Datasets
Models
Downloads: 1
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"machine_shape": "hm"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"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",
"# 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)"
],
"execution_count": 50,
"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": 51,
"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": "XPBvnB95jg4b",
"outputId": "f6ab50e3-d97d-4d0f-b04b-a06c0e7ace08",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"Dense = tf.keras.layers.Dense\n",
"fc_model = tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(2,)),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1, activation=None)])\n",
"fc_model.summary()"
],
"execution_count": 52,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_8\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_20 (Dense) (None, 1024) 3072 \n",
" \n",
" dense_21 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_22 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 1053697 (4.02 MB)\n",
"Trainable params: 1053697 (4.02 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bbKsmK8wIFTp",
"outputId": "9114f08d-f095-457c-d26e-c50708c0210f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"tn_model = tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(2,)),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" # Here, we replace the dense layer with our MPS.\n",
" TNLayer(),\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_9\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_23 (Dense) (None, 1024) 3072 \n",
" \n",
" tn_layer_10 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_24 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 9217 (36.00 KB)\n",
"Trainable params: 9217 (36.00 KB)\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(20, 2) + np.array([3, 3]),\n",
" np.random.randn(20, 2) + np.array([-3, -3]),\n",
" np.random.randn(20, 2) + np.array([-3, 3]),\n",
" np.random.randn(20, 2) + np.array([3, -3])])\n",
"\n",
"Y = np.concatenate([np.ones((40)), -np.ones((40))])"
],
"execution_count": 54,
"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": "21bed910-b9bc-4673-e150-4b4590ece06e"
},
"execution_count": 55,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1709531165.8950815\n",
"Mon Mar 4 05:46:05 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "a344d443-4472-4cd1-894f-9a8bbbcd219c",
"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": 56,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"3/3 - 1s - loss: 0.9824 - 989ms/epoch - 330ms/step\n",
"Epoch 2/300\n",
"3/3 - 0s - loss: 0.9310 - 12ms/epoch - 4ms/step\n",
"Epoch 3/300\n",
"3/3 - 0s - loss: 0.8843 - 11ms/epoch - 4ms/step\n",
"Epoch 4/300\n",
"3/3 - 0s - loss: 0.8317 - 10ms/epoch - 3ms/step\n",
"Epoch 5/300\n",
"3/3 - 0s - loss: 0.7705 - 10ms/epoch - 3ms/step\n",
"Epoch 6/300\n",
"3/3 - 0s - loss: 0.6941 - 11ms/epoch - 4ms/step\n",
"Epoch 7/300\n",
"3/3 - 0s - loss: 0.6033 - 12ms/epoch - 4ms/step\n",
"Epoch 8/300\n",
"3/3 - 0s - loss: 0.4972 - 12ms/epoch - 4ms/step\n",
"Epoch 9/300\n",
"3/3 - 0s - loss: 0.3789 - 11ms/epoch - 4ms/step\n",
"Epoch 10/300\n",
"3/3 - 0s - loss: 0.2568 - 11ms/epoch - 4ms/step\n",
"Epoch 11/300\n",
"3/3 - 0s - loss: 0.1478 - 11ms/epoch - 4ms/step\n",
"Epoch 12/300\n",
"3/3 - 0s - loss: 0.0716 - 10ms/epoch - 3ms/step\n",
"Epoch 13/300\n",
"3/3 - 0s - loss: 0.0546 - 10ms/epoch - 3ms/step\n",
"Epoch 14/300\n",
"3/3 - 0s - loss: 0.0772 - 10ms/epoch - 3ms/step\n",
"Epoch 15/300\n",
"3/3 - 0s - loss: 0.0885 - 10ms/epoch - 3ms/step\n",
"Epoch 16/300\n",
"3/3 - 0s - loss: 0.0685 - 11ms/epoch - 4ms/step\n",
"Epoch 17/300\n",
"3/3 - 0s - loss: 0.0493 - 11ms/epoch - 4ms/step\n",
"Epoch 18/300\n",
"3/3 - 0s - loss: 0.0414 - 10ms/epoch - 3ms/step\n",
"Epoch 19/300\n",
"3/3 - 0s - loss: 0.0453 - 9ms/epoch - 3ms/step\n",
"Epoch 20/300\n",
"3/3 - 0s - loss: 0.0485 - 10ms/epoch - 3ms/step\n",
"Epoch 21/300\n",
"3/3 - 0s - loss: 0.0463 - 11ms/epoch - 4ms/step\n",
"Epoch 22/300\n",
"3/3 - 0s - loss: 0.0407 - 10ms/epoch - 3ms/step\n",
"Epoch 23/300\n",
"3/3 - 0s - loss: 0.0373 - 11ms/epoch - 4ms/step\n",
"Epoch 24/300\n",
"3/3 - 0s - loss: 0.0360 - 10ms/epoch - 3ms/step\n",
"Epoch 25/300\n",
"3/3 - 0s - loss: 0.0361 - 12ms/epoch - 4ms/step\n",
"Epoch 26/300\n",
"3/3 - 0s - loss: 0.0354 - 10ms/epoch - 3ms/step\n",
"Epoch 27/300\n",
"3/3 - 0s - loss: 0.0337 - 11ms/epoch - 4ms/step\n",
"Epoch 28/300\n",
"3/3 - 0s - loss: 0.0323 - 10ms/epoch - 3ms/step\n",
"Epoch 29/300\n",
"3/3 - 0s - loss: 0.0310 - 10ms/epoch - 3ms/step\n",
"Epoch 30/300\n",
"3/3 - 0s - loss: 0.0310 - 10ms/epoch - 3ms/step\n",
"Epoch 31/300\n",
"3/3 - 0s - loss: 0.0302 - 12ms/epoch - 4ms/step\n",
"Epoch 32/300\n",
"3/3 - 0s - loss: 0.0287 - 10ms/epoch - 3ms/step\n",
"Epoch 33/300\n",
"3/3 - 0s - loss: 0.0281 - 10ms/epoch - 3ms/step\n",
"Epoch 34/300\n",
"3/3 - 0s - loss: 0.0275 - 11ms/epoch - 4ms/step\n",
"Epoch 35/300\n",
"3/3 - 0s - loss: 0.0264 - 10ms/epoch - 3ms/step\n",
"Epoch 36/300\n",
"3/3 - 0s - loss: 0.0256 - 10ms/epoch - 3ms/step\n",
"Epoch 37/300\n",
"3/3 - 0s - loss: 0.0250 - 11ms/epoch - 4ms/step\n",
"Epoch 38/300\n",
"3/3 - 0s - loss: 0.0245 - 10ms/epoch - 3ms/step\n",
"Epoch 39/300\n",
"3/3 - 0s - loss: 0.0235 - 11ms/epoch - 4ms/step\n",
"Epoch 40/300\n",
"3/3 - 0s - loss: 0.0228 - 10ms/epoch - 3ms/step\n",
"Epoch 41/300\n",
"3/3 - 0s - loss: 0.0220 - 10ms/epoch - 3ms/step\n",
"Epoch 42/300\n",
"3/3 - 0s - loss: 0.0213 - 10ms/epoch - 3ms/step\n",
"Epoch 43/300\n",
"3/3 - 0s - loss: 0.0205 - 11ms/epoch - 4ms/step\n",
"Epoch 44/300\n",
"3/3 - 0s - loss: 0.0199 - 10ms/epoch - 3ms/step\n",
"Epoch 45/300\n",
"3/3 - 0s - loss: 0.0191 - 10ms/epoch - 3ms/step\n",
"Epoch 46/300\n",
"3/3 - 0s - loss: 0.0187 - 10ms/epoch - 3ms/step\n",
"Epoch 47/300\n",
"3/3 - 0s - loss: 0.0181 - 10ms/epoch - 3ms/step\n",
"Epoch 48/300\n",
"3/3 - 0s - loss: 0.0175 - 11ms/epoch - 4ms/step\n",
"Epoch 49/300\n",
"3/3 - 0s - loss: 0.0164 - 10ms/epoch - 3ms/step\n",
"Epoch 50/300\n",
"3/3 - 0s - loss: 0.0157 - 10ms/epoch - 3ms/step\n",
"Epoch 51/300\n",
"3/3 - 0s - loss: 0.0152 - 10ms/epoch - 3ms/step\n",
"Epoch 52/300\n",
"3/3 - 0s - loss: 0.0145 - 10ms/epoch - 3ms/step\n",
"Epoch 53/300\n",
"3/3 - 0s - loss: 0.0138 - 12ms/epoch - 4ms/step\n",
"Epoch 54/300\n",
"3/3 - 0s - loss: 0.0133 - 9ms/epoch - 3ms/step\n",
"Epoch 55/300\n",
"3/3 - 0s - loss: 0.0125 - 10ms/epoch - 3ms/step\n",
"Epoch 56/300\n",
"3/3 - 0s - loss: 0.0122 - 10ms/epoch - 3ms/step\n",
"Epoch 57/300\n",
"3/3 - 0s - loss: 0.0113 - 10ms/epoch - 3ms/step\n",
"Epoch 58/300\n",
"3/3 - 0s - loss: 0.0108 - 10ms/epoch - 3ms/step\n",
"Epoch 59/300\n",
"3/3 - 0s - loss: 0.0102 - 9ms/epoch - 3ms/step\n",
"Epoch 60/300\n",
"3/3 - 0s - loss: 0.0096 - 10ms/epoch - 3ms/step\n",
"Epoch 61/300\n",
"3/3 - 0s - loss: 0.0090 - 10ms/epoch - 3ms/step\n",
"Epoch 62/300\n",
"3/3 - 0s - loss: 0.0084 - 10ms/epoch - 3ms/step\n",
"Epoch 63/300\n",
"3/3 - 0s - loss: 0.0077 - 9ms/epoch - 3ms/step\n",
"Epoch 64/300\n",
"3/3 - 0s - loss: 0.0070 - 10ms/epoch - 3ms/step\n",
"Epoch 65/300\n",
"3/3 - 0s - loss: 0.0070 - 10ms/epoch - 3ms/step\n",
"Epoch 66/300\n",
"3/3 - 0s - loss: 0.0062 - 10ms/epoch - 3ms/step\n",
"Epoch 67/300\n",
"3/3 - 0s - loss: 0.0057 - 10ms/epoch - 3ms/step\n",
"Epoch 68/300\n",
"3/3 - 0s - loss: 0.0054 - 9ms/epoch - 3ms/step\n",
"Epoch 69/300\n",
"3/3 - 0s - loss: 0.0050 - 10ms/epoch - 3ms/step\n",
"Epoch 70/300\n",
"3/3 - 0s - loss: 0.0046 - 11ms/epoch - 4ms/step\n",
"Epoch 71/300\n",
"3/3 - 0s - loss: 0.0041 - 10ms/epoch - 3ms/step\n",
"Epoch 72/300\n",
"3/3 - 0s - loss: 0.0037 - 9ms/epoch - 3ms/step\n",
"Epoch 73/300\n",
"3/3 - 0s - loss: 0.0033 - 10ms/epoch - 3ms/step\n",
"Epoch 74/300\n",
"3/3 - 0s - loss: 0.0030 - 11ms/epoch - 4ms/step\n",
"Epoch 75/300\n",
"3/3 - 0s - loss: 0.0027 - 11ms/epoch - 4ms/step\n",
"Epoch 76/300\n",
"3/3 - 0s - loss: 0.0024 - 11ms/epoch - 4ms/step\n",
"Epoch 77/300\n",
"3/3 - 0s - loss: 0.0021 - 11ms/epoch - 4ms/step\n",
"Epoch 78/300\n",
"3/3 - 0s - loss: 0.0019 - 10ms/epoch - 3ms/step\n",
"Epoch 79/300\n",
"3/3 - 0s - loss: 0.0016 - 10ms/epoch - 3ms/step\n",
"Epoch 80/300\n",
"3/3 - 0s - loss: 0.0015 - 10ms/epoch - 3ms/step\n",
"Epoch 81/300\n",
"3/3 - 0s - loss: 0.0013 - 11ms/epoch - 4ms/step\n",
"Epoch 82/300\n",
"3/3 - 0s - loss: 0.0012 - 11ms/epoch - 4ms/step\n",
"Epoch 83/300\n",
"3/3 - 0s - loss: 0.0011 - 10ms/epoch - 3ms/step\n",
"Epoch 84/300\n",
"3/3 - 0s - loss: 9.0545e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 85/300\n",
"3/3 - 0s - loss: 7.9152e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 86/300\n",
"3/3 - 0s - loss: 7.0412e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 87/300\n",
"3/3 - 0s - loss: 6.1978e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 88/300\n",
"3/3 - 0s - loss: 5.2338e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 89/300\n",
"3/3 - 0s - loss: 5.0727e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 90/300\n",
"3/3 - 0s - loss: 4.4617e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 91/300\n",
"3/3 - 0s - loss: 4.1021e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 92/300\n",
"3/3 - 0s - loss: 3.7321e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 93/300\n",
"3/3 - 0s - loss: 3.3965e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 94/300\n",
"3/3 - 0s - loss: 2.8217e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 95/300\n",
"3/3 - 0s - loss: 2.8671e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 96/300\n",
