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cleaning, prepred calling basis function

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Shin
2015-08-29 22:08:45 +02:00
parent 9e215751f0
commit 1f431ac27f
5 changed files with 44 additions and 30 deletions
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1
README.md
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@@ -1,2 +1,3 @@
Artifitial Neural Network Library
=================================
s

34
src/NeuralNetwork/FeedForward.cpp
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@@ -6,7 +6,7 @@ FFLayer::~FFLayer()
{
if(neurons!=nullptr)
{
for(size_t i=0;i<layerSize;i++)
for(size_t i=0;i<layerSize-1;i++)
{
delete neurons[i];
}
@@ -18,10 +18,10 @@ FFNeuron& FFLayer::operator[](const size_t& neuron)
{
if(neurons==nullptr)
{
neurons=new FFNeuron*[layerSize];
for(size_t i=0;i<layerSize;i++)
neurons=new FFNeuron*[layerSize-1];
for(size_t i=1;i<layerSize;i++)
{
neurons[i]=new FFNeuron(weights[i],outputs[i],inputs[i],lambda,function);
neurons[i-1]=new FFNeuron(weights[i],outputs[i],inputs[i],lambda,function);
}
}
@@ -97,18 +97,22 @@ FeedForward::~FeedForward()
}
delete[] ffLayers;
}
delete basisFunction;
}
void FeedForward::solvePart(float *newSolution, register size_t begin, size_t end,size_t prevSize, float *sol,size_t layer)
{
ActivationFunction::StreamingActivationFunction *function=dynamic_cast<ActivationFunction::StreamingActivationFunction*>(transfer[layer]);
if(prevSize >=4 && function !=nullptr)
BasisFunction::StreamingBasisFunction *bFunc=dynamic_cast<BasisFunction::StreamingBasisFunction*>(basisFunction);
size_t alignedPrev=prevSize>16?(prevSize-(prevSize%16)):0;
__m128 partialSolution;
if(prevSize >=4 && function !=nullptr && bFunc != nullptr)
{
__m128 partialSolution;
size_t alignedPrev=prevSize>16?(prevSize-(prevSize%16)):0;
for( size_t j=begin;j<end;j++)
{
partialSolution=basisFunction(prevSize,weights[layer][j],sol,alignedPrev);
partialSolution=bFunc->operator()(prevSize,weights[layer][j],sol,alignedPrev);
_mm_store_ss(inputs[layer]+j,partialSolution);
partialSolution=function->operator()(partialSolution);
_mm_store_ss(newSolution+j,partialSolution);
@@ -117,9 +121,17 @@ void FeedForward::solvePart(float *newSolution, register size_t begin, size_t en
{
for( size_t j=begin;j<end;j++)
{
const float tmp=basisFunction(prevSize,weights[layer][j],sol);
inputs[layer][j]=tmp;
newSolution[j]=transfer[layer]->operator()(tmp);
if (bFunc !=nullptr && prevSize >=4)
{
partialSolution=bFunc->operator()(prevSize,weights[layer][j],sol,alignedPrev);
_mm_store_ss(inputs[layer]+j,partialSolution);
newSolution[j]=transfer[layer]->operator()(inputs[layer][j]);
}else
{
const float tmp=basisFunction->operator()(prevSize,weights[layer][j],sol);
inputs[layer][j]=tmp;
newSolution[j]=transfer[layer]->operator()(tmp);
}
}
}
}

13
src/NeuralNetwork/FeedForward.h
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@@ -7,7 +7,9 @@
#include "ActivationFunction/Sigmoid.h"
#include "ActivationFunction/ActivationFunction.h"
#include "BasisFunction/BasisFunction.h"
#include "BasisFunction/StreamingBasisFunction.h"
#include "BasisFunction/FeedForward.h"
#include <vector>
@@ -37,8 +39,8 @@ namespace NeuralNetwork
FFNeuron(const FFNeuron&) = delete;
FFNeuron& operator=(const FFNeuron&) = delete;
inline virtual float getWeight(const size_t& i ) const override { return weights[i];}
inline virtual void setWeight(const size_t& i,const float &p) override { weights[i]=p; }
inline virtual float getWeight(const int& i ) const override { return weights[i+1];}
inline virtual void setWeight(const int& i,const float &p) override { weights[i+1]=p; }
inline virtual float output() const override { return out; }
inline virtual float input() const override { return inputs; }
@@ -61,8 +63,8 @@ namespace NeuralNetwork
FFLayer(const FFLayer &) = delete;
FFLayer& operator=(const FFLayer &) = delete;
virtual FFNeuron& operator[](const size_t& layer) override;
inline virtual size_t size() const override {return layerSize;};
virtual FFNeuron& operator[](const size_t& neuron) override;
inline virtual size_t size() const override {return layerSize-1;};
protected:
ActivationFunction::ActivationFunction &function;
FFNeuron **neurons=nullptr;
@@ -125,11 +127,10 @@ namespace NeuralNetwork
private:
FFLayer **ffLayers=nullptr;
float ***weights=nullptr;
float **potentials=nullptr;
float **outputs=nullptr;
float **inputs=nullptr;
ActivationFunction::ActivationFunction **transfer=nullptr;
BasisFunction::FeedForward basisFunction =BasisFunction::FeedForward();
BasisFunction::BasisFunction *basisFunction = new BasisFunction::FeedForward();
size_t *layerSizes=nullptr;
size_t layers;/**< Number of layers */
};

4
src/NeuralNetwork/Neuron.h
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@@ -17,14 +17,14 @@ namespace NeuralNetwork
*/
virtual ~Neuron() {};
virtual float getWeight(const size_t &w) const =0;
virtual float getWeight(const int &w) const =0;
/**
* @brief Sets weight
* @param i is number of neuron
* @param p is new weight for input neuron i
*/
virtual void setWeight(const size_t& i ,const float &p) =0;
virtual void setWeight(const int& i ,const float &p) =0;
/**
* @brief Returns output of neuron

22
tests/nn-05.cpp
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@@ -10,8 +10,8 @@ int main()
srand(time(NULL));
NeuralNetwork::FeedForward ns({1,1});
ns[1][1].setWeight(0,0);
ns[1][1].setWeight(1,1);
ns[1][0].setWeight(-1,0);
ns[1][0].setWeight(0,1);
Shin::Solution ss =ns.solve(Shin::Problem({1}));
@@ -23,17 +23,17 @@ int main()
NeuralNetwork::FeedForward xorF({2,2,1},0.8);
xorF[1][1].setWeight(0,-6.06);
xorF[1][1].setWeight(1,-11.62);
xorF[1][1].setWeight(2,10.99);
xorF[1][0].setWeight(-1,-6.06);
xorF[1][0].setWeight(0,-11.62);
xorF[1][0].setWeight(1,10.99);
xorF[1][2].setWeight(0,-7.19);
xorF[1][2].setWeight(1,12.88);
xorF[1][2].setWeight(2,-13-13);
xorF[1][1].setWeight(-1,-7.19);
xorF[1][1].setWeight(0,12.88);
xorF[1][1].setWeight(1,-13-13);
xorF[2][1].setWeight(0,-6.56);
xorF[2][1].setWeight(1,13.34);
xorF[2][1].setWeight(2,-7.19);
xorF[2][0].setWeight(-1,-6.56);
xorF[2][0].setWeight(0,13.34);
xorF[2][0].setWeight(1,-7.19);
ss= xorF.solve(Shin::Problem({0,1}));