Datasets
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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": 35,
"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": 36,
"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": "90ecd167-1989-4fb4-996d-f1a7e01e45ce",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"Dense = tf.keras.layers.Dense\n",
"tn_model = tf.keras.Sequential(\n",
" [\n",
" tf.keras.Input(shape=(2,)),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" # Start Modified Layers\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" TNLayer(),\n",
" # Finish Modified Layers\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_3\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_10 (Dense) (None, 1024) 3072 \n",
" \n",
" dense_11 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_12 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_13 (Dense) (None, 1024) 1049600 \n",
" \n",
" tn_layer_3 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_14 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 3158017 (12.05 MB)\n",
"Trainable params: 3158017 (12.05 MB)\n",
"Non-trainable params: 0 (0.00 Byte)\n",
"_________________________________________________________________\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GWwoYp0WnsLA"
},
"source": [
"# Training a model\n",
"\n",
"You can train the TN model just as you would a normal neural network model! Here, we give an example of how to do it in Keras."
]
},
{
"cell_type": "code",
"metadata": {
"id": "qDFzOC7sDBJ-"
},
"source": [
"X = np.concatenate([np.random.randn(120, 2) + np.array([3, 3]),\n",
" np.random.randn(120, 2) + np.array([-3, -3]),\n",
" np.random.randn(120, 2) + np.array([-3, 3]),\n",
" np.random.randn(120, 2) + np.array([3, -3])])\n",
"\n",
"Y = np.concatenate([np.ones((240)), -np.ones((240))])"
],
"execution_count": 38,
"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": "12d4baea-3f56-4e60-d187-828b2453f885"
},
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712558037.8686552\n",
"Mon Apr 8 06:33:57 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "0ad0e8df-0071-4347-dfdd-c9f1c4ae47b2",
"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": 40,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"15/15 - 1s - loss: 0.4296 - 1s/epoch - 88ms/step\n",
"Epoch 2/300\n",
"15/15 - 0s - loss: 0.0783 - 201ms/epoch - 13ms/step\n",
"Epoch 3/300\n",
"15/15 - 0s - loss: 0.0574 - 184ms/epoch - 12ms/step\n",
"Epoch 4/300\n",
"15/15 - 0s - loss: 0.0453 - 205ms/epoch - 14ms/step\n",
"Epoch 5/300\n",
"15/15 - 0s - loss: 0.0320 - 198ms/epoch - 13ms/step\n",
"Epoch 6/300\n",
"15/15 - 0s - loss: 0.0298 - 202ms/epoch - 13ms/step\n",
"Epoch 7/300\n",
"15/15 - 0s - loss: 0.0272 - 202ms/epoch - 13ms/step\n",
"Epoch 8/300\n",
"15/15 - 0s - loss: 0.0216 - 180ms/epoch - 12ms/step\n",
"Epoch 9/300\n",
"15/15 - 0s - loss: 0.0158 - 187ms/epoch - 12ms/step\n",
"Epoch 10/300\n",
"15/15 - 0s - loss: 0.0092 - 189ms/epoch - 13ms/step\n",
"Epoch 11/300\n",
"15/15 - 0s - loss: 0.0123 - 186ms/epoch - 12ms/step\n",
"Epoch 12/300\n",
"15/15 - 0s - loss: 0.0116 - 189ms/epoch - 13ms/step\n",
"Epoch 13/300\n",
"15/15 - 0s - loss: 0.0064 - 185ms/epoch - 12ms/step\n",
"Epoch 14/300\n",
"15/15 - 0s - loss: 0.0074 - 191ms/epoch - 13ms/step\n",
"Epoch 15/300\n",
"15/15 - 0s - loss: 0.0215 - 192ms/epoch - 13ms/step\n",
"Epoch 16/300\n",
"15/15 - 0s - loss: 0.0089 - 190ms/epoch - 13ms/step\n",
"Epoch 17/300\n",
"15/15 - 0s - loss: 0.0046 - 189ms/epoch - 13ms/step\n",