"3/3 - 0s - loss: 2.6762e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 97/300\n",
"3/3 - 0s - loss: 2.3497e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 98/300\n",
"3/3 - 0s - loss: 2.1839e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 99/300\n",
"3/3 - 0s - loss: 1.9666e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 100/300\n",
"3/3 - 0s - loss: 1.8738e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 101/300\n",
"3/3 - 0s - loss: 1.5480e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 102/300\n",
"3/3 - 0s - loss: 1.7105e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 103/300\n",
"3/3 - 0s - loss: 1.4210e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 104/300\n",
"3/3 - 0s - loss: 1.4973e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 105/300\n",
"3/3 - 0s - loss: 1.3315e-04 - 12ms/epoch - 4ms/step\n",
"Epoch 106/300\n",
"3/3 - 0s - loss: 1.2269e-04 - 12ms/epoch - 4ms/step\n",
"Epoch 107/300\n",
"3/3 - 0s - loss: 1.2132e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 108/300\n",
"3/3 - 0s - loss: 1.0276e-04 - 11ms/epoch - 4ms/step\n",
"Epoch 109/300\n",
"3/3 - 0s - loss: 1.0838e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 110/300\n",
"3/3 - 0s - loss: 1.0152e-04 - 10ms/epoch - 3ms/step\n",
"Epoch 111/300\n",
"3/3 - 0s - loss: 9.3861e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 112/300\n",
"3/3 - 0s - loss: 8.2561e-05 - 9ms/epoch - 3ms/step\n",
"Epoch 113/300\n",
"3/3 - 0s - loss: 8.2601e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 114/300\n",
"3/3 - 0s - loss: 7.4005e-05 - 9ms/epoch - 3ms/step\n",
"Epoch 115/300\n",
"3/3 - 0s - loss: 7.7541e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 116/300\n",
"3/3 - 0s - loss: 6.6006e-05 - 12ms/epoch - 4ms/step\n",
"Epoch 117/300\n",
"3/3 - 0s - loss: 6.4523e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 118/300\n",
"3/3 - 0s - loss: 6.4468e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 119/300\n",
"3/3 - 0s - loss: 6.0156e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 120/300\n",
"3/3 - 0s - loss: 5.5749e-05 - 9ms/epoch - 3ms/step\n",
"Epoch 121/300\n",
"3/3 - 0s - loss: 5.0933e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 122/300\n",
"3/3 - 0s - loss: 5.1243e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 123/300\n",
"3/3 - 0s - loss: 4.6262e-05 - 12ms/epoch - 4ms/step\n",
"Epoch 124/300\n",
"3/3 - 0s - loss: 4.6049e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 125/300\n",
"3/3 - 0s - loss: 4.2880e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 126/300\n",
"3/3 - 0s - loss: 4.0773e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 127/300\n",
"3/3 - 0s - loss: 4.0393e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 128/300\n",
"3/3 - 0s - loss: 4.4048e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 129/300\n",
"3/3 - 0s - loss: 3.5081e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 130/300\n",
"3/3 - 0s - loss: 3.8248e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 131/300\n",
"3/3 - 0s - loss: 3.2229e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 132/300\n",
"3/3 - 0s - loss: 3.0671e-05 - 9ms/epoch - 3ms/step\n",
"Epoch 133/300\n",
"3/3 - 0s - loss: 3.0762e-05 - 9ms/epoch - 3ms/step\n",
"Epoch 134/300\n",
"3/3 - 0s - loss: 2.7715e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 135/300\n",
"3/3 - 0s - loss: 2.9620e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 136/300\n",
"3/3 - 0s - loss: 2.6592e-05 - 12ms/epoch - 4ms/step\n",
"Epoch 137/300\n",
"3/3 - 0s - loss: 3.0199e-05 - 12ms/epoch - 4ms/step\n",
"Epoch 138/300\n",
"3/3 - 0s - loss: 2.5135e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 139/300\n",
"3/3 - 0s - loss: 2.4707e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 140/300\n",
"3/3 - 0s - loss: 2.3645e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 141/300\n",
"3/3 - 0s - loss: 2.3964e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 142/300\n",
"3/3 - 0s - loss: 2.2884e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 143/300\n",
"3/3 - 0s - loss: 2.0532e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 144/300\n",
"3/3 - 0s - loss: 2.1781e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 145/300\n",
"3/3 - 0s - loss: 2.0647e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 146/300\n",
"3/3 - 0s - loss: 1.9495e-05 - 12ms/epoch - 4ms/step\n",
"Epoch 147/300\n",
"3/3 - 0s - loss: 1.8582e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 148/300\n",
"3/3 - 0s - loss: 1.8290e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 149/300\n",
"3/3 - 0s - loss: 1.7448e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 150/300\n",
"3/3 - 0s - loss: 1.7882e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 151/300\n",
"3/3 - 0s - loss: 1.6310e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 152/300\n",
"3/3 - 0s - loss: 1.6778e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 153/300\n",
"3/3 - 0s - loss: 1.5487e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 154/300\n",
"3/3 - 0s - loss: 1.5726e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 155/300\n",
"3/3 - 0s - loss: 1.5094e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 156/300\n",
"3/3 - 0s - loss: 1.4633e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 157/300\n",
"3/3 - 0s - loss: 1.4032e-05 - 9ms/epoch - 3ms/step\n",
"Epoch 158/300\n",
"3/3 - 0s - loss: 1.3945e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 159/300\n",
"3/3 - 0s - loss: 1.3617e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 160/300\n",
"3/3 - 0s - loss: 1.3157e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 161/300\n",
"3/3 - 0s - loss: 1.2828e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 162/300\n",
"3/3 - 0s - loss: 1.2695e-05 - 14ms/epoch - 5ms/step\n",
"Epoch 163/300\n",
"3/3 - 0s - loss: 1.2114e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 164/300\n",
"3/3 - 0s - loss: 1.2344e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 165/300\n",
"3/3 - 0s - loss: 1.2030e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 166/300\n",
"3/3 - 0s - loss: 1.1489e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 167/300\n",
"3/3 - 0s - loss: 1.1404e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 168/300\n",
"3/3 - 0s - loss: 1.0903e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 169/300\n",
"3/3 - 0s - loss: 1.1463e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 170/300\n",
"3/3 - 0s - loss: 1.0795e-05 - 11ms/epoch - 4ms/step\n",
"Epoch 171/300\n",
"3/3 - 0s - loss: 1.0607e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 172/300\n",
"3/3 - 0s - loss: 1.1208e-05 - 10ms/epoch - 3ms/step\n",
"Epoch 173/300\n",
"3/3 - 0s - loss: 9.3615e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 174/300\n",
"3/3 - 0s - loss: 9.6801e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 175/300\n",
"3/3 - 0s - loss: 9.5731e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 176/300\n",
"3/3 - 0s - loss: 8.6858e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 177/300\n",
"3/3 - 0s - loss: 9.1015e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 178/300\n",
"3/3 - 0s - loss: 8.4440e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 179/300\n",
"3/3 - 0s - loss: 8.5789e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 180/300\n",
"3/3 - 0s - loss: 8.1958e-06 - 9ms/epoch - 3ms/step\n",
"Epoch 181/300\n",
"3/3 - 0s - loss: 8.6107e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 182/300\n",
"3/3 - 0s - loss: 8.1158e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 183/300\n",
"3/3 - 0s - loss: 8.0382e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 184/300\n",
"3/3 - 0s - loss: 8.3136e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 185/300\n",
"3/3 - 0s - loss: 7.4128e-06 - 9ms/epoch - 3ms/step\n",
"Epoch 186/300\n",
"3/3 - 0s - loss: 7.8207e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 187/300\n",
"3/3 - 0s - loss: 7.5321e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 188/300\n",
"3/3 - 0s - loss: 7.2220e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 189/300\n",
"3/3 - 0s - loss: 7.7224e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 190/300\n",
"3/3 - 0s - loss: 7.0167e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 191/300\n",
"3/3 - 0s - loss: 6.7280e-06 - 9ms/epoch - 3ms/step\n",
"Epoch 192/300\n",
"3/3 - 0s - loss: 7.3436e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 193/300\n",
"3/3 - 0s - loss: 6.4096e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 194/300\n",
"3/3 - 0s - loss: 7.1740e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 195/300\n",
"3/3 - 0s - loss: 6.2640e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 196/300\n",
"3/3 - 0s - loss: 5.9245e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 197/300\n",
"3/3 - 0s - loss: 5.7851e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 198/300\n",
"3/3 - 0s - loss: 6.1468e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 199/300\n",
"3/3 - 0s - loss: 6.6124e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 200/300\n",
"3/3 - 0s - loss: 6.1927e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 201/300\n",
"3/3 - 0s - loss: 6.0981e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 202/300\n",
"3/3 - 0s - loss: 5.6132e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 203/300\n",
"3/3 - 0s - loss: 5.4261e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 204/300\n",
