tensorflow 2，梯度是一个空列表

如何解决tensorflow 2，梯度是一个空列表

我正在重新组织我的代码以使其易于阅读，但是在编译时它说： ValueError: No gradients provided for any variable: ['enc_conv_4/kernel:0'...。我知道我的损失函数是可微的，因为代码在接触它之前就可以工作，但现在缺少我的模型的梯度。

    @tf.function
    def train_disc(self,real_imgs,gen_imgs):
      with tf.GradientTape() as disc_tape:
        d_loss = self.wasserstein_loss(real_imgs,gen_imgs)
      gradients_d = disc_tape.gradient(d_loss,self.discriminator.trainable_variables)
      self.d_optimizer.apply_gradients(zip(gradients_d,self.discriminator.trainable_variables))
      return d_loss

    @tf.function
    def train_gen(self,real_img,gen_imgs,mask,img_feat,rot_feat_mean):
      with tf.GradientTape() as gen_tape:
        g_loss_param = self.generator_loss(mask,rot_feat_mean)
        g_loss = g_loss_param(real_img,gen_imgs)
      gradients_g = gen_tape.gradient(g_loss,self.generator.trainable_variables)
      print(gradients_g)
      self.g_optimizer.apply_gradients(zip(gradients_g,self.generator.trainable_variables))

如您所见，当我对判别器和生成器执行相同操作时，生成器给了我一个空的梯度列表。

gen_imgs = self.generator([real_img,mask],training=True)


d_loss = self.train_disc(real_img,gen_imgs[:,:,:-1])

if step%self.n_critic == 0:
  masked_images = real_img * mask
  idx = 3  # index of desired layer
  layer_input = Input(shape=(self.img_shape))  #
  x = layer_input
  for layer in self.generator.layers[idx:idx+12]:
      x = layer(x)
  model_feat = Model(inputs=layer_input,outputs=x)
  model_feat.trainable = False
  img_feat = model_feat(masked_images,training=False)
  rot_feat_mean = []
  for i in range(self.batch_size):
      rot = []
      for an in [180,155,130,105,80,55,20,10]:
          r = tf.keras.preprocessing.image.random_rotation(masked_images[i],an,row_axis=0,col_axis=1,channel_axis=2)
          rot.append(r)
      rot = np.array(rot)
      rot_feat_mean.append(np.mean(model_feat(rot,training=False),axis=0))
  rot_feat_mean = np.array(rot_feat_mean)
  g_loss = self.train_gen(real_img,:-1],rot_feat_mean)

最后一段代码的最后一行给了我一个错误。不知道这个错误是不是语义错误造成的。