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):
discri_grads = Dis.get_gradient()
disc_optimizer.apply_gradients(zip(discri_grads, Dis.model.trainable_weights))
print('\tDiscriminator loss: ',Dis.__current_loss__)
print("Training Generator...")
for gen_training_loop in range(10):
painter_grads = Gen.get_gradient()
print('\t\t\t'); _grads_summ=0
for i in range(10):
_grads_summ += painter_grads[i].numpy().sum()
print("\tGenerator gradients sum: ",_grads_summ)
painter_optimizer.apply_gradients(zip(painter_grads, Gen.model.trainable_weights))
print('\tGenerator loss: ',Gen.__current_loss__)
print(f"Loss at step {i}: Painter: {Gen.__current_loss__}; Discriminator: {Dis.__current_loss__}.")
