Data.Autodiff.Ops

Reexports

Definitions

mulModel : {auto {conArg:10892} : Num t} -> Neg t => Random t => Const t -\-> Const t

Totality: total
Visibility: public export

addModel : {auto {conArg:10949} : Num t} -> Neg t => Random t => Const t -\-> Const t

Totality: total
Visibility: public export

scalarAffine : {auto {conArg:11006} : Num t} -> Neg t => Random t => Materialise t => Const t -\-> Const t

Totality: total
Visibility: public export

parallelTensor : {auto {conArg:11069} : Num a} -> {auto {conArg:11072} : Num b} -> {auto {conArg:11077} : AllC TensorMonoid shape} -> AllC IsConcrete shape => Const a =%+> Const b -> Const (Tensor shape a) =%+> Const (Tensor shape b)

  Apply a scalar lens elementwise across a tensor

Totality: total
Visibility: public export

parallelTensorModel : {auto {conArg:11238} : Num a} -> {auto {conArg:11241} : Num b} -> {auto {conArg:11246} : AllC TensorMonoid shape} -> AllC IsConcrete shape => Traversable (Tensor shape) => Const a -\-> Const b -> Const (Tensor shape a) -\-> Const (Tensor shape b)

  `Tensor shape` applied to a model: one copy per entry, each with its own parameter

Totality: total
Visibility: public export

copyN : {auto {conArg:11531} : Num a} -> Const a =%+> Const (Tensor [(axisName ~~> n)] a)

Totality: total
Visibility: public export

sameFromTensorN : {auto {conArg:11656} : Num a} -> {auto {conArg:11659} : Num b} -> Traversable (Tensor [(axisName ~~> n)]) => Const a -\-> Const b -> Const a -\-> Const (Tensor [(axisName ~~> n)] b)

Totality: total
Visibility: public export

sumAxis : IsCubical n => {auto {conArg:11806} : Num a} -> {auto {conArg:11809} : TensorMonoid (n .cont)} -> Const (Tensor [n] a) =%+> Const (Tensor [] a)

  Dual to `copyN`

Totality: total
Visibility: public export

divBy : {auto {conArg:11937} : Num a} -> Fractional a => a -> Const (Tensor [] a) =%+> Const (Tensor [] a)

  Divide by a constant, entrywise in both directions

Totality: total
Visibility: public export

meanSquaredDifference : IsCubical n => {auto {conArg:12020} : TensorMonoid (n .cont)} -> {auto {conArg:12026} : Num a} -> Neg a => Fractional a => Cast Nat a => (Const (Tensor [n] a) >*< Const (Tensor [n] a)) =%+> Const (Tensor [] a)

Totality: total
Visibility: public export

leakyReLU : {auto {conArg:12196} : Num a} -> Ord a => a -> Const a =%+> Const a

  Recovers `ReLU` when `alpha=0`
  Cannot be written as a composition of scaling and `ReLU`

Totality: total
Visibility: public export

leakyReLUModel : {auto {conArg:12314} : Num a} -> Ord a => a -> {auto {conArg:12324} : AllC TensorMonoid shape} -> AllC IsConcrete shape => Const (Tensor shape a) -\-> Const (Tensor shape a)

Totality: total
Visibility: public export

reluModel : {auto {conArg:12403} : Num a} -> Ord a => {auto {conArg:12411} : AllC TensorMonoid shape} -> AllC IsConcrete shape => Const (Tensor shape a) -\-> Const (Tensor shape a)

Totality: total
Visibility: public export

fromLogits : {auto {conArg:12480} : TensorMonoid (a .cont)} -> Const (Tensor [a] Double) =%+> Simplex a

  Interpret a vector as logits of a distribution. The backward pass is
  identity, and gradients are computed in the same way

Totality: total
Visibility: public export

fromLogitsModel : {auto {conArg:12539} : TensorMonoid (a .cont)} -> Const (Tensor [a] Double) -\-> Simplex a

Totality: total
Visibility: public export