/
exponential.py
136 lines (94 loc) · 3.07 KB
/
exponential.py
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import math
import numpy
from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check
class Exp(function_node.FunctionNode):
@property
def label(self):
return 'exp'
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.retain_outputs((0,))
return utils.force_array(numpy.exp(x[0])),
def forward_gpu(self, x):
self.retain_outputs((0,))
return cuda.cupy.exp(x[0]),
def backward(self, indexes, gy):
y = self.get_retained_outputs()[0]
return y * gy[0],
def exp(x):
"""Elementwise exponential function."""
return Exp().apply((x,))[0]
class Log(function_node.FunctionNode):
@property
def label(self):
return 'log'
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.retain_inputs((0,))
return utils.force_array(numpy.log(x[0])),
def forward_gpu(self, x):
self.retain_inputs((0,))
return cuda.cupy.log(x[0]),
def backward(self, indexes, gy):
x = self.get_retained_inputs()[0]
return utils.force_array(gy[0] / x),
def log(x):
"""Elementwise natural logarithm function."""
return Log().apply((x,))[0]
class Log2(function_node.FunctionNode):
@property
def label(self):
return 'log2'
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(self, inputs):
self.retain_inputs((0,))
x = inputs[0]
xp = cuda.get_array_module(x)
return utils.force_array(xp.log2(x)),
def backward(self, indexes, gy):
x = self.get_retained_inputs()[0]
return gy[0] / x * (1 / math.log(2)),
def log2(x):
"""Elementwise logarithm function to the base 2.
.. math::
y_i = \\log_2 x_i.
Args:
x (~chainer.Variable): Input variable.
Returns:
~chainer.Variable: Output variable.
"""
return Log2().apply((x,))[0]
class Log10(function_node.FunctionNode):
@property
def label(self):
return 'log10'
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(self, inputs):
self.retain_inputs((0,))
x = inputs[0]
xp = cuda.get_array_module(x)
return utils.force_array(xp.log10(x)),
def backward(self, indexes, gy):
x = self.get_retained_inputs()[0]
return gy[0] / x * (1 / math.log(10)),
def log10(x):
"""Elementwise logarithm function to the base 10.
.. math::
y_i = \\log_{10} x_i.
Args:
x (~chainer.Variable): Input variable.
Returns:
~chainer.Variable: Output variable.
"""
return Log10().apply((x,))[0]