diff --git a/.classpath b/.classpath
deleted file mode 100644
index fb565a5..0000000
--- a/.classpath
+++ /dev/null
@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<classpath>
-	<classpathentry kind="src" path="src"/>
-	<classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER/org.eclipse.jdt.internal.debug.ui.launcher.StandardVMType/JavaSE-1.7"/>
-	<classpathentry kind="output" path="bin"/>
-</classpath>
diff --git a/.gitignore b/.gitignore
index f837fa2..9b2785d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,7 @@
-bin/
-*.class
-*MyTest.java
-data/
-model/
+*.iml
+/.idea
+/.classpath
+/.project
+/target/
+src/test/dataset/test.predict
+src/test/model.cnn
diff --git a/.project b/.project
deleted file mode 100644
index 8f8ffe9..0000000
--- a/.project
+++ /dev/null
@@ -1,17 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<projectDescription>
-	<name>JavaCNN</name>
-	<comment></comment>
-	<projects>
-	</projects>
-	<buildSpec>
-		<buildCommand>
-			<name>org.eclipse.jdt.core.javabuilder</name>
-			<arguments>
-			</arguments>
-		</buildCommand>
-	</buildSpec>
-	<natures>
-		<nature>org.eclipse.jdt.core.javanature</nature>
-	</natures>
-</projectDescription>
diff --git a/.settings/org.eclipse.jdt.core.prefs b/.settings/org.eclipse.jdt.core.prefs
deleted file mode 100644
index 7341ab1..0000000
--- a/.settings/org.eclipse.jdt.core.prefs
+++ /dev/null
@@ -1,11 +0,0 @@
-eclipse.preferences.version=1
-org.eclipse.jdt.core.compiler.codegen.inlineJsrBytecode=enabled
-org.eclipse.jdt.core.compiler.codegen.targetPlatform=1.7
-org.eclipse.jdt.core.compiler.codegen.unusedLocal=preserve
-org.eclipse.jdt.core.compiler.compliance=1.7
-org.eclipse.jdt.core.compiler.debug.lineNumber=generate
-org.eclipse.jdt.core.compiler.debug.localVariable=generate
-org.eclipse.jdt.core.compiler.debug.sourceFile=generate
-org.eclipse.jdt.core.compiler.problem.assertIdentifier=error
-org.eclipse.jdt.core.compiler.problem.enumIdentifier=error
-org.eclipse.jdt.core.compiler.source=1.7
diff --git a/dataset/readme.md b/dataset/readme.md
deleted file mode 100644
index 3cda064..0000000
--- a/dataset/readme.md
+++ /dev/null
@@ -1,3 +0,0 @@
-<p>The dataset is part of MNIST from kaggle Digit Recognizer competition. </p>
-<p>"train.format" is the train set, which has been binarized.</p>
-<p>"test.format" is the test set, which has been binarized.</p>
\ No newline at end of file
diff --git a/dataset/test.predict b/dataset/test.predict
deleted file mode 100644
index c6eb184..0000000
--- a/dataset/test.predict
+++ /dev/null
@@ -1,28000 +0,0 @@
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diff --git a/pom.xml b/pom.xml
new file mode 100644
index 0000000..c5fff6c
--- /dev/null
+++ b/pom.xml
@@ -0,0 +1,35 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/maven-v4_0_0.xsd">
+	<modelVersion>4.0.0</modelVersion>
+
+	<!-- -->
+
+	<groupId>javacnn</groupId>
+	<artifactId>javacnn</artifactId>
+	<version>0.5-SNAPSHOT</version>
+
+	<description>Implementation of a CNN in Java</description>
+
+	<!-- -->
+
+	<build>
+
+		<plugins>
+
+			<plugin>
+				<groupId>org.apache.maven.plugins</groupId>
+				<artifactId>maven-compiler-plugin</artifactId>
+				<version>3.7.0</version>
+				<configuration>
+					<source>1.8</source>
+					<target>1.8</target>
+					<compilerArgument>-deprecation</compilerArgument>
+					<encoding>UTF-8</encoding>
+				</configuration>
+			</plugin>
+
+		</plugins>
+
+	</build>
+
+</project>
diff --git a/readme-cn.md b/readme-cn.md
deleted file mode 100644
index 49fb496..0000000
--- a/readme-cn.md
+++ /dev/null
@@ -1,25 +0,0 @@
-# JavaCNN
-一个卷积神经网络的java实现. 仿Matlab toolbox(https://github.com/rasmusbergpalm/DeepLearnToolbox )实现的，同时进行了部分改进，使得卷积核和采样块可以为矩形而不仅仅是正方形。更多细节，请查看http://www.cnblogs.com/fengfenggirl/p/cnn_implement.html
-## 创建一个卷积神经网络
-
-	LayerBuilder builder = new LayerBuilder();
-	builder.addLayer(Layer.buildInputLayer(new Size(28, 28)));
-	builder.addLayer(Layer.buildConvLayer(6, new Size(5, 5)));
-	builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
-	builder.addLayer(Layer.buildConvLayer(12, new Size(5, 5)));
-	builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
-	builder.addLayer(Layer.buildOutputLayer(10));
-	CNN cnn = new CNN(builder, 50);
-	
-## 在 MNIST 数据集上测试
-	
-	String fileName = "data/train.format";
-	Dataset dataset = Dataset.load(fileName, ",", 784);
-	cnn.train(dataset, 100);
-	Dataset testset = Dataset.load("data/test.format", ",", -1);
-	cnn.predict(testset, "data/test.predict");
-
-迭代100次，四核CPU大约需要运行一个小时后，正确率97.8%
-
-##Lisence
-	MIT
\ No newline at end of file
diff --git a/readme.md b/readme.md
index 329ada9..6520b53 100644
--- a/readme.md
+++ b/readme.md
@@ -1,5 +1,40 @@
 # JavaCNN
-A Java implement of Convolutional Neural Network. Learn from DeepLearnToolbox(https://github.com/rasmusbergpalm/DeepLearnToolbox) more detail. see here(http://www.cnblogs.com/fengfenggirl/p/cnn_implement.html)
+
+A Java implementation of Convolutional Neural Network.
+This is a mavenized fork of https://github.com/BigPeng/JavaCNN refactored for the intention to use it in productive environments.
+
+Original ideas are take from the DeepLearnToolbox (https://github.com/rasmusbergpalm/DeepLearnToolbox).
+
+
+## Include in your project
+
+If you use maven, it's simple: Just add the dependency 
+
+    <dependencies>
+
+	    <dependency>
+	        <groupId>javacnn</groupId>
+	        <artifactId>javacnn</artifactId>
+	        <version>0.4</version>
+	    </dependency>
+	   
+    </dependencies>
+
+and ratopi's repository 
+
+	<repositories>
+        <repository>
+            <id>ratopi.de releases</id>
+            <url>http://ratopi.github.io/maven/releases/</url>
+            <snapshots>
+                <enabled>false</enabled>
+            </snapshots>
+        </repository>
+    </repositories>
+
+to your project's pom.xml.
+
+
 ## Build a CNN
 
 	LayerBuilder builder = new LayerBuilder();
@@ -10,16 +45,20 @@ A Java implement of Convolutional Neural Network. Learn from DeepLearnToolbox(ht
 	builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
 	builder.addLayer(Layer.buildOutputLayer(10));
 	CNN cnn = new CNN(builder, 50);
-	
+
+
 ## Run on MNIST dataset
-	
-	String fileName = "data/train.format";
-	Dataset dataset = Dataset.load(fileName, ",", 784);
-	cnn.train(dataset, 100);
-	Dataset testset = Dataset.load("data/test.format", ",", -1);
-	cnn.predict(testset, "data/test.predict");
 
-It takes a about an hour to complete 100 iteration and get a precison of 97.8%
+For running on MNIST dataset see project https://github.com/ratopi/javacnn.mnist.
+
+
+## Source Code
+
+Get the source code from github:
+
+	git clone https://github.com/ratopi/JavaCNN.git 
+
+
+## License
 
