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": 67,
"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": 68,
"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": "1ac76f98-b378-421c-819c-d7aa9da62d40",
"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",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" Dense(1024, activation=tf.nn.relu),\n",
" # Finish Modified Layers\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 69,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_6\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_22 (Dense) (None, 1024) 3072 \n",
" \n",
" tn_layer_18 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_19 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_20 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_23 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_24 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_25 (Dense) (None, 1024) 1049600 \n",
" \n",
" dense_26 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 3168257 (12.09 MB)\n",
"Trainable params: 3168257 (12.09 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": 70,
"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": "38ef61b7-f4aa-4783-934d-f452565e2c52"
},
"execution_count": 71,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712632000.2626674\n",
"Tue Apr 9 03:06:40 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "64083e62-3942-4100-cb47-ba9d931c4eb9",
"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": 72,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"15/15 - 2s - loss: 1.0035 - 2s/epoch - 131ms/step\n",
"Epoch 2/300\n",
"15/15 - 0s - loss: 1.0007 - 210ms/epoch - 14ms/step\n",
"Epoch 3/300\n",
"15/15 - 0s - loss: 1.0008 - 216ms/epoch - 14ms/step\n",
"Epoch 4/300\n",
"15/15 - 0s - loss: 1.0005 - 212ms/epoch - 14ms/step\n",
"Epoch 5/300\n",
"15/15 - 0s - loss: 1.0004 - 216ms/epoch - 14ms/step\n",
"Epoch 6/300\n",
"15/15 - 0s - loss: 1.0005 - 218ms/epoch - 15ms/step\n",
"Epoch 7/300\n",
"15/15 - 0s - loss: 1.0006 - 213ms/epoch - 14ms/step\n",
"Epoch 8/300\n",
"15/15 - 0s - loss: 0.7357 - 208ms/epoch - 14ms/step\n",
"Epoch 9/300\n",
"15/15 - 0s - loss: 0.0965 - 213ms/epoch - 14ms/step\n",
"Epoch 10/300\n",
"15/15 - 0s - loss: 0.0140 - 211ms/epoch - 14ms/step\n",
"Epoch 11/300\n",
"15/15 - 0s - loss: 0.0066 - 210ms/epoch - 14ms/step\n",
"Epoch 12/300\n",
"15/15 - 0s - loss: 0.0050 - 220ms/epoch - 15ms/step\n",
"Epoch 13/300\n",
"15/15 - 0s - loss: 0.0085 - 212ms/epoch - 14ms/step\n",
"Epoch 14/300\n",
"15/15 - 0s - loss: 0.0020 - 215ms/epoch - 14ms/step\n",
"Epoch 15/300\n",
"15/15 - 0s - loss: 0.0010 - 212ms/epoch - 14ms/step\n",
"Epoch 16/300\n",
"15/15 - 0s - loss: 3.8659e-04 - 217ms/epoch - 14ms/step\n",
"Epoch 17/300\n",
"15/15 - 0s - loss: 1.0408e-04 - 209ms/epoch - 14ms/step\n",
"Epoch 18/300\n",
"15/15 - 0s - loss: 6.7803e-05 - 212ms/epoch - 14ms/step\n",
"Epoch 19/300\n",
"15/15 - 0s - loss: 5.0499e-05 - 227ms/epoch - 15ms/step\n",
"Epoch 20/300\n",
"15/15 - 0s - loss: 2.1185e-05 - 211ms/epoch - 14ms/step\n",
"Epoch 21/300\n",
"15/15 - 0s - loss: 1.7179e-05 - 212ms/epoch - 14ms/step\n",
"Epoch 22/300\n",
"15/15 - 0s - loss: 1.4776e-05 - 215ms/epoch - 14ms/step\n",
"Epoch 23/300\n",
"15/15 - 0s - loss: 1.4688e-05 - 208ms/epoch - 14ms/step\n",
"Epoch 24/300\n",
"15/15 - 0s - loss: 1.1310e-05 - 205ms/epoch - 14ms/step\n",
"Epoch 25/300\n",
"15/15 - 0s - loss: 1.4880e-05 - 200ms/epoch - 13ms/step\n",
"Epoch 26/300\n",
