mindspore.ops.clip_by_value

mindspore.ops.clip_by_value(x, clip_value_min, clip_value_max)

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’.

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

Note

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

Parameters

• x (Tensor) – Input data. The shape is $(N,\ast )$ where $\ast$ 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.]]