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mindspore.ops.clip_by_value

mindspore.ops.clip_by_value(x, clip_value_min=None, clip_value_max=None)

Clips tensor values to a specified min and max.

Limits the value of $x$ to a range, whose lower limit is clip_value_min and upper limit is clip_value_max .

$$\begin{array}{r}ou{t}_i=\{\begin{array}{ll}clip\mathrm{\_}value\mathrm{\_}max& \text{ if }{x}_i\ge clip\mathrm{\_}value\mathrm{\_}max\\ x_i& \text{ if }clip\mathrm{\_}value\mathrm{\_}min<x_i<clip\mathrm{\_}value\mathrm{\_}max\\ clip\mathrm{\_}value\mathrm{\_}min& \text{ if }{x}_i\le clip\mathrm{\_}value\mathrm{\_}min\end{array}\end{array}$$

Note

• clip_value_min and clip_value_max cannot be None at the same time;

• When clip_value_min is None and clip_value_max is not None, the elements in Tensor larger than clip_value_max will become clip_value_max;

• When clip_value_min is not None and clip_value_max is None, the elements in Tensor smaller than clip_value_min will become clip_value_min;

• If clip_value_min is greater than clip_value_max, the value of all elements in Tensor will be set to clip_value_max;

• The data type of x, clip_value_min and clip_value_max should support implicit type conversion and cannot be bool type.

Parameters

• x (Union(Tensor, list[Tensor], tuple[Tensor])) – Input data, which type is Tensor or a list or tuple of Tensor. Tensors of arbitrary dimensions are supported.

• clip_value_min (Union(Tensor, float, int)) – The minimum value. Default: None .

• clip_value_max (Union(Tensor, float, int)) – The maximum value. Default: None .

Returns

(Union(Tensor, tuple[Tensor], list[Tensor])), a clipped Tensor or a tuple or a list of clipped Tensor. The data type and shape are the same as x.

Raises

• ValueError – If both clip_value_min and clip_value_max are None.

• TypeError – If the type of x is not in Tensor or list[Tensor] or tuple[Tensor].

• TypeError – If the type of clip_value_min is not in None, Tensor, float or int.

• TypeError – If the type of clip_value_max is not in None, Tensor, float or int.

Supported Platforms:

Ascend GPU CPU

Examples

>>> # case 1: the data type of x is Tensor
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> min_value = Tensor(5, mindspore.float32)
>>> max_value = Tensor(20, mindspore.float32)
>>> x = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = ops.clip_by_value(x, min_value, max_value)
>>> print(output)
[[ 5. 20.  5.  7.]
 [ 5. 11.  6. 20.]]
>>> # case 2: the data type of x is list[Tensor]
>>> min_value = 5
>>> max_value = 20
>>> x = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> y = Tensor(np.array([[1., 25., 5., 7.], [4., 11., 6., 21.]]), mindspore.float32)
>>> output = ops.clip_by_value([x,y], min_value, max_value)
>>> for out in output:
...     print(out)
[[ 5. 20.  5.  7.]
 [ 5. 11.  6. 20.]]
[[ 5. 20.  5.  7.]
 [ 5. 11.  6. 20.]]