LeNet.java

package com.bsiag.anagnostes.server.neuralnetwork;

import java.io.IOException;

import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
import org.deeplearning4j.nn.weights.WeightInit;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.lossfunctions.LossFunctions;

public class LeNet {
	
	public static final int NUM_OUTPUTS = 10;
	public static final int BATCH_SIZE = 64;
	
	private static final int NUM_CHANNELS = 1;
	private static final int NUM_ITERATIONS = 1;
	private static final int SEED = 123;
	
	/**
	 * Regarding the .setInputType(InputType.convolutionalFlat(28,28,1)) line: This does a few things. (a) It adds
	 * preprocessors, which handle things like the transition between the convolutional/subsampling layers and the dense
	 * layer (b) Does some additional configuration validation (c) Where necessary, sets the nIn (number of input
	 * neurons, or input depth in the case of CNNs) values for each layer based on the size of the previous layer (but
	 * it won't override values manually set by the user) InputTypes can be used with other layer types too (RNNs, MLPs
	 * etc) not just CNNs. For normal images (when using ImageRecordReader) use
	 * InputType.convolutional(height,width,depth). MNIST record reader is a special case, that outputs 28x28 pixel
	 * grayscale (nChannels=1) images, in a "flattened" row vector format (i.e., 1x784 vectors), hence the
	 * "convolutionalFlat" input type used here.
	 */
	public static MultiLayerConfiguration networkConfiguration() {
		return new NeuralNetConfiguration.Builder()
				.seed(SEED).weightInit(WeightInit.XAVIER)
				.iterations(NUM_ITERATIONS)
				.regularization(true).l2(0.0005).learningRate(.01)
				.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
				.updater(Updater.NESTEROVS).momentum(0.9)
				.list()
				.layer(0, new ConvolutionLayer.Builder(5, 5) 
						.stride(1, 1)
						.nIn(NUM_CHANNELS)
						.nOut(20)
						.activation(Activation.IDENTITY)
						.build())
				.layer(1, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
						.kernelSize(2, 2)
						.stride(2, 2)
						.build())
				.layer(2, new ConvolutionLayer.Builder(5, 5).stride(1, 1)
						.nOut(50)
						.activation(Activation.IDENTITY)
						.build())
				.layer(3, new SubsamplingLayer.Builder(SubsamplingLayer.PoolingType.MAX)
						.kernelSize(2, 2)
						.stride(2, 2)
						.build())
				.layer(4, new DenseLayer.Builder()
						.activation(Activation.RELU)
						.nOut(500)
						.build())
				.layer(5, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
						.activation(Activation.SOFTMAX)
						.nOut(NUM_OUTPUTS)
						.build())
				.setInputType(InputType.convolutionalFlat(28, 28, 1))
				.backprop(true)
				.pretrain(false).build();
	}

	public static DataSetIterator mnistTrainSetIterator() {
		try {
			return new MnistDataSetIterator(BATCH_SIZE, true, 12345);
		} catch (IOException e) {
			throw new RuntimeException("Couldn't build the MnistDataSetIterator");
		}
	}
	
	public static DataSetIterator mnistTestSetIterator() {
		try {
			return new MnistDataSetIterator(BATCH_SIZE, false, 12345);
		} catch (IOException e) {
			throw new RuntimeException("Couldn't build the MnistDataSetIterator");
		}
	}
	
	public static DataSetIterator numbersTrainSetIterator(String numbersBaseFolder) {
		return new NumbersDatasetIterator(BATCH_SIZE, numbersBaseFolder, true);
	}
	
	public static DataSetIterator numbersTrainSetIterator() {
		return new NumbersDatasetIterator(BATCH_SIZE, true);
	}
	
	public static DataSetIterator numbersTestSetIterator(String numbersBaseFolder) {
		return new NumbersDatasetIterator(BATCH_SIZE, numbersBaseFolder, false);
	}
	
	public static DataSetIterator numbersTestSetIterator() {
		return new NumbersDatasetIterator(BATCH_SIZE, false);
	}
}