Given a list of Tensor $$t\_list$$ , calculate the global norm for the elements of all tensors in $$t\_list$$ , and limit it to clip_norm .

• If the global norm is greater than clip_norm , all elements of $$t\_list$$ will be compressed by a ratio.

• If the global norm is less than or equal to clip_norm , nothing will be done.

The list of Tensor $$t\_list$$ is not passed from this class, but the gradients of all parameters set in optimizer. If need_clip of specific param is False in its ParamAttr, then the gradients of this param will not be clipped.

Gradient clip will takes effect after being set in optimizer , see the document optimizer (for example: SGD).

The clipping formula is:

$t\_list[i] = t\_list[i] * \frac{clip\_norm}{\max(global\_norm, clip\_norm)}$

where:

$global\_norm = \sqrt{\sum_{i=0}^{N-1}(l2norm(t\_list[i]))^2}$

Note

need_clip of ClipGradyGlobalNorm HAS BEEN DEPRECATED since 2.0. Please use need_clip in ParamAttr to speficiy the clip scope.

Parameters
• clip_norm (float) – The maximum norm value.

• group_name (str, optional) – The group name for this clip. Default value is default_group.

Examples

import paddle

x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')