├── .gitignore
├── speech
├── model
│ └── model.cnn
├── test.label
├── test.predict
├── test.format
└── train.format
├── mnist
└── readme.md
├── src
├── test
│ ├── resources
│ │ └── logback-test.xml
│ └── java
│ │ └── info
│ │ └── hb
│ │ └── cnn
│ │ ├── data
│ │ └── DataSetTest.java
│ │ └── utils
│ │ ├── TimedTest.java
│ │ ├── TestArray.java
│ │ └── MathUtilsTest.java
└── main
│ ├── resources
│ └── logback.xml
│ └── java
│ └── info
│ └── hb
│ ├── ccn
│ └── main
│ │ ├── CNNSpeech.java
│ │ └── CNNMnist.java
│ └── cnn
│ ├── utils
│ ├── ConcurentRunner.java
│ └── MathUtils.java
│ ├── data
│ └── DataSet.java
│ └── core
│ ├── Layer.java
│ └── CNN.java
├── README.md
└── pom.xml
/.gitignore:
--------------------------------------------------------------------------------
1 | .classpath
2 | .project
3 | .settings/
4 | target/
5 | logs/
6 |
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/speech/model/model.cnn:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/DeepCompute/cnn/HEAD/speech/model/model.cnn
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/mnist/readme.md:
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1 | The dataset is part of MNIST from kaggle Digit Recognizer competition.
2 | "train.format" is the train set, which has been binarized.
3 | "test.format" is the test set, which has been binarized.
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/speech/test.label:
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1 | 1
2 | 1
3 | 1
4 | 1
5 | 1
6 | 1
7 | 1
8 | 1
9 | 1
10 | 1
11 | 1
12 | 1
13 | 1
14 | 1
15 | 1
16 | 1
17 | 1
18 | 1
19 | 1
20 | 1
21 | 1
22 | 1
23 | 1
24 | 1
25 | 1
26 | 1
27 | 1
28 | 1
29 | 0
30 | 0
31 | 0
32 | 0
33 | 0
34 | 0
35 | 0
36 | 0
37 | 0
38 | 0
39 | 0
40 | 0
41 | 0
42 | 0
43 | 0
44 |
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/speech/test.predict:
--------------------------------------------------------------------------------
1 | 0
2 | 0
3 | 0
4 | 0
5 | 0
6 | 0
7 | 0
8 | 0
9 | 0
10 | 0
11 | 0
12 | 0
13 | 0
14 | 0
15 | 0
16 | 0
17 | 0
18 | 0
19 | 0
20 | 0
21 | 0
22 | 0
23 | 0
24 | 0
25 | 0
26 | 0
27 | 0
28 | 0
29 | 0
30 | 0
31 | 0
32 | 0
33 | 0
34 | 0
35 | 0
36 | 0
37 | 0
38 | 0
39 | 0
40 | 0
41 | 0
42 | 0
43 | 0
44 |
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/src/test/resources/logback-test.xml:
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1 |
2 |
3 |
4 |
5 |
6 |
7 | %d{MMdd.HHmmss.SSS} [%-20t] [%-5p] [%-20c] [L:%-3L] - %m%n
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
17 |
18 |
19 |
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/src/test/java/info/hb/cnn/data/DataSetTest.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.data;
2 |
3 | import info.hb.cnn.data.DataSet.Record;
4 |
5 | import java.util.Arrays;
6 |
7 | import org.junit.Test;
8 |
9 | public class DataSetTest {
10 |
11 | @Test
12 | public void testDataSet() {
13 | DataSet dataSet = new DataSet();
14 | dataSet.setLableIndex(10);
15 | Record r = dataSet.new Record(new double[] { 3, 2, 2, 5, 4, 5, 3, 11, 3, 12, 1 });
16 | int[] encode = r.getEncodeTarget(4);
17 |
18 | System.out.println(r.getLabel());
19 | System.out.println(Arrays.toString(encode));
20 | }
21 |
22 | }
23 |
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/src/test/java/info/hb/cnn/utils/TimedTest.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.utils;
2 |
3 | public class TimedTest {
4 |
5 | private int repeat;
6 | private TestTask task;
7 |
8 | public interface TestTask {
9 | public void process();
10 | }
11 |
12 | public TimedTest(TestTask t, int repeat) {
13 | this.repeat = repeat;
14 | task = t;
15 | }
16 |
17 | public void test() {
18 | long t = System.currentTimeMillis();
19 | for (int i = 0; i < repeat; i++) {
20 | task.process();
21 | }
22 | double cost = (System.currentTimeMillis() - t) / 1000.0;
23 | System.out.println("cost \t" + cost + "s");
24 | }
25 |
26 | }
27 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # JavaCNN
2 |
3 | > 基于Java实现CNN
4 |
5 | ## 构建CNN
6 |
7 | LayerBuilder builder = new LayerBuilder();
8 | builder.addLayer(Layer.buildInputLayer(new Size(28, 28)));
9 | builder.addLayer(Layer.buildConvLayer(6, new Size(5, 5)));
10 | builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
11 | builder.addLayer(Layer.buildConvLayer(12, new Size(5, 5)));
12 | builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
13 | builder.addLayer(Layer.buildOutputLayer(10));
14 | CNN cnn = new CNN(builder, 50);
15 |
16 | ## 运行MNIST数据集
17 |
18 | String fileName = "data/train.format";
19 | Dataset dataset = Dataset.load(fileName, ",", 784);
20 | cnn.train(dataset, 100);
21 | Dataset testset = Dataset.load("data/test.format", ",", -1);
22 | cnn.predict(testset, "data/test.predict");
23 |
24 | 计算精度可以达到97.8%。
25 |
--------------------------------------------------------------------------------
/src/main/resources/logback.xml:
--------------------------------------------------------------------------------
1 |
2 |
3 |
4 |
5 |
6 |
7 | %d{ISO8601} [%thread] %-5level %logger{36} [Line:%-3L] - %msg%n
8 |
9 |
10 |
11 | INFO
12 | ACCEPT
13 | DENY
14 |
15 |
16 |
17 |
19 | logs/cnn.log
20 |
21 | %d{ISO8601} [%thread] %-5level %logger{36} [Line:%-3L] - %msg%n
22 |
23 |
24 |
25 | INFO
26 |
27 |
28 | logs/cnn.log.%d{yyyy-MM-dd}.gz
29 | 30
30 |
31 |
32 |
33 |
34 |
35 |
36 |
37 |
38 |
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/src/test/java/info/hb/cnn/utils/TestArray.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.utils;
2 |
3 | import info.hb.cnn.utils.TimedTest.TestTask;
4 |
5 | import java.util.Locale;
6 |
7 | import org.junit.Test;
8 |
9 | public class TestArray {
10 |
11 | @Test
12 | public void testOrigin() {
13 | String a = "aAdfa_";
14 | System.out.println(a.toUpperCase(Locale.CHINA));
15 | double[][] d = new double[3][];
16 | d[0] = new double[] { 1, 2, 3 };
17 | d[1] = new double[] { 3, 4, 5, 6 };
18 | System.out.println(d[1][3]);
19 | final ArrayModel t = new ArrayModel(10000, 1000);
20 | new TimedTest(new TestTask() {
21 |
22 | @Override
23 | public void process() {
24 | t.useOrigin();
25 | }
26 |
27 | }, 1).test();
28 | }
29 |
30 | @Test
31 | public void testFunc() {
32 | String a = "aAdfa_";
33 | System.out.println(a.toUpperCase(Locale.CHINA));
34 | double[][] d = new double[3][];
35 | d[0] = new double[] { 1, 2, 3 };
36 | d[1] = new double[] { 3, 4, 5, 6 };
37 | System.out.println(d[1][3]);
38 | final ArrayModel t = new ArrayModel(10000, 1000);
39 | new TimedTest(new TestTask() {
40 |
41 | @Override
42 | public void process() {
43 | t.useFunc();
44 | }
45 |
46 | }, 1).test();
47 | }
48 |
49 | public static class ArrayModel {
50 |
51 | double[][] data;
52 |
53 | public ArrayModel(int m, int n) {
54 | data = new double[m][n];
55 | }
56 |
57 | public void set(int x, int y, double value) {
58 | data[x][y] = value;
59 | }
60 |
61 | public void useOrigin() {
62 | for (int i = 0; i < data.length; i++)
63 | for (int j = 0; j < data[0].length; j++)
64 | data[i][j] = i * j;
65 | }
66 |
67 | public void useFunc() {
68 | for (int i = 0; i < data.length; i++)
69 | for (int j = 0; j < data[0].length; j++)
70 | set(i, j, i * j);
71 | }
72 |
73 | }
74 |
75 | }
76 |
--------------------------------------------------------------------------------
/src/main/java/info/hb/ccn/main/CNNSpeech.java:
--------------------------------------------------------------------------------
