diff --git a/pom.xml b/pom.xml
new file mode 100644
index 0000000..bffd62e
--- /dev/null
+++ b/pom.xml
@@ -0,0 +1,118 @@
+<!--
+   Licensed to the Apache Software Foundation (ASF) under one or more
+   contributor license agreements.  See the NOTICE file distributed with
+   this work for additional information regarding copyright ownership.
+   The ASF licenses this file to You under the Apache License, Version 2.0
+   (the "License"); you may not use this file except in compliance with
+   the License.  You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
+-->
+
+<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">
+
+  <parent>
+    <groupId>org.apache</groupId>
+    <artifactId>apache</artifactId>
+    <version>8</version>
+  </parent>
+
+  <modelVersion>4.0.0</modelVersion>
+  <groupId>org.apache.horn</groupId>
+  <artifactId>horn</artifactId>
+  <name>Horn</name>
+  <version>0.1.0-SNAPSHOT</version>
+  <packaging>jar</packaging>
+
+  <properties>
+    <hama.version>0.7.0</hama.version>
+    <hadoop.version>2.7.0</hadoop.version>
+    <protobuf.version>2.5.0</protobuf.version>
+    <junit.version>4.8.1</junit.version>
+  </properties>
+
+  <dependencies>
+    <dependency>
+      <groupId>org.apache.hama</groupId>
+      <artifactId>hama-commons</artifactId>
+      <version>${hama.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hama</groupId>
+      <artifactId>hama-core</artifactId>
+      <version>${hama.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hama</groupId>
+      <artifactId>hama-ml</artifactId>
+      <version>${hama.version}</version>
+    </dependency>
+
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-common</artifactId>
+      <version>${hadoop.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-hdfs</artifactId>
+      <version>${hadoop.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-hdfs</artifactId>
+      <classifier>tests</classifier>
+      <version>${hadoop.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-mapreduce-client-core</artifactId>
+      <version>${hadoop.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-auth</artifactId>
+      <version>${hadoop.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>com.google.protobuf</groupId>
+      <artifactId>protobuf-java</artifactId>
+      <version>${protobuf.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>org.apache.hadoop</groupId>
+      <artifactId>hadoop-yarn-client</artifactId>
+      <version>${hadoop.version}</version>
+    </dependency>
+    <dependency>
+      <groupId>junit</groupId>
+      <artifactId>junit</artifactId>
+      <version>${junit.version}</version>
+    </dependency>
+
+  </dependencies>
+
+  <build>
+    <finalName>horn-${project.version}</finalName>
+    <plugins>
+    
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-compiler-plugin</artifactId>
+        <version>2.3.2</version>
+        <configuration>
+          <source>1.7</source>
+          <target>1.7</target>
+        </configuration>
+      </plugin>
+
+    </plugins>
+  </build>
+</project>
+
diff --git a/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java b/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java
new file mode 100644
index 0000000..c29559d
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java
@@ -0,0 +1,261 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+import java.util.List;
+
+import org.apache.hadoop.io.WritableUtils;
+import org.apache.hama.commons.math.DoubleDoubleFunction;
+import org.apache.hama.commons.math.DoubleFunction;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+
+import com.google.common.base.Preconditions;
+import com.google.common.collect.Lists;
+
+/**
+ * AbstractLayeredNeuralNetwork defines the general operations for derivative
+ * layered models, include Linear Regression, Logistic Regression, Multilayer
+ * Perceptron, Autoencoder, and Restricted Boltzmann Machine, etc.
+ * 
+ * In general, these models consist of neurons which are aligned in layers.
+ * Between layers, for any two adjacent layers, the neurons are connected to
+ * form a bipartite weighted graph.
+ * 
+ */
+abstract class AbstractLayeredNeuralNetwork extends NeuralNetwork {
+
+  private static final double DEFAULT_REGULARIZATION_WEIGHT = 0;
+  private static final double DEFAULT_MOMENTUM_WEIGHT = 0.1;
+
+  double trainingError;
+
+  /* The weight of regularization */
+  protected double regularizationWeight;
+
+  /* The momentumWeight */
+  protected double momentumWeight;
+
+  /* The cost function of the model */
+  protected DoubleDoubleFunction costFunction;
+
+  /* Record the size of each layer */
+  protected List<Integer> layerSizeList;
+
+  protected TrainingMethod trainingMethod;
+  
+  protected LearningStyle learningStyle;
+
+  public static enum TrainingMethod {
+    GRADIENT_DESCENT
+  }
+  
+  public static enum LearningStyle {
+    UNSUPERVISED,
+    SUPERVISED
+  }
+  
+  public AbstractLayeredNeuralNetwork() {
+    this.regularizationWeight = DEFAULT_REGULARIZATION_WEIGHT;
+    this.momentumWeight = DEFAULT_MOMENTUM_WEIGHT;
+    this.trainingMethod = TrainingMethod.GRADIENT_DESCENT;
+    this.learningStyle = LearningStyle.SUPERVISED;
+  }
+
+  public AbstractLayeredNeuralNetwork(String modelPath) {
+    super(modelPath);
+  }
+
+  /**
+   * Set the regularization weight. Recommend in the range [0, 0.1), More
+   * complex the model is, less weight the regularization is.
+   * 
+   * @param regularizationWeight
+   */
+  public void setRegularizationWeight(double regularizationWeight) {
+    Preconditions.checkArgument(regularizationWeight >= 0
+        && regularizationWeight < 1.0,
+        "Regularization weight must be in range [0, 1.0)");
+    this.regularizationWeight = regularizationWeight;
+  }
+
+  public double getRegularizationWeight() {
+    return this.regularizationWeight;
+  }
+
+  /**
+   * Set the momemtum weight for the model. Recommend in range [0, 0.5].
+   * 
+   * @param momentumWeight
+   */
+  public void setMomemtumWeight(double momentumWeight) {
+    Preconditions.checkArgument(momentumWeight >= 0 && momentumWeight <= 1.0,
+        "Momentum weight must be in range [0, 1.0]");
+    this.momentumWeight = momentumWeight;
+  }
+
+  public double getMomemtumWeight() {
+    return this.momentumWeight;
+  }
+
+  public void setTrainingMethod(TrainingMethod method) {
+    this.trainingMethod = method;
+  }
+
+  public TrainingMethod getTrainingMethod() {
+    return this.trainingMethod;
+  }
+  
+  public void setLearningStyle(LearningStyle style) {
+    this.learningStyle = style;
+  }
+  
+  public LearningStyle getLearningStyle() {
+    return this.learningStyle;
+  }
+
+  /**
+   * Set the cost function for the model.
+   * 
+   * @param costFunction
+   */
+  public void setCostFunction(DoubleDoubleFunction costFunction) {
+    this.costFunction = costFunction;
+  }
+
+  /**
+   * Add a layer of neurons with specified size. If the added layer is not the
+   * first layer, it will automatically connects the neurons between with the
+   * previous layer.
+   * 
+   * @param size
+   * @param isFinalLayer If false, add a bias neuron.
+   * @param squashingFunction The squashing function for this layer, input layer
+   *          is f(x) = x by default.
+   * @return The layer index, starts with 0.
+   */
+  public abstract int addLayer(int size, boolean isFinalLayer,
+      DoubleFunction squashingFunction);
+
+  /**
+   * Get the size of a particular layer.
+   * 
+   * @param layer
+   * @return The layer size.
+   */
+  public int getLayerSize(int layer) {
+    Preconditions.checkArgument(
+        layer >= 0 && layer < this.layerSizeList.size(),
+        String.format("Input must be in range [0, %d]\n",
+            this.layerSizeList.size() - 1));
+    return this.layerSizeList.get(layer);
+  }
+
+  /**
+   * Get the layer size list.
+   * 
+   * @return The layer size list.
+   */
+  protected List<Integer> getLayerSizeList() {
+    return this.layerSizeList;
+  }
+
+  /**
+   * Get the weights between layer layerIdx and layerIdx + 1
+   * 
+   * @param layerIdx The index of the layer
+   * @return The weights in form of {@link DoubleMatrix}
+   */
+  public abstract DoubleMatrix getWeightsByLayer(int layerIdx);
+
+  /**
+   * Get the updated weights using one training instance.
+   * 
+   * @param trainingInstance The trainingInstance is the concatenation of
+   *          feature vector and class label vector.
+   * @return The update of each weight, in form of matrix list.
+   * @throws Exception
+   */
+  public abstract DoubleMatrix[] trainByInstance(DoubleVector trainingInstance);
+
+  /**
+   * Get the output calculated by the model.
+   * 
+   * @param instance The feature instance.
+   * @return a new vector with the result of the operation.
+   */
+  public abstract DoubleVector getOutput(DoubleVector instance);
+
+  /**
+   * Calculate the training error based on the labels and outputs.
+   * 
+   * @param labels
+   * @param output
+   */
+  protected abstract void calculateTrainingError(DoubleVector labels,
+      DoubleVector output);
+
+  @Override
+  public void readFields(DataInput input) throws IOException {
+    super.readFields(input);
+    // read regularization weight
+    this.regularizationWeight = input.readDouble();
+    // read momentum weight
+    this.momentumWeight = input.readDouble();
+
+    // read cost function
+    this.costFunction = FunctionFactory
+        .createDoubleDoubleFunction(WritableUtils.readString(input));
+
+    // read layer size list
+    int numLayers = input.readInt();
+    this.layerSizeList = Lists.newArrayList();
+    for (int i = 0; i < numLayers; ++i) {
+      this.layerSizeList.add(input.readInt());
+    }
+
+    this.trainingMethod = WritableUtils.readEnum(input, TrainingMethod.class);
+    this.learningStyle = WritableUtils.readEnum(input, LearningStyle.class);
+  }
+
+  @Override
+  public void write(DataOutput output) throws IOException {
+    super.write(output);
+    // write regularization weight
+    output.writeDouble(this.regularizationWeight);
+    // write momentum weight
+    output.writeDouble(this.momentumWeight);
+
+    // write cost function
+    WritableUtils.writeString(output, costFunction.getFunctionName());
+
+    // write layer size list
+    output.writeInt(this.layerSizeList.size());
+    for (Integer aLayerSizeList : this.layerSizeList) {
+      output.writeInt(aLayerSizeList);
+    }
+
+    WritableUtils.writeEnum(output, this.trainingMethod);
+    WritableUtils.writeEnum(output, this.learningStyle);
+  }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/AutoEncoder.java b/src/main/java/org/apache/horn/bsp/AutoEncoder.java
new file mode 100644
index 0000000..6c84dc2
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/AutoEncoder.java
@@ -0,0 +1,195 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import java.util.Map;
+
+import org.apache.hadoop.fs.Path;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleFunction;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.apache.hama.ml.util.FeatureTransformer;
+
+import com.google.common.base.Preconditions;
+
+/**
+ * AutoEncoder is a model used for dimensional reduction and feature learning.
+ * It is a special kind of {@link NeuralNetwork} that consists of three layers
+ * of neurons, where the first layer and third layer contains the same number of
+ * neurons.
+ * 
+ */
+public class AutoEncoder {
+
+  private final SmallLayeredNeuralNetwork model;
+
+  /**
+   * Initialize the autoencoder.
+   * 
+   * @param inputDimensions The number of dimensions for the input feature.
+   * @param compressedDimensions The number of dimensions for the compressed
+   *          information.
+   */
+  public AutoEncoder(int inputDimensions, int compressedDimensions) {
+    model = new SmallLayeredNeuralNetwork();
+    model.addLayer(inputDimensions, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    model.addLayer(compressedDimensions, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    model.addLayer(inputDimensions, true,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    model.setLearningStyle(AbstractLayeredNeuralNetwork.LearningStyle.UNSUPERVISED);
+    model.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+  }
+
+  public AutoEncoder(String modelPath) {
+    model = new SmallLayeredNeuralNetwork(modelPath);
+  }
+
+  public AutoEncoder setLearningRate(double learningRate) {
+    model.setLearningRate(learningRate);
+    return this;
+  }
+
+  public AutoEncoder setMomemtumWeight(double momentumWeight) {
+    model.setMomemtumWeight(momentumWeight);
+    return this;
+  }
+
+  public AutoEncoder setRegularizationWeight(double regularizationWeight) {
+    model.setRegularizationWeight(regularizationWeight);
+    return this;
+  }
+  
+  public AutoEncoder setModelPath(String modelPath) {
+    model.setModelPath(modelPath);
+    return this;
+  }
+
+  /**
+   * Train the autoencoder with given data. Note that the training data is
+   * pre-processed, where the features
+   * 
+   * @param dataInputPath
+   * @param trainingParams
+   */
+  public void train(Path dataInputPath, Map<String, String> trainingParams) {
+    model.train(dataInputPath, trainingParams);
+  }
+
+  /**
+   * Train the model with one instance.
+   * 
+   * @param trainingInstance
+   */
+  public void trainOnline(DoubleVector trainingInstance) {
+    model.trainOnline(trainingInstance);
+  }
+
+  /**
+   * Get the matrix M used to encode the input features.
+   * 
+   * @return this matrix with encode the input.
+   */
+  public DoubleMatrix getEncodeWeightMatrix() {
+    return model.getWeightsByLayer(0);
+  }
+
+  /**
+   * Get the matrix M used to decode the compressed information.
+   * 
+   * @return this matrix with decode the compressed information.
+   */
+  public DoubleMatrix getDecodeWeightMatrix() {
+    return model.getWeightsByLayer(1);
+  }
+
+  /**
+   * Transform the input features.
+   * 
+   * @param inputInstance
+   * @return The compressed information.
