Classifier

Instance Constructors

new Classifier(modelPathPrefix: String, inputDescriptors: IndexedSeq[DataDesc], contexts: Array[Context] = Context.cpu(), epoch: Option[Int] = Some(0))

modelPathPrefix
Path prefix from where to load the model artifacts These include the symbol, parameters, and synset.txt Example: file://model-dir/resnet-152 (containing resnet-152-symbol.json, resnet-152-0000.params, and synset.txt)
inputDescriptors
Descriptors defining the input node names, shape, layout and type parameters
contexts
Device contexts on which you want to run inference; defaults to CPU
epoch
Model epoch to load; defaults to 0

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 classify(input: IndexedSeq[Array[Float]], topK: Option[Int] = None): IndexedSeq[(String, Float)]

Takes flat arrays as input and returns (Label, Score) tuples.
Takes flat arrays as input and returns (Label, Score) tuples.
input
Indexed sequence one-dimensional array of floats
topK
(Optional) How many result (sorting based on the last axis) elements to return. Default returns unsorted output.
returns
Indexed sequence of (Label, Score) tuples

Definition Classes
Classifier → ClassifierBase
def classifyWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int] = None): IndexedSeq[IndexedSeq[(String, Float)]]

Perform multiple classification operations on NDArrays.
Perform multiple classification operations on NDArrays. Also works with batched input.
input
Indexed sequence of NDArrays
topK
(Optional) How many result (sorting based on the last axis) elements to return. Default returns unsorted output.
returns
Traversable sequence of (Label, Score) tuples

Definition Classes
Classifier → ClassifierBase
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val contexts: Array[Context]

Device contexts on which you want to run inference; defaults to CPU
Device contexts on which you want to run inference; defaults to CPU

Attributes
protected
val epoch: Option[Int]

Model epoch to load; defaults to 0
Model epoch to load; defaults to 0

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
val handler: MXNetHandler

Attributes
protected[org.apache.mxnet.infer]
def hashCode(): Int

Definition Classes
AnyRef → Any
val inputDescriptors: IndexedSeq[DataDesc]

Descriptors defining the input node names, shape, layout and type parameters
Descriptors defining the input node names, shape, layout and type parameters

Attributes
protected
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val predictor: PredictBase

Attributes
protected[org.apache.mxnet.infer]
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
val synset: IndexedSeq[String]

Attributes
protected[org.apache.mxnet.infer]
val synsetFilePath: String

Attributes
protected[org.apache.mxnet.infer]
def toString(): String

Definition Classes
AnyRef → Any
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( ... )

Related Doc: package infer

class Classifier extends ClassifierBase

Instance Constructors

new Classifier(modelPathPrefix: String, inputDescriptors: IndexedSeq[DataDesc], contexts: Array[Context] = Context.cpu(), epoch: Option[Int] = Some(0))

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def classify(input: IndexedSeq[Array[Float]], topK: Option[Int] = None): IndexedSeq[(String, Float)]

def classifyWithNDArray(input: IndexedSeq[NDArray], topK: Option[Int] = None): IndexedSeq[IndexedSeq[(String, Float)]]

def clone(): AnyRef

val contexts: Array[Context]

val epoch: Option[Int]

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

final def getClass(): Class[_]

val handler: MXNetHandler

def hashCode(): Int

val inputDescriptors: IndexedSeq[DataDesc]

final def isInstanceOf[T0]: Boolean

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val predictor: PredictBase

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

val synset: IndexedSeq[String]

val synsetFilePath: String

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Inherited from ClassifierBase

Inherited from AnyRef

Inherited from Any

Ungrouped