Class/Object

com.intel.analytics.zoo.pipeline.api.keras.models

Sequential

Related Docs: object Sequential | package models

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class Sequential[T] extends KerasNet[T]

Linear Supertypes
KerasNet[T], Predictable[T], Net, KerasLayer[Activity, Activity, T], Container[Activity, Activity, T], AbstractModule[Activity, Activity, T], InferShape, Serializable, Serializable, AnyRef, Any
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Inherited
  1. Sequential
  2. KerasNet
  3. Predictable
  4. Net
  5. KerasLayer
  6. Container
  7. AbstractModule
  8. InferShape
  9. Serializable
  10. Serializable
  11. AnyRef
  12. Any
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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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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. def accGradParameters(input: Activity, gradOutput: Activity): Unit

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    Definition Classes
    KerasLayer → AbstractModule
  5. def add(module: AbstractModule[_ <: Activity, _ <: Activity, T]): Sequential.this.type

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    Add a sub-module to the sequential container.

    Add a sub-module to the sequential container.

    module

    The module to be added.

    returns

    This sequential container.

  6. def add(lambda: Lambda[T]): Sequential[T]

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  7. def apply(name: String): Option[AbstractModule[Activity, Activity, T]]

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    Definition Classes
    Container → AbstractModule
  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  9. def backward(input: Activity, gradOutput: Activity): Activity

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    Definition Classes
    AbstractModule
  10. var backwardTime: Long

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    Attributes
    protected
    Definition Classes
    AbstractModule
  11. def build(calcInputShape: Shape): Shape

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    Definition Classes
    Sequential → KerasLayer → InferShape
  12. def canEqual(other: Any): Boolean

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    Definition Classes
    Container → AbstractModule
  13. final def checkEngineType(): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  14. def clearGradientClipping(): Unit

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    Clear gradient clipping parameters.

    Clear gradient clipping parameters. In this case, gradient clipping will not be applied. In order to take effect, it needs to be called before fit.

    Definition Classes
    KerasNet
  15. def clearState(): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  16. final def clone(deepCopy: Boolean): AbstractModule[Activity, Activity, T]

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    Definition Classes
    AbstractModule
  17. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  18. final def cloneModule(): Sequential.this.type

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    Definition Classes
    AbstractModule
  19. def compile(optimizer: OptimMethod[T], loss: (Variable[T], Variable[T]) ⇒ Variable[T])(implicit ev: TensorNumeric[T]): Unit

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    Definition Classes
    KerasNet
  20. def compile(optimizer: OptimMethod[T], loss: (Variable[T], Variable[T]) ⇒ Variable[T], metrics: List[ValidationMethod[T]])(implicit ev: TensorNumeric[T]): Unit

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    You can also use custom loss function during compile.

    You can also use custom loss function during compile.

    Definition Classes
    KerasNet
  21. def compile(optimizer: String, loss: String)(implicit ev: TensorNumeric[T]): Unit

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    Definition Classes
    KerasNet
  22. def compile(optimizer: String, loss: String, metrics: List[String])(implicit ev: TensorNumeric[T]): Unit

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    Alternatively, one can pass in the corresponding Keras-Style string representations when calling compile.

    Alternatively, one can pass in the corresponding Keras-Style string representations when calling compile.

    For example: optimizer = "sgd", loss = "mse", metrics = List("accuracy")

    Definition Classes
    KerasNet
  23. def compile(optimizer: OptimMethod[T], loss: Criterion[T], metrics: List[ValidationMethod[T]] = null)(implicit ev: TensorNumeric[T]): Unit

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    Configure the learning process.

    Configure the learning process. It MUST be called before fit or evaluate.

    optimizer

    Optimization method to be used.

    loss

    Criterion to be used.

    metrics

    Validation method(s) to be used. Default is null if no validation is needed.

    Definition Classes
    KerasNet
  24. def computeOutputShape(inputShape: Shape): Shape

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    Definition Classes
    Sequential → KerasLayer → InferShape
  25. def doBuild(inputShape: Shape): bigdl.nn.Sequential[T]

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    Definition Classes
    Sequential → KerasLayer
  26. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  27. def equals(other: Any): Boolean

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    Definition Classes
    Container → AbstractModule → AnyRef → Any
  28. implicit val ev: TensorNumeric[T]

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    Definition Classes
    KerasNetPredictable
  29. def evaluate(x: TextSet, batchSize: Int): Array[(ValidationResult, ValidationMethod[T])]

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    Evaluate a model on TextSet.