"15/15 - 0s - loss: 2.0525e-05 - 208ms/epoch - 14ms/step\n",
"Epoch 27/300\n",
"15/15 - 0s - loss: 1.2485e-05 - 207ms/epoch - 14ms/step\n",
"Epoch 28/300\n",
"15/15 - 0s - loss: 6.6976e-06 - 207ms/epoch - 14ms/step\n",
"Epoch 29/300\n",
"15/15 - 0s - loss: 5.8495e-06 - 209ms/epoch - 14ms/step\n",
"Epoch 30/300\n",
"15/15 - 0s - loss: 6.6519e-06 - 203ms/epoch - 14ms/step\n",
"Epoch 31/300\n",
"15/15 - 0s - loss: 5.5010e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 32/300\n",
"15/15 - 0s - loss: 5.0889e-06 - 207ms/epoch - 14ms/step\n",
"Epoch 33/300\n",
"15/15 - 0s - loss: 4.2192e-06 - 218ms/epoch - 15ms/step\n",
"Epoch 34/300\n",
"15/15 - 0s - loss: 5.2395e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 35/300\n",
"15/15 - 0s - loss: 2.9000e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 36/300\n",
"15/15 - 0s - loss: 3.6843e-06 - 219ms/epoch - 15ms/step\n",
"Epoch 37/300\n",
"15/15 - 0s - loss: 3.4082e-06 - 212ms/epoch - 14ms/step\n",
"Epoch 38/300\n",
"15/15 - 0s - loss: 2.6450e-06 - 209ms/epoch - 14ms/step\n",
"Epoch 39/300\n",
"15/15 - 0s - loss: 2.9650e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 40/300\n",
"15/15 - 0s - loss: 3.2835e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 41/300\n",
"15/15 - 0s - loss: 2.2324e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 42/300\n",
"15/15 - 0s - loss: 1.9436e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 43/300\n",
"15/15 - 0s - loss: 2.1486e-06 - 210ms/epoch - 14ms/step\n",
"Epoch 44/300\n",
"15/15 - 0s - loss: 1.8305e-06 - 210ms/epoch - 14ms/step\n",
"Epoch 45/300\n",
"15/15 - 0s - loss: 1.6079e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 46/300\n",
"15/15 - 0s - loss: 1.9244e-06 - 223ms/epoch - 15ms/step\n",
"Epoch 47/300\n",
"15/15 - 0s - loss: 1.5710e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 48/300\n",
"15/15 - 0s - loss: 1.7699e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 49/300\n",
"15/15 - 0s - loss: 2.1683e-06 - 219ms/epoch - 15ms/step\n",
"Epoch 50/300\n",
"15/15 - 0s - loss: 1.0601e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 51/300\n",
"15/15 - 0s - loss: 1.4293e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 52/300\n",
"15/15 - 0s - loss: 1.0769e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 53/300\n",
"15/15 - 0s - loss: 1.1029e-06 - 226ms/epoch - 15ms/step\n",
"Epoch 54/300\n",
"15/15 - 0s - loss: 1.6817e-06 - 223ms/epoch - 15ms/step\n",
"Epoch 55/300\n",
"15/15 - 0s - loss: 1.2723e-06 - 224ms/epoch - 15ms/step\n",
"Epoch 56/300\n",
"15/15 - 0s - loss: 1.0406e-06 - 221ms/epoch - 15ms/step\n",
"Epoch 57/300\n",
"15/15 - 0s - loss: 7.3794e-07 - 226ms/epoch - 15ms/step\n",
"Epoch 58/300\n",
"15/15 - 0s - loss: 6.8348e-07 - 226ms/epoch - 15ms/step\n",
"Epoch 59/300\n",
"15/15 - 0s - loss: 6.4114e-07 - 222ms/epoch - 15ms/step\n",
"Epoch 60/300\n",
"15/15 - 0s - loss: 5.6045e-07 - 231ms/epoch - 15ms/step\n",
"Epoch 61/300\n",
"15/15 - 0s - loss: 6.5291e-07 - 220ms/epoch - 15ms/step\n",
"Epoch 62/300\n",
"15/15 - 0s - loss: 5.8320e-07 - 212ms/epoch - 14ms/step\n",
"Epoch 63/300\n",
"15/15 - 0s - loss: 1.1800e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 64/300\n",
"15/15 - 0s - loss: 4.3819e-07 - 209ms/epoch - 14ms/step\n",
"Epoch 65/300\n",
"15/15 - 0s - loss: 7.1175e-07 - 205ms/epoch - 14ms/step\n",
"Epoch 66/300\n",
"15/15 - 0s - loss: 5.9418e-07 - 204ms/epoch - 14ms/step\n",
"Epoch 67/300\n",
"15/15 - 0s - loss: 3.6574e-07 - 210ms/epoch - 14ms/step\n",
"Epoch 68/300\n",
