dl4j如何使用遗传神经网络完成手写数字识别
今天就跟大家聊聊有关dl4j如何使用遗传神经网络完成手写数字识别，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。
企业建站必须是能够以充分展现企业形象为主要目的，是企业文化与产品对外扩展宣传的重要窗口，一个合格的网站不仅仅能为公司带来巨大的互联网上的收集和信息发布平台，创新互联面向各种领域：成都宣传片制作等成都网站设计、全网营销推广解决方案、网站设计等建站排名服务。
实现步骤
1.随机初始化若干个智能体(神经网络),并让智能体识别训练数据,并对识别结果进行排序
2.随机在排序结果中选择一个作为母本,并在比母本识别率更高的智能体中随机选择一个作为父本
3.随机选择母本或父本同位的神经网络超参组成新的智能体
4.按照母本的排序对智能体进行超参调整,排序越靠后调整幅度越大(1%~10%)之间
5.让新的智能体识别训练集并放入排行榜,并移除排行榜最后一位
6.重复2~5过程,让识别率越来越高
这个过程就类似于自然界的优胜劣汰,将神经网络超参看作dna,超参的调整看作dna的突变;当然还可以把拥有不同隐藏层的神经网络看作不同的物种,让竞争过程更加多样化.当然我们这里只讨论一种神经网络的情况
优势: 可以解决很多没有头绪的问题 劣势: 训练效率极低
gitee地址:
https://gitee.com/ichiva/gnn.git
实现步骤 1.进化接口
public interface Evolution {
/**
* 遗传
* @param mDna
* @param fDna
* @return
*/
INDArray inheritance(INDArray mDna,INDArray fDna);
/**
* 突变
* @param dna
* @param v
* @param r 突变范围
* @return
*/
INDArray mutation(INDArray dna,double v, double r);
/**
* 置换
* @param dna
* @param v
* @return
*/
INDArray substitution(INDArray dna,double v);
/**
* 外源
* @param dna
* @param v
* @return
*/
INDArray other(INDArray dna,double v);
/**
* DNA 是否同源
* @param mDna
* @param fDna
* @return
*/
boolean iSogeny(INDArray mDna, INDArray fDna);
}一个比较通用的实现
public class MnistEvolution implements Evolution {
private static final MnistEvolution instance = new MnistEvolution();
public static MnistEvolution getInstance() {
return instance;
}
@Override
public INDArray inheritance(INDArray mDna, INDArray fDna) {
if(mDna == fDna) return mDna;
long[] mShape = mDna.shape();
if(!iSogeny(mDna,fDna)){
throw new RuntimeException("非同源dna");
}
INDArray nDna = Nd4j.create(mShape);
NdIndexIterator it = new NdIndexIterator(mShape);
while (it.hasNext()){
long[] next = it.next();
double val;
if(Math.random() > 0.5){
val = fDna.getDouble(next);
}else {
val = mDna.getDouble(next);
}
nDna.putScalar(next,val);
}
return nDna;
}
@Override
public INDArray mutation(INDArray dna, double v, double r) {
long[] shape = dna.shape();
INDArray nDna = Nd4j.create(shape);
NdIndexIterator it = new NdIndexIterator(shape);
while (it.hasNext()) {
long[] next = it.next();
if(Math.random() < v){
dna.putScalar(next,dna.getDouble(next) + ((Math.random() - 0.5) * r * 2));
}else {
nDna.putScalar(next,dna.getDouble(next));
}
}
return nDna;
}
@Override
public INDArray substitution(INDArray dna, double v) {
long[] shape = dna.shape();
INDArray nDna = Nd4j.create(shape);
NdIndexIterator it = new NdIndexIterator(shape);
while (it.hasNext()) {
long[] next = it.next();
if(Math.random() > v){
long[] tag = new long[shape.length];
for (int i = 0; i < shape.length; i++) {
tag[i] = (long) (Math.random() * shape[i]);
}
nDna.putScalar(next,dna.getDouble(tag));
}else {
nDna.putScalar(next,dna.getDouble(next));
}
}
return nDna;
}
@Override
public INDArray other(INDArray dna, double v) {
long[] shape = dna.shape();
INDArray nDna = Nd4j.create(shape);
NdIndexIterator it = new NdIndexIterator(shape);
while (it.hasNext()) {
long[] next = it.next();
if(Math.random() > v){
nDna.putScalar(next,Math.random());
}else {
nDna.putScalar(next,dna.getDouble(next));
}
}
return nDna;
}
@Override
public boolean iSogeny(INDArray mDna, INDArray fDna) {
long[] mShape = mDna.shape();
long[] fShape = fDna.shape();
if (mShape.length == fShape.length) {
for (int i = 0; i < mShape.length; i++) {
if (mShape[i] != fShape[i]) {
return false;
}
}
return true;
}
return false;
}
}定义智能体配置接口
public interface AgentConfig {
/**
* 输入量
* @return
*/
int getInput();
/**
* 输出量
* @return
*/
int getOutput();
/**
* 神经网络配置
* @return
*/
MultiLayerConfiguration getMultiLayerConfiguration();
}按手写数字识别进行配置实现
public class MnistConfig implements AgentConfig {
