gan.py

def gan_compile(num_features = 100):

    # Build the Discriminator Network for DCGAN
    generator = keras.models.Sequential([
        keras.layers.Dense(7 * 7 * 128, input_shape=[num_features]),
        keras.layers.Reshape([7, 7, 128]),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2DTranspose(64, (5,5), (2,2), padding="same", activation="selu"),
        keras.layers.BatchNormalization(),
        keras.layers.Conv2DTranspose(1, (5,5), (2,2), padding="same", activation="tanh"),
    ])

    # Build the Discriminator Network for DCGAN
    discriminator = keras.models.Sequential([
        keras.layers.Conv2D(64, (5,5), (2,2), padding="same", input_shape=[28, 28, 1]),
        keras.layers.LeakyReLU(0.2),
        keras.layers.Dropout(0.3),
        keras.layers.Conv2D(128, (5,5), (2,2), padding="same"),
        keras.layers.LeakyReLU(0.2),
        keras.layers.Dropout(0.3),
        keras.layers.Flatten(),
        keras.layers.Dense(1, activation='sigmoid')
    ])

    noise = tf.random.normal(shape=[1, num_features])
    generated_images = generator(noise, training=False)
    plot_utils.show(generated_images, 1)

    # Compile the Deep Convolutional Generative Adversarial Network
    discriminator.compile(loss="binary_crossentropy", optimizer="rmsprop")
    discriminator.trainable = False
    gan = keras.models.Sequential([generator, discriminator])
    gan.compile(loss="binary_crossentropy", optimizer="rmsprop")

    return gan