"15/15 - 0s - loss: 5.7552e-07 - 217ms/epoch - 14ms/step\n",
"Epoch 69/300\n",
"15/15 - 0s - loss: 8.4107e-07 - 207ms/epoch - 14ms/step\n",
"Epoch 70/300\n",
"15/15 - 0s - loss: 5.4352e-07 - 208ms/epoch - 14ms/step\n",
"Epoch 71/300\n",
"15/15 - 0s - loss: 2.1304e-06 - 209ms/epoch - 14ms/step\n",
"Epoch 72/300\n",
"15/15 - 0s - loss: 1.4842e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 73/300\n",
"15/15 - 0s - loss: 1.1165e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 74/300\n",
"15/15 - 0s - loss: 3.8918e-07 - 221ms/epoch - 15ms/step\n",
"Epoch 75/300\n",
"15/15 - 0s - loss: 3.1255e-07 - 222ms/epoch - 15ms/step\n",
"Epoch 76/300\n",
"15/15 - 0s - loss: 3.2594e-07 - 216ms/epoch - 14ms/step\n",
"Epoch 77/300\n",
"15/15 - 0s - loss: 4.6438e-07 - 222ms/epoch - 15ms/step\n",
"Epoch 78/300\n",
"15/15 - 0s - loss: 1.1496e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 79/300\n",
"15/15 - 0s - loss: 5.7168e-07 - 218ms/epoch - 15ms/step\n",
"Epoch 80/300\n",
"15/15 - 0s - loss: 9.4330e-07 - 225ms/epoch - 15ms/step\n",
"Epoch 81/300\n",
"15/15 - 0s - loss: 2.0512e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 82/300\n",
"15/15 - 0s - loss: 3.0143e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 83/300\n",
"15/15 - 0s - loss: 4.9788e-07 - 219ms/epoch - 15ms/step\n",
"Epoch 84/300\n",
"15/15 - 0s - loss: 4.2469e-07 - 214ms/epoch - 14ms/step\n",
"Epoch 85/300\n",
"15/15 - 0s - loss: 3.1515e-07 - 219ms/epoch - 15ms/step\n",
"Epoch 86/300\n",
"15/15 - 0s - loss: 5.7052e-07 - 225ms/epoch - 15ms/step\n",
"Epoch 87/300\n",
"15/15 - 0s - loss: 1.3224e-06 - 224ms/epoch - 15ms/step\n",
"Epoch 88/300\n",
"15/15 - 0s - loss: 1.2972e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 89/300\n",
"15/15 - 0s - loss: 1.9102e-06 - 212ms/epoch - 14ms/step\n",
"Epoch 90/300\n",
"15/15 - 0s - loss: 9.0187e-07 - 226ms/epoch - 15ms/step\n",
"Epoch 91/300\n",
"15/15 - 0s - loss: 3.4419e-07 - 220ms/epoch - 15ms/step\n",
"Epoch 92/300\n",
"15/15 - 0s - loss: 7.8029e-07 - 218ms/epoch - 15ms/step\n",
"Epoch 93/300\n",
"15/15 - 0s - loss: 1.8812e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 94/300\n",
"15/15 - 0s - loss: 2.0788e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 95/300\n",
"15/15 - 0s - loss: 2.6789e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 96/300\n",
"15/15 - 0s - loss: 1.2999e-06 - 219ms/epoch - 15ms/step\n",
"Epoch 97/300\n",
"15/15 - 0s - loss: 1.0528e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 98/300\n",
"15/15 - 0s - loss: 6.5473e-07 - 216ms/epoch - 14ms/step\n",
"Epoch 99/300\n",
"15/15 - 0s - loss: 8.6112e-07 - 210ms/epoch - 14ms/step\n",
"Epoch 100/300\n",
"15/15 - 0s - loss: 6.0324e-07 - 223ms/epoch - 15ms/step\n",
"Epoch 101/300\n",
"15/15 - 0s - loss: 1.3148e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 102/300\n",
"15/15 - 0s - loss: 1.6901e-06 - 207ms/epoch - 14ms/step\n",
"Epoch 103/300\n",
"15/15 - 0s - loss: 1.2186e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 104/300\n",
"15/15 - 0s - loss: 6.2559e-07 - 208ms/epoch - 14ms/step\n",
"Epoch 105/300\n",
"15/15 - 0s - loss: 1.0925e-06 - 205ms/epoch - 14ms/step\n",
"Epoch 106/300\n",
"15/15 - 0s - loss: 5.6406e-07 - 212ms/epoch - 14ms/step\n",
"Epoch 107/300\n",
"15/15 - 0s - loss: 2.6247e-07 - 208ms/epoch - 14ms/step\n",
"Epoch 108/300\n",
"15/15 - 0s - loss: 3.0022e-07 - 211ms/epoch - 14ms/step\n",
"Epoch 109/300\n",
"15/15 - 0s - loss: 2.4898e-07 - 218ms/epoch - 15ms/step\n",
"Epoch 110/300\n",