-##Lisence
-	MIT
\ No newline at end of file
+MIT
diff --git a/src/edu/hitsz/c102c/cnn/CNN.java b/src/edu/hitsz/c102c/cnn/CNN.java
deleted file mode 100644
index 096ba73..0000000
--- a/src/edu/hitsz/c102c/cnn/CNN.java
+++ /dev/null
@@ -1,737 +0,0 @@
-package edu.hitsz.c102c.cnn;
-
-import java.io.File;
-import java.io.FileInputStream;
-import java.io.FileOutputStream;
-import java.io.IOException;
-import java.io.ObjectInputStream;
-import java.io.ObjectOutputStream;
-import java.io.PrintWriter;
-import java.io.Serializable;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Iterator;
-import java.util.List;
-import java.util.concurrent.atomic.AtomicBoolean;
-
-import edu.hitsz.c102c.cnn.Layer.Size;
-import edu.hitsz.c102c.dataset.Dataset;
-import edu.hitsz.c102c.dataset.Dataset.Record;
-import edu.hitsz.c102c.util.ConcurenceRunner.TaskManager;
-import edu.hitsz.c102c.util.Log;
-import edu.hitsz.c102c.util.Util;
-import edu.hitsz.c102c.util.Util.Operator;
-
-public class CNN implements Serializable {
-	/**
-	 * 
-	 */
-	private static final long serialVersionUID = 337920299147929932L;
-	private static double ALPHA = 0.85;
-	protected static final double LAMBDA = 0;
-	// ����ĸ���
-	private List<Layer> layers;
-	// ����
-	private int layerNum;
-
-	// �������µĴ�С
-	private int batchSize;
-	// �������������Ծ����ÿһ��Ԫ�س���һ��ֵ
-	private Operator divide_batchSize;
-
-	// �������������Ծ����ÿһ��Ԫ�س���alphaֵ
-	private Operator multiply_alpha;
-
-	// �������������Ծ����ÿһ��Ԫ�س���1-labmda*alphaֵ
-	private Operator multiply_lambda;
-
-	/**
-	 * ��ʼ������
-	 * 
-	 * @param layerBuilder
-	 *            �����
-	 * @param inputMapSize
-	 *            ����map�Ĵ�С
-	 * @param classNum
-	 *            ���ĸ�����Ҫ�����ݼ������ת��Ϊ0-classNum-1����ֵ
-	 */
-	public CNN(LayerBuilder layerBuilder, final int batchSize) {
-		layers = layerBuilder.mLayers;
-		layerNum = layers.size();
-		this.batchSize = batchSize;
-		setup(batchSize);
-		initPerator();
-	}
-
-	/**
-	 * ��ʼ��������
-	 */
-	private void initPerator() {
-		divide_batchSize = new Operator() {
-
-			private static final long serialVersionUID = 7424011281732651055L;
-
-			@Override
-			public double process(double value) {
-				return value / batchSize;
-			}
-
-		};
-		multiply_alpha = new Operator() {
-
-			private static final long serialVersionUID = 5761368499808006552L;
-
-			@Override
-			public double process(double value) {
-
-				return value * ALPHA;
-			}
-
-		};
-		multiply_lambda = new Operator() {
-
-			private static final long serialVersionUID = 4499087728362870577L;
-
-			@Override
-			public double process(double value) {
-
-				return value * (1 - LAMBDA * ALPHA);
-			}
-
-		};
-	}
-
-	/**
-	 * ��ѵ������ѵ������
-	 * 
-	 * @param trainset
-	 * @param repeat
-	 *            �����Ĵ���
-	 */
-	public void train(Dataset trainset, int repeat) {
-		// ����ֹͣ��ť
-		new Lisenter().start();
-		for (int t = 0; t < repeat && !stopTrain.get(); t++) {
-			int epochsNum = trainset.size() / batchSize;
-			if (trainset.size() % batchSize != 0)
-				epochsNum++;// ���ȡһ�Σ�������ȡ��
-			Log.i("");
-			Log.i(t + "th iter epochsNum:" + epochsNum);
-			int right = 0;
-			int count = 0;
-			for (int i = 0; i < epochsNum; i++) {
-				int[] randPerm = Util.randomPerm(trainset.size(), batchSize);
-				Layer.prepareForNewBatch();
-
-				for (int index : randPerm) {
-					boolean isRight = train(trainset.getRecord(index));
-					if (isRight)
-						right++;
-					count++;
-					Layer.prepareForNewRecord();
-				}
-
-				// ����һ��batch�����Ȩ��
-				updateParas();
-				if (i % 50 == 0) {
-					System.out.print("..");
-					if (i + 50 > epochsNum)
-						System.out.println();
-				}
-			}
-			double p = 1.0 * right / count;
-			if (t % 10 == 1 && p > 0.96) {//��̬����׼ѧϰ����
-				ALPHA = 0.001 + ALPHA * 0.9;
-				Log.i("Set alpha = " + ALPHA);
-			}
-			Log.i("precision " + right + "/" + count + "=" + p);
-		}
-	}
-
-	private static AtomicBoolean stopTrain;
-
-	static class Lisenter extends Thread {
-		Lisenter() {
-			setDaemon(true);
-			stopTrain = new AtomicBoolean(false);
-		}
-
-		@Override
-		public void run() {
-			System.out.println("Input & to stop train.");
-			while (true) {
-				try {
-					int a = System.in.read();
-					if (a == '&') {
-						stopTrain.compareAndSet(false, true);
-						break;
-					}
-				} catch (IOException e) {
-					e.printStackTrace();
-				}
-			}
-			System.out.println("Lisenter stop");
-		}
-
-	}
-
-	/**
-	 * ��������
-	 * 
-	 * @param trainset
-	 * @return
-	 */
-	public double test(Dataset trainset) {
-		Layer.prepareForNewBatch();
-		Iterator<Record> iter = trainset.iter();
-		int right = 0;
-		while (iter.hasNext()) {
-			Record record = iter.next();
-			forward(record);
-			Layer outputLayer = layers.get(layerNum - 1);
-			int mapNum = outputLayer.getOutMapNum();		
-			double[] out = new double[mapNum];
-			for (int m = 0; m < mapNum; m++) {
-				double[][] outmap = outputLayer.getMap(m);
-				out[m] = outmap[0][0];
-			}
-			if (record.getLable().intValue() == Util.getMaxIndex(out))
-				right++;		
-		}
-		double p = 1.0 * right / trainset.size();
-		Log.i("precision", p + "");
-		return p;
-	}
-
-	/**
-	 * Ԥ����
-	 * 
-	 * @param testset
-	 * @param fileName
-	 */
-	public void predict(Dataset testset, String fileName) {
-		Log.i("begin predict");
-		try {
-			int max = layers.get(layerNum - 1).getClassNum();
-			PrintWriter writer = new PrintWriter(new File(fileName));
-			Layer.prepareForNewBatch();
-			Iterator<Record> iter = testset.iter();
-			while (iter.hasNext()) {
-				Record record = iter.next();
-				forward(record);
-				Layer outputLayer = layers.get(layerNum - 1);
-
-				int mapNum = outputLayer.getOutMapNum();
-				double[] out = new double[mapNum];
-				for (int m = 0; m < mapNum; m++) {
-					double[][] outmap = outputLayer.getMap(m);
-					out[m] = outmap[0][0];
-				}
-				// int lable =
-				// Util.binaryArray2int(out);
-				int lable = Util.getMaxIndex(out);
-				// if (lable >= max)
-				// lable = lable - (1 << (out.length -
-				// 1));
-				writer.write(lable + "\n");
-			}
-			writer.flush();
-			writer.close();
-		} catch (IOException e) {
-			throw new RuntimeException(e);
-		}
-		Log.i("end predict");
-	}
-
-	private boolean isSame(double[] output, double[] target) {
-		boolean r = true;
-		for (int i = 0; i < output.length; i++)
-			if (Math.abs(output[i] - target[i]) > 0.5) {
-				r = false;
-				break;
-			}
-
-		return r;
-	}
-
-	/**
-	 * ѵ��һ����¼��ͬʱ�����Ƿ�Ԥ����ȷ��ǰ��¼
-	 * 
-	 * @param record
-	 * @return
-	 */
-	private boolean train(Record record) {
-		forward(record);
-		boolean result = backPropagation(record);
-		return result;
-		// System.exit(0);
-	}
-
-	/*
-	 * ������
-	 */
-	private boolean backPropagation(Record record) {
-		boolean result = setOutLayerErrors(record);
-		setHiddenLayerErrors();
-		return result;
-	}
-
-	/**
-	 * ���²���
-	 */
-	private void updateParas() {
-		for (int l = 1; l < layerNum; l++) {
-			Layer layer = layers.get(l);
-			Layer lastLayer = layers.get(l - 1);
-			switch (layer.getType()) {
-			case conv:
-			case output:
-				updateKernels(layer, lastLayer);
-				updateBias(layer, lastLayer);
-				break;
-			default:
-				break;
-			}
-		}
-	}
-
-	/**
-	 * ����ƫ��
-	 * 
-	 * @param layer
-	 * @param lastLayer
-	 */
-	private void updateBias(final Layer layer, Layer lastLayer) {
-		final double[][][][] errors = layer.getErrors();
-		int mapNum = layer.getOutMapNum();
-
-		new TaskManager(mapNum) {
-
-			@Override
-			public void process(int start, int end) {
-				for (int j = start; j < end; j++) {
-					double[][] error = Util.sum(errors, j);
-					// ����ƫ��
-					double deltaBias = Util.sum(error) / batchSize;
-					double bias = layer.getBias(j) + ALPHA * deltaBias;
-					layer.setBias(j, bias);
-				}
-			}
-		}.start();
-
-	}
-
-	/**
-	 * ����layer��ľ����ˣ�Ȩ�أ���ƫ��
-	 * 
-	 * @param layer
-	 *            ��ǰ��
-	 * @param lastLayer
-	 *            ǰһ��
-	 */
-	private void updateKernels(final Layer layer, final Layer lastLayer) {
-		int mapNum = layer.getOutMapNum();
-		final int lastMapNum = lastLayer.getOutMapNum();
-		new TaskManager(mapNum) {
-
-			@Override
-			public void process(int start, int end) {
-				for (int j = start; j < end; j++) {
-					for (int i = 0; i < lastMapNum; i++) {
-						// ��batch��ÿ����¼delta���
-						double[][] deltaKernel = null;
-						for (int r = 0; r < batchSize; r++) {
-							double[][] error = layer.getError(r, j);
-							if (deltaKernel == null)
-								deltaKernel = Util.convnValid(
-										lastLayer.getMap(r, i), error);
-							else {// �ۻ����
-								deltaKernel = Util.matrixOp(Util.convnValid(
-										lastLayer.getMap(r, i), error),
-										deltaKernel, null, null, Util.plus);
-							}
-						}
-
-						// ����batchSize
-						deltaKernel = Util.matrixOp(deltaKernel,
-								divide_batchSize);
-						// ���¾�����
-						double[][] kernel = layer.getKernel(i, j);
-						deltaKernel = Util.matrixOp(kernel, deltaKernel,
-								multiply_lambda, multiply_alpha, Util.plus);
-						layer.setKernel(i, j, deltaKernel);
-					}
-				}
-
-			}
-		}.start();
-
-	}
-
-	/**
-	 * �����н�����IJв�
-	 */
-	private void setHiddenLayerErrors() {
-		for (int l = layerNum - 2; l > 0; l--) {
-			Layer layer = layers.get(l);
-			Layer nextLayer = layers.get(l + 1);
-			switch (layer.getType()) {
-			case samp:
-				setSampErrors(layer, nextLayer);
-				break;
-			case conv:
-				setConvErrors(layer, nextLayer);
-				break;
-			default:// ֻ�в�����;�������Ҫ�����в�����û�вв������Ѿ�������
-				break;
-			}
-		}
-	}
-
-	/**
-	 * ���ò�����IJв�
-	 * 
-	 * @param layer
-	 * @param nextLayer
-	 */
-	private void setSampErrors(final Layer layer, final Layer nextLayer) {
-		int mapNum = layer.getOutMapNum();
-		final int nextMapNum = nextLayer.getOutMapNum();
-		new TaskManager(mapNum) {
-
-			@Override
-			public void process(int start, int end) {
-				for (int i = start; i < end; i++) {
-					double[][] sum = null;// ��ÿһ�������������
-					for (int j = 0; j < nextMapNum; j++) {
-						double[][] nextError = nextLayer.getError(j);
-						double[][] kernel = nextLayer.getKernel(i, j);
-						// �Ծ����˽���180����ת��Ȼ�����fullģʽ�µþ���
-						if (sum == null)
-							sum = Util
-									.convnFull(nextError, Util.rot180(kernel));
-						else
-							sum = Util.matrixOp(
-									Util.convnFull(nextError,
-											Util.rot180(kernel)), sum, null,
-									null, Util.plus);
-					}
-					layer.setError(i, sum);
-				}
-			}
-
-		}.start();
-
-	}
-
-	/**
-	 * ���þ�����IJв�
-	 * 
-	 * @param layer
-	 * @param nextLayer
-	 */
-	private void setConvErrors(final Layer layer, final Layer nextLayer) {
-		// ���������һ��Ϊ�����㣬�������map������ͬ����һ��mapֻ����һ���һ��map���ӣ�
-		// ���ֻ�轫��һ��IJв�kronecker��չ���õ������
-		int mapNum = layer.getOutMapNum();
-		new TaskManager(mapNum) {
-
-			@Override
-			public void process(int start, int end) {
-				for (int m = start; m < end; m++) {
-					Size scale = nextLayer.getScaleSize();
-					double[][] nextError = nextLayer.getError(m);
-					double[][] map = layer.getMap(m);
-					// ������ˣ����Եڶ��������ÿ��Ԫ��value����1-value����
-					double[][] outMatrix = Util.matrixOp(map,
-							Util.cloneMatrix(map), null, Util.one_value,
-							Util.multiply);
-					outMatrix = Util.matrixOp(outMatrix,
-							Util.kronecker(nextError, scale), null, null,
-							Util.multiply);
-					layer.setError(m, outMatrix);
-				}
-
-			}
-
-		}.start();
-
-	}
-
-	/**
-	 * ���������IJв�ֵ,�������񾭵�Ԫ�������٣��ݲ����Ƕ��߳�
-	 * 
-	 * @param record
-	 * @return
-	 */
-	private boolean setOutLayerErrors(Record record) {
-
-		Layer outputLayer = layers.get(layerNum - 1);
-		int mapNum = outputLayer.getOutMapNum();
-		// double[] target =
-		// record.getDoubleEncodeTarget(mapNum);
-		// double[] outmaps = new double[mapNum];
-		// for (int m = 0; m < mapNum; m++) {
-		// double[][] outmap = outputLayer.getMap(m);
-		// double output = outmap[0][0];
-		// outmaps[m] = output;
-		// double errors = output * (1 - output) *
-		// (target[m] - output);
-		// outputLayer.setError(m, 0, 0, errors);
-		// }
-		// // ��ȷ
-		// if (isSame(outmaps, target))
-		// return true;
-		// return false;
-
-		double[] target = new double[mapNum];
-		double[] outmaps = new double[mapNum];
-		for (int m = 0; m < mapNum; m++) {
-			double[][] outmap = outputLayer.getMap(m);
-			outmaps[m] = outmap[0][0];
-
-		}
-		int lable = record.getLable().intValue();
-		target[lable] = 1;
-		// Log.i(record.getLable() + "outmaps:" +
-		// Util.fomart(outmaps)
-		// + Arrays.toString(target));
-		for (int m = 0; m < mapNum; m++) {
-			outputLayer.setError(m, 0, 0, outmaps[m] * (1 - outmaps[m])
-					* (target[m] - outmaps[m]));
-		}
-		return lable == Util.getMaxIndex(outmaps);
-	}
-
-	/**
-	 * ǰ�����һ����¼
-	 * 
-	 * @param record
-	 */
-	private void forward(Record record) {
-		// ����������map
-		setInLayerOutput(record);
-		for (int l = 1; l < layers.size(); l++) {
-			Layer layer = layers.get(l);
-			Layer lastLayer = layers.get(l - 1);
-			switch (layer.getType()) {
-			case conv:// �������������
-				setConvOutput(layer, lastLayer);
-				break;
-			case samp:// �������������
-				setSampOutput(layer, lastLayer);
-				break;
-			case output:// �������������,�������һ������ľ�����
-				setConvOutput(layer, lastLayer);
-				break;
-			default:
-				break;
-			}
-		}
-	}
-
-	/**
-	 * ���ݼ�¼ֵ���������������ֵ
-	 * 
-	 * @param record
-	 */
-	private void setInLayerOutput(Record record) {
-		final Layer inputLayer = layers.get(0);
-		final Size mapSize = inputLayer.getMapSize();
-		final double[] attr = record.getAttrs();
-		if (attr.length != mapSize.x * mapSize.y)
-			throw new RuntimeException("���ݼ�¼�Ĵ�С�붨���map��С��һ��!");
-		for (int i = 0; i < mapSize.x; i++) {
-			for (int j = 0; j < mapSize.y; j++) {
-				// ����¼���Ե�һά����Ū�ɶ�ά����
-				inputLayer.setMapValue(0, i, j, attr[mapSize.x * i + j]);
-			}
-		}
-	}
-
-	/*
-	 * ������������ֵ,ÿ���̸߳���һ����map
-	 */
-	private void setConvOutput(final Layer layer, final Layer lastLayer) {
-		int mapNum = layer.getOutMapNum();
-		final int lastMapNum = lastLayer.getOutMapNum();
-		new TaskManager(mapNum) {
-
-			@Override
-			public void process(int start, int end) {
-				for (int j = start; j < end; j++) {
-					double[][] sum = null;// ��ÿһ������map�ľ����������
-					for (int i = 0; i < lastMapNum; i++) {
-						double[][] lastMap = lastLayer.getMap(i);
-						double[][] kernel = layer.getKernel(i, j);
-						if (sum == null)
-							sum = Util.convnValid(lastMap, kernel);
-						else
-							sum = Util.matrixOp(
-									Util.convnValid(lastMap, kernel), sum,
-									null, null, Util.plus);
-					}
-					final double bias = layer.getBias(j);
-					sum = Util.matrixOp(sum, new Operator() {
-						private static final long serialVersionUID = 2469461972825890810L;
-
-						@Override
-						public double process(double value) {
-							return Util.sigmod(value + bias);
-						}
-
-					});
-
-					layer.setMapValue(j, sum);
-				}
-			}
-
-		}.start();
-
-	}
-
-	/**
-	 * ���ò���������ֵ���������ǶԾ�����ľ�ֵ����
-	 * 
-	 * @param layer
-	 * @param lastLayer
-	 */
-	private void setSampOutput(final Layer layer, final Layer lastLayer) {
-		int lastMapNum = lastLayer.getOutMapNum();
