Class Recall<T extends TNumber>

java.lang.Object

org.tensorflow.framework.metrics.BaseMetric

org.tensorflow.framework.metrics.Recall<T>

Type Parameters:

T - The data type for the metric result

All Implemented Interfaces:

Metric

public class Recall<T extends TNumber>
extends BaseMetric

Computes the recall of the predictions with respect to the labels.

This metric creates two local variables, truePositives and falseNegatives , that are used to compute the recall. This value is ultimately returned as recall, an idempotent operation that simply divides truePositives by the sum of truePositives and falseNegatives.

If sampleWeights is null, weights default to 1. Use sampleWeights of 0 to mask values.

If topK is set, the metric calculates recall as how often on average a class among the labels of a batch entry is in the top-k predictions.

If classId is specified, the metric calculates recall by considering only the entries in the batch for which classId is in the label, and computing the fraction of them for which classId is above the threshold and/or in the top-k predictions.

Field Summary

Fields

Modifier and Type	Field	Description
static final float	DEFAULT_THRESHOLD
static final String	FALSE_NEGATIVES
static final String	TRUE_POSITIVES

Constructor Summary

Constructors

Constructor	Description
Recall(float[] thresholds, long seed, Class<T> type)	Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null.
Recall(float[] thresholds, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Recall metric with a name of Class.getSimpleName()
Recall(float threshold, long seed, Class<T> type)	Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null.
Recall(float threshold, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Recall metric with a name of Class.getSimpleName()
Recall(long seed, Class<T> type)	Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null, and thresholds set to DEFAULT_THRESHOLD
Recall(Integer topK, Integer classId, long seed, Class<T> type)	Creates a Recall metric with a name of Class.getSimpleName() and using a threshold value of DEFAULT_THRESHOLD.
Recall(String name, float[] thresholds, long seed, Class<T> type)	Creates a Recall metric with topK and classId set to null.
Recall(String name, float[] thresholds, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Recall metric.
Recall(String name, float threshold, long seed, Class<T> type)	Creates a Recall metric with topK and classId set to null.
Recall(String name, float threshold, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Recall metric.
Recall(String name, long seed, Class<T> type)	Creates a Recall metric with topK and classId set to null and thresholds set to DEFAULT_THRESHOLD.
Recall(String name, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Recall metric using a threshold value of DEFAULT_THRESHOLD.

Method Summary

Modifier and Type	Method	Description
Integer	getClassId()	Gets the class id
Variable<T>	getFalseNegatives()	Gets the falseNegatives variable
String	getFalseNegativesName()	Gets the falseNegatives variable name
float[]	getThresholds()	Gets the thresholds
Integer	getTopK()	Gets the topK value
Variable<T>	getTruePositives()	Gets the truePositives variable
String	getTruePositivesName()	Gets the truePositives variable name
protected void	init(Ops tf)	Initialize the TensorFlow Ops
Op	resetStates(Ops tf)	Resets any state variables to their initial values
<U extends TNumber> Operand<U>	result(Ops tf, Class<U> resultType)	Gets the current result of the metric
List<Op>	updateStateList(Ops tf, Operand<? extends TNumber> labels, Operand<? extends TNumber> predictions, Operand<? extends TNumber> sampleWeights)	Accumulates true positive and false negative statistics.

Methods inherited from class BaseMetric

callOnce, checkIsGraph, getName, getSeed, getTF, getVariableName, isInitialized, setInitialized, setName, setTF, updateState, updateState, updateStateList

Methods inherited from class Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

DEFAULT_THRESHOLD

public static final float DEFAULT_THRESHOLD

See Also:

• Constant Field Values

TRUE_POSITIVES

public static final String TRUE_POSITIVES

See Also:

• Constant Field Values

FALSE_NEGATIVES

public static final String FALSE_NEGATIVES

See Also:

• Constant Field Values

Constructor Details

Recall

public Recall(long seed,
 Class<T> type)

Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null, and thresholds set to DEFAULT_THRESHOLD

Parameters:

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 data type for the variables

Recall

public Recall(String name,
 long seed,
 Class<T> type)

Creates a Recall metric with topK and classId set to null and thresholds set to DEFAULT_THRESHOLD.

Parameters:

name - name of the metric instance. If null, name defaults to Class.getSimpleName().

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 data type for the variables

Recall

public Recall(float threshold,
 long seed,
 Class<T> type)

Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null.

Parameters:

threshold - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

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 data type for the variables

Recall

public Recall(float[] thresholds,
 long seed,
 Class<T> type)

Creates a Recall metric with a name of Class.getSimpleName(), and topK and classId set to null.

Parameters:

thresholds - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

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 data type for the variables

Recall

public Recall(String name,
 float threshold,
 long seed,
 Class<T> type)

Creates a Recall metric with topK and classId set to null.

Parameters:

name - name of the metric instance. If null, name defaults to Class.getSimpleName().

threshold - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

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 data type for the variables

Recall

public Recall(String name,
 float[] thresholds,
 long seed,
 Class<T> type)

Creates a Recall metric with topK and classId set to null.

Parameters:

name - name of the metric instance. If null, name defaults to Class.getSimpleName().

thresholds - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

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 data type for the variables

Recall

public Recall(Integer topK,
 Integer classId,
 long seed,
 Class<T> type)

Creates a Recall metric with a name of Class.getSimpleName() and using a threshold value of DEFAULT_THRESHOLD.