"3/3 - 0s - loss: 5.8134e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 205/300\n",
"3/3 - 0s - loss: 5.3261e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 206/300\n",
"3/3 - 0s - loss: 5.6922e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 207/300\n",
"3/3 - 0s - loss: 5.3886e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 208/300\n",
"3/3 - 0s - loss: 5.2678e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 209/300\n",
"3/3 - 0s - loss: 5.1627e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 210/300\n",
"3/3 - 0s - loss: 5.3975e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 211/300\n",
"3/3 - 0s - loss: 4.5620e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 212/300\n",
"3/3 - 0s - loss: 5.5581e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 213/300\n",
"3/3 - 0s - loss: 4.7086e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 214/300\n",
"3/3 - 0s - loss: 5.0151e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 215/300\n",
"3/3 - 0s - loss: 4.8620e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 216/300\n",
"3/3 - 0s - loss: 4.2306e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 217/300\n",
"3/3 - 0s - loss: 4.8022e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 218/300\n",
"3/3 - 0s - loss: 4.1304e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 219/300\n",
"3/3 - 0s - loss: 4.4333e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 220/300\n",
"3/3 - 0s - loss: 4.3856e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 221/300\n",
"3/3 - 0s - loss: 3.7280e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 222/300\n",
"3/3 - 0s - loss: 4.2963e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 223/300\n",
"3/3 - 0s - loss: 3.8061e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 224/300\n",
"3/3 - 0s - loss: 4.0099e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 225/300\n",
"3/3 - 0s - loss: 4.1208e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 226/300\n",
"3/3 - 0s - loss: 3.7706e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 227/300\n",
"3/3 - 0s - loss: 3.6554e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 228/300\n",
"3/3 - 0s - loss: 3.8271e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 229/300\n",
"3/3 - 0s - loss: 3.6408e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 230/300\n",
"3/3 - 0s - loss: 3.8301e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 231/300\n",
"3/3 - 0s - loss: 3.9109e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 232/300\n",
"3/3 - 0s - loss: 3.7608e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 233/300\n",
"3/3 - 0s - loss: 4.0293e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 234/300\n",
"3/3 - 0s - loss: 3.4236e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 235/300\n",
"3/3 - 0s - loss: 3.7379e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 236/300\n",
"3/3 - 0s - loss: 3.6925e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 237/300\n",
"3/3 - 0s - loss: 3.3738e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 238/300\n",
"3/3 - 0s - loss: 3.6437e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 239/300\n",
"3/3 - 0s - loss: 3.1176e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 240/300\n",
"3/3 - 0s - loss: 3.1168e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 241/300\n",
"3/3 - 0s - loss: 3.1751e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 242/300\n",
"3/3 - 0s - loss: 3.0683e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 243/300\n",
"3/3 - 0s - loss: 2.9676e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 244/300\n",
"3/3 - 0s - loss: 3.3126e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 245/300\n",
"3/3 - 0s - loss: 2.9347e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 246/300\n",
"3/3 - 0s - loss: 3.2757e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 247/300\n",
"3/3 - 0s - loss: 3.2142e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 248/300\n",
"3/3 - 0s - loss: 3.2176e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 249/300\n",
"3/3 - 0s - loss: 2.8382e-06 - 9ms/epoch - 3ms/step\n",
"Epoch 250/300\n",
"3/3 - 0s - loss: 3.0712e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 251/300\n",
"3/3 - 0s - loss: 2.9336e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 252/300\n",
"3/3 - 0s - loss: 2.7713e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 253/300\n",
"3/3 - 0s - loss: 3.1205e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 254/300\n",
"3/3 - 0s - loss: 2.7059e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 255/300\n",
"3/3 - 0s - loss: 2.9267e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 256/300\n",
"3/3 - 0s - loss: 2.6573e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 257/300\n",
"3/3 - 0s - loss: 2.7912e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 258/300\n",
"3/3 - 0s - loss: 2.9237e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 259/300\n",
"3/3 - 0s - loss: 3.3254e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 260/300\n",
"3/3 - 0s - loss: 3.2088e-06 - 9ms/epoch - 3ms/step\n",
"Epoch 261/300\n",
"3/3 - 0s - loss: 2.8664e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 262/300\n",
"3/3 - 0s - loss: 2.8006e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 263/300\n",
"3/3 - 0s - loss: 4.1102e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 264/300\n",
"3/3 - 0s - loss: 3.0658e-06 - 9ms/epoch - 3ms/step\n",
"Epoch 265/300\n",
"3/3 - 0s - loss: 2.1988e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 266/300\n",
"3/3 - 0s - loss: 2.3797e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 267/300\n",
"3/3 - 0s - loss: 2.1137e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 268/300\n",
"3/3 - 0s - loss: 2.2700e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 269/300\n",
"3/3 - 0s - loss: 2.3573e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 270/300\n",
"3/3 - 0s - loss: 2.0647e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 271/300\n",
"3/3 - 0s - loss: 2.2215e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 272/300\n",
"3/3 - 0s - loss: 2.2609e-06 - 12ms/epoch - 4ms/step\n",
"Epoch 273/300\n",
"3/3 - 0s - loss: 2.1239e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 274/300\n",
"3/3 - 0s - loss: 2.0631e-06 - 13ms/epoch - 4ms/step\n",
"Epoch 275/300\n",
"3/3 - 0s - loss: 2.1012e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 276/300\n",
"3/3 - 0s - loss: 2.0158e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 277/300\n",
"3/3 - 0s - loss: 2.0523e-06 - 13ms/epoch - 4ms/step\n",
"Epoch 278/300\n",
"3/3 - 0s - loss: 2.0416e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 279/300\n",
"3/3 - 0s - loss: 2.1009e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 280/300\n",
"3/3 - 0s - loss: 1.9421e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 281/300\n",
"3/3 - 0s - loss: 1.9659e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 282/300\n",
"3/3 - 0s - loss: 1.9462e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 283/300\n",
"3/3 - 0s - loss: 1.7357e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 284/300\n",
"3/3 - 0s - loss: 1.7213e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 285/300\n",
"3/3 - 0s - loss: 1.7748e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 286/300\n",
"3/3 - 0s - loss: 1.9336e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 287/300\n",
"3/3 - 0s - loss: 1.7405e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 288/300\n",
"3/3 - 0s - loss: 2.1245e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 289/300\n",
"3/3 - 0s - loss: 2.0561e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 290/300\n",
"3/3 - 0s - loss: 2.0798e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 291/300\n",
"3/3 - 0s - loss: 1.7068e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 292/300\n",
"3/3 - 0s - loss: 1.9205e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 293/300\n",
"3/3 - 0s - loss: 1.7442e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 294/300\n",
"3/3 - 0s - loss: 1.7597e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 295/300\n",
"3/3 - 0s - loss: 1.5517e-06 - 11ms/epoch - 4ms/step\n",
"Epoch 296/300\n",
"3/3 - 0s - loss: 1.7890e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 297/300\n",
"3/3 - 0s - loss: 1.8468e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 298/300\n",
"3/3 - 0s - loss: 1.6581e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 299/300\n",
"3/3 - 0s - loss: 1.6944e-06 - 10ms/epoch - 3ms/step\n",
"Epoch 300/300\n",
"3/3 - 0s - loss: 1.7180e-06 - 11ms/epoch - 4ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x786a65815d20>"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "e3333292-23f1-4a0a-c06f-e01a74493282",
"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": 57,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"14/14 [==============================] - 0s 2ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x786a65602620>"
]
},
"metadata": {},
"execution_count": 57
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\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": "wfZCzuq9KY9b",
"outputId": "55094d23-e05a-4e9e-9bc8-e8b5d91e44ef"
},
"execution_count": 58,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1709531172.417285\n",
"Mon Mar 4 05:46:12 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": "Ft6S13x6KuEQ",
"outputId": "425bcc82-b55e-49f2-86b6-1932e5ed8025"
