generator_model.py

def generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(4*4*512, use_bias=False, input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((4, 4, 512)))
    assert model.output_shape == (None, 4, 4, 512) # Note: None is the batch size

    model.add(layers.Conv2DTranspose(256, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 8, 8, 256)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    assert model.output_shape == (None, 16, 16, 128)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
    assert model.output_shape == (None, 32, 32, 3)

    return model