+   */
+  private DoubleVector transform(DoubleVector inputInstance, int inputLayer) {
+    DoubleVector internalInstance = new DenseDoubleVector(inputInstance.getDimension() + 1);
+    internalInstance.set(0, 1);
+    for (int i = 0; i < inputInstance.getDimension(); ++i) {
+      internalInstance.set(i + 1, inputInstance.get(i));
+    }
+    DoubleFunction squashingFunction = model
+        .getSquashingFunction(inputLayer);
+    DoubleMatrix weightMatrix = null;
+    if (inputLayer == 0) {
+      weightMatrix = this.getEncodeWeightMatrix();
+    } else {
+      weightMatrix = this.getDecodeWeightMatrix();
+    }
+    DoubleVector vec = weightMatrix.multiplyVectorUnsafe(internalInstance);
+    vec = vec.applyToElements(squashingFunction);
+    return vec;
+  }
+
+  /**
+   * Encode the input instance.
+   * @param inputInstance
+   * @return a new vector with the encode input instance.
+   */
+  public DoubleVector encode(DoubleVector inputInstance) {
+    Preconditions
+        .checkArgument(
+            inputInstance.getDimension() == model.getLayerSize(0) - 1,
+            String.format("The dimension of input instance is %d, but the model requires dimension %d.",
+                    inputInstance.getDimension(), model.getLayerSize(1) - 1));
+    return this.transform(inputInstance, 0);
+  }
+
+  /**
+   * Decode the input instance.
+   * @param inputInstance
+   * @return a new vector with the decode input instance.
+   */
+  public DoubleVector decode(DoubleVector inputInstance) {
+    Preconditions
+        .checkArgument(
+            inputInstance.getDimension() == model.getLayerSize(1) - 1,
+            String.format("The dimension of input instance is %d, but the model requires dimension %d.",
+                    inputInstance.getDimension(), model.getLayerSize(1) - 1));
+    return this.transform(inputInstance, 1);
+  }
+  
+  /**
+   * Get the label(s) according to the given features.
+   * @param inputInstance
+   * @return a new vector with output of the model according to given feature instance.
+   */
+  public DoubleVector getOutput(DoubleVector inputInstance) {
+    return model.getOutput(inputInstance);
+  }
+  
+  /**
+   * Set the feature transformer.
+   * @param featureTransformer
+   */
+  public void setFeatureTransformer(FeatureTransformer featureTransformer) {
+    this.model.setFeatureTransformer(featureTransformer);
+  }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/NeuralNetwork.java b/src/main/java/org/apache/horn/bsp/NeuralNetwork.java
new file mode 100644
index 0000000..c7f14de
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/NeuralNetwork.java
@@ -0,0 +1,271 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import com.google.common.base.Preconditions;
+import com.google.common.io.Closeables;
+import org.apache.commons.lang.SerializationUtils;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FSDataInputStream;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.Writable;
+import org.apache.hadoop.io.WritableUtils;
+import org.apache.hama.ml.util.DefaultFeatureTransformer;
+import org.apache.hama.ml.util.FeatureTransformer;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+import java.lang.reflect.Constructor;
+import java.lang.reflect.InvocationTargetException;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.Map;
+
+/**
+ * NeuralNetwork defines the general operations for all the derivative models.
+ * Typically, all derivative models such as Linear Regression, Logistic
+ * Regression, and Multilayer Perceptron consist of neurons and the weights
+ * between neurons.
+ * 
+ */
+abstract class NeuralNetwork implements Writable {
+
+  private static final double DEFAULT_LEARNING_RATE = 0.5;
+
+  protected double learningRate;
+  protected boolean learningRateDecay = false;
+
+  // the name of the model
+  protected String modelType;
+  // the path to store the model
+  protected String modelPath;
+
+  protected FeatureTransformer featureTransformer;
+
+  public NeuralNetwork() {
+    this.learningRate = DEFAULT_LEARNING_RATE;
+    this.modelType = this.getClass().getSimpleName();
+    this.featureTransformer = new DefaultFeatureTransformer();
+  }
+
+  public NeuralNetwork(String modelPath) {
+    try {
+      this.modelPath = modelPath;
+      this.readFromModel();
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+  }
+
+  /**
+   * Set the degree of aggression during model training, a large learning rate
+   * can increase the training speed, but it also decrease the chance of model
+   * converge. Recommend in range (0, 0.3).
+   * 
+   * @param learningRate
+   */
+  public void setLearningRate(double learningRate) {
+    Preconditions.checkArgument(learningRate > 0,
+        "Learning rate must be larger than 0.");
+    this.learningRate = learningRate;
+  }
+
+  public double getLearningRate() {
+    return this.learningRate;
+  }
+
+  public void isLearningRateDecay(boolean decay) {
+    this.learningRateDecay = decay;
+  }
+
+  public String getModelType() {
+    return this.modelType;
+  }
+
+  /**
+   * Train the model with the path of given training data and parameters.
+   * 
+   * @param dataInputPath The path of the training data.
+   * @param trainingParams The parameters for training.
+   * @throws IOException
+   */
+  public void train(Path dataInputPath, Map<String, String> trainingParams) {
+    Preconditions.checkArgument(this.modelPath != null,
+        "Please set the model path before training.");
+    // train with BSP job
+    try {
+      trainInternal(dataInputPath, trainingParams);
+      // write the trained model back to model path
+      this.readFromModel();
+    } catch (IOException e) {
+      e.printStackTrace();
+    } catch (InterruptedException e) {
+      e.printStackTrace();
+    } catch (ClassNotFoundException e) {
+      e.printStackTrace();
+    }
+  }
+
+  /**
+   * Train the model with the path of given training data and parameters.
+   * 
+   * @param dataInputPath
+   * @param trainingParams
+   */
+  protected abstract void trainInternal(Path dataInputPath,
+      Map<String, String> trainingParams) throws IOException,
+      InterruptedException, ClassNotFoundException;
+
+  /**
+   * Read the model meta-data from the specified location.
+   * 
+   * @throws IOException
+   */
+  protected void readFromModel() throws IOException {
+    Preconditions.checkArgument(this.modelPath != null,
+        "Model path has not been set.");
+    Configuration conf = new Configuration();
+    FSDataInputStream is = null;
+    try {
+      URI uri = new URI(this.modelPath);
+      FileSystem fs = FileSystem.get(uri, conf);
+      is = new FSDataInputStream(fs.open(new Path(modelPath)));
+      this.readFields(is);
+    } catch (URISyntaxException e) {
+      e.printStackTrace();
+    } finally {
+      Closeables.close(is, false);
+    }
+  }
+
+  /**
+   * Write the model data to specified location.
+   * 
+   * @throws IOException
+   */
+  public void writeModelToFile() throws IOException {
+    Preconditions.checkArgument(this.modelPath != null,
+        "Model path has not been set.");
+    Configuration conf = new Configuration();
+    FSDataOutputStream is = null;
+    try {
+      URI uri = new URI(this.modelPath);
+      FileSystem fs = FileSystem.get(uri, conf);
+      is = fs.create(new Path(this.modelPath), true);
+      this.write(is);
+    } catch (URISyntaxException e) {
+      e.printStackTrace();
+    }
+
+    Closeables.close(is, false);
+  }
+
+  /**
+   * Set the model path.
+   * 
+   * @param modelPath
+   */
+  public void setModelPath(String modelPath) {
+    this.modelPath = modelPath;
+  }
+
+  /**
+   * Get the model path.
+   * 
+   * @return the path to store the model.
+   */
+  public String getModelPath() {
+    return this.modelPath;
+  }
+
+  @SuppressWarnings({ "rawtypes", "unchecked" })
+  @Override
+  public void readFields(DataInput input) throws IOException {
+    // read model type
+    this.modelType = WritableUtils.readString(input);
+    // read learning rate
+    this.learningRate = input.readDouble();
+    // read model path
+    this.modelPath = WritableUtils.readString(input);
+
+    if (this.modelPath.equals("null")) {
+      this.modelPath = null;
+    }
+
+    // read feature transformer
+    int bytesLen = input.readInt();
+    byte[] featureTransformerBytes = new byte[bytesLen];
+    for (int i = 0; i < featureTransformerBytes.length; ++i) {
+      featureTransformerBytes[i] = input.readByte();
+    }
+
+    Class<? extends FeatureTransformer> featureTransformerCls = (Class<? extends FeatureTransformer>) SerializationUtils
+        .deserialize(featureTransformerBytes);
+
+    Constructor[] constructors = featureTransformerCls
+        .getDeclaredConstructors();
+    Constructor constructor = constructors[0];
+
+    try {
+      this.featureTransformer = (FeatureTransformer) constructor
+          .newInstance(new Object[] {});
+    } catch (InstantiationException e) {
+      e.printStackTrace();
+    } catch (IllegalAccessException e) {
+      e.printStackTrace();
+    } catch (IllegalArgumentException e) {
+      e.printStackTrace();
+    } catch (InvocationTargetException e) {
+      e.printStackTrace();
+    }
+  }
+
+  @Override
+  public void write(DataOutput output) throws IOException {
+    // write model type
+    WritableUtils.writeString(output, modelType);
+    // write learning rate
+    output.writeDouble(learningRate);
+    // write model path
+    if (this.modelPath != null) {
+      WritableUtils.writeString(output, modelPath);
+    } else {
+      WritableUtils.writeString(output, "null");
+    }
+
+    // serialize the class
+    Class<? extends FeatureTransformer> featureTransformerCls = this.featureTransformer
+        .getClass();
+    byte[] featureTransformerBytes = SerializationUtils
+        .serialize(featureTransformerCls);
+    output.writeInt(featureTransformerBytes.length);
+    output.write(featureTransformerBytes);
+  }
+
+  public void setFeatureTransformer(FeatureTransformer featureTransformer) {
+    this.featureTransformer = featureTransformer;
+  }
+
+  public FeatureTransformer getFeatureTransformer() {
+    return this.featureTransformer;
+  }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java b/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java
new file mode 100644
index 0000000..1c8a198
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java
@@ -0,0 +1,107 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import java.io.IOException;
+
+import org.apache.commons.logging.Log;
+import org.apache.commons.logging.LogFactory;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.NullWritable;
+import org.apache.hama.bsp.BSP;
+import org.apache.hama.bsp.BSPPeer;
+import org.apache.hama.bsp.sync.SyncException;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.ml.perception.MLPMessage;
+import org.apache.hama.ml.util.DefaultFeatureTransformer;
+import org.apache.hama.ml.util.FeatureTransformer;
+
+/**
+ * The trainer that is used to train the {@link SmallLayeredNeuralNetwork} with
+ * BSP. The trainer would read the training data and obtain the trained
+ * parameters of the model.
+ * 
+ */
+public abstract class NeuralNetworkTrainer extends
+    BSP<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> {
+
+  protected static final Log LOG = LogFactory
+      .getLog(NeuralNetworkTrainer.class);
+
+  protected Configuration conf;
+  protected int maxIteration;
+  protected int batchSize;
+  protected String trainingMode;
+  
+  protected FeatureTransformer featureTransformer;
+  
+  @Override
+  final public void setup(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+      throws IOException, SyncException, InterruptedException {
+    conf = peer.getConfiguration();
+    featureTransformer = new DefaultFeatureTransformer();
+    this.extraSetup(peer);
+  }
+
+  /**
+   * Handle extra setup for sub-classes.
+   * 
+   * @param peer
+   * @throws IOException
+   * @throws SyncException
+   * @throws InterruptedException
+   */
+  protected void extraSetup(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+      throws IOException, SyncException, InterruptedException {
+
+  }
+
+  /**
+   * {@inheritDoc}
+   */
+  @Override
+  public abstract void bsp(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+      throws IOException, SyncException, InterruptedException;
+
+  @Override
+  public void cleanup(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+      throws IOException {
+    this.extraCleanup(peer);
+    // write model to modelPath
+  }
+
+  /**
+   * Handle cleanup for sub-classes. Write the trained model back.
+   * 
+   * @param peer
+   * @throws IOException
+   * @throws SyncException
+   * @throws InterruptedException
+   */
+  protected void extraCleanup(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, MLPMessage> peer)
+      throws IOException {
+
+  }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java
new file mode 100644
index 0000000..4aee4ce
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java
@@ -0,0 +1,567 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.commons.lang.math.RandomUtils;
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.NullWritable;
+import org.apache.hadoop.io.WritableUtils;
+import org.apache.hama.HamaConfiguration;
+import org.apache.hama.bsp.BSPJob;
+import org.apache.hama.commons.io.MatrixWritable;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleFunction;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.mortbay.log.Log;
+
+import com.google.common.base.Preconditions;
+import com.google.common.collect.Lists;
+
+/**
+ * SmallLayeredNeuralNetwork defines the general operations for derivative
+ * layered models, include Linear Regression, Logistic Regression, Multilayer
+ * Perceptron, Autoencoder, and Restricted Boltzmann Machine, etc. For
+ * SmallLayeredNeuralNetwork, the training can be conducted in parallel, but the
+ * parameters of the models are assumes to be stored in a single machine.
+ * 
+ * In general, these models consist of neurons which are aligned in layers.
+ * Between layers, for any two adjacent layers, the neurons are connected to
+ * form a bipartite weighted graph.