},
"execution_count": 59,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1709531172.4279752\n",
"Mon Mar 4 05:46:12 2024\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BMxSJo5gtOmQ"
},
"source": [
"# VS Fully Connected"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NKQx7stYswzU",
"outputId": "fd3f1273-2a7c-4e72-eb34-1da87a65216d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 11458
}
},
"source": [
"fc_model.compile(optimizer=\"adam\", loss=\"mean_squared_error\")\n",
"fc_model.fit(X, Y, epochs=300, verbose=2)\n",
"# 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 = fc_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": 60,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"3/3 - 1s - loss: 0.5656 - 654ms/epoch - 218ms/step\n",
"Epoch 2/300\n",
"3/3 - 0s - loss: 0.1959 - 24ms/epoch - 8ms/step\n",
"Epoch 3/300\n",
"3/3 - 0s - loss: 0.1423 - 22ms/epoch - 7ms/step\n",
"Epoch 4/300\n",
"3/3 - 0s - loss: 0.0917 - 23ms/epoch - 8ms/step\n",
"Epoch 5/300\n",
"3/3 - 0s - loss: 0.0828 - 25ms/epoch - 8ms/step\n",
"Epoch 6/300\n",
"3/3 - 0s - loss: 0.0827 - 23ms/epoch - 8ms/step\n",
"Epoch 7/300\n",
"3/3 - 0s - loss: 0.0680 - 25ms/epoch - 8ms/step\n",
"Epoch 8/300\n",
"3/3 - 0s - loss: 0.0680 - 24ms/epoch - 8ms/step\n",
"Epoch 9/300\n",
"3/3 - 0s - loss: 0.0605 - 28ms/epoch - 9ms/step\n",
"Epoch 10/300\n",
"3/3 - 0s - loss: 0.0632 - 24ms/epoch - 8ms/step\n",
"Epoch 11/300\n",
"3/3 - 0s - loss: 0.0537 - 22ms/epoch - 7ms/step\n",
"Epoch 12/300\n",
"3/3 - 0s - loss: 0.0523 - 27ms/epoch - 9ms/step\n",
"Epoch 13/300\n",
"3/3 - 0s - loss: 0.0522 - 24ms/epoch - 8ms/step\n",
"Epoch 14/300\n",
"3/3 - 0s - loss: 0.0483 - 21ms/epoch - 7ms/step\n",
"Epoch 15/300\n",
"3/3 - 0s - loss: 0.0498 - 23ms/epoch - 8ms/step\n",
"Epoch 16/300\n",
"3/3 - 0s - loss: 0.0444 - 24ms/epoch - 8ms/step\n",
"Epoch 17/300\n",
"3/3 - 0s - loss: 0.0487 - 27ms/epoch - 9ms/step\n",
"Epoch 18/300\n",
"3/3 - 0s - loss: 0.0467 - 25ms/epoch - 8ms/step\n",
"Epoch 19/300\n",
"3/3 - 0s - loss: 0.0419 - 26ms/epoch - 9ms/step\n",
"Epoch 20/300\n",
"3/3 - 0s - loss: 0.0439 - 27ms/epoch - 9ms/step\n",
"Epoch 21/300\n",
"3/3 - 0s - loss: 0.0406 - 28ms/epoch - 9ms/step\n",
"Epoch 22/300\n",
"3/3 - 0s - loss: 0.0414 - 23ms/epoch - 8ms/step\n",
"Epoch 23/300\n",
"3/3 - 0s - loss: 0.0421 - 28ms/epoch - 9ms/step\n",
"Epoch 24/300\n",
"3/3 - 0s - loss: 0.0378 - 28ms/epoch - 9ms/step\n",
"Epoch 25/300\n",
"3/3 - 0s - loss: 0.0382 - 23ms/epoch - 8ms/step\n",
"Epoch 26/300\n",
"3/3 - 0s - loss: 0.0425 - 27ms/epoch - 9ms/step\n",
"Epoch 27/300\n",
"3/3 - 0s - loss: 0.0505 - 26ms/epoch - 9ms/step\n",
"Epoch 28/300\n",
"3/3 - 0s - loss: 0.0423 - 23ms/epoch - 8ms/step\n",
"Epoch 29/300\n",
"3/3 - 0s - loss: 0.0513 - 28ms/epoch - 9ms/step\n",
"Epoch 30/300\n",
"3/3 - 0s - loss: 0.0385 - 26ms/epoch - 9ms/step\n",
"Epoch 31/300\n",
"3/3 - 0s - loss: 0.0392 - 28ms/epoch - 9ms/step\n",
"Epoch 32/300\n",
"3/3 - 0s - loss: 0.0417 - 28ms/epoch - 9ms/step\n",
"Epoch 33/300\n",
"3/3 - 0s - loss: 0.0414 - 26ms/epoch - 9ms/step\n",
"Epoch 34/300\n",
"3/3 - 0s - loss: 0.0374 - 28ms/epoch - 9ms/step\n",
"Epoch 35/300\n",
"3/3 - 0s - loss: 0.0348 - 26ms/epoch - 9ms/step\n",
"Epoch 36/300\n",
"3/3 - 0s - loss: 0.0319 - 27ms/epoch - 9ms/step\n",
"Epoch 37/300\n",
"3/3 - 0s - loss: 0.0429 - 23ms/epoch - 8ms/step\n",
"Epoch 38/300\n",
"3/3 - 0s - loss: 0.0382 - 23ms/epoch - 8ms/step\n",
"Epoch 39/300\n",
"3/3 - 0s - loss: 0.0266 - 20ms/epoch - 7ms/step\n",
"Epoch 40/300\n",
"3/3 - 0s - loss: 0.0399 - 25ms/epoch - 8ms/step\n",
"Epoch 41/300\n",
"3/3 - 0s - loss: 0.0336 - 26ms/epoch - 9ms/step\n",
"Epoch 42/300\n",
"3/3 - 0s - loss: 0.0293 - 23ms/epoch - 8ms/step\n",
"Epoch 43/300\n",
"3/3 - 0s - loss: 0.0304 - 24ms/epoch - 8ms/step\n",
"Epoch 44/300\n",
"3/3 - 0s - loss: 0.0370 - 24ms/epoch - 8ms/step\n",
"Epoch 45/300\n",
"3/3 - 0s - loss: 0.0295 - 21ms/epoch - 7ms/step\n",
"Epoch 46/300\n",
"3/3 - 0s - loss: 0.0278 - 23ms/epoch - 8ms/step\n",
"Epoch 47/300\n",
"3/3 - 0s - loss: 0.0298 - 22ms/epoch - 7ms/step\n",
"Epoch 48/300\n",
"3/3 - 0s - loss: 0.0244 - 21ms/epoch - 7ms/step\n",
"Epoch 49/300\n",
"3/3 - 0s - loss: 0.0270 - 24ms/epoch - 8ms/step\n",
"Epoch 50/300\n",
"3/3 - 0s - loss: 0.0191 - 25ms/epoch - 8ms/step\n",
"Epoch 51/300\n",
"3/3 - 0s - loss: 0.0257 - 22ms/epoch - 7ms/step\n",
"Epoch 52/300\n",
"3/3 - 0s - loss: 0.0229 - 26ms/epoch - 9ms/step\n",
"Epoch 53/300\n",
"3/3 - 0s - loss: 0.0226 - 26ms/epoch - 9ms/step\n",
"Epoch 54/300\n",
"3/3 - 0s - loss: 0.0251 - 28ms/epoch - 9ms/step\n",
"Epoch 55/300\n",
"3/3 - 0s - loss: 0.0231 - 25ms/epoch - 8ms/step\n",
"Epoch 56/300\n",
"3/3 - 0s - loss: 0.0268 - 24ms/epoch - 8ms/step\n",
"Epoch 57/300\n",
"3/3 - 0s - loss: 0.0274 - 26ms/epoch - 9ms/step\n",
"Epoch 58/300\n",
"3/3 - 0s - loss: 0.0182 - 26ms/epoch - 9ms/step\n",
"Epoch 59/300\n",
"3/3 - 0s - loss: 0.0233 - 25ms/epoch - 8ms/step\n",
"Epoch 60/300\n",
"3/3 - 0s - loss: 0.0189 - 26ms/epoch - 9ms/step\n",
"Epoch 61/300\n",
"3/3 - 0s - loss: 0.0133 - 26ms/epoch - 9ms/step\n",
"Epoch 62/300\n",
"3/3 - 0s - loss: 0.0144 - 30ms/epoch - 10ms/step\n",
"Epoch 63/300\n",
"3/3 - 0s - loss: 0.0157 - 25ms/epoch - 8ms/step\n",
"Epoch 64/300\n",
"3/3 - 0s - loss: 0.0119 - 27ms/epoch - 9ms/step\n",
"Epoch 65/300\n",
"3/3 - 0s - loss: 0.0188 - 27ms/epoch - 9ms/step\n",
"Epoch 66/300\n",
"3/3 - 0s - loss: 0.0130 - 25ms/epoch - 8ms/step\n",
"Epoch 67/300\n",
"3/3 - 0s - loss: 0.0116 - 25ms/epoch - 8ms/step\n",
"Epoch 68/300\n",
"3/3 - 0s - loss: 0.0110 - 25ms/epoch - 8ms/step\n",
"Epoch 69/300\n",
"3/3 - 0s - loss: 0.0073 - 29ms/epoch - 10ms/step\n",
"Epoch 70/300\n",
"3/3 - 0s - loss: 0.0097 - 25ms/epoch - 8ms/step\n",
"Epoch 71/300\n",
"3/3 - 0s - loss: 0.0088 - 28ms/epoch - 9ms/step\n",
"Epoch 72/300\n",
"3/3 - 0s - loss: 0.0063 - 25ms/epoch - 8ms/step\n",
"Epoch 73/300\n",
"3/3 - 0s - loss: 0.0058 - 23ms/epoch - 8ms/step\n",
"Epoch 74/300\n",
"3/3 - 0s - loss: 0.0060 - 25ms/epoch - 8ms/step\n",
"Epoch 75/300\n",
"3/3 - 0s - loss: 0.0094 - 23ms/epoch - 8ms/step\n",
"Epoch 76/300\n",
"3/3 - 0s - loss: 0.0106 - 28ms/epoch - 9ms/step\n",
"Epoch 77/300\n",
"3/3 - 0s - loss: 0.0083 - 28ms/epoch - 9ms/step\n",
"Epoch 78/300\n",
"3/3 - 0s - loss: 0.0048 - 28ms/epoch - 9ms/step\n",
"Epoch 79/300\n",
"3/3 - 0s - loss: 0.0048 - 26ms/epoch - 9ms/step\n",
"Epoch 80/300\n",
"3/3 - 0s - loss: 0.0046 - 24ms/epoch - 8ms/step\n",
"Epoch 81/300\n",
"3/3 - 0s - loss: 0.0029 - 26ms/epoch - 9ms/step\n",
"Epoch 82/300\n",
"3/3 - 0s - loss: 0.0026 - 24ms/epoch - 8ms/step\n",
"Epoch 83/300\n",
"3/3 - 0s - loss: 0.0030 - 28ms/epoch - 9ms/step\n",
"Epoch 84/300\n",
"3/3 - 0s - loss: 0.0032 - 25ms/epoch - 8ms/step\n",
"Epoch 85/300\n",
"3/3 - 0s - loss: 0.0040 - 23ms/epoch - 8ms/step\n",
"Epoch 86/300\n",
"3/3 - 0s - loss: 0.0045 - 25ms/epoch - 8ms/step\n",
"Epoch 87/300\n",
"3/3 - 0s - loss: 0.0047 - 26ms/epoch - 9ms/step\n",
"Epoch 88/300\n",
"3/3 - 0s - loss: 0.0033 - 23ms/epoch - 8ms/step\n",
"Epoch 89/300\n",
"3/3 - 0s - loss: 0.0034 - 30ms/epoch - 10ms/step\n",
"Epoch 90/300\n",
"3/3 - 0s - loss: 0.0083 - 26ms/epoch - 9ms/step\n",
"Epoch 91/300\n",
"3/3 - 0s - loss: 0.0109 - 23ms/epoch - 8ms/step\n",
"Epoch 92/300\n",
"3/3 - 0s - loss: 0.0065 - 24ms/epoch - 8ms/step\n",
"Epoch 93/300\n",
"3/3 - 0s - loss: 0.0046 - 24ms/epoch - 8ms/step\n",
"Epoch 94/300\n",
"3/3 - 0s - loss: 0.0068 - 25ms/epoch - 8ms/step\n",
"Epoch 95/300\n",
"3/3 - 0s - loss: 0.0096 - 26ms/epoch - 9ms/step\n",
"Epoch 96/300\n",
"3/3 - 0s - loss: 0.0103 - 24ms/epoch - 8ms/step\n",
"Epoch 97/300\n",
"3/3 - 0s - loss: 0.0119 - 22ms/epoch - 7ms/step\n",
"Epoch 98/300\n",
"3/3 - 0s - loss: 0.0069 - 28ms/epoch - 9ms/step\n",
"Epoch 99/300\n",
"3/3 - 0s - loss: 0.0075 - 23ms/epoch - 8ms/step\n",
"Epoch 100/300\n",
"3/3 - 0s - loss: 0.0057 - 27ms/epoch - 9ms/step\n",
"Epoch 101/300\n",
"3/3 - 0s - loss: 0.0032 - 27ms/epoch - 9ms/step\n",
"Epoch 102/300\n",
"3/3 - 0s - loss: 0.0039 - 26ms/epoch - 9ms/step\n",
"Epoch 103/300\n",
"3/3 - 0s - loss: 0.0029 - 25ms/epoch - 8ms/step\n",
"Epoch 104/300\n",
"3/3 - 0s - loss: 0.0031 - 28ms/epoch - 9ms/step\n",
"Epoch 105/300\n",
"3/3 - 0s - loss: 0.0021 - 27ms/epoch - 9ms/step\n",
"Epoch 106/300\n",
"3/3 - 0s - loss: 0.0015 - 24ms/epoch - 8ms/step\n",
"Epoch 107/300\n",
"3/3 - 0s - loss: 0.0014 - 23ms/epoch - 8ms/step\n",
"Epoch 108/300\n",
"3/3 - 0s - loss: 0.0013 - 29ms/epoch - 10ms/step\n",
"Epoch 109/300\n",
"3/3 - 0s - loss: 0.0022 - 27ms/epoch - 9ms/step\n",
"Epoch 110/300\n",
"3/3 - 0s - loss: 0.0019 - 26ms/epoch - 9ms/step\n",
"Epoch 111/300\n",
"3/3 - 0s - loss: 0.0020 - 25ms/epoch - 8ms/step\n",
"Epoch 112/300\n",
"3/3 - 0s - loss: 6.9314e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 113/300\n",
"3/3 - 0s - loss: 9.3566e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 114/300\n",
"3/3 - 0s - loss: 0.0015 - 25ms/epoch - 8ms/step\n",
"Epoch 115/300\n",
"3/3 - 0s - loss: 0.0017 - 26ms/epoch - 9ms/step\n",
"Epoch 116/300\n",
"3/3 - 0s - loss: 0.0020 - 26ms/epoch - 9ms/step\n",
"Epoch 117/300\n",
"3/3 - 0s - loss: 0.0018 - 27ms/epoch - 9ms/step\n",
"Epoch 118/300\n",
"3/3 - 0s - loss: 0.0010 - 23ms/epoch - 8ms/step\n",
"Epoch 119/300\n",
"3/3 - 0s - loss: 8.8028e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 120/300\n",
"3/3 - 0s - loss: 7.2462e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 121/300\n",
"3/3 - 0s - loss: 8.0890e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 122/300\n",
"3/3 - 0s - loss: 9.8991e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 123/300\n",
