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": 123,
"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": 124,
"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": "d76d2f2e-af10-4eba-e577-426381961a08",
"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",
" # Finish Modified Layers\n",
" Dense(1, activation=None)])\n",
"tn_model.summary()"
],
"execution_count": 125,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential_10\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" dense_26 (Dense) (None, 1024) 3072 \n",
" \n",
" tn_layer_28 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_29 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_30 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_31 (TNLayer) (None, 1024) 5120 \n",
" \n",
" tn_layer_32 (TNLayer) (None, 1024) 5120 \n",
" \n",
" dense_27 (Dense) (None, 1) 1025 \n",
" \n",
"=================================================================\n",
"Total params: 29697 (116.00 KB)\n",
"Trainable params: 29697 (116.00 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": 126,
"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": "9d55e6c0-8688-41f5-fa0e-841df6e96fbf"
},
"execution_count": 127,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712552716.9659371\n",
"Mon Apr 8 05:05:16 2024\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "crc0q1vbIyTj",
"outputId": "64b43890-020c-4970-a0f1-20ba2097ee3d",
"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": 128,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/300\n",
"15/15 - 2s - loss: 1.0022 - 2s/epoch - 143ms/step\n",
"Epoch 2/300\n",
"15/15 - 0s - loss: 1.0001 - 101ms/epoch - 7ms/step\n",
"Epoch 3/300\n",
"15/15 - 0s - loss: 1.0005 - 103ms/epoch - 7ms/step\n",
"Epoch 4/300\n",
"15/15 - 0s - loss: 1.0004 - 102ms/epoch - 7ms/step\n",
"Epoch 5/300\n",
"15/15 - 0s - loss: 1.0003 - 104ms/epoch - 7ms/step\n",
"Epoch 6/300\n",
"15/15 - 0s - loss: 1.0003 - 106ms/epoch - 7ms/step\n",
"Epoch 7/300\n",
"15/15 - 0s - loss: 1.0005 - 99ms/epoch - 7ms/step\n",
"Epoch 8/300\n",
"15/15 - 0s - loss: 1.0003 - 93ms/epoch - 6ms/step\n",
"Epoch 9/300\n",
"15/15 - 0s - loss: 1.0008 - 94ms/epoch - 6ms/step\n",
"Epoch 10/300\n",
"15/15 - 0s - loss: 1.0004 - 96ms/epoch - 6ms/step\n",
"Epoch 11/300\n",
"15/15 - 0s - loss: 1.0004 - 95ms/epoch - 6ms/step\n",
"Epoch 12/300\n",
"15/15 - 0s - loss: 1.0002 - 97ms/epoch - 6ms/step\n",
"Epoch 13/300\n",
"15/15 - 0s - loss: 1.0007 - 93ms/epoch - 6ms/step\n",
"Epoch 14/300\n",
"15/15 - 0s - loss: 1.0005 - 98ms/epoch - 7ms/step\n",
"Epoch 15/300\n",
"15/15 - 0s - loss: 1.0003 - 98ms/epoch - 7ms/step\n",
"Epoch 16/300\n",
"15/15 - 0s - loss: 1.0002 - 96ms/epoch - 6ms/step\n",
"Epoch 17/300\n",
"15/15 - 0s - loss: 1.0002 - 95ms/epoch - 6ms/step\n",
"Epoch 18/300\n",
"15/15 - 0s - loss: 1.0002 - 95ms/epoch - 6ms/step\n",
"Epoch 19/300\n",
"15/15 - 0s - loss: 1.0004 - 93ms/epoch - 6ms/step\n",
"Epoch 20/300\n",
"15/15 - 0s - loss: 1.0003 - 95ms/epoch - 6ms/step\n",
"Epoch 21/300\n",
"15/15 - 0s - loss: 1.0002 - 99ms/epoch - 7ms/step\n",
"Epoch 22/300\n",
"15/15 - 0s - loss: 1.0007 - 98ms/epoch - 7ms/step\n",
"Epoch 23/300\n",
"15/15 - 0s - loss: 1.0004 - 97ms/epoch - 6ms/step\n",
"Epoch 24/300\n",
"15/15 - 0s - loss: 1.0001 - 93ms/epoch - 6ms/step\n",
"Epoch 25/300\n",
"15/15 - 0s - loss: 1.0002 - 84ms/epoch - 6ms/step\n",
"Epoch 26/300\n",
"15/15 - 0s - loss: 1.0002 - 87ms/epoch - 6ms/step\n",
"Epoch 27/300\n",
"15/15 - 0s - loss: 1.0003 - 94ms/epoch - 6ms/step\n",
"Epoch 28/300\n",
"15/15 - 0s - loss: 1.0004 - 91ms/epoch - 6ms/step\n",
"Epoch 29/300\n",
"15/15 - 0s - loss: 1.0002 - 92ms/epoch - 6ms/step\n",
"Epoch 30/300\n",
"15/15 - 0s - loss: 1.0002 - 91ms/epoch - 6ms/step\n",
