Numeric.SGD.Adam

Provides the adam function which implements the Adam algorithm based on the paper:

• https://arxiv.org/pdf/1412.6980

Documentation

data Config

AdaDelta configuration

Constructors

Config

Fields

• alpha0 :: Double

  Initial step size

• tau :: Double

  The step size after k * tau iterations = alpha0 / (k + 1)

• beta1 :: Double

  1st exponential moment decay

• beta2 :: Double

  1st exponential moment decay

• eps :: Double

  Epsilon

Instances

Eq Config

Defined in Numeric.SGD.Adam

Methods

(==) :: Config -> Config -> Bool

(/=) :: Config -> Config -> Bool

Ord Config

Defined in Numeric.SGD.Adam

Methods

compare :: Config -> Config -> Ordering

(<) :: Config -> Config -> Bool

(<=) :: Config -> Config -> Bool

(>) :: Config -> Config -> Bool

(>=) :: Config -> Config -> Bool

max :: Config -> Config -> Config

min :: Config -> Config -> Config

Show Config

Defined in Numeric.SGD.Adam

Methods

showsPrec :: Int -> Config -> ShowS

show :: Config -> String

showList :: [Config] -> ShowS

Generic Config

Defined in Numeric.SGD.Adam

Associated Types

type Rep Config :: Type -> Type

Methods

from :: Config -> Rep Config x

to :: Rep Config x -> Config

Default Config

Defined in Numeric.SGD.Adam

Methods

def :: Config

type Rep Config

Defined in Numeric.SGD.Adam

type Rep Config = D1 (MetaData "Config" "Numeric.SGD.Adam" "sgd-0.8.0.3-2sUF012Uk8gIqgMlYiSV0v" False) (C1 (MetaCons "Config" PrefixI True) ((S1 (MetaSel (Just "alpha0") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 Double) :*: S1 (MetaSel (Just "tau") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 Double)) :*: (S1 (MetaSel (Just "beta1") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 Double) :*: (S1 (MetaSel (Just "beta2") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 Double) :*: S1 (MetaSel (Just "eps") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 Double)))))

scaleTau :: Double -> Config -> Config

Scale the tau parameter. Useful e.g. to account for the size of the training dataset.

adam

Arguments

:: (Monad m, ParamSet p)

=> Config

Adam configuration

-> (e -> p -> p)

Gradient on a training element

-> SGD m e p

Perform gradient descent using the Adam algorithm. See Numeric.SGD.Adam for more information.