Examples with NotebookOutput com.simiacryptus.util.io.NotebookOutput used on opensource projects

Example 36 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class TrainingTester method train.

private List<StepRecord> train(@Nonnull NotebookOutput log, @Nonnull BiFunction<NotebookOutput, Trainable, List<StepRecord>> opt, @Nonnull Layer layer, @Nonnull Tensor[][] data, @Nonnull boolean... mask) {
    try {
        int inputs = data[0].length;
        @Nonnull final PipelineNetwork network = new PipelineNetwork(inputs);
        network.wrap(new MeanSqLossLayer(), network.add(layer, IntStream.range(0, inputs - 1).mapToObj(i -> network.getInput(i)).toArray(i -> new DAGNode[i])), network.getInput(inputs - 1));
        @Nonnull ArrayTrainable trainable = new ArrayTrainable(data, network);
        if (0 < mask.length)
            trainable.setMask(mask);
        List<StepRecord> history;
        try {
            history = opt.apply(log, trainable);
            if (history.stream().mapToDouble(x -> x.fitness).min().orElse(1) > 1e-5) {
                if (!network.isFrozen()) {
                    log.p("This training apply resulted in the following configuration:");
                    log.code(() -> {
                        return network.state().stream().map(Arrays::toString).reduce((a, b) -> a + "\n" + b).orElse("");
                    });
                }
                if (0 < mask.length) {
                    log.p("And regressed input:");
                    log.code(() -> {
                        return Arrays.stream(data).flatMap(x -> Arrays.stream(x)).limit(1).map(x -> x.prettyPrint()).reduce((a, b) -> a + "\n" + b).orElse("");
                    });
                }
                log.p("To produce the following output:");
                log.code(() -> {
                    Result[] array = ConstantResult.batchResultArray(pop(data));
                    @Nullable Result eval = layer.eval(array);
                    for (@Nonnull Result result : array) {
                        result.freeRef();
                        result.getData().freeRef();
                    }
                    TensorList tensorList = eval.getData();
                    eval.freeRef();
                    String str = tensorList.stream().limit(1).map(x -> {
                        String s = x.prettyPrint();
                        x.freeRef();
                        return s;
                    }).reduce((a, b) -> a + "\n" + b).orElse("");
                    tensorList.freeRef();
                    return str;
                });
            } else {
                log.p("Training Converged");
            }
        } finally {
            trainable.freeRef();
            network.freeRef();
        }
        return history;
    } finally {
        layer.freeRef();
        for (@Nonnull Tensor[] tensors : data) {
            for (@Nonnull Tensor tensor : tensors) {
                tensor.freeRef();
            }
        }
    }
}

Example 37 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class CudaLayerTester method testNonstandardBounds.

/**
 * Test nonstandard bounds tolerance statistics.
 *
 * @param log            the log
 * @param reference      the reference
 * @param inputPrototype the input prototype
 * @return the tolerance statistics
 */
@Nonnull
public ToleranceStatistics testNonstandardBounds(final NotebookOutput log, @Nullable final Layer reference, @Nonnull final Tensor[] inputPrototype) {
    log.h2("Irregular Input");
    log.p("This layer should be able to accept non-dense inputs.");
    return log.code(() -> {
        Tensor[] randomized = Arrays.stream(inputPrototype).map(x -> x.map(v -> getRandom())).toArray(i -> new Tensor[i]);
        logger.info("Input: " + Arrays.stream(randomized).map(Tensor::prettyPrint).collect(Collectors.toList()));
        Precision precision = Precision.Double;
        TensorList[] controlInput = CudaSystem.run(gpu -> {
            return Arrays.stream(randomized).map(original -> {
                TensorArray data = TensorArray.create(original);
                CudaTensorList wrap = CudaTensorList.wrap(gpu.getTensor(data, precision, MemoryType.Managed, false), 1, original.getDimensions(), precision);
                data.freeRef();
                return wrap;
            }).toArray(i -> new TensorList[i]);
        }, 0);
        @Nonnull final SimpleResult controlResult = CudaSystem.run(gpu -> {
            return SimpleGpuEval.run(reference, gpu, controlInput);
        }, 1);
        final TensorList[] irregularInput = CudaSystem.run(gpu -> {
            return Arrays.stream(randomized).map(original -> {
                return buildIrregularCudaTensor(gpu, precision, original);
            }).toArray(i -> new TensorList[i]);
        }, 0);
        @Nonnull final SimpleResult testResult = CudaSystem.run(gpu -> {
            return SimpleGpuEval.run(reference, gpu, irregularInput);
        }, 1);
        try {
            ToleranceStatistics compareOutput = compareOutput(controlResult, testResult);
            ToleranceStatistics compareDerivatives = compareDerivatives(controlResult, testResult);
            return compareDerivatives.combine(compareOutput);
        } finally {
            Arrays.stream(randomized).forEach(ReferenceCountingBase::freeRef);
            Arrays.stream(controlInput).forEach(ReferenceCounting::freeRef);
            Arrays.stream(irregularInput).forEach(x -> x.freeRef());
            controlResult.freeRef();
            testResult.freeRef();
        }
    });
}

