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": 134,
"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": 135,
"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": "6eef9b8c-ce7e-47f6-d3ca-1c15b80c088e",
"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",
" TNLayer(),\n",
" TNLayer(),\n",
" # Finish Modified Layers\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 136,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_11\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_28 (Dense) (None, 1024) 3072 \n",
" \n",
" tn_layer_33 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_34 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_35 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_36 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_37 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_38 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_29 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 34817 (136.00 KB)\n",
"Trainable params: 34817 (136.00 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(120, 2) + np.array([3, 3]),\n",
" np.random.randn(120, 2) + np.array([-3, -3]),\n",
" np.random.randn(120, 2) + np.array([-3, 3]),\n",
" np.random.randn(120, 2) + np.array([3, -3])])\n",
"\n",
"Y = np.concatenate([np.ones((240)), -np.ones((240))])"
],
"execution_count": 137,
"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": "b6b1ce64-29bd-4647-c57c-afcde41404ac"
},
"execution_count": 138,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712553038.989898\n",
"Mon Apr 8 05:10:38 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "914da226-e0fa-4a8d-d582-44b705dbbae7",
"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": 139,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"15/15 - 3s - loss: 1.0022 - 3s/epoch - 198ms/step\n",
"Epoch 2/300\n",
"15/15 - 0s - loss: 1.0001 - 112ms/epoch - 7ms/step\n",
"Epoch 3/300\n",
"15/15 - 0s - loss: 1.0005 - 111ms/epoch - 7ms/step\n",
"Epoch 4/300\n",
"15/15 - 0s - loss: 1.0005 - 112ms/epoch - 7ms/step\n",
"Epoch 5/300\n",
"15/15 - 0s - loss: 1.0004 - 115ms/epoch - 8ms/step\n",
"Epoch 6/300\n",
"15/15 - 0s - loss: 1.0004 - 110ms/epoch - 7ms/step\n",
"Epoch 7/300\n",
"15/15 - 0s - loss: 1.0005 - 108ms/epoch - 7ms/step\n",
"Epoch 8/300\n",
"15/15 - 0s - loss: 1.0003 - 109ms/epoch - 7ms/step\n",
"Epoch 9/300\n",
"15/15 - 0s - loss: 1.0009 - 111ms/epoch - 7ms/step\n",
"Epoch 10/300\n",
"15/15 - 0s - loss: 1.0004 - 111ms/epoch - 7ms/step\n",
"Epoch 11/300\n",
"15/15 - 0s - loss: 1.0004 - 108ms/epoch - 7ms/step\n",
"Epoch 12/300\n",
"15/15 - 0s - loss: 1.0003 - 109ms/epoch - 7ms/step\n",
"Epoch 13/300\n",
"15/15 - 0s - loss: 1.0008 - 108ms/epoch - 7ms/step\n",
"Epoch 14/300\n",
"15/15 - 0s - loss: 1.0006 - 110ms/epoch - 7ms/step\n",
"Epoch 15/300\n",
"15/15 - 0s - loss: 1.0004 - 105ms/epoch - 7ms/step\n",
"Epoch 16/300\n",
"15/15 - 0s - loss: 1.0003 - 102ms/epoch - 7ms/step\n",
"Epoch 17/300\n",
"15/15 - 0s - loss: 1.0002 - 109ms/epoch - 7ms/step\n",
"Epoch 18/300\n",
"15/15 - 0s - loss: 1.0003 - 100ms/epoch - 7ms/step\n",
"Epoch 19/300\n",
"15/15 - 0s - loss: 1.0004 - 107ms/epoch - 7ms/step\n",
"Epoch 20/300\n",
"15/15 - 0s - loss: 1.0004 - 106ms/epoch - 7ms/step\n",
"Epoch 21/300\n",
"15/15 - 0s - loss: 1.0003 - 107ms/epoch - 7ms/step\n",
"Epoch 22/300\n",
"15/15 - 0s - loss: 1.0009 - 106ms/epoch - 7ms/step\n",
"Epoch 23/300\n",
"15/15 - 0s - loss: 1.0004 - 106ms/epoch - 7ms/step\n",
"Epoch 24/300\n",