1 | package info.hb.ccn.main;
2 |
3 | import info.hb.cnn.core.CNN;
4 | import info.hb.cnn.core.CNN.LayerBuilder;
5 | import info.hb.cnn.core.Layer;
6 | import info.hb.cnn.core.Layer.Size;
7 | import info.hb.cnn.data.DataSet;
8 | import info.hb.cnn.utils.ConcurentRunner;
9 |
10 | public class CNNSpeech {
11 |
12 | private static final String MODEL_NAME = "speech/model/model.cnn";
13 |
14 | private static final String TRAIN_DATA = "speech/train.format";
15 |
16 | private static final String TEST_DATA = "speech/test.format";
17 |
18 | private static final String TEST_PREDICT = "speech/test.predict";
19 |
20 | public static void main(String[] args) {
21 |
22 | System.err.println("训练阶段：");
23 | runTrain();
24 | System.err.println("测试阶段：");
25 | runTest();
26 | ConcurentRunner.stop();
27 |
28 | }
29 |
30 | public static void runTrain() {
31 | // 构建网络层次结构
32 | LayerBuilder builder = new LayerBuilder();
33 | builder.addLayer(Layer.buildInputLayer(new Size(6, 6)));
34 | builder.addLayer(Layer.buildConvLayer(2, new Size(3, 3)));
35 | builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
36 | // builder.addLayer(Layer.buildConvLayer(3, new Size(3, 3)));
37 | // builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
38 | builder.addLayer(Layer.buildOutputLayer(2));
39 | CNN cnn = new CNN(builder, 10);
40 | // 加载训练数据
41 | DataSet dataset = DataSet.load(TRAIN_DATA, ",", 36);
42 | // 开始训练模型
43 | cnn.train(dataset, 3);
44 | // 保存训练好的模型
45 | cnn.saveModel(MODEL_NAME);
46 | dataset.clear();
47 | }
48 |
49 | public static void runTest() {
50 | // 加载训练好的模型
51 | CNN cnn = CNN.loadModel(MODEL_NAME);
52 | // 加载测试数据
53 | DataSet testSet = DataSet.load(TEST_DATA, ",", -1);
54 | // 预测结果
55 | cnn.predict(testSet, TEST_PREDICT);
56 | testSet.clear();
57 | }
58 |
59 | }
60 |
--------------------------------------------------------------------------------
/src/main/java/info/hb/ccn/main/CNNMnist.java:
--------------------------------------------------------------------------------
1 | package info.hb.ccn.main;
2 |
3 | import info.hb.cnn.core.CNN;
4 | import info.hb.cnn.core.CNN.LayerBuilder;
5 | import info.hb.cnn.core.Layer;
6 | import info.hb.cnn.core.Layer.Size;
7 | import info.hb.cnn.data.DataSet;
8 | import info.hb.cnn.utils.ConcurentRunner;
9 |
10 | public class CNNMnist {
11 |
12 | private static final String MODEL_NAME = "mnist/model/model.cnn";
13 |
14 | private static final String TRAIN_DATA = "mnist/train.format";
15 |
16 | private static final String TEST_DATA = "mnist/test.format";
17 |
18 | private static final String TEST_PREDICT = "mnist/test.predict";
19 |
20 | public static void main(String[] args) {
21 |
22 | System.err.println("训练阶段：");
23 | runTrain();
24 | System.err.println("测试阶段：");
25 | runTest();
26 | ConcurentRunner.stop();
27 |
28 | }
29 |
30 | public static void runTrain() {
31 | // 构建网络层次结构
32 | LayerBuilder builder = new LayerBuilder();
33 | builder.addLayer(Layer.buildInputLayer(new Size(28, 28))); // 输入层输出map大小为28×28
34 | builder.addLayer(Layer.buildConvLayer(6, new Size(5, 5))); // 卷积层输出map大小为24×24,24=28+1-5
35 | builder.addLayer(Layer.buildSampLayer(new Size(2, 2))); // 采样层输出map大小为12×12,12=24/2
36 | builder.addLayer(Layer.buildConvLayer(12, new Size(5, 5))); // 卷积层输出map大小为8×8,8=12+1-5
37 | builder.addLayer(Layer.buildSampLayer(new Size(2, 2))); // 采样层输出map大小为4×4,4=8/2
38 | builder.addLayer(Layer.buildOutputLayer(10));
39 | CNN cnn = new CNN(builder, 10);
40 | // 加载训练数据
41 | DataSet dataset = DataSet.load(TRAIN_DATA, ",", 784);
42 | // 开始训练模型
43 | cnn.train(dataset, 5);
44 | // 保存训练好的模型
45 | cnn.saveModel(MODEL_NAME);
46 | dataset.clear();
47 | }
48 |
49 | public static void runTest() {
50 | // 加载训练好的模型
51 | CNN cnn = CNN.loadModel(MODEL_NAME);
52 | // 加载测试数据
53 | DataSet testSet = DataSet.load(TEST_DATA, ",", -1);
54 | // 预测结果
55 | cnn.predict(testSet, TEST_PREDICT);
56 | testSet.clear();
57 | }
58 |
59 | }
60 |
--------------------------------------------------------------------------------
/src/main/java/info/hb/cnn/utils/ConcurentRunner.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.utils;
2 |
3 | import java.util.concurrent.CountDownLatch;
4 | import java.util.concurrent.ExecutorService;
5 | import java.util.concurrent.Executors;
6 |
7 | import org.slf4j.Logger;
8 | import org.slf4j.LoggerFactory;
9 |
10 | public class ConcurentRunner {
11 |
12 | private static Logger logger = LoggerFactory.getLogger(ConcurentRunner.class);
13 |
14 | private static final ExecutorService exec;
15 | public static final int cpuNum;
16 |
17 | static {
18 | cpuNum = Runtime.getRuntime().availableProcessors();
19 | // cpuNum = 1;
20 | logger.info("cpuNum:{}", cpuNum);
21 | exec = Executors.newFixedThreadPool(cpuNum);
22 | }
23 |
24 | public static void run(Runnable task) {
25 | exec.execute(task);
26 | }
27 |
28 | public static void stop() {
29 | exec.shutdown();
30 | }
31 |
32 | /* public abstract static class Task implements Runnable {
33 |
34 | int start, end;
35 |
36 | public Task(int start, int end) {
37 | this.start = start;
38 | this.end = end;
39 | logger.info("new Task \t start {} end {}.", start, end);
40 | }
41 |
42 | @Override
43 | public void run() {
44 | process(start, end);
45 | }
46 |
47 | public abstract void process(int start, int end);
48 |
49 | }*/
50 |
51 | public abstract static class TaskManager {
52 |
53 | private int workLength;
54 |
55 | public TaskManager(int workLength) {
56 | this.workLength = workLength;
57 | }
58 |
59 | public void start() {
60 | int runCpu = cpuNum < workLength ? cpuNum : 1;
61 | final CountDownLatch gate = new CountDownLatch(runCpu);
62 | int fregLength = (workLength + runCpu - 1) / runCpu;
63 | for (int cpu = 0; cpu < runCpu; cpu++) {
64 | final int start = cpu * fregLength;
65 | int tmp = (cpu + 1) * fregLength;
66 | final int end = tmp <= workLength ? tmp : workLength;
67 | Runnable task = new Runnable() {
68 |
69 | @Override
70 | public void run() {
71 | process(start, end);
72 | gate.countDown();
73 | }
74 |
75 | };
76 | ConcurentRunner.run(task);
77 | }
78 | try {
79 | gate.await();
80 | } catch (InterruptedException e) {
81 | e.printStackTrace();
82 | throw new RuntimeException(e);
83 | }
84 | }
85 |
86 | public abstract void process(int start, int end);
87 |
88 | }
89 |
90 | }
91 |
--------------------------------------------------------------------------------
/pom.xml:
--------------------------------------------------------------------------------
1 |
3 | 4.0.0
4 |
5 | info.hb
6 | cnn
7 | 1.0.0
8 | CNN
9 |
10 |
11 | UTF-8
12 | 4.11
13 | 1.7.7
14 | 1.1.2
15 |
16 |
17 |
18 |
19 |
20 | ch.qos.logback
21 | logback-classic
22 | ${logback.version}
23 |
24 |
25 | ch.qos.logback
26 | logback-core
27 | ${logback.version}
28 |
29 |
30 | ch.qos.logback
31 | logback-access
32 | ${logback.version}
33 |
34 |
35 | org.slf4j
36 | slf4j-api
37 | ${slf4j.version}
38 |
39 |
40 |
41 | junit
42 | junit
43 | ${junit.version}
44 | test
45 |
46 |
47 |
48 |
49 |
50 |
51 | org.apache.maven.plugins
52 | maven-compiler-plugin
53 | 3.1
54 |
55 | 1.8
56 | 1.8
57 | UTF-8
58 |
59 |
60 |
61 | org.apache.maven.plugins
62 | maven-source-plugin
63 | 2.2.1
64 |
65 |
66 | attach-sources
67 | verify
68 |
69 | jar-no-fork
70 |
71 |
72 |
73 |
74 |
75 | org.apache.maven.plugins
76 | maven-resources-plugin
77 | 2.6
78 |
79 | UTF-8
80 |
81 |
82 |
83 | org.apache.maven.plugins
84 | maven-surefire-plugin
85 | 2.16
86 |