"Epoch 18/300\n",
"15/15 - 0s - loss: 0.0334 - 192ms/epoch - 13ms/step\n",
"Epoch 19/300\n",
"15/15 - 0s - loss: 0.0114 - 185ms/epoch - 12ms/step\n",
"Epoch 20/300\n",
"15/15 - 0s - loss: 0.0021 - 183ms/epoch - 12ms/step\n",
"Epoch 21/300\n",
"15/15 - 0s - loss: 6.5101e-04 - 180ms/epoch - 12ms/step\n",
"Epoch 22/300\n",
"15/15 - 0s - loss: 1.5571e-04 - 194ms/epoch - 13ms/step\n",
"Epoch 23/300\n",
"15/15 - 0s - loss: 3.9189e-05 - 190ms/epoch - 13ms/step\n",
"Epoch 24/300\n",
"15/15 - 0s - loss: 1.7448e-05 - 187ms/epoch - 12ms/step\n",
"Epoch 25/300\n",
"15/15 - 0s - loss: 2.4584e-05 - 177ms/epoch - 12ms/step\n",
"Epoch 26/300\n",
"15/15 - 0s - loss: 9.0747e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 27/300\n",
"15/15 - 0s - loss: 8.1503e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 28/300\n",
"15/15 - 0s - loss: 4.9635e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 29/300\n",
"15/15 - 0s - loss: 4.4845e-06 - 198ms/epoch - 13ms/step\n",
"Epoch 30/300\n",
"15/15 - 0s - loss: 2.7409e-06 - 192ms/epoch - 13ms/step\n",
"Epoch 31/300\n",
"15/15 - 0s - loss: 2.0436e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 32/300\n",
"15/15 - 0s - loss: 1.7460e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 33/300\n",
"15/15 - 0s - loss: 1.7313e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 34/300\n",
"15/15 - 0s - loss: 1.4847e-06 - 178ms/epoch - 12ms/step\n",
"Epoch 35/300\n",
"15/15 - 0s - loss: 1.0916e-06 - 192ms/epoch - 13ms/step\n",
"Epoch 36/300\n",
"15/15 - 0s - loss: 1.0575e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 37/300\n",
"15/15 - 0s - loss: 1.5081e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 38/300\n",
"15/15 - 0s - loss: 1.5802e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 39/300\n",
"15/15 - 0s - loss: 1.6032e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 40/300\n",
"15/15 - 0s - loss: 1.5616e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 41/300\n",
"15/15 - 0s - loss: 6.1618e-07 - 190ms/epoch - 13ms/step\n",
"Epoch 42/300\n",
"15/15 - 0s - loss: 5.7576e-07 - 190ms/epoch - 13ms/step\n",
"Epoch 43/300\n",
"15/15 - 0s - loss: 1.0165e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 44/300\n",
"15/15 - 0s - loss: 1.7680e-05 - 185ms/epoch - 12ms/step\n",
"Epoch 45/300\n",
"15/15 - 0s - loss: 2.6954e-05 - 181ms/epoch - 12ms/step\n",
"Epoch 46/300\n",
"15/15 - 0s - loss: 1.0460e-04 - 190ms/epoch - 13ms/step\n",
"Epoch 47/300\n",
"15/15 - 0s - loss: 1.2574e-04 - 185ms/epoch - 12ms/step\n",
"Epoch 48/300\n",
"15/15 - 0s - loss: 4.2582e-05 - 194ms/epoch - 13ms/step\n",
"Epoch 49/300\n",
"15/15 - 0s - loss: 1.3570e-05 - 186ms/epoch - 12ms/step\n",
"Epoch 50/300\n",
"15/15 - 0s - loss: 1.0418e-05 - 183ms/epoch - 12ms/step\n",
"Epoch 51/300\n",
"15/15 - 0s - loss: 9.3854e-06 - 196ms/epoch - 13ms/step\n",
"Epoch 52/300\n",
"15/15 - 0s - loss: 7.9505e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 53/300\n",
"15/15 - 0s - loss: 9.9194e-07 - 200ms/epoch - 13ms/step\n",
"Epoch 54/300\n",
"15/15 - 0s - loss: 1.0344e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 55/300\n",
"15/15 - 0s - loss: 3.1597e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 56/300\n",
"15/15 - 0s - loss: 5.0569e-06 - 193ms/epoch - 13ms/step\n",
"Epoch 57/300\n",
"15/15 - 0s - loss: 3.9456e-06 - 183ms/epoch - 12ms/step\n",
"Epoch 58/300\n",
"15/15 - 0s - loss: 5.9632e-06 - 194ms/epoch - 13ms/step\n",
"Epoch 59/300\n",
"15/15 - 0s - loss: 1.3790e-06 - 197ms/epoch - 13ms/step\n",
"Epoch 60/300\n",
