Examples with Gradient org.deeplearning4j.nn.gradient.Gradient used on opensource projects

Example 41 with Gradient

use of org.deeplearning4j.nn.gradient.Gradient in project deeplearning4j by deeplearning4j.

the class RnnOutputLayer method backpropGradient.

@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon) {
    if (input.rank() != 3)
        throw new UnsupportedOperationException("Input is not rank 3");
    INDArray inputTemp = input;
    this.input = TimeSeriesUtils.reshape3dTo2d(input);
    Pair<Gradient, INDArray> gradAndEpsilonNext = super.backpropGradient(epsilon);
    this.input = inputTemp;
    INDArray epsilon2d = gradAndEpsilonNext.getSecond();
    INDArray epsilon3d = TimeSeriesUtils.reshape2dTo3d(epsilon2d, input.size(0));
    return new Pair<>(gradAndEpsilonNext.getFirst(), epsilon3d);
}

Example 42 with Gradient

use of org.deeplearning4j.nn.gradient.Gradient in project deeplearning4j by deeplearning4j.

the class LayerVertex method doBackward.

@Override
public Pair<Gradient, INDArray[]> doBackward(boolean tbptt) {
    if (!canDoBackward()) {
        throw new IllegalStateException("Cannot do backward pass: all epsilons not set. Layer " + vertexName + " (idx " + vertexIndex + ") numInputs " + getNumInputArrays() + "; numOutputs " + getNumOutputConnections());
    }
    Pair<Gradient, INDArray> pair;
    if (tbptt && layer instanceof RecurrentLayer) {
        //Truncated BPTT for recurrent layers
        pair = ((RecurrentLayer) layer).tbpttBackpropGradient(epsilon, graph.getConfiguration().getTbpttBackLength());
    } else {
        //Normal backprop
        //epsTotal may be null for OutputLayers
        pair = layer.backpropGradient(epsilon);
    }
    if (layerPreProcessor != null) {
        INDArray eps = pair.getSecond();
        eps = layerPreProcessor.backprop(eps, graph.batchSize());
        pair.setSecond(eps);
    }
    //Layers always have single activations input -> always have single epsilon output during backprop
    return new Pair<>(pair.getFirst(), new INDArray[] { pair.getSecond() });
}

Example 43 with Gradient

use of org.deeplearning4j.nn.gradient.Gradient in project deeplearning4j by deeplearning4j.

the class ActivationLayer method backpropGradient.

@Override
public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon) {
    //TODO handle activation function params
    INDArray delta = conf().getLayer().getActivationFn().backprop(input.dup(), epsilon).getFirst();
    if (maskArray != null) {
        delta.muliColumnVector(maskArray);
    }
    Gradient ret = new DefaultGradient();
    return new Pair<>(ret, delta);
}

Example 44 with Gradient

use of org.deeplearning4j.nn.gradient.Gradient in project deeplearning4j by deeplearning4j.

the class BaseLayer method error.

@Override
public Gradient error(INDArray errorSignal) {
    INDArray W = getParam(DefaultParamInitializer.WEIGHT_KEY);
    Gradient nextLayerGradient = new DefaultGradient();
    INDArray wErrorSignal = errorSignal.mmul(W.transpose());
    nextLayerGradient.gradientForVariable().put(DefaultParamInitializer.WEIGHT_KEY, wErrorSignal);
    return nextLayerGradient;
}

Example 45 with Gradient

use of org.deeplearning4j.nn.gradient.Gradient in project deeplearning4j by deeplearning4j.

the class BarnesHutTsne method gradient.

@Override
public Gradient gradient() {
    if (yIncs == null)
        yIncs = zeros(Y.shape());
    if (gains == null)
        gains = ones(Y.shape());
    AtomicDouble sumQ = new AtomicDouble(0);
    /* Calculate gradient based on barnes hut approximation with positive and negative forces */
    INDArray posF = Nd4j.create(Y.shape());
    INDArray negF = Nd4j.create(Y.shape());
    if (tree == null)
        tree = new SpTree(Y);
    tree.computeEdgeForces(rows, cols, vals, N, posF);
    for (int n = 0; n < N; n++) tree.computeNonEdgeForces(n, theta, negF.slice(n), sumQ);
    INDArray dC = posF.subi(negF.divi(sumQ));
    Gradient ret = new DefaultGradient();
    ret.gradientForVariable().put(Y_GRAD, dC);
    return ret;
}