import tensorflow_hub as hub
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.Flatten(), tf.keras.
    layers.Dense(128, activator='relu'), tf.keras.layers.Dense(10,
    activator='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics='accuracy')

5f7e1dad53f31ea179a5fe970b9acd2c609f6a8f
<mask token>

<mask token>
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics='accuracy')

<mask token>
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.Flatten(), tf.keras.
    layers.Dense(128, activator='relu'), tf.keras.layers.Dense(10,
    activator='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics='accuracy')

import tensorflow_hub as hub
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.Flatten(), tf.keras.
    layers.Dense(128, activator='relu'), tf.keras.layers.Dense(10,
    activator='softmax')])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
    metrics='accuracy')