"15/15 - 0s - loss: 2.2023e-06 - 195ms/epoch - 13ms/step\n",
"Epoch 61/300\n",
"15/15 - 0s - loss: 2.8905e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 62/300\n",
"15/15 - 0s - loss: 2.5643e-06 - 190ms/epoch - 13ms/step\n",
"Epoch 63/300\n",
"15/15 - 0s - loss: 2.8984e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 64/300\n",
"15/15 - 0s - loss: 2.6803e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 65/300\n",
"15/15 - 0s - loss: 2.4248e-06 - 185ms/epoch - 12ms/step\n",
"Epoch 66/300\n",
"15/15 - 0s - loss: 8.3010e-05 - 185ms/epoch - 12ms/step\n",
"Epoch 67/300\n",
"15/15 - 0s - loss: 8.2001e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 68/300\n",
"15/15 - 0s - loss: 9.3163e-05 - 188ms/epoch - 13ms/step\n",
"Epoch 69/300\n",
"15/15 - 0s - loss: 6.6744e-05 - 188ms/epoch - 13ms/step\n",
"Epoch 70/300\n",
"15/15 - 0s - loss: 1.1654e-05 - 187ms/epoch - 12ms/step\n",
"Epoch 71/300\n",
"15/15 - 0s - loss: 4.2087e-06 - 195ms/epoch - 13ms/step\n",
"Epoch 72/300\n",
"15/15 - 0s - loss: 4.7082e-06 - 195ms/epoch - 13ms/step\n",
"Epoch 73/300\n",
"15/15 - 0s - loss: 2.6626e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 74/300\n",
"15/15 - 0s - loss: 1.7340e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 75/300\n",
"15/15 - 0s - loss: 4.5393e-06 - 182ms/epoch - 12ms/step\n",
"Epoch 76/300\n",
"15/15 - 0s - loss: 2.5889e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 77/300\n",
"15/15 - 0s - loss: 2.5143e-06 - 192ms/epoch - 13ms/step\n",
"Epoch 78/300\n",
"15/15 - 0s - loss: 2.0497e-06 - 176ms/epoch - 12ms/step\n",
"Epoch 79/300\n",
"15/15 - 0s - loss: 3.9078e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 80/300\n",
"15/15 - 0s - loss: 3.2471e-06 - 197ms/epoch - 13ms/step\n",
"Epoch 81/300\n",
"15/15 - 0s - loss: 7.7935e-06 - 196ms/epoch - 13ms/step\n",
"Epoch 82/300\n",
"15/15 - 0s - loss: 5.8462e-06 - 194ms/epoch - 13ms/step\n",
"Epoch 83/300\n",
"15/15 - 0s - loss: 3.0795e-06 - 195ms/epoch - 13ms/step\n",
"Epoch 84/300\n",
"15/15 - 0s - loss: 2.3906e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 85/300\n",
"15/15 - 0s - loss: 1.5463e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 86/300\n",
"15/15 - 0s - loss: 1.0377e-05 - 197ms/epoch - 13ms/step\n",
"Epoch 87/300\n",
"15/15 - 0s - loss: 1.2644e-05 - 202ms/epoch - 13ms/step\n",
"Epoch 88/300\n",
"15/15 - 0s - loss: 9.0340e-06 - 184ms/epoch - 12ms/step\n",
"Epoch 89/300\n",
"15/15 - 0s - loss: 1.5783e-06 - 193ms/epoch - 13ms/step\n",
"Epoch 90/300\n",
"15/15 - 0s - loss: 1.1924e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 91/300\n",
"15/15 - 0s - loss: 3.0402e-06 - 185ms/epoch - 12ms/step\n",
"Epoch 92/300\n",
"15/15 - 0s - loss: 4.9591e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 93/300\n",
"15/15 - 0s - loss: 5.2164e-06 - 183ms/epoch - 12ms/step\n",
"Epoch 94/300\n",
"15/15 - 0s - loss: 1.7738e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 95/300\n",
"15/15 - 0s - loss: 1.2873e-06 - 182ms/epoch - 12ms/step\n",
"Epoch 96/300\n",
"15/15 - 0s - loss: 7.4306e-07 - 187ms/epoch - 12ms/step\n",
"Epoch 97/300\n",
"15/15 - 0s - loss: 1.4977e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 98/300\n",
"15/15 - 0s - loss: 1.2242e-05 - 187ms/epoch - 12ms/step\n",
"Epoch 99/300\n",
"15/15 - 0s - loss: 6.2855e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 100/300\n",
"15/15 - 0s - loss: 3.4137e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 101/300\n",
"15/15 - 0s - loss: 6.8707e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 102/300\n",
