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RT，这个Demo是我去年写，功能上没有大问题 该demo有3个缺点 1、代码性能一般，有一定的优化空间。由于是Demo，当时写的时候只考虑功能，没考虑性能 2、神经细胞层数被写死了，只有2层，如果要添加细胞层需要改源码。 3、训练任然是串行的，没有充分利用分布式框架并行计算优势。但是后续的计算任然是并行的，只要添加DRPC Client或者自定义DSpout就行了 帖子里我就不介绍Storm和神经网络原理了 我会单独写一篇博文介绍，预计本周内完成，连接：http://blog.csdn.net/tntzbzc/article/details/19974515 Spout启动+Drpc Client 训练++Drpc Client 计算 三个我放在一个main中了 第一个是服务端job 启动 第二个是训练（串行），测试的训练数据是任意整数，把它转成32个由0或1组成的double数组作为输入参数(input),以及一个double[4]的结果值(real) real[4]：1 0 0 0 正奇数 0 1 0 0 正偶数 0 0 1 0 负奇数 0 0 0 1 负偶数 两层细胞的权重weight是随机生成的 随机生成了1000个样本，训练2000次 第三个是计算，如果想实现分布式并行计算，可以自己添加Client
public class DrpcClient {
/****************************************
* by CSDN 撸大湿 Email : tntzhou@hotmail.com
****************************************/
public static void main(String[] args) throws Exception {
int InputHideCount = 32; /* Input Hide输入数量 */
int HideOutCount = 10; /* Hide Bolt输出数量，等于是Out Bolt的输入数量 */
int OutCount = 4; /* Out输出数量，等于Real的数量 */
TopologyBuilder builder = new TopologyBuilder();
DRPCSpout drpcSpout = new DRPCSpout("BPTrain");
builder.setSpout("drpcSpout", drpcSpout, 1);
builder.setBolt("hide", new HideBolt(), HideOutCount).allGrouping("drpcSpout");
// OutBolt的传参 必须等于 HideBolt的个数
builder.setBolt("out", new OutBolt(HideOutCount), OutCount).allGrouping("hide");
// TrainBPFinsh的传参 必须等于 OutBolt的个数
builder.setBolt("finsh", new TrainBPFinsh(OutCount), 1).allGrouping("out");
builder.setBolt("return", new ReturnResults(), 1).allGrouping("finsh");
Config conf = new Config();
conf.setNumWorkers(Integer.parseInt(args[1]));
StormSubmitter.submitTopology(args[0], conf, builder.createTopology());
String hideweight = getWeightStr(InputHideCount + 1, HideOutCount); // 隐藏层的权重
String outweight = getWeightStr(HideOutCount + 1, OutCount);// 输出层的权重
DRPCClient client = new DRPCClient("mynode001", 3772);
int[] ranInt = new int[1000];
for (int i = 0; i < ranInt.length; i++) {
ranInt[i] = new java.util.Random().nextInt();
}
int Num = 0;
System.out.println("开始训练");
for (int i = 0; i < 2000; i++) {
double r = 0d;
for (int j = 0; j < ranInt.length; j++) {//
RandomNum MyRandom = new RandomNum(ranInt[j]);
String input = MyRandom.getInputDataStr();
String real = MyRandom.getRealDataStr();
String[] result = client.execute(
"BPTrain",
String.valueOf(Num) + "::" + input + "::" + real + "::" + hideweight + "::"
+ outweight).split("::");
// 参数传入全部靠一个字符串，收取也是字符串，最基本的DRPC Client调用
hideweight = result[0];
outweight = result[1];
r = Double.parseDouble(result[2]);
if (j % 100 == 0)
System.out.println(r); // 输出收敛度
}
if (r < 0.005) {
System.out.println("训练结束");
break;
}
}
while (true) {
byte[] input = new byte[10];
System.in.read(input);
int value = 0;
try {
value = Integer.parseInt(new String(input).trim());
} catch (Exception e) {
break;
}
RandomNum rawVal = new RandomNum(value);
String[] resultstr = client.execute(
"BPTrain",
String.valueOf(++Num) + "::" + rawVal.getInputDataStr() + "::"
+ rawVal.getRealDataStr() + "::" + hideweight + "::" + outweight).split("::");
double max = -Integer.MIN_VALUE;
int idx = -1;
String[] result = resultstr[3].split(",");
for (int i = 0; i != result.length; i++) {
if (Double.valueOf(result[i]) > max) {
max = Double.valueOf(result[i]);