+ * 
+ */
+public class SmallLayeredNeuralNetwork extends AbstractLayeredNeuralNetwork {
+
+  /* Weights between neurons at adjacent layers */
+  protected List<DoubleMatrix> weightMatrixList;
+
+  /* Previous weight updates between neurons at adjacent layers */
+  protected List<DoubleMatrix> prevWeightUpdatesList;
+
+  /* Different layers can have different squashing function */
+  protected List<DoubleFunction> squashingFunctionList;
+
+  protected int finalLayerIdx;
+
+  public SmallLayeredNeuralNetwork() {
+    this.layerSizeList = Lists.newArrayList();
+    this.weightMatrixList = Lists.newArrayList();
+    this.prevWeightUpdatesList = Lists.newArrayList();
+    this.squashingFunctionList = Lists.newArrayList();
+  }
+
+  public SmallLayeredNeuralNetwork(String modelPath) {
+    super(modelPath);
+  }
+
+  @Override
+  /**
+   * {@inheritDoc}
+   */
+  public int addLayer(int size, boolean isFinalLayer,
+      DoubleFunction squashingFunction) {
+    Preconditions.checkArgument(size > 0,
+        "Size of layer must be larger than 0.");
+    if (!isFinalLayer) {
+      size += 1;
+    }
+
+    this.layerSizeList.add(size);
+    int layerIdx = this.layerSizeList.size() - 1;
+    if (isFinalLayer) {
+      this.finalLayerIdx = layerIdx;
+    }
+
+    // add weights between current layer and previous layer, and input layer has
+    // no squashing function
+    if (layerIdx > 0) {
+      int sizePrevLayer = this.layerSizeList.get(layerIdx - 1);
+      // row count equals to size of current size and column count equals to
+      // size of previous layer
+      int row = isFinalLayer ? size : size - 1;
+      int col = sizePrevLayer;
+      DoubleMatrix weightMatrix = new DenseDoubleMatrix(row, col);
+      // initialize weights
+      weightMatrix.applyToElements(new DoubleFunction() {
+        @Override
+        public double apply(double value) {
+          return RandomUtils.nextDouble() - 0.5;
+        }
+
+        @Override
+        public double applyDerivative(double value) {
+          throw new UnsupportedOperationException("");
+        }
+      });
+      this.weightMatrixList.add(weightMatrix);
+      this.prevWeightUpdatesList.add(new DenseDoubleMatrix(row, col));
+      this.squashingFunctionList.add(squashingFunction);
+    }
+    return layerIdx;
+  }
+
+  /**
+   * Update the weight matrices with given matrices.
+   * 
+   * @param matrices
+   */
+  public void updateWeightMatrices(DoubleMatrix[] matrices) {
+    for (int i = 0; i < matrices.length; ++i) {
+      DoubleMatrix matrix = this.weightMatrixList.get(i);
+      this.weightMatrixList.set(i, matrix.add(matrices[i]));
+    }
+  }
+
+  /**
+   * Set the previous weight matrices.
+   * @param prevUpdates
+   */
+  void setPrevWeightMatrices(DoubleMatrix[] prevUpdates) {
+    this.prevWeightUpdatesList.clear();
+    Collections.addAll(this.prevWeightUpdatesList, prevUpdates);
+  }
+
+  /**
+   * Add a batch of matrices onto the given destination matrices.
+   * 
+   * @param destMatrices
+   * @param sourceMatrices
+   */
+  static void matricesAdd(DoubleMatrix[] destMatrices,
+      DoubleMatrix[] sourceMatrices) {
+    for (int i = 0; i < destMatrices.length; ++i) {
+      destMatrices[i] = destMatrices[i].add(sourceMatrices[i]);
+    }
+  }
+
+  /**
+   * Get all the weight matrices.
+   * 
+   * @return The matrices in form of matrix array.
+   */
+  DoubleMatrix[] getWeightMatrices() {
+    DoubleMatrix[] matrices = new DoubleMatrix[this.weightMatrixList.size()];
+    this.weightMatrixList.toArray(matrices);
+    return matrices;
+  }
+
+  /**
+   * Set the weight matrices.
+   * 
+   * @param matrices
+   */
+  public void setWeightMatrices(DoubleMatrix[] matrices) {
+    this.weightMatrixList = new ArrayList<DoubleMatrix>();
+    Collections.addAll(this.weightMatrixList, matrices);
+  }
+
+  /**
+   * Get the previous matrices updates in form of array.
+   * 
+   * @return The matrices in form of matrix array.
+   */
+  public DoubleMatrix[] getPrevMatricesUpdates() {
+    DoubleMatrix[] prevMatricesUpdates = new DoubleMatrix[this.prevWeightUpdatesList
+        .size()];
+    for (int i = 0; i < this.prevWeightUpdatesList.size(); ++i) {
+      prevMatricesUpdates[i] = this.prevWeightUpdatesList.get(i);
+    }
+    return prevMatricesUpdates;
+  }
+
+  public void setWeightMatrix(int index, DoubleMatrix matrix) {
+    Preconditions.checkArgument(
+        0 <= index && index < this.weightMatrixList.size(), String.format(
+            "index [%d] should be in range[%d, %d].", index, 0,
+            this.weightMatrixList.size()));
+    this.weightMatrixList.set(index, matrix);
+  }
+
+  @Override
+  public void readFields(DataInput input) throws IOException {
+    super.readFields(input);
+
+    // read squash functions
+    int squashingFunctionSize = input.readInt();
+    this.squashingFunctionList = Lists.newArrayList();
+    for (int i = 0; i < squashingFunctionSize; ++i) {
+      this.squashingFunctionList.add(FunctionFactory
+          .createDoubleFunction(WritableUtils.readString(input)));
+    }
+
+    // read weights and construct matrices of previous updates
+    int numOfMatrices = input.readInt();
+    this.weightMatrixList = Lists.newArrayList();
+    this.prevWeightUpdatesList = Lists.newArrayList();
+    for (int i = 0; i < numOfMatrices; ++i) {
+      DoubleMatrix matrix = MatrixWritable.read(input);
+      this.weightMatrixList.add(matrix);
+      this.prevWeightUpdatesList.add(new DenseDoubleMatrix(
+          matrix.getRowCount(), matrix.getColumnCount()));
+    }
+
+  }
+
+  @Override
+  public void write(DataOutput output) throws IOException {
+    super.write(output);
+
+    // write squashing functions
+    output.writeInt(this.squashingFunctionList.size());
+    for (DoubleFunction aSquashingFunctionList : this.squashingFunctionList) {
+      WritableUtils.writeString(output, aSquashingFunctionList
+              .getFunctionName());
+    }
+
+    // write weight matrices
+    output.writeInt(this.weightMatrixList.size());
+    for (DoubleMatrix aWeightMatrixList : this.weightMatrixList) {
+      MatrixWritable.write(aWeightMatrixList, output);
+    }
+
+    // DO NOT WRITE WEIGHT UPDATE
+  }
+
+  @Override
+  public DoubleMatrix getWeightsByLayer(int layerIdx) {
+    return this.weightMatrixList.get(layerIdx);
+  }
+
+  /**
+   * Get the output of the model according to given feature instance.
+   */
+  @Override
+  public DoubleVector getOutput(DoubleVector instance) {
+    Preconditions.checkArgument(this.layerSizeList.get(0) - 1 == instance
+        .getDimension(), String.format(
+        "The dimension of input instance should be %d.",
+        this.layerSizeList.get(0) - 1));
+    // transform the features to another space
+    DoubleVector transformedInstance = this.featureTransformer
+        .transform(instance);
+    // add bias feature
+    DoubleVector instanceWithBias = new DenseDoubleVector(
+        transformedInstance.getDimension() + 1);
+    instanceWithBias.set(0, 0.99999); // set bias to be a little bit less than
+                                      // 1.0
+    for (int i = 1; i < instanceWithBias.getDimension(); ++i) {
+      instanceWithBias.set(i, transformedInstance.get(i - 1));
+    }
+
+    List<DoubleVector> outputCache = getOutputInternal(instanceWithBias);
+    // return the output of the last layer
+    DoubleVector result = outputCache.get(outputCache.size() - 1);
+    // remove bias
+    return result.sliceUnsafe(1, result.getDimension() - 1);
+  }
+
+  /**
+   * Calculate output internally, the intermediate output of each layer will be
+   * stored.
+   * 
+   * @param instanceWithBias The instance contains the features.
+   * @return Cached output of each layer.
+   */
+  public List<DoubleVector> getOutputInternal(DoubleVector instanceWithBias) {
+    List<DoubleVector> outputCache = new ArrayList<DoubleVector>();
+    // fill with instance
+    DoubleVector intermediateOutput = instanceWithBias;
+    outputCache.add(intermediateOutput);
+
+    for (int i = 0; i < this.layerSizeList.size() - 1; ++i) {
+      intermediateOutput = forward(i, intermediateOutput);
+      outputCache.add(intermediateOutput);
+    }
+    return outputCache;
+  }
+
+  /**
+   * Forward the calculation for one layer.
+   * 
+   * @param fromLayer The index of the previous layer.
+   * @param intermediateOutput The intermediateOutput of previous layer.
+   * @return a new vector with the result of the operation.
+   */
+  protected DoubleVector forward(int fromLayer, DoubleVector intermediateOutput) {
+    DoubleMatrix weightMatrix = this.weightMatrixList.get(fromLayer);
+
+    DoubleVector vec = weightMatrix.multiplyVectorUnsafe(intermediateOutput);
+    vec = vec.applyToElements(this.squashingFunctionList.get(fromLayer));
+
+    // add bias
+    DoubleVector vecWithBias = new DenseDoubleVector(vec.getDimension() + 1);
+    vecWithBias.set(0, 1);
+    for (int i = 0; i < vec.getDimension(); ++i) {
+      vecWithBias.set(i + 1, vec.get(i));
+    }
+    return vecWithBias;
+  }
+
+  /**
+   * Train the model online.
+   * 
+   * @param trainingInstance
+   */
+  public void trainOnline(DoubleVector trainingInstance) {
+    DoubleMatrix[] updateMatrices = this.trainByInstance(trainingInstance);
+    this.updateWeightMatrices(updateMatrices);
+  }
+
+  @Override
+  public DoubleMatrix[] trainByInstance(DoubleVector trainingInstance) {
+    DoubleVector transformedVector = this.featureTransformer
+        .transform(trainingInstance.sliceUnsafe(this.layerSizeList.get(0) - 1));
+
+    int inputDimension = this.layerSizeList.get(0) - 1;
+    int outputDimension;
+    DoubleVector inputInstance = null;
+    DoubleVector labels = null;
+    if (this.learningStyle == LearningStyle.SUPERVISED) {
+      outputDimension = this.layerSizeList.get(this.layerSizeList.size() - 1);
+      // validate training instance
+      Preconditions.checkArgument(
+          inputDimension + outputDimension == trainingInstance.getDimension(),
+          String
+              .format(
+                  "The dimension of training instance is %d, but requires %d.",
+                  trainingInstance.getDimension(), inputDimension
+                      + outputDimension));
+
+      inputInstance = new DenseDoubleVector(this.layerSizeList.get(0));
+      inputInstance.set(0, 1); // add bias
+      // get the features from the transformed vector
+      for (int i = 0; i < inputDimension; ++i) {
+        inputInstance.set(i + 1, transformedVector.get(i));
+      }
+      // get the labels from the original training instance
+      labels = trainingInstance.sliceUnsafe(inputInstance.getDimension() - 1,
+          trainingInstance.getDimension() - 1);
+    } else if (this.learningStyle == LearningStyle.UNSUPERVISED) {
+      // labels are identical to input features
+      outputDimension = inputDimension;
+      // validate training instance
+      Preconditions.checkArgument(inputDimension == trainingInstance
+          .getDimension(), String.format(
+          "The dimension of training instance is %d, but requires %d.",
+          trainingInstance.getDimension(), inputDimension));
+
+      inputInstance = new DenseDoubleVector(this.layerSizeList.get(0));
+      inputInstance.set(0, 1); // add bias
+      // get the features from the transformed vector
+      for (int i = 0; i < inputDimension; ++i) {
+        inputInstance.set(i + 1, transformedVector.get(i));
+      }
+      // get the labels by copying the transformed vector
+      labels = transformedVector.deepCopy();
+    }
+
+    List<DoubleVector> internalResults = this.getOutputInternal(inputInstance);
+    DoubleVector output = internalResults.get(internalResults.size() - 1);
+
+    // get the training error
+    calculateTrainingError(labels,
+        output.deepCopy().sliceUnsafe(1, output.getDimension() - 1));
+
+    if (this.trainingMethod.equals(TrainingMethod.GRADIENT_DESCENT)) {
+      return this.trainByInstanceGradientDescent(labels, internalResults);
+    } else {
+      throw new IllegalArgumentException(
+          String.format("Training method is not supported."));
+    }
+  }
+
+  /**
+   * Train by gradient descent. Get the updated weights using one training
+   * instance.
+   * 
+   * @param trainingInstance
+   * @return The weight update matrices.
+   */
+  private DoubleMatrix[] trainByInstanceGradientDescent(DoubleVector labels,
+      List<DoubleVector> internalResults) {
+
+    DoubleVector output = internalResults.get(internalResults.size() - 1);
+    // initialize weight update matrices
+    DenseDoubleMatrix[] weightUpdateMatrices = new DenseDoubleMatrix[this.weightMatrixList
+        .size()];
+    for (int m = 0; m < weightUpdateMatrices.length; ++m) {
+      weightUpdateMatrices[m] = new DenseDoubleMatrix(this.weightMatrixList
+          .get(m).getRowCount(), this.weightMatrixList.get(m).getColumnCount());
+    }
+    DoubleVector deltaVec = new DenseDoubleVector(
+        this.layerSizeList.get(this.layerSizeList.size() - 1));
+
+    DoubleFunction squashingFunction = this.squashingFunctionList
+        .get(this.squashingFunctionList.size() - 1);
+
+    DoubleMatrix lastWeightMatrix = this.weightMatrixList
+        .get(this.weightMatrixList.size() - 1);
+    for (int i = 0; i < deltaVec.getDimension(); ++i) {
+      double costFuncDerivative = this.costFunction.applyDerivative(
+          labels.get(i), output.get(i + 1));
+      // add regularization
+      costFuncDerivative += this.regularizationWeight
+          * lastWeightMatrix.getRowVector(i).sum();
+      deltaVec.set(
+          i,
+          costFuncDerivative
+              * squashingFunction.applyDerivative(output.get(i + 1)));
+    }
+
+    // start from previous layer of output layer
+    for (int layer = this.layerSizeList.size() - 2; layer >= 0; --layer) {
+      output = internalResults.get(layer);
+      deltaVec = backpropagate(layer, deltaVec, internalResults,
+          weightUpdateMatrices[layer]);
+    }
+
+    this.setPrevWeightMatrices(weightUpdateMatrices);
+
+    return weightUpdateMatrices;
+  }
+
+  /**
+   * Back-propagate the errors to from next layer to current layer. The weight
+   * updated information will be stored in the weightUpdateMatrices, and the
+   * delta of the prevLayer would be returned.
