Examples with MultiLayerNetwork org.deeplearning4j.nn.multilayer.MultiLayerNetwork used on opensource projects

Example 86 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class LayerConfigTest method testMomentumLayerwiseOverride.

@Test
public void testMomentumLayerwiseOverride() {
    Map<Integer, Double> testMomentumAfter = new HashMap<>();
    testMomentumAfter.put(0, 0.1);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().updater(Updater.NESTEROVS).momentum(1.0).momentumAfter(testMomentumAfter).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
    assertEquals(1.0, conf.getConf(0).getLayer().getMomentum(), 0.0);
    assertEquals(1.0, conf.getConf(1).getLayer().getMomentum(), 0.0);
    assertEquals(0.1, conf.getConf(0).getLayer().getMomentumSchedule().get(0), 0.0);
    assertEquals(0.1, conf.getConf(1).getLayer().getMomentumSchedule().get(0), 0.0);
    Map<Integer, Double> testMomentumAfter2 = new HashMap<>();
    testMomentumAfter2.put(0, 0.2);
    conf = new NeuralNetConfiguration.Builder().updater(Updater.NESTEROVS).momentum(1.0).momentumAfter(testMomentumAfter).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1, new DenseLayer.Builder().nIn(2).nOut(2).momentum(2.0).momentumAfter(testMomentumAfter2).build()).build();
    net = new MultiLayerNetwork(conf);
    net.init();
    assertEquals(1.0, conf.getConf(0).getLayer().getMomentum(), 0.0);
    assertEquals(2.0, conf.getConf(1).getLayer().getMomentum(), 0.0);
    assertEquals(0.1, conf.getConf(0).getLayer().getMomentumSchedule().get(0), 0.0);
    assertEquals(0.2, conf.getConf(1).getLayer().getMomentumSchedule().get(0), 0.0);
}

Example 87 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class LayerConfigValidationTest method testNesterovsNotSetGlobal.

@Test
public void testNesterovsNotSetGlobal() {
    // Warnings only thrown
    Map<Integer, Double> testMomentumAfter = new HashMap<>();
    testMomentumAfter.put(0, 0.1);
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().momentum(1.0).momentumAfter(testMomentumAfter).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
}

Example 88 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class LayerConfigValidationTest method testDropConnect.

@Test
public void testDropConnect() {
    // Warning thrown only since some layers may not have l1 or l2
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(0.3).useDropConnect(true).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
}

Example 89 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class LayerConfigValidationTest method testWeightInitDistNotSet.

@Test
public void testWeightInitDistNotSet() {
    // Warning thrown only since global dist can be set with a different weight init locally
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(0.3).dist(new GaussianDistribution(1e-3, 2)).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build()).layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
    MultiLayerNetwork net = new MultiLayerNetwork(conf);
    net.init();
}

Example 90 with MultiLayerNetwork

use of org.deeplearning4j.nn.multilayer.MultiLayerNetwork in project deeplearning4j by deeplearning4j.

the class ComputationGraph method rnnActivateUsingStoredState.

/**
     * Similar to rnnTimeStep and feedForward() methods. Difference here is that this method:<br>
     * (a) like rnnTimeStep does forward pass using stored state for RNN layers, and<br>
     * (b) unlike rnnTimeStep does not modify the RNN layer state<br>
     * Therefore multiple calls to this method with the same input should have the same output.<br>
     * Typically used during training only. Use rnnTimeStep for prediction/forward pass at test time.
     *
     * @param inputs            Input to network
     * @param training          Whether training or not
     * @param storeLastForTBPTT set to true if used as part of truncated BPTT training
     * @return Activations for each layer (including input, as per feedforward() etc)
     */
public Map<String, INDArray> rnnActivateUsingStoredState(INDArray[] inputs, boolean training, boolean storeLastForTBPTT) {
    Map<String, INDArray> layerActivations = new HashMap<>();
    //Do forward pass according to the topological ordering of the network
    for (int currVertexIdx : topologicalOrder) {
        GraphVertex current = vertices[currVertexIdx];
        if (current.isInputVertex()) {
            VertexIndices[] inputsTo = current.getOutputVertices();
            INDArray input = inputs[current.getVertexIndex()];
            layerActivations.put(current.getVertexName(), input);
            for (VertexIndices v : inputsTo) {
                int vIdx = v.getVertexIndex();
                int vIdxInputNum = v.getVertexEdgeNumber();
                //This input: the 'vIdxInputNum'th input to vertex 'vIdx'
                //TODO When to dup?
                vertices[vIdx].setInput(vIdxInputNum, input.dup());
            }
        } else {
            INDArray out;
            if (current.hasLayer()) {
                Layer l = current.getLayer();
                if (l instanceof RecurrentLayer) {
                    out = ((RecurrentLayer) l).rnnActivateUsingStoredState(current.getInputs()[0], training, storeLastForTBPTT);
                } else if (l instanceof MultiLayerNetwork) {
                    List<INDArray> temp = ((MultiLayerNetwork) l).rnnActivateUsingStoredState(current.getInputs()[0], training, storeLastForTBPTT);
                    out = temp.get(temp.size() - 1);
                } else {
                    //non-recurrent layer
                    out = current.doForward(training);
                }
                layerActivations.put(current.getVertexName(), out);
            } else {
                out = current.doForward(training);
            }
            //Now, set the inputs for the next vertices:
            VertexIndices[] outputsTo = current.getOutputVertices();
            if (outputsTo != null) {
                for (VertexIndices v : outputsTo) {
                    int vIdx = v.getVertexIndex();
                    int inputNum = v.getVertexEdgeNumber();
                    //This (jth) connection from the output: is the 'inputNum'th input to vertex 'vIdx'
                    vertices[vIdx].setInput(inputNum, out);
                }
            }
        }
    }
    return layerActivations;
}