# 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.
# ============================================================================
"""Power Bijector"""
from .power_transform import PowerTransform
[docs]class Exp(PowerTransform):
r"""
Exponential Bijector.
This Bijector performs the operation: Y = exp(x).
Args:
name (str): name of the bijector. Default: 'Exp'.
Examples:
>>> # To initialize a Exp bijector
>>> import mindspore.nn.probability.bijector as msb
>>> n = msb.Exp()
>>>
>>> # To use Exp distribution in a network
>>> class net(Cell):
>>> def __init__(self):
>>> super(net, self).__init__():
>>> self.e1 = msb.Exp()
>>>
>>> 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,
name='Exp'):
param = dict(locals())
super(Exp, self).__init__(name=name, param=param)