87 |
88 | maven-assembly-plugin
89 | 2.4
90 |
91 |
92 |
93 | info.hb.cnn.main.CNNMnist
94 |
95 |
96 |
97 | jar-with-dependencies
98 |
99 |
100 |
101 |
102 | make-assembly
103 | package
104 |
105 | single
106 |
107 |
108 |
109 |
110 |
111 |
112 | ann
113 |
114 |
115 |
116 |
--------------------------------------------------------------------------------
/src/main/java/info/hb/cnn/data/DataSet.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.data;
2 |
3 | import java.io.BufferedReader;
4 | import java.io.File;
5 | import java.io.FileReader;
6 | import java.io.IOException;
7 | import java.util.ArrayList;
8 | import java.util.Arrays;
9 | import java.util.Iterator;
10 | import java.util.List;
11 |
12 | import org.slf4j.Logger;
13 | import org.slf4j.LoggerFactory;
14 |
15 | public class DataSet {
16 |
17 | private static Logger logger = LoggerFactory.getLogger(DataSet.class);
18 |
19 | private List records;
20 |
21 | private int lableIndex;
22 |
23 | private double maxLable = -1;
24 |
25 | public DataSet(int classIndex) {
26 | this.lableIndex = classIndex;
27 | records = new ArrayList();
28 | }
29 |
30 | public DataSet(List datas) {
31 | this();
32 | for (double[] data : datas) {
33 | append(new Record(data));
34 | }
35 | }
36 |
37 | public DataSet() {
38 | this.lableIndex = -1;
39 | records = new ArrayList();
40 | }
41 |
42 | public int size() {
43 | return records.size();
44 | }
45 |
46 | public void setLableIndex(int lableIndex) {
47 | this.lableIndex = lableIndex;
48 | }
49 |
50 | public int getLableIndex() {
51 | return lableIndex;
52 | }
53 |
54 | public void append(Record record) {
55 | records.add(record);
56 | }
57 |
58 | public void clear() {
59 | records.clear();
60 | }
61 |
62 | public void append(double[] attrs, Double lable) {
63 | records.add(new Record(attrs, lable));
64 | }
65 |
66 | public Iterator iter() {
67 | return records.iterator();
68 | }
69 |
70 | public double[] getAttrs(int index) {
71 | return records.get(index).getAttrs();
72 | }
73 |
74 | public Double getLable(int index) {
75 | return records.get(index).getLabel();
76 | }
77 |
78 | public static DataSet load(String filePath, String tag, int lableIndex) {
79 | DataSet dataset = new DataSet();
80 | dataset.lableIndex = lableIndex;
81 | File file = new File(filePath);
82 | try {
83 | BufferedReader in = new BufferedReader(new FileReader(file));
84 | String line;
85 | while ((line = in.readLine()) != null) {
86 | String[] datas = line.split(tag);
87 | if (datas.length == 0)
88 | continue;
89 | double[] data = new double[datas.length];
90 | for (int i = 0; i < datas.length; i++)
91 | data[i] = Double.parseDouble(datas[i]);
92 | Record record = dataset.new Record(data);
93 | dataset.append(record);
94 | }
95 | in.close();
96 |
97 | } catch (IOException e) {
98 | e.printStackTrace();
99 | return null;
100 | }
101 | logger.info("DataSet size:{}", dataset.size());
102 | return dataset;
103 | }
104 |
105 | public class Record {
106 |
107 | private double[] attrs;
108 | private Double label;
109 |
110 | private Record(double[] attrs, Double lable) {
111 | this.attrs = attrs;
112 | this.label = lable;
113 | }
114 |
115 | public Record(double[] data) {
116 | if (lableIndex == -1)
117 | attrs = data;
118 | else {
119 | label = data[lableIndex];
120 | if (label > maxLable)
121 | maxLable = label;
122 | if (lableIndex == 0)
123 | attrs = Arrays.copyOfRange(data, 1, data.length);
124 | else
125 | attrs = Arrays.copyOfRange(data, 0, data.length - 1);
126 | }
127 | }
128 |
129 | public double[] getAttrs() {
130 | return attrs;
131 | }
132 |
133 | @Override
134 | public String toString() {
135 | StringBuilder sb = new StringBuilder();
136 | sb.append("attrs:");
137 | sb.append(Arrays.toString(attrs));
138 | sb.append("lable:");
139 | sb.append(label);
140 | return sb.toString();
141 | }
142 |
143 | public Double getLabel() {
144 | if (lableIndex == -1)
145 | return null;
146 | return label;
147 | }
148 |
149 | public int[] getEncodeTarget(int n) {
150 | String binary = Integer.toBinaryString(label.intValue());
151 | byte[] bytes = binary.getBytes();
152 | int[] encode = new int[n];
153 | int j = n;
154 | for (int i = bytes.length - 1; i >= 0; i--)
155 | encode[--j] = bytes[i] - '0';
156 |
157 | return encode;
158 | }
159 |
160 | public double[] getDoubleEncodeTarget(int n) {
161 | String binary = Integer.toBinaryString(label.intValue());
162 | byte[] bytes = binary.getBytes();
163 | double[] encode = new double[n];
164 | int j = n;
165 | for (int i = bytes.length - 1; i >= 0; i--)
166 | encode[--j] = bytes[i] - '0';
167 |
168 | return encode;
169 | }
170 |
171 | }
172 |
173 | public Record getRecord(int index) {
174 | return records.get(index);
175 | }
176 |
177 | }
178 |
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/src/test/java/info/hb/cnn/utils/MathUtilsTest.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.utils;
2 |
3 | import static org.junit.Assert.assertEquals;
4 | import info.hb.cnn.core.Layer.Size;
5 | import info.hb.cnn.utils.MathUtils.Operator;
6 | import info.hb.cnn.utils.TimedTest.TestTask;
7 |
8 | import org.junit.Test;
9 |
10 | public class MathUtilsTest {
11 |
12 | @Test
13 | public void testTimedConvn() {
14 | new TimedTest(new TestTask() {
15 |
16 | @Override
17 | public void process() {
18 | testConvn();
19 | }
20 |
21 | }, 5).test();
22 | ConcurentRunner.stop();
23 | }
24 |
25 | @Test
26 | public void testTimedScaleMatrix() {
27 | new TimedTest(new TestTask() {
28 |
29 | @Override
30 | public void process() {
31 | testScaleMatrix();
32 | }
33 |
34 | }, 5).test();
35 | ConcurentRunner.stop();
36 | }
37 |
38 | @Test
39 | public void testTimedKronecker() {
40 | new TimedTest(new TestTask() {
41 |
42 | @Override
43 | public void process() {
44 | testKronecker();
45 | }
46 |
47 | }, 5).test();
48 | ConcurentRunner.stop();
49 | }
50 |
51 | @Test
52 | public void testTimedMatrixProduct() {
53 | new TimedTest(new TestTask() {
54 |
55 | @Override
56 | public void process() {
57 | testMatrixProduct();
58 | }
59 |
60 | }, 5).test();
61 | ConcurentRunner.stop();
62 | }
63 |
64 | @Test
65 | public void testTimedCloneMatrix() {
66 | new TimedTest(new TestTask() {
67 |
68 | @Override
69 | public void process() {
70 | testCloneMatrix();
71 | }
72 |
73 | }, 5).test();
74 | ConcurentRunner.stop();
75 | }
76 |
77 | @Test
78 | public void testM() {
79 | assertEquals(0.6743346010828868d, MathUtils.sigmod(0.727855957917715), 0.0d);
80 | }
81 |
82 | private static void testConvn() {
83 | int count = 1;
84 | double[][] m = new double[5][5];
85 | for (int i = 0; i < m.length; i++)
86 | for (int j = 0; j < m[0].length; j++)
87 | m[i][j] = count++;
88 | double[][] k = new double[3][3];
89 | for (int i = 0; i < k.length; i++)
90 | for (int j = 0; j < k[0].length; j++)
91 | k[i][j] = 1;
92 | double[][] out;
93 | // out= convnValid(m, k);
94 | MathUtils.printMatrix(m);
95 | out = MathUtils.convnFull(m, k);
96 | MathUtils.printMatrix(out);
97 | // System.out.println();
98 | // out = convnFull(m, Util.rot180(k));
99 | // Util.printMatrix(out);
100 |
101 | }
102 |
103 | private static void testScaleMatrix() {
104 | int count = 1;
105 | double[][] m = new double[16][16];
106 | for (int i = 0; i < m.length; i++)
107 | for (int j = 0; j < m[0].length; j++)
108 | m[i][j] = count++;
109 | double[][] out = MathUtils.scaleMatrix(m, new Size(2, 2));
110 | MathUtils.printMatrix(m);
