| /** |
| * 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.IOException; |
| |
| import org.apache.hadoop.io.DoubleWritable; |
| import org.apache.hama.HamaConfiguration; |
| import org.apache.horn.core.Constants.TrainingMethod; |
| 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; |
| import org.apache.horn.funcs.SoftMax; |
| |
| public class MultiLayerPerceptron { |
| |
| public static class StandardNeuron extends |
| Neuron<Synapse<DoubleWritable, DoubleWritable>> { |
| |
| @Override |
| public void forward( |
| Iterable<Synapse<DoubleWritable, DoubleWritable>> messages) |
| throws IOException { |
| double sum = 0; |
| for (Synapse<DoubleWritable, DoubleWritable> m : messages) { |
| sum += m.getInput() * m.getWeight(); |
| } |
| this.feedforward(squashingFunction.apply(sum)); |
| } |
| |
| @Override |
| public void backward( |
| Iterable<Synapse<DoubleWritable, DoubleWritable>> messages) |
| throws IOException { |
| double gradient = 0; |
| for (Synapse<DoubleWritable, DoubleWritable> m : messages) { |
| // Calculates error gradient for each neuron |
| gradient += (m.getDelta() * m.getWeight()); |
| |
| // Weight corrections |
| double weight = -this.getLearningRate() * this.getOutput() |
| * m.getDelta() + this.getMomentumWeight() * m.getPrevWeight(); |
| this.push(weight); |
| } |
| |
| this.backpropagate(gradient |
| * squashingFunction.applyDerivative(getOutput())); |
| } |
| } |
| |
| public static HornJob createJob(HamaConfiguration conf, String modelPath, |
| String inputPath, double learningRate, double momemtumWeight, |
| double regularizationWeight, int features, int hu, int labels, |
| int miniBatch, int maxIteration) throws IOException { |
| |
| HornJob job = new HornJob(conf, MultiLayerPerceptron.class); |
| job.setTrainingSetPath(inputPath); |
| job.setModelPath(modelPath); |
| |
| job.setMaxIteration(maxIteration); |
| job.setLearningRate(learningRate); |
| job.setMomentumWeight(momemtumWeight); |
| job.setRegularizationWeight(regularizationWeight); |
| |
| job.setConvergenceCheckInterval(600); |
| job.setBatchSize(miniBatch); |
| |
| job.setTrainingMethod(TrainingMethod.GRADIENT_DESCENT); |
| |
| job.inputLayer(features, Sigmoid.class, StandardNeuron.class); |
| job.addLayer(hu, Sigmoid.class, StandardNeuron.class); |
| job.outputLayer(labels, SoftMax.class, StandardNeuron.class); |
| |
| job.setCostFunction(CrossEntropy.class); |
| |
| return job; |
| } |
| |
| public static void main(String[] args) throws IOException, |
| InterruptedException, ClassNotFoundException { |
| if (args.length < 9) { |
| System.out.println("Usage: <MODEL_PATH> <INPUT_PATH> " |
| + "<LEARNING_RATE> <MOMEMTUM_WEIGHT> <REGULARIZATION_WEIGHT> " |
| + "<FEATURE_DIMENSION> <HIDDEN_UNITS> <LABEL_DIMENSION> " |
| + "<BATCH_SIZE> <MAX_ITERATION>"); |
| System.exit(-1); |
| } |
| |
| HornJob ann = createJob(new HamaConfiguration(), args[0], args[1], |
| Double.parseDouble(args[2]), Double.parseDouble(args[3]), |
| Double.parseDouble(args[4]), Integer.parseInt(args[5]), |
| Integer.parseInt(args[6]), Integer.parseInt(args[7]), |
| Integer.parseInt(args[8]), Integer.parseInt(args[9])); |
| |
| long startTime = System.currentTimeMillis(); |
| if (ann.waitForCompletion(true)) { |
| System.out.println("Job Finished in " |
| + (System.currentTimeMillis() - startTime) / 1000.0 + " seconds"); |
| } |
| } |
| } |