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clipped_relu.py
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clipped_relu.py
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import numpy
import chainer
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
if cuda.cudnn_enabled:
cudnn = cuda.cudnn
_mode = cuda.cuda.cudnn.CUDNN_ACTIVATION_CLIPPED_RELU
class ClippedReLU(function_node.FunctionNode):
"""Clipped Rectifier Unit function.
Clipped ReLU is written as
:math:`ClippedReLU(x, z) = \\min(\\max(0, x), z)`,
where :math:`z(>0)` is a parameter to cap return value of ReLU.
"""
_use_cudnn = False
def __init__(self, z):
if not isinstance(z, float):
raise TypeError('z must be float value')
# z must be positive.
assert z > 0
self.cap = z
def check_type_forward(self, in_types):
type_check._argname(in_types, ('x',))
x_type = in_types[0]
type_check.expect(x_type.dtype.kind == 'f')
def forward_cpu(self, inputs):
self.retain_inputs((0,))
x, = inputs
return utils.force_array(numpy.minimum(numpy.maximum(0, x), self.cap),
x.dtype),
def forward_gpu(self, inputs):
self.retain_inputs((0,))
x, = inputs
if chainer.should_use_cudnn('==always') and x.flags.c_contiguous:
self._use_cudnn = True
y = cudnn.activation_forward(x, _mode, self.cap)
self.retain_outputs((0,))
else:
return cuda.elementwise(
'T x, T cap', 'T y', 'y = min(max(x, (T)0), cap)',
'clipped_relu_fwd')(x, self.cap),
return y,
def backward(self, indexes, grad_outputs):
x, = self.get_retained_inputs()
if chainer.should_use_cudnn('==always') and self._use_cudnn:
y = self.get_retained_outputs()[0]
return ClippedReLUGrad3(x.data, y.data, self.cap).apply(
grad_outputs)
else:
return ClippedReLUGrad2(x.data, self.cap).apply(grad_outputs)
class ClippedReLUGrad2(function_node.FunctionNode):
"""Clipped Rectifier Unit gradient function."""
def __init__(self, x, z):
self.x = x
self.cap = z
def check_type_forward(self, in_types):
type_check._argname(in_types, ('gy',))
type_check.expect(in_types[0].dtype.kind == 'f')
def forward_cpu(self, inputs):
gy, = inputs
return utils.force_array(
gy * (0 < self.x) * (self.x < self.cap), self.x.dtype),
def forward_gpu(self, inputs):
gy, = inputs
gx = cuda.elementwise(
'T x, T gy, T z', 'T gx',
'gx = ((x > 0) & (x < z)) ? gy : (T)0',
'clipped_relu_bwd')(self.x, gy, self.cap)
return gx,
def backward(self, indexes, grad_outputs):
return ClippedReLUGrad2(self.x, self.cap).apply(grad_outputs)
class ClippedReLUGrad3(function_node.FunctionNode):
"""Clipped Rectifier Unit gradient function."""
def __init__(self, x, y, z):
self.x = x
self.y = y
self.cap = z
def check_type_forward(self, in_types):
type_check._argname(in_types, ('gy',))
type_check.expect(in_types[0].dtype.kind == 'f')
def forward_cpu(self, inputs):
gy, = inputs
return utils.force_array(
gy * (0 < self.x) * (self.x < self.cap), self.x.dtype),
def forward_gpu(self, inputs):
assert chainer.should_use_cudnn('==always')
return cudnn.activation_backward(self.x, self.y, inputs[0], _mode,
self.cap),
def backward(self, indexes, grad_outputs):
return ClippedReLUGrad3(self.x, self.y, self.cap).apply(grad_outputs)
def clipped_relu(x, z=20.0):
"""Clipped Rectifier Unit function.
For a clipping value :math:`z(>0)`, it computes
.. math:: \\text{ClippedReLU}(x, z) = \\min(\\max(0, x), z).
Args:
x (:class:`~chainer.Variable` or :ref:`ndarray`):
Input variable. A :math:`(s_1, s_2, ..., s_n)`-shaped float array.
z (float): Clipping value. (default = 20.0)
Returns:
~chainer.Variable: Output variable. A
:math:`(s_1, s_2, ..., s_n)`-shaped float array.
.. admonition:: Example
>>> x = np.random.uniform(-100, 100, (10, 20)).astype(np.float32)
>>> z = 10.0
>>> np.any(x < 0)
True
>>> np.any(x > z)
True
>>> y = F.clipped_relu(x, z=z)
>>> np.any(y.data < 0)
False
>>> np.any(y.data > z)
False
"""
y, = ClippedReLU(z).apply((x,))
return y