optimizer.py 文件源码

def _clip_sparse(self, grad, var):
        assert isinstance(grad, tf.IndexedSlices)
        clip_dims = self._vars_to_clip_dims[var]
        if 0 in clip_dims:
            log.warn("Clipping norm across dims %s for %s is inefficient "
                     "when including sparse dimension 0.", clip_dims,
                     var.op.name)
            return self._clip_dense(var)

        with tf.colocate_with(var):
            var_subset = tf.gather(var, grad.indices)
        with self._maybe_colocate_with(var):
            normalized_var_subset = tf.clip_by_norm(
                var_subset, self._max_norm, clip_dims)
            delta = tf.IndexedSlices(
                var_subset - normalized_var_subset, grad.indices, grad.dense_shape)
        with tf.colocate_with(var):
            return var.scatter_sub(delta, use_locking=self._use_locking)