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object Module

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  8. def finalize(): Unit
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  9. def flatten[T](parameters: Array[Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]
  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  12. def isCompact[T](parameters: Array[Tensor[T]])(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]
  13. final def isInstanceOf[T0]: Boolean
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  14. 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

  15. 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

  16. 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

  17. def loadTorch[T](path: String)(implicit arg0: ClassTag[T]): AbstractModule[Activity, Activity, T]
  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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  20. final def notifyAll(): Unit
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  21. final def synchronized[T0](arg0: ⇒ T0): T0
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  22. def tensorflowCheckpoints[T](graphFile: String, binFile: String, byteOrder: ByteOrder = ByteOrder.LITTLE_ENDIAN)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Session[T]

    Load tensorflow checkpoints

  23. def toString(): String
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  24. final def wait(): Unit
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  25. final def wait(arg0: Long, arg1: Int): Unit
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  26. final def wait(arg0: Long): Unit
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Deprecated Value Members

  1. 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

  2. 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

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