Code refactoring
diff --git a/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java b/src/main/java/org/apache/horn/core/AbstractLayeredNeuralNetwork.java
similarity index 98%
rename from src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java
rename to src/main/java/org/apache/horn/core/AbstractLayeredNeuralNetwork.java
index b18eb44..f87e771 100644
--- a/src/main/java/org/apache/horn/bsp/AbstractLayeredNeuralNetwork.java
+++ b/src/main/java/org/apache/horn/core/AbstractLayeredNeuralNetwork.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.DataInput;
import java.io.DataOutput;
@@ -43,7 +43,7 @@
* form a bipartite weighted graph.
*
*/
-abstract class AbstractLayeredNeuralNetwork extends NeuralNetwork {
+abstract class AbstractLayeredNeuralNetwork extends AbstractNeuralNetwork {
private static final double DEFAULT_MOMENTUM_WEIGHT = 0.1;
diff --git a/src/main/java/org/apache/horn/bsp/NeuralNetwork.java b/src/main/java/org/apache/horn/core/AbstractNeuralNetwork.java
similarity index 96%
rename from src/main/java/org/apache/horn/bsp/NeuralNetwork.java
rename to src/main/java/org/apache/horn/core/AbstractNeuralNetwork.java
index 051881d..45f56a3 100644
--- a/src/main/java/org/apache/horn/bsp/NeuralNetwork.java
+++ b/src/main/java/org/apache/horn/core/AbstractNeuralNetwork.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.DataInput;
import java.io.DataOutput;
@@ -46,7 +46,7 @@
* between neurons.
*
*/
-abstract class NeuralNetwork implements Writable {
+public abstract class AbstractNeuralNetwork implements Writable {
protected HamaConfiguration conf;
protected FileSystem fs;
@@ -62,17 +62,17 @@
protected FeatureTransformer featureTransformer;
- public NeuralNetwork() {
+ public AbstractNeuralNetwork() {
this.learningRate = DEFAULT_LEARNING_RATE;
this.modelType = this.getClass().getSimpleName();
this.featureTransformer = new DefaultFeatureTransformer();
}
- public NeuralNetwork(String modelPath) {
+ public AbstractNeuralNetwork(String modelPath) {
this.modelPath = modelPath;
}
- public NeuralNetwork(HamaConfiguration conf, String modelPath) {
+ public AbstractNeuralNetwork(HamaConfiguration conf, String modelPath) {
try {
this.conf = conf;
this.fs = FileSystem.get(conf);
diff --git a/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java b/src/main/java/org/apache/horn/core/AbstractNeuralNetworkTrainer.java
similarity index 83%
rename from src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java
rename to src/main/java/org/apache/horn/core/AbstractNeuralNetworkTrainer.java
index 648e86b..3547a1a 100644
--- a/src/main/java/org/apache/horn/bsp/NeuralNetworkTrainer.java
+++ b/src/main/java/org/apache/horn/core/AbstractNeuralNetworkTrainer.java
@@ -15,13 +15,14 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
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.DoubleWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hama.bsp.BSP;
@@ -32,27 +33,28 @@
import org.apache.hama.ml.util.FeatureTransformer;
/**
- * The trainer that is used to train the {@link SmallLayeredNeuralNetwork} with
+ * The trainer that is used to train the {@link LayeredNeuralNetwork} 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, Synapse> {
+public abstract class AbstractNeuralNetworkTrainer
+ extends
+ BSP<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse<DoubleWritable, DoubleWritable>> {
protected static final Log LOG = LogFactory
- .getLog(NeuralNetworkTrainer.class);
+ .getLog(AbstractNeuralNetworkTrainer.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, Synapse> peer)
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse<DoubleWritable, DoubleWritable>> peer)
throws IOException, SyncException, InterruptedException {
conf = peer.getConfiguration();
featureTransformer = new DefaultFeatureTransformer();
@@ -68,7 +70,7 @@
* @throws InterruptedException
*/
protected void extraSetup(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse> peer)
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse<DoubleWritable, DoubleWritable>> peer)
throws IOException, SyncException, InterruptedException {
}
@@ -78,12 +80,12 @@
*/
@Override
public abstract void bsp(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse> peer)
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse<DoubleWritable, DoubleWritable>> peer)
throws IOException, SyncException, InterruptedException;
@Override
public void cleanup(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse> peer)
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse<DoubleWritable, DoubleWritable>> peer)
throws IOException {
this.extraCleanup(peer);
// write model to modelPath
@@ -98,7 +100,7 @@
* @throws InterruptedException
*/
protected void extraCleanup(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse> peer)
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, Synapse<DoubleWritable, DoubleWritable>> peer)
throws IOException {
}
diff --git a/src/main/java/org/apache/horn/bsp/AutoEncoder.java b/src/main/java/org/apache/horn/core/AutoEncoder.java
similarity index 95%
rename from src/main/java/org/apache/horn/bsp/AutoEncoder.java
rename to src/main/java/org/apache/horn/core/AutoEncoder.java
index 8ea2930..f638245 100644
--- a/src/main/java/org/apache/horn/bsp/AutoEncoder.java
+++ b/src/main/java/org/apache/horn/core/AutoEncoder.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.IOException;
import java.util.Map;
@@ -34,14 +34,14 @@
/**
* AutoEncoder is a model used for dimensional reduction and feature learning.