"Epoch 31/300\n",
"15/15 - 0s - loss: 1.0002 - 89ms/epoch - 6ms/step\n",
"Epoch 32/300\n",
"15/15 - 0s - loss: 1.0004 - 93ms/epoch - 6ms/step\n",
"Epoch 33/300\n",
"15/15 - 0s - loss: 1.0001 - 89ms/epoch - 6ms/step\n",
"Epoch 34/300\n",
"15/15 - 0s - loss: 1.0001 - 92ms/epoch - 6ms/step\n",
"Epoch 35/300\n",
"15/15 - 0s - loss: 1.0002 - 91ms/epoch - 6ms/step\n",
"Epoch 36/300\n",
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"Epoch 37/300\n",
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"Epoch 38/300\n",
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"Epoch 39/300\n",
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"Epoch 40/300\n",
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"Epoch 41/300\n",
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"Epoch 42/300\n",
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"Epoch 43/300\n",
"15/15 - 0s - loss: 1.0001 - 90ms/epoch - 6ms/step\n",
"Epoch 44/300\n",
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"Epoch 45/300\n",
"15/15 - 0s - loss: 1.0002 - 93ms/epoch - 6ms/step\n",
"Epoch 46/300\n",
"15/15 - 0s - loss: 1.0001 - 94ms/epoch - 6ms/step\n",
"Epoch 47/300\n",
"15/15 - 0s - loss: 1.0002 - 95ms/epoch - 6ms/step\n",
"Epoch 48/300\n",
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"Epoch 49/300\n",
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"Epoch 50/300\n",
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"Epoch 51/300\n",
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"Epoch 52/300\n",
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"Epoch 53/300\n",
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"Epoch 54/300\n",
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"Epoch 55/300\n",
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"Epoch 56/300\n",
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"Epoch 57/300\n",
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"Epoch 58/300\n",
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"Epoch 59/300\n",
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"Epoch 60/300\n",
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"Epoch 61/300\n",
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"Epoch 62/300\n",
"15/15 - 0s - loss: 1.0001 - 88ms/epoch - 6ms/step\n",
"Epoch 63/300\n",
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"Epoch 64/300\n",
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"Epoch 65/300\n",
"15/15 - 0s - loss: 1.0001 - 88ms/epoch - 6ms/step\n",
"Epoch 66/300\n",
"15/15 - 0s - loss: 1.0001 - 90ms/epoch - 6ms/step\n",
"Epoch 67/300\n",
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"Epoch 68/300\n",
"15/15 - 0s - loss: 1.0002 - 93ms/epoch - 6ms/step\n",
"Epoch 69/300\n",
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"Epoch 70/300\n",
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"Epoch 71/300\n",
"15/15 - 0s - loss: 1.0001 - 88ms/epoch - 6ms/step\n",
"Epoch 72/300\n",
"15/15 - 0s - loss: 1.0002 - 87ms/epoch - 6ms/step\n",
"Epoch 73/300\n",
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"Epoch 74/300\n",
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"Epoch 75/300\n",
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"Epoch 76/300\n",
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"Epoch 77/300\n",
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"Epoch 78/300\n",
"15/15 - 0s - loss: 1.0000 - 89ms/epoch - 6ms/step\n",
"Epoch 79/300\n",
"15/15 - 0s - loss: 1.0001 - 87ms/epoch - 6ms/step\n",
"Epoch 80/300\n",
"15/15 - 0s - loss: 1.0001 - 90ms/epoch - 6ms/step\n",
"Epoch 81/300\n",
"15/15 - 0s - loss: 1.0001 - 87ms/epoch - 6ms/step\n",
"Epoch 82/300\n",
"15/15 - 0s - loss: 1.0001 - 86ms/epoch - 6ms/step\n",
