| /** |
| * 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.FloatWritable; |
| 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.RecurrentDropoutNeuron; |
| import org.apache.horn.core.RecurrentLayeredNeuralNetwork; |
| import org.apache.horn.core.Synapse; |
| import org.apache.horn.funcs.CrossEntropy; |
| import org.apache.horn.funcs.ReLU; |
| import org.apache.horn.funcs.SoftMax; |
| import org.apache.horn.funcs.Tanh; |
| |
| public class MnistRecurrentMultiLayerPerceptron { |
| |
| public static class StandardNeuron extends |
| Neuron<Synapse<FloatWritable, FloatWritable>> { |
| |
| @Override |
| public void forward(Iterable<Synapse<FloatWritable, FloatWritable>> messages) |
| throws IOException { |
| float sum = 0; |
| for (Synapse<FloatWritable, FloatWritable> m : messages) { |
| sum += m.getInput() * m.getWeight(); |
| } |
| this.feedforward(squashingFunction.apply(sum)); |
| } |
| |
| @Override |
| public void backward( |
| Iterable<Synapse<FloatWritable, FloatWritable>> messages) |
| throws IOException { |
| float delta = 0; |
| |
| if (!this.isDropped()) { |
| for (Synapse<FloatWritable, FloatWritable> m : messages) { |
| // Calculates error gradient for each neuron |
| delta += (m.getDelta() * m.getWeight()); |
| |
| // Weight corrections |
| float weight = -this.getLearningRate() * m.getDelta() |
| * this.getOutput() + this.getMomentumWeight() * m.getPrevWeight(); |
| this.push(weight); |
| } |
| } |
| this.backpropagate(delta * squashingFunction.applyDerivative(getOutput())); |
| } |
| } |
| |
| public static HornJob createJob(HamaConfiguration conf, String modelPath, |
| String inputPath, float learningRate, float momemtumWeight, |
| float regularizationWeight, int features, int hu, int labels, |
| int stepSize, int numOutCells, int miniBatch, int maxIteration) |
| throws IOException, InstantiationException, IllegalAccessException { |
| |
| boolean isRecurrent = (stepSize == 1 ? false: true); |
| |
| HornJob job = new HornJob( |
| conf, RecurrentLayeredNeuralNetwork.class, MnistRecurrentMultiLayerPerceptron.class); |
| job.setTrainingSetPath(inputPath); |
| job.setModelPath(modelPath); |
| |
| job.setMaxIteration(maxIteration); |
| job.setLearningRate(learningRate); |
| job.setMomentumWeight(momemtumWeight); |
| job.setRegularizationWeight(regularizationWeight); |
| |
| job.setConvergenceCheckInterval(10); |
| job.setBatchSize(miniBatch); |
| job.setRecurrentStepSize(stepSize); |
| |
| job.setTrainingMethod(TrainingMethod.GRADIENT_DESCENT); |
| |
| job.inputLayer(features, 1.0f); // droprate |
| job.addLayer(hu, Tanh.class, RecurrentDropoutNeuron.class, isRecurrent); |
| job.outputLayer(labels, SoftMax.class, RecurrentDropoutNeuron.class, numOutCells); |
| |
| job.setCostFunction(CrossEntropy.class); |
| |
| return job; |
| } |
| |
| public static void main(String[] args) throws IOException, |
| InterruptedException, ClassNotFoundException, |
| NumberFormatException, InstantiationException, IllegalAccessException { |
| |
| if (args.length < 9) { |
| System.out.println("Usage: <MODEL_PATH> <INPUT_PATH> " |
| + "<LEARNING_RATE> <MOMEMTUM_WEIGHT> <REGULARIZATION_WEIGHT> " |
| + "<FEATURE_DIMENSION> <HIDDEN_UNITS> <LABEL_DIMENSION> " |
| + "<STEP_SIZE> <NUM_OUTPUTCELLS> <BATCH_SIZE> <MAX_ITERATION>"); |
| System.out.println("E.g. MnistRecurrentMultiLayerPerceptron" |
| + " ./model_rnn mnist.seq 0.01 0.9 0.0005 196 200 10 4 1 10 12000"); |
| System.exit(-1); |
| } |
| |
| HornJob rnn = createJob(new HamaConfiguration(), args[0], args[1], |
| Float.parseFloat(args[2]), Float.parseFloat(args[3]), |
| Float.parseFloat(args[4]), Integer.parseInt(args[5]), |
| Integer.parseInt(args[6]), Integer.parseInt(args[7]), |
| Integer.parseInt(args[8]), Integer.parseInt(args[9]), |
| Integer.parseInt(args[10]), Integer.parseInt(args[11])); |
| |
| long startTime = System.currentTimeMillis(); |
| if (rnn.waitForCompletion(true)) { |
| System.out.println("Optimization Finished! " |
| + (System.currentTimeMillis() - startTime) / 1000.0 + " seconds"); |
| } |
| } |
| } |