"3/3 - 0s - loss: 7.1008e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 124/300\n",
"3/3 - 0s - loss: 4.9597e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 125/300\n",
"3/3 - 0s - loss: 4.7966e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 126/300\n",
"3/3 - 0s - loss: 3.0518e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 127/300\n",
"3/3 - 0s - loss: 2.7030e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 128/300\n",
"3/3 - 0s - loss: 3.4302e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 129/300\n",
"3/3 - 0s - loss: 3.2476e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 130/300\n",
"3/3 - 0s - loss: 1.6305e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 131/300\n",
"3/3 - 0s - loss: 1.8642e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 132/300\n",
"3/3 - 0s - loss: 8.2074e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 133/300\n",
"3/3 - 0s - loss: 6.5955e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 134/300\n",
"3/3 - 0s - loss: 6.8692e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 135/300\n",
"3/3 - 0s - loss: 1.1016e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 136/300\n",
"3/3 - 0s - loss: 1.4056e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 137/300\n",
"3/3 - 0s - loss: 1.0764e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 138/300\n",
"3/3 - 0s - loss: 9.8001e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 139/300\n",
"3/3 - 0s - loss: 2.1907e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 140/300\n",
"3/3 - 0s - loss: 2.4921e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 141/300\n",
"3/3 - 0s - loss: 4.0704e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 142/300\n",
"3/3 - 0s - loss: 5.5095e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 143/300\n",
"3/3 - 0s - loss: 8.7078e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 144/300\n",
"3/3 - 0s - loss: 9.0852e-04 - 28ms/epoch - 9ms/step\n",
"Epoch 145/300\n",
"3/3 - 0s - loss: 0.0014 - 27ms/epoch - 9ms/step\n",
"Epoch 146/300\n",
"3/3 - 0s - loss: 0.0021 - 22ms/epoch - 7ms/step\n",
"Epoch 147/300\n",
"3/3 - 0s - loss: 0.0012 - 22ms/epoch - 7ms/step\n",
"Epoch 148/300\n",
"3/3 - 0s - loss: 0.0011 - 24ms/epoch - 8ms/step\n",
"Epoch 149/300\n",
"3/3 - 0s - loss: 0.0014 - 24ms/epoch - 8ms/step\n",
"Epoch 150/300\n",
"3/3 - 0s - loss: 0.0013 - 23ms/epoch - 8ms/step\n",
"Epoch 151/300\n",
"3/3 - 0s - loss: 0.0012 - 25ms/epoch - 8ms/step\n",
"Epoch 152/300\n",
"3/3 - 0s - loss: 0.0011 - 24ms/epoch - 8ms/step\n",
"Epoch 153/300\n",
"3/3 - 0s - loss: 8.8283e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 154/300\n",
"3/3 - 0s - loss: 5.0875e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 155/300\n",
"3/3 - 0s - loss: 4.6452e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 156/300\n",
"3/3 - 0s - loss: 4.4445e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 157/300\n",
"3/3 - 0s - loss: 4.5507e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 158/300\n",
"3/3 - 0s - loss: 5.0221e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 159/300\n",
"3/3 - 0s - loss: 7.1127e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 160/300\n",
"3/3 - 0s - loss: 5.3585e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 161/300\n",
"3/3 - 0s - loss: 3.0625e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 162/300\n",
"3/3 - 0s - loss: 3.6777e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 163/300\n",
"3/3 - 0s - loss: 2.5530e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 164/300\n",
"3/3 - 0s - loss: 1.7076e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 165/300\n",
"3/3 - 0s - loss: 2.1320e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 166/300\n",
"3/3 - 0s - loss: 2.7991e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 167/300\n",
"3/3 - 0s - loss: 3.3069e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 168/300\n",
"3/3 - 0s - loss: 2.9444e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 169/300\n",
"3/3 - 0s - loss: 4.0663e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 170/300\n",
"3/3 - 0s - loss: 3.3016e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 171/300\n",
"3/3 - 0s - loss: 2.0864e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 172/300\n",
"3/3 - 0s - loss: 3.1231e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 173/300\n",
"3/3 - 0s - loss: 2.9278e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 174/300\n",
"3/3 - 0s - loss: 3.0427e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 175/300\n",
"3/3 - 0s - loss: 4.5326e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 176/300\n",
"3/3 - 0s - loss: 3.3629e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 177/300\n",
"3/3 - 0s - loss: 2.4525e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 178/300\n",
"3/3 - 0s - loss: 2.5538e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 179/300\n",
"3/3 - 0s - loss: 3.3784e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 180/300\n",
"3/3 - 0s - loss: 1.9497e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 181/300\n",
"3/3 - 0s - loss: 1.9737e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 182/300\n",
"3/3 - 0s - loss: 2.2758e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 183/300\n",
"3/3 - 0s - loss: 2.9136e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 184/300\n",
"3/3 - 0s - loss: 1.1060e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 185/300\n",
"3/3 - 0s - loss: 5.0481e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 186/300\n",
"3/3 - 0s - loss: 4.0821e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 187/300\n",
"3/3 - 0s - loss: 5.7687e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 188/300\n",
"3/3 - 0s - loss: 5.1819e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 189/300\n",
"3/3 - 0s - loss: 4.4923e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 190/300\n",
"3/3 - 0s - loss: 5.0681e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 191/300\n",
"3/3 - 0s - loss: 3.9062e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 192/300\n",
"3/3 - 0s - loss: 3.1511e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 193/300\n",
"3/3 - 0s - loss: 3.9896e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 194/300\n",
"3/3 - 0s - loss: 3.6009e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 195/300\n",
"3/3 - 0s - loss: 3.8435e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 196/300\n",
"3/3 - 0s - loss: 6.6916e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 197/300\n",
"3/3 - 0s - loss: 1.2784e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 198/300\n",
"3/3 - 0s - loss: 8.5005e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 199/300\n",
"3/3 - 0s - loss: 6.0588e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 200/300\n",
"3/3 - 0s - loss: 6.8180e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 201/300\n",
"3/3 - 0s - loss: 4.7230e-05 - 27ms/epoch - 9ms/step\n",
"Epoch 202/300\n",
"3/3 - 0s - loss: 3.8133e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 203/300\n",
"3/3 - 0s - loss: 7.4671e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 204/300\n",
"3/3 - 0s - loss: 8.1094e-05 - 28ms/epoch - 9ms/step\n",
"Epoch 205/300\n",
"3/3 - 0s - loss: 7.8872e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 206/300\n",
"3/3 - 0s - loss: 8.7357e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 207/300\n",
"3/3 - 0s - loss: 4.8380e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 208/300\n",
"3/3 - 0s - loss: 7.0697e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 209/300\n",
"3/3 - 0s - loss: 5.2098e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 210/300\n",
"3/3 - 0s - loss: 5.4029e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 211/300\n",
"3/3 - 0s - loss: 2.8489e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 212/300\n",
"3/3 - 0s - loss: 3.3961e-05 - 21ms/epoch - 7ms/step\n",
"Epoch 213/300\n",
"3/3 - 0s - loss: 4.1667e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 214/300\n",
"3/3 - 0s - loss: 3.7597e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 215/300\n",
"3/3 - 0s - loss: 2.7004e-05 - 30ms/epoch - 10ms/step\n",
"Epoch 216/300\n",
"3/3 - 0s - loss: 2.9110e-05 - 29ms/epoch - 10ms/step\n",
"Epoch 217/300\n",
"3/3 - 0s - loss: 3.6687e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 218/300\n",
"3/3 - 0s - loss: 7.2615e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 219/300\n",
"3/3 - 0s - loss: 1.0681e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 220/300\n",
"3/3 - 0s - loss: 1.9565e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 221/300\n",
"3/3 - 0s - loss: 1.9595e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 222/300\n",
"3/3 - 0s - loss: 1.7055e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 223/300\n",
"3/3 - 0s - loss: 1.4371e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 224/300\n",
"3/3 - 0s - loss: 1.0054e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 225/300\n",
"3/3 - 0s - loss: 7.8233e-05 - 22ms/epoch - 7ms/step\n",
"Epoch 226/300\n",
"3/3 - 0s - loss: 2.0859e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 227/300\n",
"3/3 - 0s - loss: 2.3248e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 228/300\n",
"3/3 - 0s - loss: 3.5742e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 229/300\n",
"3/3 - 0s - loss: 3.2267e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 230/300\n",
