Examples with SingletonMultiDataSetIterator org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator used on opensource projects

Example 1 with SingletonMultiDataSetIterator

use of org.deeplearning4j.datasets.iterator.impl.SingletonMultiDataSetIterator in project deeplearning4j by deeplearning4j.

the class ComputationGraph method fit.

/**
     * Fit the ComputationGraph using the specified inputs and labels (and mask arrays)
     *
     * @param inputs            The network inputs (features)
     * @param labels            The network labels
     * @param featureMaskArrays Mask arrays for inputs/features. Typically used for RNN training. May be null.
     * @param labelMaskArrays   Mas arrays for the labels/outputs. Typically used for RNN training. May be null.
     */
public void fit(INDArray[] inputs, INDArray[] labels, INDArray[] featureMaskArrays, INDArray[] labelMaskArrays) {
    if (flattenedGradients == null)
        initGradientsView();
    setInputs(inputs);
    setLabels(labels);
    setLayerMaskArrays(featureMaskArrays, labelMaskArrays);
    update(TaskUtils.buildTask(inputs, labels));
    if (configuration.isPretrain()) {
        MultiDataSetIterator iter = new SingletonMultiDataSetIterator(new org.nd4j.linalg.dataset.MultiDataSet(inputs, labels, featureMaskArrays, labelMaskArrays));
        pretrain(iter);
    }
    if (configuration.isBackprop()) {
        if (configuration.getBackpropType() == BackpropType.TruncatedBPTT) {
            doTruncatedBPTT(inputs, labels, featureMaskArrays, labelMaskArrays);
        } else {
            if (solver == null) {
                solver = new Solver.Builder().configure(conf()).listeners(getListeners()).model(this).build();
            }
            solver.optimize();
        }
    }
    if (featureMaskArrays != null || labelMaskArrays != null) {
        clearLayerMaskArrays();
    }
}