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

Example 6 with TrainingMonitor

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

the class TrainingTester method trainMagic.

/**
 * Train lbfgs list.
 *
 * @param log       the log
 * @param trainable the trainable
 * @return the list
 */
@Nonnull
public List<StepRecord> trainMagic(@Nonnull final NotebookOutput log, final Trainable trainable) {
    log.p("Now we train using an experimental optimizer:");
    @Nonnull final List<StepRecord> history = new ArrayList<>();
    @Nonnull final TrainingMonitor monitor = TrainingTester.getMonitor(history);
    try {
        log.code(() -> {
            return new IterativeTrainer(trainable).setLineSearchFactory(label -> new StaticLearningRate(1.0)).setOrientation(new RecursiveSubspace() {

                @Override
                public void train(@Nonnull TrainingMonitor monitor, Layer macroLayer) {
                    @Nonnull Tensor[][] nullData = { { new Tensor() } };
                    @Nonnull BasicTrainable inner = new BasicTrainable(macroLayer);
                    @Nonnull ArrayTrainable trainable1 = new ArrayTrainable(inner, nullData);
                    inner.freeRef();
                    new IterativeTrainer(trainable1).setOrientation(new QQN()).setLineSearchFactory(n -> new QuadraticSearch().setCurrentRate(n.equals(QQN.CURSOR_NAME) ? 1.0 : 1e-4)).setMonitor(new TrainingMonitor() {

                        @Override
                        public void log(String msg) {
                            monitor.log("\t" + msg);
                        }
                    }).setMaxIterations(getIterations()).setIterationsPerSample(getIterations()).runAndFree();
                    trainable1.freeRef();
                    for (@Nonnull Tensor[] tensors : nullData) {
                        for (@Nonnull Tensor tensor : tensors) {
                            tensor.freeRef();
                        }
                    }
                }
            }).setMonitor(monitor).setTimeout(30, TimeUnit.SECONDS).setIterationsPerSample(100).setMaxIterations(250).setTerminateThreshold(0).runAndFree();
        });
    } catch (Throwable e) {
        if (isThrowExceptions())
            throw new RuntimeException(e);
    }
    return history;
}

Example 7 with TrainingMonitor

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

the class AutoencodingProblem method run.

@Nonnull
@Override
public AutoencodingProblem run(@Nonnull final NotebookOutput log) {
    @Nonnull final DAGNetwork fwdNetwork = fwdFactory.imageToVector(log, features);
    @Nonnull final DAGNetwork revNetwork = revFactory.vectorToImage(log, features);
    @Nonnull final PipelineNetwork echoNetwork = new PipelineNetwork(1);
    echoNetwork.add(fwdNetwork);
    echoNetwork.add(revNetwork);
    @Nonnull final PipelineNetwork supervisedNetwork = new PipelineNetwork(1);
    supervisedNetwork.add(fwdNetwork);
    @Nonnull final DropoutNoiseLayer dropoutNoiseLayer = new DropoutNoiseLayer().setValue(dropout);
    supervisedNetwork.add(dropoutNoiseLayer);
    supervisedNetwork.add(revNetwork);
    supervisedNetwork.add(new MeanSqLossLayer(), supervisedNetwork.getHead(), supervisedNetwork.getInput(0));
    log.h3("Network Diagrams");
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(fwdNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(revNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(supervisedNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    @Nonnull final TrainingMonitor monitor = new TrainingMonitor() {

        @Nonnull
        TrainingMonitor inner = TestUtil.getMonitor(history);

        @Override
        public void log(final String msg) {
            inner.log(msg);
        }

        @Override
        public void onStepComplete(final Step currentPoint) {
            dropoutNoiseLayer.shuffle(StochasticComponent.random.get().nextLong());
            inner.onStepComplete(currentPoint);
        }
    };
    final Tensor[][] trainingData = getTrainingData(log);
    // MonitoredObject monitoringRoot = new MonitoredObject();
    // TestUtil.addMonitoring(supervisedNetwork, monitoringRoot);
    log.h3("Training");
    TestUtil.instrumentPerformance(supervisedNetwork);
    @Nonnull final ValidatingTrainer trainer = optimizer.train(log, new SampledArrayTrainable(trainingData, supervisedNetwork, trainingData.length / 2, batchSize), new ArrayTrainable(trainingData, supervisedNetwork, batchSize), 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);
        });
    }
    TestUtil.extractPerformance(log, supervisedNetwork);
    {
        @Nonnull final String modelName = "encoder_model" + AutoencodingProblem.modelNo++ + ".json";
        log.p("Saved model as " + log.file(fwdNetwork.getJson().toString(), modelName, modelName));
    }
    @Nonnull final String modelName = "decoder_model" + AutoencodingProblem.modelNo++ + ".json";
    log.p("Saved model as " + log.file(revNetwork.getJson().toString(), modelName, modelName));
    // log.h3("Metrics");
    // log.code(() -> {
    // return TestUtil.toFormattedJson(monitoringRoot.getMetrics());
    // });
    log.h3("Validation");
    log.p("Here are some re-encoded examples:");
    log.code(() -> {
        @Nonnull final TableOutput table = new TableOutput();
        data.validationData().map(labeledObject -> {
            return toRow(log, labeledObject, echoNetwork.eval(labeledObject.data).getData().get(0).getData());
        }).filter(x -> null != x).limit(10).forEach(table::putRow);
        return table;
    });
    log.p("Some rendered unit vectors:");
    for (int featureNumber = 0; featureNumber < features; featureNumber++) {
        @Nonnull final Tensor input = new Tensor(features).set(featureNumber, 1);
        @Nullable final Tensor tensor = revNetwork.eval(input).getData().get(0);
        log.out(log.image(tensor.toImage(), ""));
    }
    return this;
}

