Safe Haskell	None
Language	Haskell2010

Backprop.Learn.Initialize

Contents

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Synopsis

class Initialize p where
- initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m p
gInitialize :: (ADTRecord p, Constraints p Initialize, ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m p
initializeNormal :: (Initialize p, PrimMonad m) => Double -> Gen (PrimState m) -> m p
initializeSingle :: (ContGen d, PrimMonad m, Fractional p) => d -> Gen (PrimState m) -> m p
reshapeR :: forall i j d m. (ContGen d, PrimMonad m, KnownNat i, KnownNat j) => d -> Gen (PrimState m) -> R i -> m (R j)
reshapeLRows :: forall i j n d m. (ContGen d, PrimMonad m, KnownNat n, KnownNat i, KnownNat j) => d -> Gen (PrimState m) -> L i n -> m (L j n)
reshapeLCols :: forall i j n d m. (ContGen d, PrimMonad m, KnownNat n, KnownNat i, KnownNat j) => d -> Gen (PrimState m) -> L n i -> m (L n j)

Documentation

class Initialize p where Source #

Class for types that are basically a bunch of Doubles, which can be initialized with a given identical and independent distribution.

Minimal complete definition

Nothing

Methods

initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m p Source #

initialize :: (ADTRecord p, Constraints p Initialize, ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m p Source #

Instances

Initialize Double Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m Double Source #
Initialize Float Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m Float Source #
Initialize () Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m () Source #
Initialize T0 Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m T0 Source #
Initialize a => Initialize (Complex a) Source #	Initializes real and imaginary components identically
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (Complex a) Source #
KnownNat n => Initialize (R n) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (R n) Source #
KnownNat n => Initialize (C n) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (C n) Source #
Initialize a => Initialize (TF a) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (TF a) Source #
RPureConstrained Initialize as => Initialize (T as) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (T as) Source #
(Initialize a, Initialize b) => Initialize (a, b) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (a, b) Source #
(KnownNat n, KnownNat m) => Initialize (L n m) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m0) => d -> Gen (PrimState m0) -> m0 (L n m) Source #
(KnownNat n, KnownNat m) => Initialize (M n m) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m0) => d -> Gen (PrimState m0) -> m0 (M n m) Source #
(Initialize a, Initialize b) => Initialize (a :# b) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (a :# b) Source #
(KnownNat o, KnownNat i) => Initialize (LRp i o) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (LRp i o) Source #
(KnownNat a, KnownNat b) => Initialize (ARIMAp a b) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (ARIMAp a b) Source #
(KnownNat i, KnownNat o) => Initialize (LSTMp i o) Source #	Forget biases initialized to 1
Instance details Defined in Backprop.Learn.Model.Neural.LSTM Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (LSTMp i o) Source #
KnownNat o => Initialize (GRUp i o) Source #
Instance details Defined in Backprop.Learn.Model.Neural.LSTM Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (GRUp i o) Source #
(Initialize a, Initialize b, Initialize c) => Initialize (a, b, c) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (a, b, c) Source #
(Vector v a, KnownNat n, Initialize a) => Initialize (Vector v n a) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (Vector v n a) Source #
KnownNat c => Initialize (ARIMAs a b c) Source #
Instance details Defined in Backprop.Learn.Model.Regression Methods initialize :: (ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m (ARIMAs a b c) Source #
(Initialize a, Initialize b, Initialize c, Initialize d) => Initialize (a, b, c, d) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d0, PrimMonad m) => d0 -> Gen (PrimState m) -> m (a, b, c, d) Source #
(Initialize a, Initialize b, Initialize c, Initialize d, Initialize e) => Initialize (a, b, c, d, e) Source #
Instance details Defined in Backprop.Learn.Initialize Methods initialize :: (ContGen d0, PrimMonad m) => d0 -> Gen (PrimState m) -> m (a, b, c, d, e) Source #

gInitialize :: (ADTRecord p, Constraints p Initialize, ContGen d, PrimMonad m) => d -> Gen (PrimState m) -> m p Source #

initialize for any instance of Generic.

initializeNormal Source #

Arguments

:: (Initialize p, PrimMonad m)
=> Double	standard deviation
-> Gen (PrimState m)
-> m p

Helper over inititialize for a gaussian distribution centered around zero.

initializeSingle :: (ContGen d, PrimMonad m, Fractional p) => d -> Gen (PrimState m) -> m p Source #

initialize definition if p is a single number.

Reshape

reshapeR :: forall i j d m. (ContGen d, PrimMonad m, KnownNat i, KnownNat j) => d -> Gen (PrimState m) -> R i -> m (R j) Source #

Reshape a vector to have a different amount of items If the matrix is grown, new weights are initialized according to the given distribution.

reshapeLRows :: forall i j n d m. (ContGen d, PrimMonad m, KnownNat n, KnownNat i, KnownNat j) => d -> Gen (PrimState m) -> L i n -> m (L j n) Source #

Reshape a matrix to have a different amount of rows If the matrix is grown, new weights are initialized according to the given distribution.

reshapeLCols :: forall i j n d m. (ContGen d, PrimMonad m, KnownNat n, KnownNat i, KnownNat j) => d -> Gen (PrimState m) -> L n i -> m (L n j) Source #

Reshape a matrix to have a different amount of columns. If the matrix is grown, new weights are initialized according to the given distribution.