Examples with ActivationLayer com.simiacryptus.mindseye.layers.cudnn.ActivationLayer used on opensource projects

Example 1 with ActivationLayer

use of com.simiacryptus.mindseye.layers.cudnn.ActivationLayer in project MindsEye by SimiaCryptus.

the class VGG16_HDF5 method phase3a.

/**
 * Phase 3 a.
 */
protected void phase3a() {
    add(new ConvolutionLayer(1, 1, 4096, 4096).setPaddingXY(0, 0).setAndFree(hdf5.readDataSet("param_0", "layer_34").permuteDimensionsAndFree(fullyconnectedOrder)));
    add(new ImgBandBiasLayer(4096).setAndFree((hdf5.readDataSet("param_1", "layer_34"))));
    add(new ActivationLayer(ActivationLayer.Mode.RELU));
    add(new ConvolutionLayer(1, 1, 4096, 1000).setPaddingXY(0, 0).setAndFree(hdf5.readDataSet("param_0", "layer_36").permuteDimensionsAndFree(fullyconnectedOrder)));
    add(new ImgBandBiasLayer(1000).setAndFree((hdf5.readDataSet("param_1", "layer_36"))));
}

Example 2 with ActivationLayer

use of com.simiacryptus.mindseye.layers.cudnn.ActivationLayer in project MindsEye by SimiaCryptus.

the class RecursiveSubspaceTest method buildModel.

@Override
public DAGNetwork buildModel(@Nonnull NotebookOutput log) {
    log.h3("Model");
    log.p("We use a multi-level convolution network");
    return log.code(() -> {
        @Nonnull final PipelineNetwork network = new PipelineNetwork();
        double weight = 1e-3;
        @Nonnull DoubleSupplier init = () -> weight * (Math.random() - 0.5);
        network.add(new ConvolutionLayer(3, 3, 1, 5).set(init));
        network.add(new ImgBandBiasLayer(5));
        network.add(new PoolingLayer().setMode(PoolingLayer.PoolingMode.Max));
        network.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        network.add(newNormalizationLayer());
        network.add(new ConvolutionLayer(3, 3, 5, 5).set(init));
        network.add(new ImgBandBiasLayer(5));
        network.add(new PoolingLayer().setMode(PoolingLayer.PoolingMode.Max));
        network.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        network.add(newNormalizationLayer());
        network.add(new BiasLayer(7, 7, 5));
        network.add(new FullyConnectedLayer(new int[] { 7, 7, 5 }, new int[] { 10 }).set(init));
        network.add(new SoftmaxActivationLayer());
        return network;
    });
}

Example 3 with ActivationLayer

use of com.simiacryptus.mindseye.layers.cudnn.ActivationLayer in project MindsEye by SimiaCryptus.

the class ImageClassifier method deepDream.

/**
 * Deep dream.
 *
 * @param log   the log
 * @param image the image
 */
public void deepDream(@Nonnull final NotebookOutput log, final Tensor image) {
    log.code(() -> {
        @Nonnull ArrayList<StepRecord> history = new ArrayList<>();
        @Nonnull PipelineNetwork clamp = new PipelineNetwork(1);
        clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        clamp.add(new LinearActivationLayer().setBias(255).setScale(-1).freeze());
        clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        clamp.add(new LinearActivationLayer().setBias(255).setScale(-1).freeze());
        @Nonnull PipelineNetwork supervised = new PipelineNetwork(1);
        supervised.add(getNetwork().freeze(), supervised.wrap(clamp, supervised.getInput(0)));
        // CudaTensorList gpuInput = CudnnHandle.apply(gpu -> {
        // Precision precision = Precision.Float;
        // return CudaTensorList.wrap(gpu.getPtr(TensorArray.wrap(image), precision, MemoryType.Managed), 1, image.getDimensions(), precision);
        // });
        // @Nonnull Trainable trainable = new TensorListTrainable(supervised, gpuInput).setVerbosity(1).setMask(true);
        @Nonnull Trainable trainable = new ArrayTrainable(supervised, 1).setVerbose(true).setMask(true, false).setData(Arrays.<Tensor[]>asList(new Tensor[] { image }));
        new IterativeTrainer(trainable).setMonitor(getTrainingMonitor(history, supervised)).setOrientation(new QQN()).setLineSearchFactory(name -> new ArmijoWolfeSearch()).setTimeout(60, TimeUnit.MINUTES).runAndFree();
        return TestUtil.plot(history);
    });
}

