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81 lines
2.7 KiB
81 lines
2.7 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
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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 "Layer.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/utils/ThreadLocal.h"
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namespace paddle {
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/**
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* @brief The Factorization Machine models pairwise (order-2) feature
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* interactions as inner product of the learned latent vectors corresponding
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* to each input feature.
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*
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* The Factorization Machine can effectively capture feature interactions
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* especially when the input is sparse. While in principle FM can model higher
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* order feature interaction, in practice usually only order-2 feature
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* interactions are considered. The Factorization Machine Layer here only
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* computes the order-2 interations with the formula:
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*
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* \f[
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* y = \sum_{i=1}^{n-1}\sum_{j=i+1}^n\langle v_i, v_j \rangle x_i x_j
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* \f]
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*
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* The detailed calculation for forward and backward can be found at this paper:
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*
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* Factorization machines.
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*
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* The config file api is factorization_machine.
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*/
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class FactorizationMachineLayer : public Layer {
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protected:
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// The latent vectors, shape: (size, factorSize_)
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// Each row of the latentVectors_ matrix is the latent vector
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// corresponding to one input feature dimension
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std::unique_ptr<Weight> latentVectors_;
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// The hyperparameter that defines the dimensionality of the factorization
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size_t factorSize_;
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private:
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// Store the square values of the letent vectors matrix
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MatrixPtr latentVectorsSquare_;
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// Store the square values of input matrix
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MatrixPtr inputSquare_;
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// The result of input matrix * latent vector matrix that will be used in
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// both forward and backward step
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MatrixPtr inputMulFactor_;
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// Store temporary calculation result
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MatrixPtr tmpOut_;
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MatrixPtr tmpSum_;
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MatrixPtr tmpInput_;
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// Negative identity matrix
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MatrixPtr negOnes_;
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public:
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explicit FactorizationMachineLayer(const LayerConfig& config)
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: Layer(config) {}
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~FactorizationMachineLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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} // namespace paddle
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