FashionClassifierWithConvolutions.py

import tensorflow as tf

mnist = tf.keras.datasets.fashion_mnist

(training_images, training_labels), (test_images, test_labels) = mnist.load_data()

training_images = training_images.reshape(60000, 28, 28, 1)
training_images = training_images / 255.0

test_images = test_images.reshape(10000, 28, 28, 1)
test_images = test_images/255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu',
                           input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Conv2D(64, (3, 3), activation='relu',
                           input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D(2, 2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation=tf.nn.relu),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy', metrics=['accuracy'])

model.summary()

model.fit(training_images, training_labels, epochs=5)

test_loss = model.evaluate(test_images, test_labels)