# 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.
# ============================================================================
"""Scalar Affine Bijector"""
from mindspore.ops import operations as P
from mindspore._checkparam import Validator as validator
from ..distribution._utils.utils import cast_to_tensor
from ..distribution._utils.custom_ops import log_generic
from .bijector import Bijector
[docs]class ScalarAffine(Bijector):
"""
Scalar Affine Bijector.
This Bijector performs the operation: Y = a * X + b, where a is the scale
factor and b is the shift factor.
Args:
scale (float): scale factor. Default: 1.0.
shift (float): shift factor. Default: 0.0.
name (str): name of the bijector. Default: 'ScalarAffine'.
Examples:
>>> # To initialize a ScalarAffine bijector of scale 1 and shift 2
>>> scalaraffine = nn.probability.bijector.ScalarAffine(1, 2)
>>>
>>> # To use ScalarAffine bijector in a network
>>> class net(Cell):
>>> def __init__(self):
>>> super(net, self).__init__():
>>> self.s1 = nn.probability.bijector.ScalarAffine(1, 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.s1.forward(value)
>>> ans2 = self.s1.inverse(value)
>>> ans3 = self.s1.forward_log_jacobian(value)
>>> ans4 = self.s1.inverse_log_jacobian(value)
"""
def __init__(self,
scale=1.0,
shift=0.0,
name='ScalarAffine'):
"""
Constructor of scalar affine bijector.
"""
param = dict(locals())
validator.check_value_type('scale', scale, [int, float], type(self).__name__)
validator.check_value_type('shift', shift, [int, float], type(self).__name__)
self._scale = cast_to_tensor(scale)
self._shift = cast_to_tensor(shift)
super(ScalarAffine, self).__init__(
is_constant_jacobian=True,
is_injective=True,
name=name,
dtype=None,
param=param)
self.abs = P.Abs()
self.oneslike = P.OnesLike()
self.log = log_generic
@property
def scale(self):
return self._scale
@property
def shift(self):
return self._shift
def extend_repr(self):
str_info = f'scale = {self.scale}, shift = {self.shift}'
return str_info
def shape_mapping(self, shape):
return shape
def _forward(self, x):
r"""
.. math::
f(x) = a * x + b
"""
x = self._check_value(x, 'value')
return self.scale * x + self.shift * self.oneslike(x)
def _inverse(self, y):
r"""
.. math::
f(y) = \frac{y - b}{a}
"""
y = self._check_value(y, 'value')
return (y - self.shift) / self.scale
def _forward_log_jacobian(self, x):
r"""
.. math::
f(x) = a * x + b
f'(x) = a
\log(f'(x)) = \log(a)
"""
x = self._check_value(x, 'value')
return self.log(self.abs(self.scale))
def _inverse_log_jacobian(self, y):
r"""
.. math::
f(y) = \frac{(y - b)}{a}
f'(x) = \frac{1.0}{a}
\log(f'(x)) = - \log(a)
"""
y = self._check_value(y, 'value')
return -1. * self.log(self.abs(self.scale))