master..10a6003 · grenade

.gitignore+1−3

@@ -1,6 +1,4 @@

1−cabal.project.local

21.cabal-sandbox/

32cabal.sandbox.config

43dist/

5−dist-newstyle/

6−.stack-work/

4+

.gitmodulesnew file+15

@@ -0,0 +1,15 @@

1+[submodule "lib/disorder"]

2+ path = lib/disorder

3+ url = https://github.com/ambiata/disorder.hs

4+[submodule "lib/x"]

5+ path = lib/x

6+ url = https://github.com/ambiata/x.git

7+[submodule "lib/p"]

8+ path = lib/p

9+ url = https://github.com/ambiata/p

10+[submodule "lib/disorder.hs"]

11+ path = lib/disorder.hs

12+ url = https://github.com/ambiata/disorder.hs.git

13+[submodule "lib/hmatrix"]

14+ path = lib/hmatrix

15+ url = https://github.com/albertoruiz/hmatrix.git

.travis.yml+14−12

@@ -2,30 +2,32 @@

22

33# The following enables several GHC versions to be tested; often it's enough to test only against the last release in a major GHC version. Feel free to omit lines listings versions you don't need/want testing for.

44env:

5− - GHCVER=8.0.2

6− - GHCVER=8.2.2

7− - GHCVER=8.4.4

8− - GHCVER=8.6.5

9− - GHCVER=8.8.3

10− - GHCVER=8.10.1

5+ - CABALVER=1.22 GHCVER=7.10.3

6+ - CABALVER=1.24 GHCVER=8.0.1

117

128# Note: the distinction between `before_install` and `install` is not important.

139before_install:

1410 - travis_retry sudo add-apt-repository -y ppa:hvr/ghc

1511 - travis_retry sudo apt-get update

16− - travis_retry sudo apt-get install cabal-install-3.2 ghc-$GHCVER libblas-dev liblapack-dev

17− - export PATH=/opt/cabal/bin:/opt/ghc/$GHCVER/bin:$PATH

12+ - travis_retry sudo apt-get install cabal-install-$CABALVER ghc-$GHCVER happy-1.19.5 alex-3.1.7 libblas-dev liblapack-dev

13+ - export PATH=/opt/alex/3.1.7/bin:/opt/happy/1.19.5/bin:/opt/ghc/$GHCVER/bin:/opt/cabal/$CABALVER/bin:$HOME/.cabal/bin:$PATH

1814

1915install:

16+ - cabal --version

2017 - echo "$(ghc --version) [$(ghc --print-project-git-commit-id 2> /dev/null || echo '?')]"

21− - travis_retry cabal-3.2 update

18+ - travis_retry cabal update

19+ - git submodule update --init

20+ - cabal sandbox init

21+ - CABAL_SOURCES=$(find lib -maxdepth 4 ! -path lib/\*/bin/\* ! -path lib/\*/lib/\* -name \*.cabal | xargs -L 1 dirname)

22+ - for CABAL_SOURCE in $CABAL_SOURCES; do cabal sandbox add-source -- $CABAL_SOURCE; done

23+

24+ - cabal install --enable-tests --enable-benchmarks

2225

2326notifications:

2427 email: false

2528

2629# Here starts the actual work to be performed for the package under test; any command which exits with a non-zero exit code causes the build to fail.

2730script:

28− - cabal-3.2 configure --enable-tests

29− - cabal-3.2 build all

30− - cabal-3.2 test --test-show-details=direct -j1

31+ - cabal build

32+ - cabal test --show-details=streaming

3133

README.md+3−11

@@ -2,9 +2,6 @@ Grenade

22=======

33

44[![Build Status](https://api.travis-ci.org/HuwCampbell/grenade.svg?branch=master)](https://travis-ci.org/HuwCampbell/grenade)

5−[![Hackage page (downloads and API reference)][hackage-png]][hackage]

6−[![Hackage-Deps][hackage-deps-png]][hackage-deps]

7−

85

96```

107First shalt thou take out the Holy Pin, then shalt thou count to three, no more, no less.

@@ -24,7 +21,7 @@ specified and initialised with random weights in a few lines of code with

2421type MNIST

2522 = Network

2623 '[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu

27− , Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, Reshape, Relu

24+ , Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, FlattenLayer, Relu

2825 , FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]

2926 '[ 'D2 28 28, 'D3 24 24 10, 'D3 12 12 10, 'D3 12 12 10

3027 , 'D3 8 8 16, 'D3 4 4 16, 'D1 256, 'D1 256

@@ -89,7 +86,7 @@ backPropagate :: forall shapes layers.

8986```

9087which takes a network, appropriate input and target data, and returns the

9188back propagated gradients for the network. The shapes of the gradients are

92−appropriate for each layer, and may be trivial for layers like `Relu` which

89+appropriate for each layer, and may be trivial for layers like `Rulu` which

9390have no learnable parameters.

9491

9592The gradients however can always be applied, yielding a new (hopefully better)

@@ -159,7 +156,7 @@ and the tests run using:

159156./mafia test

160157```

161158

162−Grenade builds with ghc 7.10, 8.0, 8.2 and 8.4.

159+Grenade builds with ghc 7.10 and 8.0.

163160

164161Thanks

165162------

@@ -185,8 +182,3 @@ core took around 12 minutes, achieving 1.5% error rate on a 1000 sample holdout

185182Contributing

186183------------

187184Contributions are welcome.

188−

189− [hackage]: http://hackage.haskell.org/package/grenade

190− [hackage-png]: http://img.shields.io/hackage/v/grenade.svg

191− [hackage-deps]: http://packdeps.haskellers.com/reverse/grenade

192− [hackage-deps-png]: https://img.shields.io/hackage-deps/v/grenade.svg

bench/bench-lstm.hs+45−4

@@ -5,26 +5,67 @@ import Criterion.Main

55

66import Grenade

77import Grenade.Recurrent

8+import Grenade.Layers.Internal.Update

9+

10+import qualified Numeric.LinearAlgebra.Static as H

811

912main :: IO ()

1013main = do

14+ layer60 :: LSTM 40 60 <- createRandom

15+ layer512 :: LSTM 40 512 <- createRandom

1116 input40 :: S ('D1 40) <- randomOfShape

12− lstm :: RecNet <- randomRecurrent

17+ rec60 :: S ('D1 60) <- randomOfShape

18+ rec512 :: S ('D1 512) <- randomOfShape

19+ (lstm, lstm') :: (RecNet, RecInput) <- randomRecurrent

20+

21+ let upIn60 :: H.R 3600 = H.randomVector 1 H.Uniform * 2 - 1

22+ let upIn512 :: H.R 262144 = H.randomVector 1 H.Uniform * 2 - 1

1323

1424 defaultMain [

15− bgroup "train" [ bench "one-time-step" $ whnf (nfT2 . trainRecurrent lp lstm 0) [(input40, Just input40)]

16− , bench "ten-time-steps" $ whnf (nfT2 . trainRecurrent lp lstm 0) $ replicate 10 (input40, Just input40)

17− , bench "fifty-time-steps" $ whnf (nfT2 . trainRecurrent lp lstm 0) $ replicate 50 (input40, Just input40)

25+ bgroup "lstm" [ bench "forwards-60" $ nf (nfT3 . uncurry (testRun60 layer60)) (rec60, input40)

26+ , bench "forwards-512" $ nf (nfT3 . uncurry (testRun512 layer512)) (rec512, input40)

27+ , bench "backwards-60" $ nf (nfT3 . uncurry4 (testRun60' layer60)) (rec60, input40, rec60, rec60)

28+ , bench "backwards-512" $ nf (nfT3 . uncurry4 (testRun512' layer512)) (rec512, input40, rec512, rec512)

29+ ]

30+ , bgroup "update" [ bench "matrix-60x60" $ nf (uncurry3 (decendVector 1 1 1)) (upIn60, upIn60, upIn60)

31+ , bench "matrix-512x512" $ nf (uncurry3 (decendVector 1 1 1)) (upIn512, upIn512, upIn512)

32+ ]

33+ , bgroup "train" [ bench "one-time-step" $ whnf (nfT2 . trainRecurrent lp lstm lstm') [(input40, Just input40)]

34+ , bench "ten-time-steps" $ whnf (nfT2 . trainRecurrent lp lstm lstm') $ replicate 10 (input40, Just input40)

35+ , bench "fifty-time-steps" $ whnf (nfT2 . trainRecurrent lp lstm lstm') $ replicate 50 (input40, Just input40)

1836 ]

1937 ]

2038

39+testRun60 :: LSTM 40 60 -> S ('D1 60) -> S ('D1 40) -> ((S ('D1 60), S ('D1 40)), S ('D1 60), S ('D1 60))

40+testRun60 = runRecurrentForwards

41+

42+testRun60' :: LSTM 40 60 -> S ('D1 60) -> S ('D1 40) -> S ('D1 60) -> S ('D1 60) -> (Gradient (LSTM 40 60), S ('D1 60), S ('D1 40))

43+testRun60' = curry . runRecurrentBackwards

44+

45+testRun512 :: LSTM 40 512 -> S ('D1 512) -> S ('D1 40) -> ((S ('D1 512), S ('D1 40)), S ('D1 512), S ('D1 512))

46+testRun512 = runRecurrentForwards

47+

48+testRun512' :: LSTM 40 512 -> S ('D1 512) -> S ('D1 40) -> S ('D1 512) -> S ('D1 512) -> (Gradient (LSTM 40 512), S ('D1 512), S ('D1 40))

49+testRun512' = curry . runRecurrentBackwards

50+

51+uncurry4 :: (t -> t1 -> t2 -> t3 -> t4) -> (t, t1, t2, t3) -> t4

52+uncurry4 f (a,b,c,d) = f a b c d

53+

54+uncurry3 :: (t -> t1 -> t2 -> t3) -> (t, t1, t2) -> t3

55+uncurry3 f (a,b,c) = f a b c

56+

2157nfT2 :: (a, b) -> (a, b)

2258nfT2 (!a, !b) = (a, b)

2359

60+nfT3 :: (a, b, c) -> (b, c)

61+nfT3 (!_, !b, !c) = (b, c)

