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| a | b/Pneumonia_Detection.ipynb | ||
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| 1 | { |
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| 2 | "nbformat": 4, |
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| 3 | "nbformat_minor": 0, |
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| 4 | "metadata": { |
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| 5 | "kernelspec": { |
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| 6 | "display_name": "Python 3", |
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| 7 | "language": "python", |
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| 8 | "name": "python3" |
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| 9 | }, |
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| 10 | "language_info": { |
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| 11 | "codemirror_mode": { |
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| 12 | "name": "ipython", |
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| 13 | "version": 3 |
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| 14 | }, |
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| 15 | "file_extension": ".py", |
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| 16 | "mimetype": "text/x-python", |
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| 17 | "name": "python", |
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| 18 | "nbconvert_exporter": "python", |
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| 19 | "pygments_lexer": "ipython3", |
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| 20 | "version": "3.7.6" |
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| 21 | }, |
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| 22 | "colab": { |
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| 23 | "name": "Pneumonia Detection.ipynb", |
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| 24 | "provenance": [], |
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| 25 | "include_colab_link": true |
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| 26 | } |
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| 27 | }, |
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| 28 | "cells": [ |
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| 29 | { |
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| 30 | "cell_type": "markdown", |
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| 31 | "metadata": { |
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| 32 | "id": "view-in-github", |
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| 33 | "colab_type": "text" |
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| 34 | }, |
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| 35 | "source": [ |
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| 36 | "<a href=\"https://colab.research.google.com/github/AnkitRajSri/Pneumonia-Detection/blob/master/Pneumonia_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" |
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| 37 | ] |
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| 38 | }, |
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| 39 | { |
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| 40 | "cell_type": "markdown", |
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| 41 | "metadata": { |
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| 42 | "id": "jETrabyQwLW2", |
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| 43 | "colab_type": "text" |
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| 44 | }, |
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| 45 | "source": [ |
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| 46 | "### Importing the libraries" |
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| 47 | ] |
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| 48 | }, |
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| 49 | { |
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| 50 | "cell_type": "code", |
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| 51 | "metadata": { |
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| 52 | "id": "U9qWWT1ewLW6", |
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| 53 | "colab_type": "code", |
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| 54 | "colab": {} |
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| 55 | }, |
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| 56 | "source": [ |
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| 57 | "import os\n", |
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| 58 | "import numpy as np\n", |
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| 59 | "import tensorflow as tf\n", |
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| 60 | "from keras.preprocessing import image\n", |
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| 61 | "from keras.preprocessing.image import ImageDataGenerator" |
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| 62 | ], |
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| 63 | "execution_count": 0, |
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| 64 | "outputs": [] |
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| 65 | }, |
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| 66 | { |
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| 67 | "cell_type": "code", |
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| 68 | "metadata": { |
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| 69 | "id": "9pmdklbswLXK", |
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| 70 | "colab_type": "code", |
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| 71 | "colab": {}, |
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| 72 | "outputId": "f9325e42-749a-48fe-ced1-7268622bee57" |
