CIFAR 100 Dataset (original) (raw)

`import tensorflow as tf import matplotlib.pyplot as plt import numpy as np

Load CIFAR-100 dataset

(cifar100_train, cifar100_test) = tf.keras.datasets.cifar100.load_data()

Unpack the dataset

(x_train, y_train) = cifar100_train (x_test, y_test) = cifar100_test

Define a function to plot a grid of images

def plot_images(images, labels, class_names, grid_size=(4, 4)): plt.figure(figsize=(8, 8)) for i in range(grid_size[0] * grid_size[1]): plt.subplot(grid_size[0], grid_size[1], i + 1) plt.xticks([]) plt.yticks([]) plt.grid(False) plt.imshow(images[i]) plt.xlabel(class_names[labels[i][0]]) plt.show()

Get class names for CIFAR-100

class_names = [ 'apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm' ]

Plot a 4x4 grid of images from the training set

plot_images(x_train, y_train, class_names)

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