master..d118c5e · grenade

examples/grenade-examples.cabal+5−6

@@ -26,7 +26,7 @@ executable feedforward

2626 , bytestring

2727 , cereal

2828 , either

29− , optparse-applicative >= 0.13 && < 0.18

29+ , optparse-applicative >= 0.13 && < 0.16

3030 , text == 1.2.*

3131 , mtl >= 2.2.1 && < 2.3

3232 , hmatrix

@@ -42,7 +42,7 @@ executable mnist

4242 , grenade

4343 , attoparsec

4444 , either

45− , optparse-applicative >= 0.13 && < 0.18

45+ , optparse-applicative >= 0.13 && < 0.16

4646 , text == 1.2.*

4747 , mtl >= 2.2.1 && < 2.3

4848 , hmatrix >= 0.18 && < 0.21

@@ -61,7 +61,7 @@ executable gan-mnist

6161 , bytestring

6262 , cereal

6363 , either

64− , optparse-applicative >= 0.13 && < 0.18

64+ , optparse-applicative >= 0.13 && < 0.16

6565 , text == 1.2.*

6666 , mtl >= 2.2.1 && < 2.3

6767 , hmatrix >= 0.18 && < 0.21

@@ -78,7 +78,7 @@ executable recurrent

7878 , grenade

7979 , attoparsec

8080 , either

81− , optparse-applicative >= 0.13 && < 0.18

81+ , optparse-applicative >= 0.13 && < 0.16

8282 , text == 1.2.*

8383 , mtl >= 2.2.1 && < 2.3

8484 , hmatrix >= 0.18 && < 0.21

@@ -96,14 +96,13 @@ executable shakespeare

9696 , bytestring

9797 , cereal

9898 , either

99− , optparse-applicative >= 0.13 && < 0.18

99+ , optparse-applicative >= 0.13 && < 0.16

100100 , text == 1.2.*

101101 , mtl >= 2.2.1 && < 2.3

102102 , hmatrix >= 0.18 && < 0.21

103103 , transformers

104104 , semigroups

105105 , singletons

106− , singletons-base

107106 , vector

108107 , MonadRandom

109108 , containers

examples/main/shakespeare.hs+7−2

@@ -14,15 +14,22 @@ import Data.Char ( isUpper, toUpper, toLower )

1414import Data.List ( foldl' )

1515import Data.Maybe ( fromMaybe )

1616

17+#if ! MIN_VERSION_base(4,13,0)

18+import Data.Semigroup ( (<>) )

19+#endif

1720

1821import qualified Data.Vector as V

1922import Data.Vector ( Vector )

2023

2124import qualified Data.Map as M

25+#if ! MIN_VERSION_base(4,13,0)

26+import Data.Proxy ( Proxy (..) )

27+#endif

2228

2329import qualified Data.ByteString as B

2430import Data.Serialize

2531

32+import Data.Singletons.Prelude

2633import GHC.TypeLits

2734

2835import Numeric.LinearAlgebra.Static ( konst )

@@ -34,8 +41,6 @@ import Grenade.Recurrent

3441import Grenade.Utils.OneHot

3542

3643import System.IO.Unsafe ( unsafeInterleaveIO )

37−import Data.Proxy

38−import Prelude.Singletons

3944

4045-- The defininition for our natural language recurrent network.

4146-- This network is able to learn and generate simple words in

grenade.cabal+1−2

@@ -38,7 +38,7 @@ source-repository head

3838library

3939 build-depends:

4040 base >= 4.8 && < 5

41− , bytestring >= 0.10.0

41+ , bytestring == 0.10.*

4242 , containers >= 0.5 && < 0.7

4343 , cereal >= 0.5 && < 0.6

4444 , deepseq >= 1.4 && < 1.5

@@ -48,7 +48,6 @@ library

4848 -- Versions of singletons are *tightly* coupled with the

4949 -- GHC version so its fine to drop version bounds.

5050 , singletons

51− , singletons-base

5251 , vector >= 0.11 && < 0.13

5352

5453 ghc-options:

src/Grenade/Core/Layer.hs+2−1

@@ -48,6 +48,7 @@ import Data.Kind (Type)

4848

4949import Grenade.Core.Shape

5050import Grenade.Core.LearningParameters

51+import Control.DeepSeq (NFData)

5152

5253-- | Class for updating a layer. All layers implement this, as it

5354-- describes how to create and update the layer.

@@ -73,7 +74,7 @@ class UpdateLayer x where

7374-- need to implement it for all shapes, only ones which are

7475-- appropriate.