Example 38 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class NotebookReportBase method run.

/**
 * Run.
 *
 * @param fn      the fn
 * @param logPath the log path
 */
public void run(@Nonnull Consumer<NotebookOutput> fn, @Nonnull CharSequence... logPath) {
    try (@Nonnull NotebookOutput log = getLog(logPath.length == 0 ? new String[] { getClass().getSimpleName() } : logPath)) {
        printHeader(log);
        @Nonnull TimedResult<Void> time = TimedResult.time(() -> {
            try {
                fn.accept(log);
                log.setFrontMatterProperty("result", "OK");
            } catch (Throwable e) {
                log.setFrontMatterProperty("result", getExceptionString(e).toString().replaceAll("\n", "<br/>").trim());
                throw (RuntimeException) (e instanceof RuntimeException ? e : new RuntimeException(e));
            }
        });
        log.setFrontMatterProperty("execution_time", String.format("%.6f", time.timeNanos / 1e9));
    } catch (IOException e) {
        throw new RuntimeException(e);
    }
}

Example 39 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class TestUtil method printHistory.

/**
 * Print history.
 *
 * @param log     the log
 * @param history the history
 */
public static void printHistory(@Nonnull final NotebookOutput log, @Nonnull final List<StepRecord> history) {
    if (!history.isEmpty()) {
        log.out("Convergence Plot: ");
        log.code(() -> {
            final DoubleSummaryStatistics valueStats = history.stream().mapToDouble(x -> x.fitness).filter(x -> x > 0).summaryStatistics();
            @Nonnull final PlotCanvas plot = ScatterPlot.plot(history.stream().map(step -> new double[] { step.iteration, Math.log10(Math.max(valueStats.getMin(), step.fitness)) }).toArray(i -> new double[i][]));
            plot.setTitle("Convergence Plot");
            plot.setAxisLabels("Iteration", "log10(Fitness)");
            plot.setSize(600, 400);
            return plot;
        });
    }
}

Example 40 with NotebookOutput

use of com.simiacryptus.util.io.NotebookOutput in project MindsEye by SimiaCryptus.

the class ReferenceIO method test.

@Nullable
@Override
public ToleranceStatistics test(@Nonnull final NotebookOutput log, @Nonnull final Layer layer, @Nonnull final Tensor... inputPrototype) {
    if (!referenceIO.isEmpty()) {
        log.h1("Reference Input/Output Pairs");
        log.p("Display pre-setBytes input/output example pairs:");
        referenceIO.forEach((input, output) -> {
            log.code(() -> {
                @Nonnull final SimpleEval eval = SimpleEval.run(layer, input);
                Tensor add = output.scale(-1).addAndFree(eval.getOutput());
                @Nonnull final DoubleStatistics error = new DoubleStatistics().accept(add.getData());
                add.freeRef();
                String format = String.format("--------------------\nInput: \n[%s]\n--------------------\nOutput: \n%s\nError: %s\n--------------------\nDerivative: \n%s", Arrays.stream(input).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).get(), eval.getOutput().prettyPrint(), error, Arrays.stream(eval.getDerivative()).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).get());
                eval.freeRef();
                return format;
            });
        });
    } else {
        log.h1("Example Input/Output Pair");
        log.p("Display input/output pairs from random executions:");
        log.code(() -> {
            @Nonnull final SimpleEval eval = SimpleEval.run(layer, inputPrototype);
            String format = String.format("--------------------\nInput: \n[%s]\n--------------------\nOutput: \n%s\n--------------------\nDerivative: \n%s", Arrays.stream(inputPrototype).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).orElse(""), eval.getOutput().prettyPrint(), Arrays.stream(eval.getDerivative()).map(t -> t.prettyPrint()).reduce((a, b) -> a + ",\n" + b).orElse(""));
            eval.freeRef();
            return format;
        });
    }
    return null;
}