    Evaluate a model on TextSet.

    x

    Evaluation dataset, TextSet.

    batchSize

    Number of samples per batch.

    Definition Classes
    KerasNet
  30. def evaluate(x: ImageSet, batchSize: Int)(implicit ev: TensorNumeric[T]): Array[(ValidationResult, ValidationMethod[T])]

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    Evaluate a model on ImageSet.

    Evaluate a model on ImageSet.

    x

    Evaluation dataset, ImageSet.

    batchSize

    Number of samples per batch.

    Definition Classes
    KerasNet
  31. def evaluate(x: LocalDataSet[MiniBatch[T]])(implicit ev: TensorNumeric[T]): Array[(ValidationResult, ValidationMethod[T])]

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    Evaluate a model in local mode.

    Evaluate a model in local mode.

    x

    Evaluation dataset, LocalDataSet.

    Definition Classes
    KerasNet
  32. def evaluate(x: RDD[Sample[T]], batchSize: Int)(implicit ev: TensorNumeric[T]): Array[(ValidationResult, ValidationMethod[T])]

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    Evaluate a model on given RDD.

    Evaluate a model on given RDD.

    x

    Evaluation dataset, RDD of Sample.

    batchSize

    Number of samples per batch.

    Definition Classes
    KerasNet
  33. final def evaluate(): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  34. final def evaluate(dataSet: LocalDataSet[MiniBatch[T]], vMethods: Array[_ <: ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

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    Definition Classes
    AbstractModule
  35. final def evaluate(dataset: RDD[MiniBatch[T]], vMethods: Array[_ <: ValidationMethod[T]]): Array[(ValidationResult, ValidationMethod[T])]

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    Definition Classes
    AbstractModule
  36. final def evaluate(dataset: RDD[Sample[T]], vMethods: Array[_ <: ValidationMethod[T]], batchSize: Option[Int]): Array[(ValidationResult, ValidationMethod[T])]

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    Definition Classes
    AbstractModule
  37. final def evaluateImage(imageFrame: ImageFrame, vMethods: Array[_ <: ValidationMethod[T]], batchSize: Option[Int]): Array[(ValidationResult, ValidationMethod[T])]

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    Definition Classes
    AbstractModule
  38. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  39. def findModules(moduleType: String): ArrayBuffer[AbstractModule[_, _, T]]

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    Definition Classes
    Container
  40. def fit(x: TextSet, batchSize: Int, nbEpoch: Int)(implicit ev: TensorNumeric[T]): Unit

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    Definition Classes
    KerasNet
  41. def fit(x: TextSet, batchSize: Int, nbEpoch: Int, validationData: TextSet)(implicit ev: TensorNumeric[T]): Unit

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    Train a model for a fixed number of epochs on TextSet.

    Train a model for a fixed number of epochs on TextSet.

    x

    Training dataset, TextSet.

    batchSize

    Number of samples per gradient update.

    nbEpoch

    Number of epochs to train.

    validationData

    TextSet, or null if validation is not configured.

    Definition Classes
    KerasNet
  42. def fit(x: ImageSet, batchSize: Int, nbEpoch: Int)(implicit ev: TensorNumeric[T]): Unit

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    Definition Classes
    KerasNet
  43. def fit(x: ImageSet, batchSize: Int, nbEpoch: Int, validationData: ImageSet)(implicit ev: TensorNumeric[T]): Unit

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    Train a model for a fixed number of epochs on ImageSet.

    Train a model for a fixed number of epochs on ImageSet.

    x

    Training dataset, ImageSet.

    batchSize

    Number of samples per gradient update.

    nbEpoch

    Number of epochs to train.

    validationData

    ImageSet, or null if validation is not configured.

    Definition Classes
    KerasNet
  44. def fit(x: RDD[Sample[T]], batchSize: Int = 32, nbEpoch: Int = 10, validationData: RDD[Sample[T]] = null, featurePaddingParam: PaddingParam[T] = null, labelPaddingParam: PaddingParam[T] = null)(implicit ev: TensorNumeric[T]): Unit

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    Train a model for a fixed number of epochs on Sample RDD.

    Train a model for a fixed number of epochs on Sample RDD.

    x

    Training dataset, RDD of Sample.

    batchSize

    Number of samples per gradient update. Default is 32.

    nbEpoch

    Number of epochs to train. Default is 10.

    validationData

    RDD of Sample, or null if validation is not configured. Default is null.

