79197c2d89a0>"
]
},
"metadata": {},
"execution_count": 95
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "87ad0ea7-106a-4a82-a0e7-ba5e99783ea3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 443
}
},
"source": [
"# Plotting code, feel free to ignore.\n",
"h = 1.0\n",
"x_min, x_max = X[:, 0].min() - 5, X[:, 0].max() + 5\n",
"y_min, y_max = X[:, 1].min() - 5, X[:, 1].max() + 5\n",
"xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
" np.arange(y_min, y_max, h))\n",
"\n",
"# here \"model\" is your model's prediction (classification) function\n",
"Z = tn_model.predict(np.c_[xx.ravel(), yy.ravel()])\n",
"\n",
"# Put the result into a color plot\n",
"Z = Z.reshape(xx.shape)\n",
"plt.contourf(xx, yy, Z)\n",
"plt.axis('off')\n",
"\n",
"# Plot also the training points\n",
"plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)"
],
"execution_count": 96,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"16/16 [==============================] - 0s 5ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x791974456ef0>"
]
},
"metadata": {},
"execution_count": 96
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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E+mQ1TIQejxAImIBlVgCmatoLmYwNDROQ9NCyCBtqg6wezsIaXEknQ9LJuP6bd6PzbEvCgUDxK+i93BHzGGOWWdPQQ7QgEKS3GvHO95+Dva0XQojRjbgAKlfNwgce+yJKFlUDAFqPNmDntx5Hf3NP2KH2a73Y9Y9PomnvhbjXNZ5iVSRM1XDBROh8yaQGgZHlf4PYiOqBQZKAvoAjb0P4kAv3qWEg+oQ/ht43jeh+Uw/XJV2E11W/DvQN9lKEPT4sFAwOHVjnBVDxUZfWf26EREU9AyQtOk83x7yjBhD3cUmWYM6zwVqUjc4zLSioLw1NEoyFKxyXd52A1+6OeZxnwA0eUDSdMxohBBytfXD3OvHiX/4SdVsXYcVDN0Jn0g9NcBwMMsYsM7Z85368+MVfwlaSg4X3rUfu4KRIn8ODK2+dxoGfvgbuH/tdXt6MYsy7axUq1s0Bkxg6rnbi1Ntn0XKuFYA6b6B/5uiJaalaYjgV+PuYOms/FgHYFvjhuqIDdw0GR0mtIlh8hyesnDAAuK/owL3xfn4M3TvHUoaaAULAUu9D0c20kyFJDeoZIGnhaO9H094Lodn3kcS74wYAJaAgf2Yp3vrXZ+HucUBwHrrzFkKEDS8Irt6V9zV04ODPX4977oDbh6vvngMPJN/4MsbQtPe8ej6vH86OfujNhojvTZIlSBLDur+6FVv++T4Uza0IPWawmTB7+zLc/j8Papr4GEvtdfNw2/88iLobF8KcZYLJakTlnHJs/8xWrLx1adznJzqREJiavQOKQ0PokQBjGcesb9pR/XkHqj7twMxv2lH+gBs66+ifE9dasJEzxK09HBOD65IeV39kU+sSEDJGFAZI2uz5rxcx0Nwd1nUueGIz7xljUPwBODsG8MIXHsHhX72B/qYuuHud6LnYhqa9F+Bo74Pf7UN/UxcO/Ow1vPw3v4HfpW3N9bHH3hpTGACAnmHDETWb5sU8n6STUTRPDQEjhygYY8itKcL7f/V5mPNtSV2LpTALG796BxhjYT0ewddaunURKueVJ3VuILENjCY7OUvD7yEHdNkcTAeYaxRYZirQ2aI/z1A8jrsIcQYRAFqfMNM+BWTMaJiApI13wI2X/urXqNu6CPU3LYG5wAZn5wCuvn0GKx7aonkzn6zSHCy6fwMa3zuHU0/vxamn96bsGgdaevDWv/4Jm//hXk09FZH4hg1HGKxGsDjzEOK9jinXii3/fB/+/MVHYs5kj2T2rcsBxqL+bLnCsfC6eWg+cy3qUAGQ3HDBVJtMqM8RsMzyRxm7VzEZyFqsfaa+sYTDWB6A99o4fbQKBn+nDHeDDMsMmkRIkkdhgGgmG3WovW4+SpfUgDGGjtPNuLzrJALu6H2jAa8f5188jPMvhu9bX7q4BuUrZ8acwBfsQZhx4yIwxrDs49fj6rtn8e6/v6C5WFE8RfMrselrd4ZeL9FA4O51oOPU0PLFgeZudRmkHPk8wfcU63UYY8ifWYKyJeoqhUQUzauI+TOVZAkltdqKHkULBOnYwOjpgeW4J/tw/ANTrGi7B40/sUEoImIgKLw5fF6AEGplQsdJPRQPg6lcQc5KH+Rh9aFKP+jG1f+yIflhgBjLCKMc7+uQYK5WAAYw6u8lSaAwQDTJn1WKrd++D8YcS6jLf8bmBVj+yRuw+5+eRvuJxoTOd/DhXbh1YTV0Jn1Y4zWyQWaMQR7W3V21djY2fu0OvPHtZ5J+L9bibMy7cxVmbF4IU64l9DqRxAsIeosRhXMr0Hm6GQBw4ZVjWPjB9TFfX0vo4AEFFWtmJRwGzPnWuMfwYasdYvUOJCNW78BkrDlgquSo+rQTbX80w9c2bFjFwlF4kxd564ZCT8DB0PQLC3xtOgQbbPsRgc6XTCh+nxt5G9SAairjyFnjQ/8+AxJt1JMLEAzdu41o/6MFgIC5TkH+dV7Y5gXiPpOQIMqQJC5jjgXb/uUBGLLM6lj0YBU+xhh0JgO2/PN9sBbnJHTOgeZuvPyVX6P1yJWwOQQBtw9euxtc4VEn4VWvn4O8GcVJvZeC2WW44yefwtw7V8GcZx21pHE4LY22pJOx9dv3wZSnNsL21l4ceVQtgzxyiSFXOAaaeyIWGhr12gBkfWJZXW81Irs8P/Z5uUDL2Wuaz5nMZMKpxlyjoPbLDtR8yY7yDztR+ZATs75pDwsCggNNP7MOCwxs6L8C6HjejP7D6j4G/j4GxT1evQKqQF/wd4rBfUVGy6+t6H5j+vwdkfSjngESV/1NS9Sx8AiNWPCufs7ty3H4l7sTOm9/Uzde//snYCnMgq0kFz6HB65uO+5/6q9jPo8HFNRsmofeK7HrCIzEJIbNf38PZKNec30BLcsfYdSj/palOPGHdwEAJx5/F472Piy6f8PQ0kGnB+dfOoITf3gXN//7R5FXVxz3vD2X2hJ6fzO3Loo7XwEMOLsvvIZBvN6B8RwumCiMAaYKDlNF5AmAros6+DqizdpXyxR2PG+CpS6Aq/9jg+IauVpAjDg+2nmSNHyIY/DPXS+bYZ0dgKl8HCc1kimLwgCJq2r97JhFhCRZQs3GuQmHgSBXlx2uwTr81uLsuMcLMVSsKBGVa+thKcxK6Dla5hBIsoTqtbNDYQAAruw+hSu7T8FanA1Jr4Ozoz9UP2Dn3z+O23/4YNRrEZwj4PHjyu5Tmq4xp6oASz56HWo2zI17vYwxuPpj118Yq2hDBQafH/o+PxSzDG6cGp2SgQGG/oMG9O2LsnthCAN3M7Q+Y1aDwKjNjVJRu2EwUAR/dDxYKznKuSWBvj0GlH4gdRspkemLwgCJS2fUx21kZENqfpXcvU743T7ozdEbe0mWMNDSE/XxaIrmlI+5yFA0xmwzzPk2uHvCdzJ0dozelMjT68QfH/wxtv/nx0M7JQZ/vsFliW/9658Q8MSfJJlXV4xb/v2jkA06zVUava7Rd/Pp7B3I77Vj/eHzmHW1HZIQEBLQP8eK9uvz4SuY+K5sX6cE+wk9FDeDoYAja4kPshno22tA+3OmYdsIx+c+r0NqGv4IJMBUocBYocDXIcN9WY79WpzB0xz532XAweDvlSCbBPSFo/dXIJmHwgCJq/tCK3KqCqI2ojyghDbhGSvuV3BxxzHMuX1FxK58IQR4QEHHySZUrJoJxRdAx+lmTVX7uCI0l0kOvZ7GDZJspbm457Evomnveez/yY5QT0fUa/ErePmvH8Wc25Zjzu0rkF2RD8UfQOM753Dqmb3oudSu6frWf+V2zcMenHO0Xe6AO05lxmgSWW4Y7B0o6urHfS/ugU7hkIIrKTiQc9aJrEsuXP5YBTwlE1NCjweA9mfMGDhsUMv7SgAUoOMFE3LWeNH3rimJs6axVeUMJXd5YCxTcOm7WRpeS4DpwlOMr5uh80UzHKeHllMaShQU3uxB1gKacJjJKAyQuM69eBizbloS9XFJJ+PcC4dS9nrHf/8OKlfPgrU4J6yR4woP9Qrc/r8Phe6mvXY3Tj21Fyef2hPzvKY8i6ZGc3gASGRPBCYxVK6ZhYLZZXjxS7+Cp9cZ83juV3DmTwdw5k8HwCSW8MZEeXXFKBjcIjoewQUggEMvH416TLIrC6L2DgiBm98+Bp2iQBrx1pgAJL9AxYuduPRgZcKvmSzuBZwXdRB+hoFjOjjPDHb/CwYM5kkREINBINnZ/ekgkL3CD1OlAn8Pg+LUNswSGGDgPkAyAL4ehqs/soF7WNgcA1+7hGu/saL0gy7krKDdDzPV1Bi4IxOq+3wrjj72FgCElRcO/vnMcwdw7dDllL2e1+7G4V+/gZ6LbVCGVfPra+iE1+5GTnVB2LCFMcuM5Q9uxp2/+Azmf2ANjFmj674XzC7D7FuWxX3tkdURg19rrZooyTLMeTYsvHedpuNDr5PEDoU5VYWaj3U7PNjxyG60XxlbUaBoqwsiUU5LKO6xjwoCQUwAllYvjB3aqkWOheBA16tGXPx2Nq79xorWP1jgPG2IUmyIjfjveAsfl2BGgYItXpTeo/bocM038AyBPgldr6o9HF0vm9QgEHE+g0D7n8zgUf4qnOd1aHrEgvPfysb5b2aj6RELnOfpXnI6ob9Nosnx37+D3oYOLPjAWhTPV+/keq904PQf92me6BaPpTALs29djjm3LYcx26yOnw82+p5+J8wFVhhspqjzF3IqC7DiwRux9KPX4e1//VPYzn+LH9gwarvgkYRQ756H9wYkU5VQkiXU37wEhx5+PX3bEDNtky0B4PCrx3DktRMpu5ZIwwWRegdKnP2azmfs9sNbnN6hgvbnTOjfm+i6/4lTcLMHpnIOphMwVyuQBqdWcB9w7XdWaO+1YOjbb0DeJi/sJ/TRd2cEg/AJ2E/okbMyvHege7cRXa+YwrZTdl3UwXVej8JbPCjYnP4wR9KPwgDRrOm982h67zwknQQwlpLd9QBA0stY+5e3YObWRWFj+sPnKBizLZoaZiYxyHodrv/m+/Hil34F+7VeLP7IJlSuqdf8/FTQW4zQmQ3wO1P/QVm6tBbrvrQdWWV5cY/1Ob04tuuU5iCQyiJEXl28Gfgqrk9vA+3tkNC/d6K29tOypHC0np0myFkCOat8MJVxwKCep2+vAb52KaFzCR+Dq0GOEQQGSYCvW4KnWYLilKDL4eA+pgYBIPz5g70LXa+oyynNNVQKeaqjMEASxgOpXbe86Wt3onr97Jh37Yncoavj78DGr92JrLJc6IzaGqWY50ywh0DxB8LKNLPBYkkzty6COd8GR1sfLu44hpaDlxLaf6B4QSW2fvs+QGNoOfnke5CPN0CZV5XQ9cejpXfgXH4ZvLIORiV6v7ZilOCsHct2vvENHNYDkojQPT4eGCALZC/1YeCYAQhouwahMAT6GLp3GtG/34DqzzugzxXo22dIeL8KAJDN2jZl6ttnQM+uoYmTkpmH9QiMIgn0vmeAuSa9y1VJ+tGcATKhCupLUbNxbswgkAxJJyO3plBzENAyJ0DrvAEeUHBl99DduN5qxPYffAzXf+NulK+oQ8GsUlStm40t/3wfbvyHeyHptS91XP7gjQBjUasYCiHAFQ7BOU488R5OPhl7UmUkOZdS05vh0+mxc8aimG1Xx/pcCF16P4YCAxP5MSegswmUfdCDWd8agGThiN+ahxcQCtgZWp9UA1OgP7FeAUBAzuKwzlJgqgqoDXsM3Bl+bu5msXsUOIOnkbZQng6oZ4BMqNrrF6Rt7X9CvQnx9gpQuKYhBK5wcIXDUpiN+578CoQQULyB0JbEwdUMwf9WrJqJ5Z+4AQd/8Xrcc9tKc0PzNWJpfO8cDv58Z9zljbGkarhgR91iFJv7sfLEZQgGCMbAhAAD0LkuF13rcsf8GvHosieyAh9DoB9o+qUFygADd0lI+NaeM7gv6eHtkCCbBQL+RMIAQ/51HjBZ3XSp+ZFo8w1E6PiRz4/7CpQFpgUKA2RCGbPT00WczA6EMc8lMTTvv4iqNfWjzj18J0Kvww1zjhWli6tDASfWtTBJwuzbluPob98OG1YoXlCFqnWzoTPp0dfQicu7TsKUG38TIqFw9FxsG1MQ0ErLUIFgDI+VX4cj82sx71ILrC4vnBYTCte5Ecgen4+fnBV+9OxOpGZAqpcUMrjO6cK+ToanSYYun2vr6RgcFslZ60XeRvXvw1qvoPwjLrQ9bVbv+CUxbOFCku+XCdjmU32C6YDCAJlQzvb+hAsBaTHWIKB28QsIAUAI7Pnhy7j02nHUb1+K5Z/cHFq+ODwIDLT2IntwUt/wno5416Iz6lE0pxytRxtgzDJj8z/cg+IFVeABBQKAJElY8Rc34uDD8XsPmCzB1T06CBjONMGX4LyBVE4mdNjMOLBkVujrbdnadi/kPsBxWo9AP4OcJZC1wA8pwiVxL+A4o4fiYtDncVhnB8BkgPsBd6MMOVuBMqD1FjZY5jf451QY+3k8zRI8DTJihxUByEDOcj9yVvvUbY2HyVoYgHWuHY5Tevi7JUgmAe7B4PLDBK+RCTAZyF1LqwmmAwoDZEJdfO04Fn94Y8LP4wEFfrcPxixzqBgRoNb1D4aLsQQCAYGm986j53I7Lr56LFRmuPVIA9hfSKG7/eGvkVWSk3SPRHAIYvM/3oPCORUAwgOFrNdhzeduQvfFNuTNKI5aPEnx+tH47rmEXz/IZDNhxuJqmGxGOHqduHKsEd1Vqe8H1rKdcd8+PTpeNEN42eAkNqBdb0bhTR7kbfKBMXWfip43DejeaYLwB49jkG0cedd50fumcbBAT/AWOLEu9slDoO89Lb0bapAJ1iSIRNIB2UuGlg/aj2stoTxseIMBTAdUfMwFfX6als+ScUVhgEwoZ0c/jv/hXSz5UPRAMPzuG1DH5b0Dbrzy1cdgK87B/HvWonxZLZgkoe9qF/oau1C7ad6YPssZGN78lz+Gff7ZSnJw+48ehN5siNjgJzsJkiscPVc6ULKoGsXzI9+9M4mBKwKKLwChcHAgLBAEQ8ihR3Zr2tNg9AsAK7cvxeLNC8AYAxcCksSw4QNrcH7/JRy61oiOzt6k3l8y+g/r0f5HC0J/AYOT2IQf6HzRDCYDeRt86HnDOLT0bdhxioOh66XhjWeivwyJTdJLf3DQfn5NKweGsc4NgBkEhC9Gb4MeKNjigeeK2mSY6wLIWeWHzkpBYLqgMEAm3LHH3sLs7UthyrVGbmQHv+d3++Dpc+LSzhM49+JhePtdsF/rRevRBvXOmjEIhUNvMaJobjksBVlJTUwUnKOvsXvUPK8b/v4e6C3GlM1FANRGXJIl3P4/D6L9RFPMyZSSLKFoXgV2fP13WPWZbcivKwk95ul14sijb+DijuNJXcfymxZjyZaFofcmDzY+OoMO8zfOwXzMQVdPP15/8zCarw1VMUxkvwKtBAc6X45dDrhrhxG2RT5074w2jBFnR7+UmUwliwFAIGdlYttKSwbAOtsPx8lom0YxwA9YqhUUbp4eW1aT0SgMEM1yqgtRNK8CEAJtxxvhaOtLyXkthVkw59liHsMDCs4+dwBHHn0z4uPBMX4A8Lu8eOVvH8MtP/gYbMU5iV8QYzj7/MGwb82+bXloh8GxGDmMEPyzKdeKmo1zNFwaQ19DJ/78hUeQV1eMrNI8eO1udJxqiltYKNq8AYNJjyU3LowbcvJzs3HvndfjyT+9gZbWrqjHadnJMBb3VRlKnEly3COh4wUzRMy5a+PRSE+uIMB0ao9JorwdceYiSAJ9+w2wzKR6AtMVhQESlznfhk1fuxOlS2pCjZkQAk17z+O9/3wRPsfY9ku3FMQOAgDUtfUa7vKzKwugN+vhaOtX7w3jjOFHe7xqbT2a9pyHp8+JeXetwqrPbEvJCoVoz5dkKWy+QzSeflfo5917uQO9lzvGdD0AUL2gErKGWgeSxMA5sHnTMvz2ydfG/LrRKE5tP2PHcUNoLsHE09JDkOpJiaOVfcQFXXbiP5BAX5z6BZzB30NlaaYzCgMkJr3FiFv+46OwFql18NmwyXmVq+ux7Xsfwst//eiYShOXLauL29BKsoSu861RH6/ZNA/LPn49sivyASBsUmEswWAz8m69fEUdbv73j2DvD1/Gqs9sC30/nZikTkyM9rPgCse5Px9K+X4HRotR81bNksRQUpSHgvxsdPcMpPQ6gvT5CdQFiFdiN/4JkLrGOfb6fdtCP2QbHyyNnMLfpcFAVHqfG1nzklvmJ1sEAlHnDKivIdsmReoiaUJRj8RUv30pbCU5Ee/KJVlCwSy1guBYZFfka7q7azt6JeL31/3Vrbj+G3eHgkDw2rRWDIzU8EqyhOzyfKz4iy3qhknjZfCah+8OGfy693I7Tj29N+UvOdBlT3hPBpt1qD6Elp0MG5qLNJ/bWMZhKFPiVstTJd9AFd/pgmWef0znGMLADOG7DUIWMJRwWOcHULDFjZxVfrgv61OaA3TZHLlrfaj9awdylkefOCq4uvNg16tGdO0wwnlRxvB/HjkrfLF/3oIhZxnNF5jOqGeAxDRr62LE+vTiCsfMLYvGtHNhwOMb3FEwele14Bx+9+gPo8Uf2YT6W5ZGfE6ku/6EMCB/ZommHoZIRq6C0PaaDDzA0Xe1AwWzygAA3gEXzr14BCeffC+5lQLDRJo30HzuGlwDLphtZs2hwOVO39pyxoCSu9xo+rkVUOLduWtdEjd43ODSw9wNXuSu8yNvvR/X/gDYj8Xa0U8LAeGTwr42FCkI9EnwtevhPD32/THCMAFzXQBVn3LFLdPh65TQ/GsL/F0yQvtJv26CoURBxcddMBRw5K73oW+/AYoTo/dwkASMZQpsC6i40HRGYYDEZMq1xGwgJFkKldpNhGzQoWLVTJhzrXD1OGLOB+AKR9PeC6M2SMoqy4u5JBEYW9c+Y2xMfWdJ1RtgDExiYEzCHz7wA8hGHbz9rvRthQx18uXbT+zFtodugOAi6r4HwWP7OvrR2dWXtusBAEutgurPONH0sBUi3g2pLAAF0BIMDKUc+dd5kb3MH2pES9/vRqBPgrtBh+SHDUaX8fW1pfjjNVQxkME6L4Dy++MHAcXJ0PhTKxTX4IHDGnpfp4Smn1lR+xU7dDaB6s85ce23FnivycPmYjBYZwdQdp+Lyg5PcxQGSEzOjgEYs81R19BzRYGjvS+hc86+dRmWP3gjDNahsWoeUABp9AY8wUbw5JPvjTpP/falmj+7I/UQaLlzH3PvQhIkWUL+zBJUrKyDuSALjAHtJ5vQHWPOxFg1nWnByz/diZW3LkNJbeQu/eDfxb7nD8U931hXFACAuUZB3novet6IMcYuCdgW+OG+ooPiwIi7e/WXI2edD4VbPZD0iFi9UDICxXe7cfWHVkCZpCOnkkDBFi8kvYB1XgDGYm3zKtS7/SibDXF134SBwwbkbfDBUMBR8yUHPM2yuvmQBFhnBWAomsi9Hch4oTBAYjr/ylGs/eItUR+XZBkXXjmm+Xz125di7Re3h74O9jowWRoqKhRQIAQg6ST4PT68/W/PRWwIcyoLEg4CgnM1gMiSehescZLhRLju7+4OzR1QJ1Bew5vf/SOcHemZuNd6sR0v/PAV2PKsmLN2FuZvmAujZWjtuXPAhfee2Y/ms9eAEWWKU1Fv4OmB5bgn+3DY93LX+tDztjHKcIF691pwgxfy7R50/tkE+0n9qG7u/j1G2A8bkLvBi8Kt3oh3uB3PmQFlMi0TDMcMAoVbEx+aGTiijzslYuCIPrQckTHAXKXAXDWO82TIpEBhgMR0aedx1N+8BPmzSkc1nIJzXDt0BS0HLmo6l6SXsfyTmyM+FrwDd/c6cWnHMchGPfoaOtDw5hkEvJHHyf0e32DDHvtDPBQEhACTJLDg25i8n/0hw3/m+XUluPn7H8ULX3gYfqcXkl5Gzca5qN4wF3qLAf2NXbjwylH0NXTGOGP8fQocvU4cevkYjuw4gYo5ZTBZTXD2OdF6sT3Um5LKfQti0ecJVHzEhZbfWgAuhu5wB7vMS+9xw1ShBqbyD7vh7/Og+RELfB0yhv8Fcy9Dzy4jfB0yyj8S3r3u65TgvjyZPwoFspIcr+cehnhzLhT3FPiHQNJuMv8LIJMA9yt47Ru/x8pPb0XdjYtC69EDXj/Ov3QEh3+1W/N4dvnyGaENfiJhjMGSb0PzgUvoPN0c93yN75xD3eaF2t4IRt/hJzqDfqJJOhnWomzMumkJrr59Btu+9yHkVBaEllGWLqrGvDtX4djv3sax37495tfjCkfT6ZYUXPnY2OYHMONv7ejba4TrvA6CA5YZAejyOPoPGtD5qgmyRSBnuQ9gGBUEhjA4TurhuqCDdbbauPq6JHS8lMiOhuNNAAzI25TchE1DkYJAf4xAwITmIQcyvVEYIHH5XT7s+a+XcPiR3civLwW4QNf5a/C7EhsTNuVYUnpc097z6G3oQE5lISTdJB3rTYOZWxZh5pZFyCrNBTDUexCchLnkw5sw0NIzphUek40hX6D4Vg9wq7oTYcuvLejbawytDlAGhFrCOF6+kwT69uthnR3AwBE9Wp9IzxbaYzc4n0UGyj7kgqksuQY7Z60ProsxVjIIBmMFDQkQqjNAEuC1u9F6+ApajzYkHAQAwNmpbazb2dGv6TjBBV77xh/Qc7l98GuuubbAVMUkBnOhTV3yGGUFhuAci+5bH/M8hjNN6bi8cdH1mgmuS4P3MaGJcYOT5Pjgn6PhDP5uGd5WSQ0CApEn1000HVB4ixd137Aja2HyS/p0WfH+PQg4z9M9IaEwQMZR27GrcHXZow4rcM7R29CJnkvtox7LrS1CyeIa2AbvhoM8vU689Fe/wqtf+y0a3jozYZP9EiGESHqpIFc4FF8gZiEkJknIrSmCOc+a7CVOWtwH9O01xGjA4/z9MwE5i6P3PcPgoZPx90VA0gvkb/JCN8aqf84zuqHaAhExeK7qNJeAJtMXRUKSNGtxDubduRK1NyyAzqRHf2MXzv35EK7sPhWxsRNcYO+PXsHm/3vPYJGhoSzKFQ4Igf0/fjXsOZVrZmH5gzcit7ow9L32E4048LPXwkJD+4lGtJ9oRG5NEXKqCpLarTCdgu9P0sloPdqAwvoydQfEBOctSLKE/obOuBs7AZh0P4NU8LbLEF4tewBEOUYw5Cz3q0MKI4vrpMTw/QeSr1nA3Qz2U3pkL9FeZIr7APsJPXwdEiQjYFvgB49VYnj4c/3A9PttIYmgMECSUji3HNv+5UOQDbrQmHVBfRk2/u0dqNk4F298548QyuhxzuZ9F/D6PzyBlZ/aGtbA913pwP6fvYaOk0Pd1zWb5uG6v7sLGNH1XzS/Erf8x8fwyt/+ZlQvwu5/fho3f/8jsBRkASyxZYHDhxgSfV6845v3X0TflQ40vKWujlh43zrM3LIIEpM1v5bgHK3HruLy7lOoWDUr5rHuXidc3XbN72EivNY2F9tKzyb0HG0/qmgHCRjLOLIW+tGZ8KTB4b+DWisijq2iYedLRnQ8b4JQAFOVgrz1PlhmqUMG0ohpAPZTOrQ9YQH3Qu3vFUDXqyYYywODQyfRSSY+5h4IMvVRGCAJk/QyNv/DvWFBABiayFa5uh7z378ap56KXEf/2sHLeP7gz5E/swSmXCtcXXb0XQ1fDifpJKz9y5sBYFTBo+DrrPrMNrz6td+GPeZo68Pzn/0FZm5bjLrNC2CwmaC3GGDMscSsrDcc9wcgG9RPWy0NvZbG/MQf3kHP5Q4sun89Fj+wcbDKoPbGwu/24fyLh3HkN+oWzqs+vRWGLHPEOgmCc5x94WDSWxpPZoYSBZKJg3uSGeFkKLrTBaYDzHUBtQSx5t4BtYRx33uGwXCa7m51FraToOsCg+v8UAIwlinI2+RF9nI/3FdkXHvMEqoYOLzx97bKg50UUa6ZCeSu8YNRS5Dx6FeAJKx6/RyYc2OMRzNg3p2rcPqZfaEGickSypbVwpJvg7vXiWuHr0ScGxBUsXoWjNnRVxVIsoSSRdWwlebC0dYX9pjf5cXZ5w7g7HMHAAA6kx7b/+sTyKuJvVlOsHGW9LpR30uWEAI8oKB4QTVWf+4mFM2rTKiiobPLjje/8wx6GzqgeIcmkr3xnWew9TsPhIYeAIS2QG492oBTT+0Z03WnQ0NzEWorY9dAiEfSA3kbfOjeZUxq4l/7MxZUf9aJvA0+2I8Y4j8hVMXQi+L3eZC1wI+2P5rVOv9pNaLxHvFevW0S2p60wH3VC19XjMIZoecJde7A8PDDBIylHAVbxrYFOZkeKAyQhBXOLYcSUCBHGZNmjMFSkAVzvg2uLjtqr5+PVZ/ZFjahzdPvwoGfvRZ1+ZutOEfTNsTWouxRYWAkxReAKUawiHT9qcIYgyTLWPnpLQmdXwgB74AbL3/l13B1je7u7zjVjBe+8DDm3bUaM26YD51Rj4GWHpx94SAu7jgecYgm1UYWHhpZhTAVJYkjKdjihbdNhuOUflgDp+1u3d8lofkRK2q+6EDxHW50PG+O81z1+3nrfWAMsMxUYJsbQO97ksZehVTtdTDytOrj/fs0FH6SBLKW+KG4GFzndAAYJIu622HBDd6IJZpJ5qEwQBImFKHp440rXB33//pdo5b8GbPN2PS1OwEgYiDw2t2aJtd57e64x2RXFkzozPpEJwn6XV5cePUYTj65B54+Z9Tj7Nd6sf/Hr46adDndMRko/4gLzrM69O03wN8lQTILeFrk+CWFBYP3mgzneR3yNvjgbZfQvy9+D4EYPo8voeH1SEElXkBIIEAwEb+HhAGyWaD8fje4D+B+BtkshipxEgIKAyQJrYcvY8EH1kR9XHCO/uYeePtdWPnpLRG7xRljEFxg5V9sQcObp0eNbzftuQDFF4DOGLlgSvA14pXeBYDihVNrXPzZh34CT59rXF5rKs4bAAAmqZUJbfOHhk663zCg62UNRYQkAftxPXQ5HPo8jrgNryygzx/qaTHXBdD7rpbb6WCjnkgYTHAin9DQK6IAhsLBPS4MgGSgyYJkNMqGJGHXjlxBX2NX1LXuTJJw8qk9KF5QBWthdtRucSYxmPNtKF1SM+oxv8uLE4+/G/F5gguAMRz+5a6416q3mbDiwc1TohiREAL21t5xCwLTTf71PhRs0zD+zQH7cT2u/lcWul4JDhNE+f2QBLKX+iEPyxi2eQHocrh6Vx5V/KGHkfT5CvKuS7zsMDMgRi0BASYD2ctSP1xDphcKAyRxAnj975+As8s+WEBHvesIhoMTT7yHyztPwBRrkuEw0Y478fh7OPzrNxDw+tWJeIPj4Io/gJb9F2HIMkM2RO/cWnjvOnzwd1+CwWqaEsWIGGOh90gSxxhQuNULXTZH7DtsFt7tH2qcRzxHEtDnchTdGh4wmAxUftIJySwGA4EY8fzEgqdk4ii81Y0ZX3OgcKt32Hm1ELDU+6HP5aMDAVMDSfFdbsjap8yQDEXDBCQpzo5+PP/Zn2PG9fNRs2ke9BYj+ho6cf7lI+i52AYAmte5R5ogF3Tyifdw7oVDmLF5ARbcswZZpXmQZAlly+tQuaYeqz6zDW9/71lcO3wl7HmL7t+AZR+/Pvk3qEEiqwK0yqksgK0sF47WvpSeN5PkbvCi6xVTnDZ55N9bsLtdbUAlo0DOah8KNnshW0efyFjGMeNvHOg/YMDAUT24l8FQoiB7qQ9tj1sinD867mHoetkEYwmHbW4AlQ+50PzwYM2A4HLBGJxn9ZjxNTt6dhkxcMgAEVCPN5YrKNzqDRtKISQaCgMkaYo3gIs7juPijuMRH+880wx7ay9sJTmjagUAane/q2sA7ScbY76O3+3FrJsWw1qUAyC8sp7BYsDmf/wgXvryr9B7uUNd1njXaiz92HUJvZdEG/Z0DjtY8rPGNQxM1XkD0eRt8MFxSg9Pkzxicl28iXkMYAJZK7zImh+AbV4g5iQ7nU2gYLMXBZuHuvadF6LtmBiLGkQ6XzTBOscBc5WCuq850H9ID8cpPdwNsc7JAAVwnNKh9P0eFN/mgb9PgmQU0OcKCA44TuvQf8iAQD+DLkcgZ6UP1jmx3xvJPPTrQNJHAPsGZ7oHhxJCDw1OGNz34x1xe1XLV9ShcHZ5xGWGTJLAmDokAADr/upWrPzUllHHRb1ELuDqtid8h5/O4ODucSR0PAkn6YGqTzmRf4MXkklr5cBBgsF+yIBrv7Hiyg9s8HVH/ohU3EDvuwY0/9KCpl9Y0LXDCH8fC92VJ0ww+DrUDZQAQLYK5F8XnAMRv/yy/Zi6IkIyAsYSDn2uAPcBzQ9b0PKoFY7TOniadHCc0qHl11Y0P2IBp2kEZBgKAyStrh28jNf/75Owj7jTtbf1Ytc/PYXmfRfinqNm49yYG/NIOhk1G+eiYs0s1N+8FIxpr+7HJIZDD78Od49jVGBJFe+AW9NcAK7wUG8KGRvJABTd4kX2Sl8C4++DBnsT/F0Srv6PFcqI1avuJhmX/y0LHc+b4Dyng+uiHt27jLj8b1nwd0uJv94wiiP8I1lo3Zogwq9X+/NmuC4Pdv4GayIMvjfXJd1gjQVCVDRMQNLu2qHL+NNf/BQFs8tgKciCu8eBrnPXND9fbzIAcdbqSzoZc29fAa4okGRt1eGEELh26DKuvHEaPMBx3Tfu1jxcwAMKXD0O2Ipz4h771veexeZ/vBeALmoRJcHVjYwOPRJ/hQTRzt8tJbxab4i6YVDjT2yo+ZIDkk7tEWh+xALuGbFkUDBACHT82QRzrQL3VTmpjZB0OeGtuqkiOBky9vCGeUb4vICAg2HgkD56DQLB0H9Ij8JbPLQvAQGQQBiYTmOKJPUMZ5riHtN9vhXdaE343P0tPTE/0IVQu/oL55RrDgKA2tVvK1Eb8znvWwHBuabnB5c2vvsfz2Pb9z4ctYHnCkfn6Wa0HbuKAz99DYs/tDFqeLC39mHPD19Gx6lmzddP4pOMYmhuYJJ87RK6dxpRdIsXA4cM4O5otQPUOQeShUOXLSHQhyjHRXqqgLFCgbFkKAx4rkno/LMpzjnU91ewNXxJovuyhjDCGdwNMrIW0gRDQj0DJEWSDYtaQsTFV45i8f0boh8gAGOWOWqBolhkgx7ZFfkoXTy61sGolxECgqv/e/v7z6H9RBOO//4dLP3o6MmKfPBO//Kuk7jrF59BdmVBaO4ADyho3nsBl3adgM5kgKOtD51nWhK+dhJf1iI/7EdjVRjUUu2PoW+PEQVbvHCcjfORyRncl9TZ/Ze/l6Wtm58JQAKK3ze0hNHTIqHxJzaImO20+vtU8n435BE1kITWfRtoJSsZpDkMDK9BTiZGzqXEC5JMdlpChA/AwV/sxKrPbAPnPGz3weDmPLI+8Y1jBOfovtiK3NrYGxgF9V5pR+O753HhlaOhSX7Hf/8OuF/Bogc2QG8eanRcHQM4/vi7WP3ZmyANXltoIySdjKr1c+Dud2Hfj15J+LqJdrZ5ARhLFXg7Iu0lMLwuQOzGk3sYfB0SBEfcYwUHdFYBQ7ECb0v81QWSScBQxDFwSA/GAFO1go7nzGoQiNqoC+iLOUrvcsMyc/R8GlNVQMP7EjBVRZ+LQzIL9QxMIekMZJM9aBw714b+X7+JZdsWoaAiHwDgdXnhcfmQlWeNu6FRJEyScO6FQ6HGOp7jv38Xje+eG/X9k0/twdkXDqJ85UwYrEbYW/vQfuIqrv/G3ZD0UpRVEAxzbluO08/sowmDacRkoPIvnGh51AJPk26oMA9nkCwCuWt86Nlt0nw+c7UC92Vd9EaaicGGGMhd7Uf7s/F/t7iHwdOog6dZRv9+I6xzfHBfjffRzFB8mydiEAAAQ76AdW4AzvO6yMMFkoB1TgD6PJovQFQUBggAbUFjogNDw/FGNBxvhDnLBFknw+/14yPf/mDMCX+xJgSeemYv2o5dhc6oh9/tC7uzH0nxBdB6pCHq4wGPH43vnA19rTMbULV+Tlgvxkhc4ZixeQGO//6dqMeQsdNlCVR/wQl3gwznOR1EgMFUocC2yA8mAe6rOrWBj0EycxhKOHLNPvS8YUTUu27BkLdBXbOXvcKHvn16eFtH1jsAhnol2NBjg42285yW4S4Bf0/sAFx6jxuNP7Gqx4WKF6lzDPT5HKX3xN/kKxrXFRm9bxvhuqiDEIB5RgD5G32wzqb5B1OV5jAwfGtSMnlkXR2/ZB8vMIxXWHDb1bFVS7Y57sz/SI8LIeDudYZm7ge8fpx6ei+WfGRT5OO5wJnnDsDv0v7+DFZjzCAQPK/Wks3plAmTgxkDLDMUWGaMvpOufMiJK/9uQ6BPQuRudYG8DT5IOkDKFyi91422p8zqWH/wrntw98DcDUMV/yQ9UPVpJ9qfM8N+TD/sDn1YEIh8tVreEWRz7H/7uiyBmi860L/fgL79Bih2CXIWR+5qH3LW+CBr7xAJ0/ueAR3PmYdtHw24LujgOqdH4c0eFNw4uXsZSWTUMzDFjTWkpTJMRAsL6QoJHqcXfq8f+gQnDjLGYMm3oXRJLdqONgAAjv/hHZhyLJh7x8qwmgaSTsbFHUdx5NdvJPQaXrsbij8AWR/9nxiTGFydAwmdl6SepAOqP+dE08+sw+62Waixsy0MhDVwOSv8MBRx9L5tgOOsHhCAqUpB3gYvbAsCGJ4nZTNQercbhgKuNsgDiexiGH3Mn+kFrPPiz06UzeoGTvnXp6bCkKdVQsdzgyli+PDD4J+7XjXBPCMQMXSRyU1zGPBWU7mqVDM2xt9HPd0ihYlU9zYMDwmpDAZc4Ti37yLmb5iT8JwBrnAU1JeGwgAEsP8nO3D2+YOYuXURLIVZcPc6cWnnCfQ3diV8bYo3gCu7T6HuxkWQdFGujQGXdp2IeZ78mSUoWz4DTJLQdbYFbceuJnwtJD59rkDtlx0YOGJA/2E9uItBX6jeRVvnji7da65WYP6wG0DsrvaAk6Hpp1b4Ood31WsVfXvighu9Sd/Zj0XfXoNaqi7aKgRJoPddAywzkh+CIBODegYmUCoDViqDRToDQjAYpCoUHHntBKrmVyArz5ZQIGAM4P7Rdy8DLT048uibKbm2Y797B5Vr6mGwGSPWLzj+u3fg7o5cetiUa8V1f3cXShfXhKoXSrKE/qYuvPGdPyYVUEhskhHIXetD7trU/btsf9YEX5cUY1VANAKFt3rQ87pJ3bBIEmoDLKtBIH/zxHTFuxuiTEgM4gyeuJMfyWREf2vTRCLBIpngkOqAkKpQ4HV68cJ/v4KVty7DrJV10A2uDOAKB5OilyVmkoTmA5fG9NrxODv68fJXHsWav7wZ5cvrQt/39Dlx/A/vomnvBeRUF8LZ0Y+AZ6jLV9LL2Pa9DyGnUl01MTzkZJXn4+bvfwQvfO4XcPc603r9ZGz8/QyOkzGqAEYjCVhmBVBwvQ9569RNl/x9EmQrR9ZC/4RuR6xpcyMqcj8laQ4DtZWd6bwOokFDs7b18PFECw6JhoRUBIRUDCF4nF6889Re7Hv+IGx5Nvh9AdQsrMS6u1ZFPJ4rHM37L8Le0pPU6yXC3tqLnd98HLaSHGRXFSDg9sNcYMPiBzZi9eduAqBOYLy08wSOPvomvHY3ajbORV6U2geSLMFgM2HO+1bi6G9S04NB0sPbEmkVQTwCuiyB0g+o3eySAchepnWDgvSzzfOrmylFe1+SgG3+5LneTPf0wHIAwDc1HEs9A1NIvEA21rAwMiSkogchkXAw1t4CvzeA3rY+AMCpt87ClmfDouvnhXoJBBeQZAkdVzux+4XD8M+r0lQBMRUc7f1wtPdjzvtWYM3nbw7bFEln1KP+lqUoW1KDl77yKGbcsABc4VGHPSRZQt2WhSkJA5mwkmDCJDy3V8BYpaDyky7orJNz/X/OGh963jJCBETk5ZIMyF1H88smUjAAJEpzGNhWejb+QSTMa21zx/X1tPTeJBIYhoeDZOckDA8HWoNBqiYc7nvuIM7vv4g5a2YhuyALHqcXlw5fQcuF1tDqrmBjOB6hwJRrxapPbwWgDlMMJ8kSbGV5WHTfehizzXHnPxisEzB7jCTEXBsAkwWEoiUVqCWJKz46eYMAAOhzBCo+4UTLr62DgQAI7snAJKDswy4Yi6nG8XhLNgAMRz0DaZSuADWWkBEtMMQLCansNRjP3oLe1j7s/dPBuMeNRyiYuXUREKMugiRLqN++FE17L6BgVikkXeTqdYJzONqoauFkJ5vVO+m+PQZNwwXlH3FCnzN5g0CQdZaCuq/b0X/AANdFGUKoNRxy1/igy5781z9dpCIADEdhYAqKFTKSDQojQ0Ii4WAscw3Gu7cgnnSGguzyPEDEfr8GqwlX3z6DmVsWRT+IMZx78UiKr46kQ9GtHvi7JbWqYKhIT/hyQUOJgrIHXDCVTZ07ap1NoGCzFwWbJ/pKMkuqA8BwmsPAPdmH03YRJLZEfgFSFRSGh4N09hpMRG+BFukIBT4NFQwFF2g7ehUXXj2KWTctGbUagiscnWeacWnn8ZRdF0kfSQ9UfMIF1wUd+g/q4e+VIGcJmKsCMJRwGEs4DIVTJwSQ8ZfOADAc9QxMAcOD2Fh+MYYHhXQFAyC5XoNkJh6OR3nkVE6wu9jUgwVRuv4BtaFvOtMCV10p3txxAj0+BYtvmA9zlhkA4Pf6cf7Ph3H0sTcj1khI13VHM/LnP/LvkAqVqZgEWOcEYJ1DdfuJNuMVAIZjQsTptxx0rqk83ddCEpCqX5ZkhxWSXbmQ7ETEdO3BMN6bL938qRtRMads1L4FggsIIfDC/7yCzsbu0PeZxJBXkgMmSejr6IcSJwSMl7EEgZFDUiN7s6gXcvKZiMaJpM43F/w57jEUBqawVP4DHcukxLEsaZxs4UCLsQQInUGHGz68AbWLqsEVDiEEZJ0Mj9OLN373DprPXkvhlcY31m2xk+0NiBcICCGpQ2EgQ6Q6tY93b8FIqSqtPJGBIZ78vGzMqquAXieju6cfFy61QOFTa+w4FcMCFApIphnvJecA8MaW/4h7DIWBaSQdXXkTHQyA8dnQaTIHh8kolfMD4gWCifjwJGQ6oTCQgdI5tjcZggEwOXZ7HKvJED7Guv11UComClK5c0LSh8JABkv3hJ9U3K2lOiSMNB1Cw2SX6hUDFArIcOn+jMgUDR//P3GPoTAwzY3HLOBUd+NO9AcAhQht0rV0MBgIJvr3gJDpgsIAATB+y4LSPbZLjcP4iReIqIYAIRMrkZuWc3//lbjHUBjIIOO5VngyTPqi8ECol4cQbWGAKhBmkGAxl/EIBdGWiI1nSJgM488USJJDjTgh44vCQAa6J/vwhFUUS2Qd+WToXRiryRBItJgsoYVCAJlMJsOqn/FCYSBDjWcvQbLGWoBmOoSJ8ZJMaJlstSQy6YObkFSjMJDh4tWBn8xhIZ5Mr2aX7jBUW9mZkkAQKQiMZ8Nus5qxeEEdyssKIbjA1eZ2nDpzBW4PTZIkmYMmEJKUmcrBIdONNTgkEwrGOwRE2lOidnE1Nn9kI5jEIEkShBCAAPy+AHY8vAttlzvSdj2EjJfXd/9d3GMoDJBpjQJK6kULDloDQSpDwFg2jcorzcXdf3MbGGNgUng1Rs45FL+CJ7/7J7gdnqRfg5DJQEsYoGECMq3RdrhDUhWMhg+/DA8GWooFaQ0C47G19IJN6rWPDAIA1C2m9cCctbNwdOfJlL+23qRH/co61Cyqgl6vQ1dzD868dx69bX0pfy1CtKAwQEiGmMhJoxMRAgxnmmI+Xj37LkiyFPVxxhiqa4twOs55EpVbU4Rt//f9MGVb1NeRGAor8zF/4xwcemQXTj29N6WvR4gW0f8lEEKmpeneW2I40xQ3CACIGQQANQxIutR+REp6GVu/ez+MWWYwaWh4QtLJAIAVD92IitWzUvqahGhBYYCQDHRP9uFpGQq0hICgjjMt4AEl6uNc4eg83ZKKywqp2TgXloKsqEGEKxwL71mb0tckRAsKA4RksOkUChIJAgBw9vmDoTvySBhjOPdian825ctnxAwgkiyhZFF1ynskCImH5gwQQpKuSrmt9OyEF3dKNAQEtR1twLHfv4MlH9oIHlBCwYAHFDBZwt4fvYyB5u5UXiqYJAFs9ITFUcfJEmo3zMXsW5chqywX3gE3Lu08gYs7jsPvSv/kSpJ5aGkhmZYEB1wXdLAf10PxMBgKFeSs9sNQwCf60ia9REPByDAQaTWBlgmEyUweTDYIDFe+og7z7lyF4oVVEFzg2uHLOPPsfnSeSe0QAQDMed8KrP7cTWBRAoHgHH2NXXD3OlG+bAa4wiHJEgRXf1bOzn688rePwdVlT/m1kenr5bYfxz2GwgCZdhQX0PwrKzyNOkASAIc6IMaBwpu8KNhCd1ZaaA0FkXoGRgaCdISBVASB8aa3GHHPb78InVGn9hJE0HLwEsqXz4j4OA8o6L7Qhpf/+tF0XyqZRrSEARqYItPOtd9Z4GkaHAvmDAAL/bdrhwn9h/QTeXlTxmSdS6B1tcBk5Hd58ca3nwYP8LC5A1xRe6wuvX4CxQuqogYFSSejaF4F8meVjsv1ksxBYYBMK54WCa6LekBEG5cV6N5lhLb+sMQIAXC/OkQxXaRrgmGyFQcnOgQYs82wFmdD0kefeBhP65EGPP/5h3H+pSNw9zjgdXjQcaoJb3znGZz782HozbE3bRJCoGRRVdKvT0gkNIGQTBlCAN4WCQGHBF02h7GMj5qL5TijHxwaiBYGGPxdMvw9DIaC1CQC7gN63zGid48ByoAESAK2+X4UbPbCVDk9kkGsgkXjMYlwokNA+fIZWPShjShZoDbCPqcXF145guO/fzepCX32lh7s/8kO7P/JjrDvF86NPxzLGEN2eX7Cr0lILBQGyJTgOKtDx59N8HcO3ZEZShWU3OGGZeZQd6uIvmorjAgwAGMPA9wHNP3MCk+LPHQ6zuA4rYfjtB4VH3fBNjcw5tdJFSEACIAl2SeY7KqDsZjIIKC3GLHuK7ehZsOcsO8brEbMu2s1ylfMxCt/85uIgSCnuhAL7lmLrNJcuHsdOPv8QXScao75er2XOyA4jzpMEGQrzU34vQCAMceCWdsWo3BuBQTnaD18BVd2n0LA60/qfGT6oDBAJj37KR2uPWYZ1Xb72iU0PWxF5UNOWGepKcBUpsToFVAxg4A+LzV37N27jINBYMRrcjVstP7egpnfGoAUu+c37bwdEnreNMJ+TA/hZ9DlcuSu8yJvvS/ha0tHIIg2eXAig4Axx4LtP/gYssrzAGDUCgBJlpBTVYBF96/H4V/uHnqAAdd9/S7UXjc/7Pja6+aj82wLdvyf30HxRQ6Iii8ArnDIccJATlVBwu+nck09rvvGXZB1MgAGAYGajXOx7OPXY+e3HkfPpfaEz0mmD5ozQCY1wYH2P5kHg8CIBlcwQAAdz5lDcwBsCwKQbRxgUe76mUDuqsQbwIjXpgB9ew0x5icwcC9gPzaxExZdV2Rc/W8bBg6rQQAAAn0MXa+Y0PhTK3gSiyvSPblwMkwSXPO5m2Ary1V3NYyyFFCSJczevixss6PVn795VBAIKpxTjhv/6YMxX9fVbUe8RV7mfFtChYlyqgtxw7feD1mnrmIIbtnMGIMhy4yt//IA9Faj5vOR6YfCAJnUXJdkdRx+ZBAIEgy+DhneFnX4gMlA+YddYDLUuQNDBwJMwFjGUXhzarakDQwwcHecf0IS4G1NfrLZWAkFuPZbizp8EtZjwgDB4L0mo2uHaaIub9Iy5VlRvXGuunthHAabCaYcddMhSaeGg2gYYyhdUgNrUbb6dYQdEzvPXIsaPoJkvQ7FC6vjXlvQvLtWRX09SZZgtJkxc8sizecj0w+FATKpBfq0/Yr6+4Y+5Cx1Cmq+6ED2Uj+YTg0EcrZA4U1eVH/WASlFN0BMyw2/AAJOBu5LzWsmynFaB8UhRe+9EAx9+w3gYxwyHr6t8XSQX1cSdyOj4fwe9Qc4Y/MCTRsgbf3u/fjw8/8HH33x73D3rz6P+e9fHVqh0HlaW4+I3qK9e6t63eyYpZfBgMq19ZrPR6YfmjNAJjXZpm2S38jjjKUcZfe5UfpBN6AALA2/6TqbgLEiAO+1CHMGggSD/agBjjN65K33onCbV+21GCeea3Kc1RWA8DH4eyQYS1K38qG2sjNiJUItJnp4ABha9x/3OM7RdrQBAbea9iwFWZqel11RELpLtxXnYMVDW1C5th47v/k4ui9qG7sfaO7RdByAuEshGWPQGag5yGTUM0AmNUt9AJKZI/rMfwFdLoe5OvIyAsbSEwSC8q/zxpgzMER4GXp2G3HtD+a01DiIRmvwSOfPaDLIrS3Cmi/cjO3/7+PY9q8fwtw7VkJvid5F1HWmBX537O4cIQQYY+B+Bbk1avDpudKh6XqGd9cHtzIunl+FhfeuQ9fZFvQ1dkUNJFzh6DzTjP7GLk2vBQDdF9piBhyucHSdb9V8PjL9UBggk4IIAO4GGc6LMgJ2BsEB53kdet8ywjIrgMhzBgQAhuLb3EkvlRsLEQB63zVC+xJFBscJA/r2jt+EQts8f5zVFQL6QgX6/MR7BSZrhcKRFt67Dnf85FOov2UpiuZWoHRRDVZ9ZhvueuSzyK2N3HsR8Ppx5rkDoT0BIglOLCxfUYfbf/QgKtfUo2X/RQQ8vrgTACORZAlzbl8BJjG89//+DK7wUQ04VzgUrx97fvhyQuc++/zBmMMXTGI4/9KRhK+ZTB8UBsiEEgLoedOAi9/NQuNPbGj+hQ2XvpOFC/+QjeZHrOh6zQjHycHGMzQhcHAegEWg7H4XshZPzDr+gWN6df+DaJMbIxLo+JMFV39iRcCeyPOSY6rgMNf5R0ymHI6hYLNXy0Z6U1Llmnosf3AzAITGzIN34sYsM7Z+5/6oXejHHnsLDW+eBqBuIBSNpJPBJAnXfeMuGLMt2P+znWCMJRUIzHlWGHMs6Dp7DS9/5ddoOXAxFEi4wtH43jm89OVfo6+hM6HzNu05j3N/PhQ6T1Dwz/t/siPlOzSSqWWadw6Sya7zzyb0vjOyu5ZB+AY/SIff1QpAX6Agb5MP+hwO6+xA2ru3AwMMnmYZYIC5RoFsGfqA799vUJcwahgmGKIe62mU0fSwFbVfdKT9PVR8xI3mX1rgaR65cRNDwVYPsldM34IzC+5ZG9r5byRJlmApyELNxrm4svvUqMcFF3j7+8+h+eAlbPrqHTFfh0kMsk7GrG2LcerpvZAkhpWf3gqdcagXKBgO4q0U4H51yKv3cgd2/9PTodUK7j4n/M7kN9na97+vov1kE+bdvRqF9aUQXKD1SANO/XEf2o42JH1eMj1QGCATxtsuRQgCQRE+MAWDv1uGPpfDNi+9vQGKS61vYD8+bJ8DWSBnlQ/Ft3sg6QF/X4xZ+vFwBl+bDPspPbKXpLcxlq0C1V9wwnle3dKZexj0hRy5q30wFE6PcsmRSDoJJQtj1/DnAQVly2ZEDANBJYurowaKcAyFcysAAOdfOgKDzYTln9wMwYXaGxEvBCgc3eevwecIX/rqc3hGfW8kg80Ec74N3n4XPP2uqMc1vHk61NtByHAUBsiE6T9oiDvTfTSBgaP6tIYB7gOafm6Dt21EY68w9O8zwN8lofIhF3RZHIG+wV0Rk8EE7MfSHwYAtfywbW4graWRJc5R0dYDk9eP/iwLGkQhJnT8QctrMxa1kZd0EtZ84WbMummJppcTQkAMdrtXrJ6J5Z9Uhycire2P+HqyhBNPvBf62pRrhd5igLvbEbVccHZFPpZ94gZUr58dKmF87fBlHHn0TXTThECSAAoDZMIE+lgS2wOwtI61K06G1sfN8LZGKXQkGFwX9XCc0SFnpX9oq+RkCAbFPT0G6/OODODTu16HxTM0A3+z7RSeWLAODbnFE3JN3K+g90oHcmsKo9b6ZxKDo6MfOTVF0JsNcLT1wdPnBACs/eJ2zNy6KO4d/fBzXTt8GXU3LsSGv3lf3OPVuQBCXV3CgIM/24nmfRcx6+YlWPqRTbAUqoWJFL+CS68dx9HH3gpdG6BWFdz+nx+HzqQLe3+lS2pxy39UY+c3H0f7iUZN104IhQEyYWSrUNvbBAOBZEzP2jx/H0PjT2zx7/aZQP9BA8ofcKH3PQN8nVKE3o3BT/jQfyOQBAyFGndWSgEhANd5HXr3GOC9JoMZBLIX+5G71gdddvI/04ID/SjfMXqZW5mjD1888Ar+e/WtaEf83fjS4cyf9mP9V26P+FhwaeDi+zdg8f0b1O9xjsY953H2Twc09wiozxPwDLjAFY6NX71D0+RBHlDQfOAS+ho6cOGVo3B123HjP38QlatmhT1f1suo374U5Svr8NKXfw1PrxoI1vzlzdCZ9KN6NiRZAmfA+r++Hc8++OOo/770FiNm3DAf2ZUF8Lt9uPr2mYQnJpLpg8IAmTDZS/3o25N4OUDb/PR0dbc+bUagX0O3v2AI9EqQDEDVZ5xoe9IM59nhqwoErPMC0Ocr6Hs3xvvjDLlrEhsi4F5g4IgB7kYZjKl1GLIW+uNOQhQCaH/WhP59xrChme5d6ryNyk85Ya5KPJhIXo7SXZFnoUsQEBy489wB/Lz0zoTPnQoXdxxH0fxK1N+8NGzcPxgERmKShKq1s1G+vE7TPIFgox3w+vH6/30C275zf9RzjyTpZdRsmIOShVXq6wigYuVM9TpGPJ8xBmtRNpZ97Drs+e+XkVWeh9JFNdHPLUnIKs1F6eIatB27OurxuhsXYu2XtkM26MAD6lbgSz60EY3vncM733+edjHMQBQGyIQx1SiwzvOrDanWiXiSwMAhPTqeN4PJArZ5AeRt8sJUPraJcN4uBvcFrev/BSCrjYDOKlD5SRd83RLcDeqQgbk2AEOBgOIDPE06dSgh7P2pvQV5G7wwVWpvgF2XZLQ8Orix0GAb1X/QgM4cjsqHnDErCA4c1KtBABixQoOB+wRafmVB3d/ZISVYAiH7rAMsEP0uWIZAfW87LIt64OrM13RO37yqlFYh3PNfL6F5/0UsvGct8meWQtLLMRtrSZbAjNo+GoNLCGW9jNzqIhizLZqvK