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76 lines
2.8 KiB
76 lines
2.8 KiB
7 years ago
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/* 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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// Out = max(X, 0) - X * Labels + log(1 + exp(-abs(X)))
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template <typename Place, typename T>
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class SigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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const framework::Tensor *X = context.Input<framework::Tensor>("X");
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const framework::Tensor *Labels =
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context.Input<framework::Tensor>("Labels");
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framework::Tensor *Out = context.Output<framework::Tensor>("Out");
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Out->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto labels = framework::EigenVector<T>::Flatten(*Labels);
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auto out = framework::EigenVector<T>::Flatten(*Out);
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auto place = context.GetEigenDevice<Place>();
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// term1 = max(x, 0)
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auto term1 = x.cwiseMax(static_cast<T>(0));
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// term2 = x * labels
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auto term2 = x * labels;
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// term3 = log(1 + exp(-abs(x)))
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auto term3 = (static_cast<T>(1) + (-(x.abs())).exp()).log();
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out.device(place) = term1 - term2 + term3;
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}
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};
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// dX = sigmoid(X) - labels
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template <typename Place, typename T>
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class SigmoidCrossEntropyWithLogitsGradKernel : public framework::OpKernel {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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const framework::Tensor *X = context.Input<framework::Tensor>("X");
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const framework::Tensor *Labels =
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context.Input<framework::Tensor>("Labels");
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const framework::Tensor *dOut =
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context.Input<framework::Tensor>(framework::GradVarName("Out"));
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framework::Tensor *dX =
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context.Output<framework::Tensor>(framework::GradVarName("X"));
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dX->mutable_data<T>(context.GetPlace());
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auto x = framework::EigenVector<T>::Flatten(*X);
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auto labels = framework::EigenVector<T>::Flatten(*Labels);
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auto dout = framework::EigenVector<T>::Flatten(*dOut);
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auto dx = framework::EigenVector<T>::Flatten(*dX);
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auto place = context.GetEigenDevice<Place>();
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auto sigmoid_x = static_cast<T>(1) / (static_cast<T>(1) + (-x).exp());
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dx.device(place) = dout * (sigmoid_x - labels);
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}
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};
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} // namespace operators
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} // namespace paddle
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