BP神经网络的六个C++范本

#include <iostream> #include <math.h> #include <time.h> #include "bp.h" using namespace std; const int TrainingSet[4][3]={ {0,0,0}, {0,1,1}, {1,0,1}, {1,1,0}, }; NeuralNetworkLayer::NeuralNetworkLayer() { ParentLayer = NULL; ChildLayer = NULL; } void NeuralNetworkLayer::Initialize(int NumNodes,NeuralNetworkLayer* parent,NeuralNetworkLayer* child) { int i, j; NeuronValues = (double*) malloc(sizeof(double) * NumberOfNodes); DesiredValues = (double*) malloc(sizeof(double) * NumberOfNodes); Errors = (double*) malloc(sizeof(double) * NumberOfNodes); if(parent != NULL) { ParentLayer = parent; } if(child != NULL) { ChildLayer = child; Weights = (double**) malloc(sizeof(double*) *NumberOfNodes); WeightChanges = (double**) malloc(sizeof(double*) *NumberOfNodes); for(i = 0; i<NumberOfNodes; i++) { Weights[i] = (double*) malloc(sizeof(double) * NumberOfChildNodes); WeightChanges[i] = (double*) malloc(sizeof(double) * NumberOfChildNodes); } BiasValues = (double*) malloc(sizeof(double) *NumberOfChildNodes); BiasWeights = (double*) malloc(sizeof(double) *NumberOfChildNodes); } else { Weights = NULL; BiasValues = NULL; BiasWeights = NULL; WeightChanges = NULL; } for(i=0; i<NumberOfNodes; i++) { NeuronValues[i] = 0; DesiredValues[i] = 0; Errors[i] = 0; if(ChildLayer != NULL) for(j=0; j<NumberOfChildNodes; j++) { Weights[i][j] = 0; WeightChanges[i][j] = 0; } } if(ChildLayer != NULL) for(j=0; j<NumberOfChildNodes; j++) { BiasValues[j] = 0; BiasWeights[j] = 0; } } void NeuralNetworkLayer::CleanUp(void) { int i; free(NeuronValues); free(DesiredValues); free(Errors); if(Weights != NULL) { for(i = 0; i<NumberOfNodes; i++) { free(Weights[i]); free(WeightChanges[i]); } free(Weights); free(WeightChanges); } if(BiasValues != NULL) free(BiasValues); if(BiasWeights != NULL) free(BiasWeights); } void NeuralNetworkLayer::RandomizeWeights(void) { int i,j; int min = 0; int max = 200; int number; srand( (unsigned)time( NULL ) ); for(i=0; i<NumberOfNodes; i++) { for(j=0; j<NumberOfChildNodes; j++) { number = (((abs(rand())%(max-min+1))+min)); if(number>max) number = max; if(number<min) number = min; Weights[i][j] = number / 100.0f - 1; } } for(j=0; j<NumberOfChildNodes; j++) { number = (((abs(rand())%(max-min+1))+min)); if(number>max) number = max; if(number<min) number = min; BiasWeights[j] = number / 100.0f - 1; } } void NeuralNetworkLayer::CalculateNeuronValues(void) { int i,j; double x; if (ParentLayer != NULL) { for(j=0; j<NumberOfNodes; j++) { x = 0; for(i=0; i<NumberOfParentNodes; i++) { x += ParentLayer->NeuronValues[i] * ParentLayer->Weights[i][j]; } x += ParentLayer->BiasValues[j] * ParentLayer->BiasWeights[j]; NeuronValues[j] = x; } } } void NeuralNetworkLayer::CalculateErrors(void) { int i, j; double sum; if(ChildLayer == NULL) { for(i=0; i<NumberOfNodes; i++) { Errors[i] = (DesiredValues[i] - NeuronValues[i]) * NeuronValues[i] * (1.0f - NeuronValues[i]); } } else if(ParentLayer == NULL) { for(i=0; i<NumberOfNodes; i++) { Errors[i] = 0.0f; } } else { for(i=0; i<NumberOfNodes; i++) { sum = 0; for(j=0; j<NumberOfChildNodes; j++) { sum += ChildLayer->Errors[j] * Weights[i][j]; } Errors[i] = sum * NeuronValues[i] * (1.0f - NeuronValues[i]); } } } void NeuralNetworkLayer::AdjustWeights(void) { int i, j; double dw; if(ChildLayer != NULL) { for(i=0; i<NumberOfNodes; i++) { for(j=0; j<NumberOfChildNodes; j++) { dw = LearningRate * ChildLayer->Errors[j] * NeuronValues[i]; Weights[i][j] += dw; } } for(j=0; j<NumberOfChildNodes; j++) { BiasWeights[j] += LearningRate * ChildLayer->Errors[j] * BiasValues[j]; } } } void NeuralNetworkLayer::OutData() { } void NeuralNetwork::Initialize(int nNodesInput, int nNodesHidden,int nNodesOutput) { InputLayer.NumberOfNodes = nNodesInput; //输入层个数 InputLayer.NumberOfChildNodes = nNodesHidden; InputLayer.NumberOfParentNodes = 0; InputLayer.Initialize(nNodesInput, NULL, &HiddenLayer); InputLayer.RandomizeWeights(); HiddenLayer.NumberOfNodes = nNodesHidden; //隐含层个数 HiddenLayer.NumberOfChildNodes = nNodesOutput; HiddenLayer.NumberOfParentNodes = nNodesInput; HiddenLayer.Initialize(nNodesHidden,&InputLayer,&OutputLayer); HiddenLayer.RandomizeWeights(); OutputLayer.NumberOfNodes = nNodesOutput; //输出层个数 OutputLayer.NumberOfChildNodes = 0; OutputLayer.NumberOfParentNodes = nNodesHidden; OutputLayer.Initialize(nNodesOutput, &HiddenLayer, NULL); } void NeuralNetwork::CleanUp() { InputLayer.CleanUp(); HiddenLayer.CleanUp(); OutputLayer.CleanUp(); } void NeuralNetwork::SetInput(int i, double value) //设定输入值 { if((i>=0) && (i<InputLayer.NumberOfNodes)) { InputLayer.NeuronValues[i] = value; } } /*double NeuralNetwork::GetOutput(int i) { return OutputLayer.NeuronValues[i]; } */ void NeuralNetwork::SetDesiredOutput(int i, double value) //期望的输出 { if((i>=0) && (i<OutputLayer.NumberOfNodes)) { OutputLayer.DesiredValues[i] = value; } } void NeuralNetwork::FeedForward(void) { InputLayer.CalculateNeuronValues (); HiddenLayer.CalculateNeuronValues (); OutputLayer.CalculateNeuronValues (); } void NeuralNetwork::BackPropagate(void) { OutputLayer.CalculateErrors(); HiddenLayer.CalculateErrors(); HiddenLayer.AdjustWeights(); InputLayer.AdjustWeights(); } double NeuralNetwork::CalculateError(void) { int i; double error = 0; for(i=0; i<OutputLayer.NumberOfNodes; i++) { error += pow(OutputLayer.NeuronValues[i] - OutputLayer.DesiredValues[i], 2); } error = error / 2; return error; } void NeuralNetwork::SetLearningRate(double rate) //设定学习率 { InputLayer.LearningRate = rate; HiddenLayer.LearningRate = rate; OutputLa