3gNphwLFty7DmCxVxowxjDrpiU4+PAuZFfE/1kKIZBVnj8qDJQvn4ENf/s+dbsOxiAPW1pZuaYeG756B978zjOa3weZHigMkAnDmLqpUPuzZgwc1g/rzoz2gaje0bqv6gDOIMAwcFSPgSN6lH9IW70Bd4OMvr0GeNtlSEaBrMV+ZC/3oe/tRHoo1A1+PK0STGVqA2wo4DAUqH8O2Bk6njeh74ABwsfU5YfD7sZ1OQL513tgnhWAp1WCoYDH3UXR1yWh+ZdWdcMhMHV54KDAAEPTz62Y8VU75Ah7DgkB9LwVLI4UeR6E4mSwn9AjZ3lid4R6hzK4TDH2cTleF1zQFgZSrX77Miy6bx1sJbman8MkSXOdAMYYIDHUbJgTWjmQKK37IDBJwqL716NpzwVN1+V3jV6KuOQjmyC4iLrcsmbDHORUFyZU4ZBMfRQGyISS9EDZB90ovNkD5zkdFA+D/Yge3mu6oTX8obX8gx+yw+9suTouf+1xC2bW2qOOfQsxrKZBqGEWcF+R0b0ruZ2Let4wovwBd9j3fL0MjT+0QXENu17B1AuQBUrudkEEJPS8YUTgeTMAgBnUbZULb/ZAMqoNf98eA5wXdYAArPUBKE61KmPEHhTBoDiAgcMG5K0fXUKXewBfR5yJjpKA+7Iu4TAQsMpxgwAADBjNCZ03VZY/uBkL710Xs5JgNMG7fi1d/kIAhmxTUkEgUTNvXISjj74JV7cd5nxb1OsLeP1oOXAp7HumPCuK5lXGPD9XOGo2zsXx37+Tsmsmkx+FATIp6HMEclerDVH+Rh9cF3UYOKJHwMGgz+fwXJXhbYu2IRADuEDffgMKtngh/OpSuuHj6P0H9EM1DUJhQv2v4kDi9QI4g+OEHuJ+d2gFW8DOcPW/bVG2NWaAArQ/bcHIWZPCx9D7ngGuBhl5G3xoe9ocdp3qBMWh643GflIXMQxoXvmYRDvWP9eK8le6wJTIjS0Hw5XcIvSYs5CizSI1m3P7Ciy8dx0A7cv7RmKMQXAedTVCiBCwX+tDfl0p9Gbtuwkmw5xvg6UwG0cfewvrv3xblMsROPXMvlE9A3pT/GsTXCS0IyKZHigMkEmHSYB1dgDW2UPd/ue+kR27wRaA/Zg6ZODvUu+CLTP9yL/BB0t9AD1vxu4mT4ZQGLp2GJG7Wp2N3/QzK3jcpYJsxH+HrsHbIqPtKfNgThjd+xHvvMIX+bVlE2AoVeBrj1EkiTNY6hKfmMlNMtqvz0PZrtE76AU7Mp6fvTLh845V/falWP35mzTf2UcihED7iUaYci3IrY69A6Okk9H47ln0XGzD6s/dlNTrJXhxuPjqMegtRiz/5A2QZBlc4YPFjYBTz+zDsd++BQDIrixAyaIqAAxd51sQ8PrDKiOOfi8S+puoNHGmoTBApgYNvby+jvC7N9dlHVyX9Cjc7g4FhPgvoLXhUI/v2WVEzy4jspb54etMwd7EI4NASPxlisaK6JMR86/3ou2JKJPbmIBsE7AtSm4GedfaXAgdQ/4b/TD5hgJFlyULjy9Yjyt541tnQG8xYs0XbgEQv/RvLIwxFC+oQn9jF048+R4W3rsu4vl4QEF/cw9aDlyC4AIF9WWo27Iw7Fjtww2xjxNCwNVlh7NzAABw5tn9uLzzBGqvnw9rURbcfS40vHUa7m4HTDkWbPzqHShfURdWCtnZNQBJJ0ecMyC4QMDrpyqFGYjCAJn0nOd1GqsUjr7bBoCuVyLMqov33ASPtx8xIGZjncQ5E3qcM+Suib7lbvYyP7zXvOh9O3zOBBggmQQqH3RCSvbTgDF0r8rFU2WrUH2tK1SB8D3/7AmpQDjvrlWaJ+TFI8kScmuKkFtbNKqhDn49cK0XO7/5BwguYMqzom7LwlHnSTSUxAoFZ58/EPa11+4ObUIUJBt1uOnfPhxadTD8XOY8G4QQo5ZOcoWDMYY9//0SAh5aWphpKAyQSa/nrXgbAsXvNmAGAeEDxtZYxzLWIJDI62DotQZ/LoU3e2Iur2QMKL7dA9t8P/r2GuBpkSEZgKzFfuSs8kFnG3shJ0Un40p1ydA3miemumLR/NgT5BI2+DakEfMGGGPgCkfX2Ra4exwAgBv/6YNxG/5YDX1Y2OAitNyQBxRIOhmKN4AVD23Bsk9sRsuBizjx5B6Y86zILs+Hz+lF057z8PQ5Ubd5IXKqCyO+jiRLoSGQwtll0A3OI+g43YTjv3+XNi3KUBQGyKQmBOC6qKUOQaz5BEmUOUxYKhq+OEMVTMBcE4DilEJDEqYqBfnXe5G1UO2eFwrguiKDuxn0BXxUQLDUKbDUuUederzZaxiyrqbn70TxpbYoVbyaBDM2L8CBn70GgKFgZknUY7WcL+w4SV3N4Pf41El9sgR5sP6BJEuoXFOPyjX1oVDCJIY1X7gZZ587gMI55eo/nmivJQCdSY8n7vsvmPOs8Lt98A5M/O8FmTgUBsjkJpCSdlz4AH0hH5w7MF538cmKfn3Fd3lgKuOhwkPDiwT17TWga4cRinPoDtZYrqDk/e6kqguOp/6ZRuRcGpr57pun7jaYTPGh8y8fQfW62TGPGcvEwpFkvQ7W4hyUL6+Lv+pAo+C1McagM+gjFiQKLn0EhuoUMJlh3l2r4Xd5Y14LkxhMuVYovgAc7f0puWYytaXmN5eQNGESYKxU1O7w6EdpORMCAxJKP+gES/uqqeTTi/peoY7rB0kCYAIl73eHihxJxvAg0POWAe3PmsOCAAB4WyU0/dQKT0v6/6lvKz2b8nMGQ0Eirh24BFe3XXPRoFTY9i8fwspPbUnLuWNtfxy5pDKD3moEV6IPG3GFw9Hel6pLJNMAhQEy6eVv8o1pvkDoSB8D0wN5G71xwkXyctZ4oS9ItjQyg79LQtkDLmQv90OXy6HL5che7kfNlxyhOgwjKe4YkyQFg1CAzpe1TKJMrdrK8E1vvNXhExztNdruzJMJBC9+8Zfw2dVubyFE6H9BqeoVCJ7flDMxRZWiESJyhcEgSZZw4eWj43dBZNKjYQIy6WUt8cN1xYv+vcbwiYSS0FT9brjW31khW4NPitQdP7YhhKwFARTf6UHDD2zwd0fZBjkG7mHoeM6MGV+LXFo4Evtx/WCZ4igEg+uCHoEBNqbdCdNt5FDBcInuV+DudeKJ+/8Lc25fgdm3LofBYoAhywzZqBs1EXAsQkv2UnjOVBBcwN7RB2tx9qj3K4TAQHMPGvecn6CrI5PR5PoNJiQCxoCSuzyo+LgTlpkBSCYB2caRs8qH/C2ehM+nOGMU3wEL76JPgGTiMNcF4L6og79bRnKhQt0nYOBI/LGMwABD7x4D7Mf1ml4qYJ/M8yTiS7iHQADnXjiEFz73Czzz8f+FLsVBAFB7GFLZy5Aqkizh1FN7cP6lIxB8RGIWQE5VAW7594/CYBv/HiMyOVHPAJkSGFO3Lh65fTEPAD27TRrrEIw6a+Rvi+D/JXbOgq1eSHrAeUEXtjFRMgaO6ODvkkJ389krfKGVAYEBhpbHLPA0Di9ypK1+vuDqPIzJYuSqgli9A8DYdjRUNxGK8XgKJxVOJCEEFF8AV944heV1JeqigmGPB0sz580oxrov30Y7FBIAFAbIFCfpgKJb3ej8s/atY+PSVJ44GBbUhkxfwCE4oLgGNxQaEwbPVT08wxrJ3neMYDoBXb6irojQsFfByGtt/J8s6HI48jZ5kbfBNyGhwFvtg7FxbDM4k11pcO3wFZSvmAFJjl4pcuQ+BJM9IIwqhDS4a+KBn74GJkmYddPiqHMHJFlC9frZsBbnwNlBKwoy3SS6RyDTnVAQtaH0dkjo2mFE+7MmdO82IjCg/QM4f5MfxXe5wfTD5wKMkSwSWMHA4O+W0PWSCRf/OQt97xni9ApovT427H+ACDD4O+TBcyfXQAX6GTr/bELr780pCC3xjZxEmEqJDhucemZf1CDAFQ6v3Y1rh6+Er0IQGNdVCVoIIUK7MPpd3rAdGe2tvXjzu3/EhVeOonBuOWR97Ps9xhhKllSn9XrJ1EA9AySthAL07Teg910D/J0ywASscwPIv8ELS60CoQBtfzRj4KBhaKxeAF2vGlG4zYv8G72aKtpmL/UBEOh43gTwFGRchUHO5lDihpIRGw+J4cML6ahnMNbhEPXP9hMGZJ30I2txagv0JCPRoYLhEhk2aD9+FXv/52Ws+cItodn2wSp/Pocbr33jD+i93AFLYRayyvIw7+5VqFo7e1L1DPCAgo4zzWjeewEdp1vQdbYF5nwbbKW58Du96Ls6FL60XveqT22Fu8uO1iMNY76+ilUzMfeOlSioL4PiD6DpvfM48/xB2FtGb2RFJhcKAyRthAK0PGaB88ywXzPB4Dyng/OsDmX3ueG+KmPg4OCC+RF30107TJCtArlro9fc91yT0LXDNPgaqa00aChW4B5I9C58ZMM7LBAwkf5CiIOMFQF4r0Xb8lm9lt73jJMiDIxVIoHg/EtH0HqkAbNvWxZqsJr3XcTl10+Gtvt1ddnhd/tQsWLmpAoCAMBkCa2HG3D6j/tD33P3OELlkIfrOt8aKmMci8FqxJZv34dXv/Y7dJ5uTvraVn12G+bduQpcUUI9MLNvXYb6W5Zi1z89hdbDV5I+N0k/CgMkbfr2GYY10sMMbpLT+mSE7XrDCHTtNCJndeTxbVeDjOZfWAe7u6NsDZwUATlLQLbwFJxvqJdAMgrkrPWi901TekOBJCA4i7PlMxu1y+N40TJvIJHeASCxQGBv7cWhh3fFPCa3pgiyYXJ+PNZvX4rTz+7DzK2LMXv7MthKc+Cze3Dp9RM498IhePpdAABvvwuXd59E3Y2LYtYcYJIEoXAs+8T12PG13yV1TbXXz8e8O1cBQNhQjKSTITjHDX//ATzz0R/B50h89Q8ZHzRngKRN77uxPvDZ4Ib3sc7AoNgleJpH39kIDrQ9YVbX1yc1az9GnWMGWGYG4DiRylKFDNzLBoNAeu82mQxAAeIljnRUYky2CmGkAkT9M43on2nUfI5kihNFwmQJc25bntBzRhY1Sla8czDGYCvOwe3/8yDWfOFm5M0ohsFqgq00F4vu34D3/fgvkFWeFzr+wE9fQ8/FtrjXJ8kSShfVwFKYldR1z797ddSKh0ySoDPoMXProqTOTcYHhQGSFiKAwX0Axt7w8RGjBIEBhrYnzfD3xOgG10yM+LOAqSagrt1P9d27iHO3nqqX8SP+XT8TyF4SffhlMkk0EIw1FKz/ym2YccMCzccHZ/QrXnXIhQeU0PcT0X2xDX53/N4QwTmyKwrUGgfS0O+TJEswZptx/TffH/qe3+XDK199DM37Lmi6BmO2tkqKTGIoX1GHuXesRN2WRSiYXRaz90FApH43SZJSk7MfjEx9EuJsO4zBIfV4jaOAoXDojsNzTULTz63g7tR035vr/fA26sD9gKGII2+9D942CZ7GoWMmp1iTEwfnKrDB40b+jJmAZABy141PGKit7ERDc9GYzpHOYYPh8meWYOaWxO5gg/MKZKMOjXvPQwQ4THlWFM4uA5OkmI3kcLaSHHSdvYby5XXxXjDqXAZJJyO/rgRF8yrQeaYFAMD9Cpr2XkDlmvqYpxVcwN09eu7BSOXLZ2DdV26DtTA7tJQxbvARCFv1QCYf6hkgacEkwDonELuan2CQrTE2IWIChlIF7qs6KC51aKDlUSu4J/mldWEkAXOlgvp/HsCc7w1gxl87kLvWpxYN0jz0MM4fcIM/K2aK97pqL4ScNRikJBH6u5CtApWfckKfO3EfziP3KQDi71WQSA8BkNywQd3WRaE7+0QxxlC1ph41G+eiZEEVPP0uKL6A5iEEY5YZZctmaHqdWLjCUTSvIux7V98+G3NrZ65wNO+/EJpvEE3xgkrc+M/3wZJvU69FGtpdMdZ7ZBJD29GGmOcmE4t6Bkja5F3vhfOsDhHvYiUBfR5HyQfcaPmlVb1rCGuA1TtaX5sOrb/XgckCljl+BPpSXE52xOn8feruhgmcIaXXM1rwA5aBGTlMVQpyV/kH50zEL7RUtN0L2SzguqiD4IC5VkHWAj/YFP2Xn+4eAnOeDZrWskYxvKG25GeBSQxeuxvGLI3d73FeO3gnHvscAFfCG2a/y4tDD+/Cmi/cPKpQEeccitePw7/cHff6ln38BvU1IpR1jnbtXOHwOTy4svtU3POTiUM9AyQtRADoe9eA8DX3Qx9Q+lyOyr9wwjpTQfUXHLDNCwzrIRh9hyEUBudpfcTHksYZ+g4Y0Pq4Ge4mGYEBhqs/skFMqqH0YUWHvAw6q0DWEj/0OdoqBulzOGzzAih+nwcld3qQvST9QSDSJMJIxYeS6R0A0ttD4O62D9aKGLtgo601CGg7KUbvNTDqdSW0Hhlaxle+fAZu/KcPYunHr4fiD4xqtCVJgqvHAa899kx/c4ENJYuq4w57DO8hEEIg4PFh57ceR8AbeddNMjlQGCBp0fGiCY5Tg/UDRlTSAwSsCwIw5KsfGqZyjoqPuVD7N3ZIZj7i2OG0Dg9o+TBXj1EGJAwc0aPxRzY0/8oCxZmiIYikxa56aD9mQMMPrdAXcuhyYi3HENBlc5hnJNflPVaTLRBodWnnibjr8icSYyxmz4UQAtcOX0F/YxcAYOnHrsfW7z6A8hV1MNpMUSsSZpXm4oZvvT/iY0GmbG0lv8PChlDDiafPqem5ZOJQGCApp7gY+vYZYu4M2L/XAGXYjYjiAZofGevEQAHIWmsDjK7K572WyFyBdIn/+r5WGS2/sqLodjciF1pSh2WK3+dO+/4D92QfjvpYugPBSCabCctuWox7v34nPvxP9+L2v7wZc9fVo7SuBPmzSuN2rwNA75UOXHj16KSe7BZvKEHSy1j35Vsx//2rsfiBDer34tzNSzoZxQuqUDC7LOoxrh5Hwj8XJjHIBh3m3L4ioeeR8TdFRw7JZOa6JANKnLFPP4P7si60C+HAIQMCvRLiN4ZDY+ijMUgmAe5MRxngSUQweFtlSDqg/MNOtL9gDiubLGcLlLzPFbW6oLddwsBhPQIDEnTZHNnL/TCWpGejgm2lZ/Fa29yw70VaXRCpENHIMsUjDZ8/kFuSg9u+cBOMFkNom2KTzYjSuuLQ8a4uO048/i7OvTgUYCyFWZi5dTGyynLhc3jQ8NYZ7P3hy8ipLkTR3IpJV4EwHsYYShdVo3hexWDBn/hzDIK4wlG+og7d51sjPu7td6Fp3wVUrp6leYUEoAaR2uvn48iv39D8HDL+KAyQlBMBbR8+Ylhb1X9IH/3A4ZhaLEd4h4WCwe2Cs9d4MbBv+DyFVEt1yBjL+QTsJ/Uo+6AbtoV2uC7LocbdUqdE7BEQHGj/kwn9+4wY3pvQ84YJpho/Kh9yQU5D73s6AwEAgAHbHrwBRvNQEABG30GbC2xY85e3wJxvw9HH3sKCD6zBsgc3D05nUV9j/vvXoHn/RTja+lA8T/u6+Mm2u2FwqENrEAAACBF3iOTwL3ejdHENdCZ9QoFAb9L475tMGBomIClnLNc2Tm2sGLobVZxaegUACKDmc3aU3O2BuU6BsUxB1mI/qj7rQPHtHm3nSNoYzx22hHLswUKd3zC4jHOWgpzlflhnRQ4CANC904j+fcHGNnweh+eqDlf+Iwtc+0T9kFhDBUFjGTKIpX+mERX1Zcgpyo7bOAUb60UPbMC8u1djxV9sgTRYB0DSyaGGsHxFHXJrCjVfw4VXj0WdHJeqyoTjQdLJ6D5/LeYxA83dePmvH0X7ycaw78d6j1zh6LnSkZJrJOlDPQMk5YwlHOYZAbivypHH4CUBy8wADAVDYUBfoCDQH79CX94mH4xlAsYy36gNjIQAmEFA+NIRCMbWeEtm9Y7dfUUGJEC2cfjax1JBMbHncS/Q/aYxxvMYlAF14mfp+9NTPz7ZHoJ4vQM5y8vAFa75TlVwgcUPbIjahS7JEvLrSsE5V6v8xbnjr7txQdTXnky9BbFwhcPdbUfLgUtxj+1v7MJrX/89bKW5yCrNhaU4Gxu+cnvU4yVZwrk/H0rl5ZI0oJ6BDKW4Aec5HRxndAjE3aY3caUfdEG2iNEFhZiAziZQ+gF32LdzV/vjNoxZS30oui16Q8UYkLvWi5QtP2TB5ZBjv4sv3OZFxcdcmPUPdsz6ezuqHnKpewgkfa0CpmrtKwVcl3VA3OEbhoGDhqR6B7RKtocg1oTCRO+8mcRgzDLH7ELnCtdcJEftURi/Rj/4fnmcJYZazgGo7zXg8WP3t59JaIKgo60PrUcbcGnHcZx8ao963mHXJLjaK3Lp9RNo2nM+6Wsl44PCQIYRAaDjBRMufScbzb+0ouXXVlz6lyy0/NaMgDN1H2iGfIHav3Igb5NvcLkgIFk48q/3ouavHNDnhX/oZC3yw1Lvj1KNUMBS70fZfe649WAKNvugy42xCVFcAuY6Pyo+4YRtfrDrd2w/F2bkyF0f3rjpsgXKP+wCZIRXaQz9OX6FQUud9u2HucbeEqEweDsSX1qnZaggKNWBoLG5PaHxa02EQPvJJlzccVzDsal96Wi4wiGEgN/pxfHH30XHySY4u+0JnSM4bBGsGBgMBft+/Cp6LrWhYtVMLPvEDVj28etRtnyG5l/9w7/cjXf+43n0Xe0Kfc/R3of9P9mBd3/wwrgX6iSJY0JjrD7XVJ7uayFpJgTQ8qhFrQo48i5cEjAUcFT/pQOyKQ2vzUdX+xuJ+4GuV03o22cIdfUzg0DuOh8Kb/JA0jioFXAwdLxggv2YPvQ+JZOALk+Bry1e17xA7d84YD+mR/dOE1LRKyBbOWb+vR2+NgkDRwwIOBh02Rw5K/wAA/reM8BxRgfBGcw1AeRt8KF7tzHy39PgNeqLOGb8jUNzsTxvu4SG/9S2I13Nl+wwVSR+1/n0QGI7/Y0cMgAQcQ+DkZMKIw0ZfOTerSgqyE1pKNj97afRfb4V9zz2xbjHxhum0DLBMNbMfyWg4Mobp9B5qhlXdp9CwOuHtTgHt/3wkzBmmzUNRwQ/6kceKziHEuDw9DpgK8kNlWOWdDL6m7qw6x+egr21N+75gww2k1p5ccAd/2AyLl5u+3HcYygMZBDnBRnND9uiH8AEirZ7kH/9xJbg417A0yqDQZ2MKGncaldwdUdDJgOyTUBxMfg6JDAdYCpX4Dirw7XfWGOdAfpCjoqPutDw/5LbyjUa20IfHCcN4b0AnCFvoxdFt3kilkVu/F8bAg4WPu9CEmA6oPozDpgqE2uwG/7bqtZSiEG2csz8pn1wCCMxiYYBIHWBIMtmxgfv2oycbPXvV0vDC4iIZXW5wuHtd+Hpj/0IQuHY+i8PoHRxTcTGXnCBgNcPvTn6LykPKIDEwlY6jDpP8GNYRL6mfT9+FedeCB93X//Xt6Nu84KUFEkSQkR8bR5Q4Olz4bnP/hx+ZxrHj0haaQkDNEyQQfoPjmiMRhJA3/40bHKfIMkIWGoVmGu1BQERALp3GXHpu1m4/L1sXPpONhr+nw2u8zpYZigwVylgMmCuCSBehT9/t4T2Z81xjgt7dU3HOk4OLq3ibOh/AHrfMaDnzdHr+fS5AjVfciBvvQ/MOHhHJwtkL/Oj9ktqEFA8QO8eA5p/ZUHTIxZ07TDC3xe9ESz/kHvw7z961cK867xJBQEgsaGCoGSHDJy14R9ddocbjz7+Kl574xC6mrs1vbZnwA2uhAcqrigQCsdb//YcxOBjB3++E4ovMOrY4J383h+9jJ5LbRE3OAqOwXt6Y1fgY4zB1e2As2Mg7PveARf2/PDlUUFAZ9Rjxg2pCQLB148UQiSdDHO+DbO2LU7J65DJi3oGMkjjj61wX419Z8gMArO/PRDzmMlEKEDzry1wXRjZpa527xds8aDwJvWOpmuHEd2vaxkDGd96ApKJY+a37JCiLMUWXO0tkQwINdSeFglND1vBXcHzMnW+BQPKPuhG9rLIS92Gnjf8g1+9vuyVPpR+YGxVC5PpHQC09RCYGg1YWlWLtTPqUZKdCy4EGpvaceDIWTQ2Dy1dy73sw31/fzesOZaoPQSCczz/+Ycxa+ti1G9fCoPVBK5wNL53Dicefxe9l8OXwuXWFmHVZ7ehbElt6Hv9TV04/MvdaNp7AaZcK278x3tROKccPKBACEDSSQi4fXjr357Dik9uRm5t9G2chRCwX+vFnz71UxQvqIKtOAdeuxutRxvA/aNDhrU4Bx949AtRz5dKQgh0nbuGl7/y6Li8Hkk9LT0DtLQwg8jZXG0woo6ZC8jW9FSiS5f+g3q4zuswurFVv+5+3YSsRX4YSjn6D2jt9dAaBAQkmwA4BhvlyOP78c7HPRLcV2VYZ0VeHcAkQB62143ihtqgu0e8pmCAEGh9wgxDEYepcvT5TBUcs75ph/2EHgNH9FDcDIYijpxVPphrlbFs2Dcm8ZYdMgDvW78Cy/JmhPo1JMZQVVmM2upS7HzjEI6dUpfF9dUZcOTV49h037qIryU4x8XXjqP/ahcOPbILRx59AzXXzcOMGxYgt7oQKz+1FZd2HkfDW2dCDXFfQyde+/rvYS3Oga1Ebaj7GoZ6MDx9Trz05V+jZFE1KtfMgqzXofdKB668cQoBjx8Fs0qxuKog5ryCrPI8zLhhAa7sPoUOxN5p0efwJFRdMNp8AS0YYzBY0zCRiEwqFAYySM4KPxwnYjSIDMhdNXl2FuNedWij/6ABATuDPpcjd40PWcv8ocmEfXvilMyTBPr2G5Czypfg1sTaFNzghblaQdPDVojA8G2Y1Q9ffSGHvyt+V67wa/1QB7p2mGKEDzUQdO0yoPJjkSdwMR2QvcwftfdgosQKBItza7E8vw5A+LuWB7u2t1y/HA1NbegfULvjz+27CHOWCctvWQpAqDUooK55v/rOWez731cBqHX8b/zHe1G+vC40CTC7sgBlS2sx/+7V2PH13yOnuhDz7liJ4oVVEApH84FLOPv8wYjvof1EI9pPNI76fvOBi1jy4U1RJxIGZ/ev/8ptuHbwMrz22JPv/C6v5tLAPocHjXvOYda2JTGPi4YHFPQ1dsU/kExpFAYyiHVOAOa6gFr4JsJqAl22GFXIZ6IE7AyNP7XC3xX8oGNQHAxtTWb07TOg6lNOSEbA1xmnciFn8LbJUBypDAJqQ2+dE1DH9GWg9st29L5jxMBRPYSXQV/IkbvWB2O5gqafxJi0OchQHL9mgOuKjLYnzfD3xAsXDM5Teiguj1rrYRzdk3046aECIHogWG+8CVxwSFHGMIQQWLxgJt7eoy4F7J9pxNGdJ3H+wGXMXlWHrHwbPC4vGp/ei95h1fBWPHQjSge7/oONavC/ubXFuOUHH0NudSF4QAmNz9ffvAT1tyzB2//6HK6+M3rOQyTLPno9hBAxJxEyxiDJEuq2LsKZZ/fHPeex376FihV1EIyN6iEQQuDqO2dx4vF30d/UDe5X0N/YjRUP3Rj2Xob/ORpJJ+PCS4nPByFTC4WBDMIkoPITTrQ/a8bAUX1YIDDXKii7zwXZOjkWBLc+YYa/Z0RDP3i9nmYZ7c+bUXavW604GKuYDhPqssKcsQ5/DHX3yzkcBTf4kLvGFxrDNxQIlNzpQcmd4UWRhAAMZQp8bVLk4ZlQNcbYP3dPs4TmX1ghEngbXTuMKLkrPdUE0ylSICi35EUNAgAgSRJKivLCvtc/0whccuHozpOh7xmGBQG9xYjZ25dFvbOWZAm51WpZ4uENZnADoE3/5050nb82atLfSNbibJSvrNO2/I8L5AXnFjDAWpgNMAZX18CogkDZFQVq9//gaYfXD+ABjsL6MlSvm4Oz3Yfg7Xfh1NN70X2hFfPuWoXSxTUQAmg92oCzzx/AnFuXo2bTPHXqyeB1Cq6e+9LO47h2+ErcaydTG4WBDCMZgbL73Si61QPnRR3AAVOVkrZd65Lh65TguhBjYxPBMHBEj6JbPche4le3S4629bBgyFqk7spnrAjAey1anYH4Y/vZq70ovt2T0GY+jAFl97nQ9BMbuF+MWiYoWwRK3h9/PXbnqyY1CCRQvrj/oGFCwsBYeweA0YGAC0Ud34iCCwGPXntFRgAonFMG2ZDcRyCTGIRgqN++DEcffTPmsTlVhQmN1Su+AGbfthwLPrAGWWVqwHH1OHD2Twdw6pm9EFygZFE1rvv6Xeq1DJ57+GvIehm20lwsemAD6rcvxStffQyO1j60HbuKtmNXR71mx8kmdF9sw7y7V8OSr/ZkubrtOP3HfTjz3AHN106mLgoDGUqXLZCzfHKNGQe5GzQsl1IYvM0y8jb60H/QMLhOevTQhz6PI2ux+j6L7/Cg6edWgI88djAIRJ1cKSAZgZL3eTTXPBjOVMZR8yUHuncbYT+ih1AYmF4gZ6UP+Td4oc+N3SsQcLIokyRjYRB+AaEg6aWCYxFcZjjWUBB0xX8JswxzIUfpHWAAznXE3mRn9JPGNltSkiWULqqO+Jgp14qZWxchuzIferP29CjpZJgLsjDn9hVhPQHmPCuWfeIGFM4tx5vf/SMWf2ijOuwQoVdjeCiQZAmmXAuu+/rdeOmvfhX1dQUXOPX0Xpz+4z5YS3IAATg7+hMqT0ymNgoDZEozFHJUPuREy28s6qS6YB0FzmAo5qj8pDM02dBSq6DqU050PGeGt3WohdTlcRTc6EX36yYEBhB+9z5YHrn0HldSQWD4dZbd60bp+93gXgbJKDQ30uruhMk0XAwiMDFhICgVvQQAcNxzGLMMcyNOwOOcw+nz4kRLIxKZ895zoQ2KX4GsT+4HJISI2FjOvnUZVn/uJrX3IIHGNHi+6nWzAYRvP8wYAxhQvX4O6rYsQtnSWs3nlWQZhbPLUDC7DN3nW2NfAxdwtPZpPjeZPigMkEnHXBcsDhS9AWSygKlarc1vmaFg5jftcJzQw9MkAzJgneOHZebopXKWGQpq/soBb6uEQJ8E2SpgqlK3/bXND6DrNSMGDhlCs/vNtQoKt3lgmZlYF3T060bESX3cD/TtNaBvjwH+XgmSSS0wlL/RC50t3pLQKHQCbBJsI59sIBg+VNCjdGGn40XcaL0VMiQEJ3FKTILD68Fj+96CX1ESCgNeuxuXd53AzK2Lo1YXBBB9+Z4QuHYkfCy9ck091n5xe+jrRGo2MMbAZBazdDFXOOq3L9N+0uClcoGiuRVxwwDJXBQGyKRjKBCwzgvAeU4XeS4AE8he7Qtbey8lsFyOMcBUzoHy8HkSOptA6d0eFN/mQcDOIBnV76Ub9wKNP7fC2yIPtnEM3MXQt8eAgYMGVH3GAdsCPxyn9dHnRowikLvWO6YCQqmUimGDBv8l/OuZZ7Eirw6VlgIoguNKQwdOtzYjkOQOfgd+thO5NcUonFMWKscbDAGubjsshZHLUgvOofgVXHzlaNj3F39oQ0LbKUcSa36BJEvILsuFz+mFwZrA5BVgVAVFQoajMEAmpbIPutH0c6vanR+8Kx78r7kugOIYWxmPlWRA3Nn9qdT1qmkwCIxoBDgD9wm0PGZB5YMuuC7owX1aeggEdHkchVsmXy35sQ4buBQv3u46E/ra2DK28tkBtw+vfu0x1G1eiPrtS2ErzoG714lLO4/j4o5jqL1uPtZ+cXvY+DxXOHhAwe5/egruYWWGzXlWFM5Of6VWv8uHxj0nMe+uVZpDB5MYWo/QigASHYUBMinJFoHqLzhgP65H/0EDFDuDLo8jd5UPtgWBCR0HTyXuA/oOGKI38IIh0Csj0Cuh+vMOtD9rhrth6J+tZBQQkoBwDzYKkkD2Cj+Kt3sgW0afTnExtWrjRR2EACx1CnJW+calByQo1ZMLx4r7FVzccQwXdxwb9diFV46i41QT5ty+Qi06FOBoOXgJ518+AldX+PbBsin+mIyW3QtjXqvCceWNUzj97H5UrJqJ7Ir8uIGAKxwtBy7Bfk37zoMk81AYIJOWpFerJuasiNz17+9l8HVLkIyAqUKZNF3iifB1SqHtmqOSBNxNMgrnBFD9OSd8nRJ8Xeq8AnO1ArDB8wQAfT4PGz4ZznVZRvOvrBB+hPYqcl3QoXunEeUfdcE2N5DS9xZPqiYXplt/Uzf2/2RH3ONcXXb43b6YOxiOBVcU+F0+nHvxMHwOD175m99g8Yc3ov7mpWGvGRymCP6351Ib3v3BC2m5JjJ90EZFZMrxdUlof84UttxOl8NReJMHOSsn53LJaLxtUvztkplA4S0eFNyQfHVIfz/DlX/PggggQi+EAGRgxl87YCgc/3HleIFgZAGikRsYDd/eeOTWxjmXwodKDGdi1/wfq5Wf2oK5d0buvhdcIODxgckSdEZtMzsF5+BcQNbJcHYOYNc/PjlqEyVJL8OSb0PA40dOTRHqb16CrLI8ePqcuLTrJJr2nA/twEgyE21URKYdXw/D1f+1gnvCl9sF+hnanrJAcbuRv2lylFTWwlDMocvmCAzEWD4oGKyzx3bX3r/PECUIAMH9DPreM6D4jokpUgSkZtjAXsNGBYLxdOx376Bs2QzkVBeGBYLg5L23v/88ShZWYd7dq2N27wsuIATHxR3H4XN40HmmBc37LkRcqsj9Chzt/QAAz/GraD8+uqgQIfFQGCBTSterJjUIjJpVr37d+bIJ2cv90E2SssrxMAnIv8GLjuej9O1LApYZAXX1wxg4zuhjTzzkDI7T+gkJA0GpHDaoLC9CXW0ZLIuBruYeXD7aACXCVsCp5nd58crf/gYL7l2HObcthzHLDMEFWg5cwokn3kXX2WtoPXIFBfVlKFlcHXG/Aq5wCC7w1veeRdOe82m/ZkIACgNkClE8gP14nOV1HLAf1SNvw9TpHchd74O/R0LvO0a1aBIfWjlhLFNQ/uH45YrjERraQS3HpNtYewlsRiM+fO9GlBbnQ1E4ALWxXXvXSuz6zVvoTPMwAaDO9j/66Js49thbMFhNCHj9UHxDPTuKL4Cd3/oD6m5ciPrty5BVng8GwO/xwdPnxLVDV3Dh5SNwdsbe84CQVKIwQKYMxSHFX2cvAf7e5GYS+vsY+vYa4DilhwioezbkrvfBUpveVpIxoPh9HmQv96H/gAG+LgmyWSBrqR+2ualZOWGuUdQdHqP9/KShIk6TTaSNiyKRGMNH11yHIls2AEAe1g2vN+px00Ob8dKxhrBdC9NJcBF1K2IeUIcALu44Pi7XQkg8FAbIlCGbBeJuKMSR1M6Lrssymn9pDRtX9/dJsB8zoGCrB4Xb0r9m31TBYapITzd97jov+vfHmOXO2ZTpTamt7Bw1iRAA6ovLUJKdG/E5ksTABcP8e9bg3X+nmfWEjDQFF2ORTCVbBSyzA6H9AiISQPaSxBo1xQ20/No6eoLd4F10904THKendm42lXMU3T54lyoN+/kN/rlgqweWukkwTjAoOFyQiPllFVBiVCKUZAm1G+eN5bIImbYoDJAppfAmr/pbGzEQCOSs9UGfn1jPwMBhA7gX0SfYMYGetxMr/ToZ5W/yofJTDlhnB8D0AkwnYJkZQMUnnePS85Eu3mo1/BlkHaQ4BX1kgy76XgOEZLCpfbtDMo65SkHVQ060PmFBoD840Q6ABORt8KFoe+Ld7K7LOnXkIVqGEAzuKzKEGPOutxPOOkuBdZZroi8jYVrmDXQ67JgTo8Kf4AKO9j7alpeQCCgMkCnHMlNB3dftcF3QqZX4DALW+YEps5yQpMfhxsvYOCt2YDj7wqFxuhpCphYaJiBTEpMA65wA8jb4kLNqbHUFLHWB6L0CAMAEzDNGb4dMJpc+twuvnVb3F+Aj5g5wztF2pQPn/kxhgJBIqGeAZLzs5T61mFG0HQEFQ/6mqTumnkn2XLkAT6MTa1bMQ2lxPgDAbffg9LvncHzXScjjUHiIkKmIwgDJeLIZqPiEc3Bp4bBAMFgAqGCrB7b5k3MNPhnt4uUWXLzcArPJCFmWoDvZH5onME02uyQk5SgMEAJ1K98Zf2ufkKJDJD3cHrU3J4cmDBISF4UBQgbpcwWKbvGi6BYaEiCEZBaaQEgIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIIYRkOAoDhBBCSIajMEAIyRi+eVUTfQmETEoUBgghhJAMR2GAEEIIyXAUBggh04q9hk30JRAy5VAYIIRMObWVnZqP7Z9pTOOVEDI9UBgghGQUmkRIyGgUBgghU5632pfQ8RQICAlHYYAQMu2MnDcQaaiAAgEhQygMEEIylm9eFYUCQkBhgBBCKBCQjEdhgBBCQIGAZDYKA4SQaWHkJEIt8wZGomEDkqkoDBBCpqREag0kigIByTQUBgghU8K20rPj+noUCEgmoTBACMkYiVYjpGEDkikoDBBCpq1U7VNAgYBMdxQGCCHTRqKVCBNBvQRkOqMwQAjJKGPduIgCAZmOKAwQQjIOBQJCwlEYIIRMSvdkH457TKTlhfHqDQRRICBkCIUBQsiUEWl54UQGAkKmCwoDhJApRWsgGIkCASHRURgghEwLIwNBpJUFFAgIiYzCACFkyolWjXC8AwHNGyDTBYUBQsiUlI5AQEimojBACJm0tKwoiCTZQEDDBSRTURgghExZsTYvokBAiHYUBgghU1oiuxmmIxDQvAEyHVAYIIRMW1pqEADUQ0AIhQFCyJSXyHABQIGAkJEoDBBCpgUKBIQkj8IAIWRSS3ZFwUjpDAQ0b4BMdRQGCCHTRrzJhNRDQEhkFAYIIdMKBQJCEkdhgBCScSgQEBKOwgAhZNrRUnuAAgEhQ5gQQkz0RRBCCCFk4lDPACGEEJLhKAwQQgghGY7CACGEEJLhKAwQQgghGY7CACGEEJLhKAwQQgghGY7CACGEEJLhKAwQQgghGY7CACGEEJLh/j8645kn9tK2vwAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "s_ukr55OORqE",
"outputId": "6b92866c-a3cb-48e6-e690-1dba3579edaa"
},
"execution_count": 97,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712560134.6738951\n",
"Mon Apr 8 07:08:54 2024\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since beginning of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "o8HTyvcHchzQ",
"outputId": "03b6969f-a4f1-4901-9e9e-23f8f950fe51"
},
"execution_count": 98,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712560134.6804094\n",
"Mon Apr 8 07:08:54 2024\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Function to compute saliency map\n",
"@tf.function\n",
"def compute_saliency(input_image):\n",
" with tf.GradientTape() as tape:\n",
" tape.watch(input_image)\n",
" predictions = tn_model(input_image)\n",
" grads = tape.gradient(predictions, input_image)\n",
" saliency_map = tf.reduce_max(tf.abs(grads), axis=-1)\n",
" return saliency_map\n",
"\n",
"# Function to compute saliency map using Gradient\n",
"@tf.function\n",
"def compute_gradient_saliency(input_image):\n",
" with tf.GradientTape() as tape:\n",
" tape.watch(input_image)\n",
" predictions = tn_model(input_image)\n",
" grads = tape.gradient(predictions, input_image)\n",
" saliency_map = tf.reduce_max(tf.abs(grads), axis=-1)\n",
" return saliency_map\n",
"\n",
"# Compute saliency map for the entire grid\n",
"def compute_saliency_map_grid():\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
" input_image = np.c_[xx.ravel(), yy.ravel()]\n",
" saliency_map = compute_saliency(tf.constant(input_image, dtype=tf.float32)).numpy()\n",
" saliency_map = saliency_map.reshape(xx.shape)\n",
" return xx, yy, saliency_map\n",
"\n",
"# Compute and plot saliency map for the entire grid\n",
"xx, yy, saliency_map = compute_saliency_map_grid()\n",
"\n",
"# Compute saliency maps for all data points\n",
"def compute_saliency_maps():\n",
" saliency_maps = []\n",
" for data_point in X:\n",
" saliency_map = compute_gradient_saliency(tf.constant(data_point[None, :], dtype=tf.float32)).numpy()\n",
" saliency_maps.append(saliency_map)\n",
" return saliency_maps\n",
"\n",
"# Find the indices of the data points with the highest saliency values\n",
"def find_top_indices(saliency_maps, top_k):\n",
" top_indices = np.argsort(np.max(saliency_maps, axis=1))[-top_k:]\n",
" return top_indices\n",
"\n",
"def plot_most_diagnostic(top_indices, top_k, normalized_saliency_values):\n",