idx = i;
}
}
switch (idx) {
case 0:
System.out.format("%d是一个正奇数\n", value);
break;
case 1:
System.out.format("%d是一个正偶数\n", value);
break;
case 2:
System.out.format("%d是一个负奇数\n", value);
break;
case 3:
System.out.format("%d是一个负偶数\n", value);
break;
}
}
}
static String getWeightStr(int inCount, int outCount) {
StringBuilder Wgt = new StringBuilder();
for (int i = 0; i < outCount; i++) {
for (int j = 0; j < inCount; j++) {
Random random = new Random();
double v = random.nextDouble();
double rand = random.nextDouble() > 0.5 ? v : -v;
Wgt.append(rand / 2);
if (j != inCount - 1)
Wgt.append(",");
}
if (i != outCount - 1)
Wgt.append(":");
}
// System.out.println(Wgt.toString().split(":")[0].split(",").length);
return Wgt.toString();
}
}隐藏层神经细胞 Hide Bolt public class HideBolt implements IRichBolt {
/****************************************
* by CSDN 撸大湿 Email : tntzhou@hotmail.com
****************************************/
private static final long serialVersionUID = -3242401692275116210L;
OutputCollector collector;
int TaskID = 0;
@SuppressWarnings("rawtypes")
@Override
public void prepare(Map stormConf, TopologyContext context, OutputCollector _collector) {
collector = _collector;
TaskID = context.getThisTaskIndex(); // 每个Bolt代表一个神经细胞
// Bolt TaskID == 细胞 id
}
@Override
public void execute(Tuple tuple) {
String[] t = tuple.getString(0).split("::");
String jobID = tuple.getString(1); // jobid，drpc rid
int TNum = Integer.valueOf(t[0]); // tuple id
String[] inputstr = t[1].split(",");
double[] input = new double[inputstr.length]; // 传入值
String[] hideweightstr = t[3].split(":");
double[][] hideweight = new double[hideweightstr.length][input.length + 1]; // 隐藏层的权重
for (int i = 0; i < inputstr.length; i++) {
input[i] = Double.parseDouble(inputstr[i]);
}
for (int i = 0; i < hideweightstr.length; i++) {
String[] tmpw = hideweightstr[i].split(",");
for (int j = 0; j < tmpw.length; j++) {
hideweight[i][j] = Double.parseDouble(tmpw[j]);
}
}
double inputsum = 0;
/****************************************
* 计算输出
****************************************/
for (int i = 0; i < input.length; i++) {
inputsum += hideweight[TaskID][i] * input[i];
}
inputsum += hideweight[TaskID][input.length];
double HideOut = 1.0 / (1.0 + Math.exp(-inputsum));
String[] realstr = t[2].split(",");
double[] real = new double[realstr.length];
for (int i = 0; i < realstr.length; i++) {
real[i] = Double.parseDouble(realstr[i]);
}
String[] outweightstr = t[4].split(":");
double[][] outweight = new double[outweightstr.length][outweightstr[0].split(",").length];
for (int i = 0; i < outweightstr.length; i++) {
String[] tmpw = outweightstr[i].split(",");
for (int j = 0; j < tmpw.length; j++) {
outweight[i][j] = Double.parseDouble(tmpw[j]);
}
}
collector.emit(new Values(TNum, TaskID, input, HideOut, real, hideweight, outweight, jobID));
collector.ack(tuple);
}
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("tnum", "HideTaskID", "Input", "HideOut", "Real", "HideWeight",
"OutWeight", "jobID"));
}
@Override
public void cleanup() {
}
@Override
public Map<String, Object> getComponentConfiguration() {
return null;
}
}//输出层神经细胞
public class OutBolt implements IRichBolt {
