/
leaky_relu.py
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/
leaky_relu.py
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from chainer import cuda
from chainer import function_node
from chainer.utils import type_check
_kern = None
def _get_kern():
global _kern
if _kern is None:
_kern = cuda.elementwise(
'T cond, T x, T slope', 'T y',
'y = cond >= 0 ? x : (T)(slope * x)', 'lrelu')
return _kern
class LeakyReLU(function_node.FunctionNode):
"""Leaky rectifier unit."""
def __init__(self, slope=0.2):
self.slope = slope
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
y = x.copy()
y[x < 0] *= self.slope
if self.slope >= 0:
self.retain_outputs((0,))
else:
self.retain_inputs((0,))
return y,
def forward_gpu(self, inputs):
x, = inputs
y = _get_kern()(x, x, self.slope)
if self.slope >= 0:
self.retain_outputs((0,))
else:
self.retain_inputs((0,))
return y,
def backward(self, indexes, grad_outputs):
if self.slope >= 0:
x = None
y = self.get_retained_outputs()[0].data
else:
x = self.get_retained_inputs()[0].data
y = None
return _LeakyReLUGrad(x, y, self.slope).apply(grad_outputs)
class _LeakyReLUGrad(function_node.FunctionNode):
def __init__(self, x, y, slope):
self.slope = slope
self.x = x
self.y = y
def forward_cpu(self, inputs):
gy, = inputs
gy = gy.copy()
if self.slope >= 0:
gy[self.y < 0] *= self.slope
else:
gy[self.x < 0] *= self.slope
return gy,
def forward_gpu(self, inputs):
gy, = inputs
if self.slope >= 0:
gy = _get_kern()(self.y, gy, self.slope)
else:
gy = _get_kern()(self.x, gy, self.slope)
return gy,
def backward(self, indexes, grad_outputs):
return _LeakyReLUGrad(self.x, self.y, self.slope).apply(grad_outputs)
def leaky_relu(x, slope=0.2):
"""Leaky Rectified Linear Unit function.
This function is expressed as
.. math:: f(x)=\\max(x, ax),
where :math:`a` is a configurable slope value.
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.
slope (float): Slope value :math:`a`.
Returns:
~chainer.Variable: Output variable. A
:math:`(s_1, s_2, ..., s_N)`-shaped float array.
.. admonition:: Example
>>> x = np.array([[-1, 0], [2, -3], [-2, 1]], 'f')
>>> x
array([[-1., 0.],
[ 2., -3.],
[-2., 1.]], dtype=float32)
>>> F.leaky_relu(x, slope=0.2).data
array([[-0.2 , 0. ],
[ 2. , -0.60000002],
[-0.40000001, 1. ]], dtype=float32)
"""
return LeakyReLU(slope).apply((x,))[0]