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_TANH
class Tanh(function.Function):
"""Hyperbolic tangent 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):
self.y = utils.force_array(numpy.tanh(x[0]))
return self.y,
def forward_gpu(self, x):
if (cuda.cudnn_enabled and self.use_cudnn and
x[0].flags.c_contiguous and
(_cudnn_version >= 3000 or x[0].dtype != numpy.float16)):
self.y = cudnn.activation_forward(x[0], _mode)
else:
self.y = cuda.cupy.empty_like(x[0])
cuda.cupy.tanh(x[0], out=self.y)
return self.y,
def backward_cpu(self, x, gy):
one = x[0].dtype.type(1)
return utils.force_array(gy[0] * (one - self.y * self.y)),
def backward_gpu(self, x, gy):
if (cuda.cudnn_enabled and self.use_cudnn and
x[0].flags.c_contiguous and gy[0].flags.c_contiguous and
(_cudnn_version >= 3000 or x[0].dtype != numpy.float16)):
gx = cudnn.activation_backward(x[0], self.y, gy[0], _mode)
else:
gx = cuda.elementwise(
'T y, T gy', 'T gx',
'gx = gy * (1 - y * y)',
'tanh_bwd')(self.y, gy[0])
return gx,
[docs]def tanh(x, use_cudnn=True):
"""Elementwise hyperbolic tangent function.
.. math:: f(x)=\\tanh(x).
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
>>> x = np.arange(-1, 4, 2).astype('f')
>>> x
array([-1., 1., 3.], dtype=float32)
>>> F.tanh(x).data
array([-0.76159418, 0.76159418, 0.99505478], dtype=float32)
"""
return Tanh(use_cudnn)(x)