"Epoch 83/300\n",
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"Epoch 84/300\n",
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"Epoch 85/300\n",
"15/15 - 0s - loss: 1.0001 - 87ms/epoch - 6ms/step\n",
"Epoch 86/300\n",
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"Epoch 87/300\n",
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"Epoch 88/300\n",
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"Epoch 89/300\n",
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"Epoch 90/300\n",
"15/15 - 0s - loss: 1.0002 - 87ms/epoch - 6ms/step\n",
"Epoch 91/300\n",
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"Epoch 92/300\n",
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"Epoch 93/300\n",
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"Epoch 94/300\n",
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"Epoch 95/300\n",
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"Epoch 96/300\n",
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"Epoch 97/300\n",
"15/15 - 0s - loss: 1.0001 - 88ms/epoch - 6ms/step\n",
"Epoch 98/300\n",
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"Epoch 99/300\n",
"15/15 - 0s - loss: 1.0001 - 89ms/epoch - 6ms/step\n",
"Epoch 100/300\n",
"15/15 - 0s - loss: 1.0001 - 88ms/epoch - 6ms/step\n",
"Epoch 101/300\n",
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"Epoch 102/300\n",
"15/15 - 0s - loss: 1.0000 - 87ms/epoch - 6ms/step\n",
"Epoch 103/300\n",
"15/15 - 0s - loss: 1.0000 - 87ms/epoch - 6ms/step\n",
"Epoch 104/300\n",
"15/15 - 0s - loss: 1.0002 - 90ms/epoch - 6ms/step\n",
"Epoch 105/300\n",
"15/15 - 0s - loss: 1.0002 - 88ms/epoch - 6ms/step\n",
"Epoch 106/300\n",
"15/15 - 0s - loss: 1.0001 - 86ms/epoch - 6ms/step\n",
"Epoch 107/300\n",
"15/15 - 0s - loss: 1.0001 - 82ms/epoch - 5ms/step\n",
"Epoch 108/300\n",
"15/15 - 0s - loss: 1.0001 - 84ms/epoch - 6ms/step\n",
"Epoch 109/300\n",
"15/15 - 0s - loss: 1.0000 - 87ms/epoch - 6ms/step\n",
"Epoch 110/300\n",
"15/15 - 0s - loss: 1.0000 - 86ms/epoch - 6ms/step\n",
"Epoch 111/300\n",
"15/15 - 0s - loss: 1.0002 - 88ms/epoch - 6ms/step\n",
"Epoch 112/300\n",
"15/15 - 0s - loss: 1.0004 - 86ms/epoch - 6ms/step\n",
"Epoch 113/300\n",
"15/15 - 0s - loss: 1.0001 - 87ms/epoch - 6ms/step\n",
"Epoch 114/300\n",
"15/15 - 0s - loss: 1.0000 - 90ms/epoch - 6ms/step\n",
"Epoch 115/300\n",
"15/15 - 0s - loss: 1.0000 - 88ms/epoch - 6ms/step\n",
"Epoch 116/300\n",
"15/15 - 0s - loss: 1.0000 - 90ms/epoch - 6ms/step\n",
"Epoch 117/300\n",
"15/15 - 0s - loss: 1.0000 - 90ms/epoch - 6ms/step\n",
"Epoch 118/300\n",
"15/15 - 0s - loss: 1.0001 - 93ms/epoch - 6ms/step\n",
"Epoch 119/300\n",
"15/15 - 0s - loss: 1.0000 - 92ms/epoch - 6ms/step\n",
"Epoch 120/300\n",
"15/15 - 0s - loss: 1.0001 - 93ms/epoch - 6ms/step\n",
"Epoch 121/300\n",
"15/15 - 0s - loss: 1.0001 - 89ms/epoch - 6ms/step\n",
"Epoch 122/300\n",
"15/15 - 0s - loss: 1.0001 - 88ms/epoch - 6ms/step\n",
"Epoch 123/300\n",
"15/15 - 0s - loss: 1.0000 - 87ms/epoch - 6ms/step\n",
"Epoch 124/300\n",
"15/15 - 0s - loss: 1.0001 - 85ms/epoch - 6ms/step\n",
"Epoch 125/300\n",
"15/15 - 0s - loss: 1.0001 - 85ms/epoch - 6ms/step\n",
"Epoch 126/300\n",
"15/15 - 0s - loss: 1.0001 - 85ms/epoch - 6ms/step\n",
"Epoch 127/300\n",
"15/15 - 0s - loss: 1.0002 - 87ms/epoch - 6ms/step\n",
"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",
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"Epoch 139/300\n",
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"Epoch 140/300\n",
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"Epoch 141/300\n",
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"Epoch 142/300\n",
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"Epoch 143/300\n",
