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{ "cells": [ { "cell_type": "markdown", "id": "8f9f0e7b", "metadata": {}, "source": [ "Display fashion_mnist dataset of clothes from Zalando" ] }, { "cell_type": "code", "execution_count": null, "id": "cc829d9a", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow import keras\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "id": "63348efe", "metadata": {}, "outputs": [], "source": [ "# Load the MNIST Fashion dataset\n", "(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()\n", "# Set the class names\n", "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n", " 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n" ] }, { "cell_type": "code", "execution_count": null, "id": "a6c86027", "metadata": {}, "outputs": [], "source": [ "# print the shape of the numpy arrays\n", "print ('Print shape of pixel data')\n", "print(x_train.shape)\n", "print ('Print shape of labels')\n", "print(y_train.shape)" ] }, { "cell_type": "code", "execution_count": null, "id": "cc58b142", "metadata": {}, "outputs": [], "source": [ "# Normalize pixel values to between 0 and 1\n", "x_train = x_train.astype(\"float32\") / 255.0\n", "x_test = x_test.astype(\"float32\") / 255.0" ] }, { "cell_type": "code", "execution_count": null, "id": "c7976111", "metadata": {}, "outputs": [], "source": [ "# choose an image num to print\n", "num = 20\n", "image = x_train[num]\n", "label = y_train[num]\n", "\n", "print ('Print normailzed pixel data of image ',num, ' :')\n", "print(x_train[num])\n", "print ('Print label of image ',num , ' :' )\n", "print(y_train[num])\n" ] }, { "cell_type": "code", "execution_count": null, "id": "64a46625", "metadata": {}, "outputs": [], "source": [ "plt.figure(figsize=(10,10))\n", "for i in range(25):\n", " plt.subplot(5,5,i+1)\n", " plt.xticks([])\n", " plt.yticks([])\n", " plt.grid(False)\n", " plt.imshow(x_train[i], cmap=plt.cm.binary)\n", " plt.xlabel(class_names[y_train[i]])\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16" } }, "nbformat": 4, "nbformat_minor": 5}
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