62+

2463

2564type R = Recurrent

2665type RecNet = RecurrentNetwork '[ R (LSTM 40 512), R (LSTM 512 40) ]

2766 '[ 'D1 40, 'D1 512, 'D1 40 ]

2867

68+type RecInput = RecurrentInputs '[ R (LSTM 40 512), R (LSTM 512 40) ]

69+

2970lp :: LearningParameters

3071lp = LearningParameters 0.1 0 0

cbits/gradient_descent.c → cbits/gradient_decent.crenamed+3−3

@@ -1,12 +1,12 @@

1−#include "gradient_descent.h"

1+#include "gradient_decent.h"

22

3−void descend_cpu(int len, double rate, double momentum, double regulariser,

3+void decend_cpu(int len, double rate, double momentum, double regulariser,

44 const double* weights,

55 const double* gradient,

66 const double* last,

77 double* outputWeights, double* outputMomentum) {

88

9− for (int i = 0; i < len; i++) {

9+ for (int i = 0; i <= len; i++) {

1010 outputMomentum[i] = momentum * last[i] - rate * gradient[i];

1111 outputWeights[i] = weights[i] + outputMomentum[i] - (rate * regulariser) * weights[i];

1212 }

cbits/gradient_descent.h → cbits/gradient_decent.hrenamed+1−1

@@ -1,7 +1,7 @@

11#include <stdio.h>

22#include <stdint.h>

33

4−void descend_cpu(int len, double rate, double momentum, double regulariser,

4+void decend_cpu(int len, double rate, double momentum, double regulariser,

55 const double* weights,

66 const double* gradient,

77 const double* last,

examples/grenade-examples.cabal+9−10

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

2626 , bytestring

2727 , cereal

2828 , either

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

29+ , optparse-applicative == 0.13.*

3030 , text == 1.2.*

3131 , mtl >= 2.2.1 && < 2.3

3232 , hmatrix

@@ -42,10 +42,10 @@ executable mnist

4242 , grenade

4343 , attoparsec

4444 , either

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

45+ , optparse-applicative == 0.13.*

4646 , text == 1.2.*

4747 , mtl >= 2.2.1 && < 2.3

48− , hmatrix >= 0.18 && < 0.21

48+ , hmatrix >= 0.18 && < 0.19

4949 , transformers

5050 , semigroups

5151 , singletons

@@ -61,10 +61,10 @@ executable gan-mnist

6161 , bytestring

6262 , cereal

6363 , either

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

64+ , optparse-applicative == 0.13.*

6565 , text == 1.2.*

6666 , mtl >= 2.2.1 && < 2.3

67− , hmatrix >= 0.18 && < 0.21

67+ , hmatrix >= 0.18 && < 0.19

6868 , transformers

6969 , semigroups

7070 , singletons

@@ -78,10 +78,10 @@ executable recurrent

7878 , grenade

7979 , attoparsec

8080 , either

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

81+ , optparse-applicative == 0.13.*

8282 , text == 1.2.*

8383 , mtl >= 2.2.1 && < 2.3

84− , hmatrix >= 0.18 && < 0.21

84+ , hmatrix >= 0.18 && < 0.19

8585 , transformers

8686 , semigroups

8787 , singletons

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

9696 , bytestring

9797 , cereal

9898 , either

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

99+ , optparse-applicative == 0.13.*

100100 , text == 1.2.*

101101 , mtl >= 2.2.1 && < 2.3

102− , hmatrix >= 0.18 && < 0.21

102+ , hmatrix >= 0.18 && < 0.19

103103 , transformers

104104 , semigroups

105105 , singletons

106− , singletons-base

107106 , vector

108107 , MonadRandom

109108 , containers

examples/mafiadeleted−62

@@ -1,62 +0,0 @@

1−#!/bin/sh -eu

2−

3−: ${MAFIA_HOME:=$HOME/.mafia}

4−: ${MAFIA_VERSIONS:=$MAFIA_HOME/versions}

5−

6−latest_version () {

7− git ls-remote https://github.com/haskell-mafia/mafia | grep refs/heads/master | cut -f 1

8−}

9−

10−build_version() {

11− MAFIA_VERSION="$1"

12− MAFIA_TEMP=$(mktemp -d 2>/dev/null || mktemp -d -t 'exec_mafia')

13− MAFIA_FILE=mafia-$MAFIA_VERSION

14− MAFIA_PATH=$MAFIA_VERSIONS/$MAFIA_FILE

15− mkdir -p $MAFIA_VERSIONS

16− echo "Building $MAFIA_FILE in $MAFIA_TEMP"

17− git clone https://github.com/haskell-mafia/mafia $MAFIA_TEMP

18− git --git-dir="$MAFIA_TEMP/.git" --work-tree="$MAFIA_TEMP" reset --hard $MAFIA_VERSION || {

19− echo "mafia version ($MAFIA_VERSION) could not be found." >&2

20− exit 1

21− }

22− (cd "$MAFIA_TEMP" && ./bin/bootstrap) || {

23− got=$?

24− echo "mafia version ($MAFIA_VERSION) could not be built." >&2

25− exit "$got"

26− }

27− chmod +x "$MAFIA_TEMP/.cabal-sandbox/bin/mafia"

28− # Ensure executable is on same file-system so final mv is atomic.

29− mv -f "$MAFIA_TEMP/.cabal-sandbox/bin/mafia" "$MAFIA_PATH.$$"

30− mv "$MAFIA_PATH.$$" "$MAFIA_PATH" || {

31− rm -f "$MAFIA_PATH.$$"

32− echo "INFO: mafia version ($MAFIA_VERSION) already exists not overiding," >&2

33− echo "INFO: this is expected if parallel builds of the same version of" >&2

34− echo "INFO: mafia occur, we are playing by first in, wins." >&2

35− exit 0

36− }

37−}

38−

39−enable_version() {

40− if [ $# -eq 0 ]; then

41− MAFIA_VERSION="$(latest_version)"

42− echo "INFO: No explicit mafia version requested installing latest ($MAFIA_VERSION)." >&2

43− else

44− MAFIA_VERSION="$1"

45− fi

46− [ -x "$MAFIA_HOME/versions/mafia-$MAFIA_VERSION" ] || build_version "$MAFIA_VERSION"

47− ln -sf "$MAFIA_HOME/versions/mafia-$MAFIA_VERSION" "$MAFIA_HOME/versions/mafia"

48−}

49−

50−exec_mafia () {

51− [ -x "$MAFIA_HOME/versions/mafia" ] || enable_version

52− "$MAFIA_HOME/versions/mafia" "$@"

53−}

54−

55−#

56−# The actual start of the script.....

57−#

58−

59−case "${1:-}" in

60−upgrade) shift; enable_version "$@" ;;

61−*) exec_mafia "$@"

62−esac

examples/main/feedforward.hs+1−3

@@ -1,5 +1,4 @@

11{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE CPP #-}

32{-# LANGUAGE DataKinds #-}

43{-# LANGUAGE ScopedTypeVariables #-}

54{-# LANGUAGE TypeOperators #-}

@@ -11,9 +10,8 @@ import Data.List ( foldl' )

1110

1211import qualified Data.ByteString as B

1312import Data.Serialize

14−#if ! MIN_VERSION_base(4,13,0)

1513import Data.Semigroup ( (<>) )

16−#endif

14+

1715import GHC.TypeLits

1816

1917import qualified Numeric.LinearAlgebra.Static as SA

examples/main/gan-mnist.hs+1−4

@@ -1,5 +1,4 @@

11{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE CPP #-}

32{-# LANGUAGE DataKinds #-}

43{-# LANGUAGE GADTs #-}

54{-# LANGUAGE ScopedTypeVariables #-}

@@ -12,7 +11,7 @@

1211-- of numbers similar to those in MNIST.

1312--

1413-- It demonstrates a different usage of the library. Within about 15

15−-- minutes it was producing examples like this:

14+-- minutes hour it was producing examples like this:

1615--

1716-- --.

1817-- .=-.--..#=###

@@ -44,9 +43,7 @@ import Control.Monad.Trans.Except

4443import qualified Data.Attoparsec.Text as A

4544import qualified Data.ByteString as B

4645import Data.List ( foldl' )

47−#if ! MIN_VERSION_base(4,13,0)

4846import Data.Semigroup ( (<>) )

49−#endif

5047import Data.Serialize

5148import qualified Data.Text as T

5249import qualified Data.Text.IO as T

examples/main/mnist.hs−4

@@ -1,5 +1,4 @@

11{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE CPP #-}

32{-# LANGUAGE DataKinds #-}

43{-# LANGUAGE ScopedTypeVariables #-}

54{-# LANGUAGE TypeOperators #-}

@@ -14,10 +13,7 @@ import Control.Monad.Trans.Except

1413

1514import qualified Data.Attoparsec.Text as A

1615import Data.List ( foldl' )

17−#if ! MIN_VERSION_base(4,13,0)

1816import Data.Semigroup ( (<>) )

19−#endif

20−

2117import qualified Data.Text as T

2218import qualified Data.Text.IO as T

2319import qualified Data.Vector.Storable as V

examples/main/recurrent.hs+10−10

@@ -9,12 +9,12 @@

99import Control.Monad ( foldM )

1010import Control.Monad.Random ( MonadRandom, getRandomR )

1111

12−#if MIN_VERSION_base(4,13,0)

12+#if __GLASGOW_HASKELL__ < 800

1313import Data.List ( unfoldr )

1414#else

1515import Data.List ( cycle, unfoldr )

16−import Data.Semigroup ( (<>) )

1716#endif

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

1818

1919import qualified Numeric.LinearAlgebra.Static as SA

2020

@@ -31,12 +31,12 @@ import Grenade.Recurrent

3131-- creation function know how to treat the layers.

