mygrad.nnet.activations.sigmoid

mygrad.nnet.activations.sigmoid(x: ArrayLike, *, constant: bool | None = None) → Tensor

Applies the sigmoid activation function:

f(x) = 1 / (1 + exp(-x))

Parameters:

xArrayLike

sigmoid is applied element-wise on x.

constantOptional[bool]

If True, the returned tensor is a constant (it does not back-propagate a gradient)

Returns:

mygrad.Tensor

Examples

>>> import mygrad as mg
>>> from mygrad.nnet import sigmoid
>>> x = mg.linspace(-5, 5, 10)
>>> sigmoid(x)
Tensor([0.00669285, 0.02005754, 0.0585369 , 0.1588691 , 0.36457644,
    0.63542356, 0.8411309 , 0.9414631 , 0.97994246, 0.99330715])

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