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- # 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.
- # ============================================================================
- """ ELBO """
-
- import mindspore.nn as nn
-
- from mindspore.ops import operations as P
-
-
- class ELBO(nn.Cell):
- """ ELBO class """
- def __init__(self, generator, variational):
- super().__init__()
- self.generator = generator
- self.variational = variational
- self.reshape_op = P.Reshape()
- self.reduce_mean = P.ReduceMean(keep_dims=False)
- self.square = P.Square()
-
- def construct(self, *inputs, **kwargs):
- if len(inputs) >= 2:
- x, y = inputs[0], inputs[1]
- else:
- x = inputs[0]
- y = None
-
- z, log_prob_z = self.variational(x, None, y)
- _, log_prob_x_, _, log_prob_z_ = self.generator(x, z, y)
-
- elbo = self.reduce_mean(log_prob_x_) + self.reduce_mean(log_prob_z_) - self.reduce_mean(log_prob_z)
- return -elbo
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