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101 lines
3.1 KiB
101 lines
3.1 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 "GruCompute.h"
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#include "Layer.h"
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#include "SequenceToBatch.h"
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#include "paddle/math/Matrix.h"
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namespace paddle {
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/**
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* @brief Please refer to "Junyoung Chung, Empirical Evaluation
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* of Gated Recurrent Neural Networks on Sequence Modeling".
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*
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* GatedRecurrentLayer takes 1 input layer with size * 3.
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* Input layer is diveded into 3 equal parts: (xz_t, xr_t, xi_t).
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* parameter and biasParameter is also diveded into 3 equal parts:
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* - parameter consists of (U_z, U_r, U)
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* - baisParameter consists of (bias_z, bias_r, bias_o)
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*
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* \f[
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* update \ gate: z_t = actGate(xz_t + U_z * h_{t-1} + bias_z) \\
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* reset \ gate: r_t = actGate(xr_t + U_r * h_{t-1} + bias_r) \\
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* output \ candidate: {h}_t = actNode(xi_t + U * dot(r_t, h_{t-1}) + bias_o) \\
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* hidden \ activation: h_t = dot((1-z_t), h_{t-1}) + dot(z_t, {h}_t) \\
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* \f]
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*
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* @note
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* - dot denotes "element-wise multiplication".
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* - actNode is defined by config active_type
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* - actGate is defined by config actvie_gate_type
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*
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* The config file is grumemory.
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*/
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class GatedRecurrentLayer : public Layer, public GruCompute {
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public:
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explicit GatedRecurrentLayer(const LayerConfig& config) : Layer(config) {}
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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) override;
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void resetState() override;
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void setState(LayerStatePtr state) override;
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LayerStatePtr getState() override;
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protected:
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void forwardSequence(int batchSize,
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size_t numSequences,
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const int* starts,
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MatrixPtr inputValue);
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void backwardSequence(int batchSize,
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size_t numSequences,
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const int* starts,
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MatrixPtr inputGrad);
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void forwardBatch(int batchSize,
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size_t numSequences,
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const int* starts,
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MatrixPtr inputValue);
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void backwardBatch(int batchSize, MatrixPtr inputGrad);
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protected:
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std::unique_ptr<Weight> weight_;
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std::unique_ptr<Weight> gateWeight_;
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std::unique_ptr<Weight> stateWeight_;
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std::unique_ptr<Weight> bias_;
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Argument gate_;
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Argument resetOutput_;
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bool reversed_;
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bool useBatch_;
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std::unique_ptr<SequenceToBatch> batchValue_;
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std::unique_ptr<SequenceToBatch> batchGrad_;
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std::unique_ptr<ActivationFunction> activationGate_;
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MatrixPtr prevOutput_;
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};
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} // namespace paddle
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