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"Epoch 144/300\n",
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"Epoch 145/300\n",
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"Epoch 146/300\n",
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"Epoch 147/300\n",
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"Epoch 148/300\n",
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"Epoch 149/300\n",
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"Epoch 150/300\n",
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"Epoch 151/300\n",
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"Epoch 152/300\n",
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"Epoch 153/300\n",
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"Epoch 154/300\n",
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"Epoch 155/300\n",
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"Epoch 156/300\n",
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"Epoch 157/300\n",
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"Epoch 158/300\n",
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"Epoch 159/300\n",
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"Epoch 160/300\n",
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"Epoch 161/300\n",
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"Epoch 162/300\n",
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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 193/300\n",
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"Epoch 201/300\n",
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"Epoch 202/300\n",
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"Epoch 211/300\n",
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"Epoch 224/300\n",
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"Epoch 226/300\n",
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"Epoch 229/300\n",
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"Epoch 230/300\n",
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"Epoch 233/300\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 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",
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"Epoch 300/300\n",
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]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<keras.src.callbacks.History at 0x7ce24445de40>"
]
},
"metadata": {},
"execution_count": 128
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "n-aNP4n3sqG_",
"outputId": "c43eaeb6-2930-466e-9338-d5c37fd1594e",
"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": 129,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"16/16 [==============================] - 1s 4ms/step\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7ce2b0762ef0>"
]
},
"metadata": {},
"execution_count": 129
},
{
"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": "46a97189-5846-48b5-f547-5cb6bfc4c4d9"
},
"execution_count": 130,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712552746.2849565\n",
"Mon Apr 8 05:05:46 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": "6ac0335f-8496-40c1-f9fe-7b46b6831333"
},
"execution_count": 131,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since beginning of run: 1712552746.2910993\n",
"Mon Apr 8 05:05:46 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": 1877
},
"id": "95xed6YyDClf",
"outputId": "fad230fd-f885-4421-d448-d5284c6a902d"
},
"execution_count": 132,
"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": "8cd26f34-8978-4d9c-aca5-8e6c35c63da3"
},
"execution_count": 133,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Time in seconds since end of run: 1712552748.2506363\n",
"Mon Apr 8 05:05:48 2024\n"
]
}
]
}
]
}