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": 13,
"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": 14,
"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": "fd330813-33f6-4482-ace1-5e819c617701",
"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",
" TNLayer(),\n",
" TNLayer(),\n",
" TNLayer(),\n",
" TNLayer(),\n",
" # Finish Modified Layers\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_1\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_2 (Dense) (None, 1024) 3072 \n",
" \n",
" tn_layer_4 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_5 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_6 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_7 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_3 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 24577 (96.00 KB)\n",
"Trainable params: 24577 (96.00 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": [
"# Generate points forming concentric circles\n",
"num_points = 240 # Number of points for each circle\n",
"\n",
"# Inner circle\n",
"r1 = np.random.rand(num_points)\n",
"theta1 = np.random.rand(num_points) * 2 * np.pi\n",
"x1 = r1 * np.cos(theta1)\n",
"y1 = r1 * np.sin(theta1)\n",
"\n",
"# Outer circle\n",
"r2 = np.random.rand(num_points) + 1\n",
"theta2 = np.random.rand(num_points) * 2 * np.pi\n",
"x2 = r2 * np.cos(theta2)\n",
"y2 = r2 * np.sin(theta2)\n",
"\n",
"# Concatenate the points and labels\n",
"X = np.concatenate([np.column_stack((x1, y1)), np.column_stack((x2, y2))])\n",
"Y = np.concatenate([np.ones(num_points), -np.ones(num_points)])\n",
"\n",
"# Shuffle the data\n",
"shuffle_index = np.random.permutation(len(X))\n",
"X_shuffled = X[shuffle_index]\n",
"Y_shuffled = Y[shuffle_index]"
],
"execution_count": 16,
"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": "fad97011-cfbd-4340-cf0f-eaff78bf766a"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712718550.7633064\n",
"Wed Apr 10 03:09:10 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "41f25d3e-ea65-4129-af14-c6de7e4b38c4",
"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": 18,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"15/15 - 2s - loss: 1.0019 - 2s/epoch - 136ms/step\n",
"Epoch 2/300\n",
"15/15 - 0s - loss: 1.0001 - 92ms/epoch - 6ms/step\n",
"Epoch 3/300\n",
"15/15 - 0s - loss: 1.0003 - 93ms/epoch - 6ms/step\n",
"Epoch 4/300\n",
"15/15 - 0s - loss: 1.0002 - 87ms/epoch - 6ms/step\n",
"Epoch 5/300\n",
"15/15 - 0s - loss: 1.0001 - 88ms/epoch - 6ms/step\n",
"Epoch 6/300\n",
"15/15 - 0s - loss: 1.0001 - 86ms/epoch - 6ms/step\n",
"Epoch 7/300\n",
"15/15 - 0s - loss: 1.0002 - 88ms/epoch - 6ms/step\n",
"Epoch 8/300\n",
"15/15 - 0s - loss: 1.0001 - 84ms/epoch - 6ms/step\n",
"Epoch 9/300\n",
"15/15 - 0s - loss: 1.0002 - 86ms/epoch - 6ms/step\n",
"Epoch 10/300\n",
"15/15 - 0s - loss: 1.0001 - 84ms/epoch - 6ms/step\n",
"Epoch 11/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 12/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 13/300\n",
"15/15 - 0s - loss: 1.0002 - 81ms/epoch - 5ms/step\n",