"15/15 - 0s - loss: 3.4035e-07 - 212ms/epoch - 14ms/step\n",
"Epoch 111/300\n",
"15/15 - 0s - loss: 1.4895e-07 - 213ms/epoch - 14ms/step\n",
"Epoch 112/300\n",
"15/15 - 0s - loss: 6.3275e-07 - 209ms/epoch - 14ms/step\n",
"Epoch 113/300\n",
"15/15 - 0s - loss: 5.3832e-07 - 215ms/epoch - 14ms/step\n",
"Epoch 114/300\n",
"15/15 - 0s - loss: 5.4055e-07 - 225ms/epoch - 15ms/step\n",
"Epoch 115/300\n",
"15/15 - 0s - loss: 3.3979e-07 - 222ms/epoch - 15ms/step\n",
"Epoch 116/300\n",
"15/15 - 0s - loss: 2.0836e-07 - 220ms/epoch - 15ms/step\n",
"Epoch 117/300\n",
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"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",
"15/15 - 0s - loss: 1.5595e-06 - 225ms/epoch - 15ms/step\n",
"Epoch 135/300\n",
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"Epoch 136/300\n",
"15/15 - 0s - loss: 1.9872e-06 - 219ms/epoch - 15ms/step\n",
"Epoch 137/300\n",
"15/15 - 0s - loss: 1.6313e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 138/300\n",
"15/15 - 0s - loss: 1.0250e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 139/300\n",
"15/15 - 0s - loss: 3.1918e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 140/300\n",
"15/15 - 0s - loss: 9.3841e-06 - 207ms/epoch - 14ms/step\n",
"Epoch 141/300\n",
"15/15 - 0s - loss: 9.1300e-06 - 208ms/epoch - 14ms/step\n",
"Epoch 142/300\n",
"15/15 - 0s - loss: 2.8924e-05 - 213ms/epoch - 14ms/step\n",
"Epoch 143/300\n",
"15/15 - 0s - loss: 2.2413e-05 - 217ms/epoch - 14ms/step\n",
"Epoch 144/300\n",
"15/15 - 0s - loss: 2.1889e-05 - 212ms/epoch - 14ms/step\n",
"Epoch 145/300\n",
"15/15 - 0s - loss: 6.5140e-05 - 204ms/epoch - 14ms/step\n",
"Epoch 146/300\n",
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"Epoch 147/300\n",
"15/15 - 0s - loss: 1.9270e-05 - 213ms/epoch - 14ms/step\n",
"Epoch 148/300\n",
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"Epoch 149/300\n",
"15/15 - 0s - loss: 2.1969e-06 - 212ms/epoch - 14ms/step\n",
"Epoch 150/300\n",
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"Epoch 151/300\n",
"15/15 - 0s - loss: 3.9271e-06 - 206ms/epoch - 14ms/step\n",
"Epoch 152/300\n",
"15/15 - 0s - loss: 2.3726e-06 - 210ms/epoch - 14ms/step\n",
"Epoch 153/300\n",
"15/15 - 0s - loss: 4.6958e-07 - 208ms/epoch - 14ms/step\n",
"Epoch 154/300\n",
"15/15 - 0s - loss: 3.2007e-07 - 216ms/epoch - 14ms/step\n",
"Epoch 155/300\n",
"15/15 - 0s - loss: 6.2525e-07 - 218ms/epoch - 15ms/step\n",
"Epoch 156/300\n",
"15/15 - 0s - loss: 1.2364e-06 - 221ms/epoch - 15ms/step\n",
"Epoch 157/300\n",
"15/15 - 0s - loss: 2.6422e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 158/300\n",
"15/15 - 0s - loss: 3.4406e-06 - 205ms/epoch - 14ms/step\n",
"Epoch 159/300\n",
"15/15 - 0s - loss: 1.2027e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 160/300\n",
"15/15 - 0s - loss: 1.6466e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 161/300\n",
"15/15 - 0s - loss: 9.8636e-07 - 213ms/epoch - 14ms/step\n",
"Epoch 162/300\n",
"15/15 - 0s - loss: 7.3531e-07 - 208ms/epoch - 14ms/step\n",
"Epoch 163/300\n",
"15/15 - 0s - loss: 1.0572e-07 - 215ms/epoch - 14ms/step\n",
"Epoch 164/300\n",
"15/15 - 0s - loss: 2.1081e-07 - 209ms/epoch - 14ms/step\n",
"Epoch 165/300\n",
"15/15 - 0s - loss: 3.1109e-07 - 211ms/epoch - 14ms/step\n",
"Epoch 166/300\n",
"15/15 - 0s - loss: 1.0728e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 167/300\n",
"15/15 - 0s - loss: 3.6950e-07 - 212ms/epoch - 14ms/step\n",
"Epoch 168/300\n",
"15/15 - 0s - loss: 2.0889e-07 - 211ms/epoch - 14ms/step\n",
