Java Code Examples for org.deeplearning4j.nn.multilayer.MultiLayerNetwork#load()

The following examples show how to use org.deeplearning4j.nn.multilayer.MultiLayerNetwork#load() . You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example 1
Source File: RegressionTest100a.java    From deeplearning4j with Apache License 2.0 6 votes vote down vote up 
@Test
public void testCustomLayer() throws Exception {
    //We dropped support for 1.0.0-alpha and earlier custom layers due to the maintenance overhead for a rarely used feature
    //An upgrade path exists as a workaround - load in beta to beta4 and re-save
    //All built-in layers can be loaded going back to 0.5.0

    File f = Resources.asFile("regression_testing/100a/CustomLayerExample_100a.bin");

    try {
        MultiLayerNetwork.load(f, true);
        fail("Expected exception");
    } catch (Exception e){
        String msg = e.getMessage();
        assertTrue(msg, msg.contains("custom") && msg.contains("1.0.0-beta") && msg.contains("saved again"));
    }
}
Example 2
Source File: RegressionTest100b4.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testVae() throws Exception {

    File f = Resources.asFile("regression_testing/100b4/VaeMNISTAnomaly_100b4.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    VariationalAutoencoder l0 = (VariationalAutoencoder) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationLReLU(), l0.getActivationFn());
    assertEquals(32, l0.getNOut());
    assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
    assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
    assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
    assertEquals(new Adam(1e-3), l0.getIUpdater());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100b4/VaeMNISTAnomaly_Output_100b4.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100b4/VaeMNISTAnomaly_Input_100b4.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}
Example 3
Source File: RegressionTest100b3.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testVae() throws Exception {

    File f = Resources.asFile("regression_testing/100b3/VaeMNISTAnomaly_100b3.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    VariationalAutoencoder l0 = (VariationalAutoencoder) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationLReLU(), l0.getActivationFn());
    assertEquals(32, l0.getNOut());
    assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
    assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
            assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new WeightDecay(1e-4, false), TestUtils.getWeightDecayReg(l0));
    assertEquals(new Adam(1e-3), l0.getIUpdater());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100b3/VaeMNISTAnomaly_Output_100b3.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100b3/VaeMNISTAnomaly_Input_100b3.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}
Example 4
Source File: RegressionTest100b6.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testVae() throws Exception {

    File f = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_100b6.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    VariationalAutoencoder l0 = (VariationalAutoencoder) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationLReLU(), l0.getActivationFn());
    assertEquals(32, l0.getNOut());
    assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
    assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
    assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
    assertEquals(new Adam(1e-3), l0.getIUpdater());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_Output_100b6.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_Input_100b6.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}
Example 5
Source File: RegressionTest100a.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testVae() throws Exception {

    File f = Resources.asFile("regression_testing/100a/VaeMNISTAnomaly_100a.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    VariationalAutoencoder l0 = (VariationalAutoencoder) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationLReLU(), l0.getActivationFn());
    assertEquals(32, l0.getNOut());
    assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
    assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
            assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new WeightDecay(1e-4, false), TestUtils.getWeightDecayReg(l0));
    assertEquals(new Adam(0.05), l0.getIUpdater());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100a/VaeMNISTAnomaly_Output_100a.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100a/VaeMNISTAnomaly_Input_100a.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}
Example 6
Source File: RegressionTest100a.java    From deeplearning4j with Apache License 2.0 5 votes vote down vote up 
@Test
public void testUpsampling2d() throws Exception {

    File f = Resources.asFile("regression_testing/100a/upsampling/net.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    INDArray in;
    File fIn = Resources.asFile("regression_testing/100a/upsampling/in.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(fIn))){
        in = Nd4j.read(dis);
    }

    INDArray label;
    File fLabels = Resources.asFile("regression_testing/100a/upsampling/labels.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(fLabels))){
        label = Nd4j.read(dis);
    }

    INDArray outExp;
    File fOutExp = Resources.asFile("regression_testing/100a/upsampling/out.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(fOutExp))){
        outExp = Nd4j.read(dis);
    }

    INDArray gradExp;
    File fGradExp = Resources.asFile("regression_testing/100a/upsampling/gradient.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(fGradExp))){
        gradExp = Nd4j.read(dis);
    }

    INDArray out = net.output(in, false);
    assertEquals(outExp, out);

    net.setInput(in);
    net.setLabels(label);
    net.computeGradientAndScore();