"3/3 - 0s - loss: 2.6533e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 231/300\n",
"3/3 - 0s - loss: 3.3579e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 232/300\n",
"3/3 - 0s - loss: 2.2141e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 233/300\n",
"3/3 - 0s - loss: 1.3816e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 234/300\n",
"3/3 - 0s - loss: 1.2997e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 235/300\n",
"3/3 - 0s - loss: 1.2696e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 236/300\n",
"3/3 - 0s - loss: 7.3166e-05 - 25ms/epoch - 8ms/step\n",
"Epoch 237/300\n",
"3/3 - 0s - loss: 4.9531e-05 - 23ms/epoch - 8ms/step\n",
"Epoch 238/300\n",
"3/3 - 0s - loss: 5.9576e-05 - 26ms/epoch - 9ms/step\n",
"Epoch 239/300\n",
"3/3 - 0s - loss: 6.9014e-05 - 24ms/epoch - 8ms/step\n",
"Epoch 240/300\n",
"3/3 - 0s - loss: 1.2079e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 241/300\n",
"3/3 - 0s - loss: 1.0165e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 242/300\n",
"3/3 - 0s - loss: 1.1189e-04 - 21ms/epoch - 7ms/step\n",
"Epoch 243/300\n",
"3/3 - 0s - loss: 1.2715e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 244/300\n",
"3/3 - 0s - loss: 2.3746e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 245/300\n",
"3/3 - 0s - loss: 7.2393e-04 - 31ms/epoch - 10ms/step\n",
"Epoch 246/300\n",
"3/3 - 0s - loss: 8.1162e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 247/300\n",
"3/3 - 0s - loss: 6.6941e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 248/300\n",
"3/3 - 0s - loss: 6.1267e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 249/300\n",
"3/3 - 0s - loss: 5.4795e-04 - 26ms/epoch - 9ms/step\n",
"Epoch 250/300\n",
"3/3 - 0s - loss: 8.4581e-04 - 25ms/epoch - 8ms/step\n",
"Epoch 251/300\n",
"3/3 - 0s - loss: 4.3189e-04 - 22ms/epoch - 7ms/step\n",
"Epoch 252/300\n",
"3/3 - 0s - loss: 6.3720e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 253/300\n",
"3/3 - 0s - loss: 8.4664e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 254/300\n",
"3/3 - 0s - loss: 0.0025 - 26ms/epoch - 9ms/step\n",
"Epoch 255/300\n",
"3/3 - 0s - loss: 0.0032 - 26ms/epoch - 9ms/step\n",
"Epoch 256/300\n",
"3/3 - 0s - loss: 0.0040 - 26ms/epoch - 9ms/step\n",
"Epoch 257/300\n",
"3/3 - 0s - loss: 0.0021 - 24ms/epoch - 8ms/step\n",
"Epoch 258/300\n",
"3/3 - 0s - loss: 0.0023 - 22ms/epoch - 7ms/step\n",
"Epoch 259/300\n",
"3/3 - 0s - loss: 0.0034 - 21ms/epoch - 7ms/step\n",
"Epoch 260/300\n",
"3/3 - 0s - loss: 0.0045 - 25ms/epoch - 8ms/step\n",
"Epoch 261/300\n",
"3/3 - 0s - loss: 0.0064 - 26ms/epoch - 9ms/step\n",
"Epoch 262/300\n",
"3/3 - 0s - loss: 0.0050 - 23ms/epoch - 8ms/step\n",
"Epoch 263/300\n",
"3/3 - 0s - loss: 0.0068 - 26ms/epoch - 9ms/step\n",
"Epoch 264/300\n",
"3/3 - 0s - loss: 0.0042 - 29ms/epoch - 10ms/step\n",
"Epoch 265/300\n",
"3/3 - 0s - loss: 0.0047 - 24ms/epoch - 8ms/step\n",
"Epoch 266/300\n",
"3/3 - 0s - loss: 0.0045 - 26ms/epoch - 9ms/step\n",
"Epoch 267/300\n",
"3/3 - 0s - loss: 0.0046 - 25ms/epoch - 8ms/step\n",
"Epoch 268/300\n",
"3/3 - 0s - loss: 0.0032 - 25ms/epoch - 8ms/step\n",
"Epoch 269/300\n",
"3/3 - 0s - loss: 0.0031 - 26ms/epoch - 9ms/step\n",
"Epoch 270/300\n",
"3/3 - 0s - loss: 0.0041 - 28ms/epoch - 9ms/step\n",
"Epoch 271/300\n",
"3/3 - 0s - loss: 0.0034 - 24ms/epoch - 8ms/step\n",
"Epoch 272/300\n",
"3/3 - 0s - loss: 0.0043 - 25ms/epoch - 8ms/step\n",
"Epoch 273/300\n",
"3/3 - 0s - loss: 0.0034 - 22ms/epoch - 7ms/step\n",
"Epoch 274/300\n",
"3/3 - 0s - loss: 0.0036 - 22ms/epoch - 7ms/step\n",
"Epoch 275/300\n",
"3/3 - 0s - loss: 0.0030 - 23ms/epoch - 8ms/step\n",
"Epoch 276/300\n",
"3/3 - 0s - loss: 0.0027 - 25ms/epoch - 8ms/step\n",
"Epoch 277/300\n",
"3/3 - 0s - loss: 0.0033 - 22ms/epoch - 7ms/step\n",
"Epoch 278/300\n",
"3/3 - 0s - loss: 0.0024 - 21ms/epoch - 7ms/step\n",
"Epoch 279/300\n",
"3/3 - 0s - loss: 0.0017 - 23ms/epoch - 8ms/step\n",
"Epoch 280/300\n",
"3/3 - 0s - loss: 0.0017 - 24ms/epoch - 8ms/step\n",
"Epoch 281/300\n",
"3/3 - 0s - loss: 0.0015 - 22ms/epoch - 7ms/step\n",
"Epoch 282/300\n",
"3/3 - 0s - loss: 0.0015 - 24ms/epoch - 8ms/step\n",
"Epoch 283/300\n",
"3/3 - 0s - loss: 0.0019 - 27ms/epoch - 9ms/step\n",
"Epoch 284/300\n",
"3/3 - 0s - loss: 0.0042 - 22ms/epoch - 7ms/step\n",
"Epoch 285/300\n",
"3/3 - 0s - loss: 0.0026 - 22ms/epoch - 7ms/step\n",
"Epoch 286/300\n",
"3/3 - 0s - loss: 0.0035 - 25ms/epoch - 8ms/step\n",
"Epoch 287/300\n",
"3/3 - 0s - loss: 0.0033 - 28ms/epoch - 9ms/step\n",
"Epoch 288/300\n",
"3/3 - 0s - loss: 0.0059 - 26ms/epoch - 9ms/step\n",
"Epoch 289/300\n",
"3/3 - 0s - loss: 0.0073 - 26ms/epoch - 9ms/step\n",
"Epoch 290/300\n",
"3/3 - 0s - loss: 0.0060 - 24ms/epoch - 8ms/step\n",
"Epoch 291/300\n",
"3/3 - 0s - loss: 0.0032 - 25ms/epoch - 8ms/step\n",
"Epoch 292/300\n",
"3/3 - 0s - loss: 0.0022 - 23ms/epoch - 8ms/step\n",
"Epoch 293/300\n",
"3/3 - 0s - loss: 0.0021 - 21ms/epoch - 7ms/step\n",
"Epoch 294/300\n",
"3/3 - 0s - loss: 0.0025 - 24ms/epoch - 8ms/step\n",
"Epoch 295/300\n",
"3/3 - 0s - loss: 0.0011 - 23ms/epoch - 8ms/step\n",
"Epoch 296/300\n",
"3/3 - 0s - loss: 6.3007e-04 - 23ms/epoch - 8ms/step\n",
"Epoch 297/300\n",
"3/3 - 0s - loss: 4.8764e-04 - 24ms/epoch - 8ms/step\n",
"Epoch 298/300\n",
"3/3 - 0s - loss: 5.1926e-04 - 27ms/epoch - 9ms/step\n",
"Epoch 299/300\n",
"3/3 - 0s - loss: 7.6698e-04 - 29ms/epoch - 10ms/step\n",
"Epoch 300/300\n",
"3/3 - 0s - loss: 7.6851e-04 - 24ms/epoch - 8ms/step\n",
"14/14 [==============================] - 0s 3ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x786a654e6ef0>"
]
},
"metadata": {},
"execution_count": 60
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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LxzDcN4Lu3V0w4v4uAlWVEAIY6R/DhfcvF+TalGSZiGDDd0dgCCYDlgBgnYyi/pIf4QYTgussmNvlTK79i5VDvv1eKOO2UyGBmnvhIt19YUXrzbjztVZ0/WgMSlQuTnUICcTtBtz5zTaotswzV5Wk2oMAUEFhgAhIFt5Z7Jkb7QhFoH1ja9rHFUVB28YWdO9ej9sX797/PiGwbls7evZ2wWwzwTPpxY0T/fBO+TJez2BU8MIfPoP2Ta2QMlkvYLaasOnABsSiMfSfG8S6rW0wWZKvmaxNGChYECFAJCR6/nkUhpCaciuddToGy0wM9Rf98K+34u5XWqFaHxgMUwSElRcq2C0XXaDHjut/3g33ZR9qhsKQCuDvscOzvQbSWD0bzRgEkhgGqKJIVSISisJiM2d+npQQGd7cVVXF1kc2LYYBi92MF7/5LJrWN0JNqBCKgFQl9j6zC2fevpDxU/z+F/agbWMrhBDLxhPFoMBkNqFtQwu+/3+9UZDDj4QQsNjNiMcSRV3mKKSibilUJZz9Qbiv+GDyJTI+dSEY1NwLo/OnE7j7tbZlj/u7bXD2B9N+vxTJ5+iJalEwc7AWMwdry30rZcEgcF9FhQEuFRAA3DzZj51Pbst4vkA2iqKgxm1f/PVzv/8UGjqSleILr7tQ6X/wpX3wz/jRf/b2ytcxKNj++NbFpkKpHnc2ONCxrQ1DvSNZ7ysdo9mIvc/sxPbDW2CdL5wcujGCT6/dwNBIdfb7tw2H0fWvYzD5EpBIzvDn8sFdSMDVH4RlMopI0/1QObvHiZZPZqDEVq6zL3zf1CPVOajqEYPActUzF0RV49JH1xAOhFMW3+X66VtKiaA3BABo7GxA26bWjA2C9j23O+VjrkZn1lmKREJFc1dTTveVitFsxOf//QvY+9yuxSAAAO2bWvHV157G1k2lC8ilPpMgHfN0FBu+OwKjPzkbILC6GXwpAGf/8p0chpiEd3MNIJeXDiw07xl5vgGBbjtI+xgEVmIYoIoT8oXws//2LsZuTyz7ejyWSPnpPRUhBAbOJZ+7fkdHxqp+oQi4W2rhqFtZRZ7Lur8AINX8dw3se3YX6tvrUhZDAsDnnn0YZrMp79fXomxLBE2fzSUr//NdeRGAEr//zbaRMDb/z0G4r/kXg8XCo9FaI/q+0YHpQ+48L0al8mXXOQaBNCpqmQDgUoEWGIwK2re0wWK3wDftx/gDg3Ip+Gf8+MX/eB+1za5kB8JEAqN9Y4iGY1i3rR3WGkvWmoGF4KAYDTnNKKTqG+Cd8sE/F0BNrT3t9RSDguEbozn+ZMsJRWDb4S1pD04SQsBoMGDHli5cuNKf1zWKqShbCqWE+6p/cf9/PoQKhFqShagiIdH1g7EVywML/zXNc3E47oYRbsu+PZHKhyEgs4oLA1ReO57Yiode3Lusot875cOxH57ASN9Yye/HM+GFZ2J5j/XPfnwaz3z9ybTfI6XEtWP39/hPD81kbRAUDUfhn13ZNlhKiUsfXsXh1w+l/D41oWJqaBoTd6ey/SgpWWussNZk3j2hqhIN9a68Xl+X5PJP9av+dgHEawzwbUpO+btuBGAKZC4+bDg1l6wXyGXHAZUcg0B2XCaggtn11HYcfv3Qiq19jvoavPjNZ9HS01ymO1vu1vk76Dt7K+VjqqrCPxtYtjtg8OoQ4tF42tkBVVVx/bO+tG2Krx27gavHktPaCwFDnV8+8E758P7f/SrvnyUey23HQDyeeTBLR4utiLPuIlAEYg5DxpOo5ZJ/ln1dAaRBYPD1FmC+6NM+FIaa4Z1SADD7EjBm2a2wGkpEhWMgCGdfAEa/PnaFaBWDQG4qcmaASwWlZ7KYcPClfSkfUxQFqqri0KsH8OZ/e6e0N5bGr777KabvzWDf87sXP1mrqsSdy/fw2RunEQ7cP6Tl4Cv7YTAZVkzzL4QD75QP5969lPF6n71xGv1nb2Pbo5vhbnYhEori1vk7uH3x7prOOoiFYxgdGEdLT1PapQKDQUHfraG8r6FH0w/VouWTmbRNggSAvm+sg6svgPpzXpgCCagGgbmdNZh6rA6RRvOyJ+cUiQrw0UrEJVo/mkb9Oe/i7IYUgGd7DUY+14SEvXoaARUCg0DuKjIMMAiUXvfuThhM6d+oFEVBS3cTnA0O+Ka1cQrflU96cfXYdTStb4TBaMDcuAchXwjNXY3Y9thmKIrA3KQXO5/cmnK9X4jkYUPxaG57+ifvTmEyz+WATM6/dwkv/fFzKXsnqKqKkbFpjIxNF/y6D1rtToJitiCefrgWtb1+WCejy9b5F7YXTjzuRrjNgnCbBRNH6iHiEtKAlNP8/m4bGk950l5LAog0mBC3K3D0B1AzFAYg4O+2IdBlzX3pQEqsf2MMzv7g8toECdT2BmCdiGLg99dBtawtdYiYitprfjhvhSASEqF2C2b2OpGoqazhgEFgdSrivz4H//KzuWyQqsx6yp7dadNMGACS1f4Td5KDks1pwxf+w4to7mqCmlAhkfxUnal4UAiBxnX1cNaX76jhkb4xfPL943jiq48mZ2Fksn++YlAwOj6Dn/7i07LcVzmpFgW3vt6O1o9mUHfJt/gpO1ZrxMThOszuX953P9Mpfr6NdkTcRpjn4ilnCASAuV0ObPnWPVjm4pDzY3Xzp7MINZlx96utiLmz7+Zw3A7B1Ze6qZGQgGUqhroLXkw/4s76WulYJqPo+d4ITP5EckukBFw3A2j+ZAb3XmuBd5sj79fWEgaB1dNlGODgrz1BT3DxsJ9MAvN79wtNCIHOHR3Y+ugmOOsdCHpD6Dt9C7cv3s3psB/FoODlP3kOtU2uxV+vhtluBmbyuvWC6DtzC4PXhrD54EbUtdYiFo3jytQohkcLPxOhF6rVgJGXmjD2TAPMszFIo0CkwbTqIj/LVBSQy7cTLvy7ADB9wInGU3MwhJOPLt3FYJ2KYsO3R9D3zc6sn+jrLnohBTJuh6w/n38YUKIqer47AmNwvvfCwnUkgASw/o1x9H/DhHBr5oJUqky6CAMc/LXvzuV7eDwah8mS+hOQqqqYuDMJf4ZPz5YaC9S4ilgktqprG4wKnv/G01i3LdkPQDEocLfUYt3Wdux+ejt+8a0PEA1lnsLu2duFulZ3yseydSxUVYnAXPo2taUSCUZx5ZPexV97NvJNHUjOEuQ7wJm8cWz49ggMkfuHGy0VajIhXmOAIZy+K6HJG4f7ig8zD2XuTmjyxDMGATF/P/lyX/HBGEiknd2QAmg8OYehL7bkfQ0t4KxAfjQZBjj46088GsepN8/h8S8/smLtWlVVSFXi5M9W/iUVisCuI9ux88lti017xu9M4uIvr2Dwam5Fbw9//gDat7QDuP+JfqGYrr6tDkd+4zF88PeZK/Y37OuCqqppi/DSURMqBq8NIezX1ml1DAKF0XhyDoaImnqgB2CbjMEQ9GZtbuS+4s8aBuIOI6SIpH0tieSWx3w50yxBLBBqcslAzxgE8lf2MMCBv3L0Hr+JRDyBgy/vh911/8CWuXEPPv3hSUwOLp+yForA8994Gp3bO5Z9vWl9A174g6M4+dMzuPyrXmRiqbFg++EtGXv/d+3qzLqmb7GbswaBFSEnoSIajuHUm8V/A6ptcsHZ4EAkGMHkvemMR+lS4bgv+7IO9KZA5mUoASzOLGQyu9uJ2hsZBmMBzO7Nv1+ESMisuyJE4XZHlhyDwNqULAxw0K8ON08NoO/MLbRuaE52IJzxY3oo9WL6lkOb0Lm9Y8U0/MKgfOgLD+Hu1aG0RwQ3dzfhc3/4TNaGQEIItG1qge9U+jAwN+FFc1dTxvMHEokEjCbj4q8Hrw3h1Jvnsh5hvBYNHfU4/PohtPTcP7vAN+3H6Z+fw60LdzN8Z2Va2mPANhJGzWByRsbfZUO4rfCzIbkM4kvrB1KRAgg3Zi8g9G22I9BphX0ovCKASAHEnAbMPJR/GAi1WuC4E0o/8yCAUGvmczS0ikFg7QoeBjjok1QlRvvHsz5v5xNbM76LSlVi26ObceqtlX/RHfUOvPRHz2bczrj4OlmOKwaAGyf6sO3RzemfIIDPfnwGo/1jsNjN8M8EECry0kB9ex1e/bPPQXngbHlHfQ2e+d0jMJqP4+apgaLeQ7GsZVuhyRPD+jfGYR+JLB4SJCQQ6LBg8PVWxF2Fe1uLutLvIlgq0+NCAjP7cxjEFYE7X2tD+9uTyTMQlgzagU4rhr7QjIQt/2WCmQMuNJ2Yy3if0w/r79RFBoHCyPtvDQd9Wit3S23GHQiKQUlb1LfzyW0wGA05rfELITB+J/PgMzk4jSuf9GLXke0pah4kxgbG0Xd6IKedCYXyyBcfgmJQVvyMC/0NHn3tYQycv4NETMdzu6ukhBPY8O2RxUK6pQOmfTSCDd8eRv8fdkI1F6a56swBF1o/St+8KJOFb5nd60Sg25bxuQtUi4Kh11ow9kxD8lO8KhHssC47SjlfMbcJwy82Yd3bk8t2LSz8+8xuBzw79LW1kEGgcHIOAxz8qdAS8UTGLXyqqiKWppnPxn1dOW3/UxMqxm5NYG48fdOYBSd+cgaeSS/2Ht0JR33yTTESjKD3+E2cf+9SSYNAjduOjs1taR8XQsBsNaF7dycGzt0p2X2lU6qji+su+ZJV9ykeE2ry0CD3JR9mDhbmE+70Q7VwX/XDOhFd9QmIcacBU4+4MXVo9WcWxF1GzO1xZn/iKs0ecCHaYELjiTk4bwUBFQg3mzF1qBZzu508W6GKlb2AkKrX7Yt3semhDWkHdUVRcPfKYMrHjObsf3SllPDPBvDxd4+teMxgVFK2Ae799CZ6j9+Eq8EJRRHwTvtLGgIW1LhXHof8IDWh5vS8fGnxXIK6y9nrM+ouFy4MSLOCW7/TjtYPp1F/IXsxoQTQ/791QLUqiLpNi+cbaEmgy4ZAlw1YaKal0wDAWYHCYhigsrn8cS82HuiBqsoVuwHUhArfrB+3L6YOA7PjHjStb0i7TCClxMTdSbzzNx8iFk72Lahx27Hn6E5sObQRJosJ0VAUN07249JHVxHyLVn/lyhqUWAuctmqKBSx7AyFBZW8rdAQVDOvzwMwBAu7bKJaDRh5uRkz+1zY/PfDaZ8nRbJbYbhdJ0cZ6zQEAAwCxcBTC6lsZsfm8N7ffrR4IqCaUJGY/xTunfLh7W99kPZTee+nNzLWCwghcPxfTy8GgdomF770F69g++Eti42RzDYzdj65Da/9xSuocdsL/NOtjXfKh8l704unG6aixlXcuZQ6LFWqaJ1xsWgwFSmAaH32yv18hNutmNnrTFk+IEXyxMPxI/VFuTYlfdl1jkGgSDgzQGU1fHMU3/s/f4SNB7rR1NmIRCKBe73DGL4+mvFMgIFzd9C9Zz26diVrWRYK/qQqIRSBCx9cxvTw/S2NT/3WYZht5hVLEopBgc1hxZNffRTv/M2HRfgJ83f6rXN48Y+eW/yZHnT+g8tZOytWmpkDtXDcTT9rknPlfp6GX2qCahJoOOtd3AkjJBBzGDD0xZaibG+kJIaA4mIYoLKLR+O4caIfN0705/w9Ukr88h8/SXYvPLINjvm189nxOVz68Cr6z95efG59ex2au5rSvRQUg4KOre1w1DsytksutZG+Mbz/vz7CE199FDW19sVdDrFoHOffu4RLH14t9y2WnGdbDXwbbHDcXrlffmGa3ruleHUUMAiMfq4Jk4/XwdkXhBJVEWk0w99j02R9QKVgECg+hgHSLalKXP74Gi7/6hpsDhukqqZcQ29or8v6WkII1Le5NRUGAOBe7zD++f9+Ax1b2uCsdyASjOJe7xBikfx71OuaInD3K21o/mQGDWc9MESTiSBhEZh+qBYTR+pLMijHHUbM5joDISXsQ2EYgyqiLiPCrWZdr9eXGoNAaTAMkP5JIORLfxpiIp5bQVmuzys1qUoMXR8p920UzFoaDgHJ44bHn2nAxJN1sE4kl0nCzWZIk/ZKoFy9frR9MA3zkgOGwo0mjLzYlKzop4wYBEpHe397iAps+OZo1oE+Folh/PYkDEYFLT1NaNvYAotdf61ZK3knwYOkSUGow4pQh1WTQcB9xYeuN8ZXnDRomYqh53sjqLlbnOO8KwWDQGlxZoAqXiQYRe/xm9j5xLaUhXhSSlz+VS/2HN2BXUe2w2xLhoBEPIH+c7dx8idnEA2v7lhlqm4iIdH23lTKbtsCyS3+be9Pof/f5tfMTcRU2IfCUBIS4WYLYgVswawFDAKF8yPvAfxlDs+rrD9BRGmcevMcbE4bNu7vTm5XnD9dRjEouHGyH856BzY91LOsDbHBaMDmhzagcV0D3vxv7yCephsi0YMc/UEYQ+mbVQkJ2MajsExEEGlexWyOKtF8bDZ5tPJ8vYRE8pCj4ZeaEHfyLb2a/Mh7oGCvxT85VBXUhIqPvv1rXP74GjYf3ACb04qAJ4S+U/0wWU149c9eTPl9ikFBfasb2x/bnPU45XIr1xJBqVoR64nJF894kuH95yUQac79dTvenkTdBd+y1xUAnP1BbPyHYfT/wTok7PkfZqQV6Qa5SpoxKORAXggMA1RVpu5NY+re9LKvPfm1x6Am1PRnHQhg2+Etmg4D1VIr8P7YtmXHGGtV3GHIGgQAIF6T+8BtHYug/kLqzphCJgNI48k5jB9tyPk19UZrA2glYRigqudscGQ89EgIUdQzANaqGEFAi+cSFJOIqnBf9aP2uh9KVEW42YKZ/S6EW/P7vfVttCNhETBEUjfOkkh2Uwy35F6kWnfRB6kkD2RKRUig/ry3osMAFQ/DAFUFs82MrYc2omdvF4wWE2ZGZtF7/AbGb08i7AtnnhlA8vRCqkzm2Rh6vjMMkze540QAsA9H0HDOi/En6jDx1OpbDEuTgrGjDeh4Z2rlY/P/b/LE0XjKg6lH3Dm9pskXB7KcmWUMqYAq2QCJVo1hgCqeu6UWr/zpC7DWWJLtY4WAu9mFTQ/14PLH19B/7jY27O9O+/2qqqLv1EDpbngVqmV5YKmCLhWoEt3fH4XJl1i+Dj8/Yrccm0Wk0QTPztUfJzzzUC2kEGh/fwpK/P4MwcJ1hAq0fTCNhFnJqYFR3G5IbgbPEAgSZsEgQHnR3uZcogISisCL33wWFrsZQhGLuwUWZgF2P70DFrsZ47cnUh6KpCZURAIRXD2mvXXqagwCheYcCMIyG0t7NLEUQNPxufvH/a6SZ0dNxu+VAFo+mUl+ms9ibrcj7RIBkLzX2b3FO5eBKhvDAFW0rl2dcNTVpF0CkKrEnqM78c7/9yHuXh2ClDL5z/yb88zoHN78q3eXH3GsAQwCheG4FYSa4V1QSMA2EYUhnGV+Pg3nQBBKhn5XAoDJn4B9OPufr+A6K7yb7SlPbZQCSFgVTD3qzus+ibhMQBWtfVMrEgkVhjRhQCgCda1uKIrAL//hV3DWO9CxtQ2KQcHk4DQmB1eu+VL5FWqpINMn7WVy+OSeiiGi5rTF0BDJ4UaEwODrLWh7dwr1F33LZjPCLWbc+2JLxTUfotLhnxyqbLkun84vH/hm/Lj+WV/x7qcAOCtQOMEOCxoybF2XAGIuQ9579yP15pz+CEbqTTm9njQqGHmlGRNP1SdPboxLhFotPDqZ1ozLBFTRxm9Ppp0VAJLLBJ5JLyIpTjvUIgaBwvJsdyBuVVJOvS+YPuTO+5TBQJcV0Vpj2teXAgh0WhGtX905GHGHEXO7nZjd72IQoIJgGKCKdvviXQR9Iahqus3ZwBUNNxNaikFguffHtq35NaRJwd2vtEIaxLIBe+HfvVtrMPVwbf4XEAJDrzZDCqwIBFIAqklg+MXG/F+fqEAYBqiiqQkV7/3tR4hH48sCwcLOgb7TA+j97Ga5bo80ILjehr5vdmL6YC1iNQYkzALBNgvufaEZg6+3rHmrXqDLhlu/2wF/9/0ji6UAvJvtGHuqHrXXA2j8bBbmabZ1pvIRUua2Z+bZo/+p2PdCVDT2Whu2H96KDfu6YDIbMTM6h2uf3sDg1aFy31pOSjkrsNrug6s9m6B73eSqnp+JHloTL2X0x2EIqbCMh7HuF1MwxORiEyIBwLOtBvdebYY083MaFc5f7nwr63MYBog0rtTLA3oKA4D+AkHdOS863k7+Hjz4Oy0B+DbZcfdrbSW/L9KP1S6Rffzsf8n6HO4m0JlqXDeuHdBHcd9aVeN/22pjmouh453UQWDha67+IGwjYYTarSW9Nyq9QtS9FArDwCrwzbo8ivH7XsyAwT8nlE7jqTlAZt7xKgVQe83PMKBDWhrcV0vXYYBvupQv/tmpHHo51hgAXNcD2fsOSOTd8ZAKS8+D+2oVNQzwDZeI6D4lkVsnw2hdbk2IKD/VNMjnKucwwIGdqPKttngwH3eGmgpeRKiX2YFQqwWOW6GsswOze1Z/SmK14sBeGLpeJiCiwilFEFhwZ6hp8d8LFQxWMyiUKzhMP1QL561Q5uc8XIu4s3Lfmjl4a1Pl/okjopKwDJpXvb1wqYVgUOjZgkzKFRx8m+2Y3udEwwVfygOMPFtrMPqCtjoScvCuDgwDRLTmWYG1BgKgPKEgF4UcDJ9vvY6Rl5sQWmdFw0kPbJPJ37NQsxkTT9TBu91R1vuj6sUwQFRlSrkckI9iLCFoxeLA3QLgC4AxnoAEkDDOn4o4Vq47o2rHMEBUwUo58BdiduBBWp0tKJS4Mb+jkYkyWRqoc8UwQFQBtP5pf60qebaAqk8+g3WxMQwQ6YyWB/5izA48qNJnC6i8tDhQlwLDAFEJaHkA1yuGgupTrQN1KTAMkG5xgNWmUswOLJVpgGBQKA0O0vrHMEBFw8G6epU6EKRT7EFKa2GDgzLli2GAUuJATpQdB1+qFAwDFYADN2mRVmYHiCg7hoEy40BORETlxjCwBhzIiTLj7ABR4VgGzUV77aoOAxzMiYqPgYD0qpiDr9boPgxwQCci0r9qGni1SBNhgAM6UWXL542eswmFw4GWsiloGOCgTtVMS4NXJbz5V8LPQKQXOYcBDvRUCbQ0YBdTPj8nB1+i6qWJZQKi1aqWQZ2IqBQYBkgzOMCXV2R9lLMDRFWKYYCKjoO8fjAQEFUnhgHKCwd4IiJtct6Vq/4ehgFahoM8cXaAKkE+A2I1YxioAhzgabUYCPSHgx+tBcOATnGAJyouDq5UTRgGNIaDPGmF3mYHOHgT5Y9hoAQ4wOtf97rJct8C7gw1lfyaWg8ElR4A7DYLNvZ0wGIxYW7Oh1t3R6Gqlf0zU3kwDKwBB3n90cKgnq+l916OYKAllR4ChBA4cngPDuzZDCEEpJRQFAXBYBjvfHgat++OlvsWqcIwDKTAQV5/9DzI52Ph5y1FKNDK7EClB4Cljj6xD/t2b4IQyTbwC/9vtVnw2suP4wc/+RjDo1PlvEWqMFUXBjjQ61O1Dfa5KtVsQbkCQTUFgAVOh31ZEFhKEQKqlHj8kV34wU8+Lv3NUcWqmDDAQV6/ONAXRqUsI1RjAFhq66ZOSAmkyAIAAEVR0NnRjBq7FYFguLQ3RxVLF2GAA72+cbAvvWIsIxRzdqDaA8BSVqsZUqoADFmftzQM1A5EinxnVMnKHgY40FcGDvjaVOjZgkIHAj2HgGINvtEmD5QDSsbnqAkVyhUPakN8/6TCKGoY4EBfWTjg61spiw4z0VoA0Non6lsX7uCx1w7CYDKkrBtQEypuXxpElEGACijvMMCBvjJxwK98aw0F+c4OlCIEaG1gz0csHMNnPzmDJ7/6KKSUywKBmlARDcdw5ufny3iHVIlyDgMc/CsLB32qJJUQApa6caIP0VAUB1/eh9omFwBAqhL3rg/j5E/PwjfjL/MdUqUpe80AFR8Hfio3rS0N6MHti3dx++Jd1Le5YbKa4Jv2I+gNlfu2qEIxDFQQDvpUClpoQFRNZkbnyn0LVAUYBnSIg35xNMz4sP/aHfTcm4AiJYZa6nBhZw+GW+tXPLd+1of2iVlIITDY1gCf016GOyagMEsEBpMBG/Z2oWVDMyCBkf4x3Lk0CDWhFuAOibSPYUCjOOCX1pZbI3j54wsAAEUmp7Q33R3H1jtj+PXBrXB/Lg4AMHrj6PzpOByD9/d3SwDebTUYeqUJqjX13vD3x7YV9f4pf01djfjcHxyF1WGFmlAhAWx7bDMCniDe/Ztf8pM5VQWGgTLjoF9+Tl8QL318AUJKLN3IZZgPBU+euYFbW9sRajFj4z8Nw+SNL/t+AcB1I4AebxwDv9sBGFZuB3u+9XrW+2BgKD17rR0v/dFzMJqTIU4x3N/fb3NY8fKfPI8f/j8/RSTIAmqqbAwDJcABX5sWBuiW3mkoWB4ElpICaDg1h9A6K0zeOESKWjghAftIBK6bAXi3O1Y+ISEhVAlpSt9MJlNgYFBIba1LBDse3wKj2QBFWfnfRTEosNgt2PLIJlz+6NqarkOkdQwDBcRBX5uyfSqvGQynHOAXCAnU3AvDMh1LrgmkIQVQd8m3LAw4BoJoOjGLmjthCADhBhOmH67FzH4XoKSLH1QqPXu7UgaBRQLo2bO+aGHAbDPDYjMj5A8jHo1n/waiImEYWCUO+NqUyzR8OlJJjvEZh2YBGIOJjM8REjD677+hN5z2oP29KUhx/7Ut0zG0vzOFmrsh3PtSS/rTaKgkjObMb4FCCBjNpoJft3FdPQ68uA+d29shhEAinsDA+Ts4985F+GcDBb8eUTYMA2lw0NeWtQz22fh77Ki5F077qV8qgL/HButkDIZwNONyQsydHDjMMzG0vZc8b37prMPC97p7A/Bt9mNutzOne3y+9XpBlwrK3ZJYK2ZGZmFzWJfVCiylJlRMD88U9Jptm1rw4jefhRBisbugwWjApgM96NzegZ/917fhm2ZTISqtqg4DHPC1o5iDfTaz+5xo/nQWiKepG1CBqUNu2EciiwN8KkICM3uTg3v9eW9y5E8XMERy5iDXMEArFWJL4bVjN9C5vSPt44pBQe/xm2u+zgIhBJ76rcchFLFieUIxKLDYzHjsSw/jvb/9qGDXJMpFxYcBDvjaUc4BP5O4w4g7X21F9w/GkoV+8wO4nB/Mh19uQqjDinCzGXUXvLBORlfUGEgB+Dba4d+Y7DdgHY9krUOwTlRWC109utc7jN7PbmL7Y1sgVQkxX8ehqioURcHFD69g4k7h3kM6trbB4a5J+7hiUNC5vQP2WjuCnmDBrkuUTUWEAQ742qHFAV8JJeDu9cPkjSNeY4Rnew3ijuV/9AM9dtz4k/WoP++FcyAIkZAIrLdh5iEXIo3JjnvSpODW19vR/u4U3Nf8EPP9aFSjwMwBF8aONizWAKgmkawVyFRwmGILYiaFXiqgpE9/eBJT96ax+6kdcLfUAgBmR+dw6aNrGDh3u6DXqm1yQVUllAzFo0II1DY5GQaopHQVBjjoa4cWB/1UGk7OofWjaYhEcu1fSKDt/SlMHnZj/Kn6ZQV8xmACUAD/BhtCLRb4ttSsGLBVqwFDX2zB6LMNsI1FAEUg2GGFalk+5evbXAPXzfRv5lIBPNvSf0KkzAp9MNGNE/24caIfJqsJkEAsEivo6y+IRWI51YzGwsW5PlE6mgsDHPC1Qy8Dfjp1571o/2B68ddiSWfZ5k/noBoVTD5RByWUwPofj8N5OwQpklP+igrE7QoGX29FoMu2+H224TAaT8zBdTMARQXCjfNbBfct3yo4t9OBlk9mYPQnVi4pLDxnlxPWsQjiNQbEnZr7q5hSpZ9LUOxBePDacHI5Is2skJQSgbkgpgpctEiUTVnegTjga4veB/2UVImWX81k3DLYfHwW0w+70P2DMdiHk+2Fhbw/tW8Iqej+/ij6v9GBSLMFtdf86PzJ+OLzAMAyFUP721NwDAQx+G9aFwOBNCm49dvt6PneKMzeeLL+AAAkIA1AuMmMnu+NLt6bv8uKsaMNCHVYM/5YXCrQt7A/jKvHbmDXkW2LOwmWEkLg7DsXM/azICqGooUBDvjak9Ogr0qYZ5OfjqJ1Jt02xrEPhWEKJDI+R4lJNJ6YQ81QOOXjQgKQEk2fzWH0uUas+9k4IJeHi4V/d90Mov6cFzMHaxcfizaYcfNP18N1IwDHQBBKQiLmMKDhrAe28eVbFGsGw9jwT8O481vty2YiqPKcevMsjCYDtj22GVKVkFJCURRIKXH65+fRd3qg3LdIVWhNYYADvjbl9UlfTQ6Mjac8i4NorMaAqUO1mHrUrbtQYAjndtpczd1wspYgzdOFCtT2+hFuMkGomRsTNZz2LAsDQLJI0LPDAc+OZFfCjX83BJFYWVi48OuOtyZw80/XsxlRBoWuFyg1qUp8+qOTuPTRVWx7dDMaO+sRDcXQd+YWBq8Olfv2qErlHAY48Gvbmqb6pUTnT8ZR2xtYNtiZAgm0fjQD21hEd93yovU5do3L0AtggZIAbKOZD6oRAKwzMYiETLtLwDIZhX00/UAmJGCZi6NmMMzZgQqnGBTsfWYXtjyyMblcIJOtkecmPPjoO8cwPcSaASqtDE25Seueb72++M9aOPuCcD8QBBYIJLvlOfv0tc0p0mhGoMNyf63+AVIAEbcRwXWZ1+glgJjDAGnKHoQWig/TMc/kVpy2sExDxWG2mWGvtS32FCiHp3/rcWx9ZBMURUl2Ipy/F1eDE5//dy+gtslVtnuj6qSPEmZaptAFf/XnPBn3xEuRfI5vi/a3wom4CvtwBCIhMX6kHl0/GoMSl8t+toVBe/jzzYjWGtF0fC7DCwIzB1yINJhRdzl9i1gpAN8Ge8blFNWaW/ZO5Pg8Wp1129qx77ndaN3QDAAIByO4fvwmLnxwpaSHBDWsq8eG/d0pH1toi7z32Z345J8/K9k9ETEM6ECxq/2tU7Gs3fIs0xr/tColmo7PofHEHIzz9QJSJKv0IQDH7fDizEegy4qxp+9X7o8/XY/Wj1fuPJACCDebMfWIG6pBIOI2wuxZeYSxnP+fqcfcGW8x0GlFzG7IeOBRwiSSoaKIqvFcgi2PbMKRrz0GVb1fHGK1W7DnmZ3o2NqOn//390oWCDYd6IGaUNOeh6AYFGw80INf/+AEpMptBVQaDAMaVOqtfgmrAulJXxwngRVNdbSm7d0pNJz1Lq/0l4DjbhgxpxE3v7luvnfAyj39k4/XIeYyovnYLCzzU/kJk8DsPhfGn6qHak7+7Hd+qx093x2B2RNfbFW8cMHhl5uyr/MrAuNP12PdL9LX30w+UQdp1vbvtd5YHVY88eVHFqv2l1IUBQ0dddhzdAfOvXupNPdTY8n6HIPRAJPZiCibD1GJMAxoQLn3+c/tcqJ1fDr9E0TyOVplmYig8aw