Examples with ConstantResult com.simiacryptus.mindseye.lang.ConstantResult used on opensource projects

Example 1 with ConstantResult

use of com.simiacryptus.mindseye.lang.ConstantResult in project MindsEye by SimiaCryptus.

the class ClassifyProblem method run.

@Nonnull
@Override
public ClassifyProblem run(@Nonnull final NotebookOutput log) {
    @Nonnull final TrainingMonitor monitor = TestUtil.getMonitor(history);
    final Tensor[][] trainingData = getTrainingData(log);
    @Nonnull final DAGNetwork network = fwdFactory.imageToVector(log, categories);
    log.h3("Network Diagram");
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(network)).height(400).width(600).render(Format.PNG).toImage();
    });
    log.h3("Training");
    @Nonnull final SimpleLossNetwork supervisedNetwork = new SimpleLossNetwork(network, new EntropyLossLayer());
    TestUtil.instrumentPerformance(supervisedNetwork);
    int initialSampleSize = Math.max(trainingData.length / 5, Math.min(10, trainingData.length / 2));
    @Nonnull final ValidatingTrainer trainer = optimizer.train(log, new SampledArrayTrainable(trainingData, supervisedNetwork, initialSampleSize, getBatchSize()), new ArrayTrainable(trainingData, supervisedNetwork, getBatchSize()), monitor);
    log.code(() -> {
        trainer.setTimeout(timeoutMinutes, TimeUnit.MINUTES).setMaxIterations(10000).run();
    });
    if (!history.isEmpty()) {
        log.code(() -> {
            return TestUtil.plot(history);
        });
        log.code(() -> {
            return TestUtil.plotTime(history);
        });
    }
    try {
        @Nonnull String filename = log.getName() + "_" + ClassifyProblem.modelNo++ + "_plot.png";
        ImageIO.write(Util.toImage(TestUtil.plot(history)), "png", log.file(filename));
        @Nonnull File file = new File(log.getResourceDir(), filename);
        log.appendFrontMatterProperty("result_plot", file.toString(), ";");
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
    TestUtil.extractPerformance(log, supervisedNetwork);
    @Nonnull final String modelName = "classification_model_" + ClassifyProblem.modelNo++ + ".json";
    log.appendFrontMatterProperty("result_model", modelName, ";");
    log.p("Saved model as " + log.file(network.getJson().toString(), modelName, modelName));
    log.h3("Validation");
    log.p("If we apply our model against the entire validation dataset, we get this accuracy:");
    log.code(() -> {
        return data.validationData().mapToDouble(labeledObject -> predict(network, labeledObject)[0] == parse(labeledObject.label) ? 1 : 0).average().getAsDouble() * 100;
    });
    log.p("Let's examine some incorrectly predicted results in more detail:");
    log.code(() -> {
        try {
            @Nonnull final TableOutput table = new TableOutput();
            Lists.partition(data.validationData().collect(Collectors.toList()), 100).stream().flatMap(batch -> {
                @Nonnull TensorList batchIn = TensorArray.create(batch.stream().map(x -> x.data).toArray(i -> new Tensor[i]));
                TensorList batchOut = network.eval(new ConstantResult(batchIn)).getData();
                return IntStream.range(0, batchOut.length()).mapToObj(i -> toRow(log, batch.get(i), batchOut.get(i).getData()));
            }).filter(x -> null != x).limit(10).forEach(table::putRow);
            return table;
        } catch (@Nonnull final IOException e) {
            throw new RuntimeException(e);
        }
    });
    return this;
}

Example 2 with ConstantResult

use of com.simiacryptus.mindseye.lang.ConstantResult in project MindsEye by SimiaCryptus.

the class TensorListTrainable method getNNContext.

/**
 * Get nn context nn result [ ].
 *
 * @param data the data
 * @param mask the mask
 * @return the nn result [ ]
 */
public static Result[] getNNContext(@Nullable final TensorList[] data, @Nullable final boolean[] mask) {
    if (null == data)
        throw new IllegalArgumentException();
    int inputs = data.length;
    assert 0 < inputs;
    int items = data[0].length();
    assert 0 < items;
    return IntStream.range(0, inputs).mapToObj(col -> {
        final Tensor[] tensors = IntStream.range(0, items).mapToObj(row -> data[col].get(row)).toArray(i -> new Tensor[i]);
        @Nonnull TensorArray tensorArray = TensorArray.create(tensors);
        if (null == mask || col >= mask.length || !mask[col]) {
            return new ConstantResult(tensorArray);
        } else {
            return new Result(tensorArray, (@Nonnull final DeltaSet<Layer> buffer, @Nonnull final TensorList delta) -> {
                for (int index = 0; index < delta.length(); index++) {
                    final Tensor dt = delta.get(index);
                    @Nullable final double[] d = dt.getData();
                    final Tensor t = tensors[index];
                    @Nullable final double[] p = t.getData();
                    @Nonnull PlaceholderLayer<double[]> layer = new PlaceholderLayer<>(p);
                    buffer.get(layer, p).addInPlace(d).freeRef();
                    dt.freeRef();
                    layer.freeRef();
                }
            }) {

                @Override
                public boolean isAlive() {
                    return true;
                }
            };
        }
    }).toArray(x1 -> new Result[x1]);
}