generator.py

def build_generator(seed_size, channels):
    model = Sequential()

    model.add(Dense(4*4*256,activation="relu",input_dim=seed_size))
    model.add(Reshape((4,4,256)))

    model.add(UpSampling2D())
    model.add(Conv2D(256,kernel_size=3,padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    model.add(UpSampling2D())
    model.add(Conv2D(256,kernel_size=3,padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))
   
    # Output resolution, additional upsampling
    model.add(UpSampling2D())
    model.add(Conv2D(128,kernel_size=3,padding="same"))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Activation("relu"))

    if GENERATE_RES>1:
      model.add(UpSampling2D(size=(GENERATE_RES,GENERATE_RES)))
      model.add(Conv2D(128,kernel_size=3,padding="same"))
      model.add(BatchNormalization(momentum=0.8))
      model.add(Activation("relu"))

    # Final CNN layer
    model.add(Conv2D(channels,kernel_size=3,padding="same"))
    model.add(Activation("tanh"))

    return model