" plt.figure(figsize=(8, 6))\n",
" plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)\n",
" plt.scatter(X[top_indices, 0], X[top_indices, 1], marker='o', s=200, facecolors='none', edgecolors='r', linewidths=2)\n",
" for i, index in enumerate(top_indices):\n",
" plt.annotate(f'{normalized_saliency_values.iloc[index][\"Saliency\"]:.4f}', (X[index, 0], X[index, 1]), xytext=(X[index, 0]+0.35, X[index, 1]+0.25), arrowprops=dict(facecolor='black', arrowstyle='->'))\n",
" plt.title(f'Saliency Most Diagnostic Data Points (Top {top_k})')\n",
" plt.xlabel('Feature 1')\n",
" plt.ylabel('Feature 2')\n",
" plt.grid(True)\n",
" plt.axis('equal')\n",
" plt.show()\n",
"\n",
"# Compute saliency maps for all data points\n",
"saliency_maps = compute_saliency_maps()\n",
"\n",
"# Find the indices of the data points with the highest saliency values\n",
"top_k = 5 # Number of top diagnostic data points to select\n",
"top_indices = find_top_indices(saliency_maps, top_k)\n",
"\n",
"# Create a DataFrame to store the saliency values\n",
"saliency_df = pd.DataFrame(data=saliency_maps, columns=[\"Saliency\"])\n",
"\n",
"# Save the saliency values to a CSV file\n",
"saliency_df.to_csv(\"saliency_values.csv\", index=False)\n",
"\n",
"print(\"Saliency values saved to saliency_values.csv\")\n",
"\n",
"# Normalizing the saliency values\n",
"normalized_saliency = (saliency_df - saliency_df.min()) / (saliency_df.max() - saliency_df.min())\n",
"\n",
"# Saving the normalized saliency values to a new CSV file\n",
"normalized_saliency.to_csv(\"normalized_saliency_values.csv\", index=False)\n",
"\n",
"# Plot the most diagnostic data points\n",
"plot_most_diagnostic(top_indices, top_k, normalized_saliency)\n",
"\n",
"print(\"Normalized saliency values saved to normalized_saliency_values.csv\")\n",
"print(\"Normalized Saliency Top-k:\")\n",
"print(normalized_saliency.nlargest(top_k, 'Saliency'))\n",
"print(\"Normalized Saliency Max:\", normalized_saliency.max())\n",
"print(\"Normalized Saliency Min:\", normalized_saliency.min())\n",
"print(\"Normalized Saliency Mean:\", normalized_saliency.mean())\n",
"print(\"Normalized Saliency Median:\", normalized_saliency.median())\n",
"print(\"Normalized Saliency Mode:\", normalized_saliency.mode())\n",
"sum_normalized_values = normalized_saliency.sum()\n",
"print(\"Normalized Saliency Sum:\", sum_normalized_values)\n",
"print(\"#\")\n",
"print(\"#\")\n",
"print(\"#\")\n",
"print(\"Normalized Saliency Standard Deviation:\", normalized_saliency.std())\n",
"print(\"Normalized Saliency Skewness:\", normalized_saliency.skew())\n",
"print(\"Normalized Saliency Kurtosis:\", normalized_saliency.kurtosis())\n",
"print(\"Normalized Saliency Variance:\", normalized_saliency.var())\n",
"coefficient_variation = (normalized_saliency.std() / normalized_saliency.mean()) * 100\n",
"print(\"Normalized Saliency Coefficient of Variation:\", coefficient_variation)\n",
"print(\"#\")\n",
"print(\"#\")\n",
"print(\"#\")\n",
"cumulative_sum = normalized_saliency.cumsum()\n",
"print(\"Cumulative Sum of Normalized Saliency Values:\", cumulative_sum)\n",
"mean_cumulative_sum = cumulative_sum / len(normalized_saliency)\n",
"print(\"Mean of Cumulative Sum of Normalized Saliency Values:\", mean_cumulative_sum)\n",
"rms = np.sqrt(np.mean(normalized_saliency**2))\n",
"print(\"Normalized Saliency Root Mean Square:\", rms)\n",
"q1 = normalized_saliency.quantile(0.25)\n",
"q2 = normalized_saliency.quantile(0.75)\n",
"iqr = q2 - q1\n",
"print(\"Normalized Saliency 25th Percentile:\", q1)\n",
"print(\"Normalized Saliency 75th Percentile:\", q2)\n",
"print(\"Normalized Saliency Interquartile Range:\", iqr)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1987
},
"id": "95xed6YyDClf",
"outputId": "695ed818-fa52-4b76-9f2f-7401b4041e17"
},
"execution_count": 99,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saliency values saved to saliency_values.csv\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Normalized saliency values saved to normalized_saliency_values.csv\n",
"Normalized Saliency Top-k:\n",
" Saliency\n",
"370 1.000000\n",
"327 0.595631\n",
"239 0.450081\n",
"377 0.309829\n",
"287 0.173656\n",
"Normalized Saliency Max: Saliency 1.0\n",
"dtype: float32\n",
"Normalized Saliency Min: Saliency 0.0\n",
"dtype: float32\n",
"Normalized Saliency Mean: Saliency 0.009756\n",
"dtype: float32\n",
"Normalized Saliency Median: Saliency 0.002819\n",
"dtype: float32\n",
"Normalized Saliency Mode: Saliency\n",
"0 0.001755\n",
"1 0.002679\n",
"2 0.002819\n",
"3 0.003298\n",
"4 0.003505\n",
"5 0.003582\n",
"6 0.007161\n",
"7 0.007519\n",
"Normalized Saliency Sum: Saliency 4.683109\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Normalized Saliency Standard Deviation: Saliency 0.059118\n",
"dtype: float32\n",
"Normalized Saliency Skewness: Saliency 13.085361\n",
"dtype: float32\n",
"Normalized Saliency Kurtosis: Saliency 191.830261\n",
"dtype: float32\n",
"Normalized Saliency Variance: Saliency 0.003495\n",
"dtype: float32\n",
"Normalized Saliency Coefficient of Variation: Saliency 605.93689\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.003288\n",
"1 0.004165\n",
"2 0.004461\n",
"3 0.006762\n",
"4 0.010534\n",
".. ...\n",
"475 4.669758\n",
"476 4.676190\n",
"477 4.677881\n",
"478 4.680497\n",
"479 4.683109\n",
"\n",
"[480 rows x 1 columns]\n",
"Mean of Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.000007\n",
"1 0.000009\n",
"2 0.000009\n",
"3 0.000014\n",
"4 0.000022\n",
".. ...\n",
"475 0.009729\n",
"476 0.009742\n",
"477 0.009746\n",
"478 0.009751\n",
"479 0.009756\n",
"\n",
"[480 rows x 1 columns]\n",
"Normalized Saliency Root Mean Square: 0.059856962\n",
"Normalized Saliency 25th Percentile: Saliency 0.001322\n",
"Name: 0.25, dtype: float64\n",
"Normalized Saliency 75th Percentile: Saliency 0.004718\n",
"Name: 0.75, dtype: float64\n",
"Normalized Saliency Interquartile Range: Saliency 0.003396\n",
"dtype: float64\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"seconds = time.time()\n",
"print(\"Time in seconds since end of run:\", seconds)\n",
"local_time = time.ctime(seconds)\n",
"print(local_time)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"id": "wfZCzuq9KY9b",
"outputId": "e3ccc530-a074-4ff4-dc67-4fd8259305d1"
},
"execution_count": 100,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712560136.4642692\n",
"Mon Apr 8 07:08:56 2024\n"
]
}
]
}
]
}