    INDArray grad = net.getFlattenedGradients();
    assertEquals(gradExp, grad);
}
Example 7
Source File: TestDL4JStep.java    From konduit-serving with Apache License 2.0 4 votes vote down vote up 
public INDArray predictFromFile(File f, INDArray in) throws Exception {
    MultiLayerNetwork net = MultiLayerNetwork.load(f, false);
    return net.output(in);
}
Example 8
Source File: RegressionTest100b4.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
    public void testCustomLayer() throws Exception {

        for (DataType dtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) {

            String dtypeName = dtype.toString().toLowerCase();

            File f = Resources.asFile("regression_testing/100b4/CustomLayerExample_100b4_" + dtypeName + ".bin");
            MultiLayerNetwork.load(f, true);

            MultiLayerNetwork net = MultiLayerNetwork.load(f, true);
//            net = net.clone();

            DenseLayer l0 = (DenseLayer) net.getLayer(0).conf().getLayer();
            assertEquals(new ActivationTanH(), l0.getActivationFn());
            assertEquals(new L2Regularization(0.03), TestUtils.getL2Reg(l0));
            assertEquals(new RmsProp(0.95), l0.getIUpdater());

            CustomLayer l1 = (CustomLayer) net.getLayer(1).conf().getLayer();
            assertEquals(new ActivationTanH(), l1.getActivationFn());
            assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction());
            assertEquals(new RmsProp(0.95), l1.getIUpdater());

            INDArray outExp;
            File f2 = Resources
                    .asFile("regression_testing/100b4/CustomLayerExample_Output_100b4_" + dtypeName + ".bin");
            try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
                outExp = Nd4j.read(dis);
            }

            INDArray in;
            File f3 = Resources.asFile("regression_testing/100b4/CustomLayerExample_Input_100b4_" + dtypeName + ".bin");
            try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
                in = Nd4j.read(dis);
            }

            assertEquals(dtype, in.dataType());
            assertEquals(dtype, outExp.dataType());
            assertEquals(dtype, net.params().dataType());
            assertEquals(dtype, net.getFlattenedGradients().dataType());
            assertEquals(dtype, net.getUpdater().getStateViewArray().dataType());

            //System.out.println(Arrays.toString(net.params().data().asFloat()));

            INDArray outAct = net.output(in);
            assertEquals(dtype, outAct.dataType());

            assertEquals(dtype, net.getLayerWiseConfigurations().getDataType());
            assertEquals(dtype, net.params().dataType());
            boolean eq = outExp.equalsWithEps(outAct, 0.01);
            assertTrue("Test for dtype: " + dtypeName + "\n" + outExp + " vs " + outAct, eq);
        }
    }
Example 9
Source File: RegressionTest100b4.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
public void testLSTM() throws Exception {

    File f = Resources.asFile("regression_testing/100b4/GravesLSTMCharModelingExample_100b4.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    LSTM l0 = (LSTM) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationTanH(), l0.getActivationFn());
    assertEquals(200, l0.getNOut());
    assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
    assertEquals(new Adam(0.005), l0.getIUpdater());

    LSTM l1 = (LSTM) net.getLayer(1).conf().getLayer();
    assertEquals(new ActivationTanH(), l1.getActivationFn());
    assertEquals(200, l1.getNOut());
    assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
    assertEquals(new Adam(0.005), l1.getIUpdater());

    RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).conf().getLayer();
    assertEquals(new ActivationSoftmax(), l2.getActivationFn());
    assertEquals(77, l2.getNOut());
    assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
    assertEquals(new Adam(0.005), l2.getIUpdater());

    assertEquals(BackpropType.TruncatedBPTT, net.getLayerWiseConfigurations().getBackpropType());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttBackLength());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttFwdLength());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100b4/GravesLSTMCharModelingExample_Output_100b4.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100b4/GravesLSTMCharModelingExample_Input_100b4.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}
Example 10
Source File: RegressionTest100b3.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
    public void testCustomLayer() throws Exception {

        for( int i=1; i<2; i++ ) {
            String dtype = (i == 0 ? "float" : "double");
            DataType dt = (i == 0 ? DataType.FLOAT : DataType.DOUBLE);

            File f = Resources.asFile("regression_testing/100b3/CustomLayerExample_100b3_" + dtype + ".bin");
            MultiLayerNetwork.load(f, true);

