# 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:
.. math::
Y = \exp(x).
Args:
name (str): The name of the Bijector. Default: 'Exp'.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import mindspore.nn as nn
>>> from mindspore import Tensor
>>>
>>> # To initialize an Exp bijector.
>>> exp_bijector = nn.probability.bijector.Exp()
>>> value = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> ans1 = exp_bijector.forward(value)
>>> print(ans1.shape)
(3,)
>>> ans2 = exp_bijector.inverse(value)
>>> print(ans2.shape)
(3,)
>>> ans3 = exp_bijector.forward_log_jacobian(value)
>>> print(ans3.shape)
(3,)
>>> ans4 = exp_bijector.inverse_log_jacobian(value)
>>> print(ans4.shape)
(3,)
"""
def __init__(self,
name='Exp'):
super(Exp, self).__init__(name=name)
def extend_repr(self):
if self.is_scalar_batch:
str_info = 'exp'
else:
str_info = f'batch_shape = {self.batch_shape}'
return str_info