variable_clipping_optimizer.py 文件源码

def apply_gradients(self, grads_and_vars, global_step=None, name=None):
    with ops.name_scope(name, self._name) as name:
      update_op = self._opt.apply_gradients(
          grads_and_vars, global_step=global_step)
      clip_update_ops = []
      with ops.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 ops.name_scope("clip_" + var.op.name):
            if isinstance(grad, ops.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 control_flow_ops.group(*([update_op] + clip_update_ops), name=name)