            MultiLayerNetwork net = MultiLayerNetwork.load(f, true);
//            net = net.clone();

            DenseLayer l0 = (DenseLayer) net.getLayer(0).conf().getLayer();
            assertEquals(new ActivationTanH(), l0.getActivationFn());
            assertEquals(new WeightDecay(0.03, false), TestUtils.getWeightDecayReg(l0));
            assertEquals(new RmsProp(0.95), l0.getIUpdater());

            CustomLayer l1 = (CustomLayer) net.getLayer(1).conf().getLayer();
            assertEquals(new ActivationTanH(), l1.getActivationFn());
            assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction());
            assertEquals(new RmsProp(0.95), l1.getIUpdater());


            INDArray outExp;
            File f2 = Resources.asFile("regression_testing/100b3/CustomLayerExample_Output_100b3_" + dtype + ".bin");
            try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
                outExp = Nd4j.read(dis);
            }

            INDArray in;
            File f3 = Resources.asFile("regression_testing/100b3/CustomLayerExample_Input_100b3_" + dtype + ".bin");
            try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
                in = Nd4j.read(dis);
            }

            assertEquals(dt, in.dataType());
            assertEquals(dt, outExp.dataType());
            assertEquals(dt, net.params().dataType());
            assertEquals(dt, net.getFlattenedGradients().dataType());
            assertEquals(dt, net.getUpdater().getStateViewArray().dataType());

            //System.out.println(Arrays.toString(net.params().data().asFloat()));

            INDArray outAct = net.output(in);
            assertEquals(dt, outAct.dataType());

            List<INDArray> activations = net.feedForward(in);

            assertEquals(dt, net.getLayerWiseConfigurations().getDataType());
            assertEquals(dt, net.params().dataType());
            assertEquals(dtype, outExp, outAct);
        }
    }
Example 11
Source File: RegressionTest100b3.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
public void testLSTM() throws Exception {

    File f = Resources.asFile("regression_testing/100b3/GravesLSTMCharModelingExample_100b3.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    LSTM l0 = (LSTM) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationTanH(), l0.getActivationFn());
    assertEquals(200, l0.getNOut());
    assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l0));
    assertEquals(new Adam(0.005), l0.getIUpdater());

    LSTM l1 = (LSTM) net.getLayer(1).conf().getLayer();
    assertEquals(new ActivationTanH(), l1.getActivationFn());
    assertEquals(200, l1.getNOut());
    assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
    assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l1));
    assertEquals(new Adam(0.005), l1.getIUpdater());

    RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).conf().getLayer();
    assertEquals(new ActivationSoftmax(), l2.getActivationFn());
    assertEquals(77, l2.getNOut());
    assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
    assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l0));
    assertEquals(new Adam(0.005), l0.getIUpdater());

    assertEquals(BackpropType.TruncatedBPTT, net.getLayerWiseConfigurations().getBackpropType());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttBackLength());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttFwdLength());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100b3/GravesLSTMCharModelingExample_Output_100b3.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100b3/GravesLSTMCharModelingExample_Input_100b3.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}
Example 12
Source File: RegressionTest100b6.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
    public void testCustomLayer() throws Exception {

        for (DataType dtype : new DataType[]{DataType.DOUBLE, DataType.FLOAT, DataType.HALF}) {

            String dtypeName = dtype.toString().toLowerCase();

            File f = Resources.asFile("regression_testing/100b6/CustomLayerExample_100b6_" + dtypeName + ".bin");
            MultiLayerNetwork.load(f, true);

            MultiLayerNetwork net = MultiLayerNetwork.load(f, true);
//            net = net.clone();

            DenseLayer l0 = (DenseLayer) net.getLayer(0).conf().getLayer();
            assertEquals(new ActivationTanH(), l0.getActivationFn());
            assertEquals(new L2Regularization(0.03), TestUtils.getL2Reg(l0));
            assertEquals(new RmsProp(0.95), l0.getIUpdater());

            CustomLayer l1 = (CustomLayer) net.getLayer(1).conf().getLayer();
            assertEquals(new ActivationTanH(), l1.getActivationFn());
            assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction());
            assertEquals(new RmsProp(0.95), l1.getIUpdater());