- * It is a special kind of {@link NeuralNetwork} that consists of three layers
+ * It is a special kind of {@link AbstractNeuralNetwork} 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;
+ private final LayeredNeuralNetwork model;
/**
* Initialize the autoencoder.
@@ -51,7 +51,7 @@
* information.
*/
public AutoEncoder(int inputDimensions, int compressedDimensions) {
- model = new SmallLayeredNeuralNetwork();
+ model = new LayeredNeuralNetwork();
model.addLayer(inputDimensions, false,
FunctionFactory.createDoubleFunction("Sigmoid"));
model.addLayer(compressedDimensions, false,
@@ -65,7 +65,7 @@
}
public AutoEncoder(HamaConfiguration conf, String modelPath) {
- model = new SmallLayeredNeuralNetwork(conf, modelPath);
+ model = new LayeredNeuralNetwork(conf, modelPath);
}
public AutoEncoder setModelPath(String modelPath) {
diff --git a/src/main/java/org/apache/horn/bsp/HornJob.java b/src/main/java/org/apache/horn/core/HornJob.java
similarity index 94%
rename from src/main/java/org/apache/horn/bsp/HornJob.java
rename to src/main/java/org/apache/horn/core/HornJob.java
index 4521b87..82dcad8 100644
--- a/src/main/java/org/apache/horn/bsp/HornJob.java
+++ b/src/main/java/org/apache/horn/core/HornJob.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.IOException;
@@ -26,14 +26,14 @@
public class HornJob extends BSPJob {
- SmallLayeredNeuralNetwork neuralNetwork;
+ LayeredNeuralNetwork neuralNetwork;
public HornJob(HamaConfiguration conf, Class<?> exampleClass)
throws IOException {
super(conf);
this.setJarByClass(exampleClass);
- neuralNetwork = new SmallLayeredNeuralNetwork();
+ neuralNetwork = new LayeredNeuralNetwork();
}
public void inputLayer(int featureDimension, Class<? extends Function> func) {
@@ -79,7 +79,7 @@
this.conf.setDouble("mlp.momentum.weight", momentumWeight);
}
- public SmallLayeredNeuralNetwork getNeuralNetwork() {
+ public LayeredNeuralNetwork getNeuralNetwork() {
return neuralNetwork;
}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java b/src/main/java/org/apache/horn/core/LayeredNeuralNetwork.java
similarity index 97%
rename from src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java
rename to src/main/java/org/apache/horn/core/LayeredNeuralNetwork.java
index 0ea8e51..afccbff 100644
--- a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetwork.java
+++ b/src/main/java/org/apache/horn/core/LayeredNeuralNetwork.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.DataInput;
import java.io.DataOutput;
@@ -62,10 +62,10 @@
* form a bipartite weighted graph.
*
*/
-public class SmallLayeredNeuralNetwork extends AbstractLayeredNeuralNetwork {
+public class LayeredNeuralNetwork extends AbstractLayeredNeuralNetwork {
private static final Log LOG = LogFactory
- .getLog(SmallLayeredNeuralNetwork.class);
+ .getLog(LayeredNeuralNetwork.class);
public static Class<Neuron<Synapse<DoubleWritable, DoubleWritable>>> neuronClass;
@@ -82,14 +82,14 @@
protected double regularizationWeight;
- public SmallLayeredNeuralNetwork() {
+ public LayeredNeuralNetwork() {
this.layerSizeList = Lists.newArrayList();
this.weightMatrixList = Lists.newArrayList();
this.prevWeightUpdatesList = Lists.newArrayList();
this.squashingFunctionList = Lists.newArrayList();
}
- public SmallLayeredNeuralNetwork(HamaConfiguration conf, String modelPath) {
+ public LayeredNeuralNetwork(HamaConfiguration conf, String modelPath) {
super(conf, modelPath);
this.regularizationWeight = conf.getDouble("regularization.weight", 0);
}
@@ -333,6 +333,7 @@
* @param intermediateOutput The intermediateOutput of previous layer.
* @return a new vector with the result of the operation.