Example 8 with TrainingMonitor

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

the class EncodingProblem method run.

@Nonnull
@Override
public EncodingProblem run(@Nonnull final NotebookOutput log) {
    @Nonnull final TrainingMonitor monitor = TestUtil.getMonitor(history);
    Tensor[][] trainingData;
    try {
        trainingData = data.trainingData().map(labeledObject -> {
            return new Tensor[] { new Tensor(features).set(this::random), labeledObject.data };
        }).toArray(i -> new Tensor[i][]);
    } catch (@Nonnull final IOException e) {
        throw new RuntimeException(e);
    }
    @Nonnull final DAGNetwork imageNetwork = revFactory.vectorToImage(log, features);
    log.h3("Network Diagram");
    log.code(() -> {
        return Graphviz.fromGraph(TestUtil.toGraph(imageNetwork)).height(400).width(600).render(Format.PNG).toImage();
    });
    @Nonnull final PipelineNetwork trainingNetwork = new PipelineNetwork(2);
    @Nullable final DAGNode image = trainingNetwork.add(imageNetwork, trainingNetwork.getInput(0));
    @Nullable final DAGNode softmax = trainingNetwork.add(new SoftmaxActivationLayer(), trainingNetwork.getInput(0));
    trainingNetwork.add(new SumInputsLayer(), trainingNetwork.add(new EntropyLossLayer(), softmax, softmax), trainingNetwork.add(new NthPowerActivationLayer().setPower(1.0 / 2.0), trainingNetwork.add(new MeanSqLossLayer(), image, trainingNetwork.getInput(1))));
    log.h3("Training");
    log.p("We start by training apply a very small population to improve initial convergence performance:");
    TestUtil.instrumentPerformance(trainingNetwork);
    @Nonnull final Tensor[][] primingData = Arrays.copyOfRange(trainingData, 0, 1000);
    @Nonnull final ValidatingTrainer preTrainer = optimizer.train(log, (SampledTrainable) new SampledArrayTrainable(primingData, trainingNetwork, trainingSize, batchSize).setMinSamples(trainingSize).setMask(true, false), new ArrayTrainable(primingData, trainingNetwork, batchSize), monitor);
    log.code(() -> {
        preTrainer.setTimeout(timeoutMinutes / 2, TimeUnit.MINUTES).setMaxIterations(batchSize).run();
    });
    TestUtil.extractPerformance(log, trainingNetwork);
    log.p("Then our main training phase:");
    TestUtil.instrumentPerformance(trainingNetwork);
    @Nonnull final ValidatingTrainer mainTrainer = optimizer.train(log, (SampledTrainable) new SampledArrayTrainable(trainingData, trainingNetwork, trainingSize, batchSize).setMinSamples(trainingSize).setMask(true, false), new ArrayTrainable(trainingData, trainingNetwork, batchSize), monitor);
    log.code(() -> {
        mainTrainer.setTimeout(timeoutMinutes, TimeUnit.MINUTES).setMaxIterations(batchSize).run();
    });
    TestUtil.extractPerformance(log, trainingNetwork);
    if (!history.isEmpty()) {
        log.code(() -> {
            return TestUtil.plot(history);
        });
        log.code(() -> {
            return TestUtil.plotTime(history);
        });
    }
    try {
        @Nonnull String filename = log.getName().toString() + EncodingProblem.modelNo++ + "_plot.png";
        ImageIO.write(Util.toImage(TestUtil.plot(history)), "png", log.file(filename));
        log.appendFrontMatterProperty("result_plot", filename, ";");
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
    // log.file()
    @Nonnull final String modelName = "encoding_model_" + EncodingProblem.modelNo++ + ".json";
    log.appendFrontMatterProperty("result_model", modelName, ";");
    log.p("Saved model as " + log.file(trainingNetwork.getJson().toString(), modelName, modelName));
    log.h3("Results");
    @Nonnull final PipelineNetwork testNetwork = new PipelineNetwork(2);
    testNetwork.add(imageNetwork, testNetwork.getInput(0));
    log.code(() -> {
        @Nonnull final TableOutput table = new TableOutput();
        Arrays.stream(trainingData).map(tensorArray -> {
            @Nullable final Tensor predictionSignal = testNetwork.eval(tensorArray).getData().get(0);
            @Nonnull final LinkedHashMap<CharSequence, Object> row = new LinkedHashMap<>();
            row.put("Source", log.image(tensorArray[1].toImage(), ""));
            row.put("Echo", log.image(predictionSignal.toImage(), ""));
            return row;
        }).filter(x -> null != x).limit(10).forEach(table::putRow);
        return table;
    });
    log.p("Learned Model Statistics:");
    log.code(() -> {
        @Nonnull final ScalarStatistics scalarStatistics = new ScalarStatistics();
        trainingNetwork.state().stream().flatMapToDouble(x -> Arrays.stream(x)).forEach(v -> scalarStatistics.add(v));
        return scalarStatistics.getMetrics();
    });
    log.p("Learned Representation Statistics:");
    log.code(() -> {
        @Nonnull final ScalarStatistics scalarStatistics = new ScalarStatistics();
        Arrays.stream(trainingData).flatMapToDouble(row -> Arrays.stream(row[0].getData())).forEach(v -> scalarStatistics.add(v));
        return scalarStatistics.getMetrics();
    });
    log.p("Some rendered unit vectors:");
    for (int featureNumber = 0; featureNumber < features; featureNumber++) {
        @Nonnull final Tensor input = new Tensor(features).set(featureNumber, 1);
        @Nullable final Tensor tensor = imageNetwork.eval(input).getData().get(0);
        TestUtil.renderToImages(tensor, true).forEach(img -> {
            log.out(log.image(img, ""));
        });
    }
    return this;
}

