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134 lines
2.9 KiB
134 lines
2.9 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3644475e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Display hand writing dataset"
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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": "8125479b",
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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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"import numpy as np\n",
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"import matplotlib.pyplot as plt"
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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": "d45b964f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load training dataset of 60000 images with greyscale values in 28 x 28\n",
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"# and labels \n",
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"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()"
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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": "fa8ae2a6",
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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": "be70973e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# normalize pixel to 0-1\n",
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"x_train = x_train / 255\n",
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"x_test = x_test / 255"
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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": "55f457d5",
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"metadata": {},
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"outputs": [],
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"source": [
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"# choose an image num to display and print\n",
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"num = 20\n",
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"\n",
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"image = x_train[num]\n",
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"label = y_train[num]"
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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": "149788b7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# plot the image using imshow\n",
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"plt.imshow(image, cmap='gray')\n",
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"# set the title\n",
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"plt.title(\"Label: %d\" % label )\n",
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"# remove the axis labels and ticks\n",
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"plt.axis('off')\n",
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"# show the plot\n",
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"plt.show()"
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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": "232ef6ca",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Plot 16 examples from the numpy array which was read in above\n",
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"# and display it\n",
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"fig, axes = plt.subplots(4, 4, figsize=(10, 10))\n",
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"for i , ax in enumerate(axes.ravel()):\n",
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" ax.imshow(x_train[num+i], cmap='gray')\n",
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" ax.set_title(\"Label: %d\" % y_train[num+i])\n",
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" ax.axis('off')\n",
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"plt.suptitle(\"Examples of training set images\")\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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