wgan_gp

wgan_gp_2.py

import tensorflow as tf
from keras import layers

# Define the discriminator model
def build_discriminator(img_shape, activation='linear', alpha=0.2):
    inputs = layers.Input(shape=img_shape, name="input")

    x = layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same', use_bias=False)(inputs)
    x = layers.LeakyReLU(alpha)(x)

    x = layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same', use_bias=False)(x)
    x = layers.LeakyReLU(alpha)(x)

    x = layers.Conv2D(256, (5, 5), strides=(2, 2), padding='same', use_bias=False)(x)
    x = layers.LeakyReLU(alpha)(x)

    x = layers.Conv2D(512, (5, 5), strides=(2, 2), padding='same', use_bias=False)(x)
    x = layers.LeakyReLU(alpha)(x)

    x = layers.Flatten()(x)
    x = layers.Dropout(0.5)(x)

    x = layers.Dense(1, activation=activation, dtype='float32')(x)

    model = tf.keras.Model(inputs=inputs, outputs=x)

    return model