Example 9 with TrainingMonitor

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

the class AutoencoderNetwork method train.

/**
 * Train autoencoder network . training parameters.
 *
 * @return the autoencoder network . training parameters
 */
@Nonnull
public AutoencoderNetwork.TrainingParameters train() {
    return new AutoencoderNetwork.TrainingParameters() {

        @Nonnull
        @Override
        public SimpleLossNetwork getTrainingNetwork() {
            @Nonnull final PipelineNetwork student = new PipelineNetwork();
            student.add(encoder);
            student.add(decoder);
            return new SimpleLossNetwork(student, new MeanSqLossLayer());
        }

        @Nonnull
        @Override
        protected TrainingMonitor wrap(@Nonnull final TrainingMonitor monitor) {
            return new TrainingMonitor() {

                @Override
                public void log(final String msg) {
                    monitor.log(msg);
                }

                @Override
                public void onStepComplete(final Step currentPoint) {
                    inputNoise.shuffle();
                    encodedNoise.shuffle(StochasticComponent.random.get().nextLong());
                    monitor.onStepComplete(currentPoint);
                }
            };
        }
    };
}

Example 10 with TrainingMonitor

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

the class QuantifyOrientationWrapper method orient.

@Override
public LineSearchCursor orient(final Trainable subject, final PointSample measurement, @Nonnull final TrainingMonitor monitor) {
    final LineSearchCursor cursor = inner.orient(subject, measurement, monitor);
    if (cursor instanceof SimpleLineSearchCursor) {
        final DeltaSet<Layer> direction = ((SimpleLineSearchCursor) cursor).direction;
        @Nonnull final StateSet<Layer> weights = ((SimpleLineSearchCursor) cursor).origin.weights;
        final Map<CharSequence, CharSequence> dataMap = weights.stream().collect(Collectors.groupingBy(x -> getId(x), Collectors.toList())).entrySet().stream().collect(Collectors.toMap(x -> x.getKey(), list -> {
            final List<Double> doubleList = list.getValue().stream().map(weightDelta -> {
                final DoubleBuffer<Layer> dirDelta = direction.getMap().get(weightDelta.layer);
                final double denominator = weightDelta.deltaStatistics().rms();
                final double numerator = null == dirDelta ? 0 : dirDelta.deltaStatistics().rms();
                return numerator / (0 == denominator ? 1 : denominator);
            }).collect(Collectors.toList());
            if (1 == doubleList.size())
                return Double.toString(doubleList.get(0));
            return new DoubleStatistics().accept(doubleList.stream().mapToDouble(x -> x).toArray()).toString();
        }));
        monitor.log(String.format("Line search stats: %s", dataMap));
    } else {
        monitor.log(String.format("Non-simple cursor: %s", cursor));
    }
    return cursor;
}