    Definition Classes
    KerasNet
  45. def fit(x: DataSet[MiniBatch[T]], nbEpoch: Int)(implicit ev: TensorNumeric[T]): Unit

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    Definition Classes
    KerasNet
  46. def fit(x: DataSet[MiniBatch[T]], nbEpoch: Int, validationData: DataSet[MiniBatch[T]])(implicit ev: TensorNumeric[T]): Unit

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    Train a model for a fixed number of epochs on a DataSet.

    Train a model for a fixed number of epochs on a DataSet.

    x

    Training dataset. If x is an instance of LocalDataSet, train in local mode.

    nbEpoch

    Number of epochs to train.

    validationData

    Dataset for validation, or null if validation is not configured.

    Definition Classes
    KerasNet
  47. final def forward(input: Activity): Activity

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    Definition Classes
    AbstractModule
  48. var forwardTime: Long

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    Attributes
    protected
    Definition Classes
    AbstractModule
  49. def freeze(names: String*): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  50. def from[T](vars: Variable[T]*)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Variable[T]

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    Build graph: some other modules point to current module

    Build graph: some other modules point to current module

    vars

    upstream variables

    returns

    Variable containing current module

    Definition Classes
    Net
  51. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  52. def getExtraParameter(): Array[Tensor[T]]

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    Definition Classes
    Container → AbstractModule
  53. final def getInputShape(): Shape

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    Definition Classes
    InferShape
  54. final def getName(): String

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    Definition Classes
    AbstractModule
  55. final def getNumericType(): TensorDataType

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    Definition Classes
    AbstractModule
  56. final def getOutputShape(): Shape

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    Definition Classes
    InferShape
  57. def getParametersTable(): Table

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    Definition Classes
    Container → AbstractModule
  58. final def getPrintName(): String

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    Attributes
    protected
    Definition Classes
    AbstractModule
  59. final def getScaleB(): Double

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    Definition Classes
    AbstractModule
  60. final def getScaleW(): Double

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    Definition Classes
    AbstractModule
  61. def getSubModules(): List[AbstractModule[Activity, Activity, T]]

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    Definition Classes
    KerasNet
  62. def getTimes(): Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)]

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    Definition Classes
    Container → AbstractModule
  63. final def getTimesGroupByModuleType(): Array[(String, Long, Long)]

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    Definition Classes
    AbstractModule
  64. def getTrainSummary(tag: String): Array[(Long, Float, Double)]

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    To get the scalar like "Loss", "LearningRate" from train summary Return is a Array of 3-tuples

    To get the scalar like "Loss", "LearningRate" from train summary Return is a Array of 3-tuples

    tag

    The string variable represents the parameter you want to return supported tags are "LearningRate", "Loss", "Throughput"

    Definition Classes
    KerasNet
  65. def getValidationSummary(tag: String): Array[(Long, Float, Double)]

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    To get the scalar like "Loss", "Top1Accuracy" from validation summary Return is a Array of 3-tuples

    To get the scalar like "Loss", "Top1Accuracy" from validation summary Return is a Array of 3-tuples

    tag

    The string variable represents the parameter you want to return supported tags are 'AUC', 'Accuracy', 'BinaryAccuracy', 'CategoricalAccuracy', 'HitRatio', 'Loss', 'MAE', 'NDCG', 'SparseCategoricalAccuracy', 'TFValidationMethod', 'Top1Accuracy', 'Top5Accuracy', 'TreeNNAccuracy'.

    Definition Classes
    KerasNet
  66. final def getWeightsBias(): Array[Tensor[T]]

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    Definition Classes
    AbstractModule
  67. var gradInput: Activity

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    Definition Classes
    AbstractModule
  68. final def hasName: Boolean

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    Definition Classes
    AbstractModule
  69. def hashCode(): Int

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    Definition Classes
    Container → AbstractModule → AnyRef → Any
  70. def inputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

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    Definition Classes
    KerasLayer → AbstractModule
  71. def inputs(nodes: Array[ModuleNode[T]]): ModuleNode[T]

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    Definition Classes
    KerasLayer → AbstractModule
  72. def inputs(nodes: ModuleNode[T]*): ModuleNode[T]

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    Definition Classes
    KerasLayer → AbstractModule
  73. def isBuilt(): Boolean

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    Definition Classes
    KerasLayer → InferShape
  74. def isFrozen[T]()(implicit arg0: ClassTag[T]): Boolean

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    Definition Classes
    Net
  75. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  76. def isKerasStyle(): Boolean