111 | MathUtils.printMatrix(out);
112 | }
113 |
114 | private static void testKronecker() {
115 | int count = 1;
116 | double[][] m = new double[5][5];
117 | for (int i = 0; i < m.length; i++)
118 | for (int j = 0; j < m[0].length; j++)
119 | m[i][j] = count++;
120 | double[][] out = MathUtils.kronecker(m, new Size(2, 2));
121 | MathUtils.printMatrix(m);
122 | System.out.println();
123 | MathUtils.printMatrix(out);
124 | }
125 |
126 | private static void testMatrixProduct() {
127 | int count = 1;
128 | double[][] m = new double[5][5];
129 | for (int i = 0; i < m.length; i++)
130 | for (int j = 0; j < m[0].length; j++)
131 | m[i][j] = count++;
132 | double[][] k = new double[5][5];
133 | for (int i = 0; i < k.length; i++)
134 | for (int j = 0; j < k[0].length; j++)
135 | k[i][j] = j;
136 |
137 | MathUtils.printMatrix(m);
138 | MathUtils.printMatrix(k);
139 | double[][] out = MathUtils.matrixOp(m, k, new Operator() {
140 |
141 | private static final long serialVersionUID = -680712567166604573L;
142 |
143 | @Override
144 | public double process(double value) {
145 | return value - 1;
146 | }
147 |
148 | }, new Operator() {
149 |
150 | private static final long serialVersionUID = -6335660830579545544L;
151 |
152 | @Override
153 | public double process(double value) {
154 |
155 | return -1 * value;
156 | }
157 |
158 | }, MathUtils.multiply);
159 | MathUtils.printMatrix(out);
160 | }
161 |
162 | private static void testCloneMatrix() {
163 | int count = 1;
164 | double[][] m = new double[5][5];
165 | for (int i = 0; i < m.length; i++)
166 | for (int j = 0; j < m[0].length; j++)
167 | m[i][j] = count++;
168 | double[][] out = MathUtils.cloneMatrix(m);
169 | MathUtils.printMatrix(m);
170 |
171 | MathUtils.printMatrix(out);
172 | }
173 |
174 | public static void testRot180() {
175 | double[][] matrix = { { 1, 2, 3, 4 }, { 4, 5, 6, 7 }, { 7, 8, 9, 10 } };
176 | MathUtils.printMatrix(matrix);
177 | MathUtils.rot180(matrix);
178 | System.out.println();
179 | MathUtils.printMatrix(matrix);
180 | }
181 |
182 | }
183 |
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/src/main/java/info/hb/cnn/core/Layer.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.core;
2 |
3 | import info.hb.cnn.utils.MathUtils;
4 |
5 | import java.io.Serializable;
6 |
7 | import org.slf4j.Logger;
8 | import org.slf4j.LoggerFactory;
9 |
10 | public class Layer implements Serializable {
11 |
12 | private static final long serialVersionUID = -5747622503947497069L;
13 |
14 | private static Logger logger = LoggerFactory.getLogger(Layer.class);
15 |
16 | private LayerType type;
17 | private int outMapNum;
18 | private Size mapSize;
19 | private Size kernelSize;
20 | private Size scaleSize;
21 | private double[][][][] kernel;
22 | private double[] bias;
23 |
24 | private double[][][][] outmaps;
25 |
26 | private double[][][][] errors;
27 |
28 | private static int recordInBatch = 0;
29 |
30 | private int classNum = -1;
31 |
32 | private Layer() {
33 | //
34 | }
35 |
36 | public static void prepareForNewBatch() {
37 | recordInBatch = 0;
38 | }
39 |
40 | public static void prepareForNewRecord() {
41 | recordInBatch++;
42 | }
43 |
44 | public static Layer buildInputLayer(Size mapSize) {
45 | Layer layer = new Layer();
46 | layer.type = LayerType.input;
47 | layer.outMapNum = 1;
48 | layer.setMapSize(mapSize);
49 | return layer;
50 | }
51 |
52 | public static Layer buildConvLayer(int outMapNum, Size kernelSize) {
53 | Layer layer = new Layer();
54 | layer.type = LayerType.conv;
55 | layer.outMapNum = outMapNum;
56 | layer.kernelSize = kernelSize;
57 | return layer;
58 | }
59 |
60 | public static Layer buildSampLayer(Size scaleSize) {
61 | Layer layer = new Layer();
62 | layer.type = LayerType.samp;
63 | layer.scaleSize = scaleSize;
64 | return layer;
65 | }
66 |
67 | public static Layer buildOutputLayer(int classNum) {
68 | Layer layer = new Layer();
69 | layer.classNum = classNum;
70 | layer.type = LayerType.output;
71 | layer.mapSize = new Size(1, 1);
72 | layer.outMapNum = classNum;
73 | // int outMapNum = 1;
74 | // while ((1 << outMapNum) < classNum)
75 | // outMapNum += 1;
76 | // layer.outMapNum = outMapNum;
77 | logger.info("outMapNum:{}", layer.outMapNum);
78 | return layer;
79 | }
80 |
81 | public Size getMapSize() {
82 | return mapSize;
83 | }
84 |
85 | public void setMapSize(Size mapSize) {
86 | this.mapSize = mapSize;
87 | }
88 |
89 | public LayerType getType() {
90 | return type;
91 | }
92 |
93 | public int getOutMapNum() {
94 | return outMapNum;
95 | }
96 |
97 | public void setOutMapNum(int outMapNum) {
98 | this.outMapNum = outMapNum;
99 | }
100 |
101 | public Size getKernelSize() {
102 | return kernelSize;
103 | }
104 |
105 | public Size getScaleSize() {
106 | return scaleSize;
107 | }
108 |
109 | enum LayerType {
110 | input, output, conv, samp
111 | }
112 |
113 | public static class Size implements Serializable {
114 |
115 | private static final long serialVersionUID = -209157832162004118L;
116 |
117 | public final int x;
118 | public final int y;
119 |
120 | public Size(int x, int y) {
121 | this.x = x;
122 | this.y = y;
123 | }
124 |
125 | @Override
126 | public String toString() {
127 | StringBuilder s = new StringBuilder("Size(").append(" x = ").append(x).append(" y= ").append(y).append(")");
128 | return s.toString();
129 | }
130 |
131 | public Size divide(Size scaleSize) {
132 | int x = this.x / scaleSize.x;
133 | int y = this.y / scaleSize.y;
134 | if (x * scaleSize.x != this.x || y * scaleSize.y != this.y)
135 | throw new RuntimeException(this + "scalesize:" + scaleSize);
136 | return new Size(x, y);
137 | }
138 |
139 | public Size subtract(Size size, int append) {
140 | int x = this.x - size.x + append;
141 | int y = this.y - size.y + append;
142 | return new Size(x, y);
143 | }
144 |
145 | }
146 |
147 | public void initKernel(int frontMapNum) {
148 | // int fan_out = getOutMapNum() * kernelSize.x * kernelSize.y;
149 | // int fan_in = frontMapNum * kernelSize.x * kernelSize.y;
150 | // double factor = 2 * Math.sqrt(6 / (fan_in + fan_out));
151 | this.kernel = new double[frontMapNum][outMapNum][kernelSize.x][kernelSize.y];
152 | for (int i = 0; i < frontMapNum; i++)
153 | for (int j = 0; j < outMapNum; j++)
154 | kernel[i][j] = MathUtils.randomMatrix(kernelSize.x, kernelSize.y, true);
155 | }
156 |
157 | public void initOutputKerkel(int frontMapNum, Size size) {
158 | kernelSize = size;
159 | // int fan_out = getOutMapNum() * kernelSize.x * kernelSize.y;
160 | // int fan_in = frontMapNum * kernelSize.x * kernelSize.y;
161 | // double factor = 2 * Math.sqrt(6 / (fan_in + fan_out));
162 | this.kernel = new double[frontMapNum][outMapNum][kernelSize.x][kernelSize.y];
163 | for (int i = 0; i < frontMapNum; i++)
164 | for (int j = 0; j < outMapNum; j++)
165 | kernel[i][j] = MathUtils.randomMatrix(kernelSize.x, kernelSize.y, false);
166 | }
167 |
168 | public void initBias(int frontMapNum) {
169 | this.bias = MathUtils.randomArray(outMapNum);
170 | }
171 |
172 | public void initOutmaps(int batchSize) {
173 | outmaps = new double[batchSize][outMapNum][mapSize.x][mapSize.y];
174 | }
175 |
176 | public void setMapValue(int mapNo, int mapX, int mapY, double value) {
177 | outmaps[recordInBatch][mapNo][mapX][mapY] = value;
178 | }
179 |
180 | static int count = 0;
181 |
182 | public void setMapValue(int mapNo, double[][] outMatrix) {