7576--

76−class UpdateLayer x => Layer x (i :: Shape) (o :: Shape) where

77+class (UpdateLayer x, NFData x) => Layer x (i :: Shape) (o :: Shape) where

7778 -- | The Wengert tape for this layer. Includes all that is required

7879 -- to generate the back propagated gradients efficiently. As a

7980 -- default, `S i` is fine.

src/Grenade/Core/Network.hs+9−2

@@ -34,6 +34,7 @@ module Grenade.Core.Network (

3434import Control.Monad.Random ( MonadRandom )

3535

3636import Data.Singletons

37+import Data.Singletons.Prelude

3738import Data.Serialize

3839

3940#if MIN_VERSION_base(4,9,0)

@@ -43,7 +44,7 @@ import Data.Kind (Type)

4344import Grenade.Core.Layer

4445import Grenade.Core.LearningParameters

4546import Grenade.Core.Shape

46−import Prelude.Singletons

47+import Control.DeepSeq

4748

4849-- | Type of a network.

4950--

@@ -68,6 +69,12 @@ instance Show (Network '[] '[i]) where

6869instance (Show x, Show (Network xs rs)) => Show (Network (x ': xs) (i ': rs)) where

6970 show (x :~> xs) = show x ++ "\n~>\n" ++ show xs

7071

72+instance NFData (Network '[] '[i]) where

73+ rnf NNil = ()

74+

75+instance NFData (Network xs rs) => NFData (Network (x ': xs) (i ': rs)) where

76+ rnf (x :~> xs) = x `deepseq` xs `deepseq` ()

77+

7178-- | Gradient of a network.

7279--

7380-- Parameterised on the layers of the network.

@@ -191,7 +198,7 @@ instance CreatableNetwork sublayers subshapes => UpdateLayer (Network sublayers

191198-- | Ultimate composition.

192199--

193200-- This allows a complete network to be treated as a layer in a larger network.

194−instance (CreatableNetwork sublayers subshapes, i ~ (Head subshapes), o ~ (Last subshapes)) => Layer (Network sublayers subshapes) i o where

201+instance (CreatableNetwork sublayers subshapes, i ~ (Head subshapes), o ~ (Last subshapes), NFData (Network sublayers subshapes)) => Layer (Network sublayers subshapes) i o where

195202 type Tape (Network sublayers subshapes) i o = Tapes sublayers subshapes

196203 runForwards = runNetwork

197204 runBackwards = runGradient

src/Grenade/Core/Runner.hs+1−2

@@ -13,12 +13,11 @@ module Grenade.Core.Runner (

1313 , runNet

1414 ) where

1515

16+import Data.Singletons.Prelude

1617

1718import Grenade.Core.LearningParameters

1819import Grenade.Core.Network

1920import Grenade.Core.Shape

20−import Data.Singletons

21−import Prelude.Singletons

2221

2322-- | Perform reverse automatic differentiation on the network

2423-- for the current input and expected output.

src/Grenade/Core/Shape.hs+31−16

@@ -21,20 +21,35 @@ Stability : experimental

2121module Grenade.Core.Shape (

2222 S (..)

2323 , Shape (..)

24+#if MIN_VERSION_singletons(2,6,0)

2425 , SShape (..)

26+#else

27+ , Sing (..)

28+#endif

29+

2530 , randomOfShape

2631 , fromStorable

2732 ) where

2833

2934import Control.DeepSeq (NFData (..))

3035import Control.Monad.Random ( MonadRandom, getRandom )

36+

37+#if MIN_VERSION_base(4,13,0)

3138import Data.Kind (Type)

39+#endif

3240import Data.Proxy

3341import Data.Serialize

3442import Data.Singletons

43+import Data.Singletons.TypeLits

3544import Data.Vector.Storable ( Vector )

3645import qualified Data.Vector.Storable as V

46+

47+#if MIN_VERSION_base(4,11,0)

48+import GHC.TypeLits hiding (natVal)

49+#else

3750import GHC.TypeLits

51+#endif

52+

3853import qualified Numeric.LinearAlgebra.Static as H

3954import Numeric.LinearAlgebra.Static

4055import qualified Numeric.LinearAlgebra as NLA

@@ -84,9 +99,9 @@ deriving instance Show (S n)

8499type instance Sing = SShape

85100

86101data SShape :: Shape -> Type where

87− D1Sing :: KnownNat a => SShape ('D1 a)

88− D2Sing :: (KnownNat a, KnownNat b) => SShape ('D2 a b)

89− D3Sing :: (KnownNat (a * c), KnownNat a, KnownNat b, KnownNat c) => SShape ('D3 a b c)

102+ D1Sing :: Sing a -> SShape ('D1 a)

103+ D2Sing :: Sing a -> Sing b -> SShape ('D2 a b)

104+ D3Sing :: KnownNat (a * c) => Sing a -> Sing b -> Sing c -> SShape ('D3 a b c)

90105#else

91106data instance Sing (n :: Shape) where

92107 D1Sing :: Sing a -> Sing ('D1 a)

@@ -95,11 +110,11 @@ data instance Sing (n :: Shape) where

95110#endif

96111

97112instance KnownNat a => SingI ('D1 a) where

98− sing = D1Sing

113+ sing = D1Sing sing

99114instance (KnownNat a, KnownNat b) => SingI ('D2 a b) where

100− sing = D2Sing

115+ sing = D2Sing sing sing

101116instance (KnownNat a, KnownNat b, KnownNat c, KnownNat (a * c)) => SingI ('D3 a b c) where

