mygrad.nnet.activations.elu

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

Returns the exponential linear activation (ELU) elementwise along x.

The ELU is given by ɑ(exp(x) - 1) for x < 0 and x for x ≥ 0.

Parameters:

xArrayLike

Input data.

alphaReal

The multiplicative factor on the negative activation.

constantOptional[bool]

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

Returns:

mygrad.Tensor

The ELU function applied to x elementwise.

Examples

>>> import mygrad as mg
>>> from mygrad.nnet.activations import elu
>>> x = mg.arange(-5, 6)
>>> x
Tensor([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5])
>>> y = elu(x, alpha=0.1); y
Tensor([-0.09932621, -0.09816844, -0.09502129, -0.08646647, -0.06321206,
         0.        ,  1.        ,  2.        ,  3.        ,  4.        ,
         5.        ])
>>> y.backward()
>>> x.grad
array([6.73794700e-04, 1.83156389e-03, 4.97870684e-03, 1.35335283e-02,
       3.67879441e-02, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00,
       1.00000000e+00, 1.00000000e+00, 1.00000000e+00])

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