lamp-core
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lamp-core/lamp/lamp.autograd/Variable

Variable

lamp.autograd.Variable
See theVariable companion object
sealed trait Variable

A value of a tensor valued function, a vertex in the computational graph.

A Variable may be constant, i.e. depends on no other Variables. Constant variables may or may not need their partial derivatives computed.

Attributes

Companion
object
Graph
Supertypes
class Object
trait Matchable
class Any
Known subtypes
trait Constant

Members list

Value members

Abstract methods

def op: Option[Op]

The parent operation of this value in the computational graph. Empty for constants.

The parent operation of this value in the computational graph. Empty for constants.

Attributes

def partialDerivative: Option[STen]

The partial derivative, or a placeholder tensor for the partial derivative.

The partial derivative, or a placeholder tensor for the partial derivative.

Returns empty iff this Variable needs no gradient computation. Otherwise a placeholder tensor is allocated upfront when the Variable is allocated.

Attributes

def value: STen

The actual tensor value of this Variable.

The actual tensor value of this Variable.

Attributes

Concrete methods

def *[S : Sc](other: Variable): Variable
def *[S : Sc](other: Double): Variable
def +[S : Sc](other: Variable): Variable
def +[S : Sc](other: Double): Variable
def -[S : Sc](other: Variable): Variable
def /[S : Sc](other: Variable): Variable
def argmax[S : Sc](dim: Long, keepDim: Boolean): Variable
def assign[S : Sc](other: Variable): Variable
def atan[S : Sc]: Variable
def backprop(): Unit

Runs the backpropagation algorithm starting from this value

Runs the backpropagation algorithm starting from this value

Only meaningful if this is scalar i.e. the number of elements in the value tensor is 1.

Attributes

def binaryCrossEntropyWithLogitsLoss[S : Sc](target: STen, posWeights: Option[STen], reduction: Reduction): Variable
def bmm[S : Sc](other: Variable): Variable
def cast[S : Sc](precision: FloatingPointPrecision): Variable
def cat[S : Sc](other: Variable, dim: Long): Variable
def choleskyLower[S : Sc]: Variable
def choleskySolve[S : Sc](factor: Variable, upper: Boolean): Variable
def clamp[S : Sc](min: Variable, max: Variable): Variable
def colSum[S : Sc]: Variable
def cos[S : Sc]: Variable
def cross[S : Sc](other: Variable, dim: Int): Variable
def crossEntropy[S : Sc](other: Variable): Variable
def debug[S : Sc](fun: (STen, Boolean, Boolean) => Unit): Variable
def detached: Constant

Returns an other Variable wrapping the same value tensor, without any parent and with needsGrad=false.

Returns an other Variable wrapping the same value tensor, without any parent and with needsGrad=false.

Attributes

def diag[S : Sc](diagonal: Long): Variable
def dropout[S : Sc](prob: Double, train: Boolean): Variable
def euclideanDistance[S : Sc](b: Variable, dim: Int): Variable
def exp[S : Sc]: Variable
def expand[S : Sc](shape: List[Long]): Variable
def expandAs[S : Sc](other: STen): Variable
def flatten[S : Sc]: Variable
def flatten[S : Sc](startDim: Int): Variable
def flatten[S : Sc](startDim: Int, endDim: Int): Variable
def flattenLastDimensions[S : Sc](dims: Int): Variable
def gelu[S : Sc]: Variable
def hardSwish[S : Sc]: Variable
def indexAdd[S : Sc](index: Variable, dim: Int, maxIndex: Long): Variable
def indexAddFromSource[S : Sc](index: Variable, dim: Int, source: Variable): Variable
def indexFill[S : Sc](index: Variable, dim: Int, fillValue: Double): Variable
def indexSelect[S : Sc](dim: Long, index: Variable): Variable
def inv[S : Sc]: Variable
def leakyRelu[S : Sc](negativeSlope: Double): Variable
def log[S : Sc]: Variable
def log1p[S : Sc]: Variable
def logSoftMax[S : Sc](dim: Int): Variable
def logdet[S : Sc]: Variable
def makeBooleanMask[S : Sc](q: Long): Variable
def maskFill[S : Sc](mask: Variable, fill: Double): Variable
def maskSelect[S : Sc](mask: Variable): Variable
def maximum[S : Sc](other: Variable): Variable
def mean[S : Sc](dim: List[Int]): Variable
def mean[S : Sc](dim: List[Int], keepDim: Boolean): Variable
def minimum[S : Sc](other: Variable): Variable
def mm[S : Sc](other: Variable): Variable
def mseLoss[S : Sc](target: STen, reduction: Reduction): Variable
def needsGrad: Boolean