102− sing = D3Sing

117+ sing = D3Sing sing sing sing

103118

104119instance SingI x => Num (S x) where

105120 (+) = n2 (+)

@@ -148,13 +163,13 @@ randomOfShape :: forall x m. ( MonadRandom m, SingI x ) => m (S x)

148163randomOfShape = do

149164 seed :: Int <- getRandom

150165 return $ case (sing :: Sing x) of

151− D1Sing ->

166+ D1Sing SNat ->

152167 S1D (randomVector seed Uniform * 2 - 1)

153168

154− D2Sing ->

169+ D2Sing SNat SNat ->

155170 S2D (uniformSample seed (-1) 1)

156171

157− D3Sing ->

172+ D3Sing SNat SNat SNat ->

158173 S3D (uniformSample seed (-1) 1)

159174

160175-- | Generate a shape from a Storable Vector.

@@ -162,13 +177,13 @@ randomOfShape = do

162177-- Returns Nothing if the vector is of the wrong size.

163178fromStorable :: forall x. SingI x => Vector Double -> Maybe (S x)

164179fromStorable xs = case sing :: Sing x of

165− D1Sing ->

180+ D1Sing SNat ->

166181 S1D <$> H.create xs

167182

168− D2Sing ->

183+ D2Sing SNat SNat ->

169184 S2D <$> mkL xs

170185

171− D3Sing ->

186+ D3Sing SNat SNat SNat ->

172187 S3D <$> mkL xs

173188 where

174189 mkL :: forall rows columns. (KnownNat rows, KnownNat columns)

@@ -205,13 +220,13 @@ n2 f (S2D x) (S2D y) = S2D (f x y)

205220n2 f (S3D x) (S3D y) = S3D (f x y)

206221

207222-- Helper function for creating the number instances

208−nk :: forall x. (SingI x) => Double -> S x

223+nk :: forall x. SingI x => Double -> S x

209224nk x = case (sing :: Sing x) of

210− D1Sing ->

225+ D1Sing SNat ->

211226 S1D (konst x)

212227

213− D2Sing ->

228+ D2Sing SNat SNat ->

214229 S2D (konst x)

215230

216− D3Sing ->

231+ D3Sing SNat SNat SNat ->

217232 S3D (konst x)

src/Grenade/Layers/Concat.hs+4

@@ -35,6 +35,7 @@ import Data.Kind (Type)

3535import Grenade.Core

3636

3737import Numeric.LinearAlgebra.Static ( row, (===), splitRows, unrow, (#), split, R )

38+import Control.DeepSeq

3839

3940-- | A Concatentating Layer.

4041--

@@ -52,6 +53,9 @@ import Numeric.LinearAlgebra.Static ( row, (===), splitRows, unrow, (#

5253data Concat :: Shape -> Type -> Shape -> Type -> Type where

5354 Concat :: x -> y -> Concat m x n y

5455

56+instance (NFData x, NFData y) => NFData (Concat m x n y) where

57+ rnf (Concat x y) = rnf x `deepseq` rnf y `deepseq` ()

58+

5559instance (Show x, Show y) => Show (Concat m x n y) where

5660 show (Concat x y) = "Concat\n" ++ show x ++ "\n" ++ show y

5761

src/Grenade/Layers/Convolution.hs+11

@@ -28,9 +28,16 @@ import Control.Monad.Random hiding ( fromList )

2828import Data.Maybe

2929import Data.Proxy

3030import Data.Serialize

31+import Data.Singletons.TypeLits

3132

33+#if MIN_VERSION_base(4,11,0)

34+import GHC.TypeLits hiding (natVal)

35+#else

3236import GHC.TypeLits

37+#endif

38+#if MIN_VERSION_base(4,9,0)

3339import Data.Kind (Type)

40+#endif

3441

3542import Numeric.LinearAlgebra hiding ( uniformSample, konst )

3643import qualified Numeric.LinearAlgebra as LA

@@ -39,6 +46,7 @@ import Numeric.LinearAlgebra.Static hiding ((|||), build, toRows)

3946import Grenade.Core

4047import Grenade.Layers.Internal.Convolution

4148import Grenade.Layers.Internal.Update

49+import Control.DeepSeq

4250

4351-- | A convolution layer for a neural network.

