CategoricalCrossentropy

public class CategoricalCrossentropy

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

This is the crossentropy metric class to be used when there are multiple label classes (2 or more). The labels should be given as a one_hot representation. eg., When labels values are [2, 0, 1], the labels Operand contains = [[0, 0, 1], [1, 0, 0], [0, 1, 0]].

Inherited Constants

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

String	COUNT
String	TOTAL

Public Constructors

	CategoricalCrossentropy(Ops tf, String name, boolean fromLogits, float labelSmoothing, long seed, Class<T> type) Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions.
	CategoricalCrossentropy(Ops tf, String name, boolean fromLogits, float labelSmoothing, int axis, long seed, Class<T> type) Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions.

Public Methods

Operand<T>

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

Calculates the weighted loss between labels and predictions

Inherited Methods

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

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.

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()

From interface org.tensorflow.framework.metrics.impl.LossMetric

abstract Operand<T>

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

Calculates the weighted loss between labels and predictions

Public Constructors

public CategoricalCrossentropy (Ops tf, String name, boolean fromLogits, float labelSmoothing, long seed, Class<T> type)

Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions.

Uses a CHANNELS_LAST for the channel axis.

Parameters

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 oras opposed to a probability distribution.
labelSmoothing	value used to smooth labels, When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. `labelSmoothing=0.2` means that we will use a value of `0.1` for label `0` and `0.9` for label `1`
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

public CategoricalCrossentropy (Ops tf, String name, boolean fromLogits, float labelSmoothing, int axis, long seed, Class<T> type)

Creates a CategoricalCrossentropy metric that computes the crossentropy metric between the labels and predictions.

Parameters

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.
labelSmoothing	value used to smooth labels, When > 0, label values are smoothed, meaning the confidence on label values are relaxed. e.g. `labelSmoothing=0.2` means that we will use a value of `0.1` for label `0` and `0.9` for label `1`
axis	Int specifying the channels axis. `axis=CHANNELS_LAST` corresponds to data format `channels_last`, and `axis=CHANNELS_FIRST` corresponds to data format `channels_first`.
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

Public Methods

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

Calculates the weighted loss between labels and predictions

Parameters

labels	the truth values or labels
predictions	the predictions

Returns

the loss