"Epoch 14/300\n",
"15/15 - 0s - loss: 1.0002 - 82ms/epoch - 5ms/step\n",
"Epoch 15/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 16/300\n",
"15/15 - 0s - loss: 1.0001 - 77ms/epoch - 5ms/step\n",
"Epoch 17/300\n",
"15/15 - 0s - loss: 1.0001 - 82ms/epoch - 5ms/step\n",
"Epoch 18/300\n",
"15/15 - 0s - loss: 1.0001 - 83ms/epoch - 6ms/step\n",
"Epoch 19/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 20/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 21/300\n",
"15/15 - 0s - loss: 1.0001 - 83ms/epoch - 6ms/step\n",
"Epoch 22/300\n",
"15/15 - 0s - loss: 1.0003 - 78ms/epoch - 5ms/step\n",
"Epoch 23/300\n",
"15/15 - 0s - loss: 1.0002 - 76ms/epoch - 5ms/step\n",
"Epoch 24/300\n",
"15/15 - 0s - loss: 1.0000 - 79ms/epoch - 5ms/step\n",
"Epoch 25/300\n",
"15/15 - 0s - loss: 1.0001 - 73ms/epoch - 5ms/step\n",
"Epoch 26/300\n",
"15/15 - 0s - loss: 1.0001 - 74ms/epoch - 5ms/step\n",
"Epoch 27/300\n",
"15/15 - 0s - loss: 1.0001 - 78ms/epoch - 5ms/step\n",
"Epoch 28/300\n",
"15/15 - 0s - loss: 1.0002 - 76ms/epoch - 5ms/step\n",
"Epoch 29/300\n",
"15/15 - 0s - loss: 1.0001 - 77ms/epoch - 5ms/step\n",
"Epoch 30/300\n",
"15/15 - 0s - loss: 1.0001 - 76ms/epoch - 5ms/step\n",
"Epoch 31/300\n",
"15/15 - 0s - loss: 1.0001 - 75ms/epoch - 5ms/step\n",
"Epoch 32/300\n",
"15/15 - 0s - loss: 1.0002 - 75ms/epoch - 5ms/step\n",
"Epoch 33/300\n",
"15/15 - 0s - loss: 1.0000 - 83ms/epoch - 6ms/step\n",
"Epoch 34/300\n",
"15/15 - 0s - loss: 1.0000 - 79ms/epoch - 5ms/step\n",
"Epoch 35/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 36/300\n",
"15/15 - 0s - loss: 1.0001 - 81ms/epoch - 5ms/step\n",
"Epoch 37/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 38/300\n",
"15/15 - 0s - loss: 1.0001 - 77ms/epoch - 5ms/step\n",
"Epoch 39/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 40/300\n",
"15/15 - 0s - loss: 1.0000 - 77ms/epoch - 5ms/step\n",
"Epoch 41/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 42/300\n",
"15/15 - 0s - loss: 1.0000 - 77ms/epoch - 5ms/step\n",
"Epoch 43/300\n",
"15/15 - 0s - loss: 1.0000 - 78ms/epoch - 5ms/step\n",
"Epoch 44/300\n",
"15/15 - 0s - loss: 1.0001 - 77ms/epoch - 5ms/step\n",
"Epoch 45/300\n",
"15/15 - 0s - loss: 1.0001 - 78ms/epoch - 5ms/step\n",
"Epoch 46/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 47/300\n",
"15/15 - 0s - loss: 1.0001 - 78ms/epoch - 5ms/step\n",
"Epoch 48/300\n",
"15/15 - 0s - loss: 1.0000 - 81ms/epoch - 5ms/step\n",
"Epoch 49/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 50/300\n",
"15/15 - 0s - loss: 1.0001 - 76ms/epoch - 5ms/step\n",
"Epoch 51/300\n",
"15/15 - 0s - loss: 1.0001 - 78ms/epoch - 5ms/step\n",
"Epoch 52/300\n",
"15/15 - 0s - loss: 1.0001 - 81ms/epoch - 5ms/step\n",
"Epoch 53/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 54/300\n",
"15/15 - 0s - loss: 1.0000 - 79ms/epoch - 5ms/step\n",
"Epoch 55/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 56/300\n",
"15/15 - 0s - loss: 1.0002 - 81ms/epoch - 5ms/step\n",
"Epoch 57/300\n",
"15/15 - 0s - loss: 1.0001 - 76ms/epoch - 5ms/step\n",
