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