Examples with UniformDistribution org.deeplearning4j.nn.conf.distribution.UniformDistribution used on opensource projects

Example 21 with UniformDistribution

use of org.deeplearning4j.nn.conf.distribution.UniformDistribution in project deeplearning4j by deeplearning4j.

the class GravesBidirectionalLSTMTest method testSerialization.

@Test
public void testSerialization() {
    final MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(5).learningRate(0.1).rmsDecay(0.95).regularization(true).l2(0.001).updater(Updater.ADAGRAD).seed(12345).list().pretrain(false).layer(0, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(1, new org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(-0.05, 0.05)).build()).layer(2, new org.deeplearning4j.nn.conf.layers.RnnOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MCXENT).nIn(2).nOut(2).activation(Activation.TANH).build()).backprop(true).build();
    final String json1 = conf1.toJson();
    final MultiLayerConfiguration conf2 = MultiLayerConfiguration.fromJson(json1);
    final String json2 = conf1.toJson();
    TestCase.assertEquals(json1, json2);
}

Example 22 with UniformDistribution

use of org.deeplearning4j.nn.conf.distribution.UniformDistribution in project deeplearning4j by deeplearning4j.

the class GravesLSTMTest method testGravesBackwardBasicHelper.

private static void testGravesBackwardBasicHelper(int nIn, int nOut, int lstmNHiddenUnits, int miniBatchSize, int timeSeriesLength) {
    INDArray inputData = Nd4j.ones(miniBatchSize, nIn, timeSeriesLength);
    NeuralNetConfiguration conf = new NeuralNetConfiguration.Builder().layer(new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(nIn).nOut(lstmNHiddenUnits).weightInit(WeightInit.DISTRIBUTION).dist(new UniformDistribution(0, 1)).activation(Activation.TANH).build()).build();
    int numParams = conf.getLayer().initializer().numParams(conf);
    INDArray params = Nd4j.create(1, numParams);
    GravesLSTM lstm = (GravesLSTM) conf.getLayer().instantiate(conf, null, 0, params, true);
    lstm.setBackpropGradientsViewArray(Nd4j.create(1, conf.getLayer().initializer().numParams(conf)));
    //Set input, do a forward pass:
    lstm.activate(inputData);
    assertNotNull(lstm.input());
    INDArray epsilon = Nd4j.ones(miniBatchSize, lstmNHiddenUnits, timeSeriesLength);
    Pair<Gradient, INDArray> out = lstm.backpropGradient(epsilon);
    Gradient outGradient = out.getFirst();
    INDArray nextEpsilon = out.getSecond();
    INDArray biasGradient = outGradient.getGradientFor(GravesLSTMParamInitializer.BIAS_KEY);
    INDArray inWeightGradient = outGradient.getGradientFor(GravesLSTMParamInitializer.INPUT_WEIGHT_KEY);
    INDArray recurrentWeightGradient = outGradient.getGradientFor(GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY);
    assertNotNull(biasGradient);
    assertNotNull(inWeightGradient);
    assertNotNull(recurrentWeightGradient);
    assertArrayEquals(biasGradient.shape(), new int[] { 1, 4 * lstmNHiddenUnits });
    assertArrayEquals(inWeightGradient.shape(), new int[] { nIn, 4 * lstmNHiddenUnits });
    assertArrayEquals(recurrentWeightGradient.shape(), new int[] { lstmNHiddenUnits, 4 * lstmNHiddenUnits + 3 });
    assertNotNull(nextEpsilon);
    assertArrayEquals(nextEpsilon.shape(), new int[] { miniBatchSize, nIn, timeSeriesLength });
    //Check update:
    for (String s : outGradient.gradientForVariable().keySet()) {
        lstm.update(outGradient.getGradientFor(s), s);
    }
}