Parameters:

topK - An optional value specifying the top-k predictions to consider when calculating precision.

classId - Optional Integer class ID for which we want binary metrics. This must be in the half-open interval [0, numClasses], where numClasses is the last dimension of predictions.

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 data type for the variables

Recall

public Recall(String name,
 Integer topK,
 Integer classId,
 long seed,
 Class<T> type)

Creates a Recall metric using a threshold value of DEFAULT_THRESHOLD.

Parameters:

name - name of the metric instance. If null, name defaults to Class.getSimpleName().

topK - An optional value specifying the top-k predictions to consider when calculating precision.

classId - Optional Integer class ID for which we want binary metrics. This must be in the half-open interval [0, numClasses], where numClasses is the last dimension of predictions.

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 data type for the variables

Recall

public Recall(float threshold,
 Integer topK,
 Integer classId,
 long seed,
 Class<T> type)

Creates a Recall metric with a name of Class.getSimpleName()

Parameters:

threshold - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

topK - An optional value specifying the top-k predictions to consider when calculating precision.

classId - Optional Integer class ID for which we want binary metrics. This must be in the half-open interval [0, numClasses], where numClasses is the last dimension of predictions.

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 data type for the variables

Recall

public Recall(float[] thresholds,
 Integer topK,
 Integer classId,
 long seed,
 Class<T> type)

Creates a Recall metric with a name of Class.getSimpleName()

Parameters:

thresholds - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

topK - An optional value specifying the top-k predictions to consider when calculating precision.

classId - Optional Integer class ID for which we want binary metrics. This must be in the half-open interval [0, numClasses], where numClasses is the last dimension of predictions.

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 data type for the variables

Recall

public Recall(String name,
 float threshold,
 Integer topK,
 Integer classId,
 long seed,
 Class<T> type)

Creates a Recall metric.

Parameters:

name - name of the metric instance. If null, name defaults to Class.getSimpleName().

threshold - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

topK - An optional value specifying the top-k predictions to consider when calculating precision.

classId - Optional Integer class ID for which we want binary metrics. This must be in the half-open interval [0, numClasses], where numClasses is the last dimension of predictions.

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 data type for the variables

Recall

public Recall(String name,
 float[] thresholds,
 Integer topK,
 Integer classId,
 long seed,
 Class<T> type)

Creates a Recall metric.

Parameters:

name - name of the metric instance. If null, name defaults to Class.getSimpleName().

thresholds - A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is `true`, below is `false`). If null, defaults to DEFAULT_THRESHOLD.

topK - An optional value specifying the top-k predictions to consider when calculating precision.

classId - Optional Integer class ID for which we want binary metrics. This must be in the half-open interval [0, numClasses], where numClasses is the last dimension of predictions.

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 data type for the variables

Method Details

init

protected void init(Ops tf)

Initialize the TensorFlow Ops

Specified by:

init in class BaseMetric

Parameters:

tf - the TensorFlow Ops encapsulating a Graph environment.

resetStates

public Op resetStates(Ops tf)

Resets any state variables to their initial values

Parameters:

tf - the TensorFlow Ops encapsulating a Graph environment.

Returns:

the operation for doing the reset

updateStateList

public List<Op> updateStateList(Ops tf,
 Operand<? extends TNumber> labels,
 Operand<? extends TNumber> predictions,
 Operand<? extends TNumber> sampleWeights)

Accumulates true positive and false negative statistics.

Specified by:

updateStateList in interface Metric

Overrides:

updateStateList in class BaseMetric

Parameters:

tf - the TensorFlow Ops encapsulating a Graph environment. The TensorFlow Ops

labels - the labels The ground truth values, with the same dimensions as predictions. Will be cast to TBool.

predictions - the predictions, each element must be in the range [0, 1].

sampleWeights - Optional weighting of each example. Defaults to 1. Rank is either 0, or * the same rank as labels, and must be broadcastable to labels.

Returns:

a List of Operations to update the metric state.

Throws:

IllegalArgumentException - if the TensorFlow Ops scope does not encapsulate a Graph environment.

result

public <U extends TNumber> Operand<U> result(Ops tf,
 Class<U> resultType)

Description copied from interface: Metric

Gets the current result of the metric

Type Parameters:

U - the date type for the result

Parameters:

tf - the TensorFlow Ops encapsulating a Graph environment.

resultType - the data type for the result

Returns:

the result, possibly with control dependencies

getThresholds

public float[] getThresholds()

Gets the thresholds

Returns:

the thresholds

getTopK

public Integer getTopK()

Gets the topK value

Returns:

the topK value

getClassId

public Integer getClassId()

Gets the class id

Returns:

the class id

getTruePositives

public Variable<T> getTruePositives()

Gets the truePositives variable

Returns:

the truePositives variable

getFalseNegatives

public Variable<T> getFalseNegatives()

Gets the falseNegatives variable

Returns:

the falseNegatives variable

getTruePositivesName

public String getTruePositivesName()

Gets the truePositives variable name

Returns:

the truePositives variable name

getFalseNegativesName

public String getFalseNegativesName()

Gets the falseNegatives variable name

Returns:

the falseNegatives variable name