4452-- This uses the im2col convolution trick popularised by Caffe, which essentially turns the

@@ -106,6 +114,9 @@ instance Show (Convolution c f k k' s s') where

106114 px = (fmap . fmap . fmap) render ms

107115 in unlines $ foldl1 (zipWith (\a' b' -> a' ++ " | " ++ b')) $ px

108116

117+instance NFData (Convolution c f k k' s s') where

118+ rnf (Convolution a b) = rnf a `deepseq` rnf b `deepseq` ()

119+

109120randomConvolution :: ( MonadRandom m

110121 , KnownNat channels

111122 , KnownNat filters

src/Grenade/Layers/Crop.hs+11

@@ -21,15 +21,23 @@ module Grenade.Layers.Crop (

2121import Data.Maybe

2222import Data.Proxy

2323import Data.Serialize

24+import Data.Singletons.TypeLits

2425

26+#if MIN_VERSION_base(4,11,0)

27+import GHC.TypeLits hiding (natVal)

28+#else

2529import GHC.TypeLits

30+#endif

31+#if MIN_VERSION_base(4,9,0)

2632import Data.Kind (Type)

33+#endif

2734

2835import Grenade.Core

2936import Grenade.Layers.Internal.Pad

3037

3138import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

3239import Numeric.LinearAlgebra.Static (extract, create)

40+import Control.DeepSeq

3341

3442-- | A cropping layer for a neural network.

3543data Crop :: Nat

@@ -38,6 +46,9 @@ data Crop :: Nat

3846 -> Nat -> Type where

3947 Crop :: Crop cropLeft cropTop cropRight cropBottom

4048

49+instance NFData (Crop cropLeft cropTop cropRight cropBottom) where

50+ rnf Crop = ()

51+

4152instance Show (Crop cropLeft cropTop cropRight cropBottom) where

4253 show Crop = "Crop"

4354

src/Grenade/Layers/Deconvolution.hs+11

@@ -32,9 +32,16 @@ import Control.Monad.Random hiding ( fromList )

3232import Data.Maybe

3333import Data.Proxy

3434import Data.Serialize

35+import Data.Singletons.TypeLits

3536

37+#if MIN_VERSION_base(4,11,0)

38+import GHC.TypeLits hiding (natVal)

39+#else

3640import GHC.TypeLits

41+#endif

42+#if MIN_VERSION_base(4,9,0)

3743import Data.Kind (Type)

44+#endif

3845

3946import Numeric.LinearAlgebra hiding ( uniformSample, konst )

4047import qualified Numeric.LinearAlgebra as LA

@@ -43,6 +50,7 @@ import Numeric.LinearAlgebra.Static hiding ((|||), build, toRows)

4350import Grenade.Core

4451import Grenade.Layers.Internal.Convolution

4552import Grenade.Layers.Internal.Update

53+import Control.DeepSeq

4654

4755-- | A Deconvolution layer for a neural network.

4856-- This uses the im2col Convolution trick popularised by Caffe.

@@ -87,6 +95,9 @@ data Deconvolution' :: Nat -- Number of channels, for the first layer this could

8795 => !(L kernelFlattened channels) -- The kernel filter gradient

8896 -> Deconvolution' channels filters kernelRows kernelColumns strideRows strideColumns

8997

98+instance NFData (Deconvolution c f k k' s s') where

99+ rnf (Deconvolution a b) = rnf a `deepseq` rnf b `deepseq` ()

100+

90101instance Show (Deconvolution c f k k' s s') where

91102 show (Deconvolution a _) = renderConv a

92103 where

src/Grenade/Layers/Dropout.hs+5−1

@@ -11,6 +11,7 @@ import Control.Monad.Random hiding (fromList)

1111

1212import GHC.TypeLits

1313import Grenade.Core

14+import Control.DeepSeq

1415

1516-- Dropout layer help to reduce overfitting.

1617-- Idea here is that the vector is a shape of 1s and 0s, which we multiply the input by.

@@ -20,7 +21,10 @@ import Grenade.Core

2021data Dropout = Dropout {

2122 dropoutRate :: Double

2223 , dropoutSeed :: Int

23− } deriving Show

24+ } deriving (Show)

25+

26+instance NFData Dropout where

27+ rnf (Dropout r s) = rnf r `deepseq` rnf s `deepseq` ()

2428

2529instance UpdateLayer Dropout where

2630 type Gradient Dropout = ()

src/Grenade/Layers/Elu.hs+4

@@ -19,6 +19,7 @@ import GHC.TypeLits

1919import Grenade.Core

2020

2121import qualified Numeric.LinearAlgebra.Static as LAS

22+import Control.DeepSeq

2223

2324-- | An exponential linear unit.

2425-- A layer which can act between any shape of the same dimension, acting as a

@@ -26,6 +27,9 @@ import qualified Numeric.LinearAlgebra.Static as LAS

2627data Elu = Elu

2728 deriving Show

2829

30+instance NFData Elu where

31+ rnf Elu = ()

32+

2933instance UpdateLayer Elu where

3034 type Gradient Elu = ()

3135 runUpdate _ _ _ = Elu

src/Grenade/Layers/FullyConnected.hs+8−1

@@ -13,6 +13,7 @@ import Control.Monad.Random hiding (fromList)

1313

1414import Data.Proxy

1515import Data.Serialize

16+import Data.Singletons.TypeLits

1617

1718import qualified Numeric.LinearAlgebra as LA

1819import Numeric.LinearAlgebra.Static

@@ -20,7 +21,7 @@ import Numeric.LinearAlgebra.Static

2021import Grenade.Core

2122

2223import Grenade.Layers.Internal.Update

23−import GHC.TypeLits

24+import Control.DeepSeq

2425

2526-- | A basic fully connected (or inner product) neural network layer.

