import numpy
from chainer import cuda
from chainer import function
from chainer import utils
from chainer.utils import type_check
if cuda.cudnn_enabled:
cudnn = cuda.cudnn
libcudnn = cudnn.cudnn
_cudnn_version = libcudnn.getVersion()
_mode = libcudnn.CUDNN_ACTIVATION_SIGMOID
class Sigmoid(function.Function):
"""Logistic sigmoid function."""
def __init__(self, use_cudnn=True):
self.use_cudnn = use_cudnn
def check_type_forward(self, in_types):
type_check.expect(in_types.size() == 1)
type_check.expect(in_types[0].dtype.kind == 'f')
def forward_cpu(self, x):
half = x[0].dtype.type(0.5)
self.y = utils.force_array(numpy.tanh(x[0] * half) * half + half)
return self.y,
def forward_gpu(self, inputs):
x = inputs[0]
if (cuda.cudnn_enabled and self.use_cudnn and x.flags.c_contiguous and
(_cudnn_version >= 3000 or x.dtype != numpy.float16)):
self.y = cuda.cupy.cudnn.activation_forward(x, _mode)
else:
self.y = cuda.elementwise(
'T x', 'T y', 'y = tanh(x * 0.5) * 0.5 + 0.5',
'sigmoid_fwd')(x)
return self.y,
def backward_cpu(self, x, gy):
one = x[0].dtype.type(1)
return utils.force_array(gy[0] * self.y * (one - self.y)),
def backward_gpu(self, inputs, grads):
x = inputs[0]
gy = grads[0]
if (cuda.cudnn_enabled and self.use_cudnn and x.flags.c_contiguous and
gy.flags.c_contiguous and
(_cudnn_version >= 3000 or x.dtype != numpy.float16)):
gx = cuda.cupy.cudnn.activation_backward(x, self.y, gy, _mode)
else:
gx = cuda.elementwise(
'T y, T gy', 'T gx',
'gx = gy * y * (1 - y)',
'sigmoid_bwd')(self.y, gy)
return gx,
[docs]def sigmoid(x, use_cudnn=True):
"""Element-wise sigmoid logistic function.
.. math:: f(x)=(1 + \\exp(-x))^{-1}.
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.
use_cudnn (bool): If ``True`` and cuDNN is enabled, then this function
uses cuDNN as the core implementation.
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.arange(-2, 3, 2).astype('f')
>>> x
array([-2., 0., 2.], dtype=float32)
>>> F.sigmoid(x).data
array([ 0.11920291, 0.5 , 0.88079709], dtype=float32)
"""
return Sigmoid(use_cudnn)(x)