"15/15 - 0s - loss: 1.0001 - 105ms/epoch - 7ms/step\n",
"Epoch 25/300\n",
"15/15 - 0s - loss: 1.0002 - 107ms/epoch - 7ms/step\n",
"Epoch 26/300\n",
"15/15 - 0s - loss: 1.0003 - 105ms/epoch - 7ms/step\n",
"Epoch 27/300\n",
"15/15 - 0s - loss: 1.0003 - 106ms/epoch - 7ms/step\n",
"Epoch 28/300\n",
"15/15 - 0s - loss: 1.0005 - 104ms/epoch - 7ms/step\n",
"Epoch 29/300\n",
"15/15 - 0s - loss: 1.0002 - 108ms/epoch - 7ms/step\n",
"Epoch 30/300\n",
"15/15 - 0s - loss: 1.0002 - 106ms/epoch - 7ms/step\n",
"Epoch 31/300\n",
"15/15 - 0s - loss: 1.0003 - 109ms/epoch - 7ms/step\n",
"Epoch 32/300\n",
"15/15 - 0s - loss: 1.0005 - 110ms/epoch - 7ms/step\n",
"Epoch 33/300\n",
"15/15 - 0s - loss: 1.0001 - 108ms/epoch - 7ms/step\n",
"Epoch 34/300\n",
"15/15 - 0s - loss: 1.0001 - 108ms/epoch - 7ms/step\n",
"Epoch 35/300\n",
"15/15 - 0s - loss: 1.0002 - 109ms/epoch - 7ms/step\n",
"Epoch 36/300\n",
"15/15 - 0s - loss: 1.0003 - 110ms/epoch - 7ms/step\n",
"Epoch 37/300\n",
"15/15 - 0s - loss: 1.0003 - 106ms/epoch - 7ms/step\n",
"Epoch 38/300\n",
"15/15 - 0s - loss: 1.0002 - 105ms/epoch - 7ms/step\n",
"Epoch 39/300\n",
"15/15 - 0s - loss: 1.0003 - 108ms/epoch - 7ms/step\n",
"Epoch 40/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"Epoch 41/300\n",
"15/15 - 0s - loss: 1.0002 - 107ms/epoch - 7ms/step\n",
"Epoch 42/300\n",
"15/15 - 0s - loss: 1.0001 - 108ms/epoch - 7ms/step\n",
"Epoch 43/300\n",
"15/15 - 0s - loss: 1.0001 - 110ms/epoch - 7ms/step\n",
"Epoch 44/300\n",
"15/15 - 0s - loss: 1.0002 - 107ms/epoch - 7ms/step\n",
"Epoch 45/300\n",
"15/15 - 0s - loss: 1.0002 - 107ms/epoch - 7ms/step\n",
"Epoch 46/300\n",
"15/15 - 0s - loss: 1.0002 - 107ms/epoch - 7ms/step\n",
"Epoch 47/300\n",
"15/15 - 0s - loss: 1.0003 - 110ms/epoch - 7ms/step\n",
"Epoch 48/300\n",
"15/15 - 0s - loss: 1.0000 - 111ms/epoch - 7ms/step\n",
"Epoch 49/300\n",
"15/15 - 0s - loss: 1.0002 - 109ms/epoch - 7ms/step\n",
"Epoch 50/300\n",
"15/15 - 0s - loss: 1.0003 - 106ms/epoch - 7ms/step\n",
"Epoch 51/300\n",
"15/15 - 0s - loss: 1.0002 - 106ms/epoch - 7ms/step\n",
"Epoch 52/300\n",
"15/15 - 0s - loss: 1.0002 - 108ms/epoch - 7ms/step\n",
"Epoch 53/300\n",
"15/15 - 0s - loss: 1.0001 - 103ms/epoch - 7ms/step\n",
"Epoch 54/300\n",
"15/15 - 0s - loss: 1.0000 - 105ms/epoch - 7ms/step\n",
"Epoch 55/300\n",
"15/15 - 0s - loss: 1.0003 - 105ms/epoch - 7ms/step\n",
"Epoch 56/300\n",
"15/15 - 0s - loss: 1.0004 - 108ms/epoch - 7ms/step\n",
"Epoch 57/300\n",
"15/15 - 0s - loss: 1.0001 - 108ms/epoch - 7ms/step\n",
"Epoch 58/300\n",
"15/15 - 0s - loss: 1.0001 - 105ms/epoch - 7ms/step\n",
"Epoch 59/300\n",
"15/15 - 0s - loss: 1.0001 - 108ms/epoch - 7ms/step\n",
"Epoch 60/300\n",
"15/15 - 0s - loss: 1.0001 - 103ms/epoch - 7ms/step\n",
"Epoch 61/300\n",
"15/15 - 0s - loss: 1.0003 - 107ms/epoch - 7ms/step\n",
"Epoch 62/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"Epoch 63/300\n",
"15/15 - 0s - loss: 1.0000 - 110ms/epoch - 7ms/step\n",
"Epoch 64/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"Epoch 65/300\n",
"15/15 - 0s - loss: 1.0001 - 107ms/epoch - 7ms/step\n",
"Epoch 66/300\n",
"15/15 - 0s - loss: 1.0001 - 104ms/epoch - 7ms/step\n",
"Epoch 67/300\n",
"15/15 - 0s - loss: 1.0000 - 107ms/epoch - 7ms/step\n",
"Epoch 68/300\n",
"15/15 - 0s - loss: 1.0003 - 108ms/epoch - 7ms/step\n",
"Epoch 69/300\n",
"15/15 - 0s - loss: 1.0001 - 114ms/epoch - 8ms/step\n",
"Epoch 70/300\n",
"15/15 - 0s - loss: 1.0000 - 114ms/epoch - 8ms/step\n",
"Epoch 71/300\n",
"15/15 - 0s - loss: 1.0001 - 111ms/epoch - 7ms/step\n",