*/
+ @SuppressWarnings("unchecked")
protected DoubleVector forward(int fromLayer, DoubleVector intermediateOutput) {
DoubleMatrix weightMatrix = this.weightMatrixList.get(fromLayer);
@@ -576,10 +577,10 @@
this.writeModelToFile();
// create job
- BSPJob job = new BSPJob(conf, SmallLayeredNeuralNetworkTrainer.class);
+ BSPJob job = new BSPJob(conf, LayeredNeuralNetworkTrainer.class);
job.setJobName("Small scale Neural Network training");
- job.setJarByClass(SmallLayeredNeuralNetworkTrainer.class);
- job.setBspClass(SmallLayeredNeuralNetworkTrainer.class);
+ job.setJarByClass(LayeredNeuralNetworkTrainer.class);
+ job.setBspClass(LayeredNeuralNetworkTrainer.class);
job.getConfiguration().setClass("neuron.class", StandardNeuron.class,
Neuron.class);
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java b/src/main/java/org/apache/horn/core/LayeredNeuralNetworkTrainer.java
similarity index 88%
rename from src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java
rename to src/main/java/org/apache/horn/core/LayeredNeuralNetworkTrainer.java
index c3e258c..effd5b0 100644
--- a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkTrainer.java
+++ b/src/main/java/org/apache/horn/core/LayeredNeuralNetworkTrainer.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.IOException;
import java.net.InetSocketAddress;
@@ -38,20 +38,20 @@
import com.google.common.base.Preconditions;
/**
- * The trainer that train the {@link SmallLayeredNeuralNetwork} based on BSP
+ * The trainer that train the {@link LayeredNeuralNetwork} based on BSP
* framework.
*
*/
-public final class SmallLayeredNeuralNetworkTrainer
+public final class LayeredNeuralNetworkTrainer
extends
- BSP<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> {
+ BSP<LongWritable, VectorWritable, NullWritable, NullWritable, ParameterMessage> {
private static final Log LOG = LogFactory
- .getLog(SmallLayeredNeuralNetworkTrainer.class);
+ .getLog(LayeredNeuralNetworkTrainer.class);
/* When given peer is master worker: base of parameter merge */
/* When given peer is slave worker: neural network for training */
- private SmallLayeredNeuralNetwork inMemoryModel;
+ private LayeredNeuralNetwork inMemoryModel;
/* Job configuration */
private HamaConfiguration conf;
@@ -76,7 +76,7 @@
* @param peer
* */
private boolean isMaster(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer) {
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, ParameterMessage> peer) {
return peer.getPeerIndex() == peer.getNumPeers() - 1;
}
@@ -85,13 +85,13 @@
* If the model path is specified, load the existing from storage location.
*/
public void setup(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer) {
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, ParameterMessage> peer) {
// At least one master & slave worker exist.
Preconditions.checkArgument(peer.getNumPeers() >= 2);
this.conf = peer.getConfiguration();
String modelPath = conf.get("model.path");
- this.inMemoryModel = new SmallLayeredNeuralNetwork(conf, modelPath);
+ this.inMemoryModel = new LayeredNeuralNetwork(conf, modelPath);
this.batchSize = conf.getInt("training.batch.size", 50);
this.isConverge = new AtomicBoolean(false);
@@ -130,7 +130,7 @@
* Write the trained model back to stored location.
*/
public void cleanup(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer) {
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, ParameterMessage> peer) {
// write model to modelPath
if (isMaster(peer)) {
try {
@@ -144,7 +144,7 @@
@Override
public void bsp(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, ParameterMessage> peer)
throws IOException, SyncException, InterruptedException {
while (!this.isConverge.get()) {
// each slave-worker calculate the matrices updates according to local
@@ -168,7 +168,7 @@
* @throws IOException
*/
private void calculateUpdates(
- BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, SmallLayeredNeuralNetworkMessage> peer)
+ BSPPeer<LongWritable, VectorWritable, NullWritable, NullWritable, ParameterMessage> peer)
throws IOException {
DoubleMatrix[] weightUpdates = new DoubleMatrix[this.inMemoryModel.weightMatrixList
@@ -189,7 +189,7 @@
peer.readNext(key, value);
}
DoubleVector trainingInstance = value.getVector();
- SmallLayeredNeuralNetwork.matricesAdd(weightUpdates,
+ LayeredNeuralNetwork.matricesAdd(weightUpdates,
this.inMemoryModel.trainByInstance(trainingInstance));
avgTrainingError += this.inMemoryModel.trainingError;
}
@@ -201,11 +201,11 @@
}
// exchange parameter update with master
- SmallLayeredNeuralNetworkMessage msg = new SmallLayeredNeuralNetworkMessage(
+ ParameterMessage msg = new ParameterMessage(