+   * 
+   * @param layer Index of current layer.
+   * @param internalOutput Internal output of current layer.
+   * @param deltaVec Delta of next layer.
+   * @return the squashing function of the specified position.
+   */
+  private DoubleVector backpropagate(int curLayerIdx,
+      DoubleVector nextLayerDelta, List<DoubleVector> outputCache,
+      DenseDoubleMatrix weightUpdateMatrix) {
+
+    // get layer related information
+    DoubleFunction squashingFunction = this.squashingFunctionList
+        .get(curLayerIdx);
+    DoubleVector curLayerOutput = outputCache.get(curLayerIdx);
+    DoubleMatrix weightMatrix = this.weightMatrixList.get(curLayerIdx);
+    DoubleMatrix prevWeightMatrix = this.prevWeightUpdatesList.get(curLayerIdx);
+
+    // next layer is not output layer, remove the delta of bias neuron
+    if (curLayerIdx != this.layerSizeList.size() - 2) {
+      nextLayerDelta = nextLayerDelta.slice(1,
+          nextLayerDelta.getDimension() - 1);
+    }
+
+    DoubleVector delta = weightMatrix.transpose()
+        .multiplyVector(nextLayerDelta);
+    for (int i = 0; i < delta.getDimension(); ++i) {
+      delta.set(
+          i,
+          delta.get(i)
+              * squashingFunction.applyDerivative(curLayerOutput.get(i)));
+    }
+
+    // update weights
+    for (int i = 0; i < weightUpdateMatrix.getRowCount(); ++i) {
+      for (int j = 0; j < weightUpdateMatrix.getColumnCount(); ++j) {
+        weightUpdateMatrix.set(i, j,
+            -learningRate * nextLayerDelta.get(i) * curLayerOutput.get(j)
+                + this.momentumWeight * prevWeightMatrix.get(i, j));
+      }
+    }
+
+    return delta;
+  }
+
+  @Override
+  protected void trainInternal(Path dataInputPath,
+      Map<String, String> trainingParams) throws IOException,
+      InterruptedException, ClassNotFoundException {
+    // add all training parameters to configuration
+    Configuration conf = new Configuration();
+    for (Map.Entry<String, String> entry : trainingParams.entrySet()) {
+      conf.set(entry.getKey(), entry.getValue());
+    }
+
+    // if training parameters contains the model path, update the model path
+    String modelPath = trainingParams.get("modelPath");
+    if (modelPath != null) {
+      this.modelPath = modelPath;
+    }
+    // modelPath must be set before training
+    if (this.modelPath == null) {
+      throw new IllegalArgumentException(
+          "Please specify the modelPath for model, "
+              + "either through setModelPath() or add 'modelPath' to the training parameters.");
+    }
+
+    conf.set("modelPath", this.modelPath);
+    this.writeModelToFile();
+
+    HamaConfiguration hamaConf = new HamaConfiguration(conf);
+
+    // create job
+    BSPJob job = new BSPJob(hamaConf, SmallLayeredNeuralNetworkTrainer.class);
+    job.setJobName("Small scale Neural Network training");
+    job.setJarByClass(SmallLayeredNeuralNetworkTrainer.class);
+    job.setBspClass(SmallLayeredNeuralNetworkTrainer.class);
+    job.setInputPath(dataInputPath);
+    job.setInputFormat(org.apache.hama.bsp.SequenceFileInputFormat.class);
+    job.setInputKeyClass(LongWritable.class);
+    job.setInputValueClass(VectorWritable.class);
+    job.setOutputKeyClass(NullWritable.class);
+    job.setOutputValueClass(NullWritable.class);
+    job.setOutputFormat(org.apache.hama.bsp.NullOutputFormat.class);
+
+    int numTasks = conf.getInt("tasks", 1);
+    Log.info(String.format("Number of tasks: %d\n", numTasks));
+    job.setNumBspTask(numTasks);
+    job.waitForCompletion(true);
+
+    // reload learned model
+    Log.info(String.format("Reload model from %s.", this.modelPath));
+    this.readFromModel();
+
+  }
+
+  @Override
+  protected void calculateTrainingError(DoubleVector labels, DoubleVector output) {
+    DoubleVector errors = labels.deepCopy().applyToElements(output,
+        this.costFunction);
+    this.trainingError = errors.sum();
+  }
+
+  /**
+   * Get the squashing function of a specified layer.
+   * 
+   * @param idx
+   * @return a new vector with the result of the operation.
+   */
+  public DoubleFunction getSquashingFunction(int idx) {
+    return this.squashingFunctionList.get(idx);
+  }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java
new file mode 100644
index 0000000..2f8c287
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java
@@ -0,0 +1,126 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import java.io.DataInput;
+import java.io.DataOutput;
+import java.io.IOException;
+
+import org.apache.hadoop.io.Writable;
+import org.apache.hama.commons.io.MatrixWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DoubleMatrix;
+
+/**
+ * NeuralNetworkMessage transmits the messages between peers during the training
+ * of neural networks.
+ * 
+ */
+public class SmallLayeredNeuralNetworkMessage implements Writable {
+
+  protected double trainingError;
+  protected DoubleMatrix[] curMatrices;
+  protected DoubleMatrix[] prevMatrices;
+  protected boolean converge;
+
+  public SmallLayeredNeuralNetworkMessage() {
+  }
+  
+  public SmallLayeredNeuralNetworkMessage(double trainingError,
+      boolean converge, DoubleMatrix[] weightMatrices,
+      DoubleMatrix[] prevMatrices) {
+    this.trainingError = trainingError;
+    this.converge = converge;
+    this.curMatrices = weightMatrices;
+    this.prevMatrices = prevMatrices;
+  }
+
+  @Override
+  public void readFields(DataInput input) throws IOException {
+    trainingError = input.readDouble();
+    converge = input.readBoolean();
+    int numMatrices = input.readInt();
+    boolean hasPrevMatrices = input.readBoolean();
+    curMatrices = new DenseDoubleMatrix[numMatrices];
+    // read matrice updates
+    for (int i = 0; i < curMatrices.length; ++i) {
+      curMatrices[i] = (DenseDoubleMatrix) MatrixWritable.read(input);
+    }
+
+    if (hasPrevMatrices) {
+      prevMatrices = new DenseDoubleMatrix[numMatrices];
+      // read previous matrices updates
+      for (int i = 0; i < prevMatrices.length; ++i) {
+        prevMatrices[i] = (DenseDoubleMatrix) MatrixWritable.read(input);
+      }
+    }
+  }
+
+  @Override
+  public void write(DataOutput output) throws IOException {
+    output.writeDouble(trainingError);
+    output.writeBoolean(converge);
+    output.writeInt(curMatrices.length);
+    if (prevMatrices == null) {
+      output.writeBoolean(false);
+    } else {
+      output.writeBoolean(true);
+    }
+    for (DoubleMatrix matrix : curMatrices) {
+      MatrixWritable.write(matrix, output);
+    }
+    if (prevMatrices != null) {
+      for (DoubleMatrix matrix : prevMatrices) {
+        MatrixWritable.write(matrix, output);
+      }
+    }
+  }
+
+  public double getTrainingError() {
+    return trainingError;
+  }
+
+  public void setTrainingError(double trainingError) {
+    this.trainingError = trainingError;
+  }
+
+  public boolean isConverge() {
+    return converge;
+  }
+
+  public void setConverge(boolean converge) {
+    this.converge = converge;
+  }
+
+  public DoubleMatrix[] getCurMatrices() {
+    return curMatrices;
+  }
+
+  public void setMatrices(DoubleMatrix[] curMatrices) {
+    this.curMatrices = curMatrices;
+  }
+
+  public DoubleMatrix[] getPrevMatrices() {
+    return prevMatrices;
+  }
+
+  public void setPrevMatrices(DoubleMatrix[] prevMatrices) {
+    this.prevMatrices = prevMatrices;
+  }
+
+}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java
new file mode 100644
index 0000000..132ec8c
--- /dev/null
+++ b/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java
@@ -0,0 +1,244 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import java.io.IOException;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.NullWritable;
+import org.apache.hama.bsp.BSP;
+import org.apache.hama.bsp.BSPPeer;
+import org.apache.hama.bsp.sync.SyncException;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.mortbay.log.Log;
+
+/**
+ * The trainer that train the {@link SmallLayeredNeuralNetwork} based on BSP
+ * framework.
+ * 
+ */
+public final class SmallLayeredNeuralNetworkTrainer
+    extends
+    BSP<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> {
+
+  private SmallLayeredNeuralNetwork inMemoryModel;
+  private Configuration conf;
+  /* Default batch size */
+  private int batchSize;
+
+  /* check the interval between intervals */
+  private double prevAvgTrainingError;
+  private double curAvgTrainingError;
+  private long convergenceCheckInterval;
+  private long iterations;
+  private long maxIterations;
+  private boolean isConverge;
+
+  private String modelPath;
+
+  @Override
+  /**
+   * If the model path is specified, load the existing from storage location.
+   */
+  public void setup(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer) {
+    if (peer.getPeerIndex() == 0) {
+      Log.info("Begin to train");
+    }
+    this.isConverge = false;
+    this.conf = peer.getConfiguration();
+    this.iterations = 0;
+    this.modelPath = conf.get("modelPath");
+    this.maxIterations = conf.getLong("training.max.iterations", 100000);
+    this.convergenceCheckInterval = conf.getLong("convergence.check.interval",
+        2000);
+    this.modelPath = conf.get("modelPath");
+    this.inMemoryModel = new SmallLayeredNeuralNetwork(modelPath);
+    this.prevAvgTrainingError = Integer.MAX_VALUE;
+    this.batchSize = conf.getInt("training.batch.size", 50);
+  }
+
+  @Override
+  /**
+   * Write the trained model back to stored location.
+   */
+  public void cleanup(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer) {
+    // write model to modelPath
+    if (peer.getPeerIndex() == 0) {
+      try {
+        Log.info(String.format("End of training, number of iterations: %d.\n",
+            this.iterations));
+        Log.info(String.format("Write model back to %s\n",
+            inMemoryModel.getModelPath()));
+        this.inMemoryModel.writeModelToFile();
+      } catch (IOException e) {
+        e.printStackTrace();
+      }
+    }
+  }
+
+  @Override
+  public void bsp(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+      throws IOException, SyncException, InterruptedException {
+    while (this.iterations++ < maxIterations) {
+      // each groom calculate the matrices updates according to local data
+      calculateUpdates(peer);
+      peer.sync();
+
+      // master merge the updates model
+      if (peer.getPeerIndex() == 0) {
+        mergeUpdates(peer);
+      }
+      peer.sync();
+      if (this.isConverge) {
+        break;
+      }
+    }
+  }
+
+  /**
+   * Calculate the matrices updates according to local partition of data.
+   * 
+   * @param peer
+   * @throws IOException
+   */
+  private void calculateUpdates(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+      throws IOException {
+    // receive update information from master
+    if (peer.getNumCurrentMessages() != 0) {
+      SmallLayeredNeuralNetworkMessage inMessage = peer.getCurrentMessage();
+      DoubleMatrix[] newWeights = inMessage.getCurMatrices();
+      DoubleMatrix[] preWeightUpdates = inMessage.getPrevMatrices();
+      this.inMemoryModel.setWeightMatrices(newWeights);
+      this.inMemoryModel.setPrevWeightMatrices(preWeightUpdates);
+      this.isConverge = inMessage.isConverge();
+      // check converge
+      if (isConverge) {
+        return;
+      }
+    }
+
+    DoubleMatrix[] weightUpdates = new DoubleMatrix[this.inMemoryModel.weightMatrixList
+        .size()];
+    for (int i = 0; i < weightUpdates.length; ++i) {
+      int row = this.inMemoryModel.weightMatrixList.get(i).getRowCount();
+      int col = this.inMemoryModel.weightMatrixList.get(i).getColumnCount();
+      weightUpdates[i] = new DenseDoubleMatrix(row, col);
+    }
+
+    // continue to train
+    double avgTrainingError = 0.0;
+    LongWritable key = new LongWritable();
+    VectorWritable value = new VectorWritable();
+    for (int recordsRead = 0; recordsRead < batchSize; ++recordsRead) {
+      if (!peer.readNext(key, value)) {
+        peer.reopenInput();
+        peer.readNext(key, value);
+      }
+      DoubleVector trainingInstance = value.getVector();
+      SmallLayeredNeuralNetwork.matricesAdd(weightUpdates,
+          this.inMemoryModel.trainByInstance(trainingInstance));
+      avgTrainingError += this.inMemoryModel.trainingError;
+    }
+    avgTrainingError /= batchSize;
+
+    // calculate the average of updates
+    for (int i = 0; i < weightUpdates.length; ++i) {
+      weightUpdates[i] = weightUpdates[i].divide(batchSize);
+    }
+
+    DoubleMatrix[] prevWeightUpdates = this.inMemoryModel
+        .getPrevMatricesUpdates();
+    SmallLayeredNeuralNetworkMessage outMessage = new SmallLayeredNeuralNetworkMessage(
+        avgTrainingError, false, weightUpdates, prevWeightUpdates);
+    peer.send(peer.getPeerName(0), outMessage);
+  }
+
+  /**
+   * Merge the updates according to the updates of the grooms.