@Override
public int getInput() {
return 28 * 28;
}
@Override
public int getOutput() {
return 10;
}
@Override
public MultiLayerConfiguration getMultiLayerConfiguration() {
return new NeuralNetConfiguration.Builder()
.seed((long) (Math.random() * Long.MAX_VALUE))
.updater(new Nesterovs(0.006, 0.9))
.l2(1e-4)
.list()
.layer(0, new DenseLayer.Builder()
.nIn(getInput())
.nOut(1000)
.activation(Activation.RELU)
.weightInit(WeightInit.XAVIER)
.build())
.layer(1, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD) //create hidden layer
.nIn(1000)
.nOut(getOutput())
.activation(Activation.SOFTMAX)
.weightInit(WeightInit.XAVIER)
.build())
.pretrain(false).backprop(true)
.build();
}
}智能体基类
@Getter
public class Agent {
private final AgentConfig config;
private final INDArray dna;
private final MultiLayerNetwork multiLayerNetwork;
/**
* 采用默认方法初始化参数
* @param config
*/
public Agent(AgentConfig config){
this(config,null);
}
/**
*
* @param config
* @param dna
*/
public Agent(AgentConfig config, INDArray dna){
if(dna == null){
this.config = config;
MultiLayerConfiguration conf = config.getMultiLayerConfiguration();
this.multiLayerNetwork = new MultiLayerNetwork(conf);
multiLayerNetwork.init();
this.dna = multiLayerNetwork.params();
}else {
this.config = config;
MultiLayerConfiguration conf = config.getMultiLayerConfiguration();
this.multiLayerNetwork = new MultiLayerNetwork(conf);
multiLayerNetwork.init(dna,true);
this.dna = dna;
}
}
}手写数字智能体实现类
@Getter
@Setter
public class MnistAgent extends Agent {
private static final AtomicInteger index = new AtomicInteger(0);
private String name;
/**
* 环境适应分数
*/
private double score;
/**
* 验证分数
*/
private double validScore;
public MnistAgent(AgentConfig config) {
this(config,null);
}
public MnistAgent(AgentConfig config, INDArray dna) {
super(config, dna);
name = "agent-" + index.incrementAndGet();
}
public static MnistConfig mnistConfig = new MnistConfig();
public static MnistAgent newInstance(){
return new MnistAgent(mnistConfig);
}
public static MnistAgent create(INDArray dna){
return new MnistAgent(mnistConfig,dna);
}
}手写数字识别环境构建
@Slf4j
public class MnistEnv {
/**
* 环境数据
*/
private static final ThreadLocal tLocal = ThreadLocal.withInitial(() -> {
try {
return new MnistDataSetIterator(128, true, 0);
} catch (IOException e) {
throw new RuntimeException("mnist 文件读取失败");
}
});
private static final ThreadLocal testLocal = ThreadLocal.withInitial(() -> {
try {
return new MnistDataSetIterator(128, false, 0);
} catch (IOException e) {
throw new RuntimeException("mnist 文件读取失败");
}
});
private static final MnistEvolution evolution = MnistEvolution.getInstance();
/**
* 环境承载上限
*
* 超过上限AI会进行激烈竞争
*/
private final int max;
private Double maxScore,minScore;
/**
* 环境中的生命体
*
* 新生代与历史代共同排序,选出最适应环境的个体
*/
//2个变量，一个队列保存KEY的顺序，一个MAP保存KEY对应的具体对象的数据 线程安全map
private final TreeMap lives = new TreeMap<>();
/**
* 初始化环境
*
* 1.向环境中初始化ai
* 2.将初始化ai进行环境适应性测试,并排序
* @param max
*/
public MnistEnv(int max){
this.max = max;
for (int i = 0; i < max; i++) {
MnistAgent agent = MnistAgent.newInstance();
test(agent);
synchronized (lives) {
lives.put(agent.getScore(),agent);
}
log.info("初始化智能体 name = {} , score = {}",i,agent.getScore());
}
synchronized (lives) {
minScore = lives.firstKey();
maxScore = lives.lastKey();
}
}
/**
* 环境适应性评估
* @param ai
*/
public void test(MnistAgent ai){
MultiLayerNetwork network = ai.getMultiLayerNetwork();
MnistDataSetIterator dataIterator = tLocal.get();
Evaluation eval = new Evaluation(ai.getConfig().getOutput());
try {
while (dataIterator.hasNext()) {
DataSet data = dataIterator.next();
INDArray output = network.output(data.getFeatures(), false);
