Package org.tensorflow.framework.metrics

package org.tensorflow.framework.metrics

Related Packages

Package	Description
org.tensorflow.framework.metrics.exceptions

Class	Description
Accuracy<T extends TNumber>	Metric that calculates how often predictions equals labels.
AUC<T extends TNumber>	Metric that computes the approximate AUC (Area under the curve) via a Riemann sum.
AUCCurve	Specifies the type of the curve to be computed, AUCCurve.ROC for a Receiver Operator Characteristic curve [default] or AUCCurve.PR for a Precision-Recall-curve.
AUCSummationMethod	Specifies the Riemann summation method used.
BaseMetric	Base class for Metrics
BinaryAccuracy<T extends TNumber>	Metric that calculates how often predictions matches binary labels.
BinaryCrossentropy<T extends TNumber>	A Metric that computes the binary cross-entropy loss between true labels and predicted labels.
CategoricalAccuracy<T extends TNumber>	Metric that calculates how often predictions matches one-hot labels.
CategoricalCrossentropy<T extends TNumber>	A Metric that computes the categorical cross-entropy loss between true labels and predicted labels.
CategoricalHinge<T extends TNumber>	A Metric that computes the categorical hinge loss metric between labels and predictions.
CosineSimilarity<T extends TNumber>	A metric that computes the cosine similarity metric between labels and predictions.
FalseNegatives<T extends TNumber>	Metric that calculates the number of false negatives.
FalsePositives<T extends TNumber>	Metric that calculates the number of false positives.
Hinge<T extends TNumber>	A metric that computes the hinge loss metric between labels and predictions.
KLDivergence<T extends TNumber>	A metric that computes the Kullback-Leibler divergence loss metric between labels and predictions.
LogCoshError<T extends TNumber>	A metric that computes the logarithm of the hyperbolic cosine of the prediction error metric between labels and predictions.
Mean<T extends TNumber>	A metric that that implements a weighted mean MetricReduction.WEIGHTED_MEAN
MeanAbsoluteError<T extends TNumber>	A metric that computes the mean of absolute difference between labels and predictions.
MeanAbsolutePercentageError<T extends TNumber>	A metric that computes the mean of absolute difference between labels and predictions.
MeanIoU<T extends TNumber>	Computes the mean Intersection-Over-Union metric.
MeanRelativeError<T extends TNumber>	Computes the mean relative error by normalizing with the given values.
MeanSquaredError<T extends TNumber>	A metric that computes the mean of absolute difference between labels and predictions.
MeanSquaredLogarithmicError<T extends TNumber>	A metric that computes the mean of absolute difference between labels and predictions.
MeanTensor<T extends TNumber>	Metric that computes the element-wise (weighted) mean of the given tensors.
Metric	Interface for metrics
MetricReduction	Defines the different types of metric reductions
Metrics	Static methods for computing metrics.
Poisson<T extends TNumber>	A metric that computes the poisson loss metric between labels and predictions.
Precision<T extends TNumber>	Computes the precision of the predictions with respect to the labels.
PrecisionAtRecall<T extends TNumber>	Computes best precision where recall is >= specified value.
Recall<T extends TNumber>	Computes the recall of the predictions with respect to the labels.
RecallAtPrecision<T extends TNumber>	Computes best recall where precision is >= specified value.
RootMeanSquaredError<T extends TNumber>	Computes root mean squared error metric between labels and predictions .
SensitivityAtSpecificity<T extends TNumber>	Computes best sensitivity where sensitivity is >= specified value.
SparseCategoricalAccuracy<T extends TNumber>	Calculates how often predictions matches integer labels.
SparseCategoricalCrossentropy<T extends TNumber>	A metric that computes the sparse categorical cross-entropy loss between true labels and predicted labels.
SparseTopKCategoricalAccuracy<T extends TNumber>	Computes how often integer targets are in the top `K` predictions.
SpecificityAtSensitivity<T extends TNumber>	Computes best specificity where sensitivity is >= specified value.
SquaredHinge<T extends TNumber>	A metric that computes the squared hinge loss metric between labels and predictions.
Sum<T extends TNumber>	Computes the (weighted) sum of the given values.
TopKCategoricalAccuracy<T extends TNumber>	Computes the poisson loss metric between labels and predictions.
TrueNegatives<T extends TNumber>	Metric that calculates the number of true negatives.
TruePositives<T extends TNumber>	Metric that calculates the number of true positives.