+   * 
+   * @param peer
+   * @throws IOException
+   */
+  private void mergeUpdates(
+      BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+      throws IOException {
+    int numMessages = peer.getNumCurrentMessages();
+    boolean isConverge = false;
+    if (numMessages == 0) { // converges
+      isConverge = true;
+      return;
+    }
+
+    double avgTrainingError = 0;
+    DoubleMatrix[] matricesUpdates = null;
+    DoubleMatrix[] prevMatricesUpdates = null;
+
+    while (peer.getNumCurrentMessages() > 0) {
+      SmallLayeredNeuralNetworkMessage message = peer.getCurrentMessage();
+      if (matricesUpdates == null) {
+        matricesUpdates = message.getCurMatrices();
+        prevMatricesUpdates = message.getPrevMatrices();
+      } else {
+        SmallLayeredNeuralNetwork.matricesAdd(matricesUpdates,
+            message.getCurMatrices());
+        SmallLayeredNeuralNetwork.matricesAdd(prevMatricesUpdates,
+            message.getPrevMatrices());
+      }
+      avgTrainingError += message.getTrainingError();
+    }
+
+    if (numMessages != 1) {
+      avgTrainingError /= numMessages;
+      for (int i = 0; i < matricesUpdates.length; ++i) {
+        matricesUpdates[i] = matricesUpdates[i].divide(numMessages);
+        prevMatricesUpdates[i] = prevMatricesUpdates[i].divide(numMessages);
+      }
+    }
+    this.inMemoryModel.updateWeightMatrices(matricesUpdates);
+    this.inMemoryModel.setPrevWeightMatrices(prevMatricesUpdates);
+
+    // check convergence
+    if (iterations % convergenceCheckInterval == 0) {
+      if (prevAvgTrainingError < curAvgTrainingError) {
+        // error cannot decrease any more
+        isConverge = true;
+      }
+      // update
+      prevAvgTrainingError = curAvgTrainingError;
+      curAvgTrainingError = 0;
+    }
+    curAvgTrainingError += avgTrainingError / convergenceCheckInterval;
+
+    // broadcast updated weight matrices
+    for (String peerName : peer.getAllPeerNames()) {
+      SmallLayeredNeuralNetworkMessage msg = new SmallLayeredNeuralNetworkMessage(
+          0, isConverge, this.inMemoryModel.getWeightMatrices(),
+          this.inMemoryModel.getPrevMatricesUpdates());
+      peer.send(peerName, msg);
+    }
+  }
+
+}
diff --git a/src/main/java/org/apache/horn/examples/NeuralNetwork.java b/src/main/java/org/apache/horn/examples/NeuralNetwork.java
new file mode 100644
index 0000000..249f0e8
--- /dev/null
+++ b/src/main/java/org/apache/horn/examples/NeuralNetwork.java
@@ -0,0 +1,216 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.examples;
+
+import java.io.BufferedReader;
+import java.io.BufferedWriter;
+import java.io.InputStreamReader;
+import java.io.OutputStreamWriter;
+import java.net.URI;
+import java.util.HashMap;
+import java.util.Map;
+
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hama.HamaConfiguration;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.apache.hama.ml.ann.SmallLayeredNeuralNetwork;
+
+/**
+ * The example of using {@link SmallLayeredNeuralNetwork}, including the
+ * training phase and labeling phase.
+ */
+public class NeuralNetwork {
+
+  public static void main(String[] args) throws Exception {
+    if (args.length < 3) {
+      printUsage();
+      return;
+    }
+    String mode = args[0];
+    if (mode.equalsIgnoreCase("label")) {
+      if (args.length < 4) {
+        printUsage();
+        return;
+      }
+      HamaConfiguration conf = new HamaConfiguration();
+
+      String featureDataPath = args[1];
+      String resultDataPath = args[2];
+      String modelPath = args[3];
+
+      SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
+
+      // process data in streaming approach
+      FileSystem fs = FileSystem.get(new URI(featureDataPath), conf);
+      BufferedReader br = new BufferedReader(new InputStreamReader(
+          fs.open(new Path(featureDataPath))));
+      Path outputPath = new Path(resultDataPath);
+      if (fs.exists(outputPath)) {
+        fs.delete(outputPath, true);
+      }
+      BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(
+          fs.create(outputPath)));
+
+      String line = null;
+
+      while ((line = br.readLine()) != null) {
+        if (line.trim().length() == 0) {
+          continue;
+        }
+        String[] tokens = line.trim().split(",");
+        double[] vals = new double[tokens.length];
+        for (int i = 0; i < tokens.length; ++i) {
+          vals[i] = Double.parseDouble(tokens[i]);
+        }
+        DoubleVector instance = new DenseDoubleVector(vals);
+        DoubleVector result = ann.getOutput(instance);
+        double[] arrResult = result.toArray();
+        StringBuilder sb = new StringBuilder();
+        for (int i = 0; i < arrResult.length; ++i) {
+          sb.append(arrResult[i]);
+          if (i != arrResult.length - 1) {
+            sb.append(",");
+          } else {
+            sb.append("\n");
+          }
+        }
+        bw.write(sb.toString());
+      }
+
+      br.close();
+      bw.close();
+    } else if (mode.equals("train")) {
+      if (args.length < 5) {
+        printUsage();
+        return;
+      }
+
+      String trainingDataPath = args[1];
+      String trainedModelPath = args[2];
+
+      int featureDimension = Integer.parseInt(args[3]);
+      int labelDimension = Integer.parseInt(args[4]);
+
+      int iteration = 1000;
+      double learningRate = 0.4;
+      double momemtumWeight = 0.2;
+      double regularizationWeight = 0.01;
+
+      // parse parameters
+      if (args.length >= 6) {
+        try {
+          iteration = Integer.parseInt(args[5]);
+          System.out.printf("Iteration: %d\n", iteration);
+        } catch (NumberFormatException e) {
+          System.err
+              .println("MAX_ITERATION format invalid. It should be a positive number.");
+          return;
+        }
+      }
+      if (args.length >= 7) {
+        try {
+          learningRate = Double.parseDouble(args[6]);
+          System.out.printf("Learning rate: %f\n", learningRate);
+        } catch (NumberFormatException e) {
+          System.err
+              .println("LEARNING_RATE format invalid. It should be a positive double in range (0, 1.0)");
+          return;
+        }
+      }
+      if (args.length >= 8) {
+        try {
+          momemtumWeight = Double.parseDouble(args[7]);
+          System.out.printf("Momemtum weight: %f\n", momemtumWeight);
+        } catch (NumberFormatException e) {
+          System.err
+              .println("MOMEMTUM_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
+          return;
+        }
+      }
+      if (args.length >= 9) {
+        try {
+          regularizationWeight = Double.parseDouble(args[8]);
+          System.out
+              .printf("Regularization weight: %f\n", regularizationWeight);
+        } catch (NumberFormatException e) {
+          System.err
+              .println("REGULARIZATION_WEIGHT format invalid. It should be a positive double in range (0, 1.0)");
+          return;
+        }
+      }
+
+      // train the model
+      SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+      ann.setLearningRate(learningRate);
+      ann.setMomemtumWeight(momemtumWeight);
+      ann.setRegularizationWeight(regularizationWeight);
+      ann.addLayer(featureDimension, false,
+          FunctionFactory.createDoubleFunction("Sigmoid"));
+      ann.addLayer(featureDimension, false,
+          FunctionFactory.createDoubleFunction("Sigmoid"));
+      ann.addLayer(labelDimension, true,
+          FunctionFactory.createDoubleFunction("Sigmoid"));
+      ann.setCostFunction(FunctionFactory
+          .createDoubleDoubleFunction("CrossEntropy"));
+      ann.setModelPath(trainedModelPath);
+
+      Map<String, String> trainingParameters = new HashMap<String, String>();
+      trainingParameters.put("tasks", "5");
+      trainingParameters.put("training.max.iterations", "" + iteration);
+      trainingParameters.put("training.batch.size", "300");
+      trainingParameters.put("convergence.check.interval", "1000");
+      ann.train(new Path(trainingDataPath), trainingParameters);
+    }
+
+  }
+
+  private static void printUsage() {
+    System.out
+        .println("USAGE: <MODE> <INPUT_PATH> <OUTPUT_PATH> <MODEL_PATH>|<FEATURE_DIMENSION> <LABEL_DIMENSION> [<MAX_ITERATION> <LEARNING_RATE> <MOMEMTUM_WEIGHT> <REGULARIZATION_WEIGHT>]");
+    System.out
+        .println("\tMODE\t- train: train the model with given training data.");
+    System.out
+        .println("\t\t- label: obtain the result by feeding the features to the neural network.");
+    System.out
+        .println("\tINPUT_PATH\tin 'train' mode, it is the path of the training data; in 'label' mode, it is the path of the to be evaluated data that lacks the label.");
+    System.out
+        .println("\tOUTPUT_PATH\tin 'train' mode, it is where the trained model is stored; in 'label' mode, it is where the labeled data is stored.");
+    System.out.println("\n\tConditional Parameters:");
+    System.out
+        .println("\tMODEL_PATH\tonly required in 'label' mode. It specifies where to load the trained neural network model.");
+    System.out
+        .println("\tMAX_ITERATION\tonly used in 'train' mode. It specifies how many iterations for the neural network to run. Default is 0.01.");
+    System.out
+        .println("\tLEARNING_RATE\tonly used to 'train' mode. It specifies the degree of aggregation for learning, usually in range (0, 1.0). Default is 0.1.");
+    System.out
+        .println("\tMOMEMTUM_WEIGHT\tonly used to 'train' mode. It specifies the weight of momemtum. Default is 0.");
+    System.out
+        .println("\tREGULARIZATION_WEIGHT\tonly required in 'train' model. It specifies the weight of reqularization.");
+    System.out.println("\nExample:");
+    System.out
+        .println("Train a neural network with with feature dimension 8, label dimension 1 and default setting:\n\tneuralnets train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1");
+    System.out
+        .println("Train a neural network with with feature dimension 8, label dimension 1 and specify learning rate as 0.1, momemtum rate as 0.2, and regularization weight as 0.01:\n\tneuralnets.train hdfs://localhost:30002/training_data hdfs://localhost:30002/model 8 1 0.1 0.2 0.01");
+    System.out
+        .println("Label the data with trained model:\n\tneuralnets evaluate hdfs://localhost:30002/unlabeled_data hdfs://localhost:30002/result hdfs://localhost:30002/model");
+  }
+
+}
diff --git a/src/test/java/org/apache/horn/bsp/MLTestBase.java b/src/test/java/org/apache/horn/bsp/MLTestBase.java
new file mode 100644
index 0000000..8001bcf
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/MLTestBase.java
@@ -0,0 +1,64 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import java.util.Arrays;
+import java.util.List;
+
+/**
+ * The common methods for testing machine learning algorithms
+ *
+ */
+public abstract class MLTestBase {
+
+  /**
+   * Conduct the 0-1 normalization.
+   * 
+   * @param instances
+   */
+  protected static void zeroOneNormalization(List<double[]> instanceList,
+      int len) {
+    int dimension = len;
+
+    double[] mins = new double[dimension];
+    double[] maxs = new double[dimension];
+    Arrays.fill(mins, Double.MAX_VALUE);
+    Arrays.fill(maxs, Double.MIN_VALUE);
+
+    for (double[] instance : instanceList) {
+      for (int i = 0; i < len; ++i) {
+        if (mins[i] > instance[i]) {
+          mins[i] = instance[i];
+        }
+        if (maxs[i] < instance[i]) {
+          maxs[i] = instance[i];
+        }
+      }
+    }
+
+    for (double[] instance : instanceList) {
+      for (int i = 0; i < len; ++i) {
+        double range = maxs[i] - mins[i];
+        if (range != 0) {
+          instance[i] = (instance[i] - mins[i]) / range;
+        }
+      }
+    }
+
+  }
+}
diff --git a/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java b/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java
new file mode 100644
index 0000000..0aaa926
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java
@@ -0,0 +1,194 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import static org.junit.Assert.assertEquals;
+
+import java.io.BufferedReader;
+import java.io.FileNotFoundException;
+import java.io.FileReader;
+import java.io.IOException;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+import java.util.Random;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.SequenceFile;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleVector;
+import org.junit.Test;
+import org.mortbay.log.Log;
+
+/**
+ * Test the functionality of {@link AutoEncoder}.
