import numpy
from chainer import cuda
from chainer import function
from chainer import utils
from chainer.utils import type_check
class HardSigmoid(function.Function):
"""Hard-sigmoid function."""
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
x_type, = in_types
type_check.expect(x_type.dtype.kind == 'f')
def forward_cpu(self, inputs):
x = inputs[0]
y = numpy.clip(x * 0.2 + 0.5, 0.0, 1.0)
return utils.force_array(y, x.dtype),
def forward_gpu(self, inputs):
x = inputs[0]
return cuda.elementwise(
'T x', 'T y',
'y = min(1.0, max(0.0, x * 0.2 + 0.5))',
'hard_sigmoid_fwd'
)(x),
def backward_cpu(self, inputs, grads):
x = inputs[0]
g = grads[0]
gx = ((-2.5 < x) & (x < 2.5)) * g * 0.2
return utils.force_array(gx, x.dtype),
def backward_gpu(self, inputs, grads):
x = inputs[0]
g = grads[0]
return cuda.elementwise(
'T x, T g', 'T gx',
'gx = fabs(x) < 2.5 ? 0.2 * g : 0',
'hard_sigmoid_bwd'
)(x, g),
[docs]def hard_sigmoid(x):
"""Element-wise hard-sigmoid function.
This function is defined as
.. math::
f(x) = \\left \\{ \\begin{array}{ll}
0 & {\\rm if}~ x < -2.5 \\\\
0.2 x + 0.5 & {\\rm if}~ -2.5 < x < 2.5 \\\\
1 & {\\rm if}~ 2.5 < x.
\\end{array} \\right.
Args:
x (:class:`~chainer.Variable` or :class:`numpy.ndarray` or \
:class:`cupy.ndarray`):
Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.
Returns:
~chainer.Variable: Output variable. A
:math:`(s_1, s_2, ..., s_N)`-shaped float array.
.. admonition:: Example
It maps the input values into the range of :math:`[0, 1]`.
>>> x = np.array([-2.6, -1, 0, 1, 2.6])
>>> x
array([-2.6, -1. , 0. , 1. , 2.6])
>>> F.hard_sigmoid(x).data
array([ 0. , 0.3, 0.5, 0.7, 1. ])
"""
return HardSigmoid()(x)