"Epoch 58/300\n",
"15/15 - 0s - loss: 1.0000 - 80ms/epoch - 5ms/step\n",
"Epoch 59/300\n",
"15/15 - 0s - loss: 1.0001 - 77ms/epoch - 5ms/step\n",
"Epoch 60/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 61/300\n",
"15/15 - 0s - loss: 1.0002 - 78ms/epoch - 5ms/step\n",
"Epoch 62/300\n",
"15/15 - 0s - loss: 1.0000 - 77ms/epoch - 5ms/step\n",
"Epoch 63/300\n",
"15/15 - 0s - loss: 1.0000 - 77ms/epoch - 5ms/step\n",
"Epoch 64/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 65/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 66/300\n",
"15/15 - 0s - loss: 1.0001 - 78ms/epoch - 5ms/step\n",
"Epoch 67/300\n",
"15/15 - 0s - loss: 1.0000 - 79ms/epoch - 5ms/step\n",
"Epoch 68/300\n",
"15/15 - 0s - loss: 1.0002 - 80ms/epoch - 5ms/step\n",
"Epoch 69/300\n",
"15/15 - 0s - loss: 1.0000 - 80ms/epoch - 5ms/step\n",
"Epoch 70/300\n",
"15/15 - 0s - loss: 1.0000 - 78ms/epoch - 5ms/step\n",
"Epoch 71/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 72/300\n",
"15/15 - 0s - loss: 1.0002 - 81ms/epoch - 5ms/step\n",
"Epoch 73/300\n",
"15/15 - 0s - loss: 1.0000 - 80ms/epoch - 5ms/step\n",
"Epoch 74/300\n",
"15/15 - 0s - loss: 1.0000 - 78ms/epoch - 5ms/step\n",
"Epoch 75/300\n",
"15/15 - 0s - loss: 1.0000 - 79ms/epoch - 5ms/step\n",
"Epoch 76/300\n",
"15/15 - 0s - loss: 1.0000 - 80ms/epoch - 5ms/step\n",
"Epoch 77/300\n",
"15/15 - 0s - loss: 1.0000 - 80ms/epoch - 5ms/step\n",
"Epoch 78/300\n",
"15/15 - 0s - loss: 1.0000 - 78ms/epoch - 5ms/step\n",
"Epoch 79/300\n",
"15/15 - 0s - loss: 1.0001 - 81ms/epoch - 5ms/step\n",
"Epoch 80/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 81/300\n",
"15/15 - 0s - loss: 1.0001 - 83ms/epoch - 6ms/step\n",
"Epoch 82/300\n",
"15/15 - 0s - loss: 1.0001 - 77ms/epoch - 5ms/step\n",
"Epoch 83/300\n",
"15/15 - 0s - loss: 1.0000 - 77ms/epoch - 5ms/step\n",
"Epoch 84/300\n",
"15/15 - 0s - loss: 1.0000 - 80ms/epoch - 5ms/step\n",
"Epoch 85/300\n",
"15/15 - 0s - loss: 1.0001 - 77ms/epoch - 5ms/step\n",
"Epoch 86/300\n",
"15/15 - 0s - loss: 1.0001 - 79ms/epoch - 5ms/step\n",
"Epoch 87/300\n",
"15/15 - 0s - loss: 1.0001 - 80ms/epoch - 5ms/step\n",
"Epoch 88/300\n",
"15/15 - 0s - loss: 1.0003 - 80ms/epoch - 5ms/step\n",
"Epoch 89/300\n",
"15/15 - 0s - loss: 0.9555 - 79ms/epoch - 5ms/step\n",
"Epoch 90/300\n",
"15/15 - 0s - loss: 0.4968 - 79ms/epoch - 5ms/step\n",
"Epoch 91/300\n",
"15/15 - 0s - loss: 0.1259 - 78ms/epoch - 5ms/step\n",
"Epoch 92/300\n",
"15/15 - 0s - loss: 0.1292 - 77ms/epoch - 5ms/step\n",
"Epoch 93/300\n",
"15/15 - 0s - loss: 0.0761 - 77ms/epoch - 5ms/step\n",
"Epoch 94/300\n",
"15/15 - 0s - loss: 0.0465 - 78ms/epoch - 5ms/step\n",
"Epoch 95/300\n",
"15/15 - 0s - loss: 0.0377 - 76ms/epoch - 5ms/step\n",
"Epoch 96/300\n",
"15/15 - 0s - loss: 0.0265 - 77ms/epoch - 5ms/step\n",
"Epoch 97/300\n",
"15/15 - 0s - loss: 0.0106 - 75ms/epoch - 5ms/step\n",
"Epoch 98/300\n",
"15/15 - 0s - loss: 0.0313 - 80ms/epoch - 5ms/step\n",
"Epoch 99/300\n",
"15/15 - 0s - loss: 0.0170 - 78ms/epoch - 5ms/step\n",
"Epoch 100/300\n",