eval.eval(data.getLabels(),output);
}
}finally {
dataIterator.reset();
}
ai.setScore(eval.accuracy());
}
/**
* 迁移评估
*
* @param ai
*/
public void validation(MnistAgent ai){
MultiLayerNetwork network = ai.getMultiLayerNetwork();
MnistDataSetIterator dataIterator = testLocal.get();
Evaluation eval = new Evaluation(ai.getConfig().getOutput());
try {
while (dataIterator.hasNext()) {
DataSet data = dataIterator.next();
INDArray output = network.output(data.getFeatures(), false);
eval.eval(data.getLabels(),output);
}
}finally {
dataIterator.reset();
}
ai.setValidScore(eval.accuracy());
}
/**
* 进化
*
* 每轮随机创建ai并放入环境中进行优胜劣汰
* @param n 进化次数
*/
public void evolution(int n){
BlockThreadPool blockThreadPool=new BlockThreadPool(2);
for (int i = 0; i < n; i++) {
blockThreadPool.execute(() -> contend(newLive()));
}
// for (int i = 0; i < n; i++) {
// contend(newLive());
// }
}
/**
* 竞争
* @param ai
*/
public void contend(MnistAgent ai){
test(ai);
quality(ai);
double score = ai.getScore();
if(score <= minScore){
UI.put("无法生存",String.format("name = %s, score = %s", ai.getName(),ai.getScore()));
return;
}
Map.Entry lastEntry;
synchronized (lives) {
lives.put(score,ai);
if (lives.size() > max) {
MnistAgent lastAI = lives.remove(lives.firstKey());
UI.put("淘 汰 ",String.format("name = %s, score = %s", lastAI.getName(),lastAI.getScore()));
}
lastEntry = lives.lastEntry();
minScore = lives.firstKey();
}
Double lastScore = lastEntry.getKey();
if(lastScore > maxScore){
maxScore = lastScore;
MnistAgent agent = lastEntry.getValue();
validation(agent);
UI.put("max验证",String.format("score = %s,validScore = %s",lastScore,agent.getValidScore()));
try {
Warehouse.write(agent);
} catch (IOException ex) {
log.error("保存对象失败",ex);
}
}
}
ArrayList scoreList = new ArrayList<>(100);
ArrayList avgList = new ArrayList<>();
private void quality(MnistAgent ai) {
synchronized (scoreList) {
scoreList.add(ai.getScore());
if (scoreList.size() >= 100) {
double avg = scoreList.stream().mapToDouble(e -> e)
.average().getAsDouble();
avgList.add((int) (avg * 1000));
StringBuffer buffer = new StringBuffer();
avgList.forEach(e -> buffer.append(e).append('\t'));
UI.put("平均得分",String.format("aix100 avg = %s",buffer.toString()));
scoreList.clear();
}
}
}
/**
* 随机生成新智能体
*
* 完全随机产生母本
* 随机从比目标相同或更高评分中选择父本
*
* 基因进化在1%~10%之间进行,评分越高基于越稳定
*/
public MnistAgent newLive(){
double r = Math.random();
//基因突变率
double v = r / 11 + 0.01;
//母本
MnistAgent mAgent = getMother(r);
//父本
MnistAgent fAgent = getFather(r);
int i = (int) (Math.random() * 3);
INDArray newDNA = evolution.inheritance(mAgent.getDna(), fAgent.getDna());
switch (i){
case 0:
newDNA = evolution.other(newDNA,v);
break;
case 1:
newDNA = evolution.mutation(newDNA,v,0.1);
break;
case 2:
newDNA = evolution.substitution(newDNA,v);
break;
}
return MnistAgent.create(newDNA);
}
/**
* 父本只选择比母本评分高的样本
* @param r
* @return
*/
private MnistAgent getFather(double r) {
r += (Math.random() * (1-r));
return getMother(r);
}
private MnistAgent getMother(double r) {
int index = (int) (r * max);
return getMnistAgent(index);
}
private MnistAgent getMnistAgent(int index) {
synchronized (lives) {
Iterator> it = lives.entrySet().iterator();
for (int i = 0; i < index; i++) {
it.next();
}
return it.next().getValue();
}
}
} 主函数
@Slf4j
public class Program {
public static void main(String[] args) {
UI.put("开始时间",new Date().toLocaleString());
MnistEnv env = new MnistEnv(128);
env.evolution(Integer.MAX_VALUE);
}
}运行截图
看完上述内容，你们对dl4j如何使用遗传神经网络完成手写数字识别有进一步的了解吗？如果还想了解更多知识或者相关内容，请关注创新互联行业资讯频道，感谢大家的支持。
本文标题：dl4j如何使用遗传神经网络完成手写数字识别
网站URL：http://azwzsj.com/article/jpsoip.html