35WNCAiZfHHVX/Bg/2pD2NeZ2OzG3ywHTXBxKXCLqNkKalg8c0ToTbv7xerhu+OHqC0LEJMItZszscyHuyu2v0ux+F5SoitaPZiASMlm1Mx8qJh6vw2SW2QU99hhw3i3vp9vND28AlpwQ+CBFUbDnmZ0YvjGK8QKeQ5COfy57/U00HEMsUrqli1QsNRZsObQR7ZtaIRSBsVsTuHGiHyFfqKz3RcXBMFAG5R78HzSz14nGk3MwBlJ0yxNAvMaweBqfFtVd8mXeHiiB+nNejD9dn3lHhBCI1WXehSCNAp6dTnh25v/7Mf2IG7N7nai9FoDJF0e8xgDPdgcSNYa8X5PSczfXAjJT+ynAaDLi1T9/EX1nbuGT7x+HlMULMH2nB7D/+d1pH1cTKm6c7C/qPWTTurEZn/uDZ2A0GwGRbIbUvrkV+5/bjV/+0yfcAlmBOB9ZIoWq/C8G1WbAra+3IzK/HU8qyX8AIFJvwq2vt0O1aXegMnnjWbcHGsNq8pO4RqhWA2YPuDDxVD1mDtYyCBTRamoBNj3Ug/0vpB+oC8E75cPFX15J+ZiaUBH0hnApzeOlYHPa8Ll/+ywMZgOEcn9GRVEUKEYFz/7eEe52qECcGSgSLQ76mUTrzej7o07U3AmhZjDZkS+w3opAt03z/QUSdkPWfgGqSaz6lECqDHcuDWLnk7ktrwghsOvIdlz88CoSscwdLNfi8sfX0LmjA3Wt7sXBVkqJkD+Mt//mA4SDEazfuQ7uZhdikTjuXrmHoLc00/PbHtsMg0lZUV8BJH9/hBDY8cRWfPbj0yW5HyoNhoEC0dvgn5IQCPTYEegpbjV7oc3tcqIhTc0AkFzqmN3j1HyoKYW17CTQ6yFFowPjGL89gab1jWkr+Jcy28xo6mzA2K0JAIDFbkZ9ez2kqmLy3vSaQ4LRbMQr870EltYxCCFgc1jxwjeOwmQxwe6yQU2oEELgsdcP4caJm/jsx2egJnLrrpmv9TvXpQwCCxSDgvW71jEMVBiGgTxUxMBfQYIdFni22JNFfSlqHlSzgslH3WW5N6M/DiUqEXMaVhQkrpYeiwdLxd1Si6b1jZCqipH+MQQ9yz9Fv/e/PsLz33garRtaIKVMW0y4QDEoMFtNePS1g9h4oAcGY3IZJxqO4eqvr+Pcuxfz3va3+eAGuFtqU96DYlBQ2+SCOv/aC+FFANj26BYYjIai9x9QcmjGZMgQFkifGAay4MCvA0Lg3pda0P7OFOou+SDk/V1/4UYz7r3WjJg7x/bEBeIYCKL5kxnUjCTbD6tGgdk9Tow/VZ9c1qCCqHHb8fRvP4G2jS2LX1NViVvnb+PYD08u1gtEglG89dfvYfOhjXjqNw5nfE01ocIz6cUr/+4F1LW6l80mmK0m7HtuF1yNTnz07V/ndc+bD21EpuM0k1sgU5xoqAhsObQJFz64Au+UL69r52L8zuSKn3spNaGWZNcFlRbDwBIc+PVLGhUMf74Z40/Vw3ErCCUuEWq1INRuKfnygPuyD+t+NrHszV6JS9Sf98JxO4iB31/HQJCnpYcUWexmvPpnL8LuWt7fQVEENuzvhr3Wjre/9cGyqvy+UwPY+fhW1LfXpRzs1ISKgfO30bOnC/VtdSlbFgshsHF/N3qP38DYwMSqfwa7K3Mr5EyzFmpCxcYD3Tj/3uVVXzdXvcdvYvvhLWkfVwwKrh7je2Wlqdq5nqXV/Vqt8qfVizuNmNvrwsxDtckOgyUOAko4gY75pkKpTiY0z8XR/GseQlMIO57YCnutLeWgrigK2je1YssjG1c89uG3f41IMLKsG6GUElKV8Ex6ceInZ7Dtsc0Zr60mVGw9tCmv+w7MBpZdezWklLDYszctWovZ0bnFeoCl9QkL/37u3Yt5hSDStoqfGeAgT6XkvuqHiMu0O9qFBOou+jD2bAOksWqzeEFsObQpY6GblBKHXz+E4Ztj8M/cP1fCO+XDG//lLex8chu2PLwRlhoLAp4grh+/id7jNxGLxFHjtmf89K4YFDgb8us1ceNkP5q78yvkVBQFvun0Z2QUyrVjNzAzOotdR7ajY0sbIATGb43jyifXMXR9pOjXp9KrqDDAgZ/KzTITy9gACQAMMQmjP4GYO/cwwOLBlayOzCdOCiGgGBQcfHkfPv7OsWWPhXxhnPnFBZz5xYWU3xsJRmCypK8zURNq3p34+s/exrbDW9C4rn5FmFlY0ki3VKCqKvrP3srruqs1NjDBGYAqosuPJqmm+BkESAsSZiVDn7v71DKcP1BpBxQFPcGsXfqEENiwtwtm6+oKSG+eGsg4la8YFPSfvb2q11ygJlS8/a0P0H/29rJp+EQ8gYHzdxDyhVdsH1z4OU/+7CwiwWhe1yXKRNMzAxzgac2kRM1gGI5byW2HwXYLvFtqMh41vBbebTVoOTab/nZE8vRCFhCu3fUTfTj0+QNZn6cYFNhdtlUd+nPt2A1sfXQzbA7ripoENZHsNzB4Lf+WvLFIDJ98/zhOvXkOzesbISExcXcKkUAEjroaPPKFh9C1e/3irgLvtA/n3rmIgXN38r4mUSaaCQMc+KnQTN44uv5lFLaJaLLFMoAmFYg5DLj7lVaE2jNPM+cj3JKh58H8/0w8WVfw61aj68f7sOfpHbA6rFn7Bqz203Q4EMFbf/0ujv7Ok2jualw2A3H3yj188i+fFeR44bA/vCJU+GcD+OU/fgKbwwpnoxOxSAyzo3NrvhZRJmULAxz8qRBEXMI2GoZISISbzEjUJP9Ii5iKnu8MwzyX3Gcu1Ps7/YyBBHq+O4K+b3YiVpucPjZ5YnDcDkGoEsF2K8KtFphnonAOzH+tzYJgZ267E+59sQWdPx1H7c0gpEjOBgg12RJ5+PPNCHSvrsMj6wVSi0ViePt//hJf+t9fSfscVVUxOjCOkD+86tf3Tfvx1l+/i7o2N5rXN0JVJUZujsI3U/wCPgAI+cN53TdRPkoWBjj4U0FJiabjc2g8MQdjOLm+KgXg2e7AyAuNcPYHYJlNfUCNkIASk2g448HEE/Xo+PkEansDi8cbCABxq4AxLJOf5kXye8KNJgy+3opIU+a2vNKsYPArbbBMRFB7PQAlqiLSYIZnp6MstQKVbGZkFtc+vYEdj29dMTugqsnuU+fevriq13Q3u7DnmZ3YuL8HBpMBQW8Qvcdv4sqvriMWyX2pgUhPihYGOPhTMbW/M4X6c95lxXpCArW9fthGw4jWmpKfyNPM5AoJuK/4YR+OwD4UXnydhf83hOX9X8+/hmU6ho3/OAzvJjvsw2EICfh7bJg+WItwy8q935FmCyaa17YnXCuzAlo+l+DET85AjavY+eQ2CEXMd/BTEPaH8KvvH19Vt7yW7ia89MfPQTEoi7UCdpcd+1/Yg569XXjrr95dVe0BkV4ULAxw8KdSsY5F0HAu9cFEC419RFymDQILDGEVNfdST8OmWgwQElAiarKXwPzX6i75UHfBh+HPN2F2r3aPda20nQRLSVXi5M/O4uKHV9G1cx1MVhM8k14MXR9Z1bq+UASe+b0jUIwrT+xTFAXu5locfHk/jr9xqtA/AlHZ5R0GOPhTudRd9GXcyy8kYAwlMs4MSJE8L0AksoeGZa/94K/V5MRBx1uTCLZZEFnjTMBSWpkV0BLPRsuylsRLhf1h3DjZn/drd27vQE1t+noOxaBgy6GNOPXWucUzD1ZDKALrd6xD1+5OmMxGzI7N4fqJfgQ9wbzvmahQcg4DHPxJK8yeGJClm6uS5b1aSCBhUxbrDdZCAJAK0HDGi5GXK/cTeKVyt9Sia1cnOra2QVXVjF0NjWYjXI1OzIyk3z6ait1lw0t//BzqWt3JHgJCoGt3J/Y9vxufvXEavcdvrvXHIFoTzWwtJMpV3G5ItsvKMI4nLApm9jnRdNKz4oA4KZJr/XGbArMnnrFbYK6ECjhuF+4THmcFis9kMeHo7zyB9TvXLTb5yRQEFiRiidVdSACf+8NnUNuUXEa637cg+afy8S8/At+sH0O9bPNL5cPSZtKduV2OjAO4FMDsHifGnm3A0EtNiLrvZ964TcHEk3W4+9U2zO6vLUgQWFTaM5FyVsn1Amvx/Deexrpt7QCwrGAwHalKeKd88EylrldJp2NzGxo66tMfCayq2PfsrlW9JlGhcWaAykdKOAeCcPUGYIiqiNSbMLPPhVhd5taxgS4bfD02OO6EVjb2EclZgalHagEhMHvAhdn9Tpi8yRmAqMsIGJKjdmC9FXPbaxa3FS57Hawc2zMcQQ8ASFjZVVAvWjc0o31z66q+RygCF355ZXF3Sa7W71yHREKFIU0YUBQFrRta8IX/8BJG+sdw/fhN+GcDq7sI0RoxDFBZGIIJdP/zCOyjye6AUAEIoOn4HMaP1mPycIYufULg7pdb0fGLSbiv3m8AIwBEGkwY/FLLYjOhhecv+/WSr9/7YguidTNoOO2BIZZ8l5cAQi1mmD3x+z0MsixLAIB9NALraAThtsrYTljJNuzrzjhAA/PHGksAUkIxKDj37kXczKNAUTHmdl5Fc1cjGjvrsefpHfjoO8dw++LdVV+LKF8MA1R6UmL9j8ZgG0u2iF2cqp//xNX60QyitUZ4dqY/IlaaFQy91oLxo/VwDISgJCRCbRYEOyw5dQlcZBAYP9qAicfrYB+JQKgSoZZkJ0ORkLCOJb8WbjRj4z8NwzIVS/vGLhWg4ZwHw6805359HdByj4F8mW3mrAO0EAJ3Lg/CM+nFzZP98E758rrWzPBsxuOQl1IUBVJKHP36E5gdm8PcuCevaxKtFsMAlZxtJAJHmv39QDITNB+bhWeHI+vAHqs1YfbA6k6kS3lNs4JAt2351wwCoY775xcYA4mMA4hQAevY2k6U46xAaXhzWPcP+cP45T/8as3X6j97G4dePQCjyZhTKBBCQKoSO5/chk9/dDLlc+wuGyx2CwKeIKIhnmJIa8cwQCXn6gtm7hMAwDoVg9GXQNylnT+iqjHzG7kEoJq1VUXI4sHUbpzsx/7n96R9XFVVXC/Qdr9YJIaPvnMMz/3+U5AJmbVQEUgWNHZub1/x9daNzTj40j60bmhZvM87Fwdx+ufnS3ZmAlUm7iagkhMJmVMNlhJf+6lwheTd5oDMMtZ7t9bk/fqcFSidwFwQp39xHgCWnUgIJI8o9kx4cenjawW73uDVIfzsv76NO5cGkUjktoVFPLDNsXNHB17+k+fR3H0/4CmKgu496/HF//gyXI3pl9WIsmEYoJILtZqhZHk/TFgUxDQ0KwAAUw/XQhpEykAgBZCwK5jdwzdkvbj04VV8/N1j8EzeXzKIR+O4fqIPb/7Vu4gV+AyCqaEZfPjtX+Pv/4/v4uqx64u9DVJREyrGBsYXfy0UgSNfOwwBsbJVskGB2WrCI198qKD3S9VFW++2VBW8Wx2I26ZgCKspWwFLAczsd0FmmZYvtVidCXd+ow1dPxyDEpk/E3n+eOJ4jQG3f7MNap7bCzkrUB79Z2+j/+xtuBqdMJgM8E3782o1vFq9n97EjsNb0z6uGBRcPXZj8dfrd66DzWnN+Pz129fB7rIh6A0V9F6pOjAMUMlJo8Dg6y3o/udRQN6vHVg4LjjUasHEkxm2FpZRoMuG63/ehdorftQMhSCFgH+DLbmEYNBWeKHc5btTIF9z4x4c++EJPPHVRyHV+3UEakKFYlBw8s2zmFhy2mJto3PxsXSEIuCsdzAMUF4YBqgsAt129H9jHZpOzKH2WgBKQiLmMmDmodrkdLwp+aYn4ircl/2ou+CF2RtHzGHA7F4XZvc4Ic3lWeVSzUqymdGBwpxSWKxZARYPatuNk/2YHpnFriPb0LG1HQLA6MA4rv76OsZuTSx7bjQcy2knQjTC45UpPwwDVDaRZguGvtCCoVdlsqHPA5+slXACPd8bgW10vh8BAKM/AdvYFBrOenDrdzqQqGHXv2KqxB4DWjJ1bxoff/fTrM+7e/keDr9+CCLN7JOUEt5JH2ZH5wp8h1QtWEBI5SfEiiAAAO3vTsE2Fl1Ymk8+df4fy3QM696aWPE9VPk8G/Po8CiAji1tOPTqATz62kFs2N+d0xY/rQj5w7h27PqKnQ8LhBAIB9L37iDKhjMDpElGfxzuq/6UBYZA8ghiZ38QptlY1rMMtIyFg8XnqKvB5/7wGdS1upPb+qTEriPbEfKH8f7ffbxsbV7LZrN0I2zpaUbHljYM3xzN+DyhCJitJsQi8Yw7Gqi6MAyQJtlGImmDwAIBoGYojDkdh4FiYb1AksFkwMt/+jwc7mT/h6VnEVjsZrz0R8/ijf/8li4a9mx9dDOkTN+UU02o2PrY5rRhwO6yYe9zu7Dl0CaYzEYk4gkMnL+DCx9chneytAWUpD36mSej6sLCfCqADfu64ax3pFwSUBQFBqMBO4/oY3bGVe+AkqGIUDEoqE3TeMhRV4PX/uJlbH9sC0zm5GdAg9GATQd68Np/fAUNHdrcvUOlwzBAmhTssCZPCsxAInkMMVE6PXvXp11nB5ID6Ib93aW7oTWIBKMZfxZVVRHyp64bOPxvDsFaY10RihSDAqPZgKd/+4mC3ivpD8MAaVLCbkhuH0zzQUgKwLutJvXRxDqh9XqBSthJYLaaV3Tse9DCJ2Wt6zszkDnYKAr6z95e8XVHXQ06t3ekLZhUFAV1re5lbY6p+jAMkGaNPN+IwLrkJ/+FULDw/6EWM4Ze4ZtXKqwXuG92dDZz219V1c0xwb2f9SHkC6f8edSEitmxOdy+cGfFY+6WWogsp39KKVHX6i7QnZIeMQyQZkmzgtu/3Y7BL7XA321DuN6EQKcV977QjFu/ty7v1r9UPa6f6Mu4hVBRFFxb0vZXyyKBCN7663cxM99LQFVVqGpypmDs1gR+/j/eRyK+MijEY9nbKwshkMjheVS59DE/RtXLIODZ4YBnh6Pcd7JIJCQcA0EY/QnEnQb4N9hX3YpY60sElWJ6eBYX3r+Mfc/vhlTlYhc/KSUggcHeIfSfWzm1rlW+aT9+8v/+HM1djWjpaYZUJUb6RhcDQioTd6YQDkRgrUnfnyERT+De9ZEi3DHpBcMA0Sq4L3nR9sE0jKH7n8DiNgWjzzdibjdPLNSiM29fwNyEB3uf3bU4FR70hnD119dx+eNrkKq2jsrOxcTdKUzcncrpuWpCxcVfXsEjX0h9qqFUJa5/1odIIFLIWySdYRggypH7sg+db07iwaHDEFLR+bMJSAXw7CxvINBavYDzrjYG2oXTCW1OK4SiIOQNZSzGqzSXP74Gm8OK3Ud3LAs/ikFB/7nbOPHTM2W8O9IChgGiXCQk2j6YhsTKFggCyW2Obb+chme7A8jhQBlaG89GC2oHVv9JNuSr3pa9p946h97PbmLLwxtR465B2B9G/9lbGZcYqHowDBClo0pYx6NQYipMvjiMwUTapwoAJl8CNYNhBLptGV9WD/UClbCtkFbyTftx9p2L5b4N0iCGAaIHSYm68z60HJuByZcMALlOKGcKDEREWsUwQPSApk/n0PqrmWUBINeJ/5irfH+ltFYvQET6wT4DREsYvXG0fDIDIHUASDdDIAFE3EYEOzIfr6uHJQIiqj4MA0RL1F3OfHrbQrHgUnL+gZEXm9IfKUdEpGFcJiBawjwXgxTIeHzyg8N9pMGE0Rca4d9gL+q9EREVC8MA0RJxuyFjEACS5yMMfL0dxrCKmMOIcKu57DMChawX4E4CourDMEC0xNxOB5qPz6V9XArAs60Goc7M2wdTYb0AEWkVawaIlog0WzC3w5Hy6GQpAKkITD5eV/obIyIqIs4MED1g6NVmJMwC9Rd9WGg5KCQQcxpx77VmhFsy7xggItIbhgGiB0ijwMgrzZg4Ug9XXwBKTCLcZIa/x1b22gAiomJgGCBKI+40YuZAbUFeq5j1Amw2RERrxZoBIlrEnQRE1YlhgIiIqMoxDBAREVU5hgGiImO9QHF4NnJXB1GhMAwQERFVOYYBIioK590sfZ2JSDMYBogIAHcSEFUzhgGiImK9ABHpAcMAERFRlWMYICIiqnIMA0RERFWOYYCoSIpZL1BoLB4kqm4MA0Q6xOJBIiokhgEiIqIqxzBARERU5RgGiIiIqhzDAFERsNkQEekJwwBRlSvGTgKeS0CkLwwDREREVY5hgIiIqMoxDBAVGOsFiEhvGAaIiIiqHMMAERFRlWMYIKpiPJOAiACGAaKCYr0AEekRwwAREVGVYxggIiKqcgwDREREVY5hgKhAilkvUAwsHiSiBQwDRDqgp+JBnktApD8MA0RERFWOYYCIiKjKMQwQFYDe6gWIiJZiGCDSOD3VCxCRPjEMEFUh7iQgoqUYBohIl2oHIuW+BaKKwTBAtEasFyAivWMYINIw1gsQUSkwDBAREVU5hgEiKhh2HyTSJ4YBojXQY70AdxIQ0YMYBog0ivUCRFQqDANEedLjrAARUSoMA0RERFWOYYCIiKjKMQwQ5aHYSwSsFyCiUmIYIKoi3ElARKkwDBCtEgsHiajSMAwQERFVOYYBIo3Ra70Auw8S6RfDANEqcImAiCoRwwBRlWDxIBGlwzBARERU5RgGiHJUiiUCvdYLEJG+MQwQERFVOYYBIiKiKscwQJQD7iIgokrGMECkEcWsF6i0nQS1A5Fy3wJRRWEYIKI1Y8MhIn1jGCDKgksERFTpGAaIiIiqHMMAkQawvwARlRPDAFEGlbBEUGnFg0RUeAwDREREVY5hgIiIqMoxDBClUQlLBEREuWAYICozFg8SUbkxDBDRmrDhEJH+MQzQCl92nSv3LZRdpSwRcCcBEeXCWO4boPLJNOh/2XUOP/IeKOHdEBFRuTAMVDh+ytc21gsQkRYwDFQADvhERLQWDAM6U8qBv1qXCiqlXqBS8fhiosJjGNAJfvonIqJi4W4CHWAQqEzFrhfgTgIiyhXDgIZ92XWu7EGg3NcnbWOPAaLKwGUCDeIAXD6sFyCiasQwoCEMAUREVA5cJtAILQcBLd8bERGtHWcGyowDrXaUcomAxYNEpCUMA2XCEEBERFrBMFBiDAFERKQ1rBkoIT0HAT3fOxERZcaZgRLgQKp9lVQvQES0WgwDRcQQQJWMDYeIKgeXCYpAC50Di6ESf6ZKxJ0ERLRaQkrJeE9ERFTFODNARERU5RgGiIiIqhzDABERUZVjGCAiIqpyDANERERVjmGAiIioyjEMEBERVTmGASIioirHMEBERFTl/n/v6EJWb2crlAAAAABJRU5ErkJggg==\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": "YyOarWssKyjN",
"outputId": "cf9f497e-f126-4cfb-b848-20723f97522f"
},
"execution_count": 61,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1709531181.5916784\n",
"Mon Mar 4 05:46:21 2024\n"
]
}
]
}
]
}