-		new TaskManager(lastMapNum) {
-
-			@Override
-			public void process(int start, int end) {
-				for (int i = start; i < end; i++) {
-					double[][] lastMap = lastLayer.getMap(i);
-					Size scaleSize = layer.getScaleSize();
-					// ��scaleSize������о�ֵ����
-					double[][] sampMatrix = Util
-							.scaleMatrix(lastMap, scaleSize);
-					layer.setMapValue(i, sampMatrix);
-				}
-			}
-
-		}.start();
-
-	}
-
-	/**
-	 * ����cnn�����ÿһ��IJ���
-	 * 
-	 * @param batchSize
-	 *            * @param classNum
-	 * @param inputMapSize
-	 */
-	public void setup(int batchSize) {
-		Layer inputLayer = layers.get(0);
-		// ÿһ�㶼��Ҫ��ʼ�����map
-		inputLayer.initOutmaps(batchSize);
-		for (int i = 1; i < layers.size(); i++) {
-			Layer layer = layers.get(i);
-			Layer frontLayer = layers.get(i - 1);
-			int frontMapNum = frontLayer.getOutMapNum();
-			switch (layer.getType()) {
-			case input:
-				break;
-			case conv:
-				// ����map�Ĵ�С
-				layer.setMapSize(frontLayer.getMapSize().subtract(
-						layer.getKernelSize(), 1));
-				// ��ʼ�������ˣ�����frontMapNum*outMapNum��������
-
-				layer.initKernel(frontMapNum);
-				// ��ʼ��ƫ�ã�����frontMapNum*outMapNum��ƫ��
-				layer.initBias(frontMapNum);
-				// batch��ÿ����¼��Ҫ����һ�ݲв�
-				layer.initErros(batchSize);
-				// ÿһ�㶼��Ҫ��ʼ�����map
-				layer.initOutmaps(batchSize);
-				break;
-			case samp:
-				// �������map��������һ����ͬ
-				layer.setOutMapNum(frontMapNum);
-				// ������map�Ĵ�С����һ��map�Ĵ�С����scale��С
-				layer.setMapSize(frontLayer.getMapSize().divide(
-						layer.getScaleSize()));
-				// batch��ÿ����¼��Ҫ����һ�ݲв�
-				layer.initErros(batchSize);
-				// ÿһ�㶼��Ҫ��ʼ�����map
-				layer.initOutmaps(batchSize);
-				break;
-			case output:
-				// ��ʼ��Ȩ�أ������ˣ��������ľ����˴�СΪ��һ���map��С
-				layer.initOutputKerkel(frontMapNum, frontLayer.getMapSize());
-				// ��ʼ��ƫ�ã�����frontMapNum*outMapNum��ƫ��
-				layer.initBias(frontMapNum);
-				// batch��ÿ����¼��Ҫ����һ�ݲв�
-				layer.initErros(batchSize);
-				// ÿһ�㶼��Ҫ��ʼ�����map
-				layer.initOutmaps(batchSize);
-				break;
-			}
-		}
-	}
-
-	/**
-	 * ������ģʽ�������,Ҫ�����ڶ������Ϊ�����������Ϊ������
-	 * 
-	 * @author jiqunpeng
-	 * 
-	 *         ����ʱ�䣺2014-7-8 ����4:54:29
-	 */
-	public static class LayerBuilder {
-		private List<Layer> mLayers;
-
-		public LayerBuilder() {
-			mLayers = new ArrayList<Layer>();
-		}
-
-		public LayerBuilder(Layer layer) {
-			this();
-			mLayers.add(layer);
-		}
-
-		public LayerBuilder addLayer(Layer layer) {
-			mLayers.add(layer);
-			return this;
-		}
-	}
-
-	/**
-	 * ���л�����ģ��
-	 * 
-	 * @param fileName
-	 */
-	public void saveModel(String fileName) {
-		try {
-			ObjectOutputStream oos = new ObjectOutputStream(
-					new FileOutputStream(fileName));
-			oos.writeObject(this);
-			oos.flush();
-			oos.close();
-		} catch (IOException e) {
-			e.printStackTrace();
-		}
-
-	}
-
-	/**
-	 * �����л�����ģ��
-	 * 
-	 * @param fileName
-	 * @return
-	 */
-	public static CNN loadModel(String fileName) {
-		try {
-			ObjectInputStream in = new ObjectInputStream(new FileInputStream(
-					fileName));
-			CNN cnn = (CNN) in.readObject();
-			in.close();
-			return cnn;
-		} catch (IOException | ClassNotFoundException e) {
-			e.printStackTrace();
-		}
-		return null;
-	}
-}
diff --git a/src/edu/hitsz/c102c/cnn/RunCNN.java b/src/edu/hitsz/c102c/cnn/RunCNN.java
deleted file mode 100644
index 7f3a1c9..0000000
--- a/src/edu/hitsz/c102c/cnn/RunCNN.java
+++ /dev/null
@@ -1,51 +0,0 @@
-package edu.hitsz.c102c.cnn;
-
-import edu.hitsz.c102c.cnn.CNN.LayerBuilder;
-import edu.hitsz.c102c.cnn.Layer.Size;
-import edu.hitsz.c102c.dataset.Dataset;
-import edu.hitsz.c102c.util.ConcurenceRunner;
-import edu.hitsz.c102c.util.TimedTest;
-import edu.hitsz.c102c.util.TimedTest.TestTask;
-
-public class RunCNN {
-
-	public static void runCnn() {
-		//����һ������������
-		LayerBuilder builder = new LayerBuilder();
-		builder.addLayer(Layer.buildInputLayer(new Size(28, 28)));
-		builder.addLayer(Layer.buildConvLayer(6, new Size(5, 5)));
-		builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
-		builder.addLayer(Layer.buildConvLayer(12, new Size(5, 5)));
-		builder.addLayer(Layer.buildSampLayer(new Size(2, 2)));
-		builder.addLayer(Layer.buildOutputLayer(10));
-		CNN cnn = new CNN(builder, 50);
-		
-		//�������ݼ�
-		String fileName = "dataset/train.format";
-		Dataset dataset = Dataset.load(fileName, ",", 784);
-		cnn.train(dataset, 3);//
-		String modelName = "model/model.cnn";
-		cnn.saveModel(modelName);		
-		dataset.clear();
-		dataset = null;
-		
-		//Ԥ��
-		// CNN cnn = CNN.loadModel(modelName);	
-		Dataset testset = Dataset.load("dataset/test.format", ",", -1);
-		cnn.predict(testset, "dataset/test.predict");
-	}
-
-	public static void main(String[] args) {
-
-		new TimedTest(new TestTask() {
-
-			@Override
-			public void process() {
-				runCnn();
-			}
-		}, 1).test();
-		ConcurenceRunner.stop();
-
-	}
-
-}
diff --git a/src/edu/hitsz/c102c/dataset/Dataset.java b/src/edu/hitsz/c102c/dataset/Dataset.java
deleted file mode 100644
index d0291c2..0000000
--- a/src/edu/hitsz/c102c/dataset/Dataset.java
+++ /dev/null
@@ -1,239 +0,0 @@
-package edu.hitsz.c102c.dataset;
-
-import java.io.BufferedReader;
-import java.io.File;
-import java.io.FileReader;
-import java.io.IOException;
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.HashMap;
-import java.util.Iterator;
-import java.util.List;
-import java.util.Map;
-
-public class Dataset {
-	// ��������
-	private List<Record> records;
-	// ����±�
-	private int lableIndex;
-
-	private double maxLable = -1;
-
-	public Dataset(int classIndex) {
-
-		this.lableIndex = classIndex;
-		records = new ArrayList<Record>();
-	}
-
-	public Dataset(List<double[]> datas) {
-		this();
-		for (double[] data : datas) {
-			append(new Record(data));
-		}
-	}
-
-	private Dataset() {
-		this.lableIndex = -1;
-		records = new ArrayList<Record>();
-	}
-
-	public int size() {
-		return records.size();
-	}
-
-	public int getLableIndex() {
-		return lableIndex;
-	}
-
-	public void append(Record record) {
-		records.add(record);
-	}
-
-	/**
-	 * �������
-	 */
-	public void clear() {
-		records.clear();
-	}
-
-	/**
-	 * ����һ����¼
-	 * 
-	 * @param attrs
-	 *            ��¼������
-	 * @param lable
-	 *            ��¼�����
-	 */
-	public void append(double[] attrs, Double lable) {
-		records.add(new Record(attrs, lable));
-	}
-
-	public Iterator<Record> iter() {
-		return records.iterator();
-	}
-
-	/**
-	 * ��ȡ��index����¼������
-	 * 
-	 * @param index
-	 * @return
-	 */
-	public double[] getAttrs(int index) {
-		return records.get(index).getAttrs();
-	}
-
-	public Double getLable(int index) {
-		return records.get(index).getLable();
-	}
-
-	/**
-	 * �������ݼ�
-	 * 
-	 * @param filePath
-	 *            �ļ�����·��
-	 * @param tag
-	 *            �ֶηָ���
-	 * @param lableIndex
-	 *            ����±꣬��0��ʼ
-	 * @return
-	 */
-	public static Dataset load(String filePath, String tag, int lableIndex) {
-		Dataset dataset = new Dataset();
-		dataset.lableIndex = lableIndex;
-		File file = new File(filePath);
-		try {
-
-			BufferedReader in = new BufferedReader(new FileReader(file));
-			String line;
-			while ((line = in.readLine()) != null) {
-				String[] datas = line.split(tag);
-				if (datas.length == 0)
-					continue;
-				double[] data = new double[datas.length];
-				for (int i = 0; i < datas.length; i++)
-					data[i] = Double.parseDouble(datas[i]);
-				Record record = dataset.new Record(data);
-				dataset.append(record);
-			}
-			in.close();
-
-		} catch (IOException e) {
-			e.printStackTrace();
-			return null;
-		}
-		System.out.println("��������:" + dataset.size());
-		return dataset;
-	}
-
-	/**
-	 * ���ݼ�¼(ʵ��),��¼�����Ժ�������,������Ϊ��һ�л������һ�л��߿�
-	 * 
-	 * @author jiqunpeng
-	 * 
-	 *         ����ʱ�䣺2014-6-15 ����8:03:29
-	 */
-	public class Record {
-		// �洢����
-		private double[] attrs;
-		private Double lable;
-
-		private Record(double[] attrs, Double lable) {
-			this.attrs = attrs;
-			this.lable = lable;
-		}
-
-		public Record(double[] data) {
-			if (lableIndex == -1)
-				attrs = data;
-			else {
-				lable = data[lableIndex];
-				if (lable > maxLable)
-					maxLable = lable;
-				if (lableIndex == 0)
-					attrs = Arrays.copyOfRange(data, 1, data.length);
-				else
-					attrs = Arrays.copyOfRange(data, 0, data.length - 1);
-			}
-		}
-
-		/**
-		 * �ü�¼������
-		 * 
-		 * @return
-		 */
-		public double[] getAttrs() {
-			return attrs;
-		}
-
-		public String toString() {
-			StringBuilder sb = new StringBuilder();
-			sb.append("attrs:");
-			sb.append(Arrays.toString(attrs));
-			sb.append("lable:");
-			sb.append(lable);
-			return sb.toString();
-		}
-
-		/**
-		 * �ü�¼�����
-		 * 
-		 * @return
-		 */
-		public Double getLable() {
-			if (lableIndex == -1)
-				return null;
-			return lable;
-		}
-
-		/**
-		 * �������ж����Ʊ���
-		 * 
-		 * @param n
-		 * @return
-		 */
-		public int[] getEncodeTarget(int n) {
-			String binary = Integer.toBinaryString(lable.intValue());
-			byte[] bytes = binary.getBytes();
-			int[] encode = new int[n];
-			int j = n;
-			for (int i = bytes.length - 1; i >= 0; i--)
-				encode[--j] = bytes[i] - '0';
-
-			return encode;
-		}
-
-		public double[] getDoubleEncodeTarget(int n) {
-			String binary = Integer.toBinaryString(lable.intValue());
-			byte[] bytes = binary.getBytes();
-			double[] encode = new double[n];
-			int j = n;
-			for (int i = bytes.length - 1; i >= 0; i--)
-				encode[--j] = bytes[i] - '0';
-
-			return encode;
-		}
-
-	}
-
-	public static void main(String[] args) {
-		Dataset d = new Dataset();
-		d.lableIndex = 10;
-		Record r = d.new Record(new double[] { 3, 2, 2, 5, 4, 5, 3, 11, 3, 12,
-				1 });
-		int[] encode = r.getEncodeTarget(4);
-
-		System.out.println(r.lable);
-		System.out.println(Arrays.toString(encode));
-	}
-
-	/**
-	 * ��ȡ��index����¼
-	 * 
-	 * @param index
-	 * @return
-	 */
-	public Record getRecord(int index) {
-		return records.get(index);
-	}
-
-}
diff --git a/src/edu/hitsz/c102c/util/ConcurenceRunner.java b/src/edu/hitsz/c102c/util/ConcurenceRunner.java
deleted file mode 100644
index 7a57a66..0000000
--- a/src/edu/hitsz/c102c/util/ConcurenceRunner.java
+++ /dev/null
@@ -1,93 +0,0 @@
-package edu.hitsz.c102c.util;
-
-import java.util.concurrent.CountDownLatch;
-import java.util.concurrent.ExecutorService;
-import java.util.concurrent.Executors;
-
-/**
- * ���������
- * 
- * @author jiqunpeng
- * 
- *         ����ʱ�䣺2014-6-16 ����3:33:41
- */
-public class ConcurenceRunner {
-
-	private static final ExecutorService exec;
-	public static final int cpuNum;
-	static {
-		cpuNum = Runtime.getRuntime().availableProcessors();
-		// cpuNum = 1;
-		System.out.println("cpuNum:" + cpuNum);
-		exec = Executors.newFixedThreadPool(cpuNum);
-	}
-
-	public static void run(Runnable task) {
-		exec.execute(task);
-	}
-
-	public static void stop() {
-		exec.shutdown();
-	}
-
-	// public abstract static class Task implements
-	// Runnable {
-	// int start, end;
-	//
-	// public Task(int start, int end) {
-	// this.start = start;
-	// this.end = end;
-	// // Log.i("new Task",
-	// // "start "+start+" end "+end);
-	// }
-	//
-	// @Override
-	// public void run() {
-	// process(start, end);
-	// }
-	//
-	// public abstract void process(int start, int
-	// end);
-	//
-	// }
-
-	public abstract static class TaskManager {
-		private int workLength;
-
-		public TaskManager(int workLength) {
-			this.workLength = workLength;
-		}
-
-		public void start() {
-			int runCpu = cpuNum < workLength ? cpuNum : 1;
-			// ��Ƭ��������ȡ��
-			final CountDownLatch gate = new CountDownLatch(runCpu);
-			int fregLength = (workLength + runCpu - 1) / runCpu;
-			for (int cpu = 0; cpu < runCpu; cpu++) {
-				final int start = cpu * fregLength;
-				int tmp = (cpu + 1) * fregLength;
-				final int end = tmp <= workLength ? tmp : workLength;
-				Runnable task = new Runnable() {
-
-					@Override
-					public void run() {
-						process(start, end);
-						gate.countDown();
-					}
-
-				};
-				ConcurenceRunner.run(task);
-			}
-			try {// �ȴ������߳�����
-				gate.await();
-			} catch (InterruptedException e) {
-				e.printStackTrace();
-				throw new RuntimeException(e);
-			}
-		}
-
-		public abstract void process(int start, int end);
-
-	}
-
-}
diff --git a/src/edu/hitsz/c102c/util/Log.java b/src/edu/hitsz/c102c/util/Log.java
deleted file mode 100644
index 9892816..0000000
--- a/src/edu/hitsz/c102c/util/Log.java
+++ /dev/null
@@ -1,16 +0,0 @@
-package edu.hitsz.c102c.util;
-
-import java.io.PrintStream;
-
-public class Log {
-	static PrintStream stream = System.out;
-	
-	public static void i(String tag,String msg){
-		stream.println(tag+"\t"+msg);
-	}
-	
-	public static void i(String msg){
-		stream.println(msg);
-	}
-
-}
diff --git a/src/main/java/javacnn/cnn/CNN.java b/src/main/java/javacnn/cnn/CNN.java
new file mode 100644
index 0000000..631362c
--- /dev/null
+++ b/src/main/java/javacnn/cnn/CNN.java
@@ -0,0 +1,589 @@
+package javacnn.cnn;
+
+import java.io.File;
+import java.io.IOException;
+import java.io.PrintWriter;
+import java.io.Serializable;
+import java.util.ArrayList;
+import java.util.Iterator;
+import java.util.List;
+
+import javacnn.dataset.Dataset;
+import javacnn.util.DotProgressIndicator;
+import javacnn.util.Log;
+import javacnn.util.ProgressIndicator;
+import javacnn.util.Runner;
+import javacnn.util.Util;
+
+public class CNN implements Serializable {
+
+	private static final long serialVersionUID = 5L;
+
+	private static final double LAMBDA = 0;
+
+	private double ALPHA = 0.85;
+
+	private final List<Layer> layers;
+	private final int layerNum;
+	private final int batchSize;
+
+	private final Util.Operator divide_batchSize;
+	private final Util.Operator multiply_alpha;
+	private final Util.Operator multiply_lambda;
+
+	private transient Runner runner;
+
+	private transient ProgressIndicator progressIndicator = new DotProgressIndicator();
+
+	public CNN(LayerBuilder layerBuilder, final int batchSize, final Runner runner) {
+
+		this.layers = layerBuilder.layerList;
+		this.layerNum = layers.size();
+		this.batchSize = batchSize;
+
+		this.runner = runner;
+
+		setup(batchSize);
+
+		// ---
+
+		final double _1_batchSize = 1. / batchSize;
+
+		divide_batchSize = new Util.Operator() {
+			private static final long serialVersionUID = 7424011281732651055L;
+
+			@Override
+			public double process(double value) {
+				return value * _1_batchSize;
+			}
+
+		};
+
+		multiply_alpha = new Util.Operator() {
+			private static final long serialVersionUID = 5761368499808006552L;
+
+			@Override
+			public double process(double value) {
+				return value * ALPHA;
+			}
+
+		};
+
+		multiply_lambda = new Util.Operator() {
+			private static final long serialVersionUID = 4499087728362870577L;
+
+			@Override
+			public double process(double value) {
+				return value * (1 - LAMBDA * ALPHA);
+			}
+		};
+	}
+
+	// === simple getters and setters ===
+
+	public void setRunner(final Runner runner) {
+		this.runner = runner;
+	}
+
+	private Runner getRunner() {
+		if (runner == null) throw new NullPointerException("'runner' is null.  Set runner before start training or test!");
+		return runner;
+	}
+
+	public ProgressIndicator getProgressIndicator() {
+		return progressIndicator;