Returns true if lamp.autograd.Variable.partialDerivative is defined.

Returns true if lamp.autograd.Variable.partialDerivative is defined.

Attributes

def nllLoss[S : Sc](target: STen, weights: STen, reduction: Reduction, ignore: Long): Variable
def norm2[S : Sc](dim: List[Int]): Variable
def norm2[S : Sc](dim: List[Int], keepDim: Boolean): Variable
def normalize[S : Sc](dim: List[Int], eps: Double): Variable
def oneHot[S : Sc](numClasses: Int): Variable
def options[S : Sc]: STenOptions

Returns the tensor options of its value.

Returns the tensor options of its value.

Attributes

def pinv[S : Sc](rcond: Double): Variable
def pow[S : Sc](const: Double): Variable
def pow[S : Sc](exponent: Variable): Variable
def relu[S : Sc]: Variable
def repeatInterleave[S : Sc](repeats: Variable, dim: Int): Variable
def reshape[S : Sc](shape: List[Long]): Variable
def rowSum[S : Sc]: Variable
def scatterAdd[S : Sc](index: Variable, dim: Int, maxIndex: Long): Variable
def select[S : Sc](dim: Long, index: Long): Variable
def shape: List[Long]

Returns the shape of its value.

Returns the shape of its value.

Attributes

def sigmoid[S : Sc]: Variable
def sin[S : Sc]: Variable
def slice[S : Sc](dim: Long, start: Long, end: Long, step: Long): Variable
def smoothL1Loss[S : Sc](target: STen, reduction: Reduction, beta: Double): Variable
def softplus[S : Sc](beta: Double, threshold: Double): Variable
def squaredFrobenius[S : Sc]: Variable
def sum[S : Sc]: Variable
def sum[S : Sc](dim: List[Int], keepDim: Boolean): Variable
def swish1[S : Sc]: Variable
def t[S : Sc]: Variable

Returns a new variable with the first two dimensions transposed.

Returns a new variable with the first two dimensions transposed.

Attributes

def tan[S : Sc]: Variable
def tanh[S : Sc]: Variable
def toDense[S : Sc]: Variable
def toDoubleArray: Array[Double]
def toLongArray: Array[Long]
override def toString: String

Returns a string representation of the object.

Returns a string representation of the object.

The default representation is platform dependent.

Attributes

Returns

a string representation of the object.

Definition Classes
Any
def transpose[S : Sc](dim1: Int, dim2: Int): Variable

Returns a new variable with the respective dimensions transposed.

Returns a new variable with the respective dimensions transposed.

Attributes

def variance[S : Sc](dim: List[Int]): Variable
def view[S : Sc](shape: List[Long]): Variable
def withGrad[S : Sc]: ConstantWithGrad

Returns an other Variable wrapping the same value tensor, without any parent and with needsGrad=true.

Returns an other Variable wrapping the same value tensor, without any parent and with needsGrad=true.

Attributes

def zeroGrad(): Unit

In place zeros out the partial derivative

In place zeros out the partial derivative

Attributes

Concrete fields

val id: UUID

Returns unique, stable and random UUID.

Returns unique, stable and random UUID.

Attributes

val sizes: List[Long]

Returns the shape of its value.

Returns the shape of its value.

Attributes

lazy val wengert: Seq[Variable]

Returns the Wengert list

Returns the Wengert list

Attributes

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