public static INDArray mean(INDArray compute, int dimension) { return compute.mean(dimension); }
INDArray maxAlong0 = originalArray.max(0); INDArray sumAlong0 = originalArray.sum(0); INDArray avgAlong0 = originalArray.mean(0); INDArray stdevAlong0 = originalArray.std(0); INDArray avgAlong1 = originalArray.mean(1); System.out.println("\n\navg along dimension 1: " + avgAlong1); System.out.println("Shape of avg along d1: " + Arrays.toString(avgAlong1.shape()));
public static INDArray mean(INDArray compute) { return compute.mean(Integer.MAX_VALUE); }
@Override public INDArray exampleMeans() { return getFeatures().mean(1); }
/** * Normalize data to zero mean and unit variance * substract by the mean and divide by the standard deviation * * @param toNormalize the ndarray to normalize * @return the normalized ndarray */ public static INDArray normalizeZeroMeanAndUnitVariance(INDArray toNormalize) { INDArray columnMeans = toNormalize.mean(0); INDArray columnStds = toNormalize.std(0); toNormalize.subiRowVector(columnMeans); //padding for non zero columnStds.addi(Nd4j.EPS_THRESHOLD); toNormalize.diviRowVector(columnStds); return toNormalize; }
public static void normalizeMatrix(INDArray toNormalize) { INDArray columnMeans = toNormalize.mean(0); toNormalize.subiRowVector(columnMeans); INDArray std = toNormalize.std(0); std.addi(Nd4j.scalar(1e-12)); toNormalize.diviRowVector(std); }
public void fit(DataSet dataSet) { mean = dataSet.getFeatureMatrix().mean(0); std = dataSet.getFeatureMatrix().std(0); std.addi(Nd4j.scalar(Nd4j.EPS_THRESHOLD)); if (std.min(1) == Nd4j.scalar(Nd4j.EPS_THRESHOLD)) logger.info("API_INFO: Std deviation found to be zero. Transform will round upto epsilon to avoid nans."); }
/** * @Deprecated * Subtract by the column means and divide by the standard deviation */ @Deprecated @Override public void normalizeZeroMeanZeroUnitVariance() { INDArray columnMeans = getFeatures().mean(0); INDArray columnStds = getFeatureMatrix().std(0); setFeatures(getFeatures().subiRowVector(columnMeans)); columnStds.addi(Nd4j.scalar(Nd4j.EPS_THRESHOLD)); setFeatures(getFeatures().diviRowVector(columnStds)); }
if (normalize) { INDArray mean = A.mean(0); A.subiRowVector(mean);
INDArray mean = A.mean(0); A.subiRowVector(mean);
INDArray mean = data.mean(0); INDArray variance = data.var(false, 0); long count = data.size(0);
mean = next.getFeatureMatrix().mean(0); std = (batchCount == 1) ? Nd4j.zeros(mean.shape()) : Transforms.pow(next.getFeatureMatrix().std(0), 2); std.muli(batchCount); INDArray meanB = next.getFeatureMatrix().mean(0); INDArray deltaSq = Transforms.pow(meanB.subRowVector(mean), 2); INDArray deltaSqScaled =
INDArray mean = array.mean(1);
@Override public INDArray preProcess(INDArray input, int miniBatchSize) { INDArray columnMeans = input.mean(0); input.subiRowVector(columnMeans); return input; }
@Override public INDArray exampleMeans() { return getFeatures().mean(1); }
@Override public Gradient calcGradient(Gradient layerError, INDArray activation) { Gradient ret = new DefaultGradient(); INDArray weightErrorSignal = layerError.getGradientFor(DefaultParamInitializer.WEIGHT_KEY); INDArray weightError = weightErrorSignal.transpose().mmul(activation).transpose(); ret.gradientForVariable().put(DefaultParamInitializer.WEIGHT_KEY, weightError); INDArray biasGradient = weightError.mean(0); ret.gradientForVariable().put(DefaultParamInitializer.BIAS_KEY, biasGradient); return ret; }
@Override public INDArray preProcess(INDArray input, int miniBatchSize) { INDArray columnMeans = input.mean(0); INDArray columnStds = input.std(0); input.subiRowVector(columnMeans); columnStds.addi(Nd4j.EPS_THRESHOLD); input.diviRowVector(columnStds); return input; }
public void fit(DataSet dataSet) { mean = dataSet.getFeatureMatrix().mean(0); std = dataSet.getFeatureMatrix().std(0); std.addi(Nd4j.scalar(Nd4j.EPS_THRESHOLD)); if (std.min(1) == Nd4j.scalar(Nd4j.EPS_THRESHOLD)) logger.info("API_INFO: Std deviation found to be zero. Transform will round upto epsilon to avoid nans."); }
public static void normalizeMatrix(INDArray toNormalize) { INDArray columnMeans = toNormalize.mean(0); toNormalize.subiRowVector(columnMeans); INDArray std = toNormalize.std(0); std.addi(Nd4j.scalar(1e-12)); toNormalize.diviRowVector(std); }
public static String summaryStatsString(INDArray d) { return new SummaryStatistics(d.mean(Integer.MAX_VALUE), d.sum(Integer.MAX_VALUE), d.min(Integer.MAX_VALUE), d.max(Integer.MAX_VALUE)).toString(); }