+	}
+
+	public void setProgressIndicator(final ProgressIndicator progressIndicator) {
+		this.progressIndicator = progressIndicator;
+	}
+
+	// === business logic ===
+
+	public void train(final Dataset trainset, final int iterationCount) {
+		for (int iteration = 0; iteration < iterationCount; iteration++) {
+
+			progressIndicator.start();
+
+			// separate trainset in batches of batchsize ... and round up the result
+			final int epochsNum = (trainset.size() + batchSize - 1) / batchSize;
+
+			Log.info(iteration + "th iter epochsNum:" + epochsNum);
+
+			int right = 0;
+			int count = 0;
+
+			for (int epoch = 0; epoch < epochsNum; epoch++) {
+
+				int[] randPerm = Util.randomPerm(trainset.size(), batchSize);
+
+				Layer.prepareForNewBatch();
+
+				for (int index : randPerm) {
+					final boolean isRight = train(trainset.getRecord(index));
+					if (isRight) right++;
+					count++;
+					Layer.prepareForNewRecord();
+				}
+
+				// After finishing a batch update weight
+				updateParas();
+
+				progressIndicator.progress();
+			}
+
+			progressIndicator.finished();
+
+			final double precision = ((double) right) / count;
+
+			if (iteration % 10 == 1 && precision > 0.96) {
+				ALPHA = 0.001 + ALPHA * 0.9; // Adjust the quasi-learning rate dynamically
+				Log.info("Set alpha = " + ALPHA);
+			}
+
+			Log.info("precision " + right + "/" + count + "=" + precision);
+		}
+	}
+
+	public double test(final Dataset dataset) {
+		Layer.prepareForNewBatch();
+
+		final Iterator<Dataset.Record> iterator = dataset.iterator();
+
+		int right = 0;
+		while (iterator.hasNext()) {
+			final Dataset.Record record = iterator.next();
+
+			final double[] out = propagate(record);
+
+			if (record.getLabel().intValue() == Util.getMaxIndex(out)) {
+				right++;
+			}
+		}
+
+		double p = 1.0 * right / dataset.size();
+
+		Log.info("precision", p + "");
+
+		return p;
+	}
+
+	private double[] getOutput() {
+		final Layer outputLayer = layers.get(layerNum - 1);
+
+		final int mapNum = outputLayer.getOutMapNum();
+		final double[] out = new double[mapNum];
+		for (int m = 0; m < mapNum; m++) {
+			final double[][] outmap = outputLayer.getMap(m);
+			out[m] = outmap[0][0];
+		}
+		return out;
+	}
+
+	// TODO: Move this method to other/new class (reduce CNN-class to the minimal CNN-logic)
+	public void predict(Dataset testset, String fileName) {
+		Log.info("begin predict");
+		try {
+			// final int max = layers.get(layerNum - 1).getClassNum();
+			final PrintWriter writer = new PrintWriter(new File(fileName));
+
+			Layer.prepareForNewBatch();
+
+			final Iterator<Dataset.Record> iter = testset.iterator();
+			while (iter.hasNext()) {
+				final Dataset.Record record = iter.next();
+				final double[] out = propagate(record);
+				// int label =
+				// Util.binaryArray2int(out);
+				final int label = Util.getMaxIndex(out);
+				// if (label >= max)
+				// label = label - (1 << (out.length -
+				// 1));
+				writer.write(label + "\n");
+			}
+			writer.flush();
+			writer.close();
+		} catch (IOException e) {
+			throw new RuntimeException(e);
+		}
+		Log.info("end predict");
+	}
+
+	private boolean train(Dataset.Record record) {
+		forward(record);
+
+		return backPropagation(record);
+	}
+
+	private boolean backPropagation(Dataset.Record record) {
+		boolean result = setOutLayerErrors(record);
+		setHiddenLayerErrors();
+		return result;
+	}
+
+	private void updateParas() {
+		for (int l = 1; l < layerNum; l++) {
+			Layer layer = layers.get(l);
+			Layer lastLayer = layers.get(l - 1);
+			switch (layer.getType()) {
+				case conv:
+				case output:
+					updateKernels(layer, lastLayer);
+					updateBias(layer);
+					break;
+				default:
+					break;
+			}
+		}
+	}
+
+	private void updateBias(final Layer layer) {
+		final double[][][][] errors = layer.getErrors();
+		int mapNum = layer.getOutMapNum();
+
+		final Process processor =
+				new Process() {
+					@Override
+					public void process(int start, int end) {
+						for (int j = start; j < end; j++) {
+							final double[][] error = Util.sum(errors, j);
+							// update offset
+							final double deltaBias = Util.sum(error) / batchSize;
+							final double bias = layer.getBias(j) + ALPHA * deltaBias;
+							layer.setBias(j, bias);
+						}
+					}
+				};
+
+		getRunner().startProcess(mapNum, processor);
+	}
+
+	private void updateKernels(final Layer layer, final Layer lastLayer) {
+		final int mapNum = layer.getOutMapNum();
+		final int lastMapNum = lastLayer.getOutMapNum();
+
+		final Process process = new Process() {
+			@Override
+			public void process(int start, int end) {
+				for (int j = start; j < end; j++) {
+					for (int i = 0; i < lastMapNum; i++) {
+						double[][] deltaKernel = null;
+						for (int r = 0; r < batchSize; r++) {
+							final double[][] error = layer.getError(r, j);
+							if (deltaKernel == null)
+								deltaKernel = Util.convnValid(lastLayer.getMap(r, i), error);
+							else {
+								deltaKernel = Util.matrixOp(Util.convnValid(lastLayer.getMap(r, i), error), deltaKernel, null, null, Util.plus);
+							}
+						}
+
+						deltaKernel = Util.matrixOp(deltaKernel, divide_batchSize);
+						final double[][] kernel = layer.getKernel(i, j);
+						deltaKernel = Util.matrixOp(kernel, deltaKernel, multiply_lambda, multiply_alpha, Util.plus);
+						layer.setKernel(i, j, deltaKernel);
+					}
+				}
+
+			}
+		};
+
+		getRunner().startProcess(mapNum, process);
+	}
+
+	private void setHiddenLayerErrors() {
+		for (int l = layerNum - 2; l > 0; l--) {
+			final Layer layer = layers.get(l);
+			final Layer nextLayer = layers.get(l + 1);
+			switch (layer.getType()) {
+				case samp:
+					setSampErrors(layer, nextLayer);
+					break;
+				case conv:
+					setConvErrors(layer, nextLayer);
+					break;
+				default:
+					break;
+			}
+		}
+	}
+
+	private void setSampErrors(final Layer layer, final Layer nextLayer) {
+		final int mapNum = layer.getOutMapNum();
+		final int nextMapNum = nextLayer.getOutMapNum();
+
+		final Process process = new Process() {
+			@Override
+			public void process(int start, int end) {
+				for (int i = start; i < end; i++) {
+					double[][] sum = null;// ��ÿһ������������
+					for (int j = 0; j < nextMapNum; j++) {
+						final double[][] nextError = nextLayer.getError(j);
+						final double[][] kernel = nextLayer.getKernel(i, j);
+						if (sum == null)
+							sum = Util.convnFull(nextError, Util.rot180(kernel));
+						else
+							sum = Util.matrixOp(Util.convnFull(nextError, Util.rot180(kernel)), sum, null, null, Util.plus);
+					}
+					layer.setError(i, sum);
+				}
+			}
+
+		};
+
+		getRunner().startProcess(mapNum, process);
+	}
+
+	private void setConvErrors(final Layer layer, final Layer nextLayer) {
+		final int mapNum = layer.getOutMapNum();
+
+		final Process process = new Process() {
+			@Override
+			public void process(int start, int end) {
+				for (int m = start; m < end; m++) {
+					final Layer.Size scale = nextLayer.getScaleSize();
+					final double[][] nextError = nextLayer.getError(m);
+					final double[][] map = layer.getMap(m);
+					double[][] outMatrix = Util.matrixOp(map, Util.cloneMatrix(map), null, Util.one_value, Util.multiply);
+					outMatrix = Util.matrixOp(outMatrix, Util.kronecker(nextError, scale), null, null, Util.multiply);
+					layer.setError(m, outMatrix);
+				}
+			}
+		};
+
+		getRunner().startProcess(mapNum, process);
+	}
+
+	private boolean setOutLayerErrors(final Dataset.Record record) {
+
+		final Layer outputLayer = layers.get(layerNum - 1);
+		final int mapNum = outputLayer.getOutMapNum();
+		final double[] target = new double[mapNum];
+		final double[] outmaps = new double[mapNum];
+
+		for (int m = 0; m < mapNum; m++) {
+			final double[][] outmap = outputLayer.getMap(m);
+			outmaps[m] = outmap[0][0];
+		}
+
+		final int label = record.getLabel().intValue();
+
+		target[label] = 1;
+
+		for (int m = 0; m < mapNum; m++) {
+			outputLayer.setError(m, 0, 0, outmaps[m] * (1 - outmaps[m]) * (target[m] - outmaps[m]));
+		}
+
+		return label == Util.getMaxIndex(outmaps);
+	}
+
+	/**
+	 * Propagate given values through the network.
+	 * Returns the results.
+	 * For each input it returns the set of output values.
+	 *
+	 * @param inputs A list of vectors of input values
+	 * @return A list of results of the network corresponding to each input vector
+	 */
+	public double[][] propagate(final double[][] inputs) {
+		final double[][] results = new double[inputs.length][];
+
+		int index = 0;
+		for (final double[] input : inputs) {
+			final Dataset.Record record = new Dataset.Record(input, -1.);
+			results[index] = propagate(record);
+			index++;
+		}
+
+		return results;
+	}
+
+	/**
+	 * Propagate given values through the network.
+	 * Returns the result.
+	 *
+	 * @param inputs A vector of input values
+	 * @return The result of the network
+	 */
+	public double[] propagate(final double[] inputs) {
+		final Dataset.Record record = new Dataset.Record(inputs, -1.);
+
+		return propagate(record);
+	}
+
+	/**
+	 * Propagate given Record through the network.
+	 * Returns the result.
+	 *
+	 * @param record A Record
+	 * @return The result of the network
+	 */
+	public double[] propagate(final Dataset.Record record) {
+		forward(record);
+
+		return getOutput();
+	}
+
+	private void forward(final Dataset.Record record) {
+		setInLayerOutput(record);
+
+		for (int l = 1; l < layers.size(); l++) {
+			final Layer layer = layers.get(l);
+			final Layer lastLayer = layers.get(l - 1);
+
+			switch (layer.getType()) {
+				case conv:
+					setConvOutput(layer, lastLayer);
+					break;
+
+				case samp:
+					setSampOutput(layer, lastLayer);
+					break;
+
+				case output:
+					setConvOutput(layer, lastLayer);
+					break;
+
+				default:
+					break;
+			}
+		}
+	}
+
+	private void setInLayerOutput(final Dataset.Record record) {
+		final Layer inputLayer = layers.get(0);
+		final Layer.Size mapSize = inputLayer.getMapSize();
+
+		final double[] attr = record.getAttrs();
+
+		if (attr.length != mapSize.x * mapSize.y) {
+			throw new RuntimeException("The size of the data record does not match the size of the map defined!");
+		}
+
+		for (int i = 0; i < mapSize.x; i++) {
+			for (int j = 0; j < mapSize.y; j++) {
+				inputLayer.setMapValue(0, i, j, attr[mapSize.x * i + j]);
+			}
+		}
+	}
+
+	/**
+	 * Compute the output of the convolutional layer, each thread is responsible for part of the map
+	 */
+	private void setConvOutput(final Layer layer, final Layer lastLayer) {
+		final int mapNum = layer.getOutMapNum();
+		final int lastMapNum = lastLayer.getOutMapNum();
+
+		final Process process = new Process() {
+			@Override
+			public void process(int start, int end) {
+				for (int j = start; j < end; j++) {
+					double[][] sum = null;
+					for (int i = 0; i < lastMapNum; i++) {
+						final double[][] lastMap = lastLayer.getMap(i);
+						final double[][] kernel = layer.getKernel(i, j);
+						if (sum == null) {
+							sum = Util.convnValid(lastMap, kernel);
+						} else {
+							sum = Util.matrixOp(Util.convnValid(lastMap, kernel), sum, null, null, Util.plus);
+						}
+					}
+					final double bias = layer.getBias(j);
+					sum = Util
+							.matrixOp(
+									sum,
+									new Util.Operator() {
+										private static final long serialVersionUID = 2469461972825890810L;
+
+										@Override
+										public double process(double value) {
+											return Util.sigmod(value + bias);
+										}
+									}
+							);
+
+					layer.setMapValue(j, sum);
+				}
+			}
+		};
+
+		getRunner().startProcess(mapNum, process);
+	}
+
+	private void setSampOutput(final Layer layer, final Layer lastLayer) {
+		final int lastMapNum = lastLayer.getOutMapNum();
+
+		final Process process = new Process() {
+			@Override
+			public void process(int start, int end) {
+				for (int i = start; i < end; i++) {
+					final double[][] lastMap = lastLayer.getMap(i);
+					final Layer.Size scaleSize = layer.getScaleSize();
+					final double[][] sampMatrix = Util.scaleMatrix(lastMap, scaleSize);
+					layer.setMapValue(i, sampMatrix);
+				}
+			}
+		};
+
+		getRunner().startProcess(lastMapNum, process);
+	}
+
+	private void setup(final int batchSize) {
+		final Layer inputLayer = layers.get(0);
+
+		inputLayer.initOutmaps(batchSize);
+
+		for (int i = 1; i < layers.size(); i++) {
+
+			final Layer layer = layers.get(i);
+			final Layer frontLayer = layers.get(i - 1);
+
+			final int frontMapNum = frontLayer.getOutMapNum();
+			switch (layer.getType()) {
+				case input:
+					break;
+				case conv:
+					layer.setMapSize(frontLayer.getMapSize().subtract(layer.getKernelSize(), 1));
+					layer.initKernel(frontMapNum);
+					layer.initBias(frontMapNum);
+					layer.initErros(batchSize);
+					layer.initOutmaps(batchSize);
+					break;
+
+				case samp:
+					layer.setOutMapNum(frontMapNum);
+					layer.setMapSize(frontLayer.getMapSize().divide(layer.getScaleSize()));
+					layer.initErros(batchSize);
+					layer.initOutmaps(batchSize);
+					break;
+
+				case output:
+					layer.initOutputKerkel(frontMapNum, frontLayer.getMapSize());
+					layer.initBias(frontMapNum);
+					layer.initErros(batchSize);
+					layer.initOutmaps(batchSize);
+					break;
+			}
+		}
+	}
+
+	// === inner classes ===
+
+	public static class LayerBuilder {
+		private List<Layer> layerList;
+
+		public LayerBuilder() {
+			layerList = new ArrayList<>();
+		}
+
+		public LayerBuilder(Layer layer) {
+			this();
+			layerList.add(layer);
+		}
+
+		public LayerBuilder addLayer(Layer layer) {
+			layerList.add(layer);
+			return this;
+		}
+	}
+
+}
diff --git a/src/main/java/javacnn/cnn/CNNLoader.java b/src/main/java/javacnn/cnn/CNNLoader.java
new file mode 100644
index 0000000..b084bba
--- /dev/null
+++ b/src/main/java/javacnn/cnn/CNNLoader.java
@@ -0,0 +1,36 @@
+package javacnn.cnn;
+
+import java.io.FileInputStream;
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.InputStream;
+import java.io.ObjectInputStream;
+import java.io.ObjectOutputStream;
+import java.io.OutputStream;
+
+public class CNNLoader {
+
+	public static void saveModel(final String fileName, final CNN cnn) throws IOException {
+		saveModel(cnn, new FileOutputStream(fileName));
+	}
+
+	public static void saveModel(final CNN cnn, final OutputStream outputStream) throws IOException {
+		ObjectOutputStream oos = new ObjectOutputStream(outputStream);
+		oos.writeObject(cnn);
+		oos.flush();
+		oos.close();
+	}
+
+	public static CNN loadModel(final String fileName) throws IOException, ClassNotFoundException {
+		return loadModel(new FileInputStream(fileName));
+	}
+
+	public static CNN loadModel(final InputStream inputStream) throws IOException, ClassNotFoundException {
+		final ObjectInputStream in = new ObjectInputStream(inputStream);
+		final CNN cnn = (CNN) in.readObject();
+		in.close();
+
+		return cnn;
+	}
+
+}
diff --git a/src/edu/hitsz/c102c/cnn/Layer.java b/src/main/java/javacnn/cnn/Layer.java
similarity index 64%
rename from src/edu/hitsz/c102c/cnn/Layer.java
rename to src/main/java/javacnn/cnn/Layer.java
index adf78e4..5d9599f 100644
--- a/src/edu/hitsz/c102c/cnn/Layer.java
+++ b/src/main/java/javacnn/cnn/Layer.java
@@ -1,75 +1,56 @@
-package edu.hitsz.c102c.cnn;
+package javacnn.cnn;
 