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| 73 | }, |
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| 74 | "source": [ |
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| 75 | "tf.__version__" |
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| 76 | ], |
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| 77 | "execution_count": 0, |
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| 78 | "outputs": [ |
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| 79 | { |
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| 80 | "output_type": "execute_result", |
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| 81 | "data": { |
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| 82 | "text/plain": [ |
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| 83 | "'2.1.0'" |
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| 84 | ] |
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| 85 | }, |
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| 86 | "metadata": { |
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| 87 | "tags": [] |
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| 88 | }, |
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| 89 | "execution_count": 8 |
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| 90 | } |
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| 91 | ] |
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| 92 | }, |
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| 93 | { |
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| 94 | "cell_type": "markdown", |
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| 95 | "metadata": { |
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| 96 | "id": "LuEvwqwAwLXb", |
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| 97 | "colab_type": "text" |
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| 98 | }, |
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| 99 | "source": [ |
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| 100 | "### Data Preprocessing" |
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| 101 | ] |
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| 102 | }, |
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| 103 | { |
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| 104 | "cell_type": "markdown", |
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| 105 | "metadata": { |
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| 106 | "id": "KxN_t-26wLXd", |
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| 107 | "colab_type": "text" |
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| 108 | }, |
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| 109 | "source": [ |
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| 110 | "##### Preprocessing the training set" |
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| 111 | ] |
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| 112 | }, |
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| 113 | { |
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| 114 | "cell_type": "code", |
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| 115 | "metadata": { |
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| 116 | "id": "k8yFRQyCwLXf", |
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| 117 | "colab_type": "code", |
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| 118 | "colab": {}, |
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| 119 | "outputId": "ad0da542-220e-4857-9927-fdf2394b37e8" |
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| 120 | }, |
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| 121 | "source": [ |
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| 122 | "train_datagen = ImageDataGenerator(\n", |
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| 123 | " rescale=1./255,\n", |
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| 124 | " shear_range=0.2,\n", |
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| 125 | " zoom_range=0.2,\n", |
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| 126 | " horizontal_flip=True)\n", |
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| 127 | "\n", |
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| 128 | "training_set = train_datagen.flow_from_directory(\n", |
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| 129 | " 'train', target_size=(64, 64),\n", |
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| 130 | " batch_size=32,\n", |
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| 131 | " class_mode='binary')" |
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| 132 | ], |
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| 133 | "execution_count": 0, |
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| 134 | "outputs": [ |
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| 135 | { |
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| 136 | "output_type": "stream", |
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| 137 | "text": [ |
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| 138 | "Found 5216 images belonging to 2 classes.\n" |
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| 139 | ], |
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| 140 | "name": "stdout" |
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| 141 | } |
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| 142 | ] |
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| 143 | }, |
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| 144 | { |
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| 145 | "cell_type": "code", |
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| 146 | "metadata": { |
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| 147 | "id": "F7A1jcC-wLXr", |