183 | // Log.i(type.toString());
184 | // Util.printMatrix(outMatrix);
185 | outmaps[recordInBatch][mapNo] = outMatrix;
186 | }
187 |
188 | public double[][] getMap(int index) {
189 | return outmaps[recordInBatch][index];
190 | }
191 |
192 | public double[][] getKernel(int i, int j) {
193 | return kernel[i][j];
194 | }
195 |
196 | public void setError(int mapNo, int mapX, int mapY, double value) {
197 | errors[recordInBatch][mapNo][mapX][mapY] = value;
198 | }
199 |
200 | public void setError(int mapNo, double[][] matrix) {
201 | // Log.i(type.toString());
202 | // Util.printMatrix(matrix);
203 | errors[recordInBatch][mapNo] = matrix;
204 | }
205 |
206 | public double[][] getError(int mapNo) {
207 | return errors[recordInBatch][mapNo];
208 | }
209 |
210 | public double[][][][] getErrors() {
211 | return errors;
212 | }
213 |
214 | public void initErros(int batchSize) {
215 | errors = new double[batchSize][outMapNum][mapSize.x][mapSize.y];
216 | }
217 |
218 | public void setKernel(int lastMapNo, int mapNo, double[][] kernel) {
219 | this.kernel[lastMapNo][mapNo] = kernel;
220 | }
221 |
222 | public double getBias(int mapNo) {
223 | return bias[mapNo];
224 | }
225 |
226 | public void setBias(int mapNo, double value) {
227 | bias[mapNo] = value;
228 | }
229 |
230 | public double[][][][] getMaps() {
231 | return outmaps;
232 | }
233 |
234 | public double[][] getError(int recordId, int mapNo) {
235 | return errors[recordId][mapNo];
236 | }
237 |
238 | public double[][] getMap(int recordId, int mapNo) {
239 | return outmaps[recordId][mapNo];
240 | }
241 |
242 | public int getClassNum() {
243 | return classNum;
244 | }
245 |
246 | public double[][][][] getKernel() {
247 | return kernel;
248 | }
249 |
250 | }
251 |
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/src/main/java/info/hb/cnn/utils/MathUtils.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.utils;
2 |
3 | import info.hb.cnn.core.Layer.Size;
4 |
5 | import java.io.Serializable;
6 | import java.util.Arrays;
7 | import java.util.HashSet;
8 | import java.util.Random;
9 | import java.util.Set;
10 |
11 | public class MathUtils {
12 |
13 | public interface Operator extends Serializable {
14 | public double process(double value);
15 | }
16 |
17 | public static final Operator one_value = new Operator() {
18 |
19 | private static final long serialVersionUID = 3752139491940330714L;
20 |
21 | @Override
22 | public double process(double value) {
23 | return 1 - value;
24 | }
25 |
26 | };
27 |
28 | public static final Operator digmod = new Operator() {
29 |
30 | private static final long serialVersionUID = -1952718905019847589L;
31 |
32 | @Override
33 | public double process(double value) {
34 | return 1 / (1 + Math.pow(Math.E, -value));
35 | }
36 |
37 | };
38 |
39 | interface OperatorOnTwo extends Serializable {
40 | public double process(double a, double b);
41 | }
42 |
43 | public static final OperatorOnTwo plus = new OperatorOnTwo() {
44 |
45 | private static final long serialVersionUID = -6298144029766839945L;
46 |
47 | @Override
48 | public double process(double a, double b) {
49 | return a + b;
50 | }
51 |
52 | };
53 |
54 | public static OperatorOnTwo multiply = new OperatorOnTwo() {
55 |
56 | private static final long serialVersionUID = -7053767821858820698L;
57 |
58 | @Override
59 | public double process(double a, double b) {
60 | return a * b;
61 | }
62 |
63 | };
64 |
65 | public static OperatorOnTwo minus = new OperatorOnTwo() {
66 |
67 | private static final long serialVersionUID = 7346065545555093912L;
68 |
69 | @Override
70 | public double process(double a, double b) {
71 | return a - b;
72 | }
73 |
74 | };
75 |
76 | public static void printMatrix(double[][] matrix) {
77 | for (int i = 0; i < matrix.length; i++) {
78 | String line = Arrays.toString(matrix[i]);
79 | line = line.replaceAll(", ", "\t");
80 | System.out.println(line);
81 | }
82 | System.out.println();
83 | }
84 |
85 | public static double[][] rot180(double[][] matrix) {
86 | matrix = cloneMatrix(matrix);
87 | int m = matrix.length;
88 | int n = matrix[0].length;
89 | for (int i = 0; i < m; i++) {
90 | for (int j = 0; j < n / 2; j++) {
91 | double tmp = matrix[i][j];
92 | matrix[i][j] = matrix[i][n - 1 - j];
93 | matrix[i][n - 1 - j] = tmp;
94 | }
95 | }
96 | for (int j = 0; j < n; j++) {
97 | for (int i = 0; i < m / 2; i++) {
98 | double tmp = matrix[i][j];
99 | matrix[i][j] = matrix[m - 1 - i][j];
100 | matrix[m - 1 - i][j] = tmp;
101 | }
102 | }
103 | return matrix;
104 | }
105 |
106 | private static Random r = new Random(2);
107 |
108 | public static double[][] randomMatrix(int x, int y, boolean b) {
109 | double[][] matrix = new double[x][y];
110 | // int tag = 1;
111 | for (int i = 0; i < x; i++) {
112 | for (int j = 0; j < y; j++) {
113 | // [-0.05,0.05)
114 | matrix[i][j] = (r.nextDouble() - 0.05) / 10;
115 | // matrix[i][j] = tag * 0.5;
116 | // if (b)
117 | // matrix[i][j] *= 1.0*(i + j + 2) / (i + 1) / (j + 1);
118 | // tag *= -1;
119 | }
120 | }
121 | // printMatrix(matrix);
122 | return matrix;
123 | }
124 |
125 | public static double[] randomArray(int len) {
126 | double[] data = new double[len];
127 | for (int i = 0; i < len; i++) {
128 | // data[i] = r.nextDouble() / 10 - 0.05;
129 | data[i] = 0;
130 | }
131 | return data;
132 | }
133 |
134 | public static int[] randomPerm(int size, int batchSize) {
135 | Set set = new HashSet();
136 | while (set.size() < batchSize) {
137 | set.add(r.nextInt(size));
138 | }
139 | int[] randPerm = new int[batchSize];
140 | int i = 0;
141 | for (Integer value : set)
142 | randPerm[i++] = value;
143 | return randPerm;
144 | }
145 |
146 | public static double[][] cloneMatrix(final double[][] matrix) {
147 | final int m = matrix.length;
148 | int n = matrix[0].length;
149 | final double[][] outMatrix = new double[m][n];
150 |
151 | for (int i = 0; i < m; i++) {
152 | for (int j = 0; j < n; j++) {
153 | outMatrix[i][j] = matrix[i][j];
154 | }
155 | }
156 | return outMatrix;
157 | }
158 |
159 | public static double[][] matrixOp(final double[][] ma, Operator operator) {
160 | final int m = ma.length;
161 | int n = ma[0].length;
162 | for (int i = 0; i < m; i++) {
163 | for (int j = 0; j < n; j++) {
164 | ma[i][j] = operator.process(ma[i][j]);
165 | }
166 | }
167 | return ma;
168 | }
169 |
170 | public static double[][] matrixOp(final double[][] ma, final double[][] mb, final Operator operatorA,
171 | final Operator operatorB, OperatorOnTwo operator) {
172 | final int m = ma.length;
173 | int n = ma[0].length;
174 | if (m != mb.length || n != mb[0].length)
175 | throw new RuntimeException("ma.length:" + ma.length + " mb.length:" + mb.length);
176 |
177 | for (int i = 0; i < m; i++) {
178 | for (int j = 0; j < n; j++) {
179 | double a = ma[i][j];
180 | if (operatorA != null)
181 | a = operatorA.process(a);
182 | double b = mb[i][j];
183 | if (operatorB != null)
184 | b = operatorB.process(b);
185 | mb[i][j] = operator.process(a, b);
186 | }
187 | }
188 | return mb;
189 | }
190 |
191 | public static double[][] kronecker(final double[][] matrix, final Size scale) {
192 | final int m = matrix.length;
193 | int n = matrix[0].length;
194 | final double[][] outMatrix = new double[m * scale.x][n * scale.y];
195 |
196 | for (int i = 0; i < m; i++) {
197 | for (int j = 0; j < n; j++) {
198 | for (int ki = i * scale.x; ki < (i + 1) * scale.x; ki++) {