2627data FullyConnected i o = FullyConnected

@@ -31,6 +32,12 @@ data FullyConnected' i o = FullyConnected'

3132 !(R o) -- Bias

3233 !(L o i) -- Activations

3334

35+instance NFData (FullyConnected' i o) where

36+ rnf (FullyConnected' a b) = a `deepseq` b `deepseq` ()

37+

38+instance NFData (FullyConnected i o) where

39+ rnf (FullyConnected a b) = a `deepseq` b `deepseq` ()

40+

3441instance Show (FullyConnected i o) where

3542 show FullyConnected {} = "FullyConnected"

3643

src/Grenade/Layers/Logit.hs+4

@@ -19,6 +19,7 @@ import Data.Serialize

1919import Data.Singletons

2020

2121import Grenade.Core

22+import Control.DeepSeq

2223

2324-- | A Logit layer.

2425--

@@ -28,6 +29,9 @@ import Grenade.Core

2829data Logit = Logit

2930 deriving Show

3031

32+instance NFData Logit where

33+ rnf Logit = ()

34+

3135instance UpdateLayer Logit where

3236 type Gradient Logit = ()

3337 runUpdate _ _ _ = Logit

src/Grenade/Layers/Merge.hs+4

@@ -29,6 +29,7 @@ import Data.Kind (Type)

2929#endif

3030

3131import Grenade.Core

32+import Control.DeepSeq

3233

3334-- | A Merging layer.

3435--

@@ -37,6 +38,9 @@ import Grenade.Core

3738data Merge :: Type -> Type -> Type where

3839 Merge :: x -> y -> Merge x y

3940

41+instance (NFData x, NFData y) => NFData (Merge x y) where

42+ rnf (Merge x y) = rnf x `deepseq` rnf y `deepseq` ()

43+

4044instance (Show x, Show y) => Show (Merge x y) where

4145 show (Merge x y) = "Merge\n" ++ show x ++ "\n" ++ show y

4246

src/Grenade/Layers/Pad.hs+11

@@ -21,15 +21,23 @@ module Grenade.Layers.Pad (

2121import Data.Maybe

2222import Data.Proxy

2323import Data.Serialize

24+import Data.Singletons.TypeLits

2425

26+#if MIN_VERSION_base(4,11,0)

27+import GHC.TypeLits hiding (natVal)

28+#else

2529import GHC.TypeLits

30+#endif

31+#if MIN_VERSION_base(4,9,0)

2632import Data.Kind (Type)

33+#endif

2734

2835import Grenade.Core

2936import Grenade.Layers.Internal.Pad

3037

3138import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

3239import Numeric.LinearAlgebra.Static (extract, create)

40+import Control.DeepSeq

3341

3442-- | A padding layer for a neural network.

3543--

@@ -40,6 +48,9 @@ data Pad :: Nat

4048 -> Nat -> Type where

4149 Pad :: Pad padLeft padTop padRight padBottom

4250

51+instance NFData (Pad padLeft padTop padRight padBottom) where

52+ rnf Pad = ()

53+

4354instance Show (Pad padLeft padTop padRight padBottom) where

4455 show Pad = "Pad"

4556

src/Grenade/Layers/Pooling.hs+11

@@ -22,14 +22,22 @@ module Grenade.Layers.Pooling (

2222import Data.Maybe

2323import Data.Proxy

2424import Data.Serialize

25+import Data.Singletons.TypeLits

2526

27+#if MIN_VERSION_base(4,11,0)

28+import GHC.TypeLits hiding (natVal)

29+#else

2630import GHC.TypeLits

31+#endif

32+#if MIN_VERSION_base(4,9,0)

2733import Data.Kind (Type)

34+#endif

2835

2936import Grenade.Core

3037import Grenade.Layers.Internal.Pooling

3138

3239import Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRows)

40+import Control.DeepSeq

3341

3442-- | A pooling layer for a neural network.

3543--

@@ -42,6 +50,9 @@ import Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRow

4250data Pooling :: Nat -> Nat -> Nat -> Nat -> Type where

4351 Pooling :: Pooling kernelRows kernelColumns strideRows strideColumns

4452

53+instance NFData (Pooling k k' s s') where

54+ rnf Pooling = ()

55+

4556instance Show (Pooling k k' s s') where

4657 show Pooling = "Pooling"

4758

src/Grenade/Layers/Relu.hs+4

@@ -19,6 +19,7 @@ import GHC.TypeLits

1919import Grenade.Core

2020

2121import qualified Numeric.LinearAlgebra.Static as LAS

22+import Control.DeepSeq

2223

2324-- | A rectifying linear unit.