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| 148 | "colab_type": "code", |
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| 149 | "colab": {}, |
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| 150 | "outputId": "eaebfab0-3fff-46f9-8977-beff4c57259a" |
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| 151 | }, |
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| 152 | "source": [ |
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| 153 | "validation_datagen = ImageDataGenerator(rescale=1./255)\n", |
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| 154 | "\n", |
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| 155 | "validation_set = validation_datagen.flow_from_directory(\n", |
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| 156 | " 'val', target_size=(64, 64),\n", |
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| 157 | " batch_size=32,\n", |
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| 158 | " class_mode='binary')" |
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| 159 | ], |
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| 160 | "execution_count": 0, |
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| 161 | "outputs": [ |
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| 162 | { |
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| 163 | "output_type": "stream", |
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| 164 | "text": [ |
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| 165 | "Found 16 images belonging to 2 classes.\n" |
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| 166 | ], |
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| 167 | "name": "stdout" |
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| 168 | } |
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| 169 | ] |
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| 170 | }, |
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| 171 | { |
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| 172 | "cell_type": "code", |
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| 173 | "metadata": { |
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| 174 | "id": "G-tm781fwLX2", |
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| 175 | "colab_type": "code", |
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| 176 | "colab": {}, |
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| 177 | "outputId": "2516ae5b-ab78-4ddb-db08-3db7cec47f38" |
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| 178 | }, |
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| 179 | "source": [ |
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| 180 | "test_datagen = ImageDataGenerator(rescale=1./255)\n", |
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| 181 | "\n", |
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| 182 | "test_set = test_datagen.flow_from_directory(\n", |
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| 183 | " 'test', target_size=(64, 64),\n", |
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| 184 | " batch_size=32,\n", |
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| 185 | " class_mode='binary')" |
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| 186 | ], |
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| 187 | "execution_count": 0, |
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| 188 | "outputs": [ |
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| 189 | { |
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| 190 | "output_type": "stream", |
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| 191 | "text": [ |
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| 192 | "Found 624 images belonging to 2 classes.\n" |
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| 193 | ], |
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| 194 | "name": "stdout" |
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| 195 | } |
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| 196 | ] |
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| 197 | }, |
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| 198 | { |
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| 199 | "cell_type": "markdown", |
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| 200 | "metadata": { |
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| 201 | "id": "P-AsgjL5wLYA", |
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| 202 | "colab_type": "text" |
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| 203 | }, |
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| 204 | "source": [ |
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| 205 | "### Building the CNN" |
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| 206 | ] |
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| 207 | }, |
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| 208 | { |
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| 209 | "cell_type": "markdown", |
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| 210 | "metadata": { |
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| 211 | "id": "Gs6izkQawLYC", |
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| 212 | "colab_type": "text" |
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| 213 | }, |
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| 214 | "source": [ |
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| 215 | "##### Initializing the CNN and adding the layers" |
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| 216 | ] |
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| 217 | }, |
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| 218 | { |
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| 219 | "cell_type": "code", |