/****************************************
* by CSDN 撸大湿 Email : tntzhou@hotmail.com
****************************************/
private static final long serialVersionUID = -7483206983562705977L;
OutputCollector collector;
int TaskID = 0;
HashMap<Integer, double[]> HideOutMap = new HashMap<Integer, double[]>();
HashMap<Integer, ArrayList<Tuple>> MyTuple = new HashMap<Integer, ArrayList<Tuple>>();
int HideTaskCount = 0;
public OutBolt(int _hidetaskcount) {
this.HideTaskCount = _hidetaskcount;
}
@SuppressWarnings("rawtypes")
@Override
public void prepare(Map stormConf, TopologyContext context, OutputCollector _collector) {
collector = _collector;
TaskID = context.getThisTaskIndex();
}
@Override
public void execute(Tuple tuple) {
int TNum = tuple.getInteger(0);
int HideTaskID = tuple.getInteger(1);
double hideout = tuple.getDouble(3);
if (!HideOutMap.containsKey(TNum)) {
HideOutMap.put(TNum, new double[HideTaskCount]);
MyTuple.put(TNum, new ArrayList<Tuple>());
}
HideOutMap.get(TNum)[HideTaskID] = hideout;
MyTuple.get(TNum).add(tuple);
double[] input = null;
double[] real = null;
double[][] hideweight = null;
double[][] outweight = null;
String jobID = null;
outweight = (double[][]) tuple.getValue(6);
jobID = tuple.getString(7);
if (MyTuple.get(TNum).size() == HideTaskCount) {
input = (double[]) tuple.getValue(2);
real = (double[]) tuple.getValue(4);
hideweight = (double[][]) tuple.getValue(5);
/****************************************
* 计算输出
****************************************/
Double sum = 0d;
for (int i = 0; i < HideOutMap.get(TNum).length; i++) {
sum += HideOutMap.get(TNum)[i] * outweight[TaskID][i];
}
sum += outweight[TaskID][HideOutMap.get(TNum).length];
double Out = 1.0 / (1.0 + Math.exp(-sum));
collector.emit(new Values(TNum, TaskID, input, HideOutMap.get(TNum), Out, real,
hideweight, outweight, jobID));
HideOutMap.remove(TNum);
MyTuple.remove(TNum);
}
collector.ack(tuple);
}
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("tnum", "OutTaskID", "Input", "HideOut", "Out", "Real",
"HideWeight", "OutWeight", "jobID"));
}
@Override
public void cleanup() {
// TODO Auto-generated method stub
}
@Override
public Map<String, Object> getComponentConfiguration() {
// TODO Auto-generated method stub
return null;
}
}BP反馈层，训练迭代终点， public class TrainBPFinsh implements IRichBolt, FinishedCallback {
/****************************************
* by CSDN 撸大湿 Email : tntzhou@hotmail.com
****************************************/
private static final long serialVersionUID = 5303881246503874591L;
OutputCollector collector;
int TaskID = 0;
int OutTaskCount = 0;
HashMap<Integer, double[]> OutMap = new HashMap<Integer, double[]>();
HashMap<Integer, ArrayList<Tuple>> MyTuple = new HashMap<Integer, ArrayList<Tuple>>();
public TrainBPFinsh(int _OutTaskCount) {
OutTaskCount = _OutTaskCount;
}
@SuppressWarnings("rawtypes")
@Override
public void prepare(Map stormConf, TopologyContext context, OutputCollector _collector) {
TaskID = context.getThisTaskIndex();
collector = _collector;
}
@Override
public void execute(Tuple tuple) {
int TNum = tuple.getInteger(0);
int OutTaskID = tuple.getInteger(1);
double out = tuple.getDouble(4);
if (!OutMap.containsKey(TNum)) {