3232type R = Recurrent

3333

34−type RecNet = RecurrentNetwork '[ R (LSTM 1 1)]

35− '[ 'D1 1, 'D1 1]

34+type RecNet = RecurrentNetwork '[ R (LSTM 1 4), R (LSTM 4 1)]

35+ '[ 'D1 1, 'D1 4, 'D1 1 ]

3636

37−type RecInput = RecurrentInputs '[ R (LSTM 1 1)]

37+type RecInput = RecurrentInputs '[ R (LSTM 1 4), R (LSTM 4 1)]

3838

39−randomNet :: MonadRandom m => m RecNet

39+randomNet :: MonadRandom m => m (RecNet, RecInput)

4040randomNet = randomRecurrent

4141

4242netTest :: MonadRandom m => RecNet -> RecInput -> LearningParameters -> Int -> m (RecNet, RecInput)

@@ -69,8 +69,8 @@ generateRecurrent n s i =

6969 unfoldr go (s, i)

7070 where

7171 go (x, y) =

72− do let (_, ns, o) = runRecurrent n x y

73− o' = heat o

72+ do let (ns, o) = runRecurrent n x y

73+ o' = heat o

7474 Just (o', (ns, fromIntegral o'))

7575

7676 heat :: S ('D1 1) -> Int

@@ -82,8 +82,8 @@ main = do

8282 FeedForwardOpts examples rate <- execParser (info (feedForward' <**> helper) idm)

8383 putStrLn "Training network..."

8484

85− net0 <- randomNet

86− (trained, bestInput) <- netTest net0 0 rate examples

85+ (net0, i0) <- randomNet

86+ (trained, bestInput) <- netTest net0 i0 rate examples

8787

8888 let results = generateRecurrent trained bestInput (c 1)

8989

examples/main/shakespeare.hs+23−26

@@ -1,5 +1,5 @@

11{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE CPP #-}

2+{-# LANGUAGE RecordWildCards #-}

33{-# LANGUAGE DataKinds #-}

44{-# LANGUAGE ScopedTypeVariables #-}

55{-# LANGUAGE TypeOperators #-}

@@ -13,16 +13,18 @@ import Control.Monad.Trans.Except

1313import Data.Char ( isUpper, toUpper, toLower )

1414import Data.List ( foldl' )

1515import Data.Maybe ( fromMaybe )

16−

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

1717

1818import qualified Data.Vector as V

1919import Data.Vector ( Vector )

2020

2121import qualified Data.Map as M

22+import Data.Proxy ( Proxy (..) )

2223

2324import qualified Data.ByteString as B

2425import Data.Serialize

2526

27+import Data.Singletons.Prelude

2628import GHC.TypeLits

2729

2830import Numeric.LinearAlgebra.Static ( konst )

@@ -34,16 +36,11 @@ import Grenade.Recurrent

3436import Grenade.Utils.OneHot

3537

3638import System.IO.Unsafe ( unsafeInterleaveIO )

37−import Data.Proxy

38−import Prelude.Singletons

3939

4040-- The defininition for our natural language recurrent network.

4141-- This network is able to learn and generate simple words in

4242-- about an hour.

4343--

44−-- Grab the input from

45−-- https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt

46−--

4744-- This is a first class recurrent net.

4845--

4946-- The F and R types are tagging types to ensure that the runner and

@@ -64,7 +61,7 @@ type Shakespeare = RecurrentNetwork '[ R (LSTM 40 80), R (LSTM 80 40), F (FullyC

6461-- The definition of the "sideways" input, which the network is fed recurrently.

6562type Shakespearian = RecurrentInputs '[ R (LSTM 40 80), R (LSTM 80 40), F (FullyConnected 40 40), F Logit]

6663

67−randomNet :: MonadRandom m => m Shakespeare

64+randomNet :: MonadRandom m => m (Shakespeare, Shakespearian)

6865randomNet = randomRecurrent

6966

7067-- | Load the data files and prepare a map of characters to a compressed int representation.

@@ -72,40 +69,40 @@ loadShakespeare :: FilePath -> ExceptT String IO (Vector Int, M.Map Char Int, Ve

7269loadShakespeare path = do

7370 contents <- lift $ readFile path

7471 let annotated = annotateCapitals contents

75− (m,cs) <- ExceptT . return $ hotMap (Proxy :: Proxy 40) annotated

72+ (m,cs) <- ExceptT . return . note "Couldn't fit data in hotMap" $ hotMap (Proxy :: Proxy 40) annotated

7673 hot <- ExceptT . return . note "Couldn't generate hot values" $ traverse (`M.lookup` m) annotated

7774 return (V.fromList hot, m, cs)

7875

7976trainSlice :: LearningParameters -> Shakespeare -> Shakespearian -> Vector Int -> Int -> Int -> (Shakespeare, Shakespearian)

80−trainSlice !lrate !net !recIns input offset size =

77+trainSlice !rate !net !recIns input offset size =

8178 let e = fmap (x . oneHot) . V.toList $ V.slice offset size input

8279 in case reverse e of

8380 (o : l : xs) ->

8481 let examples = reverse $ (l, Just o) : ((,Nothing) <$> xs)

85− in trainRecurrent lrate net recIns examples

82+ in trainRecurrent rate net recIns examples

8683 _ -> error "Not enough input"

8784 where

8885 x = fromMaybe (error "Hot variable didn't fit.")

8986

9087runShakespeare :: ShakespeareOpts -> ExceptT String IO ()

91−runShakespeare opts = do

92− (shakespeare, oneHotMap, oneHotDictionary) <- loadShakespeare $ trainingFile opts

88+runShakespeare ShakespeareOpts {..} = do

89+ (shakespeare, oneHotMap, oneHotDictionary) <- loadShakespeare trainingFile

9390 (net0, i0) <- lift $

94− case loadPath opts of

91+ case loadPath of

9592 Just loadFile -> netLoad loadFile

96− Nothing -> (,0) <$> randomNet

93+ Nothing -> randomNet

9794

9895 (trained, bestInput) <- lift $ foldM (\(!net, !io) size -> do

99− xs <- take (iterations opts `div` 10) <$> getRandomRs (0, length shakespeare - size - 1)

100− let (!trained, !bestInput) = foldl' (\(!n, !i) offset -> trainSlice (rate opts) n i shakespeare offset size) (net, io) xs

101− results <- take 1000 <$> generateParagraph trained bestInput (temperature opts) oneHotMap oneHotDictionary ( S1D $ konst 0)

96+ xs <- take (iterations `div` 10) <$> getRandomRs (0, length shakespeare - size - 1)

97+ let (!trained, !bestInput) = foldl' (\(!n, !i) offset -> trainSlice rate n i shakespeare offset size) (net, io) xs

98+ results <- take 1000 <$> generateParagraph trained bestInput temperature oneHotMap oneHotDictionary ( S1D $ konst 0)

10299 putStrLn ("TRAINING STEP WITH SIZE: " ++ show size)

103100 putStrLn (unAnnotateCapitals results)

104101 return (trained, bestInput)

105− ) (net0, i0) $ replicate 10 (sequenceSize opts)

102+ ) (net0, i0) $ replicate 10 sequenceSize

106103

107− case savePath opts of

108− Just saveFile -> lift . B.writeFile saveFile $ runPut (put trained >> put bestInput)

104+ case savePath of

105+ Just saveFile -> lift . B.writeFile saveFile $ runPut (put (trained, bestInput))

109106 Nothing -> return ()

110107

111108generateParagraph :: forall layers shapes n a. (Last shapes ~ 'D1 n, Head shapes ~ 'D1 n, KnownNat n, Ord a)

@@ -116,14 +113,14 @@ generateParagraph :: forall layers shapes n a. (Last shapes ~ 'D1 n, Head shapes

116113 -> Vector a

117114 -> S ('D1 n)

118115 -> IO [a]

119−generateParagraph n s temp hotmap hotdict =

116+generateParagraph n s temperature hotmap hotdict =

120117 go s

121118 where

122119 go x y =

123− do let (_, ns, o) = runRecurrent n x y

124− un <- sample temp hotdict o

125− Just re <- return $ makeHot hotmap un

126− rest <- unsafeInterleaveIO $ go ns re

120+ do let (ns, o) = runRecurrent n x y

121+ un <- sample temperature hotdict o

122+ Just re <- return $ makeHot hotmap un

123+ rest <- unsafeInterleaveIO $ go ns re

127124 return (un : rest)

128125

129126data ShakespeareOpts = ShakespeareOpts {

framework/mafiadeleted−62

@@ -1,62 +0,0 @@

1−#!/bin/sh -eu

2−

3−: ${MAFIA_HOME:=$HOME/.mafia}

4−: ${MAFIA_VERSIONS:=$MAFIA_HOME/versions}

5−

6−latest_version () {

7− git ls-remote https://github.com/haskell-mafia/mafia | grep refs/heads/master | cut -f 1

8−}

9−

10−build_version() {

11− MAFIA_VERSION="$1"

12− MAFIA_TEMP=$(mktemp -d 2>/dev/null || mktemp -d -t 'exec_mafia')

13− MAFIA_FILE=mafia-$MAFIA_VERSION

14− MAFIA_PATH=$MAFIA_VERSIONS/$MAFIA_FILE

15− mkdir -p $MAFIA_VERSIONS

16− echo "Building $MAFIA_FILE in $MAFIA_TEMP"

17− git clone https://github.com/haskell-mafia/mafia $MAFIA_TEMP

18− git --git-dir="$MAFIA_TEMP/.git" --work-tree="$MAFIA_TEMP" reset --hard $MAFIA_VERSION || {

19− echo "mafia version ($MAFIA_VERSION) could not be found." >&2

20− exit 1

21− }

22− (cd "$MAFIA_TEMP" && ./bin/bootstrap) || {

23− got=$?

24− echo "mafia version ($MAFIA_VERSION) could not be built." >&2

25− exit "$got"

26− }

27− chmod +x "$MAFIA_TEMP/.cabal-sandbox/bin/mafia"

28− # Ensure executable is on same file-system so final mv is atomic.

