mindspore.ops.HSwish

class mindspore.ops.HSwish

Hard swish activation function.

Applies hswish-type activation element-wise. The input is a Tensor with any valid shape.

Hard swish is defined as:

\[\text{hswish}(x_{i}) = x_{i} * \frac{ReLU6(x_{i} + 3)}{6},\]

where \(x_i\) is an element of the input Tensor.

Inputs:

• input_x (Tensor) - Tensor of shape \((N, *)\), where \(*\) means, any number of additional dimensions, with float16 or float32 data type.

Outputs:

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

Raises

• TypeError – If input_x is not a Tensor.

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

Supported Platforms:

GPU CPU

Examples

>>> hswish = ops.HSwish()
>>> input_x = Tensor(np.array([-1, -2, 0, 2, 1]), mindspore.float16)
>>> result = hswish(input_x)
>>> print(result)
[-0.3333  -0.3333  0  1.666  0.6665]