ImageFeaturizer

The ImageFeaturizer relies on a CNTK model to do the featurization, one can set this model using the modelLocation parameter. To map the nodes of the CNTK model onto the standard "layers" structure of a feed forward neural net, one needs to supply a list of node names that range from the output node, back towards the input node of the CNTK Function. This list does not need to be exhaustive, and is provided to you if you use a model downloaded from the ModelDownloader, one can find this layer list in the schema of the downloaded model.

The ImageFeaturizer takes an input column of images (the type returned by the ImageReader), and automatically resizes them to fit the CMTKModel's inputs. It then feeds them through a pre-trained CNTK model. One can truncate the model using the cutOutputLayers parameter that determines how many layers to truncate from the output of the network. For example, layer=0 means that no layers are removed, layer=2 means that the image featurizer returns the activations of the layer that is two layers from the output layer.

Annotations: @InternalWrapper()

Linear Supertypes

ComplexParamsWritable, MLWritable, HasOutputCol, HasInputCol, Wrappable, Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any

Instance Constructors

new ImageFeaturizer()
new ImageFeaturizer(uid: String)

uid
the uid of the image transformer

Value Members

final def !=(arg0: Any): Boolean

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

Definition Classes
AnyRef → Any
final def $[T](param: Param[T]): T

Attributes
protected
Definition Classes
Params
final def ==(arg0: Any): Boolean

Definition Classes
AnyRef → Any
def additionalPythonMethods(): String

Definition Classes
Wrappable
final def asInstanceOf[T0]: T0

Definition Classes
Any
final def clear(param: Param[_]): ImageFeaturizer.this.type

Definition Classes
Params
def clone(): AnyRef

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( ... )
val cntkModel: TransformerParam
def copy(extra: ParamMap): Transformer

Definition Classes
ImageFeaturizer → Transformer → PipelineStage → Params
def copyValues[T <: Params](to: T, extra: ParamMap): T

Attributes
protected
Definition Classes
Params
val cutOutputLayers: IntParam

The number of layer to cut off the end of the network; 0 leaves the network intact, 1 removes the output layer, etc.
final def defaultCopy[T <: Params](extra: ParamMap): T

Attributes
protected
Definition Classes
Params
val dropNa: BooleanParam
val emptyCntkModel: CNTKModel
final def eq(arg0: AnyRef): Boolean

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

Definition Classes
AnyRef → Any
def explainParam(param: Param[_]): String

Definition Classes
Params
def explainParams(): String

Definition Classes
Params
final def extractParamMap(): ParamMap

Definition Classes
Params
final def extractParamMap(extra: ParamMap): ParamMap

Definition Classes
Params
def finalize(): Unit

Attributes
protected[java.lang]
Definition Classes
AnyRef
Annotations
@throws( classOf[java.lang.Throwable] )
final def get[T](param: Param[T]): Option[T]

Definition Classes
Params
final def getClass(): Class[_]

Definition Classes
AnyRef → Any
def getCntkModel: CNTKModel
def getCutOutputLayers: Int
final def getDefault[T](param: Param[T]): Option[T]

Definition Classes
Params
def getDropNa: Boolean
def getInputCol: String

Definition Classes
HasInputCol
def getInputNode: Int
def getLayerNames: Array[String]
def getMiniBatchSize: Int
def getModel: SerializableFunction
final def getOrDefault[T](param: Param[T]): T

Definition Classes
Params
def getOutputCol: String

Definition Classes
HasOutputCol
def getParam(paramName: String): Param[Any]

Definition Classes
Params
final def hasDefault[T](param: Param[T]): Boolean

Definition Classes
Params
def hasParam(paramName: String): Boolean

Definition Classes
Params
def hashCode(): Int

Definition Classes
AnyRef → Any
def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

Attributes
protected
Definition Classes
Logging
def initializeLogIfNecessary(isInterpreter: Boolean): Unit

Attributes
protected
Definition Classes
Logging
val inputCol: Param[String]

The name of the input column
The name of the input column

Definition Classes
HasInputCol
final def isDefined(param: Param[_]): Boolean

Definition Classes
Params
final def isInstanceOf[T0]: Boolean

Definition Classes
Any
final def isSet(param: Param[_]): Boolean

Definition Classes
Params
def isTraceEnabled(): Boolean

Attributes
protected
Definition Classes
Logging
val layerNames: StringArrayParam

Array with valid CNTK nodes to choose from; the first entries of this array should be closer to the output node.
def log: Logger

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logDebug(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logError(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logInfo(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logName: String

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logTrace(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String, throwable: Throwable): Unit

Attributes
protected
Definition Classes
Logging
def logWarning(msg: ⇒ String): Unit

Attributes
protected
Definition Classes
Logging
final def ne(arg0: AnyRef): Boolean

Definition Classes
AnyRef
final def notify(): Unit

Definition Classes
AnyRef
final def notifyAll(): Unit

Definition Classes
AnyRef
val outputCol: Param[String]

The name of the output column
The name of the output column

Definition Classes
HasOutputCol
lazy val params: Array[Param[_]]

Definition Classes
Params
def save(path: String): Unit

Definition Classes
MLWritable
Annotations
@Since( "1.6.0" ) @throws( ... )
final def set(paramPair: ParamPair[_]): ImageFeaturizer.this.type

Attributes
protected
Definition Classes
Params
final def set(param: String, value: Any): ImageFeaturizer.this.type

Attributes
protected
Definition Classes
Params
final def set[T](param: Param[T], value: T): ImageFeaturizer.this.type

