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42 lines
1.7 KiB
42 lines
1.7 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""CrossEntropy"""
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common import dtype as mstype
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from mindspore.nn.loss.loss import _Loss
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from mindspore.ops import functional as F
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from mindspore.ops import operations as P
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class CrossEntropy(_Loss):
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"""CrossEntropy"""
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def __init__(self, smooth_factor=0., num_classes=1000):
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super(CrossEntropy, self).__init__()
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self.onehot = P.OneHot()
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self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
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self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32)
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# self.cast = P.Cast()
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self.ce = nn.SoftmaxCrossEntropyWithLogits()
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self.mean = P.ReduceMean(False)
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def construct(self, logit, label):
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# one_hot_label = self.onehot(self.cast(label, mstype.int32),
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# F.shape(logit)[1], self.on_value, self.off_value)、
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one_hot_label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value)
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loss = self.ce(logit, one_hot_label)
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loss = self.mean(loss, 0)
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return loss
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