Package com.github.neuralnetworks.samples.test

Source Code of com.github.neuralnetworks.samples.test.MnistTest

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package com.github.neuralnetworks.samples.test;

import static org.junit.Assert.assertEquals;

import org.junit.Ignore;
import org.junit.Test;

import com.amd.aparapi.Kernel.EXECUTION_MODE;
import com.github.neuralnetworks.architecture.NeuralNetworkImpl;
import com.github.neuralnetworks.architecture.types.NNFactory;
import com.github.neuralnetworks.input.MultipleNeuronsOutputError;
import com.github.neuralnetworks.input.ScalingInputFunction;
import com.github.neuralnetworks.samples.mnist.MnistInputProvider;
import com.github.neuralnetworks.training.TrainerFactory;
import com.github.neuralnetworks.training.backpropagation.BackPropagationTrainer;
import com.github.neuralnetworks.training.events.LogTrainingListener;
import com.github.neuralnetworks.training.random.MersenneTwisterRandomInitializer;
import com.github.neuralnetworks.training.random.NNRandomInitializer;
import com.github.neuralnetworks.util.Environment;

/**
* MNIST test
*/
public class MnistTest {

    @Test
    public void testSigmoidBP() {
  Environment.getInstance().setUseDataSharedMemory(false);
  Environment.getInstance().setUseWeightsSharedMemory(false);
  NeuralNetworkImpl mlp = NNFactory.mlpSigmoid(new int[] { 784, 10 }, true);

  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
  testInputProvider.addInputModifier(new ScalingInputFunction(255));

  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 0f, 1, 1000, 1);

  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));

  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);

  bpt.train();
  bpt.test();

  assertEquals(0, bpt.getOutputError().getTotalNetworkError(), 0.1);
    }

    @Ignore
    @Test
    public void testSigmoidHiddenBP() {
  NeuralNetworkImpl mlp = NNFactory.mlpSigmoid(new int[] { 784, 300, 100, 10 }, true);

  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
  testInputProvider.addInputModifier(new ScalingInputFunction(255));

  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(mlp, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.01f, 0.5f, 0f, 0f, 0f, 1, 1000, 2);

  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));

  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);

  bpt.train();
  bpt.test();

  assertEquals(0, bpt.getOutputError().getTotalNetworkError(), 0.1);
    }

    @Test
    public void testLeNetSmall() {
  // cpu execution mode
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);

  // Convolutional network
  NeuralNetworkImpl nn = NNFactory.convNN(new int[][] { { 28, 28, 1 }, { 5, 5, 20, 1 }, { 2, 2 }, { 5, 5, 50, 1 }, { 2, 2 }, {512}, {10} }, true);
  nn.setLayerCalculator(NNFactory.lcSigmoid(nn, null));
  NNFactory.lcMaxPooling(nn);

  // Mnist dataset provider
  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
  testInputProvider.addInputModifier(new ScalingInputFunction(255));

  // Backpropagation trainer that also works for convolutional and subsampling layers
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(nn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f), 0.5f), 0.01f, 0.5f, 0f, 0f, 0f, 1, 1000, 1);

  // log data
  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));

  // training
  bpt.train();

  // testing
  bpt.test();

  assertEquals(0, bpt.getOutputError().getTotalNetworkError(), 0.1);
    }

    @Test
    public void testLeNetTiny() {
  Environment.getInstance().setUseDataSharedMemory(false);
  Environment.getInstance().setUseWeightsSharedMemory(false);

  // very simple convolutional network with a single 2x2 max pooling layer
  NeuralNetworkImpl nn = NNFactory.convNN(new int[][] { { 28, 28, 1 }, { 2, 2 }, {10} }, true);
  nn.setLayerCalculator(NNFactory.lcSigmoid(nn, null));
  NNFactory.lcMaxPooling(nn);

  // MNIST dataset
  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
  testInputProvider.addInputModifier(new ScalingInputFunction(255));

  // Backpropagation trainer that also works for convolutional and subsampling layers
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(nn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 0f, 1, 1, 1);

  // log data
  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));

  // cpu execution
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);

  // training
  bpt.train();

  // testing
  bpt.test();

  assertEquals(0, bpt.getOutputError().getTotalNetworkError(), 0.1);
    }

    /**
     * MNIST small LeNet network
     */
    @Test
    public void testLeNetTiny2() {
  Environment.getInstance().setUseDataSharedMemory(false);
  Environment.getInstance().setUseWeightsSharedMemory(false);

  // very simple convolutional network with a single convolutional layer with 6 5x5 filters and a single 2x2 max pooling layer
  NeuralNetworkImpl nn = NNFactory.convNN(new int[][] { { 28, 28, 1 }, { 5, 5, 6, 1 }, {2, 2}, {10} }, true);
  nn.setLayerCalculator(NNFactory.lcSigmoid(nn, null));
  NNFactory.lcMaxPooling(nn);

  // MNIST dataset
  MnistInputProvider trainInputProvider = new MnistInputProvider("train-images.idx3-ubyte", "train-labels.idx1-ubyte");
  trainInputProvider.addInputModifier(new ScalingInputFunction(255));
  MnistInputProvider testInputProvider = new MnistInputProvider("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte");
  testInputProvider.addInputModifier(new ScalingInputFunction(255));

  // Backpropagation trainer that also works for convolutional and subsampling layers
  BackPropagationTrainer<?> bpt = TrainerFactory.backPropagation(nn, trainInputProvider, testInputProvider, new MultipleNeuronsOutputError(), new NNRandomInitializer(new MersenneTwisterRandomInitializer(-0.01f, 0.01f)), 0.02f, 0.5f, 0f, 0f, 0f, 1, 1, 1);

  // log data
  bpt.addEventListener(new LogTrainingListener(Thread.currentThread().getStackTrace()[1].getMethodName(), false, true));

  // cpu execution
  Environment.getInstance().setExecutionMode(EXECUTION_MODE.CPU);

  // training
  bpt.train();

  // testing
  bpt.test();

  assertEquals(0, bpt.getOutputError().getTotalNetworkError(), 0.1);
    }
}
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Related Classes of com.github.neuralnetworks.samples.test.MnistTest

  • com.github.neuralnetworks.architecture.NeuralNetworkImpl
  • com.github.neuralnetworks.architecture.types.Autoencoder
  • com.github.neuralnetworks.architecture.types.RBM
  • com.github.neuralnetworks.input.MultipleNeuronsOutputError
  • com.github.neuralnetworks.input.ScalingInputFunction
  • com.github.neuralnetworks.samples.mnist.MnistInputProvider
  • com.github.neuralnetworks.samples.mnist.MnistTargetMultiNeuronOutputConverter
  • com.github.neuralnetworks.training.events.LogTrainingListener
  • com.github.neuralnetworks.training.random.MersenneTwisterRandomInitializer
  • com.github.neuralnetworks.training.random.NNRandomInitializer
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