Author: yxjiang
Date: Tue Sep 24 14:49:52 2013
New Revision: 1525911
URL: http://svn.apache.org/r1525911
Log:
HAMA-804: Create NeuralNetwork Example.
Added:
hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
hama/trunk/examples/src/test/resources/neuralnets_classification_label.txt
hama/trunk/examples/src/test/resources/neuralnets_classification_test.txt
hama/trunk/examples/src/test/resources/neuralnets_classification_training.txt
Modified:
hama/trunk/CHANGES.txt
hama/trunk/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
Modified: hama/trunk/CHANGES.txt
URL:
http://svn.apache.org/viewvc/hama/trunk/CHANGES.txt?rev=1525911&r1=1525910&r2=1525911&view=diff
==============================================================================
--- hama/trunk/CHANGES.txt (original)
+++ hama/trunk/CHANGES.txt Tue Sep 24 14:49:52 2013
@@ -3,7 +3,7 @@ Hama Change Log
Release 0.6.3 (unreleased changes)
NEW FEATURES
-
+ HAMA-804: Create NeuralNetwork Example (Yexi Jiang)
HAMA-795: Implement Autoencoder based on NeuralNetwork (Yexi Jiang)
HAMA-767: Add vertex addition/removal APIs (Anastasis Andronidis via
edwardyoon)
HAMA-594: Semi-Clustering Algorithm Implementation (Renil Jeseph via
edwardyoon)
Modified:
hama/trunk/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
URL:
http://svn.apache.org/viewvc/hama/trunk/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java?rev=1525911&r1=1525910&r2=1525911&view=diff
==============================================================================
---
hama/trunk/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
(original)
+++
hama/trunk/examples/src/main/java/org/apache/hama/examples/ExampleDriver.java
Tue Sep 24 14:49:52 2013
@@ -39,6 +39,7 @@ public class ExampleDriver {
pgd.addClass("semi", SemiClusterJobDriver.class, "Semi Clustering");
pgd.addClass("kmeans", Kmeans.class, "K-Means Clustering");
pgd.addClass("gd", GradientDescentExample.class, "Gradient Descent");
+ pgd.addClass("neuralnets", NeuralNetwork.class, "Neural Network
classification");
pgd.addClass("gen", Generator.class, "Random Data Generator Util");
pgd.driver(args);
Added:
hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
URL:
http://svn.apache.org/viewvc/hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java?rev=1525911&view=auto
==============================================================================
---
hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
(added)
+++
hama/trunk/examples/src/main/java/org/apache/hama/examples/NeuralNetwork.java
Tue Sep 24 14:49:52 2013
@@ -0,0 +1,197 @@
+/**
+ * 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.hama.examples;
+
+import java.io.BufferedReader;
+import java.io.BufferedWriter;
+import java.io.InputStreamReader;
+import java.io.OutputStreamWriter;
+import java.net.URI;
+import java.util.Arrays;
+import java.util.HashMap;
+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.hama.ml.ann.SmallLayeredNeuralNetwork;
+import org.apache.hama.ml.math.DenseDoubleVector;
+import org.apache.hama.ml.math.DoubleVector;
+import org.apache.hama.ml.math.FunctionFactory;
+
+/**
+ *
+ *
+ */
+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;
+ }
+
+ String modelPath = args[1];
+ String featureDataPath = args[2];
+ String resultDataPath = args[3];
+
+ SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(modelPath);
+
+ // process data in streaming approach
+ FileSystem fs = FileSystem.get(new URI(featureDataPath), new
Configuration());
+ 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- evaluate: 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 'evaluate' 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 'evaluate' mode, it is where the labeled data is
stored.");
+ System.out.println("\n\tConditional Parameters:");
+ System.out.println("\tMODEL_PATH\tonly required in 'evaluate' 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 default
setting:\n\tneuralnets train hdfs://localhost:30002/training_data
hdfs://localhost:30002/model 8 1");
+ System.out.println("Train a neural network by 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 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");
+ }
+
+}
Added:
hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
URL:
http://svn.apache.org/viewvc/hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java?rev=1525911&view=auto
==============================================================================
---
hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
(added)
+++
hama/trunk/examples/src/test/java/org/apache/hama/examples/NeuralNetworkTest.java
Tue Sep 24 14:49:52 2013
@@ -0,0 +1,125 @@
+/**
+ * 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.hama.examples;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.List;
+
+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.ml.math.DenseDoubleVector;
+import org.apache.hama.ml.writable.VectorWritable;
+import org.junit.Test;
+
+/**
+ * Test the functionality of NeuralNetwork Example.
+ *
+ */
+public class NeuralNetworkTest {
+
+ @Test
+ public void testNeuralnetsLabeling() {
+ this.testNeuralNetworkTraining();
+
+ String dataPath = "src/test/resources/neuralnets_classification_test.txt";
+ String modelPath = "tmp/neuralnets.model";
+ String resultPath = "tmp/neuralnets.txt";
+ String mode = "label";
+ try {
+ NeuralNetwork
+ .main(new String[] { mode, modelPath, dataPath, resultPath });
+
+ // 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(resultPath));
+ 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();
+ }
+ }
+
+ private void testNeuralNetworkTraining() {
+ String mode = "train";
+ String strTrainingDataPath =
"src/test/resources/neuralnets_classification_training.txt";
+ String strSequenceTrainingDataPath = "tmp/test-neuralnets.data";
+ int featureDimension = 8;
+ int labelDimension = 1;
+
+ Path sequenceTrainingDataPath = new Path(strSequenceTrainingDataPath);
+ Configuration conf = new Configuration();
+ try {
+ FileSystem 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();
+ }
+
+ String modelPath = "tmp/neuralnets.model";
+ try {
+ NeuralNetwork.main(new String[] { mode, strSequenceTrainingDataPath,
+ modelPath, "" + featureDimension, "" + labelDimension });
+ } catch (Exception e) {
+ e.printStackTrace();
+ }
+ }
+
+}
Added:
hama/trunk/examples/src/test/resources/neuralnets_classification_label.txt
URL:
http://svn.apache.org/viewvc/hama/trunk/examples/src/test/resources/neuralnets_classification_label.txt?rev=1525911&view=auto
==============================================================================
--- hama/trunk/examples/src/test/resources/neuralnets_classification_label.txt
(added)
+++ hama/trunk/examples/src/test/resources/neuralnets_classification_label.txt
Tue Sep 24 14:49:52 2013
@@ -0,0 +1 @@
+1
0
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0
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0
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1
\ No newline at end of file
Added: hama/trunk/examples/src/test/resources/neuralnets_classification_test.txt
URL:
http://svn.apache.org/viewvc/hama/trunk/examples/src/test/resources/neuralnets_classification_test.txt?rev=1525911&view=auto
==============================================================================
--- hama/trunk/examples/src/test/resources/neuralnets_classification_test.txt
(added)
+++ hama/trunk/examples/src/test/resources/neuralnets_classification_test.txt
Tue Sep 24 14:49:52 2013
@@ -0,0 +1 @@
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0.352941176,0.492462312,0.475409836,0.333333333,0.224586288,0.506706408,0.15029889,0.366666667
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0.411764706,0.648241206,0.557377049,0.494949495,0.147754137,0.573770492,0.154141759,0.366666667
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0.235294118,0.592964824,0.573770492,0,0,0.66318927,0.352690009,0.083333333
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\ No newline at end of file