2425-- A layer which can act between any shape of the same dimension, acting as a

@@ -26,6 +27,9 @@ import qualified Numeric.LinearAlgebra.Static as LAS

2627data Relu = Relu

2728 deriving Show

2829

30+instance NFData Relu where

31+ rnf Relu = ()

32+

2933instance UpdateLayer Relu where

3034 type Gradient Relu = ()

3135 runUpdate _ _ _ = Relu

src/Grenade/Layers/Reshape.hs+5

@@ -17,12 +17,14 @@ module Grenade.Layers.Reshape (

1717

1818import Data.Serialize

1919

20+import Data.Singletons.TypeLits

2021import GHC.TypeLits

2122

2223import Numeric.LinearAlgebra.Static

2324import Numeric.LinearAlgebra.Data as LA ( flatten )

2425

2526import Grenade.Core

27+import Control.DeepSeq

2628

2729-- | Reshape Layer

2830--

@@ -35,6 +37,9 @@ import Grenade.Core

3537data Reshape = Reshape

3638 deriving Show

3739

40+instance NFData Reshape where

41+ rnf Reshape = ()

42+

3843instance UpdateLayer Reshape where

3944 type Gradient Reshape = ()

4045 runUpdate _ _ _ = Reshape

src/Grenade/Layers/Sinusoid.hs+4

@@ -18,12 +18,16 @@ import Data.Serialize

1818import Data.Singletons

1919

2020import Grenade.Core

21+import Control.DeepSeq

2122

2223-- | A Sinusoid layer.

2324-- A layer which can act between any shape of the same dimension, performing a sin function.

2425data Sinusoid = Sinusoid

2526 deriving Show

2627

28+instance NFData Sinusoid where

29+ rnf Sinusoid = ()

30+

2731instance UpdateLayer Sinusoid where

2832 type Gradient Sinusoid = ()

2933 runUpdate _ _ _ = Sinusoid

src/Grenade/Layers/Softmax.hs+4

@@ -22,6 +22,7 @@ import GHC.TypeLits

2222import Grenade.Core

2323

2424import Numeric.LinearAlgebra.Static as LAS

25+import Control.DeepSeq

2526

2627-- | A Softmax layer

2728--

@@ -33,6 +34,9 @@ import Numeric.LinearAlgebra.Static as LAS

3334data Softmax = Softmax

3435 deriving Show

3536

37+instance NFData Softmax where

38+ rnf Softmax = ()

39+

3640instance UpdateLayer Softmax where

3741 type Gradient Softmax = ()

3842 runUpdate _ _ _ = Softmax

src/Grenade/Layers/Tanh.hs+4

@@ -18,12 +18,16 @@ import Data.Serialize

1818import Data.Singletons

1919

2020import Grenade.Core

21+import Control.DeepSeq

2122

2223-- | A Tanh layer.

2324-- A layer which can act between any shape of the same dimension, performing a tanh function.

2425data Tanh = Tanh

2526 deriving Show

2627

28+instance NFData Tanh where

29+ rnf Tanh = ()

30+

2731instance UpdateLayer Tanh where

2832 type Gradient Tanh = ()

2933 runUpdate _ _ _ = Tanh

src/Grenade/Layers/Trivial.hs+4

@@ -17,6 +17,7 @@ module Grenade.Layers.Trivial (

1717import Data.Serialize

1818

1919import Grenade.Core

20+import Control.DeepSeq

2021

2122-- | A Trivial layer.

2223--

@@ -25,6 +26,9 @@ import Grenade.Core

2526data Trivial = Trivial

2627 deriving Show

2728

29+instance NFData Trivial where

30+ rnf Trivial = ()

31+

2832instance Serialize Trivial where

2933 put _ = return ()

3034 get = return Trivial

src/Grenade/Recurrent/Core/Layer.hs+2−1

@@ -14,6 +14,7 @@ import Data.Kind (Type)

1414#endif

1515

1616import Grenade.Core

17+import Control.DeepSeq (NFData)

1718

1819-- | Class for a recurrent layer.

1920-- It's quite similar to a normal layer but for the input and output

@@ -22,7 +23,7 @@ class UpdateLayer x => RecurrentUpdateLayer x where

2223 -- | Shape of data that is passed between each subsequent run of the layer

2324 type RecurrentShape x :: Type

2425

25−class (RecurrentUpdateLayer x, Num (RecurrentShape x)) => RecurrentLayer x (i :: Shape) (o :: Shape) where

26+class (RecurrentUpdateLayer x, Num (RecurrentShape x), NFData x) => RecurrentLayer x (i :: Shape) (o :: Shape) where

2627 -- | Wengert Tape

2728 type RecTape x i o :: Type

2829 -- | Used in training and scoring. Take the input from the previous

src/Grenade/Recurrent/Core/Network.hs+12−1

@@ -29,13 +29,17 @@ module Grenade.Recurrent.Core.Network (

2929

3030

3131import Control.Monad.Random ( MonadRandom )

32+import Data.Singletons ( SingI )

33+import Data.Singletons.Prelude ( Head, Last )

3234import Data.Serialize

3335

36+#if MIN_VERSION_base(4,9,0)

3437import Data.Kind (Type)

38+#endif

3539

3640import Grenade.Core

3741import Grenade.Recurrent.Core.Layer

38−import Prelude.Singletons

42+import Control.DeepSeq

3943

4044-- | Witness type to say indicate we're building up with a normal feed

4145-- forward layer.

