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76 lines
2.8 KiB
76 lines
2.8 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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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http://www.apache.org/licenses/LICENSE-2.0
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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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#pragma once
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#include "paddle/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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template <typename T, int MajorType = Eigen::RowMajor,
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typename IndexType = Eigen::DenseIndex>
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using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
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template <typename DeviceContext, typename T, typename AttrType = T>
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class LogLossKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* loss_out = ctx.Output<Tensor>("Loss");
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loss_out->mutable_data<T>(ctx.GetPlace());
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auto epsilon = static_cast<T>(ctx.Attr<AttrType>("epsilon"));
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auto prediction = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Predicted"));
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auto label = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Labels"));
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auto loss = EigenVector<T>::Flatten(*loss_out);
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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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loss.device(place) = (-(label * (prediction + epsilon).log()) -
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((static_cast<T>(1) - label) *
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(static_cast<T>(1) - prediction + epsilon).log()));
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}
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};
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template <typename DeviceContext, typename T, typename AttrType = T>
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class LogLossGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto epsilon = static_cast<T>(ctx.Attr<AttrType>("epsilon"));
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auto prediction = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Predicted"));
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auto label = EigenVector<T>::Flatten(*ctx.Input<Tensor>("Labels"));
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auto* dloss = ctx.Input<Tensor>(framework::GradVarName("Loss"));
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auto* dpred = ctx.Output<Tensor>(framework::GradVarName("Predicted"));
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auto dl = EigenVector<T>::Flatten(*dloss);
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auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
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if (dpred) {
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dpred->mutable_data<T>(ctx.GetPlace());
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auto dx = framework::EigenVector<T>::Flatten(*dpred);
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dx.device(place) = dl * (-(label / (prediction + epsilon)) +
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((static_cast<T>(1) - label) /
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(static_cast<T>(1) - prediction + epsilon)));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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