tf.clip_by_norm(t, clip_norm, axes=None, name=None)

tf.clip_by_norm(t, clip_norm, axes=None, name=None)

See the guide: Training > Gradient Clipping

Clips tensor values to a maximum L2-norm.

Given a tensor t, and a maximum clip value clip_norm, this operation normalizes t so that its L2-norm is less than or equal to clip_norm, along the dimensions given in axes. Specifically, in the default case where all dimensions are used for calculation, if the L2-norm of t is already less than or equal to clip_norm, then t is not modified. If the L2-norm is greater than clip_norm, then this operation returns a tensor of the same type and shape as t with its values set to:

t * clip_norm / l2norm(t)

In this case, the L2-norm of the output tensor is clip_norm.

As another example, if t is a matrix and axes == [1], then each row of the output will have L2-norm equal to clip_norm. If axes == [0] instead, each column of the output will be clipped.

This operation is typically used to clip gradients before applying them with an optimizer.

Args:

• t: A Tensor.
• clip_norm: A 0-D (scalar) Tensor > 0. A maximum clipping value.
• axes: A 1-D (vector) Tensor of type int32 containing the dimensions to use for computing the L2-norm. If None (the default), uses all dimensions.
• name: A name for the operation (optional).

Returns:

A clipped Tensor.

Defined in tensorflow/python/ops/clip_ops.py.