+ * 
+ */
+public class TestAutoEncoder extends MLTestBase {
+
+  @Test
+  public void testAutoEncoderSimple() {
+    double[][] instances = { { 0, 0, 0, 1 }, { 0, 0, 1, 0 }, { 0, 1, 0, 0 },
+        { 0, 0, 0, 0 } };
+    AutoEncoder encoder = new AutoEncoder(4, 2);
+    encoder.setLearningRate(0.5);
+    encoder.setMomemtumWeight(0.2);
+    
+    int maxIteration = 2000;
+    Random rnd = new Random();
+    for (int iteration = 0; iteration < maxIteration; ++iteration) {
+      for (int i = 0; i < instances.length; ++i) {
+        encoder.trainOnline(new DenseDoubleVector(instances[rnd.nextInt(instances.length)]));
+      }
+    }
+
+    for (int i = 0; i < instances.length; ++i) {
+      DoubleVector encodeVec = encoder.encode(new DenseDoubleVector(
+          instances[i]));
+      DoubleVector decodeVec = encoder.decode(encodeVec);
+      for (int d = 0; d < instances[i].length; ++d) {
+        assertEquals(instances[i][d], decodeVec.get(d), 0.1);
+      }
+    }
+
+  }
+  
+  @Test
+  public void testAutoEncoderSwissRollDataset() {
+    List<double[]> instanceList = new ArrayList<double[]>();
+    try {
+      BufferedReader br = new BufferedReader(new FileReader("src/test/resources/dimensional_reduction.txt"));
+      String line = null;
+      while ((line = br.readLine()) != null) {
+        String[] tokens = line.split("\t");
+        double[] instance = new double[tokens.length];
+        for (int i = 0; i < instance.length; ++i) {
+          instance[i] = Double.parseDouble(tokens[i]);
+        }
+        instanceList.add(instance);
+      }
+      br.close();
+      // normalize instances
+      zeroOneNormalization(instanceList, instanceList.get(0).length);
+    } catch (FileNotFoundException e) {
+      e.printStackTrace();
+    } catch (NumberFormatException e) {
+      e.printStackTrace();
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+    
+    List<DoubleVector> vecInstanceList = new ArrayList<DoubleVector>();
+    for (double[] instance : instanceList) {
+      vecInstanceList.add(new DenseDoubleVector(instance));
+    }
+    AutoEncoder encoder = new AutoEncoder(3, 2);
+    encoder.setLearningRate(0.05);
+    encoder.setMomemtumWeight(0.1);
+    int maxIteration = 2000;
+    for (int iteration = 0; iteration < maxIteration; ++iteration) {
+      for (DoubleVector vector : vecInstanceList) {
+        encoder.trainOnline(vector);
+      }
+    }
+
+    double errorInstance = 0;
+    for (DoubleVector vector : vecInstanceList) {
+      DoubleVector decoded = encoder.getOutput(vector);
+      DoubleVector diff = vector.subtract(decoded);
+      double error = diff.dot(diff);
+      if (error > 0.1) {
+        ++errorInstance;
+      }
+    }
+    Log.info(String.format("Autoecoder error rate: %f%%\n", errorInstance * 100 / vecInstanceList.size()));
+    
+  }
+  
+  @Test
+  public void testAutoEncoderSwissRollDatasetDistributed() {
+    String strDataPath = "/tmp/dimensional_reduction.txt";
+    Path path = new Path(strDataPath);
+    List<double[]> instanceList = new ArrayList<double[]>();
+    try {
+      Configuration conf = new Configuration();
+      FileSystem fs = FileSystem.get(new URI(strDataPath), conf);
+      if (fs.exists(path)) {
+        fs.delete(path, true);
+      }
+      
+      String line = null;
+      BufferedReader br = new BufferedReader(new FileReader("src/test/resources/dimensional_reduction.txt"));
+      while ((line = br.readLine()) != null) {
+        String[] tokens = line.split("\t");
+        double[] instance = new double[tokens.length];
+        for (int i = 0; i < instance.length; ++i) {
+          instance[i] = Double.parseDouble(tokens[i]);
+        }
+        instanceList.add(instance);
+      }
+      br.close();
+      // normalize instances
+      zeroOneNormalization(instanceList, instanceList.get(0).length);
+      
+      SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf, path, LongWritable.class, VectorWritable.class);
+      for (int i = 0; i < instanceList.size(); ++i) {
+        DoubleVector vector = new DenseDoubleVector(instanceList.get(i));
+        writer.append(new LongWritable(i), new VectorWritable(vector));
+      }
+      
+      writer.close();
+    } catch (FileNotFoundException e) {
+      e.printStackTrace();
+    } catch (IOException e) {
+      e.printStackTrace();
+    } catch (URISyntaxException e) {
+      e.printStackTrace();
+    }
+    
+    AutoEncoder encoder = new AutoEncoder(3, 2);
+    String modelPath = "/tmp/autoencoder-modelpath";
+    encoder.setModelPath(modelPath);
+    Map<String, String> trainingParams = new HashMap<String, String>();
+    encoder.setLearningRate(0.5);
+    trainingParams.put("tasks", "5");
+    trainingParams.put("training.max.iterations", "3000");
+    trainingParams.put("training.batch.size", "200");
+    encoder.train(path, trainingParams);
+    
+    double errorInstance = 0;
+    for (double[] instance : instanceList) {
+      DoubleVector vector = new DenseDoubleVector(instance);
+      DoubleVector decoded = encoder.getOutput(vector);
+      DoubleVector diff = vector.subtract(decoded);
+      double error = diff.dot(diff);
+      if (error > 0.1) {
+        ++errorInstance;
+      }
+    }
+    Log.info(String.format("Autoecoder error rate: %f%%\n", errorInstance * 100 / instanceList.size()));
+  }
+
+}
diff --git a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java
new file mode 100644
index 0000000..85c4b7a
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java
@@ -0,0 +1,642 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+
+import java.io.BufferedReader;
+import java.io.FileNotFoundException;
+import java.io.FileReader;
+import java.io.IOException;
+import java.net.URI;
+import java.net.URISyntaxException;
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.Date;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.SequenceFile;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DenseDoubleVector;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.hama.commons.math.DoubleVector;
+import org.apache.hama.commons.math.FunctionFactory;
+import org.apache.horn.bsp.AbstractLayeredNeuralNetwork.LearningStyle;
+import org.apache.horn.bsp.AbstractLayeredNeuralNetwork.TrainingMethod;
+import org.apache.hama.ml.util.DefaultFeatureTransformer;
+import org.apache.hama.ml.util.FeatureTransformer;
+import org.junit.Test;
+import org.mortbay.log.Log;
+
+/**
+ * Test the functionality of SmallLayeredNeuralNetwork.
+ * 
+ */
+public class TestSmallLayeredNeuralNetwork extends MLTestBase {
+
+  @Test
+  public void testReadWrite() {
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.addLayer(2, false,
+        FunctionFactory.createDoubleFunction("IdentityFunction"));
+    ann.addLayer(5, false,
+        FunctionFactory.createDoubleFunction("IdentityFunction"));
+    ann.addLayer(1, true,
+        FunctionFactory.createDoubleFunction("IdentityFunction"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+    double learningRate = 0.2;
+    ann.setLearningRate(learningRate);
+    double momentumWeight = 0.5;
+    ann.setMomemtumWeight(momentumWeight);
+    double regularizationWeight = 0.05;
+    ann.setRegularizationWeight(regularizationWeight);
+    // intentionally initialize all weights to 0.5
+    DoubleMatrix[] matrices = new DenseDoubleMatrix[2];
+    matrices[0] = new DenseDoubleMatrix(5, 3, 0.2);
+    matrices[1] = new DenseDoubleMatrix(1, 6, 0.8);
+    ann.setWeightMatrices(matrices);
+    ann.setLearningStyle(LearningStyle.UNSUPERVISED);
+    
+    FeatureTransformer defaultFeatureTransformer = new DefaultFeatureTransformer();
+    ann.setFeatureTransformer(defaultFeatureTransformer);
+    
+
+    // write to file
+    String modelPath = "/tmp/testSmallLayeredNeuralNetworkReadWrite";
+    ann.setModelPath(modelPath);
+    try {
+      ann.writeModelToFile();
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+
+    // read from file
+    SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+    assertEquals(annCopy.getClass().getSimpleName(), annCopy.getModelType());
+    assertEquals(modelPath, annCopy.getModelPath());
+    assertEquals(learningRate, annCopy.getLearningRate(), 0.000001);
+    assertEquals(momentumWeight, annCopy.getMomemtumWeight(), 0.000001);
+    assertEquals(regularizationWeight, annCopy.getRegularizationWeight(),
+        0.000001);
+    assertEquals(TrainingMethod.GRADIENT_DESCENT, annCopy.getTrainingMethod());
+    assertEquals(LearningStyle.UNSUPERVISED, annCopy.getLearningStyle());
+
+    // compare weights
+    DoubleMatrix[] weightsMatrices = annCopy.getWeightMatrices();
+    for (int i = 0; i < weightsMatrices.length; ++i) {
+      DoubleMatrix expectMat = matrices[i];
+      DoubleMatrix actualMat = weightsMatrices[i];
+      for (int j = 0; j < expectMat.getRowCount(); ++j) {
+        for (int k = 0; k < expectMat.getColumnCount(); ++k) {
+          assertEquals(expectMat.get(j, k), actualMat.get(j, k), 0.000001);
+        }
+      }
+    }
+    
+    FeatureTransformer copyTransformer = annCopy.getFeatureTransformer();
+    assertEquals(defaultFeatureTransformer.getClass().getName(), copyTransformer.getClass().getName());
+  }
+
+  @Test
+  /**
+   * Test the forward functionality.
+   */
+  public void testOutput() {
+    // first network
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.addLayer(2, false,
+        FunctionFactory.createDoubleFunction("IdentityFunction"));
+    ann.addLayer(5, false,
+        FunctionFactory.createDoubleFunction("IdentityFunction"));
+    ann.addLayer(1, true,
+        FunctionFactory.createDoubleFunction("IdentityFunction"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+    ann.setLearningRate(0.1);
+    // intentionally initialize all weights to 0.5
+    DoubleMatrix[] matrices = new DenseDoubleMatrix[2];
+    matrices[0] = new DenseDoubleMatrix(5, 3, 0.5);
+    matrices[1] = new DenseDoubleMatrix(1, 6, 0.5);
+    ann.setWeightMatrices(matrices);
+
+    double[] arr = new double[] { 0, 1 };
+    DoubleVector training = new DenseDoubleVector(arr);
+    DoubleVector result = ann.getOutput(training);
+    assertEquals(1, result.getDimension());
+    // assertEquals(3, result.get(0), 0.000001);
+
+    // second network
+    SmallLayeredNeuralNetwork ann2 = new SmallLayeredNeuralNetwork();
+    ann2.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann2.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann2.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann2.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+    ann2.setLearningRate(0.3);
+    // intentionally initialize all weights to 0.5
+    DoubleMatrix[] matrices2 = new DenseDoubleMatrix[2];
+    matrices2[0] = new DenseDoubleMatrix(3, 3, 0.5);
+    matrices2[1] = new DenseDoubleMatrix(1, 4, 0.5);
+    ann2.setWeightMatrices(matrices2);
+
+    double[] test = { 0, 0 };
+    double[] result2 = { 0.807476 };
+
+    DoubleVector vec = ann2.getOutput(new DenseDoubleVector(test));
+    assertArrayEquals(result2, vec.toArray(), 0.000001);
+
+    SmallLayeredNeuralNetwork ann3 = new SmallLayeredNeuralNetwork();
+    ann3.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann3.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann3.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann3.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+    ann3.setLearningRate(0.3);
+    // intentionally initialize all weights to 0.5
+    DoubleMatrix[] initMatrices = new DenseDoubleMatrix[2];
+    initMatrices[0] = new DenseDoubleMatrix(3, 3, 0.5);
+    initMatrices[1] = new DenseDoubleMatrix(1, 4, 0.5);
+    ann3.setWeightMatrices(initMatrices);
+
+    double[] instance = { 0, 1 };
+    DoubleVector output = ann3.getOutput(new DenseDoubleVector(instance));
+    assertEquals(0.8315410, output.get(0), 0.000001);
+  }
+
+  @Test
+  public void testXORlocal() {
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+    ann.setLearningRate(0.5);
+    ann.setMomemtumWeight(0.0);
+
+    int iterations = 50000; // iteration should be set to a very large number
+    double[][] instances = { { 0, 1, 1 }, { 0, 0, 0 }, { 1, 0, 1 }, { 1, 1, 0 } };
+    for (int i = 0; i < iterations; ++i) {
+      DoubleMatrix[] matrices = null;
+      for (int j = 0; j < instances.length; ++j) {
+        matrices = ann.trainByInstance(new DenseDoubleVector(instances[j
+            % instances.length]));
+        ann.updateWeightMatrices(matrices);
+      }
+    }
+
+    for (int i = 0; i < instances.length; ++i) {
+      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+      // the expected output is the last element in array
+      double result = instances[i][2];
+      double actual = ann.getOutput(input).get(0);
+      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+        Log.info("Neural network failes to lear the XOR.");
+      }
+    }
+
+    // write model into file and read out
+    String modelPath = "/tmp/testSmallLayeredNeuralNetworkXORLocal";
+    ann.setModelPath(modelPath);
+    try {
+      ann.writeModelToFile();
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+    SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+    // test on instances
+    for (int i = 0; i < instances.length; ++i) {
+      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+      // the expected output is the last element in array
+      double result = instances[i][2];
+      double actual = annCopy.getOutput(input).get(0);
+      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+        Log.info("Neural network failes to lear the XOR.");
+      }
+    }
+  }
+
+  @Test
+  public void testXORWithMomentum() {
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+    ann.setLearningRate(0.6);
+    ann.setMomemtumWeight(0.3);
+
+    int iterations = 2000; // iteration should be set to a very large number
+    double[][] instances = { { 0, 1, 1 }, { 0, 0, 0 }, { 1, 0, 1 }, { 1, 1, 0 } };
+    for (int i = 0; i < iterations; ++i) {
+      for (int j = 0; j < instances.length; ++j) {
+        ann.trainOnline(new DenseDoubleVector(instances[j % instances.length]));
+      }
+    }
+
+    for (int i = 0; i < instances.length; ++i) {
+      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+      // the expected output is the last element in array
+      double result = instances[i][2];
