# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Softplus Bijector"""
import numpy as np
from mindspore.ops import operations as P
from mindspore.common import dtype as mstype
from mindspore.nn.layer.activation import LogSigmoid
from mindspore._checkparam import Validator as validator
from ..distribution._utils.utils import cast_to_tensor
from ..distribution._utils.custom_ops import exp_generic, expm1_generic, log_generic
from .bijector import Bijector
[docs]class Softplus(Bijector):
r"""
Softplus Bijector.
This Bijector performs the operation, where k is the sharpness factor.
.. math::
Y = \frac{\log(1 + e ^ {kX})}{k}
Args:
sharpness (float): scale factor. Default: 1.0.
name (str): name of the bijector. Default: 'Softplus'.
Examples:
>>> # To initialize a Softplus bijector of sharpness 2
>>> softplus = nn.probability.bijector.Softfplus(2)
>>>
>>> # To use ScalarAffine bijector in a network
>>> class net(Cell):
>>> def __init__(self):
>>> super(net, self).__init__():
>>> self.sp1 = nn.probability.bijector.Softflus(2)
>>>
>>> def construct(self, value):
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'forward' with the name of the function
>>> ans1 = self.sp1.forward(value)
>>> ans2 = self.sp1.inverse(value)
>>> ans3 = self.sp1.forward_log_jacobian(value)
>>> ans4 = self.sp1.inverse_log_jacobian(value)
"""
def __init__(self,
sharpness=1.0,
name='Softplus'):
param = dict(locals())
validator.check_value_type('sharpness', sharpness, [int, float], type(self).__name__)
super(Softplus, self).__init__(name=name, param=param)
self._sharpness = cast_to_tensor(sharpness)
self.exp = exp_generic
self.log = log_generic
self.expm1 = expm1_generic
self.abs = P.Abs()
self.fill = P.Fill()
self.greater = P.Greater()
self.less = P.Less()
self.log_sigmoid = LogSigmoid()
self.logicalor = P.LogicalOr()
self.select = P.Select()
self.shape = P.Shape()
self.sigmoid = P.Sigmoid()
self.softplus = self._softplus
self.inverse_softplus = self._inverse_softplus
self.threshold = np.log(np.finfo(np.float32).eps) + 1
self.tiny = np.exp(self.threshold)
def _softplus(self, x):
too_small = self.less(x, self.threshold)
too_large = self.greater(x, -self.threshold)
too_small_value = self.exp(x)
too_large_value = x
ones = self.fill(mstype.float32, self.shape(x), 1.0)
too_small_or_too_large = self.logicalor(too_small, too_large)
x = self.select(too_small_or_too_large, ones, x)
y = self.log(self.exp(x) + 1.0)
return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y))
def _inverse_softplus(self, x):
r"""
.. math::
f(x) = \frac{\log(1 + e^{x}))}
f^{-1}(y) = \frac{\log(e^{y} - 1)}
"""
too_small = self.less(x, self.tiny)
too_large = self.greater(x, -self.threshold)
too_small_value = self.log(x)
too_large_value = x
ones = self.fill(mstype.float32, self.shape(x), 1.0)
too_small_or_too_large = self.logicalor(too_small, too_large)
x = self.select(too_small_or_too_large, ones, x)
y = x + self.log(self.abs(self.expm1(-x)))
return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y))
@property
def sharpness(self):
return self._sharpness
def extend_repr(self):
str_info = f'sharpness = {self.sharpness}'
return str_info
def shape_mapping(self, shape):
return shape
def _forward(self, x):
x = self._check_value(x, 'value')
scaled_value = self.sharpness * x
return self.softplus(scaled_value) / self.sharpness
def _inverse(self, y):
r"""
.. math::
f(x) = \frac{\log(1 + e^{kx}))}{k}
f^{-1}(y) = \frac{\log(e^{ky} - 1)}{k}
"""
y = self._check_value(y, 'value')
scaled_value = self.sharpness * y
return self.inverse_softplus(scaled_value) / self.sharpness
def _forward_log_jacobian(self, x):
r"""
.. math:
f(x) = \log(1 + e^{kx}) / k
f'(x) = \frac{e^{kx}}{ 1 + e^{kx}}
\log(f'(x)) = kx - \log(1 + e^{kx}) = kx - f(kx)
"""
x = self._check_value(x, 'value')
scaled_value = self.sharpness * x
return self.log_sigmoid(scaled_value)
def _inverse_log_jacobian(self, y):
r"""
.. math:
f(y) = \frac{\log(e^{ky} - 1)}{k}
f'(y) = \frac{e^{ky}}{e^{ky} - 1}
\log(f'(y)) = ky - \log(e^{ky} - 1) = ky - f(ky)
"""
y = self._check_value(y, 'value')
scaled_value = self.sharpness * y
return scaled_value - self.inverse_softplus(scaled_value)