GroupedPrecision

Grouped precision metric.

The metric creates two local variables, truePositives and falsePositives that are used to compute the precision of some provided predictions and targets. The predictions must be in the form of predicted label indices. The precision is ultimately returned as an idempotent operation that simply divides truePositives by truePositives + falsePositives.

For estimation of the metric over a stream of data, the function creates an update operation that updates these variables and returns the precision.

If weights is None, the weights default to 1. Use weights of 0 to mask values.

Linear Supertypes

Metric[(Output, Output), Output], AnyRef, Any

Instance Constructors

new GroupedPrecision(nameScope: String, defaultWeights: Option[tensors.Tensor[types.FLOAT32]] = None, labelID: Option[Int] = None, variablesCollections: Set[Key[variables.Variable]] = Set(METRIC_VARIABLES), valuesCollections: Set[Key[Output]] = Set(METRIC_VALUES), updatesCollections: Set[Key[Output]] = Set(METRIC_UPDATES), resetsCollections: Set[Key[Op]] = Set(METRIC_RESETS))

nameScope
Name prefix for the created ops.
defaultWeights
Default weights with which all computed metric values are multiplied.
labelID
Optional label for which we want to compute the precision.
variablesCollections
Graph collections in which to add the metric variables (for streaming metrics).
valuesCollections
Graph collections in which to add the metric values.
updatesCollections
Graph collections in which to add the metric updates.
resetsCollections
Graph collections in which to add the metric resets.

Value Members

final def !=(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def ##(): Int

Definition Classes
AnyRef → Any
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
final def asInstanceOf[T0]: T0

Definition Classes
Any
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
def compute(values: (Output, Output), weights: Option[Output] = None, name: String = s"$name/Compute"): Output

Computes the value of this metric for the provided predictions and targets, optionally weighted by weights.
Computes the value of this metric for the provided predictions and targets, optionally weighted by weights.
values
Tuple containing the predictions tensor and the targets tensor.
weights
Optional tensor containing weights for the values.
name
Name prefix for the created ops.
returns
Created output containing the metric value.

Definition Classes
GroupedPrecision → Metric
val defaultWeights: Option[tensors.Tensor[types.FLOAT32]]

Default weights with which all computed metric values are multiplied.
Default weights with which all computed metric values are multiplied.

Attributes
protected
final def eq(arg0: AnyRef): Boolean

Definition Classes
AnyRef
def equals(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getWeights(providedWeights: Option[Output]): Option[Output]

Attributes
protected
Definition Classes
Metric
def hashCode(): Int

Definition Classes
AnyRef → Any
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
val labelID: Option[Int]

Optional label for which we want to compute the precision.
def name: String

Name of this metric.
Name of this metric.

Definition Classes
GroupedPrecision → Metric
val nameScope: String

Name prefix for the created ops.
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val resetsCollections: Set[Key[Op]]

Graph collections in which to add the metric resets.
def streaming(values: (Output, Output), weights: Option[Output] = None, name: String = s"$name/Streaming"): StreamingInstance[Output]

Creates ops for computing the value of this metric in a streaming fashion.
Creates ops for computing the value of this metric in a streaming fashion. This function returns an op for obtaining the value of this metric, as well as a pair of ops to update its accumulated value and reset it.
values
Tuple containing the predictions tensor and the targets tensor.
weights
Optional tensor containing weights for the predictions.
name
Name prefix for the created ops.
returns
Tuple containing: (i) an output representing the current value of the metric, (ii) an op used to update its current value and obtain the new value, and (iii) an op used to reset its value.

Definition Classes
GroupedPrecision → Metric
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Metric → AnyRef → Any
val updatesCollections: Set[Key[Output]]

Graph collections in which to add the metric updates.
val valuesCollections: Set[Key[Output]]

Graph collections in which to add the metric values.
val variablesCollections: Set[Key[variables.Variable]]

Graph collections in which to add the metric variables (for streaming metrics).
final def wait(): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long, arg1: Int): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
final def wait(arg0: Long): Unit

Definition Classes
AnyRef
Annotations
@throws( ... )
def weights: Option[tensors.Tensor[types.FLOAT32]]

Weights to multiply the provided values with when computing the value of this metric.
Weights to multiply the provided values with when computing the value of this metric.

Definition Classes
GroupedPrecision → Metric

Related Docs: object GroupedPrecision | package metrics

class GroupedPrecision extends Metric[(Output, Output), Output]

Instance Constructors

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

def compute(values: (Output, Output), weights: Option[Output] = None, name: String = s"$name/Compute"): Output

val defaultWeights: Option[tensors.Tensor[types.FLOAT32]]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

def getWeights(providedWeights: Option[Output]): Option[Output]

def hashCode(): Int

final def isInstanceOf[T0]: Boolean

val labelID: Option[Int]

def name: String

val nameScope: String

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val resetsCollections: Set[Key[Op]]

def streaming(values: (Output, Output), weights: Option[Output] = None, name: String = s"$name/Streaming"): StreamingInstance[Output]

final def synchronized[T0](arg0: ⇒ T0): T0

def toString(): String

val updatesCollections: Set[Key[Output]]

val valuesCollections: Set[Key[Output]]

val variablesCollections: Set[Key[variables.Variable]]

final def wait(): Unit

final def wait(arg0: Long, arg1: Int): Unit

final def wait(arg0: Long): Unit

def weights: Option[tensors.Tensor[types.FLOAT32]]

Inherited from Metric[(Output, Output), Output]

Inherited from AnyRef

Inherited from Any

Ungrouped