shenjingwangluo.zip_神经网络的编程实现

简单神经网络的Java编程实现 import java.util.Random; public class BpDeep{ public double[][] layer;//神经网络各层节点 public double[][] layerErr;//神经网络各节点误差 public double[][][] layer_weight;//各层节点权重 public double[][][] layer_weight_delta;//各层节点权重动量 public double mobp;//动量系数 public double rate;//学习系数 public BpDeep(int[] layernum, double rate, double mobp){ this.mobp = mobp; this.rate = rate; layer = new double[layernum.length][]; layerErr = new double[layernum.length][]; layer_weight = new double[layernum.length][][]; layer_weight_delta = new double[layernum.length][][]; Random random = new Random(); for(int l=0;l<layernum.length;l++){ layer[l]=new double[layernum[l]]; layerErr[l]=new double[layernum[l]]; if(l+1<layernum.length){ layer_weight[l]=new double[layernum[l]+1][layernum[l+1]]; layer_weight_delta[l]=new double[layernum[l]+1][layernum[l+1]]; for(int j=0;j<layernum[l]+1;j++) for(int i=0;i<layernum[l+1];i++) layer_weight[l][j][i]=random.nextDouble();//随机初始化权重 } } } //逐层向前计算输出 public double[] computeOut(double[] in){ for(int l=1;l<layer.length;l++){ for(int j=0;j<layer[l].length;j++){ double z=layer_weight[l-1][layer[l-1].length][j]; for(int i=0;i<layer[l-1].length;i++){ layer[l-1][i]=l==1?in[i]:layer[l-1][i]; z+=layer_weight[l-1][i][j]*layer[l-1][i]; } layer[l][j]=1/(1+Math.exp(-z)); } } return layer[layer.length-1]; } //逐层反向计算误差并修改权重 public void updateWeight(double[] tar){ int l=layer.length-1; for(int j=0;j<layerErr[l].length;j++) layerErr[l][j]=layer[l][j]*(1-layer[l][j])*(tar[j]-layer[l][j]); while(l-->0){ for(int j=0;j<layerErr[l].length;j++){ double z = 0.0; for(int i=0;i<layerErr[l+1].length;i++){ z=z+l>0?layerErr[l+1][i]*layer_weight[l][j][i]:0; layer_weight_delta[l][j][i]= mobp*layer_weight_delta[l][j][i]+rate*layerErr[l+1][i]*layer[l][j];//隐含层动量调整 layer_weight[l][j][i]+=layer_weight_delta[l][j][i];//隐含层权重调整 if(j==layerErr[l].length-1){ layer_weight_delta[l][j+1][i]= mobp*layer_weight_delta[l][j+1][i]+rate*layerErr[l+1][i];//截距动量调整 layer_weight[l][j+1][i]+=layer_weight_delta[l][j+1][i];//截距权重调整 } } layerErr[l][j]=z*layer[l][j]*(1-layer[l][j]);//记录误差 } } } public void train(double[] in, double[] tar){ double[] out = computeOut(in); updateWeight(tar); } }