            INDArray outExp;
            File f2 = Resources
                    .asFile("regression_testing/100b6/CustomLayerExample_Output_100b6_" + dtypeName + ".bin");
            try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
                outExp = Nd4j.read(dis);
            }

            INDArray in;
            File f3 = Resources.asFile("regression_testing/100b6/CustomLayerExample_Input_100b6_" + dtypeName + ".bin");
            try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
                in = Nd4j.read(dis);
            }

            assertEquals(dtype, in.dataType());
            assertEquals(dtype, outExp.dataType());
            assertEquals(dtype, net.params().dataType());
            assertEquals(dtype, net.getFlattenedGradients().dataType());
            assertEquals(dtype, net.getUpdater().getStateViewArray().dataType());

            //System.out.println(Arrays.toString(net.params().data().asFloat()));

            INDArray outAct = net.output(in);
            assertEquals(dtype, outAct.dataType());

            assertEquals(dtype, net.getLayerWiseConfigurations().getDataType());
            assertEquals(dtype, net.params().dataType());
            boolean eq = outExp.equalsWithEps(outAct, 0.01);
            assertTrue("Test for dtype: " + dtypeName + " - " + outExp + " vs " + outAct, eq);
        }
    }
Example 13
Source File: RegressionTest100b6.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
public void testLSTM() throws Exception {

    File f = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_100b6.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    LSTM l0 = (LSTM) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationTanH(), l0.getActivationFn());
    assertEquals(200, l0.getNOut());
    assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
    assertEquals(new Adam(0.005), l0.getIUpdater());

    LSTM l1 = (LSTM) net.getLayer(1).conf().getLayer();
    assertEquals(new ActivationTanH(), l1.getActivationFn());
    assertEquals(200, l1.getNOut());
    assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
    assertEquals(new Adam(0.005), l1.getIUpdater());

    RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).conf().getLayer();
    assertEquals(new ActivationSoftmax(), l2.getActivationFn());
    assertEquals(77, l2.getNOut());
    assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
    assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
    assertEquals(new Adam(0.005), l2.getIUpdater());

    assertEquals(BackpropType.TruncatedBPTT, net.getLayerWiseConfigurations().getBackpropType());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttBackLength());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttFwdLength());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_Output_100b6.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f2))) {
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_Input_100b6.bin");
    try (DataInputStream dis = new DataInputStream(new FileInputStream(f3))) {
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}
Example 14
Source File: RegressionTest100a.java    From deeplearning4j with Apache License 2.0 4 votes vote down vote up 
@Test
public void testGravesLSTM() throws Exception {

    File f = Resources.asFile("regression_testing/100a/GravesLSTMCharModelingExample_100a.bin");
    MultiLayerNetwork net = MultiLayerNetwork.load(f, true);

    GravesLSTM l0 = (GravesLSTM) net.getLayer(0).conf().getLayer();
    assertEquals(new ActivationTanH(), l0.getActivationFn());
    assertEquals(200, l0.getNOut());
    assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
    assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l0));
    assertEquals(new RmsProp(0.1), l0.getIUpdater());

    GravesLSTM l1 = (GravesLSTM) net.getLayer(1).conf().getLayer();
    assertEquals(new ActivationTanH(), l1.getActivationFn());
    assertEquals(200, l1.getNOut());
    assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
    assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l1));
    assertEquals(new RmsProp(0.1), l1.getIUpdater());

    RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).conf().getLayer();
    assertEquals(new ActivationSoftmax(), l2.getActivationFn());
    assertEquals(77, l2.getNOut());
    assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
    assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l0));
    assertEquals(new RmsProp(0.1), l0.getIUpdater());

    assertEquals(BackpropType.TruncatedBPTT, net.getLayerWiseConfigurations().getBackpropType());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttBackLength());
    assertEquals(50, net.getLayerWiseConfigurations().getTbpttFwdLength());

    INDArray outExp;
    File f2 = Resources.asFile("regression_testing/100a/GravesLSTMCharModelingExample_Output_100a.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f2))){
        outExp = Nd4j.read(dis);
    }

    INDArray in;
    File f3 = Resources.asFile("regression_testing/100a/GravesLSTMCharModelingExample_Input_100a.bin");
    try(DataInputStream dis = new DataInputStream(new FileInputStream(f3))){
        in = Nd4j.read(dis);
    }

    INDArray outAct = net.output(in);

    assertEquals(outExp, outAct);
}