Examples with DL4JException org.deeplearning4j.exception.DL4JException used on opensource projects

Example 21 with DL4JException

use of org.deeplearning4j.exception.DL4JException in project deeplearning4j by deeplearning4j.

the class TestInvalidInput method testLabelsNOutMismatchOutputLayer.

@Test
public void testLabelsNOutMismatchOutputLayer() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder().nIn(10).nOut(10).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    try {
        net.fit(Nd4j.create(1, 10), Nd4j.create(1, 20));
        fail("Expected DL4JException");
    } catch (DL4JException e) {
        System.out.println("testLabelsNOutMismatchOutputLayer(): " + e.getMessage());
    } catch (Exception e) {
        e.printStackTrace();
        fail("Expected DL4JException");
    }
}

Example 22 with DL4JException

use of org.deeplearning4j.exception.DL4JException in project deeplearning4j by deeplearning4j.

the class TestInvalidInput method testInputNinMismatchDense.

@Test
public void testInputNinMismatchDense() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list().layer(0, new DenseLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder().nIn(10).nOut(10).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    try {
        net.feedForward(Nd4j.create(1, 20));
        fail("Expected DL4JException");
    } catch (DL4JException e) {
        System.out.println("testInputNinMismatchDense(): " + e.getMessage());
    } catch (Exception e) {
        e.printStackTrace();
        fail("Expected DL4JException");
    }
}

Example 23 with DL4JException

use of org.deeplearning4j.exception.DL4JException in project deeplearning4j by deeplearning4j.

the class TestInvalidInput method testInputNinMismatchLSTM.

@Test
public void testInputNinMismatchLSTM() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list().layer(0, new GravesLSTM.Builder().nIn(5).nOut(5).build()).layer(1, new RnnOutputLayer.Builder().nIn(5).nOut(5).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    try {
        net.fit(Nd4j.create(1, 10, 5), Nd4j.create(1, 5, 5));
        fail("Expected DL4JException");
    } catch (DL4JException e) {
        System.out.println("testInputNinMismatchLSTM(): " + e.getMessage());
    } catch (Exception e) {
        e.printStackTrace();
        fail("Expected DL4JException");
    }
}

Example 24 with DL4JException

use of org.deeplearning4j.exception.DL4JException in project deeplearning4j by deeplearning4j.

the class TestInvalidInput method testInputNinMismatchEmbeddingLayer.

@Test
public void testInputNinMismatchEmbeddingLayer() {
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().list().layer(0, new EmbeddingLayer.Builder().nIn(10).nOut(10).build()).layer(1, new OutputLayer.Builder().nIn(10).nOut(10).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    try {
        net.feedForward(Nd4j.create(10, 5));
        fail("Expected DL4JException");
    } catch (DL4JException e) {
        System.out.println("testInputNinMismatchEmbeddingLayer(): " + e.getMessage());
    } catch (Exception e) {
        e.printStackTrace();
        fail("Expected DL4JException");
    }
}

Example 25 with DL4JException

use of org.deeplearning4j.exception.DL4JException in project deeplearning4j by deeplearning4j.

the class TestConvolutionModes method testStrictTruncateConvolutionModeCompGraph.

@Test
public void testStrictTruncateConvolutionModeCompGraph() {
    //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();
                        }
                        ComputationGraph net = null;
                        try {
                            ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().weightInit(WeightInit.XAVIER).convolutionMode(cm).graphBuilder().addInputs("in").addLayer("0", layer, "in").addLayer("1", new OutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nOut(3).build(), "0").setOutputs("1").setInputTypes(InputType.convolutional(inSize, inSize, inDepth)).build();
                            net = new ComputationGraph(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.outputSingle(origData);
                        INDArray out2 = net.outputSingle(inputData);
                        assertEquals(out, out2);
                    }
                }
            }
        }
    }
}