"Epoch 169/300\n",
"15/15 - 0s - loss: 1.7553e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 170/300\n",
"15/15 - 0s - loss: 1.7341e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 171/300\n",
"15/15 - 0s - loss: 3.2844e-06 - 206ms/epoch - 14ms/step\n",
"Epoch 172/300\n",
"15/15 - 0s - loss: 2.6141e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 173/300\n",
"15/15 - 0s - loss: 2.8774e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 174/300\n",
"15/15 - 0s - loss: 2.5104e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 175/300\n",
"15/15 - 0s - loss: 2.8620e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 176/300\n",
"15/15 - 0s - loss: 3.3825e-05 - 215ms/epoch - 14ms/step\n",
"Epoch 177/300\n",
"15/15 - 0s - loss: 2.7779e-05 - 218ms/epoch - 15ms/step\n",
"Epoch 178/300\n",
"15/15 - 0s - loss: 1.3039e-05 - 209ms/epoch - 14ms/step\n",
"Epoch 179/300\n",
"15/15 - 0s - loss: 4.0934e-05 - 216ms/epoch - 14ms/step\n",
"Epoch 180/300\n",
"15/15 - 0s - loss: 6.3333e-05 - 211ms/epoch - 14ms/step\n",
"Epoch 181/300\n",
"15/15 - 0s - loss: 9.0939e-05 - 207ms/epoch - 14ms/step\n",
"Epoch 182/300\n",
"15/15 - 0s - loss: 8.1266e-05 - 208ms/epoch - 14ms/step\n",
"Epoch 183/300\n",
"15/15 - 0s - loss: 5.7833e-05 - 218ms/epoch - 15ms/step\n",
"Epoch 184/300\n",
"15/15 - 0s - loss: 2.2672e-05 - 224ms/epoch - 15ms/step\n",
"Epoch 185/300\n",
"15/15 - 0s - loss: 9.0902e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 186/300\n",
"15/15 - 0s - loss: 1.1610e-05 - 215ms/epoch - 14ms/step\n",
"Epoch 187/300\n",
"15/15 - 0s - loss: 3.4313e-05 - 222ms/epoch - 15ms/step\n",
"Epoch 188/300\n",
"15/15 - 0s - loss: 4.1383e-05 - 207ms/epoch - 14ms/step\n",
"Epoch 189/300\n",
"15/15 - 0s - loss: 1.8765e-05 - 215ms/epoch - 14ms/step\n",
"Epoch 190/300\n",
"15/15 - 0s - loss: 1.3273e-05 - 222ms/epoch - 15ms/step\n",
"Epoch 191/300\n",
"15/15 - 0s - loss: 1.5692e-05 - 220ms/epoch - 15ms/step\n",
"Epoch 192/300\n",
"15/15 - 0s - loss: 1.7913e-05 - 217ms/epoch - 14ms/step\n",
"Epoch 193/300\n",
"15/15 - 0s - loss: 9.0434e-05 - 223ms/epoch - 15ms/step\n",
"Epoch 194/300\n",
"15/15 - 0s - loss: 4.0499e-04 - 228ms/epoch - 15ms/step\n",
"Epoch 195/300\n",
"15/15 - 0s - loss: 2.4267e-04 - 214ms/epoch - 14ms/step\n",
"Epoch 196/300\n",
"15/15 - 0s - loss: 6.3741e-05 - 229ms/epoch - 15ms/step\n",
"Epoch 197/300\n",
"15/15 - 0s - loss: 4.2577e-05 - 222ms/epoch - 15ms/step\n",
"Epoch 198/300\n",
"15/15 - 0s - loss: 1.9206e-05 - 218ms/epoch - 15ms/step\n",
"Epoch 199/300\n",
"15/15 - 0s - loss: 7.0605e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 200/300\n",
"15/15 - 0s - loss: 4.3818e-06 - 224ms/epoch - 15ms/step\n",
"Epoch 201/300\n",
"15/15 - 0s - loss: 2.6779e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 202/300\n",
"15/15 - 0s - loss: 3.3201e-06 - 241ms/epoch - 16ms/step\n",
"Epoch 203/300\n",
"15/15 - 0s - loss: 1.6693e-06 - 229ms/epoch - 15ms/step\n",
"Epoch 204/300\n",
"15/15 - 0s - loss: 1.2018e-06 - 210ms/epoch - 14ms/step\n",
"Epoch 205/300\n",
"15/15 - 0s - loss: 8.0889e-07 - 218ms/epoch - 15ms/step\n",
"Epoch 206/300\n",
"15/15 - 0s - loss: 2.7988e-07 - 232ms/epoch - 15ms/step\n",
"Epoch 207/300\n",
"15/15 - 0s - loss: 1.4330e-07 - 216ms/epoch - 14ms/step\n",
"Epoch 208/300\n",
"15/15 - 0s - loss: 2.0310e-07 - 231ms/epoch - 15ms/step\n",
"Epoch 209/300\n",
"15/15 - 0s - loss: 1.7351e-07 - 216ms/epoch - 14ms/step\n",
"Epoch 210/300\n",
"15/15 - 0s - loss: 1.8353e-07 - 215ms/epoch - 14ms/step\n",