@@ -195,6 +199,12 @@ applyRecurrentUpdate rate (layer :~@> rest) (gradient ://> grest)

195199applyRecurrentUpdate _ RNil RGNil

196200 = RNil

197201

202+instance NFData (RecurrentNetwork '[] '[i]) where

203+ rnf RNil = ()

204+

205+instance NFData (RecurrentNetwork xs rs) => NFData (RecurrentNetwork (x ': xs) (i ': rs)) where

206+ rnf (layer :~~> rest) = rnf layer `deepseq` rnf rest `deepseq` ()

207+ rnf (layer :~@> rest) = rnf layer `deepseq` rnf rest `deepseq` ()

198208

199209instance Show (RecurrentNetwork '[] '[i]) where

200210 show RNil = "NNil"

@@ -322,6 +332,7 @@ instance CreatableRecurrent sublayers subshapes => RecurrentUpdateLayer (Recurre

322332instance ( CreatableRecurrent sublayers subshapes

323333 , i ~ (Head subshapes), o ~ (Last subshapes)

324334 , Num (RecurrentShape (RecurrentNetwork sublayers subshapes))

335+ , NFData (RecurrentNetwork sublayers subshapes)

325336 ) => RecurrentLayer (RecurrentNetwork sublayers subshapes) i o where

326337 type RecTape (RecurrentNetwork sublayers subshapes) i o = RecurrentTape sublayers subshapes

327338 runRecurrentForwards = runRecurrent

src/Grenade/Recurrent/Core/Runner.hs+1−1

@@ -17,10 +17,10 @@ module Grenade.Recurrent.Core.Runner (

1717 ) where

1818

1919import Data.List ( foldl' )

20+import Data.Singletons.Prelude

2021import Grenade.Core

2122

2223import Grenade.Recurrent.Core.Network

23−import Prelude.Singletons

2424

2525type RecurrentGradients layers = [RecurrentGradient layers]

2626

src/Grenade/Recurrent/Layers/BasicRecurrent.hs+7

@@ -16,8 +16,11 @@ module Grenade.Recurrent.Layers.BasicRecurrent (

1616

1717import Control.Monad.Random ( MonadRandom, getRandom )

1818

19+import Data.Singletons.TypeLits

1920

21+#if MIN_VERSION_base(4,9,0)

2022import Data.Kind (Type)

23+#endif

2124

2225import Numeric.LinearAlgebra.Static

2326

@@ -25,6 +28,7 @@ import GHC.TypeLits

2528

2629import Grenade.Core

2730import Grenade.Recurrent.Core

31+import Control.DeepSeq

2832

2933data BasicRecurrent :: Nat -- Input layer size

3034 -> Nat -- Output layer size

@@ -39,6 +43,9 @@ data BasicRecurrent :: Nat -- Input layer size

3943 -> !(L output matrixCols) -- Momentum

4044 -> BasicRecurrent input output

4145

46+instance NFData (BasicRecurrent input output) where

47+ rnf (BasicRecurrent b bm a aM) = rnf b `deepseq` rnf bm `deepseq` rnf a `deepseq` rnf aM `deepseq` ()

48+

4249data BasicRecurrent' :: Nat -- Input layer size

4350 -> Nat -- Output layer size

4451 -> Type where

src/Grenade/Recurrent/Layers/ConcatRecurrent.hs+10

@@ -36,6 +36,7 @@ import Grenade.Core

3636import Grenade.Recurrent.Core

3737

3838import Numeric.LinearAlgebra.Static ( (#), split, R )

39+import Control.DeepSeq

3940

4041-- | A Concatentating Layer.

4142--

@@ -55,6 +56,15 @@ data ConcatRecurrent :: Shape -> Type -> Shape -> Type -> Type where

5556 ConcatRecRight :: x -> y -> ConcatRecurrent m (FeedForward x) n (Recurrent y)

5657 ConcatRecBoth :: x -> y -> ConcatRecurrent m (Recurrent x) n (Recurrent y)

5758

59+instance (NFData x, NFData y) => NFData (ConcatRecurrent m (Recurrent x) n (Recurrent y)) where

60+ rnf (ConcatRecBoth x y) = rnf x `deepseq` rnf y `deepseq` ()

61+

62+instance (NFData x, NFData y) => NFData (ConcatRecurrent m (Recurrent x) n (FeedForward y)) where

63+ rnf (ConcatRecLeft x y) = rnf x `deepseq` rnf y `deepseq` ()