"Epoch 72/300\n",
"15/15 - 0s - loss: 1.0002 - 113ms/epoch - 8ms/step\n",
"Epoch 73/300\n",
"15/15 - 0s - loss: 1.0000 - 117ms/epoch - 8ms/step\n",
"Epoch 74/300\n",
"15/15 - 0s - loss: 1.0000 - 115ms/epoch - 8ms/step\n",
"Epoch 75/300\n",
"15/15 - 0s - loss: 1.0000 - 116ms/epoch - 8ms/step\n",
"Epoch 76/300\n",
"15/15 - 0s - loss: 1.0000 - 113ms/epoch - 8ms/step\n",
"Epoch 77/300\n",
"15/15 - 0s - loss: 1.0000 - 112ms/epoch - 7ms/step\n",
"Epoch 78/300\n",
"15/15 - 0s - loss: 1.0000 - 114ms/epoch - 8ms/step\n",
"Epoch 79/300\n",
"15/15 - 0s - loss: 1.0001 - 114ms/epoch - 8ms/step\n",
"Epoch 80/300\n",
"15/15 - 0s - loss: 1.0001 - 116ms/epoch - 8ms/step\n",
"Epoch 81/300\n",
"15/15 - 0s - loss: 1.0001 - 112ms/epoch - 7ms/step\n",
"Epoch 82/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"Epoch 83/300\n",
"15/15 - 0s - loss: 1.0000 - 110ms/epoch - 7ms/step\n",
"Epoch 84/300\n",
"15/15 - 0s - loss: 1.0000 - 113ms/epoch - 8ms/step\n",
"Epoch 85/300\n",
"15/15 - 0s - loss: 1.0002 - 110ms/epoch - 7ms/step\n",
"Epoch 86/300\n",
"15/15 - 0s - loss: 1.0001 - 114ms/epoch - 8ms/step\n",
"Epoch 87/300\n",
"15/15 - 0s - loss: 1.0002 - 121ms/epoch - 8ms/step\n",
"Epoch 88/300\n",
"15/15 - 0s - loss: 1.0001 - 108ms/epoch - 7ms/step\n",
"Epoch 89/300\n",
"15/15 - 0s - loss: 1.0003 - 107ms/epoch - 7ms/step\n",
"Epoch 90/300\n",
"15/15 - 0s - loss: 1.0004 - 105ms/epoch - 7ms/step\n",
"Epoch 91/300\n",
"15/15 - 0s - loss: 1.0001 - 106ms/epoch - 7ms/step\n",
"Epoch 92/300\n",
"15/15 - 0s - loss: 1.0001 - 110ms/epoch - 7ms/step\n",
"Epoch 93/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"Epoch 94/300\n",
"15/15 - 0s - loss: 1.0001 - 107ms/epoch - 7ms/step\n",
"Epoch 95/300\n",
"15/15 - 0s - loss: 1.0002 - 112ms/epoch - 7ms/step\n",
"Epoch 96/300\n",
"15/15 - 0s - loss: 1.0000 - 103ms/epoch - 7ms/step\n",
"Epoch 97/300\n",
"15/15 - 0s - loss: 1.0001 - 112ms/epoch - 7ms/step\n",
"Epoch 98/300\n",
"15/15 - 0s - loss: 1.0001 - 113ms/epoch - 8ms/step\n",
"Epoch 99/300\n",
"15/15 - 0s - loss: 1.0001 - 110ms/epoch - 7ms/step\n",
"Epoch 100/300\n",
"15/15 - 0s - loss: 1.0002 - 113ms/epoch - 8ms/step\n",
"Epoch 101/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"Epoch 102/300\n",
"15/15 - 0s - loss: 1.0000 - 110ms/epoch - 7ms/step\n",
"Epoch 103/300\n",
"15/15 - 0s - loss: 1.0001 - 111ms/epoch - 7ms/step\n",
"Epoch 104/300\n",
"15/15 - 0s - loss: 1.0002 - 108ms/epoch - 7ms/step\n",
"Epoch 105/300\n",
"15/15 - 0s - loss: 1.0001 - 110ms/epoch - 7ms/step\n",
"Epoch 106/300\n",
"15/15 - 0s - loss: 1.0001 - 113ms/epoch - 8ms/step\n",
"Epoch 107/300\n",
"15/15 - 0s - loss: 1.0001 - 110ms/epoch - 7ms/step\n",
"Epoch 108/300\n",
"15/15 - 0s - loss: 1.0001 - 112ms/epoch - 7ms/step\n",
"Epoch 109/300\n",
"15/15 - 0s - loss: 1.0000 - 112ms/epoch - 7ms/step\n",
"Epoch 110/300\n",
"15/15 - 0s - loss: 1.0000 - 111ms/epoch - 7ms/step\n",
"Epoch 111/300\n",
"15/15 - 0s - loss: 1.0002 - 108ms/epoch - 7ms/step\n",
"Epoch 112/300\n",
"15/15 - 0s - loss: 1.0004 - 113ms/epoch - 8ms/step\n",
"Epoch 113/300\n",
"15/15 - 0s - loss: 1.0001 - 112ms/epoch - 7ms/step\n",
"Epoch 114/300\n",
"15/15 - 0s - loss: 1.0000 - 108ms/epoch - 7ms/step\n",
"Epoch 115/300\n",
"15/15 - 0s - loss: 1.0000 - 113ms/epoch - 8ms/step\n",
"Epoch 116/300\n",
"15/15 - 0s - loss: 1.0000 - 109ms/epoch - 7ms/step\n",
"Epoch 117/300\n",
"15/15 - 0s - loss: 1.0000 - 107ms/epoch - 7ms/step\n",
"Epoch 118/300\n",
"15/15 - 0s - loss: 1.0001 - 106ms/epoch - 7ms/step\n",
