mygrad.nnet.activations.relu

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

Applies the recitfied linear unit activation function:

f(x) = {x, x > 0
        0, x <= 0 }

Parameters:

xArrayLike

relu 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 relu
>>> x = mg.linspace(-5, 5, 5)
>>> x
Tensor([-5. , -2.5,  0. ,  2.5,  5. ])
>>> relu(x)
Tensor([-0. , -0. ,  0. ,  2.5,  5. ])
>>> relu(x).backward()
>>> x.grad  # d(relu(x))/dx
array([0., 0., 0., 1., 1.])

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