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

Example 66 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 67 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 68 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());
    }
}

Example 69 with MultiLayerConfiguration

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

the class TestConvolutionModes method testStrictTruncateConvolutionModeOutput.

@Test
public void testStrictTruncateConvolutionModeOutput() {
    //Idea: with convolution mode == Truncate, input size shouldn't matter (within the bounds of truncated edge),
    // and edge data shouldn't affect the output
    //Use: 9x9, kernel 3, stride 3, padding 0
    // Should get same output for 10x10 and 11x11...
    Nd4j.getRandom().setSeed(12345);
    int[] minibatches = { 1, 3 };
    int[] inDepths = { 1, 3 };
    int[] inSizes = { 9, 10, 11 };
    for (boolean isSubsampling : new boolean[] { false, true }) {
        for (int minibatch : minibatches) {
            for (int inDepth : inDepths) {
                INDArray origData = Nd4j.rand(new int[] { minibatch, inDepth, 9, 9 });
                for (int inSize : inSizes) {
                    for (ConvolutionMode cm : new ConvolutionMode[] { ConvolutionMode.Strict, ConvolutionMode.Truncate }) {
                        INDArray inputData = Nd4j.rand(new int[] { minibatch, inDepth, inSize, inSize });
                        inputData.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 9), NDArrayIndex.interval(0, 9)).assign(origData);
                        Layer layer;
                        if (isSubsampling) {
                            layer = new SubsamplingLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).build();
                        } else {
                            layer = new ConvolutionLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).nOut(3).build();
                        }
                        MultiLayerNetwork net = null;
                        try {
                            MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).convolutionMode(cm).list().layer(0, layer).layer(1, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nOut(3).build()).setInputType(InputType.convolutional(inSize, inSize, inDepth)).build();
                            net = new MultiLayerNetwork(conf);
                            net.init();
                            if (inSize > 9 && cm == ConvolutionMode.Strict) {
                                fail("Expected exception");
                            }
                        } catch (DL4JException e) {
                            if (inSize == 9 || cm != ConvolutionMode.Strict) {
                                e.printStackTrace();
                                fail("Unexpected exception");
                            }
                            //Expected exception
                            continue;
                        } catch (Exception e) {
                            e.printStackTrace();
                            fail("Unexpected exception");
                        }
                        INDArray out = net.output(origData);
                        INDArray out2 = net.output(inputData);
                        assertEquals(out, out2);
                    }
                }
            }
        }
    }
}

Example 70 with MultiLayerConfiguration

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

the class TestConvolutionModes method testGlobalLocalConfig.

@Test
public void testGlobalLocalConfig() {
    for (ConvolutionMode cm : new ConvolutionMode[] { ConvolutionMode.Strict, ConvolutionMode.Truncate }) {
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).convolutionMode(cm).list().layer(0, new ConvolutionLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(1, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Strict).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(2, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Truncate).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(3, new ConvolutionLayer.Builder().convolutionMode(ConvolutionMode.Same).kernelSize(3, 3).stride(3, 3).padding(0, 0).nIn(3).nOut(3).build()).layer(4, new SubsamplingLayer.Builder().kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(5, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Strict).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(6, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Truncate).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(7, new SubsamplingLayer.Builder().convolutionMode(ConvolutionMode.Same).kernelSize(3, 3).stride(3, 3).padding(0, 0).build()).layer(8, new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nOut(3).build()).build();
        assertEquals(cm, ((ConvolutionLayer) conf.getConf(0).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Strict, ((ConvolutionLayer) conf.getConf(1).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Truncate, ((ConvolutionLayer) conf.getConf(2).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Same, ((ConvolutionLayer) conf.getConf(3).getLayer()).getConvolutionMode());
        assertEquals(cm, ((SubsamplingLayer) conf.getConf(4).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Strict, ((SubsamplingLayer) conf.getConf(5).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Truncate, ((SubsamplingLayer) conf.getConf(6).getLayer()).getConvolutionMode());
        assertEquals(ConvolutionMode.Same, ((SubsamplingLayer) conf.getConf(7).getLayer()).getConvolutionMode());
    }
}