Net

Value Members

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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7. def equals(arg0: Any): Boolean

   

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8. def finalize(): Unit

   

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9. final def getClass(): Class[_]

   

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10. def hashCode(): Int

    

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11. final def isInstanceOf[T0]: Boolean

    

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12. def load[T](path: String, weightPath: String = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): KerasNet[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

    An Analytics Zoo model.

13. def loadBigDL[T](path: String, weightPath: String = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): GraphNet[T]

    

    Load BigDL model from path.

    Load BigDL 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

14. def loadCaffe[T](defPath: String, modelPath: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): GraphNet[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 loadTF[T](folder: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): GraphNet[T]

    

    Load TensorFlow model from exported folder.

    Load TensorFlow model from exported folder.

    folder

    The folder path which contains 'frozen_inference_graph.pb' and 'graph_meta.json'.

    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)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): GraphNet[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

    returns

    model loaded from path

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

    

    Load tensorflow checkpoints

18. def loadTorch[T](path: String)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): GraphNet[T]

    

    Load Torch model from path.

    Load Torch model from path.

    T

    numeric type

    path

    path to load 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

19. final def ne(arg0: AnyRef): Boolean

    

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20. final def notify(): Unit

    

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21. final def notifyAll(): Unit

    

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22. def setInitMethod(module: AbstractModule[_, _, _], weightInitMethod: InitializationMethod = null, biasInitMethod: InitializationMethod = null, throwException: Boolean = true): Unit

    

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

    

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24. def toString(): String

    

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25. final def wait(): Unit

    

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

    

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27. final def wait(arg0: Long): Unit

    

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