Class

ml.dmlc.mxnet.module

BucketingModule

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class BucketingModule extends BaseModule

This module helps to deal efficiently with varying-length inputs.

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Instance Constructors

  1. new BucketingModule(symGen: (AnyRef) ⇒ (Symbol, IndexedSeq[String], IndexedSeq[String]), defaultBucketKey: AnyRef, contexts: Array[Context] = Context.cpu(), workLoadList: Option[IndexedSeq[Float]] = None, fixedParamNames: Option[Set[String]] = None)

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    symGen

    A function when called with a bucket key, returns a triple (symbol, dataNames, labelNames).

    defaultBucketKey

    The key for the default bucket.

    contexts

    Default is cpu().

    workLoadList

    Default None, indicating uniform workload.

    fixedParamNames

    Default None, indicating no network parameters are fixed.

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 backward(outGrads: Array[NDArray] = null): Unit

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    Backward computation.

    Backward computation.

    outGrads

    Gradient on the outputs to be propagated back. This parameter is only needed when bind is called on outputs that are not a loss function.

    Definition Classes
    BucketingModuleBaseModule
  6. def bind(dataShapes: IndexedSeq[DataDesc], labelShapes: Option[IndexedSeq[DataDesc]] = None, forTraining: Boolean = true, inputsNeedGrad: Boolean = false, forceRebind: Boolean = false, sharedModule: Option[BaseModule] = None, gradReq: String = "write"): Unit

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    Bind the symbols to construct executors.

    Bind the symbols to construct executors. This is necessary before one can perform computation with the module.

    dataShapes

    Typically is dataIter.provideData.

    labelShapes

    Typically is dataIter.provideLabel.

    forTraining

    Default is true. Whether the executors should be bind for training.

    inputsNeedGrad

    Default is false. Whether the gradients to the input data need to be computed. Typically this is not needed. But this might be needed when implementing composition of modules.

    forceRebind

    Default is false. This function does nothing if the executors are already binded. But with this true, the executors will be forced to rebind.

    sharedModule

    Default is None. This is used in bucketing. When not None, the shared module essentially corresponds to a different bucket -- a module with different symbol but with the same sets of parameters (e.g. unrolled RNNs with different lengths).

    gradReq

    Requirement for gradient accumulation (globally). Can be 'write', 'add', or 'null' (default to 'write').

    Definition Classes
    BucketingModuleBaseModule
  7. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  8. def dataNames: IndexedSeq[String]

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    Definition Classes
    BucketingModuleBaseModule
  9. def dataShapes: IndexedSeq[DataDesc]

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    BucketingModuleBaseModule
  10. final def eq(arg0: AnyRef): Boolean

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  11. def equals(arg0: Any): Boolean

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  12. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  13. def fit(trainData: DataIter, evalData: Option[DataIter] = None, numEpoch: Int = 1, fitParams: FitParams = new FitParams): Unit

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    Train the module parameters.

    Train the module parameters.

    evalData

    If not None, will be used as validation set and evaluate the performance after each epoch.

    numEpoch

    Number of epochs to run training.

    fitParams

    Extra parameters for training.

    Definition Classes
    BaseModule
  14. def forward(dataBatch: DataBatch, isTrain: Option[Boolean] = None): Unit

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    Forward computation.

    Forward computation.

    dataBatch

    input data

    isTrain

    Default is None, which means is_train takes the value of for_training.

    Definition Classes
    BucketingModuleBaseModule
  15. def forwardBackward(dataBatch: DataBatch): Unit

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    BaseModule
  16. final def getClass(): Class[_]

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  17. def getInputGrads(): IndexedSeq[IndexedSeq[NDArray]]

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    Get the gradients to the inputs, computed in the previous backward computation.

    Get the gradients to the inputs, computed in the previous backward computation.

    returns

    In the case when data-parallelism is used, the grads will be collected from multiple devices. The results will look like grad1_dev2], [grad2_dev1, grad2_dev2, those NDArray might live on different devices.

    Definition Classes
    BucketingModuleBaseModule
  18. def getInputGradsMerged(): IndexedSeq[NDArray]

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    Get the gradients to the inputs, computed in the previous backward computation.

