Examples with RnnToFeedForwardPreProcessor org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor used on opensource projects

Example 11 with RnnToFeedForwardPreProcessor

use of org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor in project deeplearning4j by deeplearning4j.

the class TestVariableLengthTSCG method testInputMasking.

@Test
public void testInputMasking() {
    //Idea: have masking on the input with 2 dense layers on input
    //Ensure that the parameter gradients for the inputs don't depend on the inputs when inputs are masked
    int[] miniBatchSizes = { 1, 2, 5 };
    int nIn = 2;
    Random r = new Random(12345);
    for (int nExamples : miniBatchSizes) {
        Nd4j.getRandom().setSeed(12345);
        ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1).updater(Updater.SGD).learningRate(0.1).seed(12345).graphBuilder().addInputs("in").addLayer("0", new DenseLayer.Builder().activation(Activation.TANH).nIn(2).nOut(2).build(), "in").addLayer("1", new DenseLayer.Builder().activation(Activation.TANH).nIn(2).nOut(2).build(), "0").addLayer("2", new GravesLSTM.Builder().activation(Activation.TANH).nIn(2).nOut(2).build(), "1").addLayer("3", new RnnOutputLayer.Builder().lossFunction(LossFunctions.LossFunction.MSE).nIn(2).nOut(1).build(), "2").setOutputs("3").inputPreProcessor("0", new RnnToFeedForwardPreProcessor()).inputPreProcessor("2", new FeedForwardToRnnPreProcessor()).build();
        ComputationGraph net = new ComputationGraph(conf);
        net.init();
        INDArray in1 = Nd4j.rand(new int[] { nExamples, 2, 4 });
        INDArray in2 = Nd4j.rand(new int[] { nExamples, 2, 5 });
        in2.put(new INDArrayIndex[] { NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 3, true) }, in1);
        assertEquals(in1, in2.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4)));
        INDArray labels1 = Nd4j.rand(new int[] { nExamples, 1, 4 });
        INDArray labels2 = Nd4j.create(nExamples, 1, 5);
        labels2.put(new INDArrayIndex[] { NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 3, true) }, labels1);
        assertEquals(labels1, labels2.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(0, 4)));
        INDArray inputMask = Nd4j.ones(nExamples, 5);
        for (int j = 0; j < nExamples; j++) {
            inputMask.putScalar(new int[] { j, 4 }, 0);
        }
        net.setInput(0, in1);
        net.setLabel(0, labels1);
        net.computeGradientAndScore();
        double score1 = net.score();
        Gradient g1 = net.gradient();
        Map<String, INDArray> map = g1.gradientForVariable();
        for (String s : map.keySet()) {
            //Gradients are views; need to dup otherwise they will be modified by next computeGradientAndScore
            map.put(s, map.get(s).dup());
        }
        net.setInput(0, in2);
        net.setLabel(0, labels2);
        net.setLayerMaskArrays(new INDArray[] { inputMask }, null);
        net.computeGradientAndScore();
        double score2 = net.score();
        Gradient g2 = net.gradient();
        Map<String, INDArray> activations2 = net.feedForward();
        //Scores should differ here: masking the input, not the output. Therefore 4 vs. 5 time step outputs
        assertNotEquals(score1, score2, 0.01);
        Map<String, INDArray> g1map = g1.gradientForVariable();
        Map<String, INDArray> g2map = g2.gradientForVariable();
        for (String s : g1map.keySet()) {
            INDArray g1s = g1map.get(s);
            INDArray g2s = g2map.get(s);
            assertNotEquals(s, g1s, g2s);
        }
        //Modify the values at the masked time step, and check that neither the gradients, score or activations change
        for (int j = 0; j < nExamples; j++) {
            for (int k = 0; k < nIn; k++) {
                in2.putScalar(new int[] { j, k, 4 }, r.nextDouble());
            }
            net.setInput(0, in2);
            net.computeGradientAndScore();
            double score2a = net.score();
            Gradient g2a = net.gradient();
            assertEquals(score2, score2a, 1e-12);
            for (String s : g2.gradientForVariable().keySet()) {
                assertEquals(g2.getGradientFor(s), g2a.getGradientFor(s));
            }
            Map<String, INDArray> activations2a = net.feedForward();
            for (String s : activations2.keySet()) {
                assertEquals(activations2.get(s), activations2a.get(s));
            }
        }
        //Finally: check that the activations for the first two (dense) layers are zero at the appropriate time step
        FeedForwardToRnnPreProcessor temp = new FeedForwardToRnnPreProcessor();
        INDArray l0Before = activations2.get("0");
        INDArray l1Before = activations2.get("1");
        INDArray l0After = temp.preProcess(l0Before, nExamples);
        INDArray l1After = temp.preProcess(l1Before, nExamples);
        for (int j = 0; j < nExamples; j++) {
            for (int k = 0; k < nIn; k++) {
                assertEquals(0.0, l0After.getDouble(j, k, 4), 0.0);
                assertEquals(0.0, l1After.getDouble(j, k, 4), 0.0);
            }
        }
    }
}

