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mindspore.nn.Hardtanh

class mindspore.nn.Hardtanh(min_val=- 1.0, max_val=1.0)

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

$$\begin{array}{r}\text{Hardtanh}(x)=\{\begin{array}{ll}1,& \text{ if }x>1;\\ -1,& \text{ if }x<-1;\\ x,& \text{ otherwise. }\end{array}\end{array}$$

Linear region range $[-1,1]$ can be adjusted using min_val and max_val.

Hardtanh Activation Function Graph:

Note

On Ascend, data type of float16 might lead to accidental accuracy problem.

Parameters

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

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

Inputs:

• x (Tensor) - Input Tensor with data type of float16 or float32. On CPU and Ascend support dimension 0-7D. On GPU support dimension 0-4D.

Outputs:

Tensor, with the same dtype and shape as x.

Raises

• TypeError – If x is not a Tensor.

• TypeError – If dtype of x is neither float16 nor float32.

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

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

• ValueError – If min_val is not less than max_val.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore
>>> from mindspore import Tensor, nn
>>> import numpy as np
>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> hardtanh = nn.Hardtanh(min_val=-1.0, max_val=1.0)
>>> output = hardtanh(x)
>>> print(output)
[-1. -1.  0.  1.  1.]