| /** |
| * 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.core; |
| |
| import java.io.IOException; |
| |
| import org.apache.hama.HamaConfiguration; |
| import org.apache.hama.bsp.BSPJob; |
| import org.apache.hama.commons.math.Function; |
| import org.apache.horn.core.Constants.LearningStyle; |
| import org.apache.horn.core.Constants.TrainingMethod; |
| import org.apache.horn.funcs.FunctionFactory; |
| |
| public class HornJob extends BSPJob { |
| |
| AbstractLayeredNeuralNetwork neuralNetwork; |
| |
| @Deprecated |
| public HornJob(HamaConfiguration conf, Class<?> exampleClass) |
| throws IOException { |
| super(conf); |
| this.setJarByClass(exampleClass); |
| |
| // default local file block size 10mb |
| this.getConfiguration().set("fs.local.block.size", "10358951"); |
| neuralNetwork = new LayeredNeuralNetwork(); |
| } |
| |
| public HornJob(HamaConfiguration conf, |
| Class<? extends AbstractLayeredNeuralNetwork> neuralNetworkClass, |
| Class<?> exampleClass) |
| throws IOException, InstantiationException, IllegalAccessException { |
| this.setJarByClass(exampleClass); |
| |
| // default local file block size 10mb |
| this.getConfiguration().set("fs.local.block.size", "10358951"); |
| neuralNetwork = neuralNetworkClass.newInstance(); |
| } |
| |
| public void inputLayer(int featureDimension) { |
| addLayer(featureDimension, null, null); |
| neuralNetwork.setDropRateOfInputLayer(1); |
| } |
| |
| public void inputLayer(int featureDimension, float dropRate) { |
| addLayer(featureDimension, null, null); |
| neuralNetwork.setDropRateOfInputLayer(dropRate); |
| } |
| |
| public void inputLayer(int featureDimension, float dropRate, Class<? extends Neuron<?>> neuronClass) { |
| addLayer(featureDimension, null, neuronClass); |
| neuralNetwork.setDropRateOfInputLayer(dropRate); |
| } |
| |
| public void addLayer(int featureDimension, Class<? extends Function> func, |
| Class<? extends Neuron<?>> neuronClass) { |
| neuralNetwork.addLayer( |
| featureDimension, |
| false, |
| (func != null) ? FunctionFactory.createFloatFunction(func |
| .getSimpleName()) : null, neuronClass); |
| } |
| |
| /** |
| * TODO: Adds comments |
| * @param featureDimension |
| * @param class1 |
| * @param neuronClass |
| */ |
| public void addLayer(int featureDimension, Class<? extends Function> func, |
| Class<? extends Neuron<?>> neuronClass, boolean isRecurrent) { |
| if (neuralNetwork instanceof RecurrentLayeredNeuralNetwork) { |
| ((RecurrentLayeredNeuralNetwork)neuralNetwork).addLayer( |
| featureDimension, |
| false, |
| (func != null) ? FunctionFactory.createFloatFunction(func |
| .getSimpleName()) : null, neuronClass, null, isRecurrent); |
| } else { |
| this.addLayer(featureDimension, func, neuronClass); |
| } |
| } |
| |
| public void outputLayer(int labels, Class<? extends Function> func, |
| Class<? extends Neuron<?>> neuronClass) { |
| neuralNetwork.addLayer(labels, true, |
| FunctionFactory.createFloatFunction(func.getSimpleName()), neuronClass); |
| } |
| |
| public void outputLayer(int labels, Class<? extends Function> func, |
| Class<? extends Neuron<?>> neuronClass, int numOutCells) { |
| ((RecurrentLayeredNeuralNetwork)neuralNetwork).addLayer(labels, true, |
| FunctionFactory.createFloatFunction(func.getSimpleName()), neuronClass, numOutCells); |
| } |
| |
| public void setCostFunction(Class<? extends Function> func) { |
| neuralNetwork.setCostFunction(FunctionFactory.createFloatFloatFunction(func |
| .getSimpleName())); |
| } |
| |
| public void setDouble(String name, double value) { |
| conf.setDouble(name, value); |
| } |
| |
| public void setMaxIteration(int maxIteration) { |
| this.conf.setInt("training.max.iterations", maxIteration); |
| } |
| |
| public void setBatchSize(int batchSize) { |
| this.conf.setInt("training.batch.size", batchSize); |
| } |
| |
| public void setRecurrentStepSize(int stepSize) { |
| ((RecurrentLayeredNeuralNetwork) neuralNetwork).setRecurrentStepSize(stepSize); |
| this.conf.setInt("training.recurrent.step.size", stepSize); |
| } |
| |
| public void setTrainingMethod(TrainingMethod method) { |
| this.neuralNetwork.setTrainingMethod(method); |
| } |
| |
| public void setLearningStyle(LearningStyle style) { |
| this.neuralNetwork.setLearningStyle(style); |
| } |
| |
| public void setLearningRate(float learningRate) { |
| this.neuralNetwork.setLearningRate(learningRate); |
| } |
| |
| public void setConvergenceCheckInterval(int n) { |
| this.conf.setInt("convergence.check.interval", n); |
| } |
| |
| public void setMomentumWeight(float momentumWeight) { |
| this.neuralNetwork.setMomemtumWeight(momentumWeight); |
| } |
| |
| public void setRegularizationWeight(float regularizationWeight) { |
| this.neuralNetwork.setRegularizationWeight(regularizationWeight); |
| } |
| |
| public AbstractLayeredNeuralNetwork getNeuralNetwork() { |
| return neuralNetwork; |
| } |
| |
| public boolean waitForCompletion(boolean verbose) throws IOException, |
| InterruptedException, ClassNotFoundException { |
| BSPJob job = neuralNetwork.train((HamaConfiguration) this.conf); |
| if (verbose) { |
| return job.waitForCompletion(true); |
| } else { |
| return job.waitForCompletion(false); |
| } |
| } |
| |
| public void setModelPath(String modelPath) { |
| this.conf.set("model.path", modelPath); |
| neuralNetwork.setModelPath(modelPath); |
| } |
| |
| public void setTrainingSetPath(String inputPath) { |
| this.conf.set("training.input.path", inputPath); |
| } |
| |
| } |