图像着色中的生成器和鉴别器损失

Question

我一直在关注GAN的图像着色。我曾尝试自己实施研究论文，但遇到了问题。在特定点之后，生成器和鉴别器的损失不会收敛。因此，我认为它们的实现存在问题。这是代码

def generator_loss(self,fake_output_discri,generated_image_from_generator, actual_image,regularizer_lambda=0.01):
        mse = tf.reduce_mean(regularizer_lambda*tf.keras.losses.mean_absolute_error(generated_image_from_generator, actual_image))
        return tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.ones_like(fake_output_discri),logits = fake_output_discri) + mse

def discriminator_loss(self,generated_image_from_generator,actual_image):
        actual_loss  = tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.ones_like(actual_image), logits = actual_image )
        fake_loss  = tf.nn.sigmoid_cross_entropy_with_logits(labels = tf.zeros_like(generated_image_from_generator), logits = generated_image_from_generator)
        return actual_loss + fake_loss

假设传递给他们的参数正确，我做错了什么吗？我尝试打印生成器和鉴别器损耗，并且在某些时期后保持不变！这是完成大部分工作的火车功能！

def train(self,gray_scale_image_dataset,color_image_dataset,test_image):

    generator = self.generator_model()
    discriminator = self.discriminator_model()

    gen_optimizer = tf.train.AdamOptimizer(self.learning_rate)
    dis_optimizer = tf.train.AdamOptimizer(self.learning_rate)
    for eachEpoch in range(self.epochs):

        with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
            for i in range(20):
                random.shuffle(gray_scale_image_dataset)
                random.shuffle(color_image_dataset)
            gray_scale_dataset_image = gray_scale_image_dataset[:self.batch_size]
            color_dataset_image_batch = color_image_dataset[:self.batch_size]

            generated_image = generator(gray_scale_dataset_image)
            real_output = discriminator(color_dataset_image_batch)
            fake_output = discriminator(generated_image)

            gen_loss = self.generator_loss(fake_output,generated_image,color_dataset_image_batch)
            dis_loss = self.discriminator_loss(fake_output,real_output)
            print("generator = {} discriminator =  {}".format(gen_loss,dis_loss))            
        gen_gradients = gen_tape.gradient(gen_loss,generator.trainable_variables)
        disc_gradients = disc_tape.gradient(dis_loss,discriminator.trainable_variables)
        print("APPLYING GRADENTS")
        gen_optimizer.apply_gradients(zip(gen_gradients, generator.trainable_variables))
        dis_optimizer.apply_gradients(zip(disc_gradients, discriminator.trainable_variables))

        print ("EPOCHS COMPLETED = {} ".format(eachEpoch))
        #for drawing test_image
        self.draw_images(generator,test_image)