Examples with BiasLayer com.simiacryptus.mindseye.layers.java.BiasLayer used on opensource projects

Example 1 with BiasLayer

use of com.simiacryptus.mindseye.layers.java.BiasLayer in project MindsEye by SimiaCryptus.

the class MnistTestBase method buildModel.

/**
 * Build model dag network.
 *
 * @param log the log
 * @return the dag network
 */
public DAGNetwork buildModel(@Nonnull final NotebookOutput log) {
    log.h1("Model");
    log.p("This is a very simple model that performs basic logistic regression. " + "It is expected to be trainable to about 91% accuracy on MNIST.");
    return log.code(() -> {
        @Nonnull final PipelineNetwork network = new PipelineNetwork();
        network.add(new BiasLayer(28, 28, 1));
        network.add(new FullyConnectedLayer(new int[] { 28, 28, 1 }, new int[] { 10 }).set(() -> 0.001 * (Math.random() - 0.45)));
        network.add(new SoftmaxActivationLayer());
        return network;
    });
}

Example 2 with BiasLayer

use of com.simiacryptus.mindseye.layers.java.BiasLayer in project MindsEye by SimiaCryptus.

the class SigmoidTreeNetwork method nextPhase.

@Override
public void nextPhase() {
    switch(getMode()) {
        case Linear:
            {
                head = null;
                @Nonnull final FullyConnectedLayer alpha = (FullyConnectedLayer) this.alpha;
                // alpha.weights.scale(2);
                gate = new FullyConnectedLayer(alpha.inputDims, multigate ? alpha.outputDims : new int[] { 1 });
                gateBias = new BiasLayer(alpha.inputDims);
                mode = NodeMode.Fuzzy;
                break;
            }
        case Fuzzy:
            {
                head = null;
                @Nullable final FullyConnectedLayer alpha = (FullyConnectedLayer) this.alpha;
                @Nonnull final BiasLayer alphaBias = (BiasLayer) this.alphaBias;
                beta = new FullyConnectedLayer(alpha.inputDims, alpha.outputDims).set(() -> {
                    return initialFuzzyCoeff * (FastRandom.INSTANCE.random() - 0.5);
                });
                betaBias = new BiasLayer(alphaBias.bias.length);
                copyState(alpha, beta);
                copyState(alphaBias, betaBias);
                mode = NodeMode.Bilinear;
                if (isSkipFuzzy()) {
                    nextPhase();
                }
                break;
            }
        case Bilinear:
            head = null;
            alpha = new SigmoidTreeNetwork(alpha, alphaBias);
            if (skipChildStage()) {
                ((SigmoidTreeNetwork) alpha).nextPhase();
            }
            beta = new SigmoidTreeNetwork(beta, betaBias);
            if (skipChildStage()) {
                ((SigmoidTreeNetwork) beta).nextPhase();
            }
            mode = NodeMode.Final;
            break;
        case Final:
            @Nonnull final SigmoidTreeNetwork alpha = (SigmoidTreeNetwork) this.alpha;
            @Nonnull final SigmoidTreeNetwork beta = (SigmoidTreeNetwork) this.beta;
            alpha.nextPhase();
            beta.nextPhase();
            break;
    }
}

Example 3 with BiasLayer

use of com.simiacryptus.mindseye.layers.java.BiasLayer 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 4 with BiasLayer

use of com.simiacryptus.mindseye.layers.java.BiasLayer in project MindsEye by SimiaCryptus.

the class DeepLinear method addLayer.

@Override
public void addLayer(@Nonnull final PipelineNetwork network, @Nonnull final int[] in, @Nonnull final int[] dims) {
    network.add(new FullyConnectedLayer(in, dims).set(this::random));
    network.add(new BiasLayer(dims));
    network.add(getActivation());
}