Examples with IterativeTrainer com.simiacryptus.mindseye.opt.IterativeTrainer used on opensource projects

Example 21 with IterativeTrainer

use of com.simiacryptus.mindseye.opt.IterativeTrainer in project MindsEye by SimiaCryptus.

the class L1NormalizationTest method train.

@Override
public void train(@Nonnull final NotebookOutput log, @Nonnull final Layer network, @Nonnull final Tensor[][] trainingData, final TrainingMonitor monitor) {
    log.code(() -> {
        @Nonnull final SimpleLossNetwork supervisedNetwork = new SimpleLossNetwork(network, new EntropyLossLayer());
        @Nonnull final Trainable trainable = new L12Normalizer(new SampledArrayTrainable(trainingData, supervisedNetwork, 1000)) {

            @Override
            public Layer getLayer() {
                return inner.getLayer();
            }

            @Override
            protected double getL1(final Layer layer) {
                return 1.0;
            }

            @Override
            protected double getL2(final Layer layer) {
                return 0;
            }
        };
        return new IterativeTrainer(trainable).setMonitor(monitor).setTimeout(3, TimeUnit.MINUTES).setMaxIterations(500).runAndFree();
    });
}

Example 22 with IterativeTrainer

use of com.simiacryptus.mindseye.opt.IterativeTrainer in project MindsEye by SimiaCryptus.

the class SimpleGradientDescentTest method train.

@Override
public void train(@Nonnull final NotebookOutput log, @Nonnull final Layer network, @Nonnull final Tensor[][] trainingData, final TrainingMonitor monitor) {
    log.p("Training a model involves a few different components. First, our model is combined mapCoords a loss function. " + "Then we take that model and combine it mapCoords our training data to define a trainable object. " + "Finally, we use a simple iterative scheme to refine the weights of our model. " + "The final output is the last output value of the loss function when evaluating the last batch.");
    log.code(() -> {
        @Nonnull final SimpleLossNetwork supervisedNetwork = new SimpleLossNetwork(network, new EntropyLossLayer());
        @Nonnull final ArrayList<Tensor[]> trainingList = new ArrayList<>(Arrays.stream(trainingData).collect(Collectors.toList()));
        Collections.shuffle(trainingList);
        @Nonnull final Tensor[][] randomSelection = trainingList.subList(0, 10000).toArray(new Tensor[][] {});
        @Nonnull final Trainable trainable = new ArrayTrainable(randomSelection, supervisedNetwork);
        return new IterativeTrainer(trainable).setMonitor(monitor).setTimeout(3, TimeUnit.MINUTES).setMaxIterations(500).runAndFree();
    });
}