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mindspore.ops.hardtanh

mindspore.ops.hardtanh(input, min_val=- 1.0, max_val=1.0)

Applies the hardtanh activation function element-wise. The activation function is defined as:

$$\begin{array}{r}\text{hardtanh}(input)=\{\begin{array}{ll}max\mathrm{\_}val,& \text{ if }input>max\mathrm{\_}val\\ min\mathrm{\_}val,& \text{ if }input<min\mathrm{\_}val\\ input,& \text{ otherwise. }\end{array}\end{array}$$

Linear region range $[min\mathrm{\_}val,max\mathrm{\_}val]$ can be adjusted using min_val and max_val.

Hardtanh Activation Function Graph:

Parameters

• input (Tensor) – Input Tensor.

• min_val (Union[int, float], optional) – Minimum value of the linear region range. Default: -1.0 .

• max_val (Union[int, float], optional) – Maximum value of the linear region range. Default: 1.0 .

Returns

Tensor, with the same dtype and shape as input.

Raises

• TypeError – If input is not a Tensor.

• TypeError – If dtype of min_val is neither float nor int.

• TypeError – If dtype of max_val is neither float nor int.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, ops
>>> x = Tensor([-1, -2, 0, 2, 1], mindspore.float16)
>>> output = ops.hardtanh(x, min_val=-1.0, max_val=1.0)
>>> print(output)
[-1. -1.  0.  1.  1.]