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code cleanning and mistake in HyperbolicTangent

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Shin
2014-12-13 23:27:13 +01:00
parent d63a72d82a
commit ed304e0ef2
3 changed files with 25 additions and 22 deletions
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22
src/NeuralNetwork/FeedForward.cpp
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@@ -21,7 +21,7 @@ FFNeuron& FFLayer::operator[](const size_t& neuron)
neurons=new FFNeuron*[layerSize];
for(size_t i=0;i<layerSize;i++)
{
neurons[i]=new FFNeuron(potentials[i],weights[i],sums[i],inputs[i],lambda);
neurons[i]=new FFNeuron(potentials[i],weights[i],outputs[i],inputs[i],lambda,function);
}
}
@@ -39,10 +39,10 @@ FeedForward::FeedForward(std::initializer_list<size_t> s, double lam): ACyclicNe
weights= new float**[s.size()];
potentials= new float*[s.size()];
layerSizes= new size_t[s.size()];
sums= new float*[s.size()];
outputs= new float*[s.size()];
inputs= new float*[s.size()];
int i=0;
int prev_size=1;
register int prev_size=1;
for(int layeSize:s) // TODO rename
{
transfer[i]= new TransferFunction::Sigmoid(lam);
@@ -54,11 +54,11 @@ FeedForward::FeedForward(std::initializer_list<size_t> s, double lam): ACyclicNe
layerSizes[i]=layeSize;
weights[i]= new float*[layeSize];
potentials[i]= new float[layeSize];
sums[i]= new float[layeSize];
outputs[i]= new float[layeSize];
inputs[i]= new float[layeSize];
potentials[i][0]=1.0;
sums[i][0]=1.0;
outputs[i][0]=1.0;
for (int j=1;j<layeSize;j++)
{
potentials[i][j]=1.0;
@@ -84,13 +84,13 @@ FeedForward::~FeedForward()
}
delete[] weights[i];
delete[] potentials[i];
delete[] sums[i];
delete[] outputs[i];
delete[] inputs[i];
}
delete[] weights;
delete[] potentials;
delete[] layerSizes;
delete[] sums;
delete[] outputs;
delete[] inputs;
}
if(ffLayers !=nullptr)
@@ -156,15 +156,15 @@ void FeedForward::solvePart(float *newSolution, register size_t begin, size_t en
{
tmp+=sol[k]*weights[layer][j][k];
}
newSolution[j]=transfer[layer]->operator()(tmp);
inputs[layer][j]=tmp;
newSolution[j]=transfer[layer]->operator()(tmp);
}
}
}
Shin::Solution FeedForward::solve(const Shin::Problem& p)
{
register float* sol=sums[0];
register float* sol=outputs[0];
sol[0]=1;
for(size_t i=0;i<p.size();i++)
@@ -173,7 +173,7 @@ Shin::Solution FeedForward::solve(const Shin::Problem& p)
register size_t prevSize=layerSizes[0];
for(register size_t i=1;i<layers;i++)
{
float* newSolution= sums[i];
float* newSolution= outputs[i];
if(threads > 1 && (layerSizes[i] > 700 ||prevSize > 700)) // 700 is an guess about actual size, when creating thread has some speedup
{
std::vector<std::thread> th;
@@ -211,7 +211,7 @@ FFLayer& FeedForward::operator[](const size_t& l)
ffLayers=new FFLayer*[layers];
for(size_t i=0;i<layers;i++)
{
ffLayers[i]=new FFLayer(layerSizes[i],potentials[i],weights[i],sums[i],inputs[i],lambda);
ffLayers[i]=new FFLayer(layerSizes[i],potentials[i],weights[i],outputs[i],inputs[i],lambda,*transfer[i]);
}
}

19
src/NeuralNetwork/FeedForward.h
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@@ -6,6 +6,7 @@
#include "Network"
#include "TransferFunction/Sigmoid.h"
#include "TransferFunction/TransferFunction.h"
#include "TransferFunction/HyperbolicTangent.h"
#include <vector>
#include <initializer_list>
@@ -30,25 +31,26 @@ namespace NeuralNetwork
class FFNeuron : public Neuron
{
public:
inline FFNeuron(float &pot, float *w, float &outputF, float &i,float lam,TransferFunction::TransferFunction &fun):function(fun),potential(pot),weights(w),out(outputF),inputs(i),lambda(lam) { }
FFNeuron() = delete;
FFNeuron(const FFNeuron&) = delete;
FFNeuron& operator=(const FFNeuron&) = delete;
FFNeuron(float &pot, float *w, float &s, float &i,float lam):potential(pot),weights(w),sum(s),inputs(i),lambda(lam) { }
inline virtual float getPotential() const override {return potential;}
inline virtual void setPotential(const float& p) override { potential=p;}
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 output() const override { return sum; }
inline virtual float output() const override { return out; }
inline virtual float input() const override { return inputs; }
inline virtual float derivatedOutput() const override { return lambda*output()*(1.0-output()); }
inline virtual float derivatedOutput() const override { return function.derivatedOutput(inputs,out); }
protected:
TransferFunction::TransferFunction &function;
float &potential;
float *weights;
float &sum;
float &out;
float &inputs;
float lambda;
private:
@@ -57,7 +59,7 @@ namespace NeuralNetwork
class FFLayer: public Layer
{
public:
FFLayer(size_t s, float *p,float **w,float *su,float *in,float lam): layerSize(s),potentials(p),weights(w),sums(su),inputs(in),lambda(lam) {}
inline FFLayer(size_t s, float *p,float **w,float *out,float *in,float lam,TransferFunction::TransferFunction &fun): function(fun), layerSize(s),potentials(p),weights(w),outputs(out),inputs(in),lambda(lam) {}
~FFLayer();
FFLayer(const FFLayer &) = delete;
@@ -66,11 +68,12 @@ namespace NeuralNetwork
virtual FFNeuron& operator[](const size_t& layer) override;
inline virtual size_t size() const override {return layerSize;};
protected:
TransferFunction::TransferFunction &function;
FFNeuron **neurons=nullptr;
size_t layerSize;
float *potentials;
float **weights;
float *sums;
float *outputs;
float *inputs;
float lambda;
};
@@ -93,7 +96,7 @@ namespace NeuralNetwork
FFLayer **ffLayers=nullptr;
float ***weights=nullptr;
float **potentials=nullptr;
float **sums=nullptr;
float **outputs=nullptr;
float **inputs=nullptr;
TransferFunction::TransferFunction **transfer=nullptr;
size_t *layerSizes=nullptr;

6
src/NeuralNetwork/TransferFunction/HyperbolicTangent.h
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@@ -1,5 +1,5 @@
#ifndef __TRAN_SIGMOID_H_
#define __TRAN_SIGMOID_H_
#ifndef __TRAN_HYPTAN_H_
#define __TRAN_HYPTAN_H_
#include "./TransferFunction.h"
@@ -13,7 +13,7 @@ namespace TransferFunction
{
public:
HyperbolicTangent() {}
inline virtual float derivatedOutput(const float&,const float &output) override { return 1-pow(output); }
inline virtual float derivatedOutput(const float&,const float &output) override { return 1-pow(output,2); }
inline virtual float operator()(const float &x) override { return tanh(x); };
protected:
};