Example 12 with RnnToFeedForwardPreProcessor

use of org.deeplearning4j.nn.conf.preprocessor.RnnToFeedForwardPreProcessor in project deeplearning4j by deeplearning4j.

the class MultiLayerTestRNN method testRnnTimeStepGravesLSTM.

@Test
public void testRnnTimeStepGravesLSTM() {
    Nd4j.getRandom().setSeed(12345);
    int timeSeriesLength = 12;
    //4 layer network: 2 GravesLSTM + DenseLayer + RnnOutputLayer. Hence also tests preprocessors.
    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).list().layer(0, new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(5).nOut(7).activation(Activation.TANH).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 0.5)).build()).layer(1, new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(7).nOut(8).activation(Activation.TANH).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 0.5)).build()).layer(2, new DenseLayer.Builder().nIn(8).nOut(9).activation(Activation.TANH).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 0.5)).build()).layer(3, new RnnOutputLayer.Builder(LossFunction.MCXENT).weightInit(WeightInit.DISTRIBUTION).nIn(9).nOut(4).activation(Activation.SOFTMAX).weightInit(WeightInit.DISTRIBUTION).dist(new NormalDistribution(0, 0.5)).build()).inputPreProcessor(2, new RnnToFeedForwardPreProcessor()).inputPreProcessor(3, new FeedForwardToRnnPreProcessor()).build();
    MultiLayerNetwork mln = new MultiLayerNetwork(conf);
    INDArray input = Nd4j.rand(new int[] { 3, 5, timeSeriesLength });
    List<INDArray> allOutputActivations = mln.feedForward(input, true);
    INDArray fullOutL0 = allOutputActivations.get(1);
    INDArray fullOutL1 = allOutputActivations.get(2);
    INDArray fullOutL3 = allOutputActivations.get(4);
    int[] inputLengths = { 1, 2, 3, 4, 6, 12 };
    //Should get the same result regardless of step size; should be identical to standard forward pass
    for (int i = 0; i < inputLengths.length; i++) {
        int inLength = inputLengths[i];
        //each of length inLength
        int nSteps = timeSeriesLength / inLength;
        mln.rnnClearPreviousState();
        //Reset; should be set by rnnTimeStep method
        mln.setInputMiniBatchSize(1);
        for (int j = 0; j < nSteps; j++) {
            int startTimeRange = j * inLength;
            int endTimeRange = startTimeRange + inLength;
            INDArray inputSubset;
            if (inLength == 1) {
                //Workaround to nd4j bug
                int[] sizes = new int[] { input.size(0), input.size(1), 1 };
                inputSubset = Nd4j.create(sizes);
                inputSubset.tensorAlongDimension(0, 1, 0).assign(input.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(startTimeRange)));
            } else {
                inputSubset = input.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(startTimeRange, endTimeRange));
            }
            if (inLength > 1)
                assertTrue(inputSubset.size(2) == inLength);
            INDArray out = mln.rnnTimeStep(inputSubset);
            INDArray expOutSubset;
            if (inLength == 1) {
                int[] sizes = new int[] { fullOutL3.size(0), fullOutL3.size(1), 1 };
                expOutSubset = Nd4j.create(sizes);
                expOutSubset.tensorAlongDimension(0, 1, 0).assign(fullOutL3.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.point(startTimeRange)));
            } else {
                expOutSubset = fullOutL3.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(startTimeRange, endTimeRange));
            }
            assertEquals(expOutSubset, out);
            Map<String, INDArray> currL0State = mln.rnnGetPreviousState(0);
            Map<String, INDArray> currL1State = mln.rnnGetPreviousState(1);
            INDArray lastActL0 = currL0State.get(GravesLSTM.STATE_KEY_PREV_ACTIVATION);
            INDArray lastActL1 = currL1State.get(GravesLSTM.STATE_KEY_PREV_ACTIVATION);
            INDArray expLastActL0 = fullOutL0.tensorAlongDimension(endTimeRange - 1, 1, 0);
            INDArray expLastActL1 = fullOutL1.tensorAlongDimension(endTimeRange - 1, 1, 0);
            assertEquals(expLastActL0, lastActL0);
            assertEquals(expLastActL1, lastActL1);
        }
    }
}