+      double actual = ann.getOutput(input).get(0);
+      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+        Log.info("Neural network failes to lear the XOR.");
+      }
+    }
+
+    // write model into file and read out
+    String modelPath = "/tmp/testSmallLayeredNeuralNetworkXORLocalWithMomentum";
+    ann.setModelPath(modelPath);
+    try {
+      ann.writeModelToFile();
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+    SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+    // test on instances
+    for (int i = 0; i < instances.length; ++i) {
+      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+      // the expected output is the last element in array
+      double result = instances[i][2];
+      double actual = annCopy.getOutput(input).get(0);
+      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+        Log.info("Neural network failes to lear the XOR.");
+      }
+    }
+  }
+
+  @Test
+  public void testXORLocalWithRegularization() {
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("SquaredError"));
+    ann.setLearningRate(0.7);
+    ann.setMomemtumWeight(0.5);
+    ann.setRegularizationWeight(0.002);
+
+    int iterations = 5000; // iteration should be set to a very large number
+    double[][] instances = { { 0, 1, 1 }, { 0, 0, 0 }, { 1, 0, 1 }, { 1, 1, 0 } };
+    for (int i = 0; i < iterations; ++i) {
+      for (int j = 0; j < instances.length; ++j) {
+        ann.trainOnline(new DenseDoubleVector(instances[j % instances.length]));
+      }
+    }
+
+    for (int i = 0; i < instances.length; ++i) {
+      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+      // the expected output is the last element in array
+      double result = instances[i][2];
+      double actual = ann.getOutput(input).get(0);
+      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+        Log.info("Neural network failes to lear the XOR.");
+      }
+    }
+
+    // write model into file and read out
+    String modelPath = "/tmp/testSmallLayeredNeuralNetworkXORLocalWithRegularization";
+    ann.setModelPath(modelPath);
+    try {
+      ann.writeModelToFile();
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+    SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(modelPath);
+    // test on instances
+    for (int i = 0; i < instances.length; ++i) {
+      DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
+      // the expected output is the last element in array
+      double result = instances[i][2];
+      double actual = annCopy.getOutput(input).get(0);
+      if (result < 0.5 && actual >= 0.5 || result >= 0.5 && actual < 0.5) {
+        Log.info("Neural network failes to lear the XOR.");
+      }
+    }
+  }
+
+  @Test
+  public void testTwoClassClassification() {
+    // use logistic regression data
+    String filepath = "src/test/resources/logistic_regression_data.txt";
+    List<double[]> instanceList = new ArrayList<double[]>();
+
+    try {
+      BufferedReader br = new BufferedReader(new FileReader(filepath));
+      String line = null;
+      while ((line = br.readLine()) != null) {
+        String[] tokens = line.trim().split(",");
+        double[] instance = new double[tokens.length];
+        for (int i = 0; i < tokens.length; ++i) {
+          instance[i] = Double.parseDouble(tokens[i]);
+        }
+        instanceList.add(instance);
+      }
+      br.close();
+    } catch (FileNotFoundException e) {
+      e.printStackTrace();
+    } catch (IOException e) {
+      e.printStackTrace();
+    }
+
+    zeroOneNormalization(instanceList, instanceList.get(0).length - 1);
+    
+    int dimension = instanceList.get(0).length - 1;
+
+    // divide dataset into training and testing
+    List<double[]> testInstances = new ArrayList<double[]>();
+    testInstances.addAll(instanceList.subList(instanceList.size() - 100,
+        instanceList.size()));
+    List<double[]> trainingInstances = instanceList.subList(0,
+        instanceList.size() - 100);
+
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.setLearningRate(0.001);
+    ann.setMomemtumWeight(0.1);
+    ann.setRegularizationWeight(0.01);
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("CrossEntropy"));
+
+    long start = new Date().getTime();
+    int iterations = 1000;
+    for (int i = 0; i < iterations; ++i) {
+      for (double[] trainingInstance : trainingInstances) {
+        ann.trainOnline(new DenseDoubleVector(trainingInstance));
+      }
+    }
+    long end = new Date().getTime();
+    Log.info(String.format("Training time: %fs\n",
+        (double) (end - start) / 1000));
+
+    double errorRate = 0;
+    // calculate the error on test instance
+    for (double[] testInstance : testInstances) {
+      DoubleVector instance = new DenseDoubleVector(testInstance);
+      double expected = instance.get(instance.getDimension() - 1);
+      instance = instance.slice(instance.getDimension() - 1);
+      double actual = ann.getOutput(instance).get(0);
+      if (actual < 0.5 && expected >= 0.5 || actual >= 0.5 && expected < 0.5) {
+        ++errorRate;
+      }
+    }
+    errorRate /= testInstances.size();
+
+    Log.info(String.format("Relative error: %f%%\n", errorRate * 100));
+  }
+  
+  @Test
+  public void testLogisticRegression() {
+    this.testLogisticRegressionDistributedVersion();
+    this.testLogisticRegressionDistributedVersionWithFeatureTransformer();
+  }
+
+  public void testLogisticRegressionDistributedVersion() {
+    // write data into a sequence file
+    String tmpStrDatasetPath = "/tmp/logistic_regression_data";
+    Path tmpDatasetPath = new Path(tmpStrDatasetPath);
+    String strDataPath = "src/test/resources/logistic_regression_data.txt";
+    String modelPath = "/tmp/logistic-regression-distributed-model";
+
+    Configuration conf = new Configuration();
+    List<double[]> instanceList = new ArrayList<double[]>();
+    List<double[]> trainingInstances = null;
+    List<double[]> testInstances = null;
+
+    try {
+      FileSystem fs = FileSystem.get(new URI(tmpStrDatasetPath), conf);
+      fs.delete(tmpDatasetPath, true);
+      if (fs.exists(tmpDatasetPath)) {
+        fs.createNewFile(tmpDatasetPath);
+      }
+
+      BufferedReader br = new BufferedReader(new FileReader(strDataPath));
+      String line = null;
+      int count = 0;
+      while ((line = br.readLine()) != null) {
+        String[] tokens = line.trim().split(",");
+        double[] instance = new double[tokens.length];
+        for (int i = 0; i < tokens.length; ++i) {
+          instance[i] = Double.parseDouble(tokens[i]);
+        }
+        instanceList.add(instance);
+      }
+      br.close();
+      
+      zeroOneNormalization(instanceList, instanceList.get(0).length - 1);
+      
+      // write training data to temporal sequence file
+      SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,
+          tmpDatasetPath, LongWritable.class, VectorWritable.class);
+      int testSize = 150;
+
+      Collections.shuffle(instanceList);
+      testInstances = new ArrayList<double[]>();
+      testInstances.addAll(instanceList.subList(instanceList.size() - testSize,
+          instanceList.size()));
+      trainingInstances = instanceList.subList(0, instanceList.size()
+          - testSize);
+
+      for (double[] instance : trainingInstances) {
+        DoubleVector vec = new DenseDoubleVector(instance);
+        writer.append(new LongWritable(count++), new VectorWritable(vec));
+      }
+      writer.close();
+    } catch (FileNotFoundException e) {
+      e.printStackTrace();
+    } catch (IOException e) {
+      e.printStackTrace();
+    } catch (URISyntaxException e) {
+      e.printStackTrace();
+    }
+
+    // create model
+    int dimension = 8;
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.setLearningRate(0.7);
+    ann.setMomemtumWeight(0.5);
+    ann.setRegularizationWeight(0.1);
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("CrossEntropy"));
+    ann.setModelPath(modelPath);
+
+    long start = new Date().getTime();
+    Map<String, String> trainingParameters = new HashMap<String, String>();
+    trainingParameters.put("tasks", "5");
+    trainingParameters.put("training.max.iterations", "2000");
+    trainingParameters.put("training.batch.size", "300");
+    trainingParameters.put("convergence.check.interval", "1000");
+    ann.train(tmpDatasetPath, trainingParameters);
+
+    long end = new Date().getTime();
+
+    // validate results
+    double errorRate = 0;
+    // calculate the error on test instance
+    for (double[] testInstance : testInstances) {
+      DoubleVector instance = new DenseDoubleVector(testInstance);
+      double expected = instance.get(instance.getDimension() - 1);
+      instance = instance.slice(instance.getDimension() - 1);
+      double actual = ann.getOutput(instance).get(0);
+      if (actual < 0.5 && expected >= 0.5 || actual >= 0.5 && expected < 0.5) {
+        ++errorRate;
+      }
+    }
+    errorRate /= testInstances.size();
+
+    Log.info(String.format("Training time: %fs\n",
+        (double) (end - start) / 1000));
+    Log.info(String.format("Relative error: %f%%\n", errorRate * 100));
+  }
+  
+  public void testLogisticRegressionDistributedVersionWithFeatureTransformer() {
+    // write data into a sequence file
+    String tmpStrDatasetPath = "/tmp/logistic_regression_data_feature_transformer";
+    Path tmpDatasetPath = new Path(tmpStrDatasetPath);
+    String strDataPath = "src/test/resources/logistic_regression_data.txt";
+    String modelPath = "/tmp/logistic-regression-distributed-model-feature-transformer";
+
+    Configuration conf = new Configuration();
+    List<double[]> instanceList = new ArrayList<double[]>();
+    List<double[]> trainingInstances = null;
+    List<double[]> testInstances = null;
+
+    try {
+      FileSystem fs = FileSystem.get(new URI(tmpStrDatasetPath), conf);
+      fs.delete(tmpDatasetPath, true);
+      if (fs.exists(tmpDatasetPath)) {
+        fs.createNewFile(tmpDatasetPath);
+      }
+
+      BufferedReader br = new BufferedReader(new FileReader(strDataPath));
+      String line = null;
+      int count = 0;
+      while ((line = br.readLine()) != null) {
+        String[] tokens = line.trim().split(",");
+        double[] instance = new double[tokens.length];
+        for (int i = 0; i < tokens.length; ++i) {
+          instance[i] = Double.parseDouble(tokens[i]);
+        }
+        instanceList.add(instance);
+      }
+      br.close();
+      
+      zeroOneNormalization(instanceList, instanceList.get(0).length - 1);
+      
+      // write training data to temporal sequence file
+      SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,
+          tmpDatasetPath, LongWritable.class, VectorWritable.class);
+      int testSize = 150;
+
+      Collections.shuffle(instanceList);
+      testInstances = new ArrayList<double[]>();
+      testInstances.addAll(instanceList.subList(instanceList.size() - testSize,
+          instanceList.size()));
+      trainingInstances = instanceList.subList(0, instanceList.size()
+          - testSize);
+
+      for (double[] instance : trainingInstances) {
+        DoubleVector vec = new DenseDoubleVector(instance);
+        writer.append(new LongWritable(count++), new VectorWritable(vec));
+      }
+      writer.close();
+    } catch (FileNotFoundException e) {
+      e.printStackTrace();
+    } catch (IOException e) {
+      e.printStackTrace();
+    } catch (URISyntaxException e) {
+      e.printStackTrace();
+    }
+
+    // create model
+    int dimension = 8;
+    SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+    ann.setLearningRate(0.7);
+    ann.setMomemtumWeight(0.5);
+    ann.setRegularizationWeight(0.1);
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(dimension, false,
+        FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
+    ann.setCostFunction(FunctionFactory
+        .createDoubleDoubleFunction("CrossEntropy"));
+    ann.setModelPath(modelPath);
+    
+    FeatureTransformer featureTransformer = new DefaultFeatureTransformer();
+    
+    ann.setFeatureTransformer(featureTransformer);
+
+    long start = new Date().getTime();
+    Map<String, String> trainingParameters = new HashMap<String, String>();
+    trainingParameters.put("tasks", "5");
+    trainingParameters.put("training.max.iterations", "2000");
+    trainingParameters.put("training.batch.size", "300");
+    trainingParameters.put("convergence.check.interval", "1000");
+    ann.train(tmpDatasetPath, trainingParameters);
+    
+
+    long end = new Date().getTime();
+
+    // validate results
+    double errorRate = 0;
+    // calculate the error on test instance
+    for (double[] testInstance : testInstances) {
+      DoubleVector instance = new DenseDoubleVector(testInstance);
+      double expected = instance.get(instance.getDimension() - 1);
+      instance = instance.slice(instance.getDimension() - 1);
+      instance = featureTransformer.transform(instance);
+      double actual = ann.getOutput(instance).get(0);
+      if (actual < 0.5 && expected >= 0.5 || actual >= 0.5 && expected < 0.5) {
+        ++errorRate;
+      }
+    }
+    errorRate /= testInstances.size();
+
+    Log.info(String.format("Training time: %fs\n",
+        (double) (end - start) / 1000));
+    Log.info(String.format("Relative error: %f%%\n", errorRate * 100));
+  }
+
+}
diff --git a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java
new file mode 100644
index 0000000..e422d95
--- /dev/null
+++ b/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java
@@ -0,0 +1,172 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.bsp;
+
+import static org.junit.Assert.assertArrayEquals;
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertFalse;
+import static org.junit.Assert.assertNull;
+import static org.junit.Assert.assertTrue;
+
+import java.io.IOException;
+import java.net.URI;
+import java.net.URISyntaxException;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FSDataInputStream;
+import org.apache.hadoop.fs.FSDataOutputStream;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hama.commons.math.DenseDoubleMatrix;
+import org.apache.hama.commons.math.DoubleMatrix;
+import org.junit.Test;
+
+/**
+ * Test the functionalities of SmallLayeredNeuralNetworkMessage.