"Epoch 211/300\n",
"15/15 - 0s - loss: 9.6045e-07 - 219ms/epoch - 15ms/step\n",
"Epoch 212/300\n",
"15/15 - 0s - loss: 2.2864e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 213/300\n",
"15/15 - 0s - loss: 2.3039e-06 - 219ms/epoch - 15ms/step\n",
"Epoch 214/300\n",
"15/15 - 0s - loss: 1.6817e-06 - 222ms/epoch - 15ms/step\n",
"Epoch 215/300\n",
"15/15 - 0s - loss: 1.1842e-05 - 224ms/epoch - 15ms/step\n",
"Epoch 216/300\n",
"15/15 - 0s - loss: 3.1993e-05 - 226ms/epoch - 15ms/step\n",
"Epoch 217/300\n",
"15/15 - 0s - loss: 2.4462e-05 - 221ms/epoch - 15ms/step\n",
"Epoch 218/300\n",
"15/15 - 0s - loss: 8.4272e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 219/300\n",
"15/15 - 0s - loss: 6.1416e-06 - 219ms/epoch - 15ms/step\n",
"Epoch 220/300\n",
"15/15 - 0s - loss: 9.0328e-06 - 212ms/epoch - 14ms/step\n",
"Epoch 221/300\n",
"15/15 - 0s - loss: 5.1868e-06 - 218ms/epoch - 15ms/step\n",
"Epoch 222/300\n",
"15/15 - 0s - loss: 8.6356e-07 - 212ms/epoch - 14ms/step\n",
"Epoch 223/300\n",
"15/15 - 0s - loss: 3.8953e-07 - 223ms/epoch - 15ms/step\n",
"Epoch 224/300\n",
"15/15 - 0s - loss: 6.7790e-07 - 211ms/epoch - 14ms/step\n",
"Epoch 225/300\n",
"15/15 - 0s - loss: 5.6780e-07 - 210ms/epoch - 14ms/step\n",
"Epoch 226/300\n",
"15/15 - 0s - loss: 2.0534e-05 - 218ms/epoch - 15ms/step\n",
"Epoch 227/300\n",
"15/15 - 0s - loss: 1.8788e-05 - 214ms/epoch - 14ms/step\n",
"Epoch 228/300\n",
"15/15 - 0s - loss: 2.1939e-05 - 220ms/epoch - 15ms/step\n",
"Epoch 229/300\n",
"15/15 - 0s - loss: 1.5986e-05 - 213ms/epoch - 14ms/step\n",
"Epoch 230/300\n",
"15/15 - 0s - loss: 5.2792e-05 - 214ms/epoch - 14ms/step\n",
"Epoch 231/300\n",
"15/15 - 0s - loss: 3.4207e-04 - 207ms/epoch - 14ms/step\n",
"Epoch 232/300\n",
"15/15 - 0s - loss: 2.8852e-04 - 215ms/epoch - 14ms/step\n",
"Epoch 233/300\n",
"15/15 - 0s - loss: 5.0480e-05 - 212ms/epoch - 14ms/step\n",
"Epoch 234/300\n",
"15/15 - 0s - loss: 2.4073e-05 - 230ms/epoch - 15ms/step\n",
"Epoch 235/300\n",
"15/15 - 0s - loss: 1.7500e-05 - 212ms/epoch - 14ms/step\n",
"Epoch 236/300\n",
"15/15 - 0s - loss: 8.0883e-06 - 210ms/epoch - 14ms/step\n",
"Epoch 237/300\n",
"15/15 - 0s - loss: 4.9633e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 238/300\n",
"15/15 - 0s - loss: 1.0019e-05 - 220ms/epoch - 15ms/step\n",
"Epoch 239/300\n",
"15/15 - 0s - loss: 5.0873e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 240/300\n",
"15/15 - 0s - loss: 3.0124e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 241/300\n",
"15/15 - 0s - loss: 2.1901e-06 - 227ms/epoch - 15ms/step\n",
"Epoch 242/300\n",
"15/15 - 0s - loss: 2.8596e-06 - 222ms/epoch - 15ms/step\n",
"Epoch 243/300\n",
"15/15 - 0s - loss: 3.4952e-06 - 218ms/epoch - 15ms/step\n",
"Epoch 244/300\n",
"15/15 - 0s - loss: 4.3632e-06 - 223ms/epoch - 15ms/step\n",
"Epoch 245/300\n",
"15/15 - 0s - loss: 4.5922e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 246/300\n",
"15/15 - 0s - loss: 1.2495e-06 - 206ms/epoch - 14ms/step\n",
"Epoch 247/300\n",
"15/15 - 0s - loss: 1.6412e-06 - 225ms/epoch - 15ms/step\n",
"Epoch 248/300\n",
"15/15 - 0s - loss: 1.8811e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 249/300\n",
"15/15 - 0s - loss: 3.2497e-06 - 208ms/epoch - 14ms/step\n",
"Epoch 250/300\n",
"15/15 - 0s - loss: 3.9365e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 251/300\n",
"15/15 - 0s - loss: 1.3412e-05 - 216ms/epoch - 14ms/step\n",
"Epoch 252/300\n",
"15/15 - 0s - loss: 9.8598e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 253/300\n",