OutMap.put(TNum, new double[OutTaskCount]);
MyTuple.put(TNum, new ArrayList<Tuple>());
}
OutMap.get(TNum)[OutTaskID] = out;
MyTuple.get(TNum).add(tuple);
double[] input = null;
double[] hideout = null;
double[] real = null;
double[][] hideweight = null;
double[][] outweight = null;
String jobID = null;
jobID = tuple.getString(8);
if (MyTuple.get(TNum).size() == OutTaskCount) {
input = (double[]) tuple.getValue(2);
hideout = (double[]) tuple.getValue(3);
real = (double[]) tuple.getValue(5);
hideweight = (double[][]) tuple.getValue(6);
outweight = (double[][]) tuple.getValue(7);
jobID = tuple.getString(8);
double[] outDelta = new double[OutTaskCount];
double[] hideDelta = new double[hideout.length];
double[] outout = OutMap.get(TNum);
double rate = 0.5; // 收敛速率
// 计算每个输出单元的误差项
for (int i = 0; i < hideout.length; i++) {
System.out.println("hideout " + i + " : " + hideout[i]);
}
for (int i = 0; i < outout.length; i++) {
System.out.println("outout " + i + " : " + outout[i]);
}
double outErrSum = 0;
for (int i = 0; i < OutTaskCount; ++i) {
outDelta[i] = outout[i] * (1.0 - outout[i]) * (real[i] - outout[i]);
System.out.println("real " + i + " : " + real[i]);
System.out.println("outDelta " + i + " : " + outDelta[i]);
outErrSum += Math.pow((real[i] - outout[i]), 2);
}
// 计算每个隐藏单元的误差项
for (int i = 0; i < hideout.length; ++i) {
double sum = 0;
for (int j = 0; j < OutTaskCount; ++j) {
sum += outweight[j][i] * outDelta[j];
}
hideDelta[i] = hideout[i] * (1d - hideout[i]) * sum;
System.out.println("hideDelta " + i + " : " + hideDelta[i]);
}
StringBuilder hideweightstr = new StringBuilder();
StringBuilder outweightstr = new StringBuilder();
// 收敛输出层的权值
for (int i = 0; i < OutTaskCount; ++i) {
for (int j = 0; j < hideout.length; ++j) {
outweight[i][j] += rate * outDelta[i] * hideout[j];
outweightstr.append(outweight[i][j]);
outweightstr.append(",");
}
outweight[i][hideout.length] += rate * outDelta[i];
outweightstr.append(outweight[i][hideout.length]);
if (i != OutTaskCount - 1) {
outweightstr.append(":");
}
}
// 收敛隐含层的权值
for (int i = 0; i < hideout.length; ++i) {
for (int j = 0; j < input.length; ++j) {
hideweight[i][j] += rate * hideDelta[i] * input[j];
hideweightstr.append(hideweight[i][j]);
hideweightstr.append(",");
}
hideweight[i][input.length] += rate * hideDelta[i];
hideweightstr.append(hideweight[i][input.length]);
if (i != hideout.length - 1) {
hideweightstr.append(":");
}
}
String outStr = "";
for (int i = 0; i < outout.length; i++) {
outStr += String.valueOf(outout[i]);
if (i != outout.length - 1)
outStr += ",";
}
String result = hideweightstr + "::" + outweightstr + "::" + outErrSum + "::" + outStr;
collector.emit(new Values(result, jobID));
OutMap.remove(TNum);
MyTuple.remove(TNum);
}
collector.ack(tuple);
}
@Override
public void declareOutputFields(OutputFieldsDeclarer declarer) {
declarer.declare(new Fields("result", "returnInfo"));
}
@Override
public void cleanup() {
// TODO Auto-generated method stub
}
@Override
public Map<String, Object> getComponentConfiguration() {
// TODO Auto-generated method stub
return null;
}
@Override
public void finishedId(Object id) {
}
}把代码打包成bpdemo.jar上传storm server，运行命令 开始训练
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