64+

65+instance (NFData x, NFData y) => NFData (ConcatRecurrent m (FeedForward x) n (Recurrent y)) where

66+ rnf (ConcatRecRight x y) = rnf x `deepseq` rnf y `deepseq` ()

67+

5868instance (Show x, Show y) => Show (ConcatRecurrent m (p x) n (q y)) where

5969 show (ConcatRecLeft x y) = "ConcatRecLeft\n" ++ show x ++ "\n" ++ show y

6070 show (ConcatRecRight x y) = "ConcatRecRight\n" ++ show x ++ "\n" ++ show y

src/Grenade/Recurrent/Layers/LSTM.hs+15−1

@@ -21,7 +21,11 @@ import Control.Monad.Random ( MonadRandom, getRandom )

2121-- import Data.List ( foldl1' )

2222import Data.Proxy

2323import Data.Serialize

24+import Data.Singletons.TypeLits

25+

26+#if MIN_VERSION_base(4,9,0)

2427import Data.Kind (Type)

28+#endif

2529

2630import qualified Numeric.LinearAlgebra as LA

2731import Numeric.LinearAlgebra.Static

@@ -29,7 +33,7 @@ import Numeric.LinearAlgebra.Static

2933import Grenade.Core

3034import Grenade.Recurrent.Core

3135import Grenade.Layers.Internal.Update

32−import GHC.TypeLits

36+import Control.DeepSeq

3337

3438

3539-- | Long Short Term Memory Recurrent unit

@@ -61,6 +65,16 @@ data LSTMWeights :: Nat -> Nat -> Type where

6165 , lstmBc :: !(R output) -- Bias Cell (b_c)

6266 } -> LSTMWeights input output

6367

68+instance NFData (LSTMWeights input output) where

69+ rnf (LSTMWeights wf uf bf wi ui bi wo uo bo wc bc) =

70+ rnf wf `deepseq` rnf uf `deepseq` rnf bf `deepseq`

71+ rnf wi `deepseq` rnf ui `deepseq` rnf bi `deepseq`

72+ rnf wo `deepseq` rnf uo `deepseq` rnf bo `deepseq`

73+ rnf wc `deepseq` rnf bc `deepseq` ()

74+

75+instance NFData (LSTM input output) where

76+ rnf (LSTM w m) = rnf w `deepseq` rnf m `deepseq` ()

77+

6478instance Show (LSTM i o) where

6579 show LSTM {} = "LSTM"

6680

src/Grenade/Utils/OneHot.hs+1−1

@@ -22,6 +22,7 @@ import Data.Map ( Map )

2222import qualified Data.Map as M

2323

2424import Data.Proxy

25+import Data.Singletons.TypeLits

2526

2627import Data.Vector ( Vector )

2728import qualified Data.Vector as V

@@ -30,7 +31,6 @@ import qualified Data.Vector.Storable as VS

3031import Numeric.LinearAlgebra ( maxIndex )

3132import Numeric.LinearAlgebra.Devel

3233import Numeric.LinearAlgebra.Static

33−import GHC.TypeLits

3434

3535import Grenade.Core.Shape

3636

stack.yaml+1−7

@@ -20,7 +20,7 @@

2020#

2121# resolver: ./custom-snapshot.yaml

2222# resolver: https://example.com/snapshots/2018-01-01.yaml

23−resolver: lts-20.18

23+resolver: lts-15.5

2424

2525# User packages to be built.

2626# Various formats can be used as shown in the example below.

@@ -70,9 +70,3 @@ extra-deps:

7070#

7171# Allow a newer minor version of GHC than the snapshot specifies

7272# compiler-check: newer-minor

73−

74−nix:

75− enable: true

76− packages:

77− - blas

78− - lapack

stack.yaml.lock+5−5

@@ -7,13 +7,13 @@ packages:

77- completed:

88 hackage: typelits-witnesses-0.3.0.3@sha256:2d9df4ac6ff3077bfd2bf659e4b495e157723ac5b45c519762853f55df5c16db,2738

99 pantry-tree:

10− sha256: 6a42a462f98e94933b6e9721acd912c6c6b6a4743635efd15cc5871908c816a0

1110 size: 469

11+ sha256: 6a42a462f98e94933b6e9721acd912c6c6b6a4743635efd15cc5871908c816a0

1212 original:

1313 hackage: typelits-witnesses-0.3.0.3

1414snapshots:

1515- completed:

16− sha256: 9fa4bece7acfac1fc7930c5d6e24606004b09e80aa0e52e9f68b148201008db9

17− size: 649606

18− url: https://raw.githubusercontent.com/commercialhaskell/stackage-snapshots/master/lts/20/18.yaml

19− original: lts-20.18

16+ size: 491372

17+ url: https://raw.githubusercontent.com/commercialhaskell/stackage-snapshots/master/lts/15/5.yaml

18+ sha256: 1b549cfff328040c382a70a84a2087aac8dab6d778bf92f32a93a771a1980dfc

19+ original: lts-15.5