199 | for (int kj = j * scale.y; kj < (j + 1) * scale.y; kj++) {
200 | outMatrix[ki][kj] = matrix[i][j];
201 | }
202 | }
203 | }
204 | }
205 | return outMatrix;
206 | }
207 |
208 | public static double[][] scaleMatrix(final double[][] matrix, final Size scale) {
209 | int m = matrix.length;
210 | int n = matrix[0].length;
211 | final int sm = m / scale.x;
212 | final int sn = n / scale.y;
213 | final double[][] outMatrix = new double[sm][sn];
214 | if (sm * scale.x != m || sn * scale.y != n)
215 | throw new RuntimeException("scale matrix");
216 | final int size = scale.x * scale.y;
217 | for (int i = 0; i < sm; i++) {
218 | for (int j = 0; j < sn; j++) {
219 | double sum = 0.0;
220 | for (int si = i * scale.x; si < (i + 1) * scale.x; si++) {
221 | for (int sj = j * scale.y; sj < (j + 1) * scale.y; sj++) {
222 | sum += matrix[si][sj];
223 | }
224 | }
225 | outMatrix[i][j] = sum / size;
226 | }
227 | }
228 | return outMatrix;
229 | }
230 |
231 | public static double[][] convnFull(double[][] matrix, final double[][] kernel) {
232 | int m = matrix.length;
233 | int n = matrix[0].length;
234 | final int km = kernel.length;
235 | final int kn = kernel[0].length;
236 | final double[][] extendMatrix = new double[m + 2 * (km - 1)][n + 2 * (kn - 1)];
237 | for (int i = 0; i < m; i++) {
238 | for (int j = 0; j < n; j++)
239 | extendMatrix[i + km - 1][j + kn - 1] = matrix[i][j];
240 | }
241 | return convnValid(extendMatrix, kernel);
242 | }
243 |
244 | public static double[][] convnValid(final double[][] matrix, double[][] kernel) {
245 | // kernel = rot180(kernel);
246 | int m = matrix.length;
247 | int n = matrix[0].length;
248 | final int km = kernel.length;
249 | final int kn = kernel[0].length;
250 | int kns = n - kn + 1;
251 | final int kms = m - km + 1;
252 | final double[][] outMatrix = new double[kms][kns];
253 |
254 | for (int i = 0; i < kms; i++) {
255 | for (int j = 0; j < kns; j++) {
256 | double sum = 0.0;
257 | for (int ki = 0; ki < km; ki++) {
258 | for (int kj = 0; kj < kn; kj++)
259 | sum += matrix[i + ki][j + kj] * kernel[ki][kj];
260 | }
261 | outMatrix[i][j] = sum;
262 |
263 | }
264 | }
265 | return outMatrix;
266 | }
267 |
268 | public static double[][] convnValid(final double[][][][] matrix, int mapNoX, double[][][][] kernel, int mapNoY) {
269 | int m = matrix.length;
270 | int n = matrix[0][mapNoX].length;
271 | int h = matrix[0][mapNoX][0].length;
272 | int km = kernel.length;
273 | int kn = kernel[0][mapNoY].length;
274 | int kh = kernel[0][mapNoY][0].length;
275 | int kms = m - km + 1;
276 | int kns = n - kn + 1;
277 | int khs = h - kh + 1;
278 | if (matrix.length != kernel.length)
279 | throw new RuntimeException("length");
280 | final double[][][] outMatrix = new double[kms][kns][khs];
281 | for (int i = 0; i < kms; i++) {
282 | for (int j = 0; j < kns; j++)
283 | for (int k = 0; k < khs; k++) {
284 | double sum = 0.0;
285 | for (int ki = 0; ki < km; ki++) {
286 | for (int kj = 0; kj < kn; kj++)
287 | for (int kk = 0; kk < kh; kk++) {
288 | sum += matrix[i + ki][mapNoX][j + kj][k + kk] * kernel[ki][mapNoY][kj][kk];
289 | }
290 | }
291 | outMatrix[i][j][k] = sum;
292 | }
293 | }
294 | return outMatrix[0];
295 | }
296 |
297 | public static double sigmod(double x) {
298 | return 1 / (1 + Math.pow(Math.E, -x));
299 | }
300 |
301 | public static double sum(double[][] error) {
302 | int m = error.length;
303 | int n = error[0].length;
304 | double sum = 0.0;
305 | for (int i = 0; i < m; i++) {
306 | for (int j = 0; j < n; j++) {
307 | sum += error[i][j];
308 | }
309 | }
310 | return sum;
311 | }
312 |
313 | public static double[][] sum(double[][][][] errors, int j) {
314 | int m = errors[0][j].length;
315 | int n = errors[0][j][0].length;
316 | double[][] result = new double[m][n];
317 | for (int mi = 0; mi < m; mi++) {
318 | for (int nj = 0; nj < n; nj++) {
319 | double sum = 0;
320 | for (int i = 0; i < errors.length; i++)
321 | sum += errors[i][j][mi][nj];
322 | result[mi][nj] = sum;
323 | }
324 | }
325 | return result;
326 | }
327 |
328 | public static int binaryArray2int(double[] array) {
329 | int[] d = new int[array.length];
330 | for (int i = 0; i < d.length; i++) {
331 | if (array[i] >= 0.500000001)
332 | d[i] = 1;
333 | else
334 | d[i] = 0;
335 | }
336 | String s = Arrays.toString(d);
337 | String binary = s.substring(1, s.length() - 1).replace(", ", "");
338 | int data = Integer.parseInt(binary, 2);
339 | return data;
340 |
341 | }
342 |
343 | public static int getMaxIndex(double[] out) {
344 | double max = out[0];
345 | int index = 0;
346 | for (int i = 1; i < out.length; i++)
347 | if (out[i] > max) {
348 | max = out[i];
349 | index = i;
350 | }
351 | return index;
352 | }
353 |
354 | public static String fomart(double[] data) {
355 | StringBuilder sb = new StringBuilder("[");
356 | for (double each : data)
357 | sb.append(String.format("%4f,", each));
358 | sb.append("]");
359 | return sb.toString();
360 | }
361 |
362 | }
363 |
--------------------------------------------------------------------------------
/src/main/java/info/hb/cnn/core/CNN.java:
--------------------------------------------------------------------------------
1 | package info.hb.cnn.core;
2 |
3 | import info.hb.cnn.core.Layer.Size;
4 | import info.hb.cnn.data.DataSet;
5 | import info.hb.cnn.data.DataSet.Record;
6 | import info.hb.cnn.utils.ConcurentRunner.TaskManager;
7 | import info.hb.cnn.utils.MathUtils;
8 | import info.hb.cnn.utils.MathUtils.Operator;
9 |
10 | import java.io.File;
11 | import java.io.FileInputStream;
12 | import java.io.FileOutputStream;
13 | import java.io.IOException;
14 | import java.io.ObjectInputStream;
15 | import java.io.ObjectOutputStream;
16 | import java.io.PrintWriter;
17 | import java.io.Serializable;
18 | import java.util.ArrayList;
19 | import java.util.Iterator;
20 | import java.util.List;
21 | import java.util.concurrent.atomic.AtomicBoolean;
22 |
23 | import org.slf4j.Logger;
24 | import org.slf4j.LoggerFactory;
25 |
26 | public class CNN implements Serializable {
27 |
28 | private static final long serialVersionUID = 337920299147929932L;
29 |
30 | private static Logger logger = LoggerFactory.getLogger(CNN.class);
31 |
32 | private static double ALPHA = 0.85;
33 | protected static final double LAMBDA = 0;
34 | private List layers;
35 | private int layerNum;
36 |
37 | private int batchSize;
38 | private Operator divide_batchSize;
39 |
40 | private Operator multiply_alpha;
41 |
42 | private Operator multiply_lambda;
43 |
44 | public CNN(LayerBuilder layerBuilder, final int batchSize) {
45 | layers = layerBuilder.mLayers;
46 | layerNum = layers.size();
47 | this.batchSize = batchSize;
48 | setup(batchSize);
49 | initPerator();
50 | }
51 |
52 | private void initPerator() {
53 |
54 | divide_batchSize = new Operator() {
55 |
56 | private static final long serialVersionUID = 7424011281732651055L;
57 |
58 | @Override
59 | public double process(double value) {
60 | return value / batchSize;
61 | }
62 |
63 | };
64 |
65 | multiply_alpha = new Operator() {
66 |
67 | private static final long serialVersionUID = 5761368499808006552L;
68 |
69 | @Override
70 | public double process(double value) {
71 |
72 | return value * ALPHA;
73 | }
74 |
75 | };
76 |
77 | multiply_lambda = new Operator() {
78 |
79 | private static final long serialVersionUID = 4499087728362870577L;
80 |
81 | @Override
82 | public double process(double value) {
83 |
