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