List of applyDerivative() Examples

Examples of applyDerivative()

org.apache.hama.commons.math.DoubleFunction.applyDerivative()
Apply the gradient of the function. @param elem @return
org.apache.hama.ml.math.DoubleFunction.applyDerivative()
Apply the gradient of the function. @param elem @return
tv.floe.metronome.deeplearning.neuralnetwork.activation.ActivationFunction.applyDerivative()
Applies the derivative of this function @param input the input to apply it to @return the derivative of this function applied to the input

Examples of org.apache.hama.commons.math.DoubleFunction.applyDerivative()

          * lastWeightMatrix.getRowVector(i).sum();
      deltaVec.set(i, costFuncDerivative);
      deltaVec.set(
          i,
          deltaVec.get(i)
              * squashingFunction.applyDerivative(output.get(i + 1)));
    }


    // start from previous layer of output layer
    for (int layer = this.layerSizeList.size() - 2; layer >= 0; --layer) {
      output = internalResults.get(layer);

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Examples of org.apache.hama.commons.math.DoubleFunction.applyDerivative()

        .multiplyVector(nextLayerDelta);
    for (int i = 0; i < delta.getDimension(); ++i) {
      delta.set(
          i,
          delta.get(i)
              * squashingFunction.applyDerivative(curLayerOutput.get(i)));
    }


    // update weights
    for (int i = 0; i < weightUpdateMatrix.getRowCount(); ++i) {
      for (int j = 0; j < weightUpdateMatrix.getColumnCount(); ++j) {

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Examples of org.apache.hama.commons.math.DoubleFunction.applyDerivative()

          * lastWeightMatrix.getRowVector(i).sum();
      deltaVec.set(i, costFuncDerivative);
      deltaVec.set(
          i,
          deltaVec.get(i)
              * squashingFunction.applyDerivative(output.get(i + 1)));
    }


    // start from previous layer of output layer
    for (int layer = this.layerSizeList.size() - 2; layer >= 0; --layer) {
      output = internalResults.get(layer);

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Examples of org.apache.hama.commons.math.DoubleFunction.applyDerivative()

        .multiplyVector(nextLayerDelta);
    for (int i = 0; i < delta.getDimension(); ++i) {
      delta.set(
          i,
          delta.get(i)
              * squashingFunction.applyDerivative(curLayerOutput.get(i)));
    }


    // update weights
    for (int i = 0; i < weightUpdateMatrix.getRowCount(); ++i) {
      for (int j = 0; j < weightUpdateMatrix.getColumnCount(); ++j) {

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Examples of org.apache.hama.ml.math.DoubleFunction.applyDerivative()

          * lastWeightMatrix.getRowVector(i).sum();
      deltaVec.set(i, costFuncDerivative);
      deltaVec.set(
          i,
          deltaVec.get(i)
              * squashingFunction.applyDerivative(output.get(i + 1)));
    }


    // start from previous layer of output layer
    for (int layer = this.layerSizeList.size() - 2; layer >= 0; --layer) {
      output = internalResults.get(layer);

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Examples of org.apache.hama.ml.math.DoubleFunction.applyDerivative()

        .multiplyVector(nextLayerDelta);
    for (int i = 0; i < delta.getDimension(); ++i) {
      delta.set(
          i,
          delta.get(i)
              * squashingFunction.applyDerivative(curLayerOutput.get(i)));
    }


    // System.out.printf("Delta layer: %d, %s\n", curLayerIdx,
    // delta.toString());

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Examples of tv.floe.metronome.deeplearning.neuralnetwork.activation.ActivationFunction.applyDerivative()


                //-( y - h) .* f'(z^l) where l is the output layer
                //delta = labels.sub(activations.get(i)).neg().mul(softMaxDerivative.applyDerivative(activations.get(i)));
                Matrix tmpDelta = MatrixUtils.neg( labels.minus( activations.get( i ) ) );


                delta = MatrixUtils.elementWiseMultiplication( tmpDelta, softMaxDerivative.applyDerivative(activations.get(i)) );


                deltas[i] = delta;




            } else {

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