29− mv -f "$MAFIA_TEMP/.cabal-sandbox/bin/mafia" "$MAFIA_PATH.$$"

30− mv "$MAFIA_PATH.$$" "$MAFIA_PATH" || {

31− rm -f "$MAFIA_PATH.$$"

32− echo "INFO: mafia version ($MAFIA_VERSION) already exists not overiding," >&2

33− echo "INFO: this is expected if parallel builds of the same version of" >&2

34− echo "INFO: mafia occur, we are playing by first in, wins." >&2

35− exit 0

36− }

37−}

38−

39−enable_version() {

40− if [ $# -eq 0 ]; then

41− MAFIA_VERSION="$(latest_version)"

42− echo "INFO: No explicit mafia version requested installing latest ($MAFIA_VERSION)." >&2

43− else

44− MAFIA_VERSION="$1"

45− fi

46− [ -x "$MAFIA_HOME/versions/mafia-$MAFIA_VERSION" ] || build_version "$MAFIA_VERSION"

47− ln -sf "$MAFIA_HOME/versions/mafia-$MAFIA_VERSION" "$MAFIA_HOME/versions/mafia"

48−}

49−

50−exec_mafia () {

51− [ -x "$MAFIA_HOME/versions/mafia" ] || enable_version

52− "$MAFIA_HOME/versions/mafia" "$@"

53−}

54−

55−#

56−# The actual start of the script.....

57−#

58−

59−case "${1:-}" in

60−upgrade) shift; enable_version "$@" ;;

61−*) exec_mafia "$@"

62−esac

grenade.cabal+29−77

@@ -1,33 +1,21 @@

11name: grenade

2−version: 0.1.0

2+version: 0.0.1

33license: BSD2

44license-file: LICENSE

55author: Huw Campbell <huw.campbell@gmail.com>

66maintainer: Huw Campbell <huw.campbell@gmail.com>

8−synopsis: Practical Deep Learning in Haskell

9−category: AI, Machine Learning

10−cabal-version: >= 1.10

8+synopsis: grenade

9+category: System

10+cabal-version: >= 1.8

1111build-type: Simple

12−description:

13− Grenade is a composable, dependently typed, practical, and fast

14− recurrent neural network library for precise specifications and

15− complex deep neural networks in Haskell.

16− .

17− Grenade provides an API for composing layers of a neural network

18− into a sequence parallel graph in a type safe manner; running

19− networks with reverse automatic differentiation to calculate their

20− gradients; and applying gradient descent for learning.

21− .

22− Documentation and examples are available on github

23− <https://github.com/HuwCampbell/grenade>.

12+description: grenade.

2413

2514extra-source-files:

26− README.md

2715 cbits/im2col.h

2816 cbits/im2col.c

29− cbits/gradient_descent.h

30− cbits/gradient_descent.c

17+ cbits/gradient_decent.h

18+ cbits/gradient_decent.c

3119 cbits/pad.h

3220 cbits/pad.c

3321

@@ -38,17 +26,17 @@ source-repository head

3826library

3927 build-depends:

4028 base >= 4.8 && < 5

41− , bytestring >= 0.10.0

42− , containers >= 0.5 && < 0.7

29+ , bytestring == 0.10.*

30+ , containers >= 0.5 && < 0.6

4331 , cereal >= 0.5 && < 0.6

4432 , deepseq >= 1.4 && < 1.5

45− , hmatrix >= 0.18 && < 0.21

33+ , exceptions == 0.8.*

34+ , hmatrix == 0.18.*

4635 , MonadRandom >= 0.4 && < 0.6

47− , primitive >= 0.6 && < 0.8

48− -- Versions of singletons are *tightly* coupled with the

49− -- GHC version so its fine to drop version bounds.

50− , singletons

51− , singletons-base

36+ , mtl >= 2.2.1 && < 2.3

37+ , primitive >= 0.6 && < 0.7

38+ , text == 1.2.*

39+ , singletons >= 2.1 && < 2.3

5240 , vector >= 0.11 && < 0.13

5341

5442 ghc-options:

@@ -56,14 +44,9 @@ library

5644 hs-source-dirs:

5745 src

5846

59− default-language: Haskell2010

60−

6147 if impl(ghc < 8.0)

6248 ghc-options: -fno-warn-incomplete-patterns

63− cpp-options: -DType=*

6449

65− if impl(ghc >= 8.6)

66− default-extensions: NoStarIsType

6750

6851 exposed-modules:

6952 Grenade

@@ -89,7 +72,6 @@ library

8972 Grenade.Layers.Pooling

9073 Grenade.Layers.Relu

9174 Grenade.Layers.Reshape

92− Grenade.Layers.Sinusoid

9375 Grenade.Layers.Softmax

9476 Grenade.Layers.Tanh

9577 Grenade.Layers.Trivial

@@ -108,16 +90,15 @@ library

10890

10991 Grenade.Recurrent.Layers

11092 Grenade.Recurrent.Layers.BasicRecurrent

111− Grenade.Recurrent.Layers.ConcatRecurrent

11293 Grenade.Recurrent.Layers.LSTM

11394

11495 Grenade.Utils.OneHot

11596

11697 includes: cbits/im2col.h

117− cbits/gradient_descent.h

98+ cbits/gradient_decent.h

11899 cbits/pad.h

119100 c-sources: cbits/im2col.c

120− cbits/gradient_descent.c

101+ cbits/gradient_decent.c

121102 cbits/pad.c

122103

123104 cc-options: -std=c99 -O3 -msse4.2 -Wall -Werror -DCABAL=1

@@ -132,48 +113,23 @@ test-suite test

132113 hs-source-dirs:

133114 test

134115

135− default-language: Haskell2010

136−

137− other-modules: Test.Hedgehog.Compat

138− Test.Hedgehog.Hmatrix

139− Test.Hedgehog.TypeLits

140−

141− Test.Grenade.Network

142− Test.Grenade.Layers.Convolution

143− Test.Grenade.Layers.FullyConnected

144− Test.Grenade.Layers.Nonlinear

145− Test.Grenade.Layers.PadCrop

146− Test.Grenade.Layers.Pooling

147− Test.Grenade.Layers.Internal.Convolution

148− Test.Grenade.Layers.Internal.Pooling

149− Test.Grenade.Layers.Internal.Reference

150−

151− Test.Grenade.Recurrent.Layers.LSTM

152− Test.Grenade.Recurrent.Layers.LSTM.Reference

153−

154− if impl(ghc < 8.0)

155− ghc-options: -fno-warn-incomplete-patterns

156− cpp-options: -DType=*

157−

158− if impl(ghc >= 8.6)

159− default-extensions: NoStarIsType

160−

161116 build-depends:

162− base

117+ base >= 4.8 && < 5

163118 , grenade

164− , hedgehog >= 1.0 && < 1.1

119+ , ambiata-disorder-core

120+ , ambiata-disorder-jack

165121 , hmatrix

166122 , mtl

167123 , singletons

168124 , text == 1.2.*

169− , typelits-witnesses < 0.4

170− , transformers

125+ , typelits-witnesses

171126 , constraints

127+ , QuickCheck >= 2.7 && < 2.9

128+ , quickcheck-instances == 0.3.*

172129 , MonadRandom

173130 , random

174131 , ad

175132 , reflection

176− , vector

177133

178134

179135benchmark bench

@@ -186,12 +142,10 @@ benchmark bench

186142 hs-source-dirs:

187143 bench

188144

189− default-language: Haskell2010

190−

191145 build-depends:

192− base

193− , bytestring

194− , criterion >= 1.1 && < 1.6

146+ base >= 3 && < 5

147+ , bytestring == 0.10.*

148+ , criterion == 1.1.*

195149 , grenade

196150 , hmatrix

197151

@@ -205,11 +159,9 @@ benchmark bench-lstm

205159 hs-source-dirs:

206160 bench

207161

208− default-language: Haskell2010

209−

210162 build-depends:

211− base

212− , bytestring

213− , criterion

163+ base >= 3 && < 5

164+ , bytestring == 0.10.*

165+ , criterion == 1.1.*

214166 , grenade

215167 , hmatrix

lib/disorder.hsnew file+1

Submodule changed.

lib/pnew file+1

Submodule changed.

mafiamode 120000 → 100755+123−1

@@ -1 +0,0 @@

1−framework/mafia

@@ -0,0 +1,123 @@

1+#!/bin/sh -eu

2+

3+: ${MAFIA_HOME:=$HOME/.mafia}

4+

5+fetch_latest () {

6+ if [ -z ${MAFIA_TEST_MODE+x} ]; then

7+ TZ=$(date +"%T")

8+ curl --silent "https://raw.githubusercontent.com/ambiata/mafia/master/script/mafia?$TZ"

9+ else

10+ cat ../script/mafia

11+ fi

12+}

13+

14+latest_version () {

15+ git ls-remote https://github.com/ambiata/mafia | grep refs/heads/master | cut -f 1

16+}

17+

18+local_version () {

19+ awk '/^# Version: / { print $3; exit 0; }' $0

20+}

21+

22+run_upgrade () {

23+ MAFIA_TEMP=$(mktemp 2>/dev/null || mktemp -t 'upgrade_mafia')

24+

25+ clean_up () {

26+ rm -f "$MAFIA_TEMP"

27+ }

28+

29+ trap clean_up EXIT

30+

31+ MAFIA_CUR="$0"

32+

33+ if [ -L "$MAFIA_CUR" ]; then

34+ echo 'Refusing to overwrite a symlink; run `upgrade` from the canonical path.' >&2

35+ exit 1

36+ fi

37+

38+ echo "Checking for a new version of mafia ..."

39+ fetch_latest > $MAFIA_TEMP

40+

41+ LATEST_VERSION=$(latest_version)

42+ echo "# Version: $LATEST_VERSION" >> $MAFIA_TEMP

43+

44+ if ! cmp $MAFIA_CUR $MAFIA_TEMP >/dev/null 2>&1; then

45+ mv $MAFIA_TEMP $MAFIA_CUR

46+ chmod +x $MAFIA_CUR

47+ echo "New version found and upgraded. You can now commit it to your git repo."

48+ else

49+ echo "You have latest mafia."

50+ fi

51+}

52+

53+exec_mafia () {

54+ MAFIA_VERSION=$(local_version)

55+

56+ if [ "x$MAFIA_VERSION" = "x" ]; then

57+ # If we can't find the mafia version, then we need to upgrade the script.

58+ run_upgrade

59+ else

60+ MAFIA_BIN=$MAFIA_HOME/bin

61+ MAFIA_FILE=mafia-$MAFIA_VERSION

62+ MAFIA_PATH=$MAFIA_BIN/$MAFIA_FILE

63+

64+ [ -f "$MAFIA_PATH" ] || {

65+ # Create a temporary directory which will be deleted when the script

66+ # terminates. Unfortunately `mktemp` doesn't behave the same on

67+ # Linux and OS/X so we need to try two different approaches.

