Module

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final def !=(arg0: Any): Boolean

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final def ##(): Int

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final def ==(arg0: Any): Boolean

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final def asInstanceOf[T0]: T0

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def clone(): AnyRef

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final def eq(arg0: AnyRef): Boolean

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def finalize(): Unit

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protected[java.lang]
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def flatten[T](parameters: Array[Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]
final def getClass(): Class[_]

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def hashCode(): Int

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def isCompact[T](parameters: Array[Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]
final def isInstanceOf[T0]: Boolean

Definition Classes
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def loadCaffeModel[T](defPath: String, modelPath: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T]

Loaf caffe trained model from prototxt and weight files
Loaf caffe trained model from prototxt and weight files
defPath
caffe model definition file path
modelPath
caffe model binary file containing weight and bias
def loadModule[T](path: String, weightPath: String = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T]

Load model from path.
Load model from path.
T
numeric type
path
path to save module, local file system, HDFS and Amazon S3 is supported. HDFS path should be like "hdfs://[host]:[port]/xxx" Amazon S3 path should be like "s3a://bucket/xxx"
weightPath
: where weight is stored
returns
model loaded from path
def loadTF[T](graphFile: String, inputs: Seq[String], outputs: Seq[String], byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, binFile: Option[String] = None, generatedBackward: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]

Load tensorflow model from its saved protobuf file.
Load tensorflow model from its saved protobuf file.
graphFile
where is the protobuf model file
inputs
input node names
outputs
output node names, the output tensor order is same with the node order
byteOrder
byte order in the tensorflow file. The default value is little endian
binFile
where is the model variable file
generatedBackward
if generate backward graph
returns
BigDL model
def loadTorch[T](path: String)(implicit arg0: ClassTag[T]): AbstractModule[Activity, Activity, T]
final def ne(arg0: AnyRef): Boolean

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final def notify(): Unit

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final def notifyAll(): Unit

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

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def tensorflowCheckpoints[T](graphFile: String, binFile: String, byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Session[T]

Load tensorflow checkpoints
def toString(): String

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final def wait(): Unit

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

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Deprecated Value Members

def load[T](path: String)(implicit arg0: ClassTag[T]): AbstractModule[Activity, Activity, T]

Load model from path.
Load model from path.
T
numeric type
path
path to save module, local file system, HDFS and Amazon S3 is supported. HDFS path should be like "hdfs://[host]:[port]/xxx" Amazon S3 path should be like "s3a://bucket/xxx"
returns
model loaded from path

Annotations
@deprecated
Deprecated
(Since version 0.3) Java based serialization not recommended any more, please use loadModule instead
def loadCaffe[T](model: AbstractModule[Activity, Activity, T], defPath: String, modelPath: String, matchAll: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T]

Annotations
@deprecated
Deprecated
(Since version 0.2) Please try to use the loadCaffeModel API

Related Doc: package nn

object Module

Value Members

final def !=(arg0: Any): Boolean

final def ##(): Int

final def ==(arg0: Any): Boolean

final def asInstanceOf[T0]: T0

def clone(): AnyRef

final def eq(arg0: AnyRef): Boolean

def equals(arg0: Any): Boolean

def finalize(): Unit

def flatten[T](parameters: Array[Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

final def getClass(): Class[_]

def hashCode(): Int

def isCompact[T](parameters: Array[Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]

final def isInstanceOf[T0]: Boolean

def loadCaffeModel[T](defPath: String, modelPath: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T]

def loadModule[T](path: String, weightPath: String = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T]

def loadTF[T](graphFile: String, inputs: Seq[String], outputs: Seq[String], byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN, binFile: Option[String] = None, generatedBackward: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Module[T]

def loadTorch[T](path: String)(implicit arg0: ClassTag[T]): AbstractModule[Activity, Activity, T]

final def ne(arg0: AnyRef): Boolean

final def notify(): Unit

final def notifyAll(): Unit

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

def tensorflowCheckpoints[T](graphFile: String, binFile: String, byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Session[T]

def toString(): String

final def wait(): Unit

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

final def wait(arg0: Long): Unit

Deprecated Value Members

def load[T](path: String)(implicit arg0: ClassTag[T]): AbstractModule[Activity, Activity, T]

def loadCaffe[T](model: AbstractModule[Activity, Activity, T], defPath: String, modelPath: String, matchAll: Boolean = true)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): AbstractModule[Activity, Activity, T]

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