object Utils
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!=(arg0: Any): Boolean
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##(): Int
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asInstanceOf[T0]: T0
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def
calculateFwdBwdTime[T](times: Array[(AbstractModule[_ <: Activity, _ <: Activity, T], Long, Long)])(implicit arg0: ClassTag[T]): (Long, Long)
Calculate forward time and backward time.
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clone(): AnyRef
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def
copyModule[T](src: Module[T], dst: Module[T]): Module[T]
copy src's parameters and running status to dst
copy src's parameters and running status to dst
- src
source model
- dst
destination model
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eq(arg0: AnyRef): Boolean
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equals(arg0: Any): Boolean
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finalize(): Unit
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def
getClass(): Class[_]
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def
getNamedModules[T](model: Module[T]): Map[String, Module[T]]
get all modules and map by name
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hashCode(): Int
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def
isInstanceOf[T0]: Boolean
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def
isLayerwiseScaled[T](model: Module[T]): Boolean
get whether the module is layerwise scaled
get whether the module is layerwise scaled
- model
input module
- returns
whether the module is layerwise scaled
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final
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ne(arg0: AnyRef): Boolean
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notify(): Unit
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notifyAll(): Unit
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def
recursiveAdd[T](y: Activity, alpha: Double = 1.0, x: Activity)(implicit ev: TensorNumeric[T]): Activity
Apply a add operation on table x and table y one by one.
Apply a add operation on table x and table y one by one. y := y + alpha * x
Table x should have the same size with y.
- returns
y
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def
recursiveCopy[T](y: Activity, x: Activity)(implicit ev: TensorNumeric[T]): Activity
copy table x's tensor to table y.
copy table x's tensor to table y.
Table x should have the same size with y.
- returns
y
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def
recursiveFill[T](x: Activity, value: Double)(implicit ev: TensorNumeric[T]): Unit
Fill the value to each Tensor in the table recursively
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def
recursiveResizeAs[T](target: Activity, src: Activity)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Activity
Resize table target as table src.
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def
recursiveTensorApply1[T](x: Activity, func: (Tensor[T]) ⇒ Tensor[T])(implicit ev: TensorNumeric[T]): Unit
Apply function 'func' on all tensor in the table.
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def
recursiveTensorApply2[T](x: Activity, y: Activity, func: (Tensor[T], Tensor[T]) ⇒ Tensor[T])(implicit ev: TensorNumeric[T]): Activity
Apply function 'func' on each tensor in table x and table y recursively.
Apply function 'func' on each tensor in table x and table y recursively.
Table x should have the same size with table y.
- def shuffle[T](src: Tensor[T], permutation: Array[Int], buffer: Tensor[T] = null)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Tensor[T]
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synchronized[T0](arg0: ⇒ T0): T0
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toString(): String
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wait(): Unit
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wait(arg0: Long, arg1: Int): Unit
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wait(arg0: Long): Unit
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def
zeroTableCopy[T](t1: Table, t2: Table)(implicit arg0: ClassTag[T], ev: TensorNumeric[T]): Table
This method recursively keep the shape of the source table
t2and set the elements of each tensor to zero, saving the result on the destination tablet1Notice thatt1andt2can only contain tables or tensorsThis method recursively keep the shape of the source table
t2and set the elements of each tensor to zero, saving the result on the destination tablet1Notice thatt1andt2can only contain tables or tensors- t1
is the destination table
- t2
is the source table