avgTrainingError, false, weightUpdates,
this.inMemoryModel.getPrevMatricesUpdates());
- SmallLayeredNeuralNetworkMessage inMessage = proxy.merge(msg);
+ ParameterMessage inMessage = proxy.merge(msg);
DoubleMatrix[] newWeights = inMessage.getCurMatrices();
DoubleMatrix[] preWeightUpdates = inMessage.getPrevMatrices();
this.inMemoryModel.setWeightMatrices(newWeights);
diff --git a/src/main/java/org/apache/horn/bsp/Neuron.java b/src/main/java/org/apache/horn/core/Neuron.java
similarity index 98%
rename from src/main/java/org/apache/horn/bsp/Neuron.java
rename to src/main/java/org/apache/horn/core/Neuron.java
index f122b6d..357b42f 100644
--- a/src/main/java/org/apache/horn/bsp/Neuron.java
+++ b/src/main/java/org/apache/horn/core/Neuron.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import org.apache.hadoop.io.Writable;
import org.apache.hama.commons.math.DoubleFunction;
diff --git a/src/main/java/org/apache/horn/bsp/NeuronInterface.java b/src/main/java/org/apache/horn/core/NeuronInterface.java
similarity index 97%
rename from src/main/java/org/apache/horn/bsp/NeuronInterface.java
rename to src/main/java/org/apache/horn/core/NeuronInterface.java
index bcc1a5a..5e4c113 100644
--- a/src/main/java/org/apache/horn/bsp/NeuronInterface.java
+++ b/src/main/java/org/apache/horn/core/NeuronInterface.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.IOException;
diff --git a/src/main/java/org/apache/horn/bsp/ParameterMerger.java b/src/main/java/org/apache/horn/core/ParameterMerger.java
similarity index 89%
rename from src/main/java/org/apache/horn/bsp/ParameterMerger.java
rename to src/main/java/org/apache/horn/core/ParameterMerger.java
index 6df719a..512b402 100644
--- a/src/main/java/org/apache/horn/bsp/ParameterMerger.java
+++ b/src/main/java/org/apache/horn/core/ParameterMerger.java
@@ -15,13 +15,13 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import org.apache.hama.ipc.VersionedProtocol;
public interface ParameterMerger extends VersionedProtocol {
long versionID = 1L;
- SmallLayeredNeuralNetworkMessage merge(SmallLayeredNeuralNetworkMessage msg);
+ ParameterMessage merge(ParameterMessage msg);
}
diff --git a/src/main/java/org/apache/horn/bsp/ParameterMergerServer.java b/src/main/java/org/apache/horn/core/ParameterMergerServer.java
similarity index 92%
rename from src/main/java/org/apache/horn/bsp/ParameterMergerServer.java
rename to src/main/java/org/apache/horn/core/ParameterMergerServer.java
index 47aab84..c76a4d0 100644
--- a/src/main/java/org/apache/horn/bsp/ParameterMergerServer.java
+++ b/src/main/java/org/apache/horn/core/ParameterMergerServer.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.IOException;
import java.util.concurrent.atomic.AtomicBoolean;
@@ -31,7 +31,7 @@
private static final Log LOG = LogFactory.getLog(ParameterMergerServer.class);
/* The parameter merge base. */
- protected SmallLayeredNeuralNetwork inMemoryModel;
+ protected LayeredNeuralNetwork inMemoryModel;
/* To terminate or not to terminate. */
protected AtomicBoolean isConverge;
@@ -60,7 +60,7 @@
/* how many merges have been conducted? */
protected int mergeCount = 0;
- public ParameterMergerServer(SmallLayeredNeuralNetwork inMemoryModel,
+ public ParameterMergerServer(LayeredNeuralNetwork inMemoryModel,
AtomicBoolean isConverge, int slaveCount, int mergeLimit,
int convergenceCheckInterval) {
this.inMemoryModel = inMemoryModel;
@@ -76,8 +76,8 @@
}
@Override
- public SmallLayeredNeuralNetworkMessage merge(
- SmallLayeredNeuralNetworkMessage msg) {
+ public ParameterMessage merge(
+ ParameterMessage msg) {
double trainingError = msg.getTrainingError();
DoubleMatrix[] weightUpdates = msg.getCurMatrices();
@@ -124,7 +124,7 @@
}
}
- return new SmallLayeredNeuralNetworkMessage(0, this.isConverge.get(),
+ return new ParameterMessage(0, this.isConverge.get(),
this.inMemoryModel.getWeightMatrices(),
this.inMemoryModel.getPrevMatricesUpdates());
}
diff --git a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java b/src/main/java/org/apache/horn/core/ParameterMessage.java
similarity index 88%
rename from src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java
rename to src/main/java/org/apache/horn/core/ParameterMessage.java
index 2f8c287..3905e25 100644
--- a/src/main/java/org/apache/horn/bsp/SmallLayeredNeuralNetworkMessage.java
+++ b/src/main/java/org/apache/horn/core/ParameterMessage.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.DataInput;
import java.io.DataOutput;
@@ -27,23 +27,22 @@
import org.apache.hama.commons.math.DoubleMatrix;
/**
- * NeuralNetworkMessage transmits the messages between peers during the training
- * of neural networks.