"Epoch 119/300\n",
"15/15 - 0s - loss: 1.0000 - 104ms/epoch - 7ms/step\n",
"Epoch 120/300\n",
"15/15 - 0s - loss: 1.0001 - 108ms/epoch - 7ms/step\n",
"Epoch 121/300\n",
"15/15 - 0s - loss: 1.0001 - 111ms/epoch - 7ms/step\n",
"Epoch 122/300\n",
"15/15 - 0s - loss: 1.0001 - 112ms/epoch - 7ms/step\n",
"Epoch 123/300\n",
"15/15 - 0s - loss: 1.0000 - 110ms/epoch - 7ms/step\n",
"Epoch 124/300\n",
"15/15 - 0s - loss: 1.0001 - 117ms/epoch - 8ms/step\n",
"Epoch 125/300\n",
"15/15 - 0s - loss: 1.0001 - 116ms/epoch - 8ms/step\n",
"Epoch 126/300\n",
"15/15 - 0s - loss: 1.0001 - 113ms/epoch - 8ms/step\n",
"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",
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"Epoch 138/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"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",
"15/15 - 0s - loss: 1.0002 - 109ms/epoch - 7ms/step\n",
"Epoch 146/300\n",
"15/15 - 0s - loss: 1.0004 - 116ms/epoch - 8ms/step\n",
"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",
"15/15 - 0s - loss: 1.0000 - 112ms/epoch - 7ms/step\n",
"Epoch 152/300\n",
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"Epoch 153/300\n",
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"Epoch 154/300\n",
"15/15 - 0s - loss: 1.0000 - 113ms/epoch - 8ms/step\n",
"Epoch 155/300\n",
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"Epoch 156/300\n",
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"Epoch 157/300\n",
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"Epoch 158/300\n",
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"Epoch 159/300\n",
"15/15 - 0s - loss: 1.0003 - 113ms/epoch - 8ms/step\n",
"Epoch 160/300\n",
"15/15 - 0s - loss: 1.0001 - 109ms/epoch - 7ms/step\n",
"Epoch 161/300\n",
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"Epoch 162/300\n",
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"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",
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"Epoch 168/300\n",
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"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",
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"Epoch 187/300\n",
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"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",
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"Epoch 204/300\n",
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"Epoch 205/300\n",
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"Epoch 206/300\n",
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"Epoch 207/300\n",
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"Epoch 208/300\n",
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"Epoch 210/300\n",
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"Epoch 211/300\n",
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"Epoch 212/300\n",
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"Epoch 213/300\n",
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"Epoch 214/300\n",
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"Epoch 215/300\n",
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"Epoch 217/300\n",
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"Epoch 219/300\n",
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"Epoch 220/300\n",
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"Epoch 221/300\n",
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"Epoch 223/300\n",