68+ MAFIA_TEMP=$(mktemp -d 2>/dev/null || mktemp -d -t 'exec_mafia')

69+

70+ # Create a temporary file in MAFIA_BIN so we can do an atomic copy/move dance.

71+ mkdir -p $MAFIA_BIN

72+

73+ clean_up () {

74+ rm -rf "$MAFIA_TEMP"

75+ }

76+

77+ trap clean_up EXIT

78+

79+ echo "Building $MAFIA_FILE in $MAFIA_TEMP"

80+

81+ ( cd "$MAFIA_TEMP"

82+

83+ git clone https://github.com/ambiata/mafia

84+ cd mafia

85+

86+ git reset --hard $MAFIA_VERSION

87+

88+ bin/bootstrap ) || exit $?

89+

90+ MAFIA_PATH_TEMP=$(mktemp --tmpdir=$MAFIA_BIN $MAFIA_FILE-XXXXXX 2>/dev/null || TMPDIR=$MAFIA_BIN mktemp -t $MAFIA_FILE)

91+

92+ clean_up_temp () {

93+ clean_up

94+ rm -f "$MAFIA_PATH_TEMP"

95+ }

96+ trap clean_up_temp EXIT

97+

98+ cp "$MAFIA_TEMP/mafia/.cabal-sandbox/bin/mafia" "$MAFIA_PATH_TEMP"

99+ chmod 755 "$MAFIA_PATH_TEMP"

100+ mv "$MAFIA_PATH_TEMP" "$MAFIA_PATH"

101+

102+ clean_up_temp

103+ }

104+

105+ exec $MAFIA_PATH "$@"

106+ fi

107+}

108+

109+#

110+# The actual start of the script.....

111+#

112+

113+if [ $# -gt 0 ]; then

114+ MODE="$1"

115+else

116+ MODE=""

117+fi

118+

119+case "$MODE" in

120+upgrade) shift; run_upgrade "$@" ;;

121+*) exec_mafia "$@"

122+esac

123+# Version: 360716306a06db842ec022a7b9f161d2208483f0

src/Grenade.hs+1−2

@@ -29,7 +29,6 @@ It provides an expressive type level API for the construction

2929of complex neural network architectures. Backing this API is and

3030implementation written using BLAS and LAPACK, mostly provided by

3131the hmatrix library.

32−

3332-}

3433

3534{- $example

@@ -46,7 +45,7 @@ is a simple example which runs a logistic regression.

4645> randomMyNet = randomNetwork

4746

4847The function `randomMyNet` witnesses the `CreatableNetwork`

49−constraint of the neural network, and in doing so, ensures the network

48+constraint of the neural network, that is it ensures the network

5049can be built, and hence, that the architecture is sound.

5150-}

5251

src/Grenade/Core/Layer.hs+4−25

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

43{-# LANGUAGE TypeOperators #-}

@@ -8,30 +7,14 @@

87{-# LANGUAGE RankNTypes #-}

98{-# LANGUAGE FlexibleInstances #-}

109{-|

11−Module : Grenade.Core.Layer

12−Description : Defines the Layer Classes

10+Module : Grenade.Core.Network

11+Description : Core definition a simple neural etwork

1312Copyright : (c) Huw Campbell, 2016-2017

1413License : BSD2

1514Stability : experimental

1615

1716This module defines what a Layer is in a Grenade

1817neural network.

19−

20−There are two classes of interest: `UpdateLayer` and `Layer`.

21−

22−`UpdateLayer` is required for all types which are used as a layer

23−in a network. Having no shape information, this class is agnostic

24−to the input and output data of the layer.

25−

26−An instance of `Layer` on the other hand is required for usage in

27−a neural network, but also specifies the shapes of data that the

28−network can transform. Multiple instance of `Layer` are permitted

29−for a single type, to transform different shapes. The `Reshape` layer

30−for example can act as a flattening layer, and its inverse, projecting

31−a 1D shape up to 2 or 3 dimensions.

32−

33−Instances of `Layer` should be as strict as possible, and not emit

34−runtime errors.

3518-}

3619module Grenade.Core.Layer (

3720 Layer (..)

@@ -42,10 +25,6 @@ import Control.Monad.Random ( MonadRandom )

4225

4326import Data.List ( foldl' )

4427

45−#if MIN_VERSION_base(4,9,0)

46−import Data.Kind (Type)

47−#endif

48−

4928import Grenade.Core.Shape

5029import Grenade.Core.LearningParameters

5130

@@ -55,7 +34,7 @@ import Grenade.Core.LearningParameters

5534class UpdateLayer x where

5635 -- | The type for the gradient for this layer.

5736 -- Unit if there isn't a gradient to pass back.

58− type Gradient x :: Type

37+ type Gradient x :: *

5938

6039 -- | Update a layer with its gradient and learning parameters

6140 runUpdate :: LearningParameters -> x -> Gradient x -> x

@@ -77,7 +56,7 @@ class UpdateLayer x => Layer x (i :: Shape) (o :: Shape) where

7756 -- | The Wengert tape for this layer. Includes all that is required

7857 -- to generate the back propagated gradients efficiently. As a

7958 -- default, `S i` is fine.

80− type Tape x i o :: Type

59+ type Tape x i o :: *

8160

8261 -- | Used in training and scoring. Take the input from the previous

8362 -- layer, and give the output from this layer.

src/Grenade/Core/LearningParameters.hs+1−8

@@ -1,13 +1,6 @@

1−{-|

2−Module : Grenade.Core.LearningParameters

3−Description : Stochastic gradient descent learning parameters

4−Copyright : (c) Huw Campbell, 2016-2017

5−License : BSD2

6−Stability : experimental

7−-}

81module Grenade.Core.LearningParameters (

92 -- | This module contains learning algorithm specific

10− -- code. Currently, this module should be considered

3+ -- code. Currently, this module should be consifered

114 -- unstable, due to issue #26.

125

136 LearningParameters (..)

src/Grenade/Core/Network.hs+10−16

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE BangPatterns #-}

43{-# LANGUAGE GADTs #-}

@@ -10,7 +9,7 @@

109{-# LANGUAGE FlexibleInstances #-}

1110{-|

1211Module : Grenade.Core.Network

13−Description : Core definition of a Neural Network

12+Description : Core definition a simple neural etwork

1413Copyright : (c) Huw Campbell, 2016-2017

1514License : BSD2

1615Stability : experimental

@@ -34,16 +33,12 @@ module Grenade.Core.Network (

3433import Control.Monad.Random ( MonadRandom )

3534

3635import Data.Singletons

36+import Data.Singletons.Prelude

3737import Data.Serialize

3838

39−#if MIN_VERSION_base(4,9,0)

40−import Data.Kind (Type)

41−#endif

42−

4339import Grenade.Core.Layer

4440import Grenade.Core.LearningParameters

4541import Grenade.Core.Shape

46−import Prelude.Singletons

4742

4843-- | Type of a network.

4944--

@@ -53,7 +48,7 @@ import Prelude.Singletons

5348--

5449-- Can be considered to be a heterogeneous list of layers which are able to

5550-- transform the data shapes of the network.

56−data Network :: [Type] -> [Shape] -> Type where

51+data Network :: [*] -> [Shape] -> * where

5752 NNil :: SingI i

5853 => Network '[] '[i]

5954

@@ -71,7 +66,7 @@ instance (Show x, Show (Network xs rs)) => Show (Network (x ': xs) (i ': rs)) wh

7166-- | Gradient of a network.

7267--

7368-- Parameterised on the layers of the network.

74−data Gradients :: [Type] -> Type where

69+data Gradients :: [*] -> * where

7570 GNil :: Gradients '[]

7671

7772 (:/>) :: UpdateLayer x

@@ -82,7 +77,7 @@ data Gradients :: [Type] -> Type where

8277-- | Wegnert Tape of a network.

8378--

8479-- Parameterised on the layers and shapes of the network.

85−data Tapes :: [Type] -> [Shape] -> Type where

80+data Tapes :: [*] -> [Shape] -> * where

8681 TNil :: SingI i

8782 => Tapes '[] '[i]

8883

@@ -96,6 +91,7 @@ data Tapes :: [Type] -> [Shape] -> Type where

9691--

9792-- This gives the output, and the Wengert tape required for back

9893-- propagation.

94+--

9995runNetwork :: forall layers shapes.

10096 Network layers shapes

10197 -> S (Head shapes)

@@ -123,6 +119,7 @@ runNetwork =

123119--

124120-- Gives the gradients for the layer, and the gradient across the

125121-- input (which may not be required).

122+--

126123runGradient :: forall layers shapes.

127124 Network layers shapes

128125 -> Tapes layers shapes

@@ -144,7 +141,7 @@ runGradient net tapes o =

144141 = (GNil, o)

145142

146143

147−-- | Apply one step of stochastic gradient descent across the network.

144+-- | Apply one step of stochastic gradient decent across the network.

148145applyUpdate :: LearningParameters

149146 -> Network layers shapes

150147 -> Gradients layers

@@ -157,7 +154,7 @@ applyUpdate _ NNil GNil

157154

158155-- | A network can easily be created by hand with (:~>), but an easy way to

159156-- initialise a random network is with the randomNetwork.

160−class CreatableNetwork (xs :: [Type]) (ss :: [Shape]) where

157+class CreatableNetwork (xs :: [*]) (ss :: [Shape]) where

161158 -- | Create a network with randomly initialised weights.

162159 --

163160 -- Calls to this function will not compile if the type of the neural

@@ -182,15 +179,12 @@ instance (SingI i, SingI o, Layer x i o, Serialize x, Serialize (Network xs (o '

182179

183180-- | Ultimate composition.

184181--

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

182+-- This allows a complete network to be treated as a layer in a bigger network.

186183instance CreatableNetwork sublayers subshapes => UpdateLayer (Network sublayers subshapes) where

187184 type Gradient (Network sublayers subshapes) = Gradients sublayers

188185 runUpdate = applyUpdate

189186 createRandom = randomNetwork

190187

191−-- | Ultimate composition.

