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157 lines
4.3 KiB
157 lines
4.3 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "50680cbc",
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"metadata": {},
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"source": [
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"Read/load the cifar10 dataset using tf.keras.datasets\n",
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"- Display the first 25 images\n",
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"- Convert them to greyscale images by reducing the 3 colors (r,g,b) to \n",
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" one greyscale\n",
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" using the formula gray = 0.2989 * r + 0.5870 * g + 0.1140 * b\n",
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"- Display the first 25 images in greyscale"
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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": "2dc5ea2f",
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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 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": "e846355a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load the CIFAR-10 dataset\n",
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"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.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": "85a493d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# In case of special pictures\n",
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"selectPicture = -1 # -1 for all or number for a class starting from 0\n",
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"\n",
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"# Define a list of class names for CIFAR-10\n",
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"class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
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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": "4eacb734",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the indices of images with a special label in the training set\n",
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"special_indices = np.where(y_train == selectPicture)[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": "be2d0cd2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Display the first 25 images in the training set\n",
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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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" if selectPicture == -1 :\n",
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" plt.imshow(x_train[i]) # all images\n",
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" plt.title(class_names[y_train[i][0]])\n",
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" else : \n",
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" plt.imshow(x_train[special_indices[i]]) # special Picture only\n",
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" plt.title(class_names[selectPicture]) \n",
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" plt.xticks([])\n",
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" plt.yticks([])\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": "938e632d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Convert images to grayscale\n",
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"# The ... notation is used to select all dimensions of the array up to the last one\n",
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"# The vector [0.2989, 0.5870, 0.1140] converts a color image to grayscale by taking \n",
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"# a weighted average of the color channels.\n",
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"train_images_gray = np.dot(x_train[..., :3], [0.2989, 0.5870, 0.1140])\n",
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"test_images_gray = np.dot(x_test[..., :3], [0.2989, 0.5870, 0.1140])"
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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": "96333686",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Normalize pixel values to [0, 1]\n",
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"train_images_gray = train_images_gray / 255.0\n",
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"test_images_gray = test_images_gray / 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": "64d3d2ae",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Display the first 25 images in the training set\n",
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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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" if selectPicture == -1 :\n",
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" plt.imshow(train_images_gray[i],cmap='gray') # all images\n",
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" plt.title(class_names[y_train[i][0]])\n",
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" else : \n",
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" plt.imshow(train_images_gray[special_indices[i]],cmap='gray') # special Picture only\n",
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" plt.title(class_names[selectPicture]) \n",
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" plt.xticks([])\n",
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" plt.yticks([])\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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