 import java.io.Serializable;
 
-import edu.hitsz.c102c.util.Log;
-import edu.hitsz.c102c.util.Util;
+import javacnn.util.Log;
+import javacnn.util.Util;
 
 /**
- * cnn����IJ�
- * 
+ * cnn����IJ�
+ *
  * @author jiqunpeng
- * 
- *         ����ʱ�䣺2014-7-8 ����3:58:46
+ * <p>
+ * ����ʱ�䣺2014-7-8 ����3:58:46
  */
 public class Layer implements Serializable {
 	/**
-	 * 
+	 *
 	 */
 	private static final long serialVersionUID = -5747622503947497069L;
-	private LayerType type;// �������
-	private int outMapNum;// ���map�ĸ���
-	private Size mapSize;// map�Ĵ�С
-	private Size kernelSize;// �����˴�С��ֻ�о�������
-	private Size scaleSize;// ������С��ֻ�в�������
-	private double[][][][] kernel;// �����ˣ�ֻ�о�������������
-	private double[] bias;// ÿ��map��Ӧһ��ƫ�ã�ֻ�о�������������
-	// �������batch�����map��outmaps[0][0]��ʾ��һ����¼ѵ���µ�0�����map
+	private LayerType type;
+	private int outMapNum;
+	private Size mapSize;
+	private Size kernelSize;
+	private Size scaleSize;
+	private double[][][][] kernel;
+	private double[] bias;
 	private double[][][][] outmaps;
-	// �в��matlab toolbox��d��Ӧ
 	private double[][][][] errors;
 
-	private static int recordInBatch = 0;// ��¼��ǰѵ������batch�ĵڼ�����¼
+	private static int recordInBatch = 0;
 
-	private int classNum = -1;// ������
+	private int classNum = -1;
 
 	private Layer() {
 
 	}
 
-	/**
-	 * ׼����һ��batch��ѵ��
-	 */
 	public static void prepareForNewBatch() {
 		recordInBatch = 0;
 	}
 
-	/**
-	 * ׼����һ����¼��ѵ��
-	 */
 	public static void prepareForNewRecord() {
 		recordInBatch++;
 	}
 
-	/**
-	 * ��ʼ�������
-	 * 
-	 * @param mapSize
-	 * @return
-	 */
 	public static Layer buildInputLayer(Size mapSize) {
 		Layer layer = new Layer();
 		layer.type = LayerType.input;
-		layer.outMapNum = 1;// ������map����Ϊ1����һ��ͼ
+		layer.outMapNum = 1;// ������map����Ϊ1����һ��ͼ
 		layer.setMapSize(mapSize);//
 		return layer;
 	}
 
-	/**
-	 * ���������
-	 * 
-	 * @return
-	 */
 	public static Layer buildConvLayer(int outMapNum, Size kernelSize) {
 		Layer layer = new Layer();
 		layer.type = LayerType.conv;
@@ -78,12 +59,6 @@ public static Layer buildConvLayer(int outMapNum, Size kernelSize) {
 		return layer;
 	}
 
-	/**
-	 * ���������
-	 * 
-	 * @param scaleSize
-	 * @return
-	 */
 	public static Layer buildSampLayer(Size scaleSize) {
 		Layer layer = new Layer();
 		layer.type = LayerType.samp;
@@ -91,11 +66,6 @@ public static Layer buildSampLayer(Size scaleSize) {
 		return layer;
 	}
 
-	/**
-	 * ���������,���������������ĸ��������������Ԫ�ĸ���
-	 * 
-	 * @return
-	 */
 	public static Layer buildOutputLayer(int classNum) {
 		Layer layer = new Layer();
 		layer.classNum = classNum;
@@ -106,86 +76,44 @@ public static Layer buildOutputLayer(int classNum) {
 		// while ((1 << outMapNum) < classNum)
 		// outMapNum += 1;
 		// layer.outMapNum = outMapNum;
-		Log.i("outMapNum:" + layer.outMapNum);
+		Log.info("outMapNum:" + layer.outMapNum);
 		return layer;
 	}
 