192−--

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

194188instance (CreatableNetwork sublayers subshapes, i ~ (Head subshapes), o ~ (Last subshapes)) => Layer (Network sublayers subshapes) i o where

195189 type Tape (Network sublayers subshapes) i o = Tapes sublayers subshapes

196190 runForwards = runNetwork

src/Grenade/Core/Runner.hs+6−4

@@ -1,11 +1,14 @@

11{-# LANGUAGE DataKinds #-}

22{-# LANGUAGE FlexibleContexts #-}

33{-|

4−Module : Grenade.Core.Runner

5−Description : Functions to perform training and backpropagation

4+Module : Grenade.Core.Shape

5+Description : Core definition of the Shapes of data we understand

66Copyright : (c) Huw Campbell, 2016-2017

77License : BSD2

88Stability : experimental

9+

10+This module defines simple back propagation and training functions

11+for a network.

912-}

1013module Grenade.Core.Runner (

1114 train

@@ -13,12 +16,11 @@ module Grenade.Core.Runner (

1316 , runNet

1417 ) where

1518

19+import Data.Singletons.Prelude

1620

1721import Grenade.Core.LearningParameters

1822import Grenade.Core.Network

1923import Grenade.Core.Shape

20−import Data.Singletons

21−import Prelude.Singletons

2224

2325-- | Perform reverse automatic differentiation on the network

2426-- for the current input and expected output.

src/Grenade/Core/Shape.hs+24−59

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

43{-# LANGUAGE KindSignatures #-}

@@ -8,33 +7,36 @@

87{-# LANGUAGE FlexibleContexts #-}

98{-# LANGUAGE ScopedTypeVariables #-}

109{-# LANGUAGE RankNTypes #-}

11−{-# LANGUAGE UndecidableInstances #-}

1210{-|

1311Module : Grenade.Core.Shape

14−Description : Dependently typed shapes of data which are passed between layers of a network

12+Description : Core definition of the Shapes of data we understand

1513Copyright : (c) Huw Campbell, 2016-2017

1614License : BSD2

1715Stability : experimental

1816

19−

17+This module defines the core data types for the shapes of data that

18+are understood by Grenade.

2019-}

2120module Grenade.Core.Shape (

22− S (..)

23− , Shape (..)

24− , SShape (..)

21+ Shape (..)

22+ , S (..)

23+ , Sing (..)

24+

2525 , randomOfShape

2626 , fromStorable

2727 ) where

2828

2929import Control.DeepSeq (NFData (..))

3030import Control.Monad.Random ( MonadRandom, getRandom )

31−import Data.Kind (Type)

31+

3232import Data.Proxy

33−import Data.Serialize

3433import Data.Singletons

34+import Data.Singletons.TypeLits

3535import Data.Vector.Storable ( Vector )

3636import qualified Data.Vector.Storable as V

37+

3738import GHC.TypeLits

39+

3840import qualified Numeric.LinearAlgebra.Static as H

3941import Numeric.LinearAlgebra.Static

4042import qualified Numeric.LinearAlgebra as NLA

@@ -79,20 +81,10 @@ deriving instance Show (S n)

7981-- These could probably be derived with template haskell, but this seems

8082-- clear and makes adding the KnownNat constraints simple.

8183-- We can also keep our code TH free, which is great.

82−#if MIN_VERSION_singletons(2,6,0)

83−-- In singletons 2.6 Sing switched from a data family to a type family.

84−type instance Sing = SShape

85−

86−data 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)

90−#else

9184data instance Sing (n :: Shape) where

92− D1Sing :: Sing a -> Sing ('D1 a)

93− D2Sing :: Sing a -> Sing b -> Sing ('D2 a b)

94− D3Sing :: KnownNat (a * c) => Sing a -> Sing b -> Sing c -> Sing ('D3 a b c)

95−#endif

85+ D1Sing :: KnownNat a => Sing ('D1 a)

86+ D2Sing :: (KnownNat a, KnownNat b) => Sing ('D2 a b)

87+ D3Sing :: (KnownNat a, KnownNat b, KnownNat c, KnownNat (a * c)) => Sing ('D3 a b c)

9688

9789instance KnownNat a => SingI ('D1 a) where

9890 sing = D1Sing

@@ -148,28 +140,18 @@ randomOfShape :: forall x m. ( MonadRandom m, SingI x ) => m (S x)

148140randomOfShape = do

149141 seed :: Int <- getRandom

150142 return $ case (sing :: Sing x) of

151− D1Sing ->

152− S1D (randomVector seed Uniform * 2 - 1)

153−

154− D2Sing ->

155− S2D (uniformSample seed (-1) 1)

156−

157− D3Sing ->

158− S3D (uniformSample seed (-1) 1)

143+ D1Sing -> S1D (randomVector seed Uniform * 2 - 1)

144+ D2Sing -> S2D (uniformSample seed (-1) 1)

145+ D3Sing -> S3D (uniformSample seed (-1) 1)

159146

160147-- | Generate a shape from a Storable Vector.

161148--

162149-- Returns Nothing if the vector is of the wrong size.

163150fromStorable :: forall x. SingI x => Vector Double -> Maybe (S x)

164151fromStorable xs = case sing :: Sing x of

165− D1Sing ->

166− S1D <$> H.create xs

167−

168− D2Sing ->

169− S2D <$> mkL xs

170−

171− D3Sing ->

172− S3D <$> mkL xs

152+ D1Sing -> S1D <$> H.create xs

153+ D2Sing -> S2D <$> mkL xs

154+ D3Sing -> S3D <$> mkL xs

173155 where

174156 mkL :: forall rows columns. (KnownNat rows, KnownNat columns)

175157 => Vector Double -> Maybe (L rows columns)

@@ -180,18 +162,6 @@ fromStorable xs = case sing :: Sing x of

180162 then H.create $ NLA.reshape columns v

181163 else Nothing

182164

183−

184−instance SingI x => Serialize (S x) where

185− put i = (case i of

186− (S1D x) -> putListOf put . NLA.toList . H.extract $ x

187− (S2D x) -> putListOf put . NLA.toList . NLA.flatten . H.extract $ x

188− (S3D x) -> putListOf put . NLA.toList . NLA.flatten . H.extract $ x

189− ) :: PutM ()

190−

191− get = do

192− Just i <- fromStorable . V.fromList <$> getListOf get

193− return i

194−

195165-- Helper function for creating the number instances

196166n1 :: ( forall a. Floating a => a -> a ) -> S x -> S x

197167n1 f (S1D x) = S1D (f x)

@@ -205,13 +175,8 @@ n2 f (S2D x) (S2D y) = S2D (f x y)

205175n2 f (S3D x) (S3D y) = S3D (f x y)

206176

207177-- Helper function for creating the number instances

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

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

209179nk x = case (sing :: Sing x) of

210− D1Sing ->

211− S1D (konst x)

212−

213− D2Sing ->

214− S2D (konst x)

215−

216− D3Sing ->

217− S3D (konst x)

180+ D1Sing -> S1D (konst x)

181+ D2Sing -> S2D (konst x)

182+ D3Sing -> S3D (konst x)

src/Grenade/Layers/Concat.hs+2−14

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

43{-# LANGUAGE TypeOperators #-}

@@ -9,7 +8,6 @@

98{-# LANGUAGE FlexibleInstances #-}

109{-# LANGUAGE ScopedTypeVariables #-}

1110{-# LANGUAGE StandaloneDeriving #-}

12−{-# LANGUAGE UndecidableInstances #-}

1311{-|

1412Module : Grenade.Layers.Concat

1513Description : Concatenation layer

@@ -17,21 +15,15 @@ Copyright : (c) Huw Campbell, 2016-2017

1715License : BSD2

1816Stability : experimental

1917

20−This module provides the concatenation layer, which runs two chilld layers in parallel and combines their outputs.

18+This module provides the concatenation layer, whic used to run two separate layers in parallel and combine their outputs.

2119-}

2220module Grenade.Layers.Concat (

2321 Concat (..)

2422 ) where

2523

26−import Data.Serialize

27−

2824import Data.Singletons

2925import GHC.TypeLits

3026

31−#if MIN_VERSION_base(4,9,0)

32−import Data.Kind (Type)

33−#endif

34−

3527import Grenade.Core

3628

3729import Numeric.LinearAlgebra.Static ( row, (===), splitRows, unrow, (#), split, R )

@@ -49,7 +41,7 @@ import Numeric.LinearAlgebra.Static ( row, (===), splitRows, unrow, (#

4941--

5042-- 3D images become 3D images with more channels. The sizes must be the same, one can use Pad

5143-- and Crop layers to ensure this is the case.

52−data Concat :: Shape -> Type -> Shape -> Type -> Type where

44+data Concat :: Shape -> * -> Shape -> * -> * where

5345 Concat :: x -> y -> Concat m x n y

5446

5547instance (Show x, Show y) => Show (Concat m x n y) where

@@ -133,7 +125,3 @@ instance ( SingI i

133125 (x', xB :: S i) = runBackwards x xTape (S3D ox :: S ('D3 rows cols m))

134126 (y', yB :: S i) = runBackwards y yTape (S3D oy :: S ('D3 rows cols n))

135127 in ((x', y'), xB + yB)

136−

137−instance (Serialize a, Serialize b) => Serialize (Concat sa a sb b) where

138− put (Concat a b) = put a *> put b

139− get = Concat <$> get <*> get

src/Grenade/Layers/Convolution.hs+7−18

@@ -1,23 +1,12 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE ScopedTypeVariables #-}

3+{-# LANGUAGE RecordWildCards #-}

44{-# LANGUAGE GADTs #-}

55{-# LANGUAGE TypeOperators #-}

66{-# LANGUAGE TypeFamilies #-}

77{-# LANGUAGE MultiParamTypeClasses #-}

88{-# LANGUAGE FlexibleInstances #-}

99{-# LANGUAGE FlexibleContexts #-}

10−{-# LANGUAGE UndecidableInstances #-}

11−{-|

12−Module : Grenade.Layers.Convolution

13−Description : Convolution layer

14−Copyright : (c) Huw Campbell, 2016-2017

15−License : BSD2

16−Stability : experimental

17−

18−This module provides the Convolution layer, which is critical in many computer vision tasks.

