Package tv.floe.metronome.deeplearning.neuralnetwork.gradient

Examples of tv.floe.metronome.deeplearning.neuralnetwork.gradient.LogisticRegressionGradient

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  }

  @Override
  public void getValueGradient(double[] buffer) {
   
    LogisticRegressionGradient grad = logReg.getGradient( lr );
   
    for (int i = 0; i < buffer.length; i++) {
   
      if ( i < MatrixUtils.length( logReg.connectionWeights )) {
       
        buffer[ i ] = MatrixUtils.getElement( grad.getwGradient(), i );
       
      } else {
       
        buffer[ i ] = MatrixUtils.getElement( grad.getbGradient(), i - MatrixUtils.length( logReg.connectionWeights ) );
       
      }
     
    }
   
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  }

  @Override
  public Matrix getValueGradient() {
   
    LogisticRegressionGradient grad = logReg.getGradient( lr );
    Matrix ret = new DenseMatrix( 1, getNumParameters() );
   
    for (int i = 0; i < MatrixUtils.length( ret ); i++) {
     
      if ( i < MatrixUtils.length( logReg.connectionWeights  ) ) {
       
        MatrixUtils.setElement( ret, i, MatrixUtils.getElement( grad.getwGradient(), i ) );
       
      } else {
       
        MatrixUtils.setElement( ret, i, MatrixUtils.getElement( grad.getbGradient(), i - MatrixUtils.length( logReg.connectionWeights ) ) );
       
      }
     
    }
    return ret;
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        if (params.length >= 2) {
            lr = (Double) params[1];
        }

        this.feedForward();
        LogisticRegressionGradient g2 = this.logisticRegressionLayer.getGradient(lr);


        MultiLayerGradient ret =  new MultiLayerGradient(gradient,g2);
/*
    if(multiLayerGradientListeners != null && !multiLayerGradientListeners.isEmpty()) {
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    }

    this.input = x;
    this.labels = y;

    LogisticRegressionGradient gradient = getGradient(lr);

    //W.addi(gradient.getwGradient());
    this.connectionWeights = this.connectionWeights.plus(gradient.getwGradient());
   
    //b.addi(gradient.getbGradient());
    this.biasTerms = this.biasTerms.plus(gradient.getbGradient());

  }
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    }
   
    Matrix bGradient = dy;
   
    return new LogisticRegressionGradient( wGradient, bGradient );
   
   
  }
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Related Classes of tv.floe.metronome.deeplearning.neuralnetwork.gradient.LogisticRegressionGradient

  • tv.floe.metronome.deeplearning.neuralnetwork.core.BaseMultiLayerNeuralNetworkVectorized
  • tv.floe.metronome.deeplearning.neuralnetwork.core.LogisticRegression
  • tv.floe.metronome.deeplearning.neuralnetwork.optimize.LogisticRegressionOptimizer
  • java.io.DataInput
  • java.io.DataOutputStream
  • java.io.DataOutput
  • java.io.DataInputStream
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