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| 220 | "metadata": { |
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| 221 | "id": "qhRn_PPMwLYD", |
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| 222 | "colab_type": "code", |
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| 223 | "colab": {} |
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| 224 | }, |
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| 225 | "source": [ |
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| 226 | "cnn = tf.keras.Sequential()\n", |
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| 227 | "\n", |
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| 228 | "cnn.add(tf.keras.layers.Conv2D(filters=32, \n", |
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| 229 | " kernel_size=3,\n", |
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| 230 | " activation='relu',\n", |
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| 231 | " input_shape=[64, 64, 3]))\n", |
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| 232 | "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n", |
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| 233 | "cnn.add(tf.keras.layers.Conv2D(filters=32, \n", |
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| 234 | " kernel_size=3,\n", |
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| 235 | " activation='relu'))\n", |
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| 236 | "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\n", |
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| 237 | "cnn.add(tf.keras.layers.Flatten())\n", |
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| 238 | "cnn.add(tf.keras.layers.Dense(units=256, activation='relu'))\n", |
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| 239 | "cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))" |
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| 240 | ], |
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| 241 | "execution_count": 0, |
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| 242 | "outputs": [] |
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| 243 | }, |
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| 244 | { |
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| 245 | "cell_type": "markdown", |
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| 246 | "metadata": { |
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| 247 | "id": "dtzJ9tbqwLYS", |
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| 248 | "colab_type": "text" |
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| 249 | }, |
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| 250 | "source": [ |
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| 251 | "#### Compiling the CNN" |
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| 252 | ] |
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| 253 | }, |
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| 254 | { |
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| 255 | "cell_type": "code", |
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| 256 | "metadata": { |
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| 257 | "id": "EL6XoP-pwLYU", |
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| 258 | "colab_type": "code", |
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| 259 | "colab": {} |
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| 260 | }, |
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| 261 | "source": [ |
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| 262 | "cnn.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])" |
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| 263 | ], |
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| 264 | "execution_count": 0, |
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| 265 | "outputs": [] |
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| 266 | }, |
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| 267 | { |
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| 268 | "cell_type": "markdown", |
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| 269 | "metadata": { |
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| 270 | "id": "EkeukAK2wLYh", |
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| 271 | "colab_type": "text" |
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| 272 | }, |
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| 273 | "source": [ |
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| 274 | "##### Training the CNN on the training set and evaluating it on the validation set" |
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| 275 | ] |
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| 276 | }, |
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| 277 | { |
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| 278 | "cell_type": "code", |
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| 279 | "metadata": { |
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| 280 | "id": "BAKUV9CywLYj", |
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| 281 | "colab_type": "code", |
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| 282 | "colab": {}, |
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| 283 | "outputId": "9432f8a2-8939-40c5-b2ca-d5c057b4e777" |
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| 284 | }, |
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| 285 | "source": [ |
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| 286 | "cnn.fit(x=training_set, validation_data=test_set, epochs=25)" |
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| 287 | ], |
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| 288 | "execution_count": 0, |
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| 289 | "outputs": [ |
