mindspore.nn.HSigmoid

class mindspore.nn.HSigmoid[source]

Hard sigmoid activation function.

Applies hard sigmoid activation element-wise. The input is a Tensor with any valid shape.

Hard sigmoid is defined as:

\[\text{hsigmoid}(x_{i}) = max(0, min(1, \frac{x_{i} + 3}{6})),\]

where \(x_{i}\) is the \(i\)-th slice in the given dimension of the input Tensor.

Inputs:

x (Tensor) - The input of HSigmoid, data type must be float16 or float32. The shape is \((N,*)\) where \(*\) means, any number of additional dimensions.

Outputs:

Tensor, with the same type and shape as the x.

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

Supported Platforms:: GPU CPU

Examples

>>> x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> hsigmoid = nn.HSigmoid()
>>> result = hsigmoid(x)
>>> print(result)
[0.3333  0.1666  0.5  0.833  0.6665]