class
Sequential extends Model
Instance Constructors
-
new
Sequential(rngSeed: Long = 0)
Value Members
-
final
def
!=(arg0: AnyRef): Boolean
-
final
def
!=(arg0: Any): Boolean
-
final
def
##(): Int
-
final
def
==(arg0: AnyRef): Boolean
-
final
def
==(arg0: Any): Boolean
-
def
add(layer: Node): Unit
-
final
def
asInstanceOf[T0]: T0
-
def
buildModelConfig(optimizer: Optimizer, seed: Long): Builder
-
def
buildOutput(lossFunction: LossFunction): Unit
-
def
checkShape(layer: Node): Unit
-
def
clone(): AnyRef
-
def
compile(lossFunction: LossFunction, optimizer: Optimizer = defaultOptimizer): Unit
-
val
defaultEpochs: Int
-
val
defaultOptimizer: SGD
-
final
def
eq(arg0: AnyRef): Boolean
-
def
equals(arg0: Any): Boolean
-
def
finalize(): Unit
-
def
fit(iter: DataSetIterator, nbEpoch: Int = defaultEpochs, listeners: List[IterationListener]): Unit
-
final
def
getClass(): Class[_]
-
def
getLayers: List[Node]
-
def
getNetwork: MultiLayerNetwork
-
def
getPreprocessors: Map[Int, Node]
-
def
hashCode(): Int
-
def
inferInputShape(layer: Node): List[Int]
-
def
inputShape: List[Int]
-
final
def
isInstanceOf[T0]: Boolean
-
var
layers: List[Node]
-
var
model: MultiLayerNetwork
-
final
def
ne(arg0: AnyRef): Boolean
-
final
def
notify(): Unit
-
final
def
notifyAll(): Unit
-
def
predict(x: DataSet): INDArray
-
def
predict(x: INDArray): INDArray
-
val
rngSeed: Long
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
-
def
toJson: String
-
def
toString(): String
-
def
toYaml: String
-
final
def
wait(): Unit
-
final
def
wait(arg0: Long, arg1: Int): Unit
-
final
def
wait(arg0: Long): Unit
Inherited from AnyRef
Inherited from Any
Class for keras-style simple sequential neural net architectures with one input node and one output node for each node in computational graph.
Wraps DL4J MultiLayerNetwork. Enforces keras model construction pattern: preprocessing (reshaping) layers should be explicitly provided by the user, while last layer is treated implicitly as an output layer.