"15/15 - 0s - loss: 0.0460 - 76ms/epoch - 5ms/step\n",
"Epoch 101/300\n",
"15/15 - 0s - loss: 0.0599 - 79ms/epoch - 5ms/step\n",
"Epoch 102/300\n",
"15/15 - 0s - loss: 0.0335 - 80ms/epoch - 5ms/step\n",
"Epoch 103/300\n",
"15/15 - 0s - loss: 0.0249 - 77ms/epoch - 5ms/step\n",
"Epoch 104/300\n",
"15/15 - 0s - loss: 0.0172 - 78ms/epoch - 5ms/step\n",
"Epoch 105/300\n",
"15/15 - 0s - loss: 0.0287 - 80ms/epoch - 5ms/step\n",
"Epoch 106/300\n",
"15/15 - 0s - loss: 0.0161 - 80ms/epoch - 5ms/step\n",
"Epoch 107/300\n",
"15/15 - 0s - loss: 0.0054 - 78ms/epoch - 5ms/step\n",
"Epoch 108/300\n",
"15/15 - 0s - loss: 0.0041 - 77ms/epoch - 5ms/step\n",
"Epoch 109/300\n",
"15/15 - 0s - loss: 0.0185 - 79ms/epoch - 5ms/step\n",
"Epoch 110/300\n",
"15/15 - 0s - loss: 0.0334 - 77ms/epoch - 5ms/step\n",
"Epoch 111/300\n",
"15/15 - 0s - loss: 0.1220 - 78ms/epoch - 5ms/step\n",
"Epoch 112/300\n",
"15/15 - 0s - loss: 0.1316 - 77ms/epoch - 5ms/step\n",
"Epoch 113/300\n",
"15/15 - 0s - loss: 0.0972 - 78ms/epoch - 5ms/step\n",
"Epoch 114/300\n",
"15/15 - 0s - loss: 0.0450 - 79ms/epoch - 5ms/step\n",
"Epoch 115/300\n",
"15/15 - 0s - loss: 0.0159 - 80ms/epoch - 5ms/step\n",
"Epoch 116/300\n",
"15/15 - 0s - loss: 0.0060 - 80ms/epoch - 5ms/step\n",
"Epoch 117/300\n",
"15/15 - 0s - loss: 0.0040 - 78ms/epoch - 5ms/step\n",
"Epoch 118/300\n",
"15/15 - 0s - loss: 0.0031 - 80ms/epoch - 5ms/step\n",
"Epoch 119/300\n",
"15/15 - 0s - loss: 0.0034 - 77ms/epoch - 5ms/step\n",
"Epoch 120/300\n",
"15/15 - 0s - loss: 0.0078 - 80ms/epoch - 5ms/step\n",
"Epoch 121/300\n",
"15/15 - 0s - loss: 0.0079 - 76ms/epoch - 5ms/step\n",
"Epoch 122/300\n",
"15/15 - 0s - loss: 0.0463 - 79ms/epoch - 5ms/step\n",
"Epoch 123/300\n",
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"Epoch 124/300\n",
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"Epoch 125/300\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 129/300\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",
"15/15 - 0s - loss: 0.0018 - 76ms/epoch - 5ms/step\n",
"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",
"15/15 - 0s - loss: 3.1640e-06 - 88ms/epoch - 6ms/step\n",
"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: 2.1414e-06 - 82ms/epoch - 5ms/step\n",
"Epoch 163/300\n",
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"Epoch 164/300\n",
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"Epoch 165/300\n",
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"Epoch 166/300\n",
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"Epoch 167/300\n",
"15/15 - 0s - loss: 1.7253e-06 - 78ms/epoch - 5ms/step\n",
"Epoch 168/300\n",
"15/15 - 0s - loss: 1.9873e-06 - 79ms/epoch - 5ms/step\n",
"Epoch 169/300\n",
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"Epoch 170/300\n",
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"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",
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"Epoch 175/300\n",
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"Epoch 176/300\n",
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"Epoch 177/300\n",
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"Epoch 178/300\n",
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"Epoch 179/300\n",
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"Epoch 180/300\n",
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"Epoch 181/300\n",
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"Epoch 182/300\n",
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"Epoch 183/300\n",
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"Epoch 184/300\n",
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"Epoch 185/300\n",
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"Epoch 186/300\n",
"15/15 - 0s - loss: 9.1642e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 187/300\n",
"15/15 - 0s - loss: 1.0425e-06 - 82ms/epoch - 5ms/step\n",
"Epoch 188/300\n",
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"Epoch 189/300\n",
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"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",
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"Epoch 194/300\n",
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"Epoch 195/300\n",
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"Epoch 196/300\n",
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"Epoch 197/300\n",
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"Epoch 198/300\n",
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"Epoch 199/300\n",
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"Epoch 200/300\n",
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"Epoch 201/300\n",
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"Epoch 202/300\n",
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"Epoch 203/300\n",
"15/15 - 0s - loss: 7.9795e-07 - 83ms/epoch - 6ms/step\n",
"Epoch 204/300\n",
"15/15 - 0s - loss: 1.0404e-06 - 84ms/epoch - 6ms/step\n",
"Epoch 205/300\n",
"15/15 - 0s - loss: 7.4247e-07 - 83ms/epoch - 6ms/step\n",
"Epoch 206/300\n",
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"Epoch 207/300\n",
"15/15 - 0s - loss: 3.7028e-06 - 81ms/epoch - 5ms/step\n",
"Epoch 208/300\n",
"15/15 - 0s - loss: 6.8299e-06 - 80ms/epoch - 5ms/step\n",
"Epoch 209/300\n",
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"Epoch 210/300\n",
"15/15 - 0s - loss: 1.4607e-06 - 85ms/epoch - 6ms/step\n",
"Epoch 211/300\n",
"15/15 - 0s - loss: 4.9968e-07 - 85ms/epoch - 6ms/step\n",
"Epoch 212/300\n",
"15/15 - 0s - loss: 5.5134e-07 - 84ms/epoch - 6ms/step\n",
"Epoch 213/300\n",
"15/15 - 0s - loss: 9.4944e-07 - 80ms/epoch - 5ms/step\n",
"Epoch 214/300\n",
"15/15 - 0s - loss: 4.8437e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 215/300\n",
"15/15 - 0s - loss: 6.0213e-07 - 81ms/epoch - 5ms/step\n",
"Epoch 216/300\n",
"15/15 - 0s - loss: 6.7136e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 217/300\n",
"15/15 - 0s - loss: 5.4196e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 218/300\n",
"15/15 - 0s - loss: 5.6960e-07 - 79ms/epoch - 5ms/step\n",