+ * ParameterMessage transmits the messages between workers and parameter
+ * servers during the training of neural networks.
*
*/
-public class SmallLayeredNeuralNetworkMessage implements Writable {
+public class ParameterMessage implements Writable {
protected double trainingError;
protected DoubleMatrix[] curMatrices;
protected DoubleMatrix[] prevMatrices;
protected boolean converge;
- public SmallLayeredNeuralNetworkMessage() {
+ public ParameterMessage() {
}
-
- public SmallLayeredNeuralNetworkMessage(double trainingError,
- boolean converge, DoubleMatrix[] weightMatrices,
- DoubleMatrix[] prevMatrices) {
+
+ public ParameterMessage(double trainingError, boolean converge,
+ DoubleMatrix[] weightMatrices, DoubleMatrix[] prevMatrices) {
this.trainingError = trainingError;
this.converge = converge;
this.curMatrices = weightMatrices;
diff --git a/src/main/java/org/apache/horn/bsp/Synapse.java b/src/main/java/org/apache/horn/core/Synapse.java
similarity index 98%
rename from src/main/java/org/apache/horn/bsp/Synapse.java
rename to src/main/java/org/apache/horn/core/Synapse.java
index 61725f9..714767b 100644
--- a/src/main/java/org/apache/horn/bsp/Synapse.java
+++ b/src/main/java/org/apache/horn/core/Synapse.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.io.DataInput;
import java.io.DataOutput;
diff --git a/src/main/java/org/apache/horn/examples/MultiLayerPerceptron.java b/src/main/java/org/apache/horn/examples/MultiLayerPerceptron.java
index f66344c..c3bf180 100644
--- a/src/main/java/org/apache/horn/examples/MultiLayerPerceptron.java
+++ b/src/main/java/org/apache/horn/examples/MultiLayerPerceptron.java
@@ -21,9 +21,9 @@
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hama.HamaConfiguration;
-import org.apache.horn.bsp.HornJob;
-import org.apache.horn.bsp.Neuron;
-import org.apache.horn.bsp.Synapse;
+import org.apache.horn.core.HornJob;
+import org.apache.horn.core.Neuron;
+import org.apache.horn.core.Synapse;
import org.apache.horn.funcs.CrossEntropy;
import org.apache.horn.funcs.Sigmoid;
@@ -101,7 +101,7 @@
InterruptedException, ClassNotFoundException {
if (args.length < 9) {
System.out
- .println("Usage: model_path training_set learning_rate momentum regularization_weight feature_dimension label_dimension max_iteration num_tasks");
+ .println("Usage: <MODEL_PATH> <INPUT_PATH> <LEARNING_RATE> <MOMEMTUM_WEIGHT> <REGULARIZATION_WEIGHT> <FEATURE_DIMENSION> <LABEL_DIMENSION> <MAX_ITERATION> <NUM_TASKS>");
System.exit(1);
}
HornJob ann = createJob(new HamaConfiguration(), args[0], args[1],
diff --git a/src/main/java/org/apache/horn/examples/NeuralNetwork.java b/src/main/java/org/apache/horn/examples/NeuralNetwork.java
deleted file mode 100644
index 5c0afdf..0000000
--- a/src/main/java/org/apache/horn/examples/NeuralNetwork.java
+++ /dev/null
@@ -1,217 +0,0 @@
-/**
- * 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.horn.bsp.SmallLayeredNeuralNetwork;
-import org.apache.horn.funcs.FunctionFactory;
-
-/**
- * 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;
- }
- HamaConfiguration conf = new HamaConfiguration();
- String mode = args[0];
-
- if (mode.equalsIgnoreCase("label")) {
- if (args.length < 4) {
- printUsage();
- return;
- }
-
- String featureDataPath = args[1];
- String resultDataPath = args[2];
- String modelPath = args[3];
-
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(conf, 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", "2");
- trainingParameters.put("training.max.iterations", "" + iteration);
- trainingParameters.put("training.batch.size", "300");
- trainingParameters.put("convergence.check.interval", "1000");
- // ann.train(conf, 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/core/MLTestBase.java
similarity index 98%
rename from src/test/java/org/apache/horn/bsp/MLTestBase.java
rename to src/test/java/org/apache/horn/core/MLTestBase.java
index 8001bcf..3f02600 100644
--- a/src/test/java/org/apache/horn/bsp/MLTestBase.java
+++ b/src/test/java/org/apache/horn/core/MLTestBase.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import java.util.Arrays;
import java.util.List;
diff --git a/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java b/src/test/java/org/apache/horn/core/TestAutoEncoder.java
similarity index 98%
rename from src/test/java/org/apache/horn/bsp/TestAutoEncoder.java
