optimizer.py 文件源码

def apply_gradients(self, grads_and_vars, global_step=None, name=None):
        with tf.name_scope(name, self._name) as name:
            update_op = self._opt.apply_gradients(
                grads_and_vars, global_step=global_step)
            clip_update_ops = []
            with tf.control_dependencies([update_op]):
                for grad, var in grads_and_vars:
                    if grad is None or var not in self._vars_to_clip_dims:
                        continue
                    with tf.name_scope("clip_" + var.op.name):
                        if isinstance(grad, tf.Tensor):
                            clip_update_ops.append(self._clip_dense(var))
                        else:
                            clip_update_ops.append(
                                self._clip_sparse(grad, var))

            # In case no var was clipped, still need to run the update_op.
            return tf.group(*([update_op] + clip_update_ops), name=name)