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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.
- # ============================================================================
- """define loss function for network"""
- from mindspore.nn.loss.loss import _Loss
- from mindspore.ops import operations as P
- from mindspore.ops import functional as F
- from mindspore import Tensor
- from mindspore.common import dtype as mstype
- import mindspore.nn as nn
-
-
- class CrossEntropy(_Loss):
- """the redefined loss function with SoftmaxCrossEntropyWithLogits"""
-
- def __init__(self, smooth_factor=0., num_classes=1001):
- super(CrossEntropy, self).__init__()
- self.onehot = P.OneHot()
- self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
- self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
- self.ce = nn.SoftmaxCrossEntropyWithLogits()
- self.mean = P.ReduceMean(False)
-
- def construct(self, logit, label):
- one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
- loss = self.ce(logit, one_hot_label)
- loss = self.mean(loss, 0)
- return loss
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