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| 290 | { |
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| 291 | "output_type": "stream", |
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| 292 | "text": [ |
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| 293 | "WARNING:tensorflow:sample_weight modes were coerced from\n", |
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| 294 | " ...\n", |
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| 295 | " to \n", |
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| 296 | " ['...']\n", |
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| 297 | "WARNING:tensorflow:sample_weight modes were coerced from\n", |
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| 298 | " ...\n", |
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| 299 | " to \n", |
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| 300 | " ['...']\n", |
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| 301 | "Train for 163 steps, validate for 20 steps\n", |
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| 302 | "Epoch 1/25\n", |
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| 303 | "163/163 [==============================] - 119s 731ms/step - loss: 0.0716 - accuracy: 0.9730 - val_loss: 0.2369 - val_accuracy: 0.9311\n", |
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| 304 | "Epoch 2/25\n", |
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| 305 | "163/163 [==============================] - 124s 761ms/step - loss: 0.0677 - accuracy: 0.9755 - val_loss: 0.4190 - val_accuracy: 0.8974\n", |
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| 306 | "Epoch 3/25\n", |
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| 307 | "163/163 [==============================] - 119s 732ms/step - loss: 0.0667 - accuracy: 0.9747 - val_loss: 0.3130 - val_accuracy: 0.9054\n", |
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| 308 | "Epoch 4/25\n", |
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| 309 | "163/163 [==============================] - 120s 738ms/step - loss: 0.0571 - accuracy: 0.9793 - val_loss: 0.3345 - val_accuracy: 0.8958\n", |
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| 310 | "Epoch 5/25\n", |
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| 311 | "163/163 [==============================] - 118s 725ms/step - loss: 0.0593 - accuracy: 0.9760 - val_loss: 0.2856 - val_accuracy: 0.9151\n", |
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| 312 | "Epoch 6/25\n", |
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| 313 | "163/163 [==============================] - 108s 665ms/step - loss: 0.0584 - accuracy: 0.9799 - val_loss: 0.2743 - val_accuracy: 0.9263\n", |
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| 314 | "Epoch 7/25\n", |
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| 315 | "163/163 [==============================] - 108s 662ms/step - loss: 0.0560 - accuracy: 0.9791 - val_loss: 0.2714 - val_accuracy: 0.9087\n", |
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| 316 | "Epoch 8/25\n", |
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| 317 | "163/163 [==============================] - 113s 693ms/step - loss: 0.0550 - accuracy: 0.9793 - val_loss: 0.4636 - val_accuracy: 0.8734\n", |
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| 318 | "Epoch 9/25\n", |
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| 319 | "163/163 [==============================] - 128s 788ms/step - loss: 0.0581 - accuracy: 0.9789 - val_loss: 0.2477 - val_accuracy: 0.9151\n", |
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| 320 | "Epoch 10/25\n", |
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| 321 | "163/163 [==============================] - 117s 719ms/step - loss: 0.0547 - accuracy: 0.9812 - val_loss: 0.2690 - val_accuracy: 0.9151\n", |
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| 322 | "Epoch 11/25\n", |
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| 323 | "163/163 [==============================] - 110s 678ms/step - loss: 0.0546 - accuracy: 0.9810 - val_loss: 0.2803 - val_accuracy: 0.9167\n", |
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| 324 | "Epoch 12/25\n", |
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| 325 | "163/163 [==============================] - 113s 693ms/step - loss: 0.0574 - accuracy: 0.9776 - val_loss: 0.4011 - val_accuracy: 0.8830\n", |
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| 326 | "Epoch 13/25\n", |
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| 327 | "163/163 [==============================] - 110s 677ms/step - loss: 0.0516 - accuracy: 0.9816 - val_loss: 0.2724 - val_accuracy: 0.9263\n", |
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| 328 | "Epoch 14/25\n", |
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| 329 | "163/163 [==============================] - 121s 744ms/step - loss: 0.0463 - accuracy: 0.9833 - val_loss: 0.3101 - val_accuracy: 0.9183\n", |
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| 330 | "Epoch 15/25\n", |
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| 331 | "163/163 [==============================] - 115s 703ms/step - loss: 0.0514 - accuracy: 0.9806 - val_loss: 0.3937 - val_accuracy: 0.9022\n", |
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| 332 | "Epoch 16/25\n", |
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| 333 | "163/163 [==============================] - 113s 692ms/step - loss: 0.0527 - accuracy: 0.9812 - val_loss: 0.2983 - val_accuracy: 0.9279\n", |
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| 334 | "Epoch 17/25\n", |
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| 335 | "163/163 [==============================] - 124s 758ms/step - loss: 0.0451 - accuracy: 0.9843 - val_loss: 0.2324 - val_accuracy: 0.9279\n", |
