Builder

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 build(): FeedForward

   

   Construct the FeedForward model and fit on the input training data

   Construct the FeedForward model and fit on the input training data

   returns

   the trained model

6. def clone(): AnyRef

   

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7. final def eq(arg0: AnyRef): Boolean

   

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8. def equals(arg0: Any): Boolean

   

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9. def finalize(): Unit

   

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

    

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11. def hashCode(): Int

    

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12. final def isInstanceOf[T0]: Boolean

    

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

    

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14. final def notify(): Unit

    

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15. final def notifyAll(): Unit

    

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16. def setAllowExtraParams(allowExtraParams: Boolean): Builder

    

    Whether allow extra parameters that are not needed by symbol to be passed by aux_params and arg_params.

    Whether allow extra parameters that are not needed by symbol to be passed by aux_params and arg_params. If this is True, no error will be thrown when aux_params and arg_params contain extra parameters than needed.

17. def setArgParams(argParams: Map[String, NDArray]): Builder

    

    Set the model parameter, dict of name to NDArray of net's weights.

18. def setAuxParams(auxParams: Map[String, NDArray]): Builder

    

    Set the model parameter, dict of name to NDArray of net's auxiliary states

19. def setBatchEndCallback(batchEndCallback: BatchEndCallback): Builder

    

    batchEndCallback A callback that is invoked at end of each batch.

    batchEndCallback A callback that is invoked at end of each batch. For print purpose.

20. def setBatchSize(batchSize: Int): Builder

    

    Set the batch size of training data.

21. def setBeginEpoch(beginEpoch: Int): Builder

    

    Set the beginning training epoch.

22. def setContext(ctx: Array[Context]): Builder

    

    Set ctx The device context of training and prediction.

    Set ctx The device context of training and prediction. To use multi GPU training, pass in a list of gpu contexts.

23. def setEpochEndCallback(epochEndCallback: EpochEndCallback): Builder

    

    A callback that is invoked at end of each epoch.

    A callback that is invoked at end of each epoch. This can be used to checkpoint model each epoch.

24. def setEpochSize(epochSize: Int): Builder

    

    Set number of batches in a epoch.

    Set number of batches in a epoch. In default, it is set to ceil(num_train_examples / batch_size)

25. def setEvalData(evalData: DataIter): Builder

    

    Set the evaluation data

26. def setEvalMetric(metric: EvalMetric): Builder

    

    Set the evaluation metric.

    Set the evaluation metric. Default Accuracy()

27. def setInitializer(initializer: Initializer): Builder

    

    Set the initialization scheme used.

    Set the initialization scheme used. Default Uniform(0.01f).

28. def setKVStore(kv: String): Builder

    

    A string kvstore type: 'local' : multi-devices on a single machine, will automatically choose one from 'local_update_cpu', 'local_allreduce_cpu', and 'local_allreduce_device' 'dist_sync' : multi-machines with BSP 'dist_async' : multi-machines with partical asynchronous In default uses 'local', often no need to change for single machine.

29. def setKVStore(kv: KVStore): Builder

    

    this will take precedence over the setKVStore(String) version

30. def setLogger(logger: Logger): Builder

    

    When not specified, default logger will be used.

31. def setNumEpoch(numEpoch: Int): Builder

    

    Set number of training epochs

32. def setOptimizer(opt: Optimizer): Builder

    

    Set optimizer for training.

    Set optimizer for training. Default SGD.

33. def setTrainData(trainData: DataIter): Builder

    

    Set the training data

34. def setWorkLoadList(workLoadList: Seq[Float]): Builder

    

    Set the list of work load for different devices, in the same order as ctx

35. def setup(): FeedForward

    

    Construct the FeedForward model but do NOT train

    Construct the FeedForward model but do NOT train

    returns

    the un-trained model

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

    

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37. def toString(): String

    

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38. final def wait(): Unit

    

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

    

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

    

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