-	/**
-	 * ��ȡmap�Ĵ�С
-	 * 
-	 * @return
-	 */
 	public Size getMapSize() {
 		return mapSize;
 	}
 
-	/**
-	 * ����map�Ĵ�С
-	 * 
-	 * @param mapSize
-	 */
 	public void setMapSize(Size mapSize) {
 		this.mapSize = mapSize;
 	}
 
-	/**
-	 * ��ȡ�������
-	 * 
-	 * @return
-	 */
 	public LayerType getType() {
 		return type;
 	}
 
-	/**
-	 * ��ȡ�����������
-	 * 
-	 * @return
-	 */
-
 	public int getOutMapNum() {
 		return outMapNum;
 	}
 
-	/**
-	 * �������map�ĸ���
-	 * 
-	 * @param outMapNum
-	 */
 	public void setOutMapNum(int outMapNum) {
 		this.outMapNum = outMapNum;
 	}
 
-	/**
-	 * ��ȡ�����˵Ĵ�С��ֻ�о�������kernelSize���������δnull
-	 * 
-	 * @return
-	 */
 	public Size getKernelSize() {
 		return kernelSize;
 	}
 
-	/**
-	 * ��ȡ������С��ֻ�в�������scaleSize���������δnull
-	 * 
-	 * @return
-	 */
 	public Size getScaleSize() {
 		return scaleSize;
 	}
 
 	enum LayerType {
-		// ���������ͣ�����㡢����㡢�����㡢������
 		input, output, conv, samp
 	}
 
-	/**
-	 * �����˻��߲�����scale�Ĵ�С,��������Բ���.���Ͱ�ȫ�����Ժ󲻿��޸�
-	 * 
-	 * @author jiqunpeng
-	 * 
-	 *         ����ʱ�䣺2014-7-8 ����4:11:00
-	 */
+	// ---
+
 	public static class Size implements Serializable {
 
 		private static final long serialVersionUID = -209157832162004118L;
@@ -197,30 +125,34 @@ public Size(int x, int y) {
 			this.y = y;
 		}
 
+		@Override
 		public String toString() {
-			StringBuilder s = new StringBuilder("Size(").append(" x = ")
-					.append(x).append(" y= ").append(y).append(")");
-			return s.toString();
+			return "Size{" +
+					"x=" + x +
+					", y=" + y +
+					'}';
 		}
 
 		/**
-		 * ����scaleSize�õ�һ���µ�Size��Ҫ��this.x��this.
-		 * y�ֱܷ�scaleSize.x��scaleSize.y����
-		 * 
+		 * ����scaleSize�õ�һ���µ�Size��Ҫ��this.x��this.
+		 * y�ֱܷ�scaleSize.x��scaleSize.y����
+		 *
 		 * @param scaleSize
 		 * @return
 		 */
 		public Size divide(Size scaleSize) {
 			int x = this.x / scaleSize.x;
 			int y = this.y / scaleSize.y;
-			if (x * scaleSize.x != this.x || y * scaleSize.y != this.y)
-				throw new RuntimeException(this + "��������" + scaleSize);
+			if (x * scaleSize.x != this.x || y * scaleSize.y != this.y) {
+				throw new RuntimeException(this + " can not be divisible " + scaleSize);
+			}
+
 			return new Size(x, y);
 		}
 
 		/**
-		 * ��ȥsize��С����x��y�ֱ𸽼�һ��ֵappend
-		 * 
+		 * ��ȥsize��С����x��y�ֱ𸽼�һ��ֵappend
+		 *
 		 * @param size
 		 * @param append
 		 * @return
@@ -233,8 +165,8 @@ public Size subtract(Size size, int append) {
 	}
 
 	/**
-	 * �����ʼ��������
-	 * 
+	 * �����ʼ�������
+	 *
 	 * @param frontMapNum
 	 */
 	public void initKernel(int frontMapNum) {
@@ -244,12 +176,12 @@ public void initKernel(int frontMapNum) {
 		this.kernel = new double[frontMapNum][outMapNum][kernelSize.x][kernelSize.y];
 		for (int i = 0; i < frontMapNum; i++)
 			for (int j = 0; j < outMapNum; j++)
-				kernel[i][j] = Util.randomMatrix(kernelSize.x, kernelSize.y,true);
+				kernel[i][j] = Util.randomMatrix(kernelSize.x, kernelSize.y, true);
 	}
 
 	/**
-	 * �����ľ����˵Ĵ�С����һ���map��С
-	 * 
+	 * �����ľ���˵Ĵ�С����һ���map��С
+	 *
 	 * @param frontMapNum
 	 * @param size
 	 */
@@ -261,12 +193,12 @@ public void initOutputKerkel(int frontMapNum, Size size) {
 		this.kernel = new double[frontMapNum][outMapNum][kernelSize.x][kernelSize.y];
 		for (int i = 0; i < frontMapNum; i++)
 			for (int j = 0; j < outMapNum; j++)
-				kernel[i][j] = Util.randomMatrix(kernelSize.x, kernelSize.y,false);
+				kernel[i][j] = Util.randomMatrix(kernelSize.x, kernelSize.y, false);
 	}
 
 	/**
-	 * ��ʼ��ƫ��
-	 * 
+	 * ��ʼ��ƫ��
+	 *
 	 * @param frontMapNum
 	 */
 	public void initBias(int frontMapNum) {
@@ -274,8 +206,8 @@ public void initBias(int frontMapNum) {
 	}
 
 	/**
-	 * ��ʼ�����map
-	 * 
+	 * ��ʼ�����map
+	 *
 	 * @param batchSize
 	 */
 	public void initOutmaps(int batchSize) {
@@ -283,14 +215,11 @@ public void initOutmaps(int batchSize) {
 	}
 
 	/**
-	 * ����mapֵ
-	 * 
-	 * @param mapNo
-	 *            �ڼ���map
-	 * @param mapX
-	 *            map�ĸ�
-	 * @param mapY
-	 *            map�Ŀ�
+	 * ����mapֵ
+	 *
+	 * @param mapNo �ڼ���map
+	 * @param mapX  map�ĸ�
+	 * @param mapY  map�Ŀ�
 	 * @param value
 	 */
 	public void setMapValue(int mapNo, int mapX, int mapY, double value) {
@@ -300,8 +229,8 @@ public void setMapValue(int mapNo, int mapX, int mapY, double value) {
 	static int count = 0;
 
 	/**
-	 * �Ծ�����ʽ���õ�mapNo��map��ֵ
-	 * 
+	 * �Ծ�����ʽ���õ�mapNo��map��ֵ
+	 *
 	 * @param mapNo
 	 * @param outMatrix
 	 */
@@ -312,9 +241,9 @@ public void setMapValue(int mapNo, double[][] outMatrix) {
 	}
 
 	/**
-	 * ��ȡ��index��map���󡣴������ܿ��ǣ�û�з��ظ��ƶ��󣬶���ֱ�ӷ������ã����ö��������
-	 * �����޸�outmaps�������޸������setMapValue(...)
-	 * 
+	 * ��ȡ��index��map���󡣴������ܿ��ǣ�û�з��ظ��ƶ��󣬶���ֱ�ӷ������ã����ö��������
+	 * �����޸�outmaps�������޸������setMapValue(...)
+	 *
 	 * @param index
 	 * @return
 	 */
@@ -323,12 +252,10 @@ public double[][] getMap(int index) {
 	}
 
 	/**
-	 * ��ȡǰһ���i��map����ǰ���j��map�ľ�����
-	 * 
-	 * @param i
-	 *            ��һ���map�±�
-	 * @param j
-	 *            ��ǰ���map�±�
+	 * ��ȡǰһ���i��map����ǰ���j��map�ľ����
+	 *
+	 * @param i ��һ���map�±�
+	 * @param j ��ǰ���map�±�
 	 * @return
 	 */
 	public double[][] getKernel(int i, int j) {
@@ -336,8 +263,8 @@ public double[][] getKernel(int i, int j) {
 	}
 
 	/**
-	 * ���òв�ֵ
-	 * 
+	 * ���òв�ֵ
+	 *
 	 * @param mapNo
 	 * @param mapX
 	 * @param mapY
@@ -348,8 +275,8 @@ public void setError(int mapNo, int mapX, int mapY, double value) {
 	}
 
 	/**
-	 * ��map�������ʽ���òв�ֵ
-	 * 
+	 * ��map�������ʽ���òв�ֵ
+	 *
 	 * @param mapNo
 	 * @param matrix
 	 */
@@ -360,9 +287,9 @@ public void setError(int mapNo, double[][] matrix) {
 	}
 
 	/**
-	 * ��ȡ��mapNo��map�IJв�.û�з��ظ��ƶ��󣬶���ֱ�ӷ������ã����ö��������
-	 * �����޸�errors�������޸������setError(...)
-	 * 
+	 * ��ȡ��mapNo��map�IJв�.û�з��ظ��ƶ��󣬶���ֱ�ӷ������ã����ö��������
+	 * �����޸�errors�������޸������setError(...)
+	 *
 	 * @param mapNo
 	 * @return
 	 */
@@ -371,8 +298,8 @@ public double[][] getError(int mapNo) {
 	}
 
 	/**
-	 * ��ȡ����(ÿ����¼��ÿ��map)�IJв�
-	 * 
+	 * ��ȡ����(ÿ����¼��ÿ��map)�IJв�
+	 *
 	 * @return
 	 */
 	public double[][][][] getErrors() {
@@ -380,8 +307,8 @@ public double[][][][] getErrors() {
 	}
 
 	/**
-	 * ��ʼ���в�����
-	 * 
+	 * ��ʼ���в�����
+	 *
 	 * @param batchSize
 	 */
 	public void initErros(int batchSize) {
@@ -389,7 +316,6 @@ public void initErros(int batchSize) {
 	}
 
 	/**
-	 * 
 	 * @param lastMapNo
 	 * @param mapNo
 	 * @param kernel
@@ -399,8 +325,8 @@ public void setKernel(int lastMapNo, int mapNo, double[][] kernel) {
 	}
 
 	/**
-	 * ��ȡ��mapNo��
-	 * 
+	 * ��ȡ��mapNo��
+	 *
 	 * @param mapNo
 	 * @return
 	 */
@@ -409,8 +335,8 @@ public double getBias(int mapNo) {
 	}
 
 	/**
-	 * ���õ�mapNo��map��ƫ��ֵ
-	 * 
+	 * ���õ�mapNo��map��ƫ��ֵ
+	 *
 	 * @param mapNo
 	 * @param value
 	 */
@@ -419,8 +345,8 @@ public void setBias(int mapNo, double value) {
 	}
 
 	/**
-	 * ��ȡbatch����map����
-	 * 
+	 * ��ȡbatch����map����
+	 *
 	 * @return
 	 */
 
@@ -429,8 +355,8 @@ public double[][][][] getMaps() {
 	}
 
 	/**
-	 * ��ȡ��recordId��¼�µ�mapNo�IJв�
-	 * 
+	 * ��ȡ��recordId��¼�µ�mapNo�IJв�
+	 *
 	 * @param recordId
 	 * @param mapNo
 	 * @return
@@ -440,8 +366,8 @@ public double[][] getError(int recordId, int mapNo) {
 	}
 
 	/**
-	 * ��ȡ��recordId��¼�µ�mapNo�����map
-	 * 
+	 * ��ȡ��recordId��¼�µ�mapNo�����map
+	 *
 	 * @param recordId
 	 * @param mapNo
 	 * @return
@@ -451,8 +377,8 @@ public double[][] getMap(int recordId, int mapNo) {
 	}
 
 	/**
-	 * ��ȡ������
-	 * 
+	 * ��ȡ������
+	 *
 	 * @return
 	 */
 	public int getClassNum() {
@@ -460,8 +386,8 @@ public int getClassNum() {
 	}
 
