gans.py

# DCGAN

import tensorflow as tf
from tensorflow.keras import layers

# the generator
def make_generator():
    model = tf.keras.Sequential()
    model.add(layers.Dense(7*7*256, use_bias=False, input_shape = (100,))) # the encoder
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((7,7,256)))
    assert model.output_shape == (None, 7, 7, 256)

    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 7, 7, 128)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 14, 14, 64)
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2DTranspose(1, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    assert model.output_shape == (None, 28, 28, 1)

    return model


generator = make_generator()
noise = tf.random.normal([1, 100])
generated_image = generator(noise, training=False)



# the discriminator
def make_discriminator():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', input_shape=[28, 28, 1]))

    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(.3))

    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(.3))

    model.add(layers.Flatten())
    model.add(layers.Dense(1))

    return model


discriminator = make_discriminator()
decision = discriminator(generated_image)
print (decision)