ClipGradByNorm¶

class paddle.nn. ClipGradByNorm ( clip_norm ) [source]

Limit the l2 norm of multi-dimensional Tensor $X$ to clip_norm .

• If the l2 norm of $X$ is greater than clip_norm , $X$ will be compressed by a ratio.

• If the l2 norm of $X$ is less than or equal to clip_norm , nothing will be done.

The multidimensional Tensor $X$ 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:

$$\begin{split}Out = \left\{ \begin{array}{ccl} X & & if (norm(X) \leq clip\_norm) \\ \frac{clip\_norm*X}{norm(X)} & & if (norm(X) > clip\_norm) \\ \end{array} \right.\end{split}$$

where $norm(X)$ represents the L2 norm of $X$.

$$norm(X) = ( \sum_{i=1}^{n}|x\_i|^2)^{ \frac{1}{2}}$$

Note

need_clip of ClipGradByNorm 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.

Examples

import paddle

x = paddle.uniform([10, 10], min=-1.0, max=1.0, dtype='float32')
linear = paddle.nn.Linear(in_features=10, out_features=10,
                          weight_attr=paddle.ParamAttr(need_clip=True),
                          bias_attr=paddle.ParamAttr(need_clip=False))
out = linear(x)
loss = paddle.mean(out)
loss.backward()

clip = paddle.nn.ClipGradByNorm(clip_norm=1.0)
sdg = paddle.optimizer.SGD(learning_rate=0.1, parameters=linear.parameters(), grad_clip=clip)
sdg.step()