    Get the gradients to the inputs, computed in the previous backward computation.

    returns

    In the case when data-parallelism is used, the grads will be merged from multiple devices, as they look like from a single executor. The results will look like [grad1, grad2]

    Definition Classes
    BucketingModuleBaseModule
  19. def getOutputs(): IndexedSeq[IndexedSeq[NDArray]]

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    Get outputs of the previous forward computation.

    Get outputs of the previous forward computation.

    returns

    In the case when data-parallelism is used, the outputs will be collected from multiple devices. The results will look like out1_dev2], [out2_dev1, out2_dev2, those NDArray might live on different devices.

    Definition Classes
    BucketingModuleBaseModule
  20. def getOutputsMerged(): IndexedSeq[NDArray]

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    Get outputs of the previous forward computation.

    Get outputs of the previous forward computation.

    returns

    In the case when data-parallelism is used, the outputs will be merged from multiple devices, as they look like from a single executor. The results will look like [out1, out2]

    Definition Classes
    BucketingModuleBaseModule
  21. def getParams: (Map[String, NDArray], Map[String, NDArray])

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    Get current parameters.

    Get current parameters. (arg_params, aux_params), each a dictionary of name to parameters (in NDArray) mapping.

    returns

    (argParams, auxParams), a pair of dictionary of name to value mapping.

    Definition Classes
    BucketingModuleBaseModule
  22. def getSymbol: Symbol

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    Definition Classes
    BucketingModuleBaseModule
  23. def hashCode(): Int

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  24. def initOptimizer(kvstore: String = "local", optimizer: Optimizer = new SGD(), resetOptimizer: Boolean = true, forceInit: Boolean = false): Unit

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    Install and initialize optimizers.

    Install and initialize optimizers.

    resetOptimizer

    Default True, indicating whether we should set rescaleGrad & idx2name for optimizer according to executorGroup

    forceInit

    Default False, indicating whether we should force re-initializing the optimizer in the case an optimizer is already installed.

    Definition Classes
    BucketingModuleBaseModule
  25. def initParams(initializer: Initializer = new Uniform(0.01f), argParams: Map[String, NDArray] = null, auxParams: Map[String, NDArray] = null, allowMissing: Boolean = false, forceInit: Boolean = false, allowExtra: Boolean = false): Unit

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    Initialize the parameters and auxiliary states.

    Initialize the parameters and auxiliary states.

    initializer

    Called to initialize parameters if needed.

    argParams

    If not None, should be a dictionary of existing arg_params. Initialization will be copied from that.

    auxParams

    If not None, should be a dictionary of existing aux_params. Initialization will be copied from that.

    allowMissing

    If true, params could contain missing values, and the initializer will be called to fill those missing params.

    forceInit

    If true, will force re-initialize even if already initialized.

    allowExtra

    Whether allow extra parameters that are not needed by symbol. If this is True, no error will be thrown when argParams or auxParams contain extra parameters that is not needed by the executor.

    Definition Classes
    BucketingModuleBaseModule
  26. def installMonitor(monitor: Monitor): Unit

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    BucketingModuleBaseModule
  27. final def isInstanceOf[T0]: Boolean

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  28. def labelShapes: IndexedSeq[DataDesc]

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    A list of (name, shape) pairs specifying the label inputs to this module.

    A list of (name, shape) pairs specifying the label inputs to this module. If this module does not accept labels -- either it is a module without loss function, or it is not binded for training, then this should return an empty list [].

    Definition Classes
    BucketingModuleBaseModule
  29. def loadParams(fname: String): Unit

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    Load model parameters from file.

    Load model parameters from file.

    fname

    Path to input param file.

    Definition Classes
    BaseModule
    Annotations
    @throws( classOf[IOException] )
    Exceptions thrown

    IOException if param file is invalid

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

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  31. final def notify(): Unit

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  32. final def notifyAll(): Unit

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  33. def outputNames: IndexedSeq[String]

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    BucketingModuleBaseModule
  34. def outputShapes: IndexedSeq[(String, Shape)]

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    BucketingModuleBaseModule
  35. def predict(evalData: DataIter, numBatch: Int = 1, reset: Boolean = true): IndexedSeq[NDArray]

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    Run prediction and collect the outputs.