Example 4 with ActivationLayer

use of com.simiacryptus.mindseye.layers.cudnn.ActivationLayer in project MindsEye by SimiaCryptus.

the class ImageClassifier method deepDream.

/**
 * Deep dream.
 *
 * @param log                 the log
 * @param image               the image
 * @param targetCategoryIndex the target category index
 * @param totalCategories     the total categories
 * @param config              the config
 * @param network             the network
 * @param lossLayer           the loss layer
 * @param targetValue         the target value
 */
public void deepDream(@Nonnull final NotebookOutput log, final Tensor image, final int targetCategoryIndex, final int totalCategories, Function<IterativeTrainer, IterativeTrainer> config, final Layer network, final Layer lossLayer, final double targetValue) {
    @Nonnull List<Tensor[]> data = Arrays.<Tensor[]>asList(new Tensor[] { image, new Tensor(1, 1, totalCategories).set(targetCategoryIndex, targetValue) });
    log.code(() -> {
        for (Tensor[] tensors : data) {
            ImageClassifier.log.info(log.image(tensors[0].toImage(), "") + tensors[1]);
        }
    });
    log.code(() -> {
        @Nonnull ArrayList<StepRecord> history = new ArrayList<>();
        @Nonnull PipelineNetwork clamp = new PipelineNetwork(1);
        clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        clamp.add(new LinearActivationLayer().setBias(255).setScale(-1).freeze());
        clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
        clamp.add(new LinearActivationLayer().setBias(255).setScale(-1).freeze());
        @Nonnull PipelineNetwork supervised = new PipelineNetwork(2);
        supervised.wrap(lossLayer, supervised.add(network.freeze(), supervised.wrap(clamp, supervised.getInput(0))), supervised.getInput(1));
        // TensorList[] gpuInput = data.stream().map(data1 -> {
        // return CudnnHandle.apply(gpu -> {
        // Precision precision = Precision.Float;
        // return CudaTensorList.wrap(gpu.getPtr(TensorArray.wrap(data1), precision, MemoryType.Managed), 1, image.getDimensions(), precision);
        // });
        // }).toArray(i -> new TensorList[i]);
        // @Nonnull Trainable trainable = new TensorListTrainable(supervised, gpuInput).setVerbosity(1).setMask(true);
        @Nonnull Trainable trainable = new ArrayTrainable(supervised, 1).setVerbose(true).setMask(true, false).setData(data);
        config.apply(new IterativeTrainer(trainable).setMonitor(getTrainingMonitor(history, supervised)).setOrientation(new QQN()).setLineSearchFactory(name -> new ArmijoWolfeSearch()).setTimeout(60, TimeUnit.MINUTES)).setTerminateThreshold(Double.NEGATIVE_INFINITY).runAndFree();
        return TestUtil.plot(history);
    });
}

Example 5 with ActivationLayer

use of com.simiacryptus.mindseye.layers.cudnn.ActivationLayer in project MindsEye by SimiaCryptus.

the class ArtistryUtil method getClamp.

/**
 * Gets clamp.
 *
 * @param max the max
 * @return the clamp
 */
@Nonnull
public static PipelineNetwork getClamp(final int max) {
    @Nonnull PipelineNetwork clamp = new PipelineNetwork(1);
    clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
    clamp.add(new LinearActivationLayer().setBias(max).setScale(-1).freeze());
    clamp.add(new ActivationLayer(ActivationLayer.Mode.RELU));
    clamp.add(new LinearActivationLayer().setBias(max).setScale(-1).freeze());
    return clamp;
}