2-discriminator.py

def define_discriminator(image_shape):
    #weight initialization
    init = RandomNormal(stddev=0.02)
    #source image input
    in_image = Input(shape=image_shape)
    #64
    d = Conv2D(64,(4,4),strides=(2,2),padding='same',kernel_initializer=init)(in_image)
    d = LeakyReLU(alpha=0.2)(d)
    #128
    d = Conv2D(128,(4,4),strides=(2,2),padding='same',kernel_initializer=init)(d)
    d = InstanceNormalization(axis=-1)(d)
    d = LeakyReLU(alpha=0.2)(d)
    #256
    d = Conv2D(256,(4,4),strides=(2,2),padding='same',kernel_initializer=init)(d)
    d = InstanceNormalization(axis=-1)(d)
    d = LeakyReLU(alpha=0.2)(d)
    #512
    d = Conv2D(512,(4,4),strides=(2,2),padding='same',kernel_initializer=init)(d)
    d =InstanceNormalization(axis=-1)(d)
    d = LeakyReLU(alpha=0.2)(d)
    #prefinal output layer
    d = Conv2D(512,(4,4),padding='same',kernel_initializer=init)(d)
    d = InstanceNormalization(axis=-1)(d)
    d = LeakyReLU(alpha=0.2)(d)
    #patch output
    patch_out = Conv2D(1,(4,4),padding='same',kernel_initializer=init)(d)
    #definig the model
    model = Model(in_image,patch_out)
    #compiling the model
    model.compile(loss = 'mse',optimizer = Adam(lr=0.0002,beta_1=0.5),loss_weights=[0.5])
    return model