discriminator.py

from keras.layers import Input
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import Conv2D
from keras.layers.core import Dense, Flatten
from keras.layers.normalization import BatchNormalization
from keras.models import Model

ndf = 64
output_nc = 3
input_shape_discriminator = (256, 256, output_nc)


def discriminator_model():
    """Build discriminator architecture."""
    n_layers, use_sigmoid = 3, False
    inputs = Input(shape=input_shape_discriminator)

    x = Conv2D(filters=ndf, kernel_size=(4,4), strides=2, padding='same')(inputs)
    x = LeakyReLU(0.2)(x)

    nf_mult, nf_mult_prev = 1, 1
    for n in range(n_layers):
        nf_mult_prev, nf_mult = nf_mult, min(2**n, 8)
        x = Conv2D(filters=ndf*nf_mult, kernel_size=(4,4), strides=2, padding='same')(x)
        x = BatchNormalization()(x)
        x = LeakyReLU(0.2)(x)

    nf_mult_prev, nf_mult = nf_mult, min(2**n_layers, 8)
    x = Conv2D(filters=ndf*nf_mult, kernel_size=(4,4), strides=1, padding='same')(x)
    x = BatchNormalization()(x)
    x = LeakyReLU(0.2)(x)

    x = Conv2D(filters=1, kernel_size=(4,4), strides=1, padding='same')(x)
    if use_sigmoid:
        x = Activation('sigmoid')(x)

    x = Flatten()(x)
    x = Dense(1024, activation='tanh')(x)
    x = Dense(1, activation='sigmoid')(x)

    model = Model(inputs=inputs, outputs=x, name='Discriminator')
    return model