mindspore.ops.SeLU

class mindspore.ops.SeLU

Computes SeLU (scaled exponential Linear Unit) of input tensors element-wise.

The activation function is defined as:

$$E_i=scale\ast \{\begin{array}{ll}{x}_i,& \text{if }{x}_i\ge 0;\\ \text{alpha}\ast (\exp (x_i)-1),& \text{otherwise.}\end{array}$$

where $alpha$ and $scale$ are pre-defined constants($alpha=1.67326324$ and $scale=1.05070098$).

See more details in Self-Normalizing Neural Networks.

Inputs:

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

Outputs:

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

Supported Platforms:

Ascend

Raises

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

Examples

>>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32)
>>> selu = ops.SeLU()
>>> output = selu(input_x)
>>> print(output)
[[-1.1113307 4.202804 -1.7575096]
[ 2.101402 -1.7462534 9.456309 ]]