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First CPU implementation of HashedNets compression algorithm for DNNs #220

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818afb9
included xxhash.h
adi-sharma Jun 23, 2016
db6990f
added hashing initializations
adi-sharma Jun 24, 2016
efa2efb
added weightBucket
adi-sharma Jun 24, 2016
c821a57
edited applyWeights() without drop-out as an example
adi-sharma Jun 28, 2016
b6328a7
Updated all applyWeights() with HashedNets
adi-sharma Jul 1, 2016
a58ac1d
Update NeuralNet.h
adi-sharma Jul 1, 2016
c81f248
updated update() and Steepest Gradient Descent
adi-sharma Jul 1, 2016
96c8f42
Updated SGD operator() with HashedNets
adi-sharma Jul 1, 2016
0250775
updated weightDecay()
adi-sharma Jul 1, 2016
7c62f3b
updated weightDecay() with HashedNets
adi-sharma Jul 1, 2016
fe93bb2
Edited dropOutWeightFactor()
adi-sharma Jul 13, 2016
13d151a
Edited dropOutWeightFactor()
adi-sharma Jul 13, 2016
725bba3
Updated with HashedNets
adi-sharma Jul 18, 2016
4ec8917
Updated train() with HashedNets
adi-sharma Jul 18, 2016
b1caf73
Updated with complete HashedNets
adi-sharma Aug 23, 2016
c696958
Updated with complete HashedNets
adi-sharma Aug 23, 2016
8d6b2d4
Updated with Complete HashedNets
adi-sharma Aug 23, 2016
e277c73
Update MethodDNN.h
adi-sharma Aug 23, 2016
a820db6
Update NeuralNet.h
adi-sharma Aug 23, 2016
919a319
Updated with HashedNets
adi-sharma Aug 23, 2016
4d9e46c
Error corrections
adi-sharma Aug 24, 2016
3d7eec3
Error corrections NeuralNet.icc
adi-sharma Aug 24, 2016
d202ae1
Error corrections MethodDNN.cxx
adi-sharma Aug 24, 2016
52dc851
Successful compile
adi-sharma Aug 25, 2016
70484aa
Successful compile
adi-sharma Aug 25, 2016
6a0932f
Successful compile
adi-sharma Aug 25, 2016
4f5b919
Successful compile
adi-sharma Aug 25, 2016
96b29df
Successful build NeuralNet.h
adi-sharma Aug 25, 2016
f301784
Successful build NeuralNet.icc
adi-sharma Aug 25, 2016
9f1dce5
Successful build NeuralNet.cxx
adi-sharma Aug 25, 2016
f5e6943
Update NeuralNet.icc
adi-sharma Aug 26, 2016
c4f8749
Update NeuralNet.h
adi-sharma Aug 26, 2016
9637a69
Update MethodDNN.cxx
adi-sharma Aug 26, 2016
f62fcf2
Update NeuralNet.cxx
adi-sharma Aug 26, 2016
e48d24f
Made some logical changes in HashedNets
adi-sharma Aug 27, 2016
2bf3295
Made some logical changes in HashedNets
adi-sharma Aug 27, 2016
e048347
Update NeuralNet.icc
adi-sharma Aug 28, 2016
24c19c2
Production version v1.0
adi-sharma Aug 28, 2016
3c7f7ac
Production Version v1.0
adi-sharma Aug 28, 2016
e6332d5
Production Version v1.0
adi-sharma Aug 28, 2016
5c33612
Production Version v1.0
adi-sharma Aug 28, 2016
6eed664
Production Version v1.0
adi-sharma Aug 28, 2016
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Made some logical changes in HashedNets
  • Loading branch information
@adi-sharma
adi-sharma authored Aug 27, 2016
commit e48d24f443799e86be22da2bab29385fbc1954da
20 changes: 10 additions & 10 deletions tmva/tmva/inc/TMVA/NeuralNet.h
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Original file line number Diff line number Diff line change
Expand Up @@ -268,12 +268,12 @@ namespace TMVA


template <typename ItSource, typename ItTarget>
void applyWeights (ItSource itSourceBegin, ItSource itSourceEnd, int itWeight, std::vector<double>& weightBucket, ItTarget itTargetBegin, ItTarget itTargetEnd);
void applyWeights (ItSource itSourceBegin, ItSource itSourceEnd, int itWeight, std::vector<double>& weightBucket, size_t layerNumber, ItTarget itTargetBegin, ItTarget itTargetEnd);



template <bool HasDropOut, typename ItSource, typename ItPrev, typename ItDrop>
void applyWeightsBackwards (ItSource itCurrBegin, ItSource itCurrEnd, int itWeight, std::vector<double>& weightBucket, ItPrev itPrevBegin, ItPrev itPrevEnd, ItDrop itDrop);
template <typename ItSource, typename ItPrev, typename ItDrop>
void applyWeightsBackwards (ItSource itCurrBegin, ItSource itCurrEnd, int itWeight, std::vector<double>& weightBucket, size_t layerNumber, ItPrev itPrevBegin, ItPrev itPrevEnd, ItDrop itDrop);



Expand All @@ -292,7 +292,7 @@ namespace TMVA
void update (ItSource itSource, ItSource itSourceEnd,
ItDelta itTargetDeltaBegin, ItDelta itTargetDeltaEnd,
ItTargetGradient itTargetGradientBegin,
int itGradient, std::vector<double>& gradientBucket);
int itGradient, std::vector<double>& gradientBucket, size_t layerNumber);



Expand All @@ -301,7 +301,7 @@ namespace TMVA
ItDelta itTargetDeltaBegin, ItDelta itTargetDeltaEnd,
ItTargetGradient itTargetGradientBegin,
int itGradient, std::vector<double>& gradientBucket,
int itWeight, std::vector<double>& weightBucket, double& weightDecay);
int itWeight, std::vector<double>& weightBucket, double& factorWeightDecay, size_t layerNumber);



Expand Down Expand Up @@ -420,7 +420,7 @@ namespace TMVA


template <typename EnumRegularization>
double weightDecay (double error, int currLayerWeightIndex, int nextLayerWeightIndex, std::vector<double>& weightBucket, double factorWeightDecay, EnumRegularization eRegularization);
double weightDecay (double error, int currLayerWeightIndex, int nextLayerWeightIndex, std::vector<double>& weightBucket, double factorWeightDecay, EnumRegularization eRegularization, size_t layerNumber);



Expand Down Expand Up @@ -726,15 +726,15 @@ namespace TMVA


template <typename LAYERDATA>
void forward (const LAYERDATA& prevLayerData, LAYERDATA& currLayerData, std::vector<double>& weightBucket);
void forward (const LAYERDATA& prevLayerData, LAYERDATA& currLayerData, std::vector<double>& weightBucket, size_t layerNumber);


template <typename LAYERDATA>
void backward (LAYERDATA& prevLayerData, LAYERDATA& currLayerData, std::vector<double>& weightBucket);
void backward (LAYERDATA& prevLayerData, LAYERDATA& currLayerData, std::vector<double>& weightBucket, size_t layerNumber);


template <typename LAYERDATA>
void update (const LAYERDATA& prevLayerData, LAYERDATA& currLayerData, double factorWeightDecay, EnumRegularization regularization, std::vector<double>& weightBucket, std::vector<double>& gradientBucket);
void update (const LAYERDATA& prevLayerData, LAYERDATA& currLayerData, double factorWeightDecay, EnumRegularization regularization, std::vector<double>& weightBucket, std::vector<double>& gradientBucket, size_t layerNumber);



Expand Down Expand Up @@ -1255,7 +1255,7 @@ namespace TMVA
double patternWeight,
std::vector<double>& weightBucket,
double factorWeightDecay,
EnumRegularization eRegularization) const;
EnumRegularization eRegularization, size_t layerNumber) const;


const std::vector<Layer>& layers () const { return m_layers; } ///< returns the layers (structure)
Expand Down