class RBM[T] extends TrainableModel[ops.Output[T], ops.Output[T], ops.Output[T], ops.Output[T], Float, ops.Output[T]]
Linear Supertypes
Ordering
- Alphabetic
- By Inheritance
Inherited
- RBM
- TrainableModel
- InferenceModel
- Model
- AnyRef
- Any
- Hide All
- Show All
Visibility
- Public
- All
Instance Constructors
- new RBM(input: Input[ops.Output[T]], numHidden: Int, meanField: Boolean = true, numSamples: Int = 100, meanFieldCD: Boolean = false, cdSteps: Int = 1, optimizer: Optimizer, colocateGradientsWithOps: Boolean = false, name: String = "RBM")(implicit arg0: core.types.TF[T], arg1: core.types.IsIntOrLongOrHalfOrFloatOrDouble[T])
Value Members
-
final
def
!=(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
##(): Int
- Definition Classes
- AnyRef → Any
-
final
def
==(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
final
def
asInstanceOf[T0]: T0
- Definition Classes
- Any
-
def
buildEvalOps(metrics: Seq[Metric[ops.Output[T], ops.Output[Float]]]): EvalOps[ops.Output[T], ops.Output[T]]
- Definition Classes
- RBM → TrainableModel
-
def
buildInferOps(): InferOps[ops.Output[T], ops.Output[T]]
- Definition Classes
- RBM → InferenceModel
-
def
buildTrainOps(): TrainOps[ops.Output[T], ops.Output[T], Float]
- Definition Classes
- RBM → TrainableModel
- val cdSteps: Int
-
def
clone(): AnyRef
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )
- val colocateGradientsWithOps: Boolean
-
def
contrastiveDivergence(initialV: ops.Output[T], vb: ops.variables.Variable[T], hb: ops.variables.Variable[T], w: ops.variables.Variable[T]): ops.Output[T]
- Attributes
- protected
-
final
def
eq(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
def
equals(arg0: Any): Boolean
- Definition Classes
- AnyRef → Any
-
val
evalOpsCache: Map[core.Graph, EvalOps[ops.Output[T], ops.Output[T]]]
- Attributes
- protected
-
def
finalize(): Unit
- Attributes
- protected[java.lang]
- Definition Classes
- AnyRef
- Annotations
- @throws( classOf[java.lang.Throwable] )
-
final
def
getClass(): Class[_]
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
def
hashCode(): Int
- Definition Classes
- AnyRef → Any
- Annotations
- @native()
-
val
inferOpsCache: Map[core.Graph, InferOps[ops.Output[T], ops.Output[T]]]
- Attributes
- protected
- val input: Input[ops.Output[T]]
-
final
def
isInstanceOf[T0]: Boolean
- Definition Classes
- Any
- val meanField: Boolean
- val meanFieldCD: Boolean
- val name: String
-
final
def
ne(arg0: AnyRef): Boolean
- Definition Classes
- AnyRef
-
val
nextInputCache: Map[core.Graph, ops.Output[T]]
- Attributes
- protected
-
final
def
notify(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
-
final
def
notifyAll(): Unit
- Definition Classes
- AnyRef
- Annotations
- @native()
- val numHidden: Int
- val numInputs: Int
- val numSamples: Int
- val optimizer: Optimizer
-
final
def
synchronized[T0](arg0: ⇒ T0): T0
- Definition Classes
- AnyRef
-
def
toString(): String
- Definition Classes
- AnyRef → Any
-
val
trainOpsCache: Map[core.Graph, TrainOps[ops.Output[T], ops.Output[T], Float]]
- Attributes
- protected
-
def
variables(): (ops.variables.Variable[T], ops.variables.Variable[T], ops.variables.Variable[T])
- Attributes
- protected
-
val
variablesCache: Map[core.Graph, (ops.variables.Variable[T], ops.variables.Variable[T], ops.variables.Variable[T])]
- Attributes
- protected
-
final
def
wait(): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long, arg1: Int): Unit
- Definition Classes
- AnyRef
- Annotations
- @throws( ... )
-
final
def
wait(arg0: Long): Unit
- Definition Classes
- AnyRef
- Annotations
- @native() @throws( ... )