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    Definition Classes
    KerasLayer → InferShape
  77. final def isTraining(): Boolean

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    Definition Classes
    AbstractModule
  78. def labor: AbstractModule[Activity, Activity, T]

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    Definition Classes
    KerasLayer
  79. def labor_=(value: AbstractModule[Activity, Activity, T]): Unit

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    Definition Classes
    KerasLayer
  80. var line: String

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    Attributes
    protected
    Definition Classes
    AbstractModule
  81. final def loadModelWeights(srcModel: Module[Float], matchAll: Boolean): Sequential.this.type

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    Definition Classes
    AbstractModule
  82. final def loadWeights(weightPath: String, matchAll: Boolean): Sequential.this.type

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    Definition Classes
    AbstractModule
  83. val module: Module[T]

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    Attributes
    protected
    Definition Classes
    KerasNetPredictable
  84. val modules: ArrayBuffer[AbstractModule[Activity, Activity, T]]

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    Definition Classes
    Container
  85. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  86. final def notify(): Unit

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    Definition Classes
    AnyRef
  87. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  88. var output: Activity

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    Definition Classes
    AbstractModule
  89. def parameters(): (Array[Tensor[T]], Array[Tensor[T]])

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    Definition Classes
    Container → AbstractModule
  90. def predict(x: TextSet): TextSet

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    The default batchPerThread is 4.

    The default batchPerThread is 4. For DistributedTextSet, the total batchSize is batchPerThread * rdd.getNumPartitions. For LocalTextSet, the total batchSize is batchPerThread * numOfCores.

    x

    Prediction data, TextSet.

    Definition Classes
    Predictable
  91. def predict(x: TextSet, batchPerThread: Int): TextSet

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    Use a model to do prediction on TextSet.

    Use a model to do prediction on TextSet.

    x

    Prediction data, TextSet.

    batchPerThread

    The total batch size is batchPerThread * rdd.getNumPartitions(distributed mode) or batchPerThread * numOfCores(local mode)

    Definition Classes
    Predictable
  92. def predict(x: ImageSet): ImageSet

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    The default batchPerThread is 4.

    The default batchPerThread is 4. For DistributedImageSet, the total batchSize is batchPerThread * rdd.getNumPartitions. For LocalImageSet, the total batchSize is batchPerThread * numOfCores.

    x

    Prediction data, ImageSet.

    Definition Classes
    Predictable
  93. def predict(x: ImageSet, batchPerThread: Int): ImageSet

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    Use a model to do prediction on ImageSet.

    Use a model to do prediction on ImageSet.

    x

    Prediction data, ImageSet.

    batchPerThread

    The total batch size is batchPerThread * rdd.getNumPartitions(distributed mode) or batchPerThread * numOfCores(local mode)

    Definition Classes
    Predictable
  94. def predict(x: Array[Sample[T]])(implicit ev: TensorNumeric[T]): Array[Activity]

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    Use a model to do prediction in local mode.

    Use a model to do prediction in local mode. The total batch size is batchPerThread * numOfCores, and batchPerThread is 4 by default.

    x

    Prediction data, array of Sample.

    Definition Classes
    Predictable
  95. def predict(x: Array[Sample[T]], batchPerThread: Int)(implicit ev: TensorNumeric[T]): Array[Activity]

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    Use a model to do prediction in local mode.

    Use a model to do prediction in local mode.

    x

    Prediction data, array of Sample.

    batchPerThread

    The total batchSize is batchPerThread * numOfCores.

    Definition Classes
    Predictable
  96. def predict(x: LocalDataSet[MiniBatch[T]])(implicit ev: TensorNumeric[T]): Array[Activity]

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    Use a model to do prediction in local mode.

    Use a model to do prediction in local mode. The total batch size is batchPerThread * numOfCores, and batchPerThread is 4 by default.

    x

    Prediction data, LocalDataSet.

    Definition Classes
    Predictable
  97. def predict(x: LocalDataSet[MiniBatch[T]], batchPerThread: Int)(implicit ev: TensorNumeric[T]): Array[Activity]

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    Use a model to do prediction in local mode.

    Use a model to do prediction in local mode.

    x

    Prediction data, LocalDataSet.

    batchPerThread

    The total batchSize is batchPerThread * numOfCores.

    Definition Classes
    Predictable
  98. def predict(x: RDD[Sample[T]])(implicit ev: TensorNumeric[T]): RDD[Activity]

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    Use a model to do prediction for RDD.

