memnet.py 文件源码

def build_train(self, total_loss):
    with self.G.as_default():
      self.opt = tf.train.AdamOptimizer(learning_rate=self.learning_rate)

      # can't use opt.minimize because we need to clip the gradients
      grads_and_vars = self.opt.compute_gradients(self.loss)
      grads_and_vars = [(tf.clip_by_norm(g, self.max_grad_norm), v) for g,v in grads_and_vars]
      grads_and_vars = [(add_gradient_noise(g), v) for g,v in grads_and_vars]
      nil_grads_and_vars = []
      for g, v in grads_and_vars:
        if v.name in self.nil_vars:
          nil_grads_and_vars.append((zero_nil_slot(g), v))
        else:
          nil_grads_and_vars.append((g, v))

      self.train_op = self.opt.apply_gradients(nil_grads_and_vars, name="train_op")

    return self.train_op