SparseCategoricalCrossentropy

public class SparseCategoricalCrossentropy

A metric that computes the sparse categorical cross-entropy loss between true labels and predicted labels.

String	COUNT
String	TOTAL

SparseCategoricalCrossentropy(Ops tf, String name, boolean fromLogits, int axis, long seed, Class<T> type)

Creates a SparseCategoricalCrossentropy metric

Operand<T>

call(Operand<? extends TNumber> labels, Operand<? extends TNumber> predictions)

Calculates the weighted loss between labels and predictions

LossMetric<T>	getLoss() Gets the loss function.
List<Op>	updateStateList(Operand<? extends TNumber> labels, Operand<? extends TNumber> predictions, Operand<? extends TNumber> sampleWeights) Creates Operations that update the state of the mean metric, by calling the loss function and passing the loss to the Mean metric to calculate the weighted mean of the loss over many iterations.

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.

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

abstract Operand<T>

call(Operand<? extends TNumber> labels, Operand<? extends TNumber> predictions)

Calculates the weighted loss between labels and predictions

Public Constructors

Creates a SparseCategoricalCrossentropy metric

tf	the TensorFlow Ops
name	the name of this metric, if null then metric name is `getSimpleName()`.
fromLogits	Whether to interpret predictions as a tensor of logit values as opposed to a probability distribution.
axis	The dimension along which the entropy is computed.
seed	the seed for random number generation. An initializer created with a given seed will always produce the same random tensor for a given shape and data type.
type	the type for the variables and result

Calculates the weighted loss between labels and predictions

labels	the truth values or labels
predictions	the predictions