    Use a model to do prediction for RDD. The default batchPerThread is 4, and the total batchSize is batchPerThread * rdd.getNumPartitions.

    x

    Prediction data, RDD of Sample.

    Definition Classes
    Predictable
  99. def predict(x: RDD[Sample[T]], batchPerThread: Int)(implicit ev: TensorNumeric[T]): RDD[Activity]

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    Use a model to do prediction for RDD.

    Use a model to do prediction for RDD.

    x

    Prediction data, RDD of Sample.

    batchPerThread

    The total batchSize is batchPerThread * rdd.getNumPartitions.

    Definition Classes
    Predictable
  100. final def predict(dataset: RDD[Sample[T]], batchSize: Int, shareBuffer: Boolean): RDD[Activity]

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    Definition Classes
    AbstractModule
  101. final def predictClass(dataset: RDD[Sample[T]], batchSize: Int): RDD[Int]

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    Definition Classes
    AbstractModule
  102. def predictClasses(x: RDD[Sample[T]], batchPerThread: Int = 4, zeroBasedLabel: Boolean = true): RDD[Int]

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    Use a model to predict for classes.

    Use a model to predict for classes. By default, label predictions start from 0.

    x

    Prediction data, RDD of Sample.

    batchPerThread

    The default batchPerThread is 4, and the total batchSize is batchPerThread * rdd.getNumPartitions.

    zeroBasedLabel

    Boolean. Whether result labels start from 0. Default is true. If false, result labels start from 1.

    Definition Classes
    Predictable
  103. final def predictImage(imageFrame: ImageFrame, outputLayer: String, shareBuffer: Boolean, batchPerPartition: Int, predictKey: String, featurePaddingParam: Option[PaddingParam[T]]): ImageFrame

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    Definition Classes
    AbstractModule
  104. def processInputs(first: (ModuleNode[T], Int), nodesWithIndex: (ModuleNode[T], Int)*): ModuleNode[T]

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    Attributes
    protected
    Definition Classes
    AbstractModule
  105. def processInputs(nodes: Seq[ModuleNode[T]]): ModuleNode[T]

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    Attributes
    protected
    Definition Classes
    AbstractModule
  106. final def quantize(): Module[T]

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    Definition Classes
    AbstractModule
  107. def release(): Unit

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    Definition Classes
    Container → AbstractModule
  108. def releaseDataSets(dataSets: Array[DataSet[MiniBatch[T]]]): Unit

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    Release DataSet from memory.

    Release DataSet from memory. This method is used to release the rdd which is cached when toDataSet() method is called and rdd is cached TODO: modify this when BigDL fix this issue

    Definition Classes
    KerasNet
  109. def reset(): Unit

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    Definition Classes
    Container → AbstractModule
  110. def resetTimes(): Unit

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    Definition Classes
    Container → AbstractModule
  111. final def saveCaffe(prototxtPath: String, modelPath: String, useV2: Boolean, overwrite: Boolean): Sequential.this.type

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    Definition Classes
    AbstractModule
  112. final def saveDefinition(path: String, overWrite: Boolean): Sequential.this.type

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    Definition Classes
    AbstractModule
  113. final def saveModule(path: String, weightPath: String, overWrite: Boolean): Sequential.this.type

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    Definition Classes
    AbstractModule
  114. final def saveTF(inputs: Seq[(String, Seq[Int])], path: String, byteOrder: ByteOrder, dataFormat: TensorflowDataFormat): Sequential.this.type

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    Definition Classes
    AbstractModule
  115. final def saveTorch(path: String, overWrite: Boolean): Sequential.this.type

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    Definition Classes
    AbstractModule
  116. final def saveWeights(path: String, overWrite: Boolean): Unit

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    Definition Classes
    AbstractModule
  117. var scaleB: Double

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    Attributes
    protected
    Definition Classes
    AbstractModule
  118. var scaleW: Double

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    Attributes
    protected
    Definition Classes
    AbstractModule
  119. def setCheckpoint(path: String, overWrite: Boolean = true): Unit

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    Configure checkpoint settings to write snapshots every epoch during the training process.

    Configure checkpoint settings to write snapshots every epoch during the training process. In order to take effect, it needs to be called before fit.

    path

    The path to save snapshots. Make sure this path exists beforehand.

    overWrite

    Whether to overwrite existing snapshots in the given path. Default is true.

    Definition Classes
    KerasNet
  120. def setConstantGradientClipping(min: Float, max: Float): Unit

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    Set constant gradient clipping during the training process.