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"Epoch 224/300\n",
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"Epoch 225/300\n",
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"Epoch 226/300\n",
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"Epoch 227/300\n",
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"Epoch 228/300\n",
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"Epoch 229/300\n",
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"Epoch 230/300\n",
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"Epoch 231/300\n",
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"Epoch 232/300\n",
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"Epoch 233/300\n",
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"Epoch 234/300\n",
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"Epoch 235/300\n",
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"Epoch 236/300\n",
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"Epoch 237/300\n",
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"Epoch 239/300\n",
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"Epoch 245/300\n",
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"Epoch 255/300\n",
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"Epoch 256/300\n",
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"Epoch 260/300\n",
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"Epoch 261/300\n",
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"Epoch 264/300\n",
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"Epoch 265/300\n",
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"Epoch 266/300\n",
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"Epoch 267/300\n",
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"Epoch 269/300\n",
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"Epoch 270/300\n",
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"Epoch 271/300\n",
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"Epoch 272/300\n",
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"Epoch 273/300\n",
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"Epoch 274/300\n",
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"Epoch 275/300\n",
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"Epoch 276/300\n",
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"Epoch 277/300\n",
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"Epoch 278/300\n",
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"Epoch 279/300\n",
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"Epoch 280/300\n",
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"Epoch 281/300\n",
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"Epoch 282/300\n",
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"Epoch 283/300\n",
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"Epoch 284/300\n",
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"Epoch 285/300\n",
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"Epoch 286/300\n",
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"Epoch 287/300\n",
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"Epoch 288/300\n",
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"Epoch 289/300\n",
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"Epoch 290/300\n",
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"Epoch 291/300\n",
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"Epoch 292/300\n",
