Class Precision<T extends TNumber>

java.lang.Object

org.tensorflow.framework.metrics.BaseMetric

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

Type Parameters:

T - The data type for the metric result

All Implemented Interfaces:

Metric

public class Precision<T extends TNumber>
extends BaseMetric

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

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

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

If topK is set, the metric calculates precision as how often on average a class among the top-k classes with the highest predicted values of a batch entry is correct and can be found in the label for that entry.

If classId is specified, the metric calculates precision by considering only the entries in the batch for which classId is above the thresholds and/or in the top-k highest predictions, and computing the fraction of them for which classId is indeed a correct label.

Field Summary

Fields

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

Constructor Summary

Constructors

Constructor	Description
Precision(float[] thresholds, long seed, Class<T> type)	Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values.
Precision(float[] thresholds, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Precision Metric with a name of Class.getSimpleName()
Precision(float threshold, long seed, Class<T> type)	Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values.
Precision(float threshold, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Precision Metric with a name of Class.getSimpleName()
Precision(long seed, Class<T> type)	Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values and with a threshold of DEFAULT_THRESHOLD.
Precision(String name, float[] thresholds, long seed, Class<T> type)	Creates a Precision Metric with no topK or classId values.
Precision(String name, float[] thresholds, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Precision Metric.
Precision(String name, float threshold, long seed, Class<T> type)	Creates a Precision Metric with no topK or classId values.
Precision(String name, float threshold, Integer topK, Integer classId, long seed, Class<T> type)	Creates a Precision Metric.
Precision(String name, long seed, Class<T> type)	Creates a Precision Metric with no topK or classId values with a threshold of DEFAULT_THRESHOLD.

Method Summary

Modifier and Type	Method	Description
Integer	getClassId()	Gets the classId, may be null
Variable<T>	getFalsePositives()	Gets the falsePositives variable
String	getFalsePositivesName()	Gets the name of the falsePositives variable
float[]	getThresholds()	Gets the thresholds
Integer	getTopK()	Gets the topK value, may be null
Variable<T>	getTruePositives()	Gets the truePositives variable
String	getTruePositivesName()	Gets the name of the truePositives variable
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 positive 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

TRUE_POSITIVES

public static final String TRUE_POSITIVES

See Also:

• Constant Field Values

FALSE_POSITIVES

public static final String FALSE_POSITIVES

See Also:

• Constant Field Values

DEFAULT_THRESHOLD

public static final float DEFAULT_THRESHOLD

See Also:

• Constant Field Values

Constructor Details

Precision

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

Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values and with a threshold of 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

Precision

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

Creates a Precision Metric with no topK or classId values with a threshold of 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

Precision

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

Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values.

Parameters:

threshold - Optional threshold value in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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

Precision

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

Creates a Precision Metric with a name of Class.getSimpleName() and no topK or classId values.

Parameters:

thresholds - Optional threshold values in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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

Precision

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

Creates a Precision Metric with no topK or classId values.

Parameters:

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

threshold - Optional threshold value in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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

Precision

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

Creates a Precision Metric with no topK or classId values.

Parameters:

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

thresholds - Optional threshold values in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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

Precision

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

Creates a Precision Metric with a name of Class.getSimpleName()

Parameters:

threshold - Optional threshold value in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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

Precision

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

Creates a Precision Metric with a name of Class.getSimpleName()

Parameters:

thresholds - Optional threshold values in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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

Precision

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

Creates a Precision Metric.

Parameters:

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

threshold - Optional threshold value in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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

Precision

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

Creates a Precision Metric.

Parameters:

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

thresholds - Optional threshold values in the range [0, 1]. A threshold is compared with prediction values to determine the truth value of predictions (i.e., above the threshold is true, below is false). One metric value is generated for each threshold value.

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.

updateStateList

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

Accumulates true positive and false positive statistics.

Specified by:

updateStateList in interface Metric

Overrides:

updateStateList in class BaseMetric

Parameters:

tf - the TensorFlow Ops encapsulating a Graph environment.

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.

result

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

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

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

getThresholds

public float[] getThresholds()

Gets the thresholds

Returns:

the thresholds

getTopK

public Integer getTopK()

Gets the topK value, may be null

Returns:

the topK value or null

getClassId

public Integer getClassId()

Gets the classId, may be null

Returns:

the classId or null

getTruePositives

public Variable<T> getTruePositives()

Gets the truePositives variable

Returns:

the truePositives

getFalsePositives

public Variable<T> getFalsePositives()

Gets the falsePositives variable

Returns:

the falsePositives

getTruePositivesName

public String getTruePositivesName()

Gets the name of the truePositives variable

Returns:

the truePositivesName

getFalsePositivesName

public String getFalsePositivesName()

Gets the name of the falsePositives variable

Returns:

the falsePositivesName