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| 336 | "Epoch 18/25\n", |
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| 337 | "163/163 [==============================] - 177s 1s/step - loss: 0.0487 - accuracy: 0.9837 - val_loss: 0.3608 - val_accuracy: 0.9087\n", |
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| 338 | "Epoch 19/25\n", |
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| 339 | "163/163 [==============================] - 199s 1s/step - loss: 0.0475 - accuracy: 0.9841 - val_loss: 0.2932 - val_accuracy: 0.9038\n", |
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| 340 | "Epoch 20/25\n", |
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| 341 | "163/163 [==============================] - 199s 1s/step - loss: 0.0559 - accuracy: 0.9793 - val_loss: 0.2245 - val_accuracy: 0.9183\n", |
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| 342 | "Epoch 21/25\n", |
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| 343 | "163/163 [==============================] - 195s 1s/step - loss: 0.0458 - accuracy: 0.9843 - val_loss: 0.3297 - val_accuracy: 0.9215\n", |
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| 344 | "Epoch 22/25\n", |
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| 345 | "163/163 [==============================] - 192s 1s/step - loss: 0.0539 - accuracy: 0.9806 - val_loss: 0.3120 - val_accuracy: 0.9071\n", |
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| 346 | "Epoch 23/25\n", |
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| 347 | "163/163 [==============================] - 200s 1s/step - loss: 0.0417 - accuracy: 0.9833 - val_loss: 0.6652 - val_accuracy: 0.8574\n", |
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| 348 | "Epoch 24/25\n", |
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| 349 | "163/163 [==============================] - 199s 1s/step - loss: 0.0503 - accuracy: 0.9822 - val_loss: 0.4144 - val_accuracy: 0.8926\n", |
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| 350 | "Epoch 25/25\n", |
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| 351 | "163/163 [==============================] - 201s 1s/step - loss: 0.0442 - accuracy: 0.9850 - val_loss: 0.4151 - val_accuracy: 0.8958\n" |
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| 352 | ], |
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| 353 | "name": "stdout" |
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| 354 | }, |
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| 355 | { |
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| 356 | "output_type": "execute_result", |
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| 357 | "data": { |
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| 358 | "text/plain": [ |
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| 359 | "<tensorflow.python.keras.callbacks.History at 0x226801ea688>" |
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| 360 | ] |
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| 361 | }, |
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| 362 | "metadata": { |
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| 363 | "tags": [] |
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| 364 | }, |
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| 365 | "execution_count": 37 |
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| 366 | } |
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| 367 | ] |
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| 368 | }, |
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| 369 | { |
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| 370 | "cell_type": "markdown", |
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| 371 | "metadata": { |
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| 372 | "id": "AiCsHRNnwLYw", |
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| 373 | "colab_type": "text" |
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| 374 | }, |
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| 375 | "source": [ |
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| 376 | "### Making a prediction" |
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| 377 | ] |
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| 378 | }, |
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| 379 | { |
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| 380 | "cell_type": "code", |
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| 381 | "metadata": { |
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| 382 | "id": "N5SdP_4dwLYy", |
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| 383 | "colab_type": "code", |
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| 384 | "colab": {}, |
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| 385 | "outputId": "08cd96f7-2355-46b3-9f9c-375cc240637c" |
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| 386 | }, |
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| 387 | "source": [ |
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| 388 | "test_image = image.load_img('test/PNEUMONIA/person1_virus_7.jpeg', target_size=(64, 64))\n", |
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| 389 | "test_image" |
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| 390 | ], |
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| 391 | "execution_count": 0, |
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| 392 | "outputs": [ |
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| 393 | { |
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| 394 | "output_type": "execute_result", |
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| 395 | "data": { |
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| 396 | "image/png": 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\n", |
||
| 397 | "text/plain": [ |
||
