satishgunjal

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os, datetime

satishgunjal

# List all files under the input directory
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# List of physical devices
tf.config.experimental.list_physical_devices() 

satishgunjal

fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

# Creating class label array
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

satishgunjal

# Shape of training nad test data
print(f'Shape of train_images: {train_images.shape}')
print(f'Shape of train_labels: {train_labels.shape}')
print(f'Shape of test_images: {test_images.shape}')
print(f'Shape of test_labels: {test_labels.shape}')

satishgunjal

# There are 10 labels starting from 0 to 9
print(f'Unique train labels: {np.unique(train_labels)}')
print(f'Unique test labels: {np.unique(test_labels)}')

satishgunjal

# The images are 28x28 NumPy arrays, with pixel values ranging from 0 to 255
train_images

satishgunjal

plt.figure()
plt.imshow(train_images[0])
plt.colorbar()
plt.grid(False)
plt.show()

satishgunjal

# Images labels(classes) possible values from 0 to 9
train_labels

satishgunjal

# Display the first 25 images from the training set and display the class name below each image.
plt.figure(figsize=(10,10))
for i in range(25):
    plt.subplot(5,5,i+1)
    plt.xticks([])
    plt.yticks([])
    plt.grid(False)
    plt.imshow(train_images[i], cmap=plt.cm.binary)
    plt.xlabel(class_names[train_labels[i]])
plt.show()

satishgunjal

train_images = train_images / 255.0
test_images = test_images / 255.0