rename to src/test/java/org/apache/horn/core/TestAutoEncoder.java
index a42fd72..10ae738 100644
--- a/src/test/java/org/apache/horn/bsp/TestAutoEncoder.java
+++ b/src/test/java/org/apache/horn/core/TestAutoEncoder.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import static org.junit.Assert.assertEquals;
@@ -39,6 +39,7 @@
import org.apache.hama.commons.io.VectorWritable;
import org.apache.hama.commons.math.DenseDoubleVector;
import org.apache.hama.commons.math.DoubleVector;
+import org.apache.horn.core.AutoEncoder;
import org.junit.Test;
import org.mortbay.log.Log;
diff --git a/src/test/java/org/apache/horn/trainer/TestNeuron.java b/src/test/java/org/apache/horn/core/TestNeuron.java
similarity index 96%
rename from src/test/java/org/apache/horn/trainer/TestNeuron.java
rename to src/test/java/org/apache/horn/core/TestNeuron.java
index b5f6bfc..f2fe4e1 100644
--- a/src/test/java/org/apache/horn/trainer/TestNeuron.java
+++ b/src/test/java/org/apache/horn/core/TestNeuron.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.trainer;
+package org.apache.horn.core;
import java.io.IOException;
import java.util.ArrayList;
@@ -25,8 +25,8 @@
import org.apache.hadoop.io.DoubleWritable;
import org.apache.hama.HamaConfiguration;
-import org.apache.horn.bsp.Neuron;
-import org.apache.horn.bsp.Synapse;
+import org.apache.horn.core.Neuron;
+import org.apache.horn.core.Synapse;
import org.apache.horn.funcs.Sigmoid;
public class TestNeuron extends TestCase {
diff --git a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java b/src/test/java/org/apache/horn/core/TestSmallLayeredNeuralNetwork.java
similarity index 94%
rename from src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java
rename to src/test/java/org/apache/horn/core/TestSmallLayeredNeuralNetwork.java
index ee48136..7e4328f 100644
--- a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetwork.java
+++ b/src/test/java/org/apache/horn/core/TestSmallLayeredNeuralNetwork.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
@@ -46,8 +46,8 @@
import org.apache.hama.commons.math.DoubleVector;
import org.apache.hama.ml.util.DefaultFeatureTransformer;
import org.apache.hama.ml.util.FeatureTransformer;
-import org.apache.horn.bsp.AbstractLayeredNeuralNetwork.LearningStyle;
-import org.apache.horn.bsp.AbstractLayeredNeuralNetwork.TrainingMethod;
+import org.apache.horn.core.AbstractLayeredNeuralNetwork.LearningStyle;
+import org.apache.horn.core.AbstractLayeredNeuralNetwork.TrainingMethod;
import org.apache.horn.funcs.FunctionFactory;
import org.junit.Test;
import org.mortbay.log.Log;
@@ -60,7 +60,7 @@
@Test
public void testReadWrite() {
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
ann.addLayer(2, false,
FunctionFactory.createDoubleFunction("IdentityFunction"));
ann.addLayer(5, false,
@@ -96,7 +96,7 @@
}
// read from file
- SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(new HamaConfiguration(), modelPath);
+ LayeredNeuralNetwork annCopy = new LayeredNeuralNetwork(new HamaConfiguration(), modelPath);
assertEquals(annCopy.getClass().getSimpleName(), annCopy.getModelType());
assertEquals(modelPath, annCopy.getModelPath());
// assertEquals(learningRate, annCopy.getLearningRate(), 0.000001);
@@ -128,7 +128,7 @@
*/
public void testOutput() {
// first network
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
ann.addLayer(2, false,
FunctionFactory.createDoubleFunction("IdentityFunction"));
ann.addLayer(5, false,
@@ -151,7 +151,7 @@
// assertEquals(3, result.get(0), 0.000001);
// second network
- SmallLayeredNeuralNetwork ann2 = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann2 = new LayeredNeuralNetwork();
ann2.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann2.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann2.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
@@ -170,7 +170,7 @@
DoubleVector vec = ann2.getOutput(new DenseDoubleVector(test));
assertArrayEquals(result2, vec.toArray(), 0.000001);
- SmallLayeredNeuralNetwork ann3 = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann3 = new LayeredNeuralNetwork();
ann3.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann3.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann3.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
@@ -190,7 +190,7 @@