19−

20−-}

2110module Grenade.Layers.Convolution (

2211 Convolution (..)

2312 , Convolution' (..)

@@ -28,9 +17,9 @@ import Control.Monad.Random hiding ( fromList )

2817import Data.Maybe

2918import Data.Proxy

3019import Data.Serialize

20+import Data.Singletons.TypeLits

3121

3222import GHC.TypeLits

33−import Data.Kind (Type)

3423

3524import Numeric.LinearAlgebra hiding ( uniformSample, konst )

3625import qualified Numeric.LinearAlgebra as LA

@@ -57,7 +46,7 @@ data Convolution :: Nat -- Number of channels, for the first layer this could be

5746 -> Nat -- The number of column in the kernel filter

5847 -> Nat -- The row stride of the convolution filter

5948 -> Nat -- The columns stride of the convolution filter

60− -> Type where

49+ -> * where

6150 Convolution :: ( KnownNat channels

6251 , KnownNat filters

6352 , KnownNat kernelRows

@@ -76,7 +65,7 @@ data Convolution' :: Nat -- Number of channels, for the first layer this could b

7665 -> Nat -- The number of column in the kernel filter

7766 -> Nat -- The row stride of the convolution filter

7867 -> Nat -- The columns stride of the convolution filter

79− -> Type where

68+ -> * where

8069 Convolution' :: ( KnownNat channels

8170 , KnownNat filters

8271 , KnownNat kernelRows

@@ -130,9 +119,9 @@ instance ( KnownNat channels

130119 , KnownNat strideColumns

131120 , KnownNat (kernelRows * kernelColumns * channels)

132121 ) => UpdateLayer (Convolution channels filters kernelRows kernelColumns strideRows strideColumns) where

133− type Gradient (Convolution channels filters kernelRows kernelColumns strideRows strideColumns) = (Convolution' channels filters kernelRows kernelColumns strideRows strideColumns)

134− runUpdate lp (Convolution oldKernel oldMomentum) (Convolution' kernelGradient) =

135− let (newKernel, newMomentum) = descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldKernel kernelGradient oldMomentum

122+ type Gradient (Convolution channels filters kernelRows kernelCols strideRows strideCols) = (Convolution' channels filters kernelRows kernelCols strideRows strideCols)

123+ runUpdate LearningParameters {..} (Convolution oldKernel oldMomentum) (Convolution' kernelGradient) =

124+ let (newKernel, newMomentum) = decendMatrix learningRate learningMomentum learningRegulariser oldKernel kernelGradient oldMomentum

136125 in Convolution newKernel newMomentum

137126

138127 createRandom = randomConvolution

src/Grenade/Layers/Crop.hs+4−19

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE ScopedTypeVariables #-}

43{-# LANGUAGE GADTs #-}

@@ -6,24 +5,14 @@

65{-# LANGUAGE TypeFamilies #-}

76{-# LANGUAGE FlexibleContexts #-}

87{-# LANGUAGE MultiParamTypeClasses #-}

9−{-# LANGUAGE UndecidableInstances #-}

10−{-|

11−Module : Grenade.Layers.Crop

12−Description : Cropping layer

13−Copyright : (c) Huw Campbell, 2016-2017

14−License : BSD2

15−Stability : experimental

16−-}

178module Grenade.Layers.Crop (

189 Crop (..)

1910 ) where

2011

2112import Data.Maybe

2213import Data.Proxy

23−import Data.Serialize

24−

14+import Data.Singletons.TypeLits

2515import GHC.TypeLits

26−import Data.Kind (Type)

2716

2817import Grenade.Core

2918import Grenade.Layers.Internal.Pad

@@ -35,7 +24,7 @@ import Numeric.LinearAlgebra.Static (extract, create)

3524data Crop :: Nat

3625 -> Nat

3726 -> Nat

38− -> Nat -> Type where

27+ -> Nat -> * where

3928 Crop :: Crop cropLeft cropTop cropRight cropBottom

4029

4130instance Show (Crop cropLeft cropTop cropRight cropBottom) where

@@ -46,10 +35,6 @@ instance UpdateLayer (Crop l t r b) where

4635 runUpdate _ x _ = x

4736 createRandom = return Crop

4837

49−instance Serialize (Crop l t r b) where

50− put _ = return ()

51− get = return Crop

52−

5338-- | A two dimentional image can be cropped.

5439instance ( KnownNat cropLeft

5540 , KnownNat cropTop

@@ -59,8 +44,8 @@ instance ( KnownNat cropLeft

5944 , KnownNat inputColumns

6045 , KnownNat outputRows

6146 , KnownNat outputColumns

62− , (outputRows + cropTop + cropBottom) ~ inputRows

63− , (outputColumns + cropLeft + cropRight) ~ inputColumns

47+ , (inputRows - cropTop - cropBottom) ~ outputRows

48+ , (inputColumns - cropLeft - cropRight) ~ outputColumns

6449 ) => Layer (Crop cropLeft cropTop cropRight cropBottom) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) where

6550 type Tape (Crop cropLeft cropTop cropRight cropBottom) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) = ()

6651 runForwards Crop (S2D input) =

src/Grenade/Layers/Deconvolution.hs+35−45

@@ -1,27 +1,12 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE ScopedTypeVariables #-}

3+{-# LANGUAGE RecordWildCards #-}

44{-# LANGUAGE GADTs #-}

55{-# LANGUAGE TypeOperators #-}

66{-# LANGUAGE TypeFamilies #-}

77{-# LANGUAGE MultiParamTypeClasses #-}

88{-# LANGUAGE FlexibleInstances #-}

99{-# LANGUAGE FlexibleContexts #-}

10−{-# LANGUAGE UndecidableInstances #-}

11−{-|

12−Module : Grenade.Layers.Deconvolution

13−Description : Deconvolution layer

14−Copyright : (c) Huw Campbell, 2016-2017

15−License : BSD2

16−Stability : experimental

17−

18−A deconvolution layer is in many ways a convolution layer in reverse.

19−It learns a kernel to apply to each pixel location, spreading it out

20−into a larger layer.

21−

22−This layer is important for image generation tasks, such as GANs on

23−images.

24−-}

2510module Grenade.Layers.Deconvolution (

2611 Deconvolution (..)

2712 , Deconvolution' (..)

@@ -32,9 +17,9 @@ import Control.Monad.Random hiding ( fromList )

3217import Data.Maybe

3318import Data.Proxy

3419import Data.Serialize

20+import Data.Singletons.TypeLits

3521

3622import GHC.TypeLits

37−import Data.Kind (Type)

3823

3924import Numeric.LinearAlgebra hiding ( uniformSample, konst )

4025import qualified Numeric.LinearAlgebra as LA

@@ -45,37 +30,42 @@ import Grenade.Layers.Internal.Convolution

4530import Grenade.Layers.Internal.Update

4631

4732-- | A Deconvolution layer for a neural network.

48−-- This uses the im2col Convolution trick popularised by Caffe.

33+-- This uses the im2col Deconvolution trick popularised by Caffe, which essentially turns the

34+-- many, many, many, many loop Deconvolution into a single matrix multiplication.

35+--

36+-- The Deconvolution layer takes all of the kernels for the Deconvolution, which are flattened

37+-- and then put into columns in the matrix.

4938--

50−-- The Deconvolution layer is a way of spreading out a single response

51−-- into a larger image, and is useful in generating images.

39+-- The kernel size dictates which input and output sizes will "fit". Fitting the equation:

40+-- `out = (in - kernel) / stride + 1` for both dimensions.

5241--

42+-- One probably shouldn't build their own layer, but rather use the randomDeconvolution function.

5343data Deconvolution :: Nat -- Number of channels, for the first layer this could be RGB for instance.

54− -> Nat -- Number of filters, this is the number of channels output by the layer.

55− -> Nat -- The number of rows in the kernel filter

56− -> Nat -- The number of column in the kernel filter

57− -> Nat -- The row stride of the Deconvolution filter

58− -> Nat -- The columns stride of the Deconvolution filter

59− -> Type where

44+ -> Nat -- Number of filters, this is the number of channels output by the layer.

45+ -> Nat -- The number of rows in the kernel filter

46+ -> Nat -- The number of column in the kernel filter

47+ -> Nat -- The row stride of the Deconvolution filter

48+ -> Nat -- The columns stride of the Deconvolution filter

49+ -> * where

6050 Deconvolution :: ( KnownNat channels

61− , KnownNat filters

62− , KnownNat kernelRows

63− , KnownNat kernelColumns

64− , KnownNat strideRows

65− , KnownNat strideColumns

66− , KnownNat kernelFlattened

67− , kernelFlattened ~ (kernelRows * kernelColumns * filters))

68− => !(L kernelFlattened channels) -- The kernel filter weights

69− -> !(L kernelFlattened channels) -- The last kernel update (or momentum)

70− -> Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns

51+ , KnownNat filters

52+ , KnownNat kernelRows

53+ , KnownNat kernelColumns

54+ , KnownNat strideRows

55+ , KnownNat strideColumns

56+ , KnownNat kernelFlattened

57+ , kernelFlattened ~ (kernelRows * kernelColumns * filters))

58+ => !(L kernelFlattened channels) -- The kernel filter weights

59+ -> !(L kernelFlattened channels) -- The last kernel update (or momentum)

60+ -> Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns

7161

7262data Deconvolution' :: Nat -- Number of channels, for the first layer this could be RGB for instance.

73− -> Nat -- Number of filters, this is the number of channels output by the layer.

74− -> Nat -- The number of rows in the kernel filter

75− -> Nat -- The number of column in the kernel filter

76− -> Nat -- The row stride of the Deconvolution filter

77− -> Nat -- The columns stride of the Deconvolution filter

78− -> Type where

63+ -> Nat -- Number of filters, this is the number of channels output by the layer.

