# mindspore.ops.clip_by_value

mindspore.ops.clip_by_value(x, clip_value_min, clip_value_max)[source]

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{split}out_i= \left\{ \begin{array}{align} clip\_value_{max} & \text{ if } x_i\ge clip\_value_{max} \\ x_i & \text{ if } clip\_value_{min} \lt x_i \lt clip\_value_{max} \\ clip\_value_{min} & \text{ if } x_i \le clip\_value_{min} \\ \end{array}\right.\end{split}

Note

‘clip_value_min’ needs to be less than or equal to ‘clip_value_max’.

Parameters
• x (Tensor) – Input data. The shape is $$(N,*)$$ where $$*$$ means, any number of additional dimensions.

• clip_value_min (Tensor) – The minimum value.

• clip_value_max (Tensor) – The maximum value.

Returns

Tensor, a clipped Tensor. It has the same shape and data type as x.

Supported Platforms:

Ascend GPU CPU

Examples

>>> 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.]]