    Run prediction and collect the outputs.

    numBatch

    Default is -1, indicating running all the batches in the data iterator.

    reset

    Default is True, indicating whether we should reset the data iter before start doing prediction.

    returns

    The return value will be a list [out1, out2, out3]. Where each element is concatenation of the outputs for all the mini-batches.

    Definition Classes
    BaseModule
  36. def predict(batch: DataBatch): IndexedSeq[NDArray]

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    BaseModule
  37. def predictEveryBatch(evalData: DataIter, numBatch: Int = 1, reset: Boolean = true): IndexedSeq[IndexedSeq[NDArray]]

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    Run prediction and collect the outputs.

    Run prediction and collect the outputs.

    numBatch

    Default is -1, indicating running all the batches in the data iterator.

    reset

    Default is True, indicating whether we should reset the data iter before start doing prediction.

    returns

    The return value will be a nested list like out2_batch1, ...], [out1_batch2, out2_batch2, ... This mode is useful because in some cases (e.g. bucketing), the module does not necessarily produce the same number of outputs.

    Definition Classes
    BaseModule
  38. def prepare(dataBatch: DataBatch): Unit

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    Prepares a data batch for forward.

    Prepares a data batch for forward.

    dataBatch

    input data

  39. def saveParams(fname: String): Unit

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    Save model parameters to file.

    Save model parameters to file.

    fname

    Path to output param file.

    Definition Classes
    BaseModule
  40. def score(evalData: DataIter, evalMetric: EvalMetric, numBatch: Int = Integer.MAX_VALUE, batchEndCallback: Option[BatchEndCallback] = None, scoreEndCallback: Option[BatchEndCallback] = None, reset: Boolean = true, epoch: Int = 0): EvalMetric

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    Run prediction on eval_data and evaluate the performance according to eval_metric.

    Run prediction on eval_data and evaluate the performance according to eval_metric.

    evalData

    : DataIter

    evalMetric

    : EvalMetric

    numBatch

    Number of batches to run. Default is Integer.MAX_VALUE, indicating run until the DataIter finishes.

    batchEndCallback

    Could also be a list of functions.

    reset

    Default True, indicating whether we should reset eval_data before starting evaluating.

    epoch

    Default 0. For compatibility, this will be passed to callbacks (if any). During training, this will correspond to the training epoch number.

    Definition Classes
    BaseModule
  41. def setParams(argParams: Map[String, NDArray], auxParams: Map[String, NDArray], allowMissing: Boolean = false, forceInit: Boolean = true, allowExtra: Boolean = false): Unit

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    Assign parameter and aux state values.

    Assign parameter and aux state values.

    argParams

    Dictionary of name to value (NDArray) mapping.

    auxParams

    Dictionary of name to value (NDArray) mapping.

    allowMissing

    If true, params could contain missing values, and the initializer will be called to fill those missing params.

    forceInit

    If true, will force re-initialize even if already initialized.

    allowExtra

    Whether allow extra parameters that are not needed by symbol. If this is True, no error will be thrown when argParams or auxParams contain extra parameters that is not needed by the executor.

    Definition Classes
    BucketingModuleBaseModule
  42. def switchBucket(bucketKey: AnyRef, dataShapes: IndexedSeq[DataDesc], labelShapes: Option[IndexedSeq[DataDesc]] = None): Unit

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    Switches to a different bucket.

    Switches to a different bucket. This will change this._currModule.

    bucketKey

    The key of the target bucket.

    dataShapes

    Typically is dataIter.provideData.

    labelShapes

    Typically is dataIter.provideLabel.

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

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  44. def toString(): String

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  45. def update(): Unit

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    BucketingModuleBaseModule
  46. def updateMetric(evalMetric: EvalMetric, labels: IndexedSeq[NDArray]): Unit

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    Evaluate and accumulate evaluation metric on outputs of the last forward computation.

    Evaluate and accumulate evaluation metric on outputs of the last forward computation.

    Definition Classes
    BucketingModuleBaseModule
  47. final def wait(): Unit

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    @throws( ... )
  48. final def wait(arg0: Long, arg1: Int): Unit

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

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