TensorFlow 2 version | View source on GitHub |
Clips tensor values to a maximum L2-norm.
tf.clip_by_norm(
t, clip_norm, axes=None, name=None
)
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 less than or 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 or IndexedSlices .
|
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 or IndexedSlices .
|
Raises | |
---|---|
ValueError
|
If the clip_norm tensor is not a 0-D scalar tensor. |
TypeError
|
If dtype of the input is not a floating point or complex type. |