Examples with MultiLayerConfiguration org.deeplearning4j.nn.conf.MultiLayerConfiguration used on opensource projects

Example 11 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method testCNNTooLargeKernel.

@Test(expected = DL4JException.class)
public void testCNNTooLargeKernel() {
    int imageHeight = 20;
    int imageWidth = 23;
    int nChannels = 1;
    int classes = 2;
    int numSamples = 200;
    int kernelHeight = imageHeight;
    int kernelWidth = imageWidth + 1;
    DataSet trainInput;
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(123).iterations(1).list().layer(0, //(img-kernel+2*padding)/stride + 1: must be >= 1. Therefore: with p=0, kernel <= img size
    new ConvolutionLayer.Builder(kernelHeight, kernelWidth).stride(1, 1).nOut(2).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new OutputLayer.Builder().nOut(classes).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).setInputType(InputType.convolutionalFlat(imageHeight, imageWidth, nChannels)).backprop(true).pretrain(false);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    INDArray emptyFeatures = Nd4j.zeros(numSamples, imageWidth * imageHeight * nChannels);
    INDArray emptyLables = Nd4j.zeros(numSamples, classes);
    trainInput = new DataSet(emptyFeatures, emptyLables);
    model.fit(trainInput);
}

Example 12 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class ConvolutionLayerTest method testCNNZeroStride.

@Test(expected = Exception.class)
public void testCNNZeroStride() {
    int imageHeight = 20;
    int imageWidth = 23;
    int nChannels = 1;
    int classes = 2;
    int numSamples = 200;
    int kernelHeight = imageHeight;
    int kernelWidth = imageWidth;
    DataSet trainInput;
    MultiLayerConfiguration.Builder builder = new NeuralNetConfiguration.Builder().seed(123).iterations(1).list().layer(0, new ConvolutionLayer.Builder(kernelHeight, kernelWidth).stride(1, 0).nOut(2).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new OutputLayer.Builder().nOut(classes).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build()).backprop(true).pretrain(false);
    new ConvolutionLayerSetup(builder, imageHeight, imageWidth, nChannels);
    MultiLayerConfiguration conf = builder.build();
    MultiLayerNetwork model = new MultiLayerNetwork(conf);
    model.init();
    INDArray emptyFeatures = Nd4j.zeros(numSamples, imageWidth * imageHeight * nChannels);
    INDArray emptyLables = Nd4j.zeros(numSamples, classes);
    trainInput = new DataSet(emptyFeatures, emptyLables);
    model.fit(trainInput);
}

Example 13 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class ActivationLayerTest method testDenseActivationLayer.

@Test
public void testDenseActivationLayer() throws Exception {
    DataSetIterator iter = new MnistDataSetIterator(2, 2);
    DataSet next = iter.next();
    // Run without separate activation layer
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new DenseLayer.Builder().nIn(28 * 28 * 1).nOut(10).activation(Activation.RELU).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network = new MultiLayerNetwork(conf);
    network.init();
    network.fit(next);
    // Run with separate activation layer
    MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).seed(123).list().layer(0, new DenseLayer.Builder().nIn(28 * 28 * 1).nOut(10).activation(Activation.IDENTITY).weightInit(WeightInit.XAVIER).build()).layer(1, new org.deeplearning4j.nn.conf.layers.ActivationLayer.Builder().activation(Activation.RELU).build()).layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).nIn(10).nOut(10).build()).backprop(true).pretrain(false).build();
    MultiLayerNetwork network2 = new MultiLayerNetwork(conf2);
    network2.init();
    network2.fit(next);
    // check parameters
    assertEquals(network.getLayer(0).getParam("W"), network2.getLayer(0).getParam("W"));
    assertEquals(network.getLayer(1).getParam("W"), network2.getLayer(2).getParam("W"));
    assertEquals(network.getLayer(0).getParam("b"), network2.getLayer(0).getParam("b"));
    assertEquals(network.getLayer(1).getParam("b"), network2.getLayer(2).getParam("b"));
    // check activations
    network.init();
    network.setInput(next.getFeatureMatrix());
    List<INDArray> activations = network.feedForward(true);
    network2.init();
    network2.setInput(next.getFeatureMatrix());
    List<INDArray> activations2 = network2.feedForward(true);
    assertEquals(activations.get(1).reshape(activations2.get(2).shape()), activations2.get(2));
    assertEquals(activations.get(2), activations2.get(3));
}

Example 14 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class BaseLayerTest method configureMultiLayer.

public MultiLayerNetwork configureMultiLayer() {
    int nIn = 2;
    int nOut = 2;
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list().layer(0, new DenseLayer.Builder().nIn(nIn).nOut(nOut).build()).layer(1, new OutputLayer.Builder().nIn(nIn).nOut(nOut).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    return net;
}

Example 15 with MultiLayerConfiguration

use of org.deeplearning4j.nn.conf.MultiLayerConfiguration in project deeplearning4j by deeplearning4j.

the class TestConvolutionModes method testSameModeActivationSizes.

@Test
public void testSameModeActivationSizes() {
    int inH = 3;
    int inW = 4;
    int inDepth = 3;
    int minibatch = 5;
    int sH = 2;
    int sW = 2;
    int kH = 3;
    int kW = 3;
    Layer[] l = new Layer[2];
    l[0] = new ConvolutionLayer.Builder().nOut(4).kernelSize(kH, kW).stride(sH, sW).build();
    l[1] = new SubsamplingLayer.Builder().kernelSize(kH, kW).stride(sH, sW).build();
    for (int i = 0; i < l.length; i++) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().convolutionMode(ConvolutionMode.Same).list().layer(0, l[i]).layer(1, new OutputLayer.Builder().nOut(3).build()).setInputType(InputType.convolutional(inH, inW, inDepth)).build();
        MultiLayerNetwork net = new MultiLayerNetwork(conf);
        net.init();
        INDArray inData = Nd4j.create(minibatch, inDepth, inH, inW);
        List<INDArray> activations = net.feedForward(inData);
        INDArray actL0 = activations.get(1);
        int outH = (int) Math.ceil(inH / ((double) sH));
        int outW = (int) Math.ceil(inW / ((double) sW));
        System.out.println(Arrays.toString(actL0.shape()));
        assertArrayEquals(new int[] { minibatch, (i == 0 ? 4 : inDepth), outH, outW }, actL0.shape());
    }
}