@Test
public void testXORlocal() {
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
@@ -228,7 +228,7 @@
} catch (IOException e) {
e.printStackTrace();
}
- SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(new HamaConfiguration(), modelPath);
+ LayeredNeuralNetwork annCopy = new LayeredNeuralNetwork(new HamaConfiguration(), modelPath);
// test on instances
for (int i = 0; i < instances.length; ++i) {
DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
@@ -243,7 +243,7 @@
@Test
public void testXORWithMomentum() {
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
@@ -278,7 +278,7 @@
} catch (IOException e) {
e.printStackTrace();
}
- SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(new HamaConfiguration(), modelPath);
+ LayeredNeuralNetwork annCopy = new LayeredNeuralNetwork(new HamaConfiguration(), modelPath);
// test on instances
for (int i = 0; i < instances.length; ++i) {
DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
@@ -293,7 +293,7 @@
@Test
public void testXORLocalWithRegularization() {
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
ann.addLayer(2, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann.addLayer(3, false, FunctionFactory.createDoubleFunction("Sigmoid"));
ann.addLayer(1, true, FunctionFactory.createDoubleFunction("Sigmoid"));
@@ -329,7 +329,7 @@
} catch (IOException e) {
e.printStackTrace();
}
- SmallLayeredNeuralNetwork annCopy = new SmallLayeredNeuralNetwork(new HamaConfiguration(), modelPath);
+ LayeredNeuralNetwork annCopy = new LayeredNeuralNetwork(new HamaConfiguration(), modelPath);
// test on instances
for (int i = 0; i < instances.length; ++i) {
DoubleVector input = new DenseDoubleVector(instances[i]).slice(2);
@@ -377,7 +377,7 @@
List<double[]> trainingInstances = instanceList.subList(0,
instanceList.size() - 100);
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
// ann.setLearningRate(0.001);
// ann.setMomemtumWeight(0.1);
//ann.setRegularizationWeight(0.01);
@@ -485,7 +485,7 @@
// create model
int dimension = 8;
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
// ann.setLearningRate(0.7);
// ann.setMomemtumWeight(0.5);
//ann.setRegularizationWeight(0.1);
@@ -590,7 +590,7 @@
// create model
int dimension = 8;
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork();
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork();
// ann.setLearningRate(0.7);
// ann.setMomemtumWeight(0.5);
//ann.setRegularizationWeight(0.1);
diff --git a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java b/src/test/java/org/apache/horn/core/TestSmallLayeredNeuralNetworkMessage.java
similarity index 93%
rename from src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java
rename to src/test/java/org/apache/horn/core/TestSmallLayeredNeuralNetworkMessage.java
index e422d95..a0c66d2 100644
--- a/src/test/java/org/apache/horn/bsp/TestSmallLayeredNeuralNetworkMessage.java
+++ b/src/test/java/org/apache/horn/core/TestSmallLayeredNeuralNetworkMessage.java
@@ -15,7 +15,7 @@
* See the License for the specific language governing permissions and
* limitations under the License.
*/
-package org.apache.horn.bsp;
+package org.apache.horn.core;
import static org.junit.Assert.assertArrayEquals;
import static org.junit.Assert.assertEquals;
@@ -34,6 +34,7 @@
import org.apache.hadoop.fs.Path;
import org.apache.hama.commons.math.DenseDoubleMatrix;
import org.apache.hama.commons.math.DoubleMatrix;
+import org.apache.horn.core.ParameterMessage;
import org.junit.Test;
/**
@@ -54,7 +55,7 @@
boolean isConverge = false;
- SmallLayeredNeuralNetworkMessage message = new SmallLayeredNeuralNetworkMessage(
+ ParameterMessage message = new ParameterMessage(
error, isConverge, matrices, null);
Configuration conf = new Configuration();
String strPath = "/tmp/testReadWriteSmallLayeredNeuralNetworkMessage";
@@ -66,7 +67,7 @@
out.close();
FSDataInputStream in = fs.open(path);
- SmallLayeredNeuralNetworkMessage readMessage = new SmallLayeredNeuralNetworkMessage(
+ ParameterMessage readMessage = new ParameterMessage(
0, isConverge, null, null);
readMessage.readFields(in);
in.close();
@@ -117,7 +118,7 @@
prevMatrices[0] = new DenseDoubleMatrix(prevMatrix1);
prevMatrices[1] = new DenseDoubleMatrix(prevMatrix2);
- SmallLayeredNeuralNetworkMessage message = new SmallLayeredNeuralNetworkMessage(
