public CliquePotentialFunction getCliquePotentialFunction(double[] x) { double[] rawScales = separateLopScales(x); double[] scales = ArrayMath.softmax(rawScales); double[][][] learnedLopExpertWeights2D = lopExpertWeights2D; if (backpropTraining) { learnedLopExpertWeights2D = separateLopExpertWeights2D(x); } double[][] combinedWeights2D = combineAndScaleLopWeights2D(numLopExpert, learnedLopExpertWeights2D, scales); return new LinearCliquePotentialFunction(combinedWeights2D); }
double[] rawScales = func.separateLopScales(learnedParams); double[] lopScales = ArrayMath.softmax(rawScales); log.info("After SoftMax Transformation, learned scales are:");
double[] eScales = new double[numLopExpert]; double[] rawScales = separateLopScales(x); double[] scales = ArrayMath.softmax(rawScales); double[][][] learnedLopExpertWeights2D = lopExpertWeights2D;
public CliquePotentialFunction getCliquePotentialFunction(double[] x) { double[] rawScales = separateLopScales(x); double[] scales = ArrayMath.softmax(rawScales); double[][][] learnedLopExpertWeights2D = lopExpertWeights2D; if (backpropTraining) { learnedLopExpertWeights2D = separateLopExpertWeights2D(x); } double[][] combinedWeights2D = combineAndScaleLopWeights2D(numLopExpert, learnedLopExpertWeights2D, scales); return new LinearCliquePotentialFunction(combinedWeights2D); }
public CliquePotentialFunction getCliquePotentialFunction(double[] x) { double[] rawScales = separateLopScales(x); double[] scales = ArrayMath.softmax(rawScales); double[][][] learnedLopExpertWeights2D = lopExpertWeights2D; if (backpropTraining) { learnedLopExpertWeights2D = separateLopExpertWeights2D(x); } double[][] combinedWeights2D = combineAndScaleLopWeights2D(numLopExpert, learnedLopExpertWeights2D, scales); return new LinearCliquePotentialFunction(combinedWeights2D); }
double[] rawScales = func.separateLopScales(learnedParams); double[] lopScales = ArrayMath.softmax(rawScales); System.err.println("After SoftMax Transformation, learned scales are:");
double[] rawScales = func.separateLopScales(learnedParams); double[] lopScales = ArrayMath.softmax(rawScales); log.info("After SoftMax Transformation, learned scales are:");
double[] eScales = new double[numLopExpert]; double[] rawScales = separateLopScales(x); double[] scales = ArrayMath.softmax(rawScales); double[][][] learnedLopExpertWeights2D = lopExpertWeights2D;
double[] eScales = new double[numLopExpert]; double[] rawScales = separateLopScales(x); double[] scales = ArrayMath.softmax(rawScales); double[][][] learnedLopExpertWeights2D = lopExpertWeights2D;