Mean

public class Mean

Known Direct Subclasses MeanMetricWrapper<T extends TNumber> MeanMetricWrapper<T extends TNumber> A class that bridges a stateless loss function with the Mean metric using a reduction of WEIGHTED_MEAN.

Known Indirect Subclasses BinaryCrossentropy<T extends TNumber>, CategoricalCrossentropy<T extends TNumber>, CategoricalHinge<T extends TNumber>, CosineSimilarity<T extends TNumber>, Hinge<T extends TNumber>, KLDivergence<T extends TNumber>, LogCoshError<T extends TNumber>, MeanAbsoluteError<T extends TNumber>, MeanAbsolutePercentageError<T extends TNumber>, MeanSquaredError<T extends TNumber>, MeanSquaredLogarithmicError<T extends TNumber>, Poisson<T extends TNumber>, SparseCategoricalCrossentropy<T extends TNumber>, SquaredHinge<T extends TNumber> BinaryCrossentropy<T extends TNumber> A Metric that computes the binary cross-entropy loss between true labels and predicted 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.  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.  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.  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.  Poisson<T extends TNumber> A metric that computes the poisson loss metric between labels and predictions.  SparseCategoricalCrossentropy<T extends TNumber> A metric that computes the sparse categorical cross-entropy loss between true labels and predicted labels.  SquaredHinge<T extends TNumber> A metric that computes the squared hinge loss metric between labels and predictions.

A metric that that implements a weighted mean WEIGHTED_MEAN

Inherited Constants

From class org.tensorflow.framework.metrics.impl.Reduce

String	COUNT
String	TOTAL

Inherited Methods

From class org.tensorflow.framework.metrics.impl.Reduce

Variable<T>	getCount() Gets the count variable
Class<T>	getResultType() Gets the type for the variables
Variable<T>	getTotal() Gets the total variable
Op	resetStates() Resets any state variables to their initial values
Operand<T>	result() Gets the current result of the metric
List<Op>	updateStateList(Operand<? extends TNumber> values, Operand<? extends TNumber> sampleWeights) Updates the metric variables based on the inputs.

From class org.tensorflow.framework.metrics.Metric

final Operand<T>	callOnce(Operand<? extends TNumber> values, Operand<? extends TNumber> sampleWeights) Calls update state once, followed by a call to get the result
String	getName() Gets the name of this metric.
long	getSeed() Gets the random number generator seed value
Ops	getTF() Gets the TensorFlow Ops
abstract Op	resetStates() Resets any state variables to their initial values
abstract Operand<T>	result() Gets the current result of the metric
final Op	updateState(Operand<? extends TNumber> labels, Operand<? extends TNumber> predictions, Operand<? extends TNumber> sampleWeights) Creates a NoOp Operation with control dependencies to update the metric state
final Op	updateState(Operand<? extends TNumber> values, Operand<? extends TNumber> sampleWeights) Creates a NoOp Operation with control dependencies to update the metric state
List<Op>	updateStateList(Operand<? extends TNumber> labels, Operand<? extends TNumber> predictions, Operand<? extends TNumber> sampleWeights) Creates a List of Operations to update the metric state based on labels and predictions.
List<Op>	updateStateList(Operand<? extends TNumber> values, Operand<? extends TNumber> sampleWeights) Creates a List of Operations to update the metric state based on input values.

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()