 	/**
-	 * ��ȡ���еľ�����
-	 * 
+	 * ��ȡ���еľ����
+	 *
 	 * @return
 	 */
 	public double[][][][] getKernel() {
diff --git a/src/main/java/javacnn/cnn/Process.java b/src/main/java/javacnn/cnn/Process.java
new file mode 100644
index 0000000..0ba7194
--- /dev/null
+++ b/src/main/java/javacnn/cnn/Process.java
@@ -0,0 +1,11 @@
+package javacnn.cnn;
+
+/**
+ * <p/>
+ * Created: 2018-02-19 08:29
+ *
+ * @author Ralf Th. Pietsch &lt;ratopi@abwesend.de&gt;
+ */
+public interface Process {
+	void process(int start, int end);
+}
diff --git a/src/main/java/javacnn/dataset/Dataset.java b/src/main/java/javacnn/dataset/Dataset.java
new file mode 100644
index 0000000..877d47b
--- /dev/null
+++ b/src/main/java/javacnn/dataset/Dataset.java
@@ -0,0 +1,89 @@
+package javacnn.dataset;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Iterator;
+import java.util.List;
+
+public class Dataset {
+	private List<Record> records;
+
+	public Dataset() {
+		records = new ArrayList<>();
+	}
+
+	public Dataset(final List<double[]> datas, final List<Double> labels) {
+		this();
+
+		if (datas.size() != labels.size()) {
+			throw new IllegalArgumentException("Lengths differs: " + datas.size() + " datas and " + labels.size() + " labels");
+		}
+
+		for (int i = 0; i < datas.size(); i++) {
+			final double[] data = datas.get(i);
+			final Double label = labels.get(i);
+			append(new Record(data, label));
+		}
+	}
+
+	public int size() {
+		return records.size();
+	}
+
+	public void append(Record record) {
+		records.add(record);
+	}
+
+	public void clear() {
+		records.clear();
+	}
+
+	public void append(double[] attrs, Double label) {
+		records.add(new Record(attrs, label));
+	}
+
+	public Iterator<Record> iterator() {
+		return records.iterator();
+	}
+
+	public double[] getAttrs(int index) {
+		return records.get(index).getAttrs();
+	}
+
+	public Double getLabel(int index) {
+		return records.get(index).getLabel();
+	}
+
+	public Record getRecord(int index) {
+		return records.get(index);
+	}
+
+	// ---
+
+	public static class Record {
+		private double[] attrs;
+		private Double label;
+
+		public Record(double[] attrs, Double label) {
+			this.attrs = attrs;
+			this.label = label;
+		}
+
+		public double[] getAttrs() {
+			return attrs;
+		}
+
+		public Double getLabel() {
+			return label;
+		}
+
+		@Override
+		public String toString() {
+			return "Record{" +
+					"attrs=" + Arrays.toString(attrs) +
+					", label=" + label +
+					'}';
+		}
+	}
+
+}
diff --git a/src/main/java/javacnn/dataset/DatasetLoader.java b/src/main/java/javacnn/dataset/DatasetLoader.java
new file mode 100644
index 0000000..f5a663d
--- /dev/null
+++ b/src/main/java/javacnn/dataset/DatasetLoader.java
@@ -0,0 +1,41 @@
+package javacnn.dataset;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+
+public class DatasetLoader {
+	public static Dataset load(final String filePath, final String tag, final int labelIndex) throws IOException {
+		final Dataset dataset = new Dataset();
+
+		final BufferedReader in = new BufferedReader(new FileReader(filePath));
+
+		String line;
+		while ((line = in.readLine()) != null) {
+
+			final String[] datas = line.split(tag);
+
+			if (datas.length == 0) {
+				continue;
+			}
+
+			final int vectorLength = labelIndex < 0 ? datas.length : datas.length - 1;
+
+			final double[] data = new double[vectorLength];
+
+			for (int i = 0; i < vectorLength; i++) {
+				data[i] = Double.parseDouble(datas[i]);
+			}
+
+			final Double label = labelIndex < 0 ? null : Double.parseDouble(datas[labelIndex]);
+
+			dataset.append(data, label);
+		}
+		in.close();
+
+		System.out.println("Read " + dataset.size() + " records");
+
+		return dataset;
+	}
+
+}
diff --git a/src/main/java/javacnn/util/ConcurenceRunner.java b/src/main/java/javacnn/util/ConcurenceRunner.java
new file mode 100644
index 0000000..e364195
--- /dev/null
+++ b/src/main/java/javacnn/util/ConcurenceRunner.java
@@ -0,0 +1,75 @@
+package javacnn.util;
+
+import java.util.concurrent.CountDownLatch;
+import java.util.concurrent.ExecutorService;
+import java.util.concurrent.Executors;
+
+import javacnn.cnn.Process;
+
+/**
+ * Concurrent running tools
+ * <p>
+ * Created: 2014-6-16 at 3:33:41 PM
+ *
+ * @author jiqunpeng
+ */
+public class ConcurenceRunner implements Runner {
+
+	private final ExecutorService exec;
+	private final int threadCount;
+
+	/**
+	 * Starting ConcurrenceRunner with one thread for each CPU.
+	 */
+	public ConcurenceRunner() {
+		this(Runtime.getRuntime().availableProcessors());
+	}
+
+	/**
+	 * Starting ConcurenceRunner with the given count of threads.
+	 *
+	 * @param threadCount Threads to start (must be &gt; 0).
+	 */
+	public ConcurenceRunner(final int threadCount) {
+		this.threadCount = threadCount;
+		exec = Executors.newFixedThreadPool(this.threadCount);
+	}
+
+	public void shutdown() {
+		exec.shutdown();
+	}
+
+	@Override
+	public void startProcess(final int mapNum, final Process process) {
+		final int runCpu = threadCount < mapNum ? threadCount : 1;
+
+		// Fragment length rounded up
+		final CountDownLatch gate = new CountDownLatch(runCpu);
+
+		final int fregLength = (mapNum + runCpu - 1) / runCpu;
+
+		for (int cpu = 0; cpu < runCpu; cpu++) {
+			final int start = cpu * fregLength;
+
+			final int tmp = (cpu + 1) * fregLength;
+			final int end = tmp <= mapNum ? tmp : mapNum;
+
+			final Runnable task = new Runnable() {
+				@Override
+				public void run() {
+					process.process(start, end);
+					gate.countDown();
+				}
+			};
+
+			exec.execute(task);
+		}
+		try {// Wait for all threads to finish running
+			gate.await();
+		} catch (InterruptedException e) {
+			e.printStackTrace();
+			throw new RuntimeException(e);
+		}
+	}
+
+}
diff --git a/src/main/java/javacnn/util/DirectRunner.java b/src/main/java/javacnn/util/DirectRunner.java
new file mode 100644
index 0000000..ad8bd4c
--- /dev/null
+++ b/src/main/java/javacnn/util/DirectRunner.java
@@ -0,0 +1,29 @@
+package javacnn.util;
+
+import javacnn.cnn.Process;
+
+/**
+ * <p/>
+ * Created: 20.02.2018 11:03
+ *
+ * @author Ralf Th. Pietsch &lt;ratopi@abwesend.de&gt;
+ */
+public class DirectRunner implements Runner {
+
+	@Override
+	public void startProcess(final int mapNum, final Process process) {
+		final int runCpu = 1;
+
+		// Fragment length rounded up
+		final int fregLength = (mapNum + runCpu - 1) / runCpu;
+
+		for (int cpu = 0; cpu < runCpu; cpu++) {
+			final int start = cpu * fregLength;
+
+			final int tmp = (cpu + 1) * fregLength;
+			final int end = tmp <= mapNum ? tmp : mapNum;
+
+			process.process(start, end);
+		}
+	}
+}
diff --git a/src/main/java/javacnn/util/DotProgressIndicator.java b/src/main/java/javacnn/util/DotProgressIndicator.java
new file mode 100644
index 0000000..5996de8
--- /dev/null
+++ b/src/main/java/javacnn/util/DotProgressIndicator.java
@@ -0,0 +1,47 @@
+package javacnn.util;
+
+import java.io.Serializable;
+
+/**
+ * <p/>
+ * Created: 19.02.2018 10:53
+ *
+ * @author Ralf Th. Pietsch &lt;ratopi@abwesend.de&gt;
+ */
+public class DotProgressIndicator implements ProgressIndicator, Serializable {
+
+	private static final long serialVersionUID = 1L;
+
+	private int cycle;
+
+	private int count = 0;
+
+
+	public DotProgressIndicator() {
+		this(50);
+	}
+
+	public DotProgressIndicator(final int cycle) {
+		this.cycle = cycle;
+	}
+
+
+	@Override
+	public void start() {
+		count = 0;
+	}
+
+	@Override
+	public void progress() {
+		count++;
+		if (count > cycle) {
+			System.out.print(".");
+			count = 0;
+		}
+	}
+
+	@Override
+	public void finished() {
+		System.out.println();
+	}
+}
diff --git a/src/main/java/javacnn/util/Log.java b/src/main/java/javacnn/util/Log.java
new file mode 100644
index 0000000..683cc86
--- /dev/null
+++ b/src/main/java/javacnn/util/Log.java
@@ -0,0 +1,22 @@
+package javacnn.util;
+
+import java.io.PrintStream;
+
+public class Log {
+	private static final PrintStream stream = System.out;
+
+	private static boolean on = false;
+
+	public static void switchOn() {
+		on = true;
+	}
+
+	public static void info(String tag, String msg) {
+		if (on) stream.println(tag + "\t" + msg);
+	}
+
+	public static void info(String msg) {
+		if (on) stream.println(msg);
+	}
+
+}
diff --git a/src/main/java/javacnn/util/ProgressIndicator.java b/src/main/java/javacnn/util/ProgressIndicator.java
new file mode 100644
index 0000000..1bb4af4
--- /dev/null
+++ b/src/main/java/javacnn/util/ProgressIndicator.java
@@ -0,0 +1,14 @@
+package javacnn.util;
+
+/**
+ * Interface for feedback progress of any kind
+ * <p/>
+ * Created: 19.02.2018 10:52
+ *
+ * @author Ralf Th. Pietsch &lt;ratopi@abwesend.de&gt;
+ */
+public interface ProgressIndicator {
+	void start();
+	void progress();
+	void finished();
+}
diff --git a/src/main/java/javacnn/util/Runner.java b/src/main/java/javacnn/util/Runner.java
new file mode 100644
index 0000000..b404a49
--- /dev/null
+++ b/src/main/java/javacnn/util/Runner.java
@@ -0,0 +1,13 @@
+package javacnn.util;
+
+import javacnn.cnn.Process;
+
+/**
+ * <p/>
+ * Created: 2018-02-19 08:57
+ *
+ * @author Ralf Th. Pietsch &lt;ratopi@abwesend.de&gt;
+ */
+public interface Runner {
+	void startProcess(int mapNum, Process process);
+}
diff --git a/src/edu/hitsz/c102c/util/TestArray.java b/src/main/java/javacnn/util/TestArray.java
similarity index 73%
rename from src/edu/hitsz/c102c/util/TestArray.java
rename to src/main/java/javacnn/util/TestArray.java
index b946432..4ccf55f 100644
--- a/src/edu/hitsz/c102c/util/TestArray.java
+++ b/src/main/java/javacnn/util/TestArray.java
@@ -1,15 +1,15 @@
-package edu.hitsz.c102c.util;
+package javacnn.util;
 
 import java.util.Locale;
 
-import edu.hitsz.c102c.util.TimedTest.TestTask;
+import javacnn.util.TimedTest.TestTask;
 
 /**
- * ����Ԫ��ֱ�ӷ���������ͨ���������������Ч�ʣ� ���ۣ�������ʽ���ʲ�û�н����ٶ�
+ * ����Ԫ��ֱ�ӷ���������ͨ���������������Ч�ʣ� ���ۣ�������ʽ���ʲ�û�н����ٶ�
  * 
  * @author jiqunpeng
  * 
- *         ����ʱ�䣺2014-7-9 ����3:18:30
+ *         ����ʱ�䣺2014-7-9 ����3:18:30
  */
 public class TestArray {
 	double[][] data;
@@ -35,7 +35,7 @@ private void useFunc() {
 	}
 
 	public static void main(String[] args) {
-		String a = "aAdfa��_";
+		String a = "aAdfa��_";
 		System.out.println(a.toUpperCase(Locale.CHINA));
 		double[][] d = new double[3][];
 //		d[0] = new double[] { 1,2,3 };
diff --git a/src/edu/hitsz/c102c/util/TimedTest.java b/src/main/java/javacnn/util/TimedTest.java
similarity index 74%
rename from src/edu/hitsz/c102c/util/TimedTest.java
rename to src/main/java/javacnn/util/TimedTest.java
index 71125aa..ea5959c 100644
--- a/src/edu/hitsz/c102c/util/TimedTest.java
+++ b/src/main/java/javacnn/util/TimedTest.java
@@ -1,11 +1,11 @@
-package edu.hitsz.c102c.util;
+package javacnn.util;
 
 /**
- * ��ʱ�IJ��Թ���
+ * ��ʱ�IJ��Թ���
  * 
  * @author jiqunpeng
  * 
- *         ����ʱ�䣺2014-7-8 ����8:21:56
+ *         ����ʱ�䣺2014-7-8 ����8:21:56
  */
 public class TimedTest {
 	private int repeat;
@@ -26,6 +26,6 @@ public void test() {
 			task.process();
 		}
 		double cost = (System.currentTimeMillis() - t) / 1000.0;
-		Log.i("cost ", cost + "s");
+		Log.info("cost ", cost + "s");
 	}
 }
diff --git a/src/edu/hitsz/c102c/util/Util.java b/src/main/java/javacnn/util/Util.java
similarity index 84%
rename from src/edu/hitsz/c102c/util/Util.java
rename to src/main/java/javacnn/util/Util.java
index e0d0077..62b3020 100644
--- a/src/edu/hitsz/c102c/util/Util.java
+++ b/src/main/java/javacnn/util/Util.java
@@ -1,27 +1,26 @@
-package edu.hitsz.c102c.util;
+package javacnn.util;
 
 import java.io.Serializable;
 import java.util.Arrays;
 import java.util.HashSet;
 import java.util.Random;
 import java.util.Set;
-import edu.hitsz.c102c.cnn.Layer.Size;
-import edu.hitsz.c102c.util.TimedTest.TestTask;
+import javacnn.cnn.Layer.Size;
 
 public class Util {
 
 	/**
-	 * �����ӦԪ�����ʱ��ÿ��Ԫ���ϵIJ���
+	 * �����ӦԪ�����ʱ��ÿ��Ԫ���ϵIJ���
 	 * 
 	 * @author jiqunpeng
 	 * 
-	 *         ����ʱ�䣺2014-7-9 ����9:28:35
+	 *         ����ʱ�䣺2014-7-9 ����9:28:35
 	 */
 	public interface Operator extends Serializable {
 		public double process(double value);
 	}
 