+ * 
+ */
+public class TestSmallLayeredNeuralNetworkMessage {
+
+  @Test
+  public void testReadWriteWithoutPrev() {
+    double error = 0.22;
+    double[][] matrix1 = new double[][] { { 0.1, 0.2, 0.8, 0.5 },
+        { 0.3, 0.4, 0.6, 0.2 }, { 0.5, 0.6, 0.1, 0.5 } };
+    double[][] matrix2 = new double[][] { { 0.8, 1.2, 0.5 } };
+    DoubleMatrix[] matrices = new DoubleMatrix[2];
+    matrices[0] = new DenseDoubleMatrix(matrix1);
+    matrices[1] = new DenseDoubleMatrix(matrix2);
+
+    boolean isConverge = false;
+
+    SmallLayeredNeuralNetworkMessage message = new SmallLayeredNeuralNetworkMessage(
+        error, isConverge, matrices, null);
+    Configuration conf = new Configuration();
+    String strPath = "/tmp/testReadWriteSmallLayeredNeuralNetworkMessage";
+    Path path = new Path(strPath);
+    try {
+      FileSystem fs = FileSystem.get(new URI(strPath), conf);
+      FSDataOutputStream out = fs.create(path);
+      message.write(out);
+      out.close();
+
+      FSDataInputStream in = fs.open(path);
+      SmallLayeredNeuralNetworkMessage readMessage = new SmallLayeredNeuralNetworkMessage(
+          0, isConverge, null, null);
+      readMessage.readFields(in);
+      in.close();
+      assertEquals(error, readMessage.getTrainingError(), 0.000001);
+      assertFalse(readMessage.isConverge());
+      DoubleMatrix[] readMatrices = readMessage.getCurMatrices();
+      assertEquals(2, readMatrices.length);
+      for (int i = 0; i < readMatrices.length; ++i) {
+        double[][] doubleMatrices = ((DenseDoubleMatrix) readMatrices[i])
+            .getValues();
+        double[][] doubleExpected = ((DenseDoubleMatrix) matrices[i])
+            .getValues();
+        for (int r = 0; r < doubleMatrices.length; ++r) {
+          assertArrayEquals(doubleExpected[r], doubleMatrices[r], 0.000001);
+        }
+      }
+
+      DoubleMatrix[] readPrevMatrices = readMessage.getPrevMatrices();
+      assertNull(readPrevMatrices);
+
+      // delete
+      fs.delete(path, true);
+    } catch (IOException e) {
+      e.printStackTrace();
+    } catch (URISyntaxException e) {
+      e.printStackTrace();
+    }
+  }
+
+  @Test
+  public void testReadWriteWithPrev() {
+    double error = 0.22;
+    boolean isConverge = true;
+
+    double[][] matrix1 = new double[][] { { 0.1, 0.2, 0.8, 0.5 },
+        { 0.3, 0.4, 0.6, 0.2 }, { 0.5, 0.6, 0.1, 0.5 } };
+    double[][] matrix2 = new double[][] { { 0.8, 1.2, 0.5 } };
+    DoubleMatrix[] matrices = new DoubleMatrix[2];
+    matrices[0] = new DenseDoubleMatrix(matrix1);
+    matrices[1] = new DenseDoubleMatrix(matrix2);
+
+    double[][] prevMatrix1 = new double[][] { { 0.1, 0.1, 0.2, 0.3 },
+        { 0.2, 0.4, 0.1, 0.5 }, { 0.5, 0.1, 0.5, 0.2 } };
+    double[][] prevMatrix2 = new double[][] { { 0.1, 0.2, 0.5, 0.9 },
+        { 0.3, 0.5, 0.2, 0.6 }, { 0.6, 0.8, 0.7, 0.5 } };
+
+    DoubleMatrix[] prevMatrices = new DoubleMatrix[2];
+    prevMatrices[0] = new DenseDoubleMatrix(prevMatrix1);
+    prevMatrices[1] = new DenseDoubleMatrix(prevMatrix2);
+
+    SmallLayeredNeuralNetworkMessage message = new SmallLayeredNeuralNetworkMessage(
+        error, isConverge, matrices, prevMatrices);
+    Configuration conf = new Configuration();
+    String strPath = "/tmp/testReadWriteSmallLayeredNeuralNetworkMessageWithPrev";
+    Path path = new Path(strPath);
+    try {
+      FileSystem fs = FileSystem.get(new URI(strPath), conf);
+      FSDataOutputStream out = fs.create(path);
+      message.write(out);
+      out.close();
+
+      FSDataInputStream in = fs.open(path);
+      SmallLayeredNeuralNetworkMessage readMessage = new SmallLayeredNeuralNetworkMessage(
+          0, isConverge, null, null);
+      readMessage.readFields(in);
+      in.close();
+
+      assertTrue(readMessage.isConverge());
+
+      DoubleMatrix[] readMatrices = readMessage.getCurMatrices();
+      assertEquals(2, readMatrices.length);
+      for (int i = 0; i < readMatrices.length; ++i) {
+        double[][] doubleMatrices = ((DenseDoubleMatrix) readMatrices[i])
+            .getValues();
+        double[][] doubleExpected = ((DenseDoubleMatrix) matrices[i])
+            .getValues();
+        for (int r = 0; r < doubleMatrices.length; ++r) {
+          assertArrayEquals(doubleExpected[r], doubleMatrices[r], 0.000001);
+        }
+      }
+
+      DoubleMatrix[] readPrevMatrices = readMessage.getPrevMatrices();
+      assertEquals(2, readPrevMatrices.length);
+      for (int i = 0; i < readPrevMatrices.length; ++i) {
+        double[][] doubleMatrices = ((DenseDoubleMatrix) readPrevMatrices[i])
+            .getValues();
+        double[][] doubleExpected = ((DenseDoubleMatrix) prevMatrices[i])
+            .getValues();
+        for (int r = 0; r < doubleMatrices.length; ++r) {
+          assertArrayEquals(doubleExpected[r], doubleMatrices[r], 0.000001);
+        }
+      }
+
+      // delete
+      fs.delete(path, true);
+    } catch (IOException e) {
+      e.printStackTrace();
+    } catch (URISyntaxException e) {
+      e.printStackTrace();
+    }
+  }
+
+}
diff --git a/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java b/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java
new file mode 100644
index 0000000..462140c
--- /dev/null
+++ b/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java
@@ -0,0 +1,140 @@
+/**
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.horn.examples;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+import junit.framework.TestCase;
+
+import org.apache.hadoop.conf.Configuration;
+import org.apache.hadoop.fs.FileSystem;
+import org.apache.hadoop.fs.Path;
+import org.apache.hadoop.io.LongWritable;
+import org.apache.hadoop.io.SequenceFile;
+import org.apache.hama.HamaConfiguration;
+import org.apache.hama.commons.io.VectorWritable;
+import org.apache.hama.commons.math.DenseDoubleVector;
+
+/**
+ * Test the functionality of NeuralNetwork Example.
+ * 
+ */
+public class NeuralNetworkTest extends TestCase {
+  private Configuration conf = new HamaConfiguration();
+  private FileSystem fs;
+  private String MODEL_PATH = "/tmp/neuralnets.model";
+  private String RESULT_PATH = "/tmp/neuralnets.txt";
+  private String SEQTRAIN_DATA = "/tmp/test-neuralnets.data";
+  
+  @Override
+  protected void setUp() throws Exception {
+    super.setUp();
+    fs = FileSystem.get(conf);
+  }
+
+  public void testNeuralnetsLabeling() throws IOException {
+    this.neuralNetworkTraining();
+
+    String dataPath = "src/test/resources/neuralnets_classification_test.txt";
+    String mode = "label";
+    try {
+      NeuralNetwork
+          .main(new String[] { mode, dataPath, RESULT_PATH, MODEL_PATH });
+
+      // compare results with ground-truth
+      BufferedReader groundTruthReader = new BufferedReader(new FileReader(
+          "src/test/resources/neuralnets_classification_label.txt"));
+      List<Double> groundTruthList = new ArrayList<Double>();
+      String line = null;
+      while ((line = groundTruthReader.readLine()) != null) {
+        groundTruthList.add(Double.parseDouble(line));
+      }
+      groundTruthReader.close();
+
+      BufferedReader resultReader = new BufferedReader(new FileReader(
+          RESULT_PATH));
+      List<Double> resultList = new ArrayList<Double>();
+      while ((line = resultReader.readLine()) != null) {
+        resultList.add(Double.parseDouble(line));
+      }
+      resultReader.close();
+      int total = resultList.size();
+      double correct = 0;
+      for (int i = 0; i < groundTruthList.size(); ++i) {
+        double actual = resultList.get(i);
+        double expected = groundTruthList.get(i);
+        if (actual < 0.5 && expected < 0.5 || actual >= 0.5 && expected >= 0.5) {
+          ++correct;
+        }
+      }
+      System.out.printf("Precision: %f\n", correct / total);
+
+    } catch (Exception e) {
+      e.printStackTrace();
+    } finally {
+      fs.delete(new Path(RESULT_PATH), true);
+      fs.delete(new Path(MODEL_PATH), true);
+      fs.delete(new Path(SEQTRAIN_DATA), true);
+    }
+  }
+
+  private void neuralNetworkTraining() {
+    String mode = "train";
+    String strTrainingDataPath = "src/test/resources/neuralnets_classification_training.txt";
+    int featureDimension = 8;
+    int labelDimension = 1;
+
+    Path sequenceTrainingDataPath = new Path(SEQTRAIN_DATA);
+    Configuration conf = new Configuration();
+    FileSystem fs;
+    try {
+      fs = FileSystem.get(conf);
+      SequenceFile.Writer writer = new SequenceFile.Writer(fs, conf,
+          sequenceTrainingDataPath, LongWritable.class, VectorWritable.class);
+      BufferedReader br = new BufferedReader(
+          new FileReader(strTrainingDataPath));
+      String line = null;
+      // convert the data in sequence file format
+      while ((line = br.readLine()) != null) {
+        String[] tokens = line.split(",");
+        double[] vals = new double[tokens.length];
+        for (int i = 0; i < tokens.length; ++i) {
+          vals[i] = Double.parseDouble(tokens[i]);
+        }
+        writer.append(new LongWritable(), new VectorWritable(
+            new DenseDoubleVector(vals)));
+      }
+      writer.close();
+      br.close();
+    } catch (IOException e1) {
+      e1.printStackTrace();
+    }
+
+    try {
+      NeuralNetwork.main(new String[] { mode, SEQTRAIN_DATA,
+          MODEL_PATH, "" + featureDimension, "" + labelDimension });
+    } catch (Exception e) {
+      e.printStackTrace();
+    }
+  }
+
+}
diff --git a/src/test/resources/dimensional_reduction.txt b/src/test/resources/dimensional_reduction.txt
new file mode 100644
index 0000000..0528d42
--- /dev/null
+++ b/src/test/resources/dimensional_reduction.txt
@@ -0,0 +1,1600 @@
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+12.440832	12.176057	-1.032280
+9.612878	11.898241	-7.069101
+-0.945664	11.501216	-10.867711
\ No newline at end of file
diff --git a/src/test/resources/logistic_regression_data.txt b/src/test/resources/logistic_regression_data.txt
new file mode 100644
index 0000000..b874bd8
--- /dev/null
+++ b/src/test/resources/logistic_regression_data.txt
@@ -0,0 +1,768 @@
+6,148,72,35,0,33.6,0.627,50,1
+1,85,66,29,0,26.6,0.351,31,0
+8,183,64,0,0,23.3,0.672,32,1
+1,89,66,23,94,28.1,0.167,21,0
+0,137,40,35,168,43.1,2.288,33,1
+5,116,74,0,0,25.6,0.201,30,0
+3,78,50,32,88,31,0.248,26,1
+10,115,0,0,0,35.3,0.134,29,0
+2,197,70,45,543,30.5,0.158,53,1
+8,125,96,0,0,0,0.232,54,1
+4,110,92,0,0,37.6,0.191,30,0
+10,168,74,0,0,38,0.537,34,1
+10,139,80,0,0,27.1,1.441,57,0
+1,189,60,23,846,30.1,0.398,59,1
+5,166,72,19,175,25.8,0.587,51,1
+7,100,0,0,0,30,0.484,32,1
+0,118,84,47,230,45.8,0.551,31,1
+7,107,74,0,0,29.6,0.254,31,1
+1,103,30,38,83,43.3,0.183,33,0
+1,115,70,30,96,34.6,0.529,32,1
+3,126,88,41,235,39.3,0.704,27,0
+8,99,84,0,0,35.4,0.388,50,0
+7,196,90,0,0,39.8,0.451,41,1
+9,119,80,35,0,29,0.263,29,1
+11,143,94,33,146,36.6,0.254,51,1
+10,125,70,26,115,31.1,0.205,41,1
+7,147,76,0,0,39.4,0.257,43,1
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\ No newline at end of file
diff --git a/src/test/resources/neuralnets_classification_label.txt b/src/test/resources/neuralnets_classification_label.txt
new file mode 100644
index 0000000..e1b6789
--- /dev/null
+++ b/src/test/resources/neuralnets_classification_label.txt
Binary files differ
diff --git a/src/test/resources/neuralnets_classification_test.txt b/src/test/resources/neuralnets_classification_test.txt
new file mode 100644
index 0000000..b19107d
--- /dev/null
+++ b/src/test/resources/neuralnets_classification_test.txt
Binary files differ
diff --git a/src/test/resources/neuralnets_classification_training.txt b/src/test/resources/neuralnets_classification_training.txt
new file mode 100644
index 0000000..405fb69
--- /dev/null
+++ b/src/test/resources/neuralnets_classification_training.txt
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\ No newline at end of file