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"Epoch 293/300\n",
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"Epoch 294/300\n",
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"Epoch 295/300\n",
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"Epoch 296/300\n",
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"Epoch 297/300\n",
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"Epoch 298/300\n",
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"Epoch 299/300\n",
"15/15 - 0s - loss: 1.0000 - 109ms/epoch - 7ms/step\n",
"Epoch 300/300\n",
"15/15 - 0s - loss: 1.0000 - 105ms/epoch - 7ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x7ce2b054ce80>"
]
},
"metadata": {},
"execution_count": 139
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "b0219040-cd74-415f-84aa-6f55337e1c7b",
"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": 140,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"16/16 [==============================] - 1s 6ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7ce2b0431900>"
]
},
"metadata": {},
"execution_count": 140
},
{
"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": "bedc7faa-796a-4ef2-c53e-2be5a88edb5e"
},
"execution_count": 141,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712553075.8306277\n",
"Mon Apr 8 05:11:15 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": "1d02a907-ac4f-485b-dd5e-87942dc2e82a"
},
"execution_count": 142,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712553075.8361459\n",
"Mon Apr 8 05:11:15 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": 1904
},
"id": "95xed6YyDClf",
"outputId": "04e58e20-eeb4-4671-d1ee-55875e3b2e4e"
},
"execution_count": 143,
"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",
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
"4 NaN\n",
"Normalized Saliency Max: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Min: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Mean: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Median: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Mode: Empty DataFrame\n",
"Columns: [Saliency]\n",
"Index: []\n",
"Normalized Saliency Sum: Saliency 0.0\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Normalized Saliency Standard Deviation: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Skewness: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Kurtosis: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Variance: Saliency NaN\n",
"dtype: float32\n",
"Normalized Saliency Coefficient of Variation: Saliency NaN\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
"4 NaN\n",
".. ...\n",
"475 NaN\n",
"476 NaN\n",
"477 NaN\n",
"478 NaN\n",
"479 NaN\n",
"\n",
"[480 rows x 1 columns]\n",
"Mean of Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 NaN\n",
"1 NaN\n",
"2 NaN\n",
"3 NaN\n",
"4 NaN\n",
".. ...\n",
"475 NaN\n",
"476 NaN\n",
"477 NaN\n",
"478 NaN\n",
"479 NaN\n",
"\n",
"[480 rows x 1 columns]\n",
"Normalized Saliency Root Mean Square: nan\n",
"Normalized Saliency 25th Percentile: Saliency NaN\n",
"Name: 0.25, dtype: float32\n",
"Normalized Saliency 75th Percentile: Saliency NaN\n",
"Name: 0.75, dtype: float32\n",
"Normalized Saliency Interquartile Range: Saliency NaN\n",
"dtype: float32\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": "0da6e8db-83e9-422c-eff3-81dadf2ee3d8"
},
"execution_count": 144,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712553077.9567401\n",
"Mon Apr 8 05:11:17 2024\n"
]
}
]
}
]
}