-	// ����ÿ��Ԫ��value������1-value�IJ���
+	// ����ÿ��Ԫ��value������1-value�IJ���
 	public static final Operator one_value = new Operator() {
 		/**
 		 * 
@@ -34,7 +33,7 @@ public double process(double value) {
 		}
 	};
 
-	// digmod����
+	// digmod����
 	public static final Operator digmod = new Operator() {
 		/**
 		 * 
@@ -52,7 +51,7 @@ interface OperatorOnTwo extends Serializable {
 	}
 
 	/**
-	 * ��������ӦԪ�صļӷ�����
+	 * ��������ӦԪ�صļӷ�����
 	 */
 	public static final OperatorOnTwo plus = new OperatorOnTwo() {
 		/**
@@ -66,7 +65,7 @@ public double process(double a, double b) {
 		}
 	};
 	/**
-	 * ��������ӦԪ�صij˷�����
+	 * ��������ӦԪ�صij˷�����
 	 */
 	public static OperatorOnTwo multiply = new OperatorOnTwo() {
 		/**
@@ -81,7 +80,7 @@ public double process(double a, double b) {
 	};
 
 	/**
-	 * ��������ӦԪ�صļ�������
+	 * ��������ӦԪ�صļ�������
 	 */
 	public static OperatorOnTwo minus = new OperatorOnTwo() {
 		/**
@@ -105,7 +104,7 @@ public static void printMatrix(double[][] matrix) {
 	}
 
 	/**
-	 * �Ծ������180����ת,����matrix�ĸ����ϸ��ƣ������ԭ���ľ�������޸�
+	 * �Ծ������180����ת,����matrix�ĸ����ϸ��ƣ������ԭ���ľ�������޸�
 	 * 
 	 * @param matrix
 	 */
@@ -113,7 +112,7 @@ public static double[][] rot180(double[][] matrix) {
 		matrix = cloneMatrix(matrix);
 		int m = matrix.length;
 		int n = matrix[0].length;
-		// ���жԳƽ��н���
+		// ���жԳƽ��н���
 		for (int i = 0; i < m; i++) {
 			for (int j = 0; j < n / 2; j++) {
 				double tmp = matrix[i][j];
@@ -121,7 +120,7 @@ public static double[][] rot180(double[][] matrix) {
 				matrix[i][n - 1 - j] = tmp;
 			}
 		}
-		// ���жԳƽ��н���
+		// ���жԳƽ��н���
 		for (int j = 0; j < n; j++) {
 			for (int i = 0; i < m / 2; i++) {
 				double tmp = matrix[i][j];
@@ -135,7 +134,7 @@ public static double[][] rot180(double[][] matrix) {
 	private static Random r = new Random(2);
 
 	/**
-	 * �����ʼ������
+	 * �����ʼ������
 	 * 
 	 * @param x
 	 * @param y
@@ -147,7 +146,7 @@ public static double[][] randomMatrix(int x, int y, boolean b) {
 		int tag = 1;
 		for (int i = 0; i < x; i++) {
 			for (int j = 0; j < y; j++) {
-				// ���ֵ��[-0.05,0.05)֮�䣬��Ȩ�س�ʼ��ֵ��С���������ڱ�������
+				// ���ֵ��[-0.05,0.05)֮�䣬��Ȩ�س�ʼ��ֵ��С���������ڱ�������
 				matrix[i][j] = (r.nextDouble() - 0.05) / 10;
 //				matrix[i][j] = tag * 0.5;
 //				if (b)
@@ -160,7 +159,7 @@ public static double[][] randomMatrix(int x, int y, boolean b) {
 	}
 
 	/**
-	 * �����ʼ��һά����
+	 * �����ʼ��һά����
 	 * 
 	 * @param len
 	 * @return
@@ -175,7 +174,7 @@ public static double[] randomArray(int len) {
 	}
 
 	/**
-	 * ������еij����������ȡbatchSize��[0,size)����
+	 * ������еij����������ȡbatchSize��[0,size)����
 	 * 
 	 * @param size
 	 * @param batchSize
@@ -194,7 +193,7 @@ public static int[] randomPerm(int size, int batchSize) {
 	}
 
 	/**
-	 * ���ƾ���
+	 * ���ƾ���
 	 * 
 	 * @param matrix
 	 * @return
@@ -214,7 +213,7 @@ public static double[][] cloneMatrix(final double[][] matrix) {
 	}
 
 	/**
-	 * �Ե���������в���
+	 * �Ե���������в���
 	 * 
 	 * @param ma
 	 * @param operator
@@ -233,15 +232,15 @@ public static double[][] matrixOp(final double[][] ma, Operator operator) {
 	}
 
 	/**
-	 * ����ά����ͬ�ľ����ӦԪ�ز���,�õ��Ľ������mb�У���mb[i][j] = (op_a
+	 * ����ά����ͬ�ľ����ӦԪ�ز���,�õ��Ľ������mb�У���mb[i][j] = (op_a
 	 * ma[i][j]) op (op_b mb[i][j])
 	 * 
 	 * @param ma
 	 * @param mb
 	 * @param operatorB
-	 *            �ڵ�mb�����ϵIJ���
+	 *            �ڵ�mb�����ϵIJ���
 	 * @param operatorA
-	 *            ��ma����Ԫ���ϵIJ���
+	 *            ��ma����Ԫ���ϵIJ���
 	 * @return
 	 * 
 	 */
@@ -251,7 +250,7 @@ public static double[][] matrixOp(final double[][] ma, final double[][] mb,
 		final int m = ma.length;
 		int n = ma[0].length;
 		if (m != mb.length || n != mb[0].length)
-			throw new RuntimeException("���������С��һ�� ma.length:" + ma.length
+			throw new RuntimeException("���������С��һ�� ma.length:" + ma.length
 					+ "  mb.length:" + mb.length);
 
 		for (int i = 0; i < m; i++) {
@@ -269,7 +268,7 @@ public static double[][] matrixOp(final double[][] ma, final double[][] mb,
 	}
 
 	/**
-	 * �����ڿ˻�,�Ծ��������չ
+	 * �����ڿ˻�,�Ծ��������չ
 	 * 
 	 * @param matrix
 	 * @param scale
@@ -293,7 +292,7 @@ public static double[][] kronecker(final double[][] matrix, final Size scale) {
 	}
 
 	/**
-	 * �Ծ�����о�ֵ��С
+	 * �Ծ�����о�ֵ��С
 	 * 
 	 * @param matrix
 	 * @param scaleSize
@@ -307,7 +306,7 @@ public static double[][] scaleMatrix(final double[][] matrix,
 		final int sn = n / scale.y;
 		final double[][] outMatrix = new double[sm][sn];
 		if (sm * scale.x != m || sn * scale.y != n)
-			throw new RuntimeException("scale��������matrix");
+			throw new RuntimeException("scale��������matrix");
 		final int size = scale.x * scale.y;
 		for (int i = 0; i < sm; i++) {
 			for (int j = 0; j < sn; j++) {
@@ -324,7 +323,7 @@ public static double[][] scaleMatrix(final double[][] matrix,
 	}
 
 	/**
-	 * ����fullģʽ�ľ���
+	 * ����fullģʽ�ľ��
 	 * 
 	 * @param matrix
 	 * @param kernel
@@ -336,7 +335,7 @@ public static double[][] convnFull(double[][] matrix,
 		int n = matrix[0].length;
 		final int km = kernel.length;
 		final int kn = kernel[0].length;
-		// ��չ����
+		// ��չ����
 		final double[][] extendMatrix = new double[m + 2 * (km - 1)][n + 2
 				* (kn - 1)];
 		for (int i = 0; i < m; i++) {
@@ -347,7 +346,7 @@ public static double[][] convnFull(double[][] matrix,
 	}
 
 	/**
-	 * ����validģʽ�ľ���
+	 * ����validģʽ�ľ��
 	 * 
 	 * @param matrix
 	 * @param kernel
@@ -360,11 +359,11 @@ public static double[][] convnValid(final double[][] matrix,
 		int n = matrix[0].length;
 		final int km = kernel.length;
 		final int kn = kernel[0].length;
-		// ��Ҫ������������
+		// ��Ҫ�����������
 		int kns = n - kn + 1;
-		// ��Ҫ������������
+		// ��Ҫ�����������
 		final int kms = m - km + 1;
-		// �������
+		// �������
 		final double[][] outMatrix = new double[kms][kns];
 
 		for (int i = 0; i < kms; i++) {
@@ -383,7 +382,7 @@ public static double[][] convnValid(final double[][] matrix,
 	}
 
 	/**
-	 * ��ά����ľ���,����Ҫ�����������һά��ͬ
+	 * ��ά����ľ��,����Ҫ�����������һά��ͬ
 	 * 
 	 * @param matrix
 	 * @param kernel
@@ -401,8 +400,8 @@ public static double[][] convnValid(final double[][][][] matrix,
 		int kns = n - kn + 1;
 		int khs = h - kh + 1;
 		if (matrix.length != kernel.length)
-			throw new RuntimeException("������������ڵ�һά�ϲ�ͬ");
-		// �������
+			throw new RuntimeException("�����������ڵ�һά�ϲ�ͬ");
+		// �������
 		final double[][][] outMatrix = new double[kms][kns][khs];
 		for (int i = 0; i < kms; i++) {
 			for (int j = 0; j < kns; j++)
@@ -426,10 +425,10 @@ public static double sigmod(double x) {
 	}
 
 	/**
-	 * �Ծ���Ԫ�����
+	 * �Ծ���Ԫ�����
 	 * 
 	 * @param error
-	 * @return ע�������ͺܿ��ܻ����
+	 * @return ע�������ͺܿ��ܻ����
 	 */
 
 	public static double sum(double[][] error) {
@@ -445,7 +444,7 @@ public static double sum(double[][] error) {
 	}
 
 	/**
-	 * ��errors[...][j]Ԫ�����
+	 * ��errors[...][j]Ԫ�����
 	 * 
 	 * @param errors
 	 * @param j
@@ -482,7 +481,7 @@ public static int binaryArray2int(double[] array) {
 	}
 
 	/**
-	 * ���Ծ���,���Խ����4���²������еľ�����߲���2��
+	 * ���Ծ��,���Խ����4���²������еľ����߲���2��
 	 */
 	private static void testConvn() {
 		int count = 1;
@@ -608,7 +607,7 @@ public static void main(String[] args) {
 	}
 
 	/**
-	 * ȡ����Ԫ�ص��±�
+	 * ȡ����Ԫ�ص��±�
 	 * 
 	 * @param out
 	 * @return
diff --git a/src/test/dataset/readme.md b/src/test/dataset/readme.md
new file mode 100644
index 0000000..01c8d6d
--- /dev/null
+++ b/src/test/dataset/readme.md
@@ -0,0 +1,3 @@
+The dataset is part of MNIST from kaggle Digit Recognizer competition:
+* "train.format" is the train set, which has been binarized.
+* "test.format" is the test set, which has been binarized.
diff --git a/dataset/test.format b/src/test/dataset/test.format
similarity index 100%
rename from dataset/test.format
rename to src/test/dataset/test.format
diff --git a/dataset/train.format b/src/test/dataset/train.format
similarity index 100%
rename from dataset/train.format
rename to src/test/dataset/train.format
diff --git a/src/test/java/javacnn/RunCNN.java b/src/test/java/javacnn/RunCNN.java
new file mode 100644
index 0000000..cae9051
--- /dev/null
+++ b/src/test/java/javacnn/RunCNN.java
@@ -0,0 +1,52 @@
+package javacnn;
+
+import java.io.IOException;
+
+import javacnn.cnn.CNN;
+import javacnn.cnn.CNNLoader;
+import javacnn.cnn.Layer;
+import javacnn.dataset.Dataset;
+import javacnn.dataset.DatasetLoader;
+import javacnn.util.ConcurenceRunner;
+
+public class RunCNN {
+
+	public static void main(String[] args) throws IOException, ClassNotFoundException {
+
+		final ConcurenceRunner concurenceRunner = new ConcurenceRunner();
+
+		try {
+
+			final CNN.LayerBuilder builder =
+					new CNN.LayerBuilder()
+							.addLayer(Layer.buildInputLayer(new Layer.Size(28, 28)))
+							.addLayer(Layer.buildConvLayer(6, new Layer.Size(5, 5)))
+							.addLayer(Layer.buildSampLayer(new Layer.Size(2, 2)))
+							.addLayer(Layer.buildConvLayer(12, new Layer.Size(5, 5)))
+							.addLayer(Layer.buildSampLayer(new Layer.Size(2, 2)))
+							.addLayer(Layer.buildOutputLayer(10));
+
+			final CNN cnn = new CNN(builder, 50, concurenceRunner);
+			// final CNN cnn = new CNN(builder, 50, new DirectRunner());
+
+			final String fileName = "src/test/dataset/train.format";
+			final Dataset dataset = DatasetLoader.load(fileName, ",", 784);
+			cnn.train(dataset, 3);
+
+			CNNLoader.saveModel("src/test/model.cnn", cnn);
+			dataset.clear();
+
+			/*
+			final CNN cnn = CNNLoader.loadModel("model.cnn");
+			cnn.setRunner(concurenceRunner);
+			*/
+
+			final Dataset testset = DatasetLoader.load("src/test/dataset/test.format", ",", -1);
+			cnn.predict(testset, "src/test/dataset/test.predict");
+
+		} finally {
+			concurenceRunner.shutdown();
+		}
+	}
+
+}