"Epoch 219/300\n",
"15/15 - 0s - loss: 4.5855e-07 - 85ms/epoch - 6ms/step\n",
"Epoch 220/300\n",
"15/15 - 0s - loss: 3.7956e-07 - 83ms/epoch - 6ms/step\n",
"Epoch 221/300\n",
"15/15 - 0s - loss: 4.5322e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 222/300\n",
"15/15 - 0s - loss: 6.1718e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 223/300\n",
"15/15 - 0s - loss: 3.9799e-07 - 81ms/epoch - 5ms/step\n",
"Epoch 224/300\n",
"15/15 - 0s - loss: 1.2093e-06 - 83ms/epoch - 6ms/step\n",
"Epoch 225/300\n",
"15/15 - 0s - loss: 2.1275e-06 - 83ms/epoch - 6ms/step\n",
"Epoch 226/300\n",
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"Epoch 227/300\n",
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"Epoch 228/300\n",
"15/15 - 0s - loss: 7.5661e-07 - 81ms/epoch - 5ms/step\n",
"Epoch 229/300\n",
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"Epoch 230/300\n",
"15/15 - 0s - loss: 4.9856e-06 - 79ms/epoch - 5ms/step\n",
"Epoch 231/300\n",
"15/15 - 0s - loss: 4.3746e-06 - 80ms/epoch - 5ms/step\n",
"Epoch 232/300\n",
"15/15 - 0s - loss: 1.5431e-06 - 77ms/epoch - 5ms/step\n",
"Epoch 233/300\n",
"15/15 - 0s - loss: 1.5790e-06 - 78ms/epoch - 5ms/step\n",
"Epoch 234/300\n",
"15/15 - 0s - loss: 8.1042e-07 - 81ms/epoch - 5ms/step\n",
"Epoch 235/300\n",
"15/15 - 0s - loss: 4.7891e-07 - 79ms/epoch - 5ms/step\n",
"Epoch 236/300\n",
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"Epoch 237/300\n",
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"Epoch 238/300\n",
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"Epoch 239/300\n",
"15/15 - 0s - loss: 4.1100e-07 - 80ms/epoch - 5ms/step\n",
"Epoch 240/300\n",
"15/15 - 0s - loss: 2.7570e-07 - 81ms/epoch - 5ms/step\n",
"Epoch 241/300\n",
"15/15 - 0s - loss: 3.0445e-07 - 80ms/epoch - 5ms/step\n",
"Epoch 242/300\n",
"15/15 - 0s - loss: 3.1757e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 243/300\n",
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"Epoch 244/300\n",
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"Epoch 245/300\n",
"15/15 - 0s - loss: 2.3585e-07 - 80ms/epoch - 5ms/step\n",
"Epoch 246/300\n",
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"Epoch 247/300\n",
"15/15 - 0s - loss: 3.5606e-07 - 79ms/epoch - 5ms/step\n",
"Epoch 248/300\n",
"15/15 - 0s - loss: 2.8918e-07 - 81ms/epoch - 5ms/step\n",
"Epoch 249/300\n",
"15/15 - 0s - loss: 3.2523e-07 - 82ms/epoch - 5ms/step\n",
"Epoch 250/300\n",
"15/15 - 0s - loss: 1.6684e-06 - 80ms/epoch - 5ms/step\n",
"Epoch 251/300\n",
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"Epoch 252/300\n",
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"Epoch 253/300\n",
"15/15 - 0s - loss: 0.0029 - 77ms/epoch - 5ms/step\n",
"Epoch 254/300\n",
"15/15 - 0s - loss: 0.0205 - 80ms/epoch - 5ms/step\n",
"Epoch 255/300\n",
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"Epoch 256/300\n",
"15/15 - 0s - loss: 0.0688 - 78ms/epoch - 5ms/step\n",
"Epoch 257/300\n",
"15/15 - 0s - loss: 0.0742 - 83ms/epoch - 6ms/step\n",
"Epoch 258/300\n",
