mygrad.nnet.activations.hard_tanh

mygrad.nnet.activations.hard_tanh(x: ArrayLike, *, lower_bound: Real = -1, upper_bound: Real = 1, constant: bool | None = None) → Tensor

Returns the hard hyperbolic tangent function.

The hard_tanh function is lower_bound where x <= lower_bound, upper_bound where x >= upper_bound, and x where lower_bound < x < upper_bound.

Parameters:

xArrayLike

The input, to which to apply the hard tanh function.

lower_boundReal, optional (default=-1)

The lower bound on the hard tanh.

upper_boundReal, optional (default=1)

The upper bound on the hard tanh.

constantOptional[bool]

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

Returns:

mygrad.Tensor

The result of applying the “hard-tanh” function elementwise to x.

Examples

>>> import mygrad as mg
>>> from mygrad.nnet.activations import hard_tanh
>>> x = mg.arange(-5, 6)
>>> x
Tensor([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5])
>>> y = hard_tanh(x, lower_bound=-3, upper_bound=3); y
Tensor([-3, -3, -3, -2, -1,  0,  1,  2,  3,  3,  3])
>>> y.backward()
>>> x.grad
array([0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0.])

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