"15/15 - 0s - loss: 2.2387e-04 - 191ms/epoch - 13ms/step\n",
"Epoch 103/300\n",
"15/15 - 0s - loss: 5.9916e-04 - 199ms/epoch - 13ms/step\n",
"Epoch 104/300\n",
"15/15 - 0s - loss: 2.2667e-04 - 195ms/epoch - 13ms/step\n",
"Epoch 105/300\n",
"15/15 - 0s - loss: 1.1028e-04 - 188ms/epoch - 13ms/step\n",
"Epoch 106/300\n",
"15/15 - 0s - loss: 3.8122e-05 - 188ms/epoch - 13ms/step\n",
"Epoch 107/300\n",
"15/15 - 0s - loss: 3.1527e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 108/300\n",
"15/15 - 0s - loss: 3.3642e-05 - 180ms/epoch - 12ms/step\n",
"Epoch 109/300\n",
"15/15 - 0s - loss: 7.0959e-05 - 193ms/epoch - 13ms/step\n",
"Epoch 110/300\n",
"15/15 - 0s - loss: 1.1226e-04 - 192ms/epoch - 13ms/step\n",
"Epoch 111/300\n",
"15/15 - 0s - loss: 5.4224e-05 - 185ms/epoch - 12ms/step\n",
"Epoch 112/300\n",
"15/15 - 0s - loss: 4.4090e-05 - 194ms/epoch - 13ms/step\n",
"Epoch 113/300\n",
"15/15 - 0s - loss: 3.7923e-05 - 191ms/epoch - 13ms/step\n",
"Epoch 114/300\n",
"15/15 - 0s - loss: 3.6064e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 115/300\n",
"15/15 - 0s - loss: 1.2832e-06 - 184ms/epoch - 12ms/step\n",
"Epoch 116/300\n",
"15/15 - 0s - loss: 1.4854e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 117/300\n",
"15/15 - 0s - loss: 1.0191e-06 - 193ms/epoch - 13ms/step\n",
"Epoch 118/300\n",
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"Epoch 119/300\n",
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"Epoch 120/300\n",
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"Epoch 121/300\n",
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"Epoch 122/300\n",
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"Epoch 123/300\n",
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"Epoch 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",
"15/15 - 0s - loss: 7.5885e-07 - 189ms/epoch - 13ms/step\n",
"Epoch 136/300\n",
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"Epoch 137/300\n",
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"Epoch 138/300\n",
"15/15 - 0s - loss: 1.0221e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 139/300\n",
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"Epoch 140/300\n",
"15/15 - 0s - loss: 7.0238e-06 - 189ms/epoch - 13ms/step\n",
"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",
"15/15 - 0s - loss: 5.9436e-05 - 180ms/epoch - 12ms/step\n",
"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",
"15/15 - 0s - loss: 6.7882e-05 - 193ms/epoch - 13ms/step\n",
"Epoch 152/300\n",
"15/15 - 0s - loss: 8.5441e-05 - 192ms/epoch - 13ms/step\n",
"Epoch 153/300\n",
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"Epoch 154/300\n",
"15/15 - 0s - loss: 5.1148e-05 - 195ms/epoch - 13ms/step\n",
"Epoch 155/300\n",
"15/15 - 0s - loss: 2.7253e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 156/300\n",
"15/15 - 0s - loss: 1.1951e-05 - 194ms/epoch - 13ms/step\n",
"Epoch 157/300\n",
"15/15 - 0s - loss: 8.8577e-06 - 193ms/epoch - 13ms/step\n",
"Epoch 158/300\n",
"15/15 - 0s - loss: 6.6463e-06 - 194ms/epoch - 13ms/step\n",
"Epoch 159/300\n",
"15/15 - 0s - loss: 5.6882e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 160/300\n",
"15/15 - 0s - loss: 1.9305e-05 - 190ms/epoch - 13ms/step\n",
"Epoch 161/300\n",
"15/15 - 0s - loss: 1.8758e-05 - 191ms/epoch - 13ms/step\n",
"Epoch 162/300\n",
"15/15 - 0s - loss: 1.7258e-04 - 193ms/epoch - 13ms/step\n",
"Epoch 163/300\n",
"15/15 - 0s - loss: 1.7194e-04 - 191ms/epoch - 13ms/step\n",
"Epoch 164/300\n",