    Set constant gradient clipping during the training process. In order to take effect, it needs to be called before fit.

    min

    The minimum value to clip by. Double.

    max

    The maximum value to clip by. Double.

    Definition Classes
    KerasNet
  121. def setEvaluateStatus(): Sequential.this.type

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    Set the model to be in evaluate status, i.e.

    Set the model to be in evaluate status, i.e. remove the effect of Dropout, etc.

    Definition Classes
    KerasNet
  122. final def setExtraParameter(extraParam: Array[Tensor[T]]): Sequential.this.type

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    Definition Classes
    AbstractModule
  123. def setGradientClippingByL2Norm(clipNorm: Float): Unit

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    Clip gradient to a maximum L2-Norm during the training process.

    Clip gradient to a maximum L2-Norm during the training process. In order to take effect, it needs to be called before fit.

    clipNorm

    Gradient L2-Norm threshold. Double.

    Definition Classes
    KerasNet
  124. final def setLine(line: String): Sequential.this.type

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    Definition Classes
    AbstractModule
  125. final def setName(name: String): Sequential.this.type

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    Definition Classes
    AbstractModule
  126. def setScaleB(b: Double): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  127. def setScaleW(w: Double): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  128. def setTensorBoard(logDir: String, appName: String): Unit

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    Set summary information during the training process for visualization purposes.

    Set summary information during the training process for visualization purposes. Saved summary can be viewed via TensorBoard. In order to take effect, it needs to be called before fit.

    Training summary will be saved to 'logDir/appName/train' and validation summary (if any) will be saved to 'logDir/appName/validation'.

    logDir

    The base directory path to store training and validation logs.

    appName

    The name of the application.

    Definition Classes
    KerasNet
  129. final def setWeightsBias(newWeights: Array[Tensor[T]]): Sequential.this.type

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    Definition Classes
    AbstractModule
  130. def summary(lineLength: Int = 120, positions: Array[Double] = Array(.33, .55, .67, 1)): Unit

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    Print out the summary information of an Analytics Zoo Keras Model.

    Print out the summary information of an Analytics Zoo Keras Model.

    For each layer in the model, there will be a separate row containing four columns: Layer (type) Output Shape Param # Connected to

    In addition, total number of parameters of this model, separated into trainable and non-trainable counts, will be printed out after the table.

    lineLength

    The total length of one row. Default is 120.

    positions

    The maximum absolute length proportion(%) of each field. Array of Double of length 4. Usually you don't need to adjust this parameter. Default is Array(.33, .55, .67, 1), meaning that the first field will occupy up to 33% of lineLength, the second field will occupy up to (55-33)% of lineLength, the third field will occupy up to (67-55)% of lineLength, the fourth field will occupy the remaining line (100-67)%. If the field has a larger length, the remaining part will be trimmed. If the field has a smaller length, the remaining part will be white spaces.

    Definition Classes
    SequentialKerasNet
  131. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  132. implicit val tag: ClassTag[T]

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    Definition Classes
    KerasNetPredictable
  133. def toGraph(startNodes: ModuleNode[T]*): Graph[T]

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    Definition Classes
    AbstractModule
  134. def toModel(): Model[T]

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    Definition Classes
    SequentialKerasNet
  135. def toString(): String

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    Definition Classes
    AbstractModule → AnyRef → Any
  136. var train: Boolean

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    Attributes
    protected
    Definition Classes
    AbstractModule
  137. final def training(): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  138. def unFreeze(names: String*): Sequential.this.type

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    Definition Classes
    Container → AbstractModule
  139. def updateGradInput(input: Activity, gradOutput: Activity): Activity

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    Definition Classes
    KerasLayer → AbstractModule
  140. def updateOutput(input: Activity): Activity

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    Definition Classes
    KerasLayer → AbstractModule
  141. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  142. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  143. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  144. def zeroGradParameters(): Unit

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    Definition Classes
    AbstractModule

Deprecated Value Members

  1. final def save(path: String, overWrite: Boolean): Sequential.this.type

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    Definition Classes
    AbstractModule
    Annotations
    @deprecated
    Deprecated

    (Since version 0.3.0) please use recommended saveModule(path, overWrite)

Inherited from KerasNet[T]

Inherited from Predictable[T]

Inherited from Net

Inherited from KerasLayer[Activity, Activity, T]

Inherited from Container[Activity, Activity, T]

Inherited from AbstractModule[Activity, Activity, T]

Inherited from InferShape

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped