1# Copyright 2020 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15"""define loss function for network""" 16import mindspore.nn as nn 17from mindspore import Tensor 18from mindspore.common import dtype as mstype 19from mindspore.nn.loss.loss import LossBase 20from mindspore.ops import functional as F 21from mindspore.ops import operations as P 22 23 24class CrossEntropySmooth(LossBase): 25 """CrossEntropy""" 26 def __init__(self, sparse=True, reduction='mean', smooth_factor=0., num_classes=1000): 27 super(CrossEntropySmooth, self).__init__() 28 self.onehot = P.OneHot() 29 self.sparse = sparse 30 self.on_value = Tensor(1.0 - smooth_factor, mstype.float32) 31 self.off_value = Tensor(1.0 * smooth_factor / (num_classes - 1), mstype.float32) 32 self.ce = nn.SoftmaxCrossEntropyWithLogits(reduction=reduction) 33 34 def construct(self, logit, label): 35 if self.sparse: 36 label = self.onehot(label, F.shape(logit)[1], self.on_value, self.off_value) 37 loss = self.ce(logit, label) 38 return loss 39