org.apache.mxnet.FeedForward

Builder

Related Doc: package FeedForward

class Builder extends AnyRef

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

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