|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 1, |
| 6 | + "metadata": {}, |
| 7 | + "outputs": [], |
| 8 | + "source": [ |
| 9 | + "import matplotlib.pyplot as plt" |
| 10 | + ] |
| 11 | + }, |
| 12 | + { |
| 13 | + "cell_type": "code", |
| 14 | + "execution_count": 2, |
| 15 | + "metadata": {}, |
| 16 | + "outputs": [], |
| 17 | + "source": [ |
| 18 | + "import tensorflow as tf" |
| 19 | + ] |
| 20 | + }, |
| 21 | + { |
| 22 | + "cell_type": "code", |
| 23 | + "execution_count": 1, |
| 24 | + "metadata": {}, |
| 25 | + "outputs": [], |
| 26 | + "source": [ |
| 27 | + "from tensorflow import keras" |
| 28 | + ] |
| 29 | + }, |
| 30 | + { |
| 31 | + "cell_type": "code", |
| 32 | + "execution_count": 3, |
| 33 | + "metadata": {}, |
| 34 | + "outputs": [ |
| 35 | + { |
| 36 | + "data": { |
| 37 | + "text/plain": [ |
| 38 | + "'2.7.0'" |
| 39 | + ] |
| 40 | + }, |
| 41 | + "execution_count": 3, |
| 42 | + "metadata": {}, |
| 43 | + "output_type": "execute_result" |
| 44 | + } |
| 45 | + ], |
| 46 | + "source": [ |
| 47 | + "tf.__version__" |
| 48 | + ] |
| 49 | + }, |
| 50 | + { |
| 51 | + "cell_type": "code", |
| 52 | + "execution_count": 4, |
| 53 | + "metadata": {}, |
| 54 | + "outputs": [ |
| 55 | + { |
| 56 | + "data": { |
| 57 | + "text/plain": [ |
| 58 | + "'2.7.0'" |
| 59 | + ] |
| 60 | + }, |
| 61 | + "execution_count": 4, |
| 62 | + "metadata": {}, |
| 63 | + "output_type": "execute_result" |
| 64 | + } |
| 65 | + ], |
| 66 | + "source": [ |
| 67 | + "keras.__version__" |
| 68 | + ] |
| 69 | + }, |
| 70 | + { |
| 71 | + "cell_type": "code", |
| 72 | + "execution_count": 5, |
| 73 | + "metadata": {}, |
| 74 | + "outputs": [ |
| 75 | + { |
| 76 | + "data": { |
| 77 | + "text/plain": [ |
| 78 | + "<tf.Tensor: shape=(), dtype=string, numpy=b'Hello World'>" |
| 79 | + ] |
| 80 | + }, |
| 81 | + "execution_count": 5, |
| 82 | + "metadata": {}, |
| 83 | + "output_type": "execute_result" |
| 84 | + } |
| 85 | + ], |
| 86 | + "source": [ |
| 87 | + "hello = tf.constant(\"Hello World\")\n", |
| 88 | + "hello " |
| 89 | + ] |
| 90 | + }, |
| 91 | + { |
| 92 | + "cell_type": "code", |
| 93 | + "execution_count": 6, |
| 94 | + "metadata": {}, |
| 95 | + "outputs": [ |
| 96 | + { |
| 97 | + "name": "stdout", |
| 98 | + "output_type": "stream", |
| 99 | + "text": [ |
| 100 | + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n", |
| 101 | + "32768/29515 [=================================] - 0s 1us/step\n", |
| 102 | + "40960/29515 [=========================================] - 0s 1us/step\n", |
| 103 | + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n", |
| 104 | + "26427392/26421880 [==============================] - 4s 0us/step\n", |
| 105 | + "26435584/26421880 [==============================] - 4s 0us/step\n", |
| 106 | + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n", |
| 107 | + "16384/5148 [===============================================================================================] - 0s 0s/step\n", |
| 108 | + "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n", |
| 109 | + "4423680/4422102 [==============================] - 1s 0us/step\n", |
| 110 | + "4431872/4422102 [==============================] - 1s 0us/step\n" |
| 111 | + ] |
| 112 | + } |
| 113 | + ], |
| 114 | + "source": [ |
| 115 | + "fashion_mnist = keras.datasets.fashion_mnist\n", |
| 116 | + "(X_train_full, y_train_full),(X_test, y_test) = fashion_mnist.load_data()" |
| 117 | + ] |
| 118 | + }, |
| 119 | + { |
| 120 | + "cell_type": "code", |
| 121 | + "execution_count": 7, |
| 122 | + "metadata": {}, |
| 123 | + "outputs": [ |
| 124 | + { |
| 125 | + "data": { |
| 126 | + "text/plain": [ |
| 127 | + "(60000, 28, 28)" |
| 128 | + ] |
| 129 | + }, |
| 130 | + "execution_count": 7, |
| 131 | + "metadata": {}, |
| 132 | + "output_type": "execute_result" |
| 133 | + } |
| 134 | + ], |
| 135 | + "source": [ |
| 136 | + "X_train_full.shape" |
| 137 | + ] |
| 138 | + }, |
| 139 | + { |
| 140 | + "cell_type": "code", |
| 141 | + "execution_count": 8, |
| 142 | + "metadata": {}, |
| 143 | + "outputs": [ |
| 144 | + { |
| 145 | + "data": { |
| 146 | + "text/plain": [ |
| 147 | + "dtype('uint8')" |
| 148 | + ] |
| 149 | + }, |
| 150 | + "execution_count": 8, |
| 151 | + "metadata": {}, |
| 152 | + "output_type": "execute_result" |
| 153 | + } |
| 154 | + ], |
| 155 | + "source": [ |
| 156 | + "X_train_full.dtype" |
| 157 | + ] |
| 158 | + } |
| 159 | + ], |
| 160 | + "metadata": { |
| 161 | + "interpreter": { |
| 162 | + "hash": "0989d4cb382ec003e6ad9ee0079fe5a34620af18f47069c43c62ee5030c1ec77" |
| 163 | + }, |
| 164 | + "kernelspec": { |
| 165 | + "display_name": "Python 3.7.9 64-bit ('myenv': conda)", |
| 166 | + "name": "python3" |
| 167 | + }, |
| 168 | + "language_info": { |
| 169 | + "codemirror_mode": { |
| 170 | + "name": "ipython", |
| 171 | + "version": 3 |
| 172 | + }, |
| 173 | + "file_extension": ".py", |
| 174 | + "mimetype": "text/x-python", |
| 175 | + "name": "python", |
| 176 | + "nbconvert_exporter": "python", |
| 177 | + "pygments_lexer": "ipython3", |
| 178 | + "version": "3.8.12" |
| 179 | + }, |
| 180 | + "orig_nbformat": 2 |
| 181 | + }, |
| 182 | + "nbformat": 4, |
| 183 | + "nbformat_minor": 2 |
| 184 | +} |
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