Examples with Solver org.deeplearning4j.optimize.Solver used on opensource projects

Example 11 with Solver

use of org.deeplearning4j.optimize.Solver in project deeplearning4j by deeplearning4j.

the class MultiLayerNetwork method doTruncatedBPTT.

protected void doTruncatedBPTT(INDArray input, INDArray labels, INDArray featuresMaskArray, INDArray labelsMaskArray) {
    if (input.rank() != 3 || labels.rank() != 3) {
        log.warn("Cannot do truncated BPTT with non-3d inputs or labels. Expect input with shape [miniBatchSize,nIn,timeSeriesLength], got " + Arrays.toString(input.shape()) + "\tand labels with shape " + Arrays.toString(labels.shape()));
        return;
    }
    if (input.size(2) != labels.size(2)) {
        log.warn("Input and label time series have different lengths: {} input length, {} label length", input.size(2), labels.size(2));
        return;
    }
    int fwdLen = layerWiseConfigurations.getTbpttFwdLength();
    update(TaskUtils.buildTask(input, labels));
    int timeSeriesLength = input.size(2);
    int nSubsets = timeSeriesLength / fwdLen;
    if (timeSeriesLength % fwdLen != 0)
        //Example: 100 fwdLen with timeSeriesLength=100 -> want 2 subsets (1 of size 100, 1 of size 20)
        nSubsets++;
    rnnClearPreviousState();
    for (int i = 0; i < nSubsets; i++) {
        int startTimeIdx = i * fwdLen;
        int endTimeIdx = startTimeIdx + fwdLen;
        if (endTimeIdx > timeSeriesLength)
            endTimeIdx = timeSeriesLength;
        INDArray inputSubset = input.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(startTimeIdx, endTimeIdx));
        INDArray labelSubset = labels.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(startTimeIdx, endTimeIdx));
        setInput(inputSubset);
        setLabels(labelSubset);
        INDArray featuresMaskSubset = null;
        INDArray labelsMaskSubset = null;
        if (featuresMaskArray != null) {
            featuresMaskSubset = featuresMaskArray.get(NDArrayIndex.all(), NDArrayIndex.interval(startTimeIdx, endTimeIdx));
        }
        if (labelsMaskArray != null) {
            labelsMaskSubset = labelsMaskArray.get(NDArrayIndex.all(), NDArrayIndex.interval(startTimeIdx, endTimeIdx));
        }
        if (featuresMaskSubset != null || labelsMaskSubset != null)
            setLayerMaskArrays(featuresMaskSubset, labelsMaskSubset);
        if (solver == null) {
            solver = new Solver.Builder().configure(conf()).listeners(getListeners()).model(this).build();
        }
        solver.optimize();
        //Finally, update the state of the RNN layers:
        updateRnnStateWithTBPTTState();
    }
    rnnClearPreviousState();
    if (featuresMaskArray != null || labelsMaskArray != null)
        clearLayerMaskArrays();
}

Example 12 with Solver

use of org.deeplearning4j.optimize.Solver in project deeplearning4j by deeplearning4j.

the class LossLayer method fit.

/**
     * Fit the model
     *
     * @param input the examples to classify (one example in each row)
     * @param labels   the example labels(a binary outcome matrix)
     */
@Override
public void fit(INDArray input, INDArray labels) {
    setInput(input);
    setLabels(labels);
    applyDropOutIfNecessary(true);
    if (solver == null) {
        solver = new Solver.Builder().configure(conf()).listeners(getListeners()).model(this).build();
        //Set the updater state view array. For MLN and CG, this is done by MultiLayerUpdater and ComputationGraphUpdater respectively
        Updater updater = solver.getOptimizer().getUpdater();
        int updaterStateSize = updater.stateSizeForLayer(this);
        if (updaterStateSize > 0)
            updater.setStateViewArray(this, Nd4j.createUninitialized(new int[] { 1, updaterStateSize }, Nd4j.order()), true);
    }
    solver.optimize();
}