Definition Classes
Params
def setCntkModel(value: CNTKModel): ImageFeaturizer.this.type
def setCutOutputLayers(value: Int): ImageFeaturizer.this.type
final def setDefault(paramPairs: ParamPair[_]*): ImageFeaturizer.this.type

Attributes
protected
Definition Classes
Params
final def setDefault[T](param: Param[T], value: T): ImageFeaturizer.this.type

Attributes
protected
Definition Classes
Params
def setDropNa(value: Boolean): ImageFeaturizer.this.type
def setInputCol(value: String): ImageFeaturizer.this.type

Definition Classes
HasInputCol
def setInputNode(value: Int): ImageFeaturizer.this.type
def setLayerNames(value: Array[String]): ImageFeaturizer.this.type
def setMiniBatchSize(value: Int): ImageFeaturizer.this.type
def setModel(model: SerializableFunction): ImageFeaturizer.this.type
def setModel(modelSchema: ModelSchema): ImageFeaturizer.this.type
def setModelLocation(path: String): ImageFeaturizer.this.type
def setOutputCol(value: String): ImageFeaturizer.this.type

Definition Classes
HasOutputCol
final def synchronized[T0](arg0: ⇒ T0): T0

Definition Classes
AnyRef
def toString(): String

Definition Classes
Identifiable → AnyRef → Any
def transform(dataset: Dataset[_]): DataFrame

Definition Classes
ImageFeaturizer → Transformer
def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" )
def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

Definition Classes
Transformer
Annotations
@Since( "2.0.0" ) @varargs()
def transformSchema(schema: StructType): StructType

Add the features column to the schema
Add the features column to the schema
returns
schema with features column

Definition Classes
ImageFeaturizer → PipelineStage
def transformSchema(schema: StructType, logging: Boolean): StructType

Attributes
protected
Definition Classes
PipelineStage
Annotations
@DeveloperApi()
val uid: String

the uid of the image transformer
the uid of the image transformer

Definition Classes
ImageFeaturizer → Identifiable
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 write: MLWriter

Definition Classes
ComplexParamsWritable → MLWritable

Related Docs: object ImageFeaturizer | package image

class ImageFeaturizer extends Transformer with HasInputCol with HasOutputCol with Wrappable with ComplexParamsWritable

Instance Constructors

new ImageFeaturizer()

new ImageFeaturizer(uid: String)

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def $[T](param: Param[T]): T

final def ==(arg0: Any): Boolean

def additionalPythonMethods(): String

final def asInstanceOf[T0]: T0

final def clear(param: Param[_]): ImageFeaturizer.this.type

def clone(): AnyRef

val cntkModel: TransformerParam

def copy(extra: ParamMap): Transformer

def copyValues[T <: Params](to: T, extra: ParamMap): T

val cutOutputLayers: IntParam

final def defaultCopy[T <: Params](extra: ParamMap): T

val dropNa: BooleanParam

val emptyCntkModel: CNTKModel

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def explainParam(param: Param[_]): String

def explainParams(): String

final def extractParamMap(): ParamMap

final def extractParamMap(extra: ParamMap): ParamMap

def finalize(): Unit

final def get[T](param: Param[T]): Option[T]

final def getClass(): Class[_]

def getCntkModel: CNTKModel

def getCutOutputLayers: Int

final def getDefault[T](param: Param[T]): Option[T]

def getDropNa: Boolean

def getInputCol: String

def getInputNode: Int

def getLayerNames: Array[String]

def getMiniBatchSize: Int

def getModel: SerializableFunction

final def getOrDefault[T](param: Param[T]): T

def getOutputCol: String

def getParam(paramName: String): Param[Any]

final def hasDefault[T](param: Param[T]): Boolean

def hasParam(paramName: String): Boolean

def hashCode(): Int

def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

def initializeLogIfNecessary(isInterpreter: Boolean): Unit

val inputCol: Param[String]

final def isDefined(param: Param[_]): Boolean

final def isInstanceOf[T0]: Boolean

final def isSet(param: Param[_]): Boolean

def isTraceEnabled(): Boolean

val layerNames: StringArrayParam

def log: Logger

def logDebug(msg: ⇒ String, throwable: Throwable): Unit

def logDebug(msg: ⇒ String): Unit

def logError(msg: ⇒ String, throwable: Throwable): Unit

def logError(msg: ⇒ String): Unit

def logInfo(msg: ⇒ String, throwable: Throwable): Unit

def logInfo(msg: ⇒ String): Unit

def logName: String

def logTrace(msg: ⇒ String, throwable: Throwable): Unit

def logTrace(msg: ⇒ String): Unit

def logWarning(msg: ⇒ String, throwable: Throwable): Unit

def logWarning(msg: ⇒ String): Unit

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

val outputCol: Param[String]

lazy val params: Array[Param[_]]

def save(path: String): Unit

final def set(paramPair: ParamPair[_]): ImageFeaturizer.this.type

final def set(param: String, value: Any): ImageFeaturizer.this.type

final def set[T](param: Param[T], value: T): ImageFeaturizer.this.type

def setCntkModel(value: CNTKModel): ImageFeaturizer.this.type

def setCutOutputLayers(value: Int): ImageFeaturizer.this.type

final def setDefault(paramPairs: ParamPair[_]*): ImageFeaturizer.this.type

final def setDefault[T](param: Param[T], value: T): ImageFeaturizer.this.type

def setDropNa(value: Boolean): ImageFeaturizer.this.type