input_shape = (1024,1024, 3) output_shape = (256, 256, 3) Dis = Discriminator(input_shape=output_shape) Dis.input() Dis.conv_pool_block(n_filter=16, filter_size=(5,5)) Dis.conv_pool_block(n_filter=8, filter_size=(7,7)) Dis.fully_connected(4) Dis.fully_connected(1) # for W-Loss Dis.build() Gen = Painter(Dis, input_shape, output_shape) Gen.input() Gen.conv_block(16, filter_size=(5,5), padding="same") Gen.conv_block(16, filter_size=(7,7), padding="same") Gen.pooling_block((2,2)) Gen.conv_block(32, filter_size=(9,9), padding="same") Gen.conv_block(32, filter_size=(11,11), padding="same") Gen.pooling_block((2,2)) Gen.top_block() #Gen.build_monitored([2,5],[0.5,0.5]) Gen.build() painter_optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07) disc_optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07) for i in range(10): print("Training Discriminator...") for dis_training_loop in range(25):