| 398 | "<PIL.Image.Image image mode=RGB size=64x64 at 0x22680735AC8>" |
||
| 399 | ] |
||
| 400 | }, |
||
| 401 | "metadata": { |
||
| 402 | "tags": [] |
||
| 403 | }, |
||
| 404 | "execution_count": 38 |
||
| 405 | } |
||
| 406 | ] |
||
| 407 | }, |
||
| 408 | { |
||
| 409 | "cell_type": "code", |
||
| 410 | "metadata": { |
||
| 411 | "id": "ojQqZNx1wLY8", |
||
| 412 | "colab_type": "code", |
||
| 413 | "colab": {}, |
||
| 414 | "outputId": "8dd2143c-ac51-49a8-80ae-72a16ecec5e8" |
||
| 415 | }, |
||
| 416 | "source": [ |
||
| 417 | "test_image = image.img_to_array(test_image)\n", |
||
| 418 | "test_image" |
||
| 419 | ], |
||
| 420 | "execution_count": 0, |
||
| 421 | "outputs": [ |
||
| 422 | { |
||
| 423 | "output_type": "execute_result", |
||
| 424 | "data": { |
||
| 425 | "text/plain": [ |
||
| 426 | "array([[[134., 134., 134.],\n", |
||
| 427 | " [ 45., 45., 45.],\n", |
||
| 428 | " [ 49., 49., 49.],\n", |
||
| 429 | " ...,\n", |
||
| 430 | " [222., 222., 222.],\n", |
||
| 431 | " [205., 205., 205.],\n", |
||
| 432 | " [213., 213., 213.]],\n", |
||
| 433 | "\n", |
||
| 434 | " [[ 53., 53., 53.],\n", |
||
| 435 | " [ 40., 40., 40.],\n", |
||
| 436 | " [ 60., 60., 60.],\n", |
||
| 437 | " ...,\n", |
||
| 438 | " [234., 234., 234.],\n", |
||
| 439 | " [209., 209., 209.],\n", |
||
| 440 | " [211., 211., 211.]],\n", |
||
| 441 | "\n", |
||
| 442 | " [[ 53., 53., 53.],\n", |
||
| 443 | " [ 48., 48., 48.],\n", |
||
| 444 | " [ 70., 70., 70.],\n", |
||
| 445 | " ...,\n", |
||
| 446 | " [185., 185., 185.],\n", |
||
| 447 | " [212., 212., 212.],\n", |
||
| 448 | " [216., 216., 216.]],\n", |
||
| 449 | "\n", |
||
| 450 | " ...,\n", |
||
| 451 | "\n", |
||
| 452 | " [[ 24., 24., 24.],\n", |
||
| 453 | " [ 26., 26., 26.],\n", |
||
| 454 | " [ 26., 26., 26.],\n", |
||
| 455 | " ...,\n", |
||
| 456 | " [ 17., 17., 17.],\n", |
||
| 457 | " [ 19., 19., 19.],\n", |
||
| 458 | " [ 18., 18., 18.]],\n", |
||
| 459 | "\n", |
||
| 460 | " [[ 26., 26., 26.],\n", |
||
| 461 | " [ 26., 26., 26.],\n", |
||
| 462 | " [ 26., 26., 26.],\n", |
||
| 463 | " ...,\n", |
||
| 464 | " [ 16., 16., 16.],\n", |
||
| 465 | " [ 19., 19., 19.],\n", |
||
| 466 | " [ 20., 20., 20.]],\n", |
||
| 467 | "\n", |
||
| 468 | " [[ 26., 26., 26.],\n", |
||
| 469 | " [ 26., 26., 26.],\n", |
||
| 470 | " [ 24., 24., 24.],\n", |
||
| 471 | " ...,\n", |
||
| 472 | " [ 16., 16., 16.],\n", |
||
| 473 | " [ 19., 19., 19.],\n", |
||
| 474 | " [ 19., 19., 19.]]], dtype=float32)" |
||
| 475 | ] |
||
| 476 | }, |
||
| 477 | "metadata": { |
||
| 478 | "tags": [] |
||
| 479 | }, |
||
| 480 | "execution_count": 39 |
||
| 481 | } |
||
| 482 | ] |
||
| 483 | }, |
||
| 484 | { |
||
| 485 | "cell_type": "code", |
||
| 486 | "metadata": { |
||
| 487 | "id": "7LjOjssdwLZJ", |
||
| 488 | "colab_type": "code", |
||
| 489 | "colab": {}, |
||
| 490 | "outputId": "869f1d47-d72f-4b60-f4c9-f9693da95a47" |
||
| 491 | }, |
||
| 492 | "source": [ |
||
| 493 | "test_image = np.expand_dims(test_image, axis=0)\n", |
||
| 494 | "test_image" |
||
| 495 | ], |
||
| 496 | "execution_count": 0, |
||
| 497 | "outputs": [ |
||
| 498 | { |
||
| 499 | "output_type": "execute_result", |
||
| 500 | "data": { |
||
| 501 | "text/plain": [ |
||
| 502 | "array([[[[134., 134., 134.],\n", |
||
| 503 | " [ 45., 45., 45.],\n", |
||
| 504 | " [ 49., 49., 49.],\n", |
||
| 505 | " ...,\n", |
||
| 506 | " [222., 222., 222.],\n", |
||
| 507 | " [205., 205., 205.],\n", |
||
| 508 | " [213., 213., 213.]],\n", |
||
| 509 | "\n", |
||
| 510 | " [[ 53., 53., 53.],\n", |
||
| 511 | " [ 40., 40., 40.],\n", |
||
| 512 | " [ 60., 60., 60.],\n", |
||
| 513 | " ...,\n", |
||
| 514 | " [234., 234., 234.],\n", |
||
| 515 | " [209., 209., 209.],\n", |
||
| 516 | " [211., 211., 211.]],\n", |
||
| 517 | "\n", |
||
| 518 | " [[ 53., 53., 53.],\n", |
||
| 519 | " [ 48., 48., 48.],\n", |
||
| 520 | " [ 70., 70., 70.],\n", |
||
| 521 | " ...,\n", |
||
| 522 | " [185., 185., 185.],\n", |
||
| 523 | " [212., 212., 212.],\n", |
||
| 524 | " [216., 216., 216.]],\n", |
||
| 525 | "\n", |
||
| 526 | " ...,\n", |
||
| 527 | "\n", |
||
| 528 | " [[ 24., 24., 24.],\n", |
||
| 529 | " [ 26., 26., 26.],\n", |
||
| 530 | " [ 26., 26., 26.],\n", |
||
| 531 | " ...,\n", |
||
| 532 | " [ 17., 17., 17.],\n", |
||
| 533 | " [ 19., 19., 19.],\n", |
||
| 534 | " [ 18., 18., 18.]],\n", |
||
| 535 | "\n", |
||
| 536 | " [[ 26., 26., 26.],\n", |
||
| 537 | " [ 26., 26., 26.],\n", |
||
| 538 | " [ 26., 26., 26.],\n", |
||
| 539 | " ...,\n", |
||
| 540 | " [ 16., 16., 16.],\n", |
||
| 541 | " [ 19., 19., 19.],\n", |
||
| 542 | " [ 20., 20., 20.]],\n", |
||
| 543 | "\n", |
||
| 544 | " [[ 26., 26., 26.],\n", |
||
| 545 | " [ 26., 26., 26.],\n", |
||
| 546 | " [ 24., 24., 24.],\n", |
||
| 547 | " ...,\n", |
||
| 548 | " [ 16., 16., 16.],\n", |
||
| 549 | " [ 19., 19., 19.],\n", |
||
| 550 | " [ 19., 19., 19.]]]], dtype=float32)" |
||
| 551 | ] |
||
| 552 | }, |
||
| 553 | "metadata": { |
||
| 554 | "tags": [] |
||
| 555 | }, |
||
| 556 | "execution_count": 40 |
||
| 557 | } |
||
| 558 | ] |
||
| 559 | }, |
||
| 560 | { |
||
| 561 | "cell_type": "code", |
||
| 562 | "metadata": { |
||
| 563 | "id": "3chAjHsEwLZV", |
||
| 564 | "colab_type": "code", |
||
| 565 | "colab": {}, |
||
| 566 | "outputId": "f375eb94-3dca-47d3-de20-82ced809c582" |
||
| 567 | }, |
||
| 568 | "source": [ |
||
| 569 | "result = cnn.predict(test_image)\n", |
||
| 570 | "result" |
||
| 571 | ], |
||
| 572 | "execution_count": 0, |
||
| 573 | "outputs": [ |
||
| 574 | { |
||
| 575 | "output_type": "execute_result", |
||
| 576 | "data": { |
||
| 577 | "text/plain": [ |
||
| 578 | "array([[1.]], dtype=float32)" |
||
| 579 | ] |
||
| 580 | }, |
||
| 581 | "metadata": { |
||
| 582 | "tags": [] |
||
| 583 | }, |
||
| 584 | "execution_count": 41 |
||
| 585 | } |
||
| 586 | ] |
||
| 587 | }, |
||
| 588 | { |
||