"15/15 - 0s - loss: 4.0685e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 254/300\n",
"15/15 - 0s - loss: 1.4190e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 255/300\n",
"15/15 - 0s - loss: 7.5167e-07 - 223ms/epoch - 15ms/step\n",
"Epoch 256/300\n",
"15/15 - 0s - loss: 2.7946e-07 - 216ms/epoch - 14ms/step\n",
"Epoch 257/300\n",
"15/15 - 0s - loss: 1.5494e-07 - 222ms/epoch - 15ms/step\n",
"Epoch 258/300\n",
"15/15 - 0s - loss: 4.3300e-07 - 211ms/epoch - 14ms/step\n",
"Epoch 259/300\n",
"15/15 - 0s - loss: 2.2933e-07 - 204ms/epoch - 14ms/step\n",
"Epoch 260/300\n",
"15/15 - 0s - loss: 1.1742e-06 - 203ms/epoch - 14ms/step\n",
"Epoch 261/300\n",
"15/15 - 0s - loss: 1.0040e-06 - 213ms/epoch - 14ms/step\n",
"Epoch 262/300\n",
"15/15 - 0s - loss: 1.1727e-06 - 212ms/epoch - 14ms/step\n",
"Epoch 263/300\n",
"15/15 - 0s - loss: 2.6091e-06 - 214ms/epoch - 14ms/step\n",
"Epoch 264/300\n",
"15/15 - 0s - loss: 8.0253e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 265/300\n",
"15/15 - 0s - loss: 2.3669e-06 - 221ms/epoch - 15ms/step\n",
"Epoch 266/300\n",
"15/15 - 0s - loss: 3.8771e-05 - 220ms/epoch - 15ms/step\n",
"Epoch 267/300\n",
"15/15 - 0s - loss: 2.0598e-04 - 219ms/epoch - 15ms/step\n",
"Epoch 268/300\n",
"15/15 - 0s - loss: 0.0027 - 214ms/epoch - 14ms/step\n",
"Epoch 269/300\n",
"15/15 - 0s - loss: 0.0442 - 207ms/epoch - 14ms/step\n",
"Epoch 270/300\n",
"15/15 - 0s - loss: 0.0425 - 239ms/epoch - 16ms/step\n",
"Epoch 271/300\n",
"15/15 - 0s - loss: 0.0560 - 218ms/epoch - 15ms/step\n",
"Epoch 272/300\n",
"15/15 - 0s - loss: 0.0152 - 215ms/epoch - 14ms/step\n",
"Epoch 273/300\n",
"15/15 - 0s - loss: 0.0129 - 215ms/epoch - 14ms/step\n",
"Epoch 274/300\n",
"15/15 - 0s - loss: 0.0147 - 216ms/epoch - 14ms/step\n",
"Epoch 275/300\n",
"15/15 - 0s - loss: 0.0085 - 216ms/epoch - 14ms/step\n",
"Epoch 276/300\n",
"15/15 - 0s - loss: 0.0082 - 222ms/epoch - 15ms/step\n",
"Epoch 277/300\n",
"15/15 - 0s - loss: 0.0076 - 217ms/epoch - 14ms/step\n",
"Epoch 278/300\n",
"15/15 - 0s - loss: 0.0072 - 219ms/epoch - 15ms/step\n",
"Epoch 279/300\n",
"15/15 - 0s - loss: 2.7397e-04 - 215ms/epoch - 14ms/step\n",
"Epoch 280/300\n",
"15/15 - 0s - loss: 6.9670e-05 - 219ms/epoch - 15ms/step\n",
"Epoch 281/300\n",
"15/15 - 0s - loss: 3.7510e-05 - 213ms/epoch - 14ms/step\n",
"Epoch 282/300\n",
"15/15 - 0s - loss: 2.2864e-05 - 215ms/epoch - 14ms/step\n",
"Epoch 283/300\n",
"15/15 - 0s - loss: 2.1669e-05 - 211ms/epoch - 14ms/step\n",
"Epoch 284/300\n",
"15/15 - 0s - loss: 1.5721e-05 - 212ms/epoch - 14ms/step\n",
"Epoch 285/300\n",
"15/15 - 0s - loss: 8.1817e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 286/300\n",
"15/15 - 0s - loss: 8.8259e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 287/300\n",
"15/15 - 0s - loss: 7.5924e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 288/300\n",
"15/15 - 0s - loss: 5.0791e-06 - 220ms/epoch - 15ms/step\n",
"Epoch 289/300\n",
"15/15 - 0s - loss: 5.6192e-06 - 204ms/epoch - 14ms/step\n",
"Epoch 290/300\n",
"15/15 - 0s - loss: 6.5361e-06 - 211ms/epoch - 14ms/step\n",
"Epoch 291/300\n",
"15/15 - 0s - loss: 4.1514e-06 - 215ms/epoch - 14ms/step\n",
"Epoch 292/300\n",
"15/15 - 0s - loss: 3.3945e-06 - 212ms/epoch - 14ms/step\n",
"Epoch 293/300\n",
"15/15 - 0s - loss: 4.4908e-06 - 227ms/epoch - 15ms/step\n",
"Epoch 294/300\n",
"15/15 - 0s - loss: 3.8311e-06 - 209ms/epoch - 14ms/step\n",
"Epoch 295/300\n",
"15/15 - 0s - loss: 5.3792e-06 - 217ms/epoch - 14ms/step\n",
