mindspore.nn.LogSigmoid

class mindspore.nn.LogSigmoid[source]

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

Logsigmoid is defined as:

\[\text{logsigmoid}(x_{i}) = log(\frac{1}{1 + \exp(-x_i)}),\]

where \(x_{i}\) is the element of the input.

Inputs:

x (Tensor) - The input of LogSigmoid with data type of 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:: Ascend GPU CPU

Examples

>>> net = nn.LogSigmoid()
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> output = net(x)
>>> print(output)
[-0.31326166 -0.12692806 -0.04858734]