"15/15 - 0s - loss: 3.2836e-04 - 190ms/epoch - 13ms/step\n",
"Epoch 165/300\n",
"15/15 - 0s - loss: 4.6629e-04 - 197ms/epoch - 13ms/step\n",
"Epoch 166/300\n",
"15/15 - 0s - loss: 2.2543e-04 - 192ms/epoch - 13ms/step\n",
"Epoch 167/300\n",
"15/15 - 0s - loss: 1.1173e-04 - 195ms/epoch - 13ms/step\n",
"Epoch 168/300\n",
"15/15 - 0s - loss: 5.2963e-05 - 194ms/epoch - 13ms/step\n",
"Epoch 169/300\n",
"15/15 - 0s - loss: 1.9526e-05 - 190ms/epoch - 13ms/step\n",
"Epoch 170/300\n",
"15/15 - 0s - loss: 4.5748e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 171/300\n",
"15/15 - 0s - loss: 2.6322e-06 - 185ms/epoch - 12ms/step\n",
"Epoch 172/300\n",
"15/15 - 0s - loss: 2.4484e-06 - 192ms/epoch - 13ms/step\n",
"Epoch 173/300\n",
"15/15 - 0s - loss: 1.2697e-06 - 194ms/epoch - 13ms/step\n",
"Epoch 174/300\n",
"15/15 - 0s - loss: 1.0113e-06 - 183ms/epoch - 12ms/step\n",
"Epoch 175/300\n",
"15/15 - 0s - loss: 9.1881e-07 - 177ms/epoch - 12ms/step\n",
"Epoch 176/300\n",
"15/15 - 0s - loss: 7.8648e-07 - 182ms/epoch - 12ms/step\n",
"Epoch 177/300\n",
"15/15 - 0s - loss: 4.7581e-07 - 183ms/epoch - 12ms/step\n",
"Epoch 178/300\n",
"15/15 - 0s - loss: 4.1811e-07 - 197ms/epoch - 13ms/step\n",
"Epoch 179/300\n",
"15/15 - 0s - loss: 8.5406e-07 - 185ms/epoch - 12ms/step\n",
"Epoch 180/300\n",
"15/15 - 0s - loss: 9.6913e-07 - 189ms/epoch - 13ms/step\n",
"Epoch 181/300\n",
"15/15 - 0s - loss: 1.5965e-06 - 193ms/epoch - 13ms/step\n",
"Epoch 182/300\n",
"15/15 - 0s - loss: 1.9572e-06 - 204ms/epoch - 14ms/step\n",
"Epoch 183/300\n",
"15/15 - 0s - loss: 4.3940e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 184/300\n",
"15/15 - 0s - loss: 4.5502e-06 - 181ms/epoch - 12ms/step\n",
"Epoch 185/300\n",
"15/15 - 0s - loss: 5.3744e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 186/300\n",
"15/15 - 0s - loss: 9.3889e-06 - 181ms/epoch - 12ms/step\n",
"Epoch 187/300\n",
"15/15 - 0s - loss: 1.0897e-05 - 198ms/epoch - 13ms/step\n",
"Epoch 188/300\n",
"15/15 - 0s - loss: 3.9473e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 189/300\n",
"15/15 - 0s - loss: 1.1040e-06 - 182ms/epoch - 12ms/step\n",
"Epoch 190/300\n",
"15/15 - 0s - loss: 6.2190e-06 - 192ms/epoch - 13ms/step\n",
"Epoch 191/300\n",
"15/15 - 0s - loss: 5.5368e-05 - 186ms/epoch - 12ms/step\n",
"Epoch 192/300\n",
"15/15 - 0s - loss: 7.3528e-05 - 180ms/epoch - 12ms/step\n",
"Epoch 193/300\n",
"15/15 - 0s - loss: 6.0684e-05 - 185ms/epoch - 12ms/step\n",
"Epoch 194/300\n",
"15/15 - 0s - loss: 3.8646e-05 - 188ms/epoch - 13ms/step\n",
"Epoch 195/300\n",
"15/15 - 0s - loss: 3.1695e-05 - 190ms/epoch - 13ms/step\n",
"Epoch 196/300\n",
"15/15 - 0s - loss: 3.2691e-05 - 202ms/epoch - 13ms/step\n",
"Epoch 197/300\n",
"15/15 - 0s - loss: 3.1272e-05 - 182ms/epoch - 12ms/step\n",
"Epoch 198/300\n",
"15/15 - 0s - loss: 2.3967e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 199/300\n",
"15/15 - 0s - loss: 5.4592e-06 - 195ms/epoch - 13ms/step\n",
"Epoch 200/300\n",
"15/15 - 0s - loss: 1.2851e-06 - 181ms/epoch - 12ms/step\n",
"Epoch 201/300\n",
"15/15 - 0s - loss: 8.5098e-07 - 193ms/epoch - 13ms/step\n",
"Epoch 202/300\n",
"15/15 - 0s - loss: 1.1917e-06 - 177ms/epoch - 12ms/step\n",
"Epoch 203/300\n",
"15/15 - 0s - loss: 1.7254e-06 - 179ms/epoch - 12ms/step\n",
"Epoch 204/300\n",
"15/15 - 0s - loss: 2.2643e-07 - 183ms/epoch - 12ms/step\n",
"Epoch 205/300\n",
"15/15 - 0s - loss: 1.4561e-07 - 181ms/epoch - 12ms/step\n",
"Epoch 206/300\n",
"15/15 - 0s - loss: 1.0475e-07 - 185ms/epoch - 12ms/step\n",
"Epoch 207/300\n",
