Class

ml.dmlc.mxnet.module

SequentialModule

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class SequentialModule extends BaseModule

A SequentialModule is a container module that can chain multiple modules together. Note building a computation graph with this kind of imperative container is less flexible and less efficient than the symbolic graph. So this should be only used as a handy utility.

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Instance Constructors

  1. new SequentialModule()

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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    Definition Classes
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  4. def add(module: BaseModule, kwargs: (String, Boolean)*): SequentialModule

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    Add a module to the chain.

    Add a module to the chain. An example of addinging two modules to a chain: val seqMod = new SequentialModule() seqMod.add(mod1).add(mod2)

    module

    The new module to add.

    kwargs

    All the keyword arguments are saved as meta information for the added module. The currently known meta includes

    • "take_labels": indicating whether the module expect to take labels when doing computation. Note any module in the chain can take labels (not necessarily only the top most one), and they all take the same labels passed from the original data batch for the SequentialModule.
    returns

    This function returns this to allow us to easily chain a series of add calls.

  5. final def asInstanceOf[T0]: T0

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    Definition Classes
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  6. 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
    SequentialModuleBaseModule
  7. 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 data_iter.provide_label.

    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
    SequentialModuleBaseModule
  8. def clone(): AnyRef

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

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    returns

    A list of names for data required by this module.

    Definition Classes
    SequentialModuleBaseModule
  10. def dataShapes: IndexedSeq[DataDesc]

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    Get data shapes.

    Get data shapes.

    returns

    The data shapes of the first module is the data shape of a SequentialModule.

    Definition Classes
    SequentialModuleBaseModule
  11. final def eq(arg0: AnyRef): Boolean

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  12. def equals(arg0: Any): Boolean

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  13. def finalize(): Unit

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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  14. 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
  15. 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 isTrain takes the value of forTraining.

    Definition Classes
    SequentialModuleBaseModule
  16. def forwardBackward(dataBatch: DataBatch): Unit

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    Definition Classes
    BaseModule
  17. final def getClass(): Class[_]

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    Definition Classes
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  18. 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
    SequentialModuleBaseModule
  19. 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
    SequentialModuleBaseModule
  20. 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
    SequentialModuleBaseModule
  21. 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
    SequentialModuleBaseModule
  22. def getParams: (Map[String, NDArray], Map[String, NDArray])

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    Get current parameters.

    Get current parameters.

    returns

    (argParams, auxParams), each a Map of name to parameters (in NDArray) mapping.

    Definition Classes
    SequentialModuleBaseModule
  23. def getSymbol: Symbol

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    Definition Classes
    BaseModule
  24. def hashCode(): Int

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  25. 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
    SequentialModuleBaseModule
  26. 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
    SequentialModuleBaseModule
  27. def installMonitor(monitor: Monitor): Unit

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    Definition Classes
    SequentialModuleBaseModule
  28. final def isInstanceOf[T0]: Boolean

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    Definition Classes
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  29. def labelShapes: IndexedSeq[DataDesc]

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    Get label shapes.

    Get label shapes.

    returns

    The return value could be null if the module does not need labels, or if the module is not binded for training (in this case, label information is not available).

    Definition Classes
    SequentialModuleBaseModule
  30. 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

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

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  32. final def notify(): Unit

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  33. final def notifyAll(): Unit

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  34. def outputNames: IndexedSeq[String]

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    returns

    A list of names for the outputs of this module.

    Definition Classes
    SequentialModuleBaseModule
  35. def outputShapes: IndexedSeq[(String, Shape)]

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    Get output shapes.

    Get output shapes.

    returns

    The output shapes of the last module is the output shape of a SequentialModule.

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

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    Definition Classes
    BaseModule
  38. 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
  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 : dict Dictionary of name to value (NDArray) mapping. auxParams : dict Dictionary of name to value (NDArray) mapping. allowMissing : bool If true, params could contain missing values, and the initializer will be called to fill those missing params. forceInit : bool If true, will force re-initialize even if already initialized. allowExtra : bool 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
    BaseModule
  42. final def synchronized[T0](arg0: ⇒ T0): T0

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  43. def toString(): String

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  44. def update(): Unit

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    Definition Classes
    SequentialModuleBaseModule
  45. 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
    SequentialModuleBaseModule
  46. final def wait(): Unit

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

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

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