+ ParameterMessage message = new ParameterMessage(
error, isConverge, matrices, prevMatrices);
Configuration conf = new Configuration();
String strPath = "/tmp/testReadWriteSmallLayeredNeuralNetworkMessageWithPrev";
@@ -129,7 +130,7 @@
out.close();
FSDataInputStream in = fs.open(path);
- SmallLayeredNeuralNetworkMessage readMessage = new SmallLayeredNeuralNetworkMessage(
+ ParameterMessage readMessage = new ParameterMessage(
0, isConverge, null, null);
readMessage.readFields(in);
in.close();
diff --git a/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java b/src/test/java/org/apache/horn/examples/MultiLayerPerceptronTest.java
similarity index 74%
rename from src/test/java/org/apache/horn/examples/NeuralNetworkTest.java
rename to src/test/java/org/apache/horn/examples/MultiLayerPerceptronTest.java
index 932b17a..fd24c4f 100644
--- a/src/test/java/org/apache/horn/examples/NeuralNetworkTest.java
+++ b/src/test/java/org/apache/horn/examples/MultiLayerPerceptronTest.java
@@ -18,14 +18,10 @@
package org.apache.horn.examples;
import java.io.BufferedReader;
-import java.io.BufferedWriter;
import java.io.FileReader;
import java.io.IOException;
import java.io.InputStreamReader;
-import java.io.OutputStreamWriter;
import java.net.URI;
-import java.util.ArrayList;
-import java.util.List;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
@@ -37,21 +33,20 @@
import org.apache.hama.commons.io.VectorWritable;
import org.apache.hama.commons.math.DenseDoubleVector;
import org.apache.hama.commons.math.DoubleVector;
-import org.apache.horn.bsp.HornJob;
-import org.apache.horn.bsp.SmallLayeredNeuralNetwork;
+import org.apache.horn.core.HornJob;
+import org.apache.horn.core.LayeredNeuralNetwork;
/**
* Test the functionality of NeuralNetwork Example.
- *
*/
-public class NeuralNetworkTest extends HamaCluster {
+public class MultiLayerPerceptronTest extends HamaCluster {
private HamaConfiguration conf;
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";
- public NeuralNetworkTest() {
+ public MultiLayerPerceptronTest() {
conf = new HamaConfiguration();
conf.set("bsp.master.address", "localhost");
conf.setBoolean("hama.child.redirect.log.console", true);
@@ -82,22 +77,23 @@
String featureDataPath = "src/test/resources/neuralnets_classification_test.txt";
try {
- SmallLayeredNeuralNetwork ann = new SmallLayeredNeuralNetwork(conf,
+ LayeredNeuralNetwork ann = new LayeredNeuralNetwork(conf,
MODEL_PATH);
// 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(RESULT_PATH);
- if (fs.exists(outputPath)) {
- fs.delete(outputPath, true);
- }
- BufferedWriter bw = new BufferedWriter(new OutputStreamWriter(
- fs.create(outputPath)));
String line = null;
+ line = null;
+ // compare results with ground-truth
+ BufferedReader groundTruthReader = new BufferedReader(new FileReader(
+ "src/test/resources/neuralnets_classification_label.txt"));
+
+ double correct = 0;
+ int samples = 0;
while ((line = br.readLine()) != null) {
if (line.trim().length() == 0) {
continue;
@@ -109,52 +105,21 @@
}
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());
- }
+ double actual = result.toArray()[0];
+ double expected = Double.parseDouble(groundTruthReader.readLine());
- br.close();
- bw.close();
-
- // compare results with ground-truth
- BufferedReader groundTruthReader = new BufferedReader(new FileReader(
- "src/test/resources/neuralnets_classification_label.txt"));
- List<Double> groundTruthList = new ArrayList<Double>();
- 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);
LOG.info("evaluated: " + actual + ", expected: " + expected);
if (actual < 0.5 && expected < 0.5 || actual >= 0.5 && expected >= 0.5) {
++correct;
}
+ samples++;
}
- LOG.info("## Precision: " + (correct / total));
- assertTrue((correct / total) > 0.5);
+ groundTruthReader.close();
+ br.close();
+
+ LOG.info("## Precision: " + (correct / samples));
+ assertTrue((correct / samples) > 0.5);
} catch (Exception e) {
e.printStackTrace();
@@ -201,10 +166,10 @@
long startTime = System.currentTimeMillis();
if (ann.waitForCompletion(true)) {
- LOG.info("Job Finished in "
- + (System.currentTimeMillis() - startTime) / 1000.0 + " seconds");
+ LOG.info("Job Finished in " + (System.currentTimeMillis() - startTime)
+ / 1000.0 + " seconds");
}
-
+
} catch (Exception e) {
e.printStackTrace();
}