"15/15 - 0s - loss: 1.5852e-07 - 186ms/epoch - 12ms/step\n",
"Epoch 208/300\n",
"15/15 - 0s - loss: 1.8224e-07 - 180ms/epoch - 12ms/step\n",
"Epoch 209/300\n",
"15/15 - 0s - loss: 2.3922e-07 - 184ms/epoch - 12ms/step\n",
"Epoch 210/300\n",
"15/15 - 0s - loss: 5.6019e-07 - 183ms/epoch - 12ms/step\n",
"Epoch 211/300\n",
"15/15 - 0s - loss: 1.3751e-06 - 178ms/epoch - 12ms/step\n",
"Epoch 212/300\n",
"15/15 - 0s - loss: 1.2773e-06 - 178ms/epoch - 12ms/step\n",
"Epoch 213/300\n",
"15/15 - 0s - loss: 1.0616e-06 - 180ms/epoch - 12ms/step\n",
"Epoch 214/300\n",
"15/15 - 0s - loss: 1.1698e-06 - 177ms/epoch - 12ms/step\n",
"Epoch 215/300\n",
"15/15 - 0s - loss: 7.0334e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 216/300\n",
"15/15 - 0s - loss: 9.9544e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 217/300\n",
"15/15 - 0s - loss: 9.5961e-06 - 177ms/epoch - 12ms/step\n",
"Epoch 218/300\n",
"15/15 - 0s - loss: 8.4263e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 219/300\n",
"15/15 - 0s - loss: 4.4950e-05 - 184ms/epoch - 12ms/step\n",
"Epoch 220/300\n",
"15/15 - 0s - loss: 3.9351e-05 - 184ms/epoch - 12ms/step\n",
"Epoch 221/300\n",
"15/15 - 0s - loss: 1.0234e-05 - 190ms/epoch - 13ms/step\n",
"Epoch 222/300\n",
"15/15 - 0s - loss: 2.5016e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 223/300\n",
"15/15 - 0s - loss: 7.8064e-07 - 186ms/epoch - 12ms/step\n",
"Epoch 224/300\n",
"15/15 - 0s - loss: 4.5610e-07 - 183ms/epoch - 12ms/step\n",
"Epoch 225/300\n",
"15/15 - 0s - loss: 4.9718e-07 - 182ms/epoch - 12ms/step\n",
"Epoch 226/300\n",
"15/15 - 0s - loss: 8.2101e-07 - 180ms/epoch - 12ms/step\n",
"Epoch 227/300\n",
"15/15 - 0s - loss: 4.4336e-07 - 185ms/epoch - 12ms/step\n",
"Epoch 228/300\n",
"15/15 - 0s - loss: 1.6072e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 229/300\n",
"15/15 - 0s - loss: 2.5595e-06 - 178ms/epoch - 12ms/step\n",
"Epoch 230/300\n",
"15/15 - 0s - loss: 5.4622e-06 - 190ms/epoch - 13ms/step\n",
"Epoch 231/300\n",
"15/15 - 0s - loss: 6.7577e-06 - 185ms/epoch - 12ms/step\n",
"Epoch 232/300\n",
"15/15 - 0s - loss: 4.0132e-06 - 195ms/epoch - 13ms/step\n",
"Epoch 233/300\n",
"15/15 - 0s - loss: 2.2396e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 234/300\n",
"15/15 - 0s - loss: 8.0027e-07 - 197ms/epoch - 13ms/step\n",
"Epoch 235/300\n",
"15/15 - 0s - loss: 5.6226e-07 - 192ms/epoch - 13ms/step\n",
"Epoch 236/300\n",
"15/15 - 0s - loss: 1.1617e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 237/300\n",
"15/15 - 0s - loss: 3.3872e-06 - 186ms/epoch - 12ms/step\n",
"Epoch 238/300\n",
"15/15 - 0s - loss: 3.7099e-06 - 181ms/epoch - 12ms/step\n",
"Epoch 239/300\n",
"15/15 - 0s - loss: 2.2923e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 240/300\n",
"15/15 - 0s - loss: 2.2427e-06 - 184ms/epoch - 12ms/step\n",
"Epoch 241/300\n",
"15/15 - 0s - loss: 3.9913e-06 - 185ms/epoch - 12ms/step\n",
"Epoch 242/300\n",
"15/15 - 0s - loss: 6.7509e-07 - 190ms/epoch - 13ms/step\n",
"Epoch 243/300\n",
"15/15 - 0s - loss: 9.7690e-07 - 196ms/epoch - 13ms/step\n",
"Epoch 244/300\n",
"15/15 - 0s - loss: 1.8328e-06 - 184ms/epoch - 12ms/step\n",
"Epoch 245/300\n",
"15/15 - 0s - loss: 6.6998e-06 - 182ms/epoch - 12ms/step\n",
"Epoch 246/300\n",
"15/15 - 0s - loss: 1.2469e-05 - 180ms/epoch - 12ms/step\n",
"Epoch 247/300\n",
"15/15 - 0s - loss: 9.5216e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 248/300\n",
"15/15 - 0s - loss: 6.6577e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 249/300\n",
"15/15 - 0s - loss: 1.3174e-06 - 187ms/epoch - 12ms/step\n",