"15/15 - 0s - loss: 0.0276 - 81ms/epoch - 5ms/step\n",
"Epoch 259/300\n",
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"Epoch 260/300\n",
"15/15 - 0s - loss: 0.0691 - 81ms/epoch - 5ms/step\n",
"Epoch 261/300\n",
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"Epoch 262/300\n",
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"Epoch 263/300\n",
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"Epoch 264/300\n",
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"Epoch 265/300\n",
"15/15 - 0s - loss: 0.0149 - 78ms/epoch - 5ms/step\n",
"Epoch 266/300\n",
"15/15 - 0s - loss: 0.0033 - 82ms/epoch - 5ms/step\n",
"Epoch 267/300\n",
"15/15 - 0s - loss: 0.0015 - 80ms/epoch - 5ms/step\n",
"Epoch 268/300\n",
"15/15 - 0s - loss: 7.5240e-04 - 79ms/epoch - 5ms/step\n",
"Epoch 269/300\n",
"15/15 - 0s - loss: 0.0056 - 77ms/epoch - 5ms/step\n",
"Epoch 270/300\n",
"15/15 - 0s - loss: 0.0242 - 84ms/epoch - 6ms/step\n",
"Epoch 271/300\n",
"15/15 - 0s - loss: 0.0289 - 75ms/epoch - 5ms/step\n",
"Epoch 272/300\n",
"15/15 - 0s - loss: 0.0128 - 72ms/epoch - 5ms/step\n",
"Epoch 273/300\n",
"15/15 - 0s - loss: 0.0093 - 76ms/epoch - 5ms/step\n",
"Epoch 274/300\n",
"15/15 - 0s - loss: 0.0015 - 76ms/epoch - 5ms/step\n",
"Epoch 275/300\n",
"15/15 - 0s - loss: 0.0146 - 82ms/epoch - 5ms/step\n",
"Epoch 276/300\n",
"15/15 - 0s - loss: 0.0092 - 79ms/epoch - 5ms/step\n",
"Epoch 277/300\n",
"15/15 - 0s - loss: 0.0306 - 79ms/epoch - 5ms/step\n",
"Epoch 278/300\n",
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"Epoch 279/300\n",
"15/15 - 0s - loss: 0.0202 - 79ms/epoch - 5ms/step\n",
"Epoch 280/300\n",
"15/15 - 0s - loss: 0.0061 - 78ms/epoch - 5ms/step\n",
"Epoch 281/300\n",
"15/15 - 0s - loss: 0.0050 - 79ms/epoch - 5ms/step\n",
"Epoch 282/300\n",
"15/15 - 0s - loss: 0.0046 - 72ms/epoch - 5ms/step\n",
"Epoch 283/300\n",
"15/15 - 0s - loss: 0.0072 - 80ms/epoch - 5ms/step\n",
"Epoch 284/300\n",
"15/15 - 0s - loss: 0.0034 - 80ms/epoch - 5ms/step\n",
"Epoch 285/300\n",
"15/15 - 0s - loss: 1.0488e-04 - 78ms/epoch - 5ms/step\n",
"Epoch 286/300\n",
"15/15 - 0s - loss: 3.4497e-04 - 77ms/epoch - 5ms/step\n",
"Epoch 287/300\n",
"15/15 - 0s - loss: 3.6634e-05 - 73ms/epoch - 5ms/step\n",
"Epoch 288/300\n",
"15/15 - 0s - loss: 5.3274e-05 - 80ms/epoch - 5ms/step\n",
"Epoch 289/300\n",
"15/15 - 0s - loss: 3.2084e-05 - 77ms/epoch - 5ms/step\n",
"Epoch 290/300\n",
"15/15 - 0s - loss: 2.5528e-05 - 78ms/epoch - 5ms/step\n",
"Epoch 291/300\n",
"15/15 - 0s - loss: 2.3218e-05 - 84ms/epoch - 6ms/step\n",
"Epoch 292/300\n",
"15/15 - 0s - loss: 2.3699e-05 - 80ms/epoch - 5ms/step\n",
"Epoch 293/300\n",
"15/15 - 0s - loss: 1.7401e-05 - 80ms/epoch - 5ms/step\n",
"Epoch 294/300\n",
"15/15 - 0s - loss: 1.6556e-05 - 79ms/epoch - 5ms/step\n",
"Epoch 295/300\n",
"15/15 - 0s - loss: 1.6340e-05 - 78ms/epoch - 5ms/step\n",
"Epoch 296/300\n",
"15/15 - 0s - loss: 1.6619e-05 - 80ms/epoch - 5ms/step\n",
"Epoch 297/300\n",
"15/15 - 0s - loss: 1.3705e-05 - 81ms/epoch - 5ms/step\n",
"Epoch 298/300\n",
"15/15 - 0s - loss: 1.2345e-05 - 79ms/epoch - 5ms/step\n",
