| /** |
| * 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.fs.Path; |
| import org.apache.hadoop.io.DoubleWritable; |
| import org.apache.hama.HamaConfiguration; |
| import org.apache.hama.bsp.TextInputFormat; |
| import org.apache.horn.bsp.HornJob; |
| import org.apache.horn.funcs.CrossEntropy; |
| import org.apache.horn.funcs.Sigmoid; |
| import org.apache.horn.trainer.Neuron; |
| import org.apache.horn.trainer.PropMessage; |
| |
| public class MultiLayerPerceptron { |
| |
| public static class StandardNeuron extends |
| Neuron<PropMessage<DoubleWritable, DoubleWritable>> { |
| |
| private double learningRate; |
| private double lambda; |
| private double momentum; |
| private static double bias = -1; |
| |
| @Override |
| public void setup(HamaConfiguration conf) { |
| this.learningRate = conf.getDouble("mlp.learning.rate", 0.1); |
| this.lambda = conf.getDouble("mlp.regularization.weight", 0.01); |
| this.momentum = conf.getDouble("mlp.momentum.weight", 0.2); |
| } |
| |
| @Override |
| public void forward( |
| Iterable<PropMessage<DoubleWritable, DoubleWritable>> messages) |
| throws IOException { |
| double sum = 0; |
| |
| for (PropMessage<DoubleWritable, DoubleWritable> m : messages) { |
| sum += m.getInput() * m.getWeight(); |
| } |
| sum += bias * this.getTheta(); // add bias feature |
| feedforward(activation(sum)); |
| } |
| |
| @Override |
| public void backward( |
| Iterable<PropMessage<DoubleWritable, DoubleWritable>> messages) |
| throws IOException { |
| for (PropMessage<DoubleWritable, DoubleWritable> m : messages) { |
| // Calculates error gradient for each neuron |
| double gradient = this.getOutput() * (1 - this.getOutput()) |
| * m.getDelta() * m.getWeight(); |
| backpropagate(gradient); |
| |
| // Weight corrections |
| double weight = -learningRate * this.getOutput() * m.getDelta() |
| + momentum * this.getPreviousWeight(); |
| this.push(weight); |
| } |
| } |
| |
| } |
| |
| public static void main(String[] args) throws IOException, |
| InterruptedException, ClassNotFoundException { |
| HamaConfiguration conf = new HamaConfiguration(); |
| HornJob job = new HornJob(conf, MultiLayerPerceptron.class); |
| |
| job.setDouble("mlp.learning.rate", 0.1); |
| job.setDouble("mlp.regularization.weight", 0.01); |
| job.setDouble("mlp.momentum.weight", 0.2); |
| |
| // initialize the topology of the model. |
| // a three-layer model is created in this example |
| job.addLayer(1000, StandardNeuron.class, Sigmoid.class); // 1st layer |
| job.addLayer(800, StandardNeuron.class, Sigmoid.class); // 2nd layer |
| job.addLayer(300, StandardNeuron.class, Sigmoid.class); // total classes |
| |
| // set the cost function to evaluate the error |
| job.setCostFunction(CrossEntropy.class); |
| |
| // set I/O and others |
| job.setInputFormat(TextInputFormat.class); |
| job.setOutputPath(new Path("/tmp/")); |
| job.setMaxIteration(10000); |
| job.setNumBspTask(3); |
| |
| long startTime = System.currentTimeMillis(); |
| |
| if (job.waitForCompletion(true)) { |
| System.out.println("Job Finished in " |
| + (System.currentTimeMillis() - startTime) / 1000.0 + " seconds"); |
| } |
| } |
| } |