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neural_network.cpp
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neural_network.cpp
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// This code is a modification of the code from http://robotics.hobbizine.com/arduinoann.html
#include <arduino.h>
#include "neural_network.h"
#include <math.h>
void NeuralNetwork::initialize(float LearningRate, float Momentum, int DropoutRate) {
this->LearningRate = LearningRate;
this->Momentum = Momentum;
this->DropoutRate = DropoutRate;
for (int i = 0; i < (InputNodes+1) * HiddenNodes; ++i) {
HiddenWeights[i] = setWeight(random(InitialWeightMin*100, InitialWeightMax*100)/100.f); // Random generates ints
}
for (int i = 0; i < (HiddenNodes+1) * OutputNodes; ++i) {
OutputWeights[i] = setWeight(random(InitialWeightMin*100, InitialWeightMax*100)/100.f);
}
}
float NeuralNetwork::forward(const float Input[], const float Target[]){
float error = 0;
/******************************************************************
* Compute hidden layer activations
******************************************************************/
for (int i = 0; i < HiddenNodes; i++) {
float Accum = getWeight(HiddenWeights[InputNodes*HiddenNodes + i]);
for (int j = 0; j < InputNodes; j++) {
Accum += Input[j] * getWeight(HiddenWeights[j*HiddenNodes + i]);
}
Hidden[i] = 1.0 / (1.0 + exp(-Accum));
}
/******************************************************************
* Compute output layer activations and calculate errors
******************************************************************/
for (int i = 0; i < OutputNodes; i++) {
float Accum = getWeight(OutputWeights[HiddenNodes*OutputNodes + i]);
for (int j = 0; j < HiddenNodes; j++) {
Accum += Hidden[j] * getWeight(OutputWeights[j*OutputNodes + i]);
}
Output[i] = 1.0 / (1.0 + exp(-Accum));
// OutputDelta[i] = (Target[i] - Output[i]) * Output[i] * (1.0 - Output[i]);
error += 0.33333 * (Target[i] - Output[i]) * (Target[i] - Output[i]);
}
return error;
}
float NeuralNetwork::getWeight(weightType val) {
return val / weightFactor;
}
weightType NeuralNetwork::setWeight(float val) {
return val * weightFactor;
}
// Input will be changed!!
float NeuralNetwork::backward(float Input[], const float Target[]){
float error = 0;
for (int i = 0; i < InputNodes; i++) {
if (rand() % 100 < this->DropoutRate) {
Input[i] = 0;
}
}
// Forward
/******************************************************************
* Compute hidden layer activations
******************************************************************/
for (int i = 0; i < HiddenNodes; i++) {
float Accum = getWeight(HiddenWeights[InputNodes*HiddenNodes + i]);
for (int j = 0; j < InputNodes; j++) {
Accum += Input[j] * getWeight(HiddenWeights[j*HiddenNodes + i]);
}
Hidden[i] = 1.0 / (1.0 + exp(-Accum));
}
/******************************************************************
* Compute output layer activations and calculate errors
******************************************************************/
for (int i = 0; i < OutputNodes; i++) {
float Accum = getWeight(OutputWeights[HiddenNodes*OutputNodes + i]);
for (int j = 0; j < HiddenNodes; j++) {
Accum += Hidden[j] * getWeight(OutputWeights[j*OutputNodes + i]);
}
Output[i] = 1.0 / (1.0 + exp(-Accum)); // Sigmoid, from 0 to 1
OutputDelta[i] = (Target[i] - Output[i]) * Output[i] * (1.0 - Output[i]);
/*Serial.print("OutputDelta "); Serial.print(i); Serial.print(": "); Serial.println(OutputDelta[i]);
Serial.print("OutputDelta "); Serial.print(i); Serial.print(": "); Serial.println(OutputDelta[i]);
Serial.print("OutputAccoum "); Serial.print(i); Serial.print(": "); Serial.println(Accum);*/
error += 1/OutputNodes * (Target[i] - Output[i]) * (Target[i] - Output[i]);
}
// End forward
// Backward
/******************************************************************
* Backpropagate errors to hidden layer
******************************************************************/
for(int i = 0 ; i < HiddenNodes ; i++ ) {
float Accum = 0.0 ;
for(int j = 0 ; j < OutputNodes ; j++ ) {
Accum += getWeight(OutputWeights[i*OutputNodes + j]) * OutputDelta[j] ;
}
HiddenDelta[i] = Accum * Hidden[i] * (1.0 - Hidden[i]) ;
}
/******************************************************************
* Update Inner-->Hidden Weights
******************************************************************/
for(int i = 0 ; i < HiddenNodes ; i++ ) {
ChangeHiddenWeights[InputNodes*HiddenNodes + i] = setWeight(LearningRate * HiddenDelta[i] + Momentum * getWeight(ChangeHiddenWeights[InputNodes*HiddenNodes + i]));
HiddenWeights[InputNodes*HiddenNodes + i] += ChangeHiddenWeights[InputNodes*HiddenNodes + i];
for(int j = 0 ; j < InputNodes ; j++ ) {
ChangeHiddenWeights[j*HiddenNodes + i] = setWeight(LearningRate * Input[j] * HiddenDelta[i] + Momentum * getWeight(ChangeHiddenWeights[j*HiddenNodes + i]));
HiddenWeights[j*HiddenNodes + i] += ChangeHiddenWeights[j*HiddenNodes + i];
}
}
/******************************************************************
* Update Hidden-->Output Weights
******************************************************************/
for(int i = 0 ; i < OutputNodes ; i ++ ) {
ChangeOutputWeights[HiddenNodes*OutputNodes + i] = setWeight(LearningRate * OutputDelta[i] + Momentum * getWeight(ChangeOutputWeights[HiddenNodes*OutputNodes + i]));
OutputWeights[HiddenNodes*OutputNodes + i] += ChangeOutputWeights[HiddenNodes*OutputNodes + i];
for(int j = 0 ; j < HiddenNodes ; j++ ) {
ChangeOutputWeights[j*OutputNodes + i] = setWeight(LearningRate * Hidden[j] * OutputDelta[i] + Momentum * getWeight(ChangeOutputWeights[j*OutputNodes + i]));
OutputWeights[j*OutputNodes + i] += ChangeOutputWeights[j*OutputNodes + i];
}
}
return error;
}
float* NeuralNetwork::get_output(){
return Output;
}
weightType* NeuralNetwork::get_HiddenWeights(){
return HiddenWeights;
}
weightType* NeuralNetwork::get_OutputWeights(){
return OutputWeights;
}
float NeuralNetwork::get_error(){
return Error;
}