"Epoch 299/300\n",
"15/15 - 0s - loss: 1.1798e-05 - 83ms/epoch - 6ms/step\n",
"Epoch 300/300\n",
"15/15 - 0s - loss: 1.2129e-05 - 81ms/epoch - 5ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x7f9d1c7f72e0>"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "1b3fba76-8599-438c-b3ed-1f52b41dc768",
"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": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"7/7 [==============================] - 1s 5ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f9d1c790df0>"
]
},
"metadata": {},
"execution_count": 19
},
{
"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": "s_ukr55OORqE",
"outputId": "4052d4ad-3bff-444e-d494-67c789ce809d"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712718578.174219\n",
"Wed Apr 10 03:09:38 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": "35fabe50-bf6d-42aa-f12c-85f37b31820a"
},
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712718578.1807635\n",
"Wed Apr 10 03:09:38 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": 1886
},
"id": "95xed6YyDClf",
"outputId": "3768a265-aa24-4113-8897-806af47422ed"
},
"execution_count": 22,
"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",
"288 1.000000\n",
"154 0.761622\n",
"52 0.495627\n",
"50 0.439939\n",
"134 0.355489\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 0.015697\n",
"dtype: float32\n",
"Normalized Saliency Median: Saliency 0.001944\n",
"dtype: float32\n",
"Normalized Saliency Mode: Saliency\n",
"0 0.0\n",
"Normalized Saliency Sum: Saliency 7.534386\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Normalized Saliency Standard Deviation: Saliency 0.072634\n",
"dtype: float32\n",
"Normalized Saliency Skewness: Saliency 9.071964\n",
"dtype: float32\n",
"Normalized Saliency Kurtosis: Saliency 100.143044\n",
"dtype: float32\n",
"Normalized Saliency Variance: Saliency 0.005276\n",
"dtype: float32\n",
"Normalized Saliency Coefficient of Variation: Saliency 462.734497\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.001428\n",
"1 0.025819\n",
"2 0.033251\n",
"3 0.039543\n",
"4 0.039543\n",
".. ...\n",
"475 7.527632\n",
"476 7.527637\n",
"477 7.528818\n",
"478 7.533586\n",
"479 7.534388\n",
"\n",
"[480 rows x 1 columns]\n",
"Mean of Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.000003\n",
"1 0.000054\n",
"2 0.000069\n",
"3 0.000082\n",
"4 0.000082\n",
".. ...\n",
"475 0.015683\n",
"476 0.015683\n",
"477 0.015685\n",
"478 0.015695\n",
"479 0.015697\n",
"\n",
"[480 rows x 1 columns]\n",
"Normalized Saliency Root Mean Square: 0.07423649\n",
"Normalized Saliency 25th Percentile: Saliency 0.000678\n",
"Name: 0.25, dtype: float64\n",
"Normalized Saliency 75th Percentile: Saliency 0.004892\n",
"Name: 0.75, dtype: float64\n",
"Normalized Saliency Interquartile Range: Saliency 0.004214\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": "f0a4e6bf-7868-4315-d865-9d18e5fcaec5"
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712718579.844518\n",
"Wed Apr 10 03:09:39 2024\n"
]
}
]
}
]
}