84 | return value * (1 - LAMBDA * ALPHA);
85 | }
86 |
87 | };
88 |
89 | }
90 |
91 | public void train(DataSet trainset, int repeat) {
92 |
93 | new Lisenter().start();
94 |
95 | for (int t = 0; t < repeat && !stopTrain.get(); t++) {
96 | int epochsNum = trainset.size() / batchSize;
97 | if (trainset.size() % batchSize != 0)
98 | epochsNum++;
99 | logger.info("第{}次迭代，epochsNum: {}", t, epochsNum);
100 | int right = 0;
101 | int count = 0;
102 | for (int i = 0; i < epochsNum; i++) {
103 | int[] randPerm = MathUtils.randomPerm(trainset.size(), batchSize);
104 | Layer.prepareForNewBatch();
105 |
106 | for (int index : randPerm) {
107 | boolean isRight = train(trainset.getRecord(index));
108 | if (isRight)
109 | right++;
110 | count++;
111 | Layer.prepareForNewRecord();
112 | }
113 |
114 | updateParas();
115 | if (i % 50 == 0) {
116 | System.out.print("..");
117 | if (i + 50 > epochsNum)
118 | System.out.println();
119 | }
120 | }
121 | double p = 1.0 * right / count;
122 | if (t % 10 == 1 && p > 0.96) {
123 | ALPHA = 0.001 + ALPHA * 0.9;
124 | logger.info("设置 alpha = {}", ALPHA);
125 | }
126 | logger.info("计算精度： {}/{}={}.", right, count, p);
127 | }
128 |
129 | }
130 |
131 | private static AtomicBoolean stopTrain;
132 |
133 | static class Lisenter extends Thread {
134 |
135 | Lisenter() {
136 | setDaemon(true);
137 | stopTrain = new AtomicBoolean(false);
138 | }
139 |
140 | @Override
141 | public void run() {
142 | logger.info("输入&符号停止训练.");
143 | while (true) {
144 | try {
145 | int a = System.in.read();
146 | if (a == '&') {
147 | stopTrain.compareAndSet(false, true);
148 | break;
149 | }
150 | } catch (IOException e) {
151 | e.printStackTrace();
152 | }
153 | }
154 | System.out.println("监听停止.");
155 | }
156 |
157 | }
158 |
159 | @SuppressWarnings("unused")
160 | private double test(DataSet trainset) {
161 | Layer.prepareForNewBatch();
162 | Iterator iter = trainset.iter();
163 | int right = 0;
164 | while (iter.hasNext()) {
165 | Record record = iter.next();
166 | forward(record);
167 | Layer outputLayer = layers.get(layerNum - 1);
168 | int mapNum = outputLayer.getOutMapNum();
169 | double[] out = new double[mapNum];
170 | for (int m = 0; m < mapNum; m++) {
171 | double[][] outmap = outputLayer.getMap(m);
172 | out[m] = outmap[0][0];
173 | }
174 | if (record.getLabel().intValue() == MathUtils.getMaxIndex(out))
175 | right++;
176 | }
177 | double p = 1.0 * right / trainset.size();
178 | logger.info("计算精度为：\t{}", p + "");
179 | return p;
180 | }
181 |
182 | public void predict(DataSet testset, String fileName) {
183 | logger.info("开始预测 ...");
184 | try {
185 | // int max = layers.get(layerNum - 1).getClassNum();
186 | PrintWriter writer = new PrintWriter(new File(fileName));
187 | Layer.prepareForNewBatch();
188 | Iterator iter = testset.iter();
189 | while (iter.hasNext()) {
190 | Record record = iter.next();
191 | forward(record);
192 | Layer outputLayer = layers.get(layerNum - 1);
193 |
194 | int mapNum = outputLayer.getOutMapNum();
195 | double[] out = new double[mapNum];
196 | for (int m = 0; m < mapNum; m++) {
197 | double[][] outmap = outputLayer.getMap(m);
198 | out[m] = outmap[0][0];
199 | }
200 | // int lable = MathUtils.binaryArray2int(out);
201 | int lable = MathUtils.getMaxIndex(out);
202 | // if (lable >= max)
203 | // lable = lable - (1 << (out.length - 1));
204 | writer.write(lable + "\n");
205 | }
206 | writer.flush();
207 | writer.close();
208 | } catch (IOException e) {
209 | throw new RuntimeException(e);
210 | }
211 | logger.info("完成预测 ...");
212 | }
213 |
214 | @SuppressWarnings("unused")
215 | private boolean isSame(double[] output, double[] target) {
216 | boolean r = true;
217 | for (int i = 0; i < output.length; i++)
218 | if (Math.abs(output[i] - target[i]) > 0.5) {
219 | r = false;
220 | break;
221 | }
222 |
223 | return r;
224 | }
225 |
226 | private boolean train(Record record) {
227 | forward(record);
228 | boolean result = backPropagation(record);
229 | return result;
230 | // System.exit(0);
231 | }
232 |
233 | private boolean backPropagation(Record record) {
234 | boolean result = setOutLayerErrors(record);
235 | setHiddenLayerErrors();
236 | return result;
237 | }
238 |
239 | private void updateParas() {
240 | for (int l = 1; l < layerNum; l++) {
241 | Layer layer = layers.get(l);
242 | Layer lastLayer = layers.get(l - 1);
243 | switch (layer.getType()) {
244 | case conv:
245 | case output:
246 | updateKernels(layer, lastLayer);
247 | updateBias(layer, lastLayer);
248 | break;
249 | default:
250 | break;
251 | }
252 | }
253 | }
254 |
255 | private void updateBias(final Layer layer, Layer lastLayer) {
256 | final double[][][][] errors = layer.getErrors();
257 | int mapNum = layer.getOutMapNum();
258 |
259 | new TaskManager(mapNum) {
260 | @Override
261 | public void process(int start, int end) {
262 | for (int j = start; j < end; j++) {
263 | double[][] error = MathUtils.sum(errors, j);
264 | double deltaBias = MathUtils.sum(error) / batchSize;
265 | double bias = layer.getBias(j) + ALPHA * deltaBias;
266 | layer.setBias(j, bias);
267 | }
268 | }
269 | }.start();
270 |
271 | }
272 |
273 | private void updateKernels(final Layer layer, final Layer lastLayer) {
274 |
275 | int mapNum = layer.getOutMapNum();
276 | final int lastMapNum = lastLayer.getOutMapNum();
277 |
278 | new TaskManager(mapNum) {
279 |
280 | @Override
281 | public void process(int start, int end) {
282 | for (int j = start; j < end; j++) {
283 | for (int i = 0; i < lastMapNum; i++) {
284 | double[][] deltaKernel = null;
285 | for (int r = 0; r < batchSize; r++) {
286 | double[][] error = layer.getError(r, j);
287 | if (deltaKernel == null)
288 | deltaKernel = MathUtils.convnValid(lastLayer.getMap(r, i), error);
289 | else {
290 | deltaKernel = MathUtils.matrixOp(MathUtils.convnValid(lastLayer.getMap(r, i), error),
291 | deltaKernel, null, null, MathUtils.plus);
292 | }
293 | }
294 |
295 | deltaKernel = MathUtils.matrixOp(deltaKernel, divide_batchSize);
296 | double[][] kernel = layer.getKernel(i, j);
297 | deltaKernel = MathUtils.matrixOp(kernel, deltaKernel, multiply_lambda, multiply_alpha,
298 | MathUtils.plus);
299 | layer.setKernel(i, j, deltaKernel);
300 | }
301 | }
302 |
303 | }
304 | }.start();
305 |
306 | }
307 |
308 | private void setHiddenLayerErrors() {
309 | for (int l = layerNum - 2; l > 0; l--) {
310 | Layer layer = layers.get(l);
311 | Layer nextLayer = layers.get(l + 1);
312 | switch (layer.getType()) {
313 | case samp:
314 | setSampErrors(layer, nextLayer);
315 | break;
316 | case conv:
317 | setConvErrors(layer, nextLayer);
318 | break;
319 | default:
320 | break;
321 | }
322 | }
323 | }
324 |
325 | private void setSampErrors(final Layer layer, final Layer nextLayer) {
326 |
327 | int mapNum = layer.getOutMapNum();
328 | final int nextMapNum = nextLayer.getOutMapNum();
329 |
330 | new TaskManager(mapNum) {
331 |
332 | @Override
333 | public void process(int start, int end) {
334 | for (int i = start; i < end; i++) {
335 | double[][] sum = null;
336 | for (int j = 0; j < nextMapNum; j++) {
337 | double[][] nextError = nextLayer.getError(j);
338 | double[][] kernel = nextLayer.getKernel(i, j);
339 | if (sum == null)
340 | sum = MathUtils.convnFull(nextError, MathUtils.rot180(kernel));
341 | else
342 | sum = MathUtils.matrixOp(MathUtils.convnFull(nextError, MathUtils.rot180(kernel)), sum,
343 | null, null, MathUtils.plus);
