SparseConvNet/SparseConvNet.h at master · SpiderMonkey1975/SparseConvNet · GitHub

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//Ben Graham, University of Warwick, 2015, b.graham@warwick.ac.uk
//SparseConvNet is free software: you can redistribute it and/or modify
//it under the terms of the GNU General Public License as published by
//the Free Software Foundation, either version 3 of the License, or
//(at your option) any later version.

//SparseConvNet is distributed in the hope that it will be useful,
//but WITHOUT ANY WARRANTY; without even the implied warranty of
//MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
//GNU General Public License for more details.

#pragma once
#include "SpatiallySparseDataset.h"
#include <memory>
#include <string>
#include <fstream>

class SparseConvNetCUDA;

class SparseConvNet {
private:
  std::unique_ptr<SparseConvNetCUDA> cnn;
public:
  SparseConvNet(int dimension, int nInputFeatures, int nClasses, int nTop=1);
  ~SparseConvNet();
  void addLeNetLayerMP(int nFeatures, int filterSize, int filterStride, int poolSize, int poolStride, ActivationFunction activationFn=RELU, float dropout=0.0f, int minActiveInputs=1);
  void addLeNetLayerPOFMP(int nFeatures, int filterSize, int filterStride, int poolSize, float fmpShrink, ActivationFunction activationFn=RELU, float dropout=0.0f, int minActiveInputs=1);
  void addLeNetLayerROFMP(int nFeatures, int filterSize, int filterStride, int poolSize, float fmpShrink, ActivationFunction activationFn=RELU, float dropout=0.0f, int minActiveInputs=1);
  void addTerminalPoolingLayer(int poolSize);
  void addSoftmaxLayer();
  void addIndexLearnerLayer();
  float processDataset(SpatiallySparseDataset &dataset, int batchSize=100, float learningRate=0, float momentum=0.99);
  void processDatasetRepeatTest(SpatiallySparseDataset &dataset, int batchSize=100, int nReps=12, std::string predictionsFilename="",std::string header="",std::string confusionMatrixFilename="");
  float processIndexLearnerDataset(SpatiallySparseDataset &dataset, int batchSize=100, float learningRate=0.0,float momentum=0.99);
  void processDatasetDumpTopLevelFeatures(SpatiallySparseDataset &dataset, int batchSize, int reps=1);
  void loadWeights(std::string baseName, int epoch, int firstNlayers=1000000);
  void saveWeights(std::string baseName, int epoch);
  void calculateInputRegularizingConstants(SpatiallySparseDataset dataset);
};

class SparseConvTriangLeNet {
private:
  std::auto_ptr<SparseConvNetCUDA> cnn;
public:
  SparseConvTriangLeNet(int dimension, int nInputFeatures, int nClasses, int nTop=1);
  ~SparseConvTriangLeNet();
  void addLeNetLayerMP(int nFeatures, int filterSize, int filterStride, int poolSize, int poolStride, ActivationFunction activationFn=RELU, float dropout=0.0f, int minActiveInputs=1);
  void addTerminalPoolingLayer(int poolSize);
  void addSoftmaxLayer();
  void addIndexLearnerLayer();
  float processDataset(SpatiallySparseDataset &dataset, int batchSize=100, float learningRate=0, float momentum=0.99);
  void processDatasetRepeatTest(SpatiallySparseDataset &dataset, int batchSize=100, int nReps=12, std::string predictionsFilename="",std::string header="",std::string confusionMatrixFilename="");
  float processIndexLearnerDataset(SpatiallySparseDataset &dataset, int batchSize=100, float learningRate=0, float momentum=0.99);
  void processDatasetDumpTopLevelFeatures(SpatiallySparseDataset &dataset, int batchSize, int reps=1);
  void loadWeights(std::string baseName, int epoch, int firstNlayers=1000000);
  void saveWeights(std::string baseName, int epoch);
  void calculateInputRegularizingConstants(SpatiallySparseDataset dataset);
};