Reduce

public abstract class Reduce

Known Direct Subclasses

Mean<T extends TNumber>

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

Encapsulates metrics that perform a reduce operation on the metric values.

Constants

String	COUNT
String	TOTAL

Public Methods

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.

Inherited Methods

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()

Constants

public static final String COUNT

Constant Value: "count"

public static final String TOTAL

Constant Value: "total"

Public Methods

public Variable<T> getCount ()

Gets the count variable

Returns

the count variable

public Class<T> getResultType ()

Gets the type for the variables

Returns

the type for the variables

public Variable<T> getTotal ()

Gets the total variable

Returns

the total variable

public Op resetStates ()

Resets any state variables to their initial values

Returns

the control operation for doing the reset

public Operand<T> result ()

Gets the current result of the metric

Returns

the result, possibly with control dependencies

public List<Op> updateStateList (Operand<? extends TNumber> values, Operand<? extends TNumber> sampleWeights)

Updates the metric variables based on the inputs. At least one input arg required for values, an optional additional input for the sampleWeights

Parameters

values	the inputs to be passed to update state, this may not be null
sampleWeights	sample weights to be applied to values, may be null.

Returns

the result with a control dependency on update state Operands

Throws

IllegalArgumentException	if values is null