344 | }
345 | layer.setError(i, sum);
346 | }
347 | }
348 |
349 | }.start();
350 |
351 | }
352 |
353 | private void setConvErrors(final Layer layer, final Layer nextLayer) {
354 |
355 | int mapNum = layer.getOutMapNum();
356 |
357 | new TaskManager(mapNum) {
358 |
359 | @Override
360 | public void process(int start, int end) {
361 | for (int m = start; m < end; m++) {
362 | Size scale = nextLayer.getScaleSize();
363 | double[][] nextError = nextLayer.getError(m);
364 | double[][] map = layer.getMap(m);
365 | double[][] outMatrix = MathUtils.matrixOp(map, MathUtils.cloneMatrix(map), null,
366 | MathUtils.one_value, MathUtils.multiply);
367 | outMatrix = MathUtils.matrixOp(outMatrix, MathUtils.kronecker(nextError, scale), null, null,
368 | MathUtils.multiply);
369 | layer.setError(m, outMatrix);
370 | }
371 |
372 | }
373 |
374 | }.start();
375 |
376 | }
377 |
378 | private boolean setOutLayerErrors(Record record) {
379 |
380 | Layer outputLayer = layers.get(layerNum - 1);
381 | int mapNum = outputLayer.getOutMapNum();
382 | // double[] target =
383 | // record.getDoubleEncodeTarget(mapNum);
384 | // double[] outmaps = new double[mapNum];
385 | // for (int m = 0; m < mapNum; m++) {
386 | // double[][] outmap = outputLayer.getMap(m);
387 | // double output = outmap[0][0];
388 | // outmaps[m] = output;
389 | // double errors = output * (1 - output) *
390 | // (target[m] - output);
391 | // outputLayer.setError(m, 0, 0, errors);
392 | // }
393 | // if (isSame(outmaps, target))
394 | // return true;
395 | // return false;
396 |
397 | double[] target = new double[mapNum];
398 | double[] outmaps = new double[mapNum];
399 | for (int m = 0; m < mapNum; m++) {
400 | double[][] outmap = outputLayer.getMap(m);
401 | outmaps[m] = outmap[0][0];
402 |
403 | }
404 | int lable = record.getLabel().intValue();
405 | target[lable] = 1;
406 | // Log.i(record.getLable() + "outmaps:" +
407 | // Util.fomart(outmaps)
408 | // + Arrays.toString(target));
409 | for (int m = 0; m < mapNum; m++) {
410 | outputLayer.setError(m, 0, 0, outmaps[m] * (1 - outmaps[m]) * (target[m] - outmaps[m]));
411 | }
412 |
413 | return lable == MathUtils.getMaxIndex(outmaps);
414 | }
415 |
416 | private void forward(Record record) {
417 | setInLayerOutput(record);
418 | for (int l = 1; l < layers.size(); l++) {
419 | Layer layer = layers.get(l);
420 | Layer lastLayer = layers.get(l - 1);
421 | switch (layer.getType()) {
422 | case conv:
423 | setConvOutput(layer, lastLayer);
424 | break;
425 | case samp:
426 | setSampOutput(layer, lastLayer);
427 | break;
428 | case output:
429 | setConvOutput(layer, lastLayer);
430 | break;
431 | default:
432 | break;
433 | }
434 | }
435 | }
436 |
437 | private void setInLayerOutput(Record record) {
438 | final Layer inputLayer = layers.get(0);
439 | final Size mapSize = inputLayer.getMapSize();
440 | final double[] attr = record.getAttrs();
441 | if (attr.length != mapSize.x * mapSize.y)
442 | throw new RuntimeException("map");
443 | for (int i = 0; i < mapSize.x; i++) {
444 | for (int j = 0; j < mapSize.y; j++) {
445 | // inputLayer.setMapValue(0, i, j, attr[mapSize.x * i + j]);
446 | inputLayer.setMapValue(0, i, j, attr[mapSize.y * i + j]);
447 | }
448 | }
449 | }
450 |
451 | private void setConvOutput(final Layer layer, final Layer lastLayer) {
452 |
453 | int mapNum = layer.getOutMapNum();
454 | final int lastMapNum = lastLayer.getOutMapNum();
455 |
456 | new TaskManager(mapNum) {
457 |
458 | @Override
459 | public void process(int start, int end) {
460 | for (int j = start; j < end; j++) {
461 | double[][] sum = null;
462 | for (int i = 0; i < lastMapNum; i++) {
463 | double[][] lastMap = lastLayer.getMap(i);
464 | double[][] kernel = layer.getKernel(i, j);
465 | if (sum == null)
466 | sum = MathUtils.convnValid(lastMap, kernel);
467 | else
468 | sum = MathUtils.matrixOp(MathUtils.convnValid(lastMap, kernel), sum, null, null,
469 | MathUtils.plus);
470 | }
471 | final double bias = layer.getBias(j);
472 | sum = MathUtils.matrixOp(sum, new Operator() {
473 | private static final long serialVersionUID = 2469461972825890810L;
474 |
475 | @Override
476 | public double process(double value) {
477 | return MathUtils.sigmod(value + bias);
478 | }
479 |
480 | });
481 |
482 | layer.setMapValue(j, sum);
483 | }
484 | }
485 |
486 | }.start();
487 |
488 | }
489 |
490 | private void setSampOutput(final Layer layer, final Layer lastLayer) {
491 |
492 | int lastMapNum = lastLayer.getOutMapNum();
493 |
494 | new TaskManager(lastMapNum) {
495 |
496 | @Override
497 | public void process(int start, int end) {
498 | for (int i = start; i < end; i++) {
499 | double[][] lastMap = lastLayer.getMap(i);
500 | Size scaleSize = layer.getScaleSize();
501 | double[][] sampMatrix = MathUtils.scaleMatrix(lastMap, scaleSize);
502 | layer.setMapValue(i, sampMatrix);
503 | }
504 | }
505 |
506 | }.start();
507 |
508 | }
509 |
510 | public void setup(int batchSize) {
511 |
512 | Layer inputLayer = layers.get(0);
513 | inputLayer.initOutmaps(batchSize);
514 |
515 | for (int i = 1; i < layers.size(); i++) {
516 | Layer layer = layers.get(i);
517 | Layer frontLayer = layers.get(i - 1);
518 | int frontMapNum = frontLayer.getOutMapNum();
519 | switch (layer.getType()) {
520 | case input:
521 | break;
522 | case conv:
523 | layer.setMapSize(frontLayer.getMapSize().subtract(layer.getKernelSize(), 1));
524 | layer.initKernel(frontMapNum);
525 | layer.initBias(frontMapNum);
526 | layer.initErros(batchSize);
527 | layer.initOutmaps(batchSize);
528 | break;
529 | case samp:
530 | layer.setOutMapNum(frontMapNum);
531 | layer.setMapSize(frontLayer.getMapSize().divide(layer.getScaleSize()));
532 | layer.initErros(batchSize);
533 | layer.initOutmaps(batchSize);
534 | break;
535 | case output:
536 | layer.initOutputKerkel(frontMapNum, frontLayer.getMapSize());
537 | layer.initBias(frontMapNum);
538 | layer.initErros(batchSize);
539 | layer.initOutmaps(batchSize);
540 | break;
541 | }
542 | }
543 | }
544 |
545 | public static class LayerBuilder {
546 |
547 | private List mLayers;
548 |
549 | public LayerBuilder() {
550 | mLayers = new ArrayList();
551 | }
552 |
553 | public LayerBuilder(Layer layer) {
554 | this();
555 | mLayers.add(layer);
556 | }
557 |
558 | public LayerBuilder addLayer(Layer layer) {
559 | mLayers.add(layer);
560 | return this;
561 | }
562 |
563 | }
564 |
565 | public void saveModel(String fileName) {
566 | try (ObjectOutputStream oos = new ObjectOutputStream(new FileOutputStream(fileName));) {
567 | oos.writeObject(this);
568 | oos.flush();
569 | } catch (IOException e) {
570 | logger.error("IOException:{}", e);
571 | throw new RuntimeException(e);
572 | }
573 | }
574 |
575 | public static CNN loadModel(String fileName) {
576 | try (ObjectInputStream in = new ObjectInputStream(new FileInputStream(fileName));) {
577 | CNN cnn = (CNN) in.readObject();
578 | return cnn;
579 | } catch (IOException | ClassNotFoundException e) {
580 | logger.error("IOException or ClassNotFoundException:{}", e);
581 | throw new RuntimeException(e);
582 | }
583 | }
584 |
585 | }
586 |
--------------------------------------------------------------------------------
/speech/test.format:
--------------------------------------------------------------------------------
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201 |
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