| 589 | "cell_type": "code", |
||
| 590 | "metadata": { |
||
| 591 | "id": "ew5t1Q2KwLZf", |
||
| 592 | "colab_type": "code", |
||
| 593 | "colab": {}, |
||
| 594 | "outputId": "9a393185-1313-4a92-cdb4-d97a25d307de" |
||
| 595 | }, |
||
| 596 | "source": [ |
||
| 597 | "training_set.class_indices" |
||
| 598 | ], |
||
| 599 | "execution_count": 0, |
||
| 600 | "outputs": [ |
||
| 601 | { |
||
| 602 | "output_type": "execute_result", |
||
| 603 | "data": { |
||
| 604 | "text/plain": [ |
||
| 605 | "{'NORMAL': 0, 'PNEUMONIA': 1}" |
||
| 606 | ] |
||
| 607 | }, |
||
| 608 | "metadata": { |
||
| 609 | "tags": [] |
||
| 610 | }, |
||
| 611 | "execution_count": 42 |
||
| 612 | } |
||
| 613 | ] |
||
| 614 | }, |
||
| 615 | { |
||
| 616 | "cell_type": "code", |
||
| 617 | "metadata": { |
||
| 618 | "id": "0PRTcxumwLZt", |
||
| 619 | "colab_type": "code", |
||
| 620 | "colab": {}, |
||
| 621 | "outputId": "9c48017f-be59-4f78-bc99-feffaa49fcc3" |
||
| 622 | }, |
||
| 623 | "source": [ |
||
| 624 | "if result[0][0] == 1:\n", |
||
| 625 | " prediction = 'Yes'\n", |
||
| 626 | "else:\n", |
||
| 627 | " prediction = 'No'\n", |
||
| 628 | " \n", |
||
| 629 | "print(prediction)" |
||
| 630 | ], |
||
| 631 | "execution_count": 0, |
||
| 632 | "outputs": [ |
||
| 633 | { |
||
| 634 | "output_type": "stream", |
||
| 635 | "text": [ |
||
| 636 | "Yes\n" |
||
| 637 | ], |
||
| 638 | "name": "stdout" |
||
| 639 | } |
||
| 640 | ] |
||
| 641 | }, |
||
| 642 | { |
||
| 643 | "cell_type": "code", |
||
| 644 | "metadata": { |
||
| 645 | "id": "iTJzvNZswLaC", |
||
| 646 | "colab_type": "code", |
||
| 647 | "colab": {}, |
||
| 648 | "outputId": "b973828e-51a1-48f4-deb5-f7fd3acf70e4" |
||
| 649 | }, |
||
| 650 | "source": [ |
||
| 651 | "predictions = cnn.predict(test_set)\n", |
||
| 652 | "predictions" |
||
| 653 | ], |
||
| 654 | "execution_count": 0, |
||
| 655 | "outputs": [ |
||
| 656 | { |
||
| 657 | "output_type": "execute_result", |
||
| 658 | "data": { |
||
| 659 | "text/plain": [ |
||
| 660 | "array([[9.99127209e-01],\n", |
||
| 661 | " [1.00000000e+00],\n", |
||
| 662 | " [9.99928474e-01],\n", |
||
| 663 | " [1.09114801e-03],\n", |
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| 664 | " [9.99967813e-01],\n", |
||
| 665 | " [9.99459326e-01],\n", |
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| 666 | " [9.99999881e-01],\n", |
||
| 667 | " [1.00000000e+00],\n", |
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| 668 | " [9.99017596e-01],\n", |
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| 669 | " [1.00000000e+00],\n", |
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| 670 | " [1.29443448e-04],\n", |
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| 671 | " [9.79463816e-01],\n", |
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| 672 | " [1.37045123e-02],\n", |
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| 673 | " [2.46368870e-02],\n", |
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| 680 | " [6.15069061e-04],\n", |
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| 756 | " [1.17318169e-03],\n", |
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| 776 | " [7.64466599e-02],\n", |
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| 777 | " [1.80779747e-03],\n", |
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| 778 | " [5.24111152e-01],\n", |
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| 792 | " [9.19544101e-01],\n", |
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| 793 | " [9.34216261e-01],\n", |
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| 794 | " [1.67280678e-02],\n", |
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| 795 | " [2.88470894e-01],\n", |
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| 796 | " [1.00000000e+00],\n", |
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| 798 | " [9.99920726e-01],\n", |
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| 799 | " [5.99860251e-02],\n", |
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| 800 | " [1.14257149e-02],\n", |
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| 801 | " [1.00000000e+00],\n", |
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| 802 | " [2.70382106e-01],\n", |
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| 803 | " [9.99763191e-01],\n", |
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| 810 | " [3.24655920e-01],\n", |
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| 818 | " [1.63357193e-03],\n", |
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
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||
| 1263 | " [9.99982119e-01],\n", |
||
| 1264 | " [6.42004192e-01],\n", |
||
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||
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||
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||
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||
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||
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||
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||
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||
| 1280 | " [6.34111580e-04],\n", |
||
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||
| 1282 | " [9.46811199e-01],\n", |
||
| 1283 | " [9.99915361e-01]], dtype=float32)" |
||
| 1284 | ] |
||
| 1285 | }, |
||
| 1286 | "metadata": { |
||
| 1287 | "tags": [] |
||
| 1288 | }, |
||
| 1289 | "execution_count": 44 |
||
| 1290 | } |
||
| 1291 | ] |
||
| 1292 | }, |
||
| 1293 | { |
||
| 1294 | "cell_type": "code", |
||
| 1295 | "metadata": { |
||
| 1296 | "id": "yIkNpzCuwLaO", |
||
| 1297 | "colab_type": "code", |
||
| 1298 | "colab": {} |
||
| 1299 | }, |
||
| 1300 | "source": [ |
||
| 1301 | "" |
||
| 1302 | ], |
||
| 1303 | "execution_count": 0, |
||
| 1304 | "outputs": [] |
||
| 1305 | } |
||
| 1306 | ] |
||
| 1307 | } |