"Epoch 296/300\n",
"15/15 - 0s - loss: 3.0223e-06 - 204ms/epoch - 14ms/step\n",
"Epoch 297/300\n",
"15/15 - 0s - loss: 3.4727e-06 - 216ms/epoch - 14ms/step\n",
"Epoch 298/300\n",
"15/15 - 0s - loss: 2.0669e-06 - 210ms/epoch - 14ms/step\n",
"Epoch 299/300\n",
"15/15 - 0s - loss: 2.4571e-06 - 208ms/epoch - 14ms/step\n",
"Epoch 300/300\n",
"15/15 - 0s - loss: 2.5337e-06 - 219ms/epoch - 15ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x7f3b9c3b5c60>"
]
},
"metadata": {},
"execution_count": 72
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "552791f9-fbae-446b-e119-4c638bf3a125",
"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": 73,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"16/16 [==============================] - 0s 6ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7f3a587ba4d0>"
]
},
"metadata": {},
"execution_count": 73
},
{
"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": "e204cd9d-c722-41c8-d19e-b6e15a1b971f"
},
"execution_count": 74,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712632068.1383727\n",
"Tue Apr 9 03:07:48 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": "de797f77-6124-4802-df20-1d85a84c8898"
},
"execution_count": 75,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712632068.1437232\n",
"Tue Apr 9 03:07:48 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": 1914
},
"id": "95xed6YyDClf",
"outputId": "228ce65d-4fb9-4a21-bf87-cc28329900f3"
},
"execution_count": 76,
"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",
"239 1.000000\n",
"377 0.363529\n",
"327 0.337469\n",
"307 0.144629\n",
"287 0.096564\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 0.008464\n",
"dtype: float32\n",
"Normalized Saliency Median: Saliency 0.002957\n",
"dtype: float32\n",
"Normalized Saliency Mode: Saliency\n",
"0 0.001517\n",
"1 0.003418\n",
"2 0.003778\n",
"3 0.005802\n",
"Normalized Saliency Sum: Saliency 4.062913\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Normalized Saliency Standard Deviation: Saliency 0.051485\n",
"dtype: float32\n",
"Normalized Saliency Skewness: Saliency 16.264872\n",
"dtype: float32\n",
"Normalized Saliency Kurtosis: Saliency 296.22757\n",
"dtype: float32\n",
"Normalized Saliency Variance: Saliency 0.002651\n",
"dtype: float32\n",
"Normalized Saliency Coefficient of Variation: Saliency 608.255127\n",
"dtype: float32\n",
"#\n",
"#\n",
"#\n",
"Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.005108\n",
"1 0.006376\n",
"2 0.008419\n",
"3 0.008764\n",
"4 0.010040\n",
".. ...\n",
"475 4.048548\n",
"476 4.057882\n",
"477 4.060352\n",
"478 4.061864\n",
"479 4.062913\n",
"\n",
"[480 rows x 1 columns]\n",
"Mean of Cumulative Sum of Normalized Saliency Values: Saliency\n",
"0 0.000011\n",
"1 0.000013\n",
"2 0.000018\n",
"3 0.000018\n",
"4 0.000021\n",
".. ...\n",
"475 0.008434\n",
"476 0.008454\n",
"477 0.008459\n",
"478 0.008462\n",
"479 0.008464\n",
"\n",
"[480 rows x 1 columns]\n",
"Normalized Saliency Root Mean Square: 0.05212336\n",
"Normalized Saliency 25th Percentile: Saliency 0.001779\n",
"Name: 0.25, dtype: float64\n",
"Normalized Saliency 75th Percentile: Saliency 0.005117\n",
"Name: 0.75, dtype: float64\n",
"Normalized Saliency Interquartile Range: Saliency 0.003337\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": "5d9139d2-be7a-4a06-a5ea-28f91d60ddad"
},
"execution_count": 77,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712632070.328312\n",
"Tue Apr 9 03:07:50 2024\n"
]
}
]
}
]
}