"Epoch 250/300\n",
"15/15 - 0s - loss: 2.0465e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 251/300\n",
"15/15 - 0s - loss: 4.0642e-04 - 187ms/epoch - 12ms/step\n",
"Epoch 252/300\n",
"15/15 - 0s - loss: 0.0013 - 185ms/epoch - 12ms/step\n",
"Epoch 253/300\n",
"15/15 - 0s - loss: 8.1537e-04 - 186ms/epoch - 12ms/step\n",
"Epoch 254/300\n",
"15/15 - 0s - loss: 8.6432e-04 - 191ms/epoch - 13ms/step\n",
"Epoch 255/300\n",
"15/15 - 0s - loss: 0.0042 - 185ms/epoch - 12ms/step\n",
"Epoch 256/300\n",
"15/15 - 0s - loss: 0.0264 - 184ms/epoch - 12ms/step\n",
"Epoch 257/300\n",
"15/15 - 0s - loss: 0.0983 - 188ms/epoch - 13ms/step\n",
"Epoch 258/300\n",
"15/15 - 0s - loss: 0.0313 - 189ms/epoch - 13ms/step\n",
"Epoch 259/300\n",
"15/15 - 0s - loss: 0.0164 - 189ms/epoch - 13ms/step\n",
"Epoch 260/300\n",
"15/15 - 0s - loss: 0.0238 - 200ms/epoch - 13ms/step\n",
"Epoch 261/300\n",
"15/15 - 0s - loss: 0.0039 - 186ms/epoch - 12ms/step\n",
"Epoch 262/300\n",
"15/15 - 0s - loss: 9.3715e-04 - 184ms/epoch - 12ms/step\n",
"Epoch 263/300\n",
"15/15 - 0s - loss: 2.7948e-04 - 199ms/epoch - 13ms/step\n",
"Epoch 264/300\n",
"15/15 - 0s - loss: 2.0170e-04 - 186ms/epoch - 12ms/step\n",
"Epoch 265/300\n",
"15/15 - 0s - loss: 7.1950e-05 - 184ms/epoch - 12ms/step\n",
"Epoch 266/300\n",
"15/15 - 0s - loss: 4.9808e-05 - 185ms/epoch - 12ms/step\n",
"Epoch 267/300\n",
"15/15 - 0s - loss: 2.9839e-05 - 182ms/epoch - 12ms/step\n",
"Epoch 268/300\n",
"15/15 - 0s - loss: 8.5867e-06 - 192ms/epoch - 13ms/step\n",
"Epoch 269/300\n",
"15/15 - 0s - loss: 7.9386e-05 - 199ms/epoch - 13ms/step\n",
"Epoch 270/300\n",
"15/15 - 0s - loss: 4.4635e-05 - 192ms/epoch - 13ms/step\n",
"Epoch 271/300\n",
"15/15 - 0s - loss: 3.9340e-05 - 189ms/epoch - 13ms/step\n",
"Epoch 272/300\n",
"15/15 - 0s - loss: 1.1161e-05 - 178ms/epoch - 12ms/step\n",
"Epoch 273/300\n",
"15/15 - 0s - loss: 5.1339e-06 - 185ms/epoch - 12ms/step\n",
"Epoch 274/300\n",
"15/15 - 0s - loss: 2.2416e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 275/300\n",
"15/15 - 0s - loss: 1.6499e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 276/300\n",
"15/15 - 0s - loss: 1.3978e-06 - 193ms/epoch - 13ms/step\n",
"Epoch 277/300\n",
"15/15 - 0s - loss: 1.2741e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 278/300\n",
"15/15 - 0s - loss: 1.6154e-06 - 183ms/epoch - 12ms/step\n",
"Epoch 279/300\n",
"15/15 - 0s - loss: 7.5531e-07 - 188ms/epoch - 13ms/step\n",
"Epoch 280/300\n",
"15/15 - 0s - loss: 9.0422e-07 - 191ms/epoch - 13ms/step\n",
"Epoch 281/300\n",
"15/15 - 0s - loss: 8.5166e-07 - 195ms/epoch - 13ms/step\n",
"Epoch 282/300\n",
"15/15 - 0s - loss: 5.2687e-07 - 198ms/epoch - 13ms/step\n",
"Epoch 283/300\n",
"15/15 - 0s - loss: 4.9499e-07 - 188ms/epoch - 13ms/step\n",
"Epoch 284/300\n",
"15/15 - 0s - loss: 3.8474e-06 - 192ms/epoch - 13ms/step\n",
"Epoch 285/300\n",
"15/15 - 0s - loss: 5.3604e-06 - 178ms/epoch - 12ms/step\n",
"Epoch 286/300\n",
"15/15 - 0s - loss: 1.6075e-05 - 192ms/epoch - 13ms/step\n",
"Epoch 287/300\n",
"15/15 - 0s - loss: 9.1206e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 288/300\n",
"15/15 - 0s - loss: 7.1723e-06 - 189ms/epoch - 13ms/step\n",
"Epoch 289/300\n",
"15/15 - 0s - loss: 9.8859e-06 - 191ms/epoch - 13ms/step\n",
"Epoch 290/300\n",
"15/15 - 0s - loss: 2.7601e-05 - 196ms/epoch - 13ms/step\n",
"Epoch 291/300\n",
"15/15 - 0s - loss: 1.4416e-05 - 197ms/epoch - 13ms/step\n",
"Epoch 292/300\n",
"15/15 - 0s - loss: 9.9736e-06 - 194ms/epoch - 13ms/step\n",
"Epoch 293/300\n",
"15/15 - 0s - loss: 8.5270e-06 - 182ms/epoch - 12ms/step\n",
"Epoch 294/300\n",
"15/15 - 0s - loss: 1.2650e-05 - 188ms/epoch - 13ms/step\n",
"Epoch 295/300\n",
"15/15 - 0s - loss: 2.4732e-06 - 183ms/epoch - 12ms/step\n",
"Epoch 296/300\n",
"15/15 - 0s - loss: 2.5235e-06 - 193ms/epoch - 13ms/step\n",
"Epoch 297/300\n",
"15/15 - 0s - loss: 1.5025e-06 - 188ms/epoch - 13ms/step\n",
"Epoch 298/300\n",
"15/15 - 0s - loss: 4.1802e-07 - 187ms/epoch - 12ms/step\n",
"Epoch 299/300\n",
"15/15 - 0s - loss: 4.0912e-07 - 186ms/epoch - 12ms/step\n",
"Epoch 300/300\n",
"15/15 - 0s - loss: 3.3231e-07 - 190ms/epoch - 13ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x791a8870d8a0>"
]
},
"metadata": {},
"execution_count": 40
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "34818c12-179e-4a73-be74-1ca7481bd25b",
"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": 41,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"16/16 [==============================] - 0s 5ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x79197c75dde0>"
]
},
"metadata": {},
"execution_count": 41
},
{
"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": "0f9fe440-9177-4761-adfa-7870eb81d1df"
},
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712558096.875877\n",
"Mon Apr 8 06:34:56 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": "aa93a3a8-64bb-4cb9-d0aa-c57ebe5ca129"
},
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712558096.8823998\n",
"Mon Apr 8 06:34:56 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": 1859
},
"id": "95xed6YyDClf",
"outputId": "72345a1a-d870-4cb6-89bb-784e40bf027b"
},
"execution_count": 44,
"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",
"377 1.000000\n",
"327 0.350421\n",
"239 0.234272\n",
"287 0.206172\n",
"370 0.176374\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 0.006949\n",
"dtype: float32\n",
"Normalized Saliency Median: Saliency 0.001556\n",
"dtype: float32\n",
"Normalized Saliency Mode: Saliency\n",
"0 0.001593\n",
"Normalized Saliency Sum: Saliency 3.335494\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Normalized Saliency Standard Deviation: Saliency 0.051421\n",
"dtype: float32\n",
"Normalized Saliency Skewness: Saliency 16.156046\n",
"dtype: float32\n",
"Normalized Saliency Kurtosis: Saliency 296.914551\n",
"dtype: float32\n",
"Normalized Saliency Variance: Saliency 0.002644\n",
"dtype: float32\n",
"Normalized Saliency Coefficient of Variation: Saliency 739.978027\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.001239\n",
"1 0.001979\n",
"2 0.002947\n",
"3 0.003496\n",
"4 0.005373\n",
".. ...\n",
"475 3.321666\n",
"476 3.327693\n",
"477 3.329568\n",
"478 3.332645\n",
"479 3.335494\n",
"\n",
"[480 rows x 1 columns]\n",
"Mean of Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.000003\n",
"1 0.000004\n",
"2 0.000006\n",
"3 0.000007\n",
"4 0.000011\n",
".. ...\n",
"475 0.006920\n",
"476 0.006933\n",
"477 0.006937\n",
"478 0.006943\n",
"479 0.006949\n",
"\n",
"[480 rows x 1 columns]\n",
"Normalized Saliency Root Mean Square: 0.051834974\n",
"Normalized Saliency 25th Percentile: Saliency 0.000922\n",
"Name: 0.25, dtype: float64\n",
"Normalized Saliency 75th Percentile: Saliency 0.002889\n",
"Name: 0.75, dtype: float64\n",
"Normalized Saliency Interquartile Range: Saliency 0.001967\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": "9ec3fc56-e849-44ed-8002-d8e191fb6a2d"
},
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712558098.5333471\n",
"Mon Apr 8 06:34:58 2024\n"
]
}
]
}
]
}