64+ -> Nat -- The number of rows in the kernel filter

65+ -> Nat -- The number of column in the kernel filter

66+ -> Nat -- The row stride of the Deconvolution filter

67+ -> Nat -- The columns stride of the Deconvolution filter

68+ -> * where

7969 Deconvolution' :: ( KnownNat channels

8070 , KnownNat filters

8171 , KnownNat kernelRows

@@ -129,9 +119,9 @@ instance ( KnownNat channels

129119 , KnownNat strideColumns

130120 , KnownNat (kernelRows * kernelColumns * filters)

131121 ) => UpdateLayer (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) where

132− type Gradient (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) = (Deconvolution' channels filters kernelRows kernelColumns strideRows strideColumns)

133− runUpdate lp (Deconvolution oldKernel oldMomentum) (Deconvolution' kernelGradient) =

134− let (newKernel, newMomentum) = descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldKernel kernelGradient oldMomentum

122+ type Gradient (Deconvolution channels filters kernelRows kernelCols strideRows strideCols) = (Deconvolution' channels filters kernelRows kernelCols strideRows strideCols)

123+ runUpdate LearningParameters {..} (Deconvolution oldKernel oldMomentum) (Deconvolution' kernelGradient) =

124+ let (newKernel, newMomentum) = decendMatrix learningRate learningMomentum learningRegulariser oldKernel kernelGradient oldMomentum

135125 in Deconvolution newKernel newMomentum

136126

137127 createRandom = randomDeconvolution

src/Grenade/Layers/Elu.hs+3−10

@@ -2,13 +2,6 @@

22{-# LANGUAGE TypeOperators #-}

33{-# LANGUAGE TypeFamilies #-}

44{-# LANGUAGE MultiParamTypeClasses #-}

5−{-|

6−Module : Grenade.Layers.Logit

7−Description : Exponential linear unit layer

8−Copyright : (c) Huw Campbell, 2016-2017

9−License : BSD2

10−Stability : experimental

11−-}

125module Grenade.Layers.Elu (

136 Elu (..)

147 ) where

@@ -36,12 +29,12 @@ instance Serialize Elu where

3629 get = return Elu

3730

3831instance ( KnownNat i) => Layer Elu ('D1 i) ('D1 i) where

39− type Tape Elu ('D1 i) ('D1 i) = LAS.R i

32+ type Tape Elu ('D1 i) ('D1 i) = S ('D1 i)

4033

41− runForwards _ (S1D y) = (y, S1D (elu y))

34+ runForwards _ (S1D y) = (S1D y, S1D (elu y))

4235 where

4336 elu = LAS.dvmap (\a -> if a <= 0 then exp a - 1 else a)

44− runBackwards _ y (S1D dEdy) = ((), S1D (elu' y * dEdy))

37+ runBackwards _ (S1D y) (S1D dEdy) = ((), S1D (elu' y * dEdy))

4538 where

4639 elu' = LAS.dvmap (\a -> if a <= 0 then exp a else 1)

4740

src/Grenade/Layers/FullyConnected.hs+8−7

@@ -1,4 +1,5 @@

11{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE RecordWildCards #-}

23{-# LANGUAGE TypeOperators #-}

34{-# LANGUAGE TypeFamilies #-}

45{-# LANGUAGE MultiParamTypeClasses #-}

@@ -13,6 +14,7 @@ import Control.Monad.Random hiding (fromList)

1314

1415import Data.Proxy

1516import Data.Serialize

17+import Data.Singletons.TypeLits

1618

1719import qualified Numeric.LinearAlgebra as LA

1820import Numeric.LinearAlgebra.Static

@@ -20,7 +22,6 @@ import Numeric.LinearAlgebra.Static

2022import Grenade.Core

2123

2224import Grenade.Layers.Internal.Update

23−import GHC.TypeLits

2425

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

2627data FullyConnected i o = FullyConnected

@@ -37,20 +38,20 @@ instance Show (FullyConnected i o) where

3738instance (KnownNat i, KnownNat o) => UpdateLayer (FullyConnected i o) where

3839 type Gradient (FullyConnected i o) = (FullyConnected' i o)

3940

40− runUpdate lp (FullyConnected (FullyConnected' oldBias oldActivations) (FullyConnected' oldBiasMomentum oldMomentum)) (FullyConnected' biasGradient activationGradient) =

41− let (newBias, newBiasMomentum) = descendVector (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldBias biasGradient oldBiasMomentum

42− (newActivations, newMomentum) = descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldActivations activationGradient oldMomentum

41+ runUpdate LearningParameters {..} (FullyConnected (FullyConnected' oldBias oldActivations) (FullyConnected' oldBiasMomentum oldMomentum)) (FullyConnected' biasGradient activationGradient) =

42+ let (newBias, newBiasMomentum) = decendVector learningRate learningMomentum learningRegulariser oldBias biasGradient oldBiasMomentum

43+ (newActivations, newMomentum) = decendMatrix learningRate learningMomentum learningRegulariser oldActivations activationGradient oldMomentum

4344 in FullyConnected (FullyConnected' newBias newActivations) (FullyConnected' newBiasMomentum newMomentum)

4445

4546 createRandom = randomFullyConnected

4647

4748instance (KnownNat i, KnownNat o) => Layer (FullyConnected i o) ('D1 i) ('D1 o) where

48− type Tape (FullyConnected i o) ('D1 i) ('D1 o) = R i

49+ type Tape (FullyConnected i o) ('D1 i) ('D1 o) = S ('D1 i)

4950 -- Do a matrix vector multiplication and return the result.

50− runForwards (FullyConnected (FullyConnected' wB wN) _) (S1D v) = (v, S1D (wB + wN #> v))

51+ runForwards (FullyConnected (FullyConnected' wB wN) _) (S1D v) = (S1D v, S1D (wB + wN #> v))

5152

5253 -- Run a backpropogation step for a full connected layer.

53− runBackwards (FullyConnected (FullyConnected' _ wN) _) x (S1D dEdy) =

54+ runBackwards (FullyConnected (FullyConnected' _ wN) _) (S1D x) (S1D dEdy) =

5455 let wB' = dEdy

5556 mm' = dEdy `outer` x

5657 -- calcluate derivatives for next step

src/Grenade/Layers/Internal/Update.hs+12−12

@@ -1,7 +1,7 @@

11{-# LANGUAGE ForeignFunctionInterface #-}

22module Grenade.Layers.Internal.Update (

3− descendMatrix

4− , descendVector

3+ decendMatrix

4+ , decendVector

55 ) where

66

77import Data.Maybe ( fromJust )

@@ -17,8 +17,8 @@ import qualified Numeric.LinearAlgebra.Devel as U

1717

1818import System.IO.Unsafe ( unsafePerformIO )

1919

20−descendMatrix :: (KnownNat rows, KnownNat columns) => Double -> Double -> Double -> L rows columns -> L rows columns -> L rows columns -> (L rows columns, L rows columns)

21−descendMatrix rate momentum regulariser weights gradient lastUpdate =

20+decendMatrix :: (KnownNat rows, KnownNat columns) => Double -> Double -> Double -> L rows columns -> L rows columns -> L rows columns -> (L rows columns, L rows columns)

21+decendMatrix rate momentum regulariser weights gradient lastUpdate =

2222 let (rows, cols) = size weights

2323 len = rows * cols

2424 -- Most gradients come in in ColumnMajor,

@@ -29,7 +29,7 @@ descendMatrix rate momentum regulariser weights gradient lastUpdate =

2929 weights' = flatten . tr . extract $ weights

3030 gradient' = flatten . tr . extract $ gradient

3131 lastUpdate' = flatten . tr . extract $ lastUpdate

32− (vw, vm) = descendUnsafe len rate momentum regulariser weights' gradient' lastUpdate'

32+ (vw, vm) = decendUnsafe len rate momentum regulariser weights' gradient' lastUpdate'

3333

3434 -- Note that it's ColumnMajor, as we did a transpose before

3535 -- using the internal vectors.

@@ -37,17 +37,17 @@ descendMatrix rate momentum regulariser weights gradient lastUpdate =

3737 mm = U.matrixFromVector U.ColumnMajor rows cols vm

3838 in (fromJust . create $ mw, fromJust . create $ mm)

3939

40−descendVector :: (KnownNat r) => Double -> Double -> Double -> R r -> R r -> R r -> (R r, R r)

41−descendVector rate momentum regulariser weights gradient lastUpdate =

40+decendVector :: (KnownNat r) => Double -> Double -> Double -> R r -> R r -> R r -> (R r, R r)

41+decendVector rate momentum regulariser weights gradient lastUpdate =

4242 let len = size weights

4343 weights' = extract weights

4444 gradient' = extract gradient

4545 lastUpdate' = extract lastUpdate

46− (vw, vm) = descendUnsafe len rate momentum regulariser weights' gradient' lastUpdate'

46+ (vw, vm) = decendUnsafe len rate momentum regulariser weights' gradient' lastUpdate'

4747 in (fromJust $ create vw, fromJust $ create vm)

4848

49−descendUnsafe :: Int -> Double -> Double -> Double -> Vector Double -> Vector Double -> Vector Double -> (Vector Double, Vector Double)

50−descendUnsafe len rate momentum regulariser weights gradient lastUpdate =

49+decendUnsafe :: Int -> Double -> Double -> Double -> Vector Double -> Vector Double -> Vector Double -> (Vector Double, Vector Double)

50+decendUnsafe len rate momentum regulariser weights gradient lastUpdate =

5151 unsafePerformIO $ do

5252 outWPtr <- mallocForeignPtrArray len

5353 outMPtr <- mallocForeignPtrArray len

@@ -60,11 +60,11 @@ descendUnsafe len rate momentum regulariser weights gradient lastUpdate =

6060 withForeignPtr lPtr $ \lPtr' ->

6161 withForeignPtr outWPtr $ \outWPtr' ->

6262 withForeignPtr outMPtr $ \outMPtr' ->

63− descend_cpu len rate momentum regulariser wPtr' gPtr' lPtr' outWPtr' outMPtr'

63+ decend_cpu len rate momentum regulariser wPtr' gPtr' lPtr' outWPtr' outMPtr'

6464

6565 return (U.unsafeFromForeignPtr0 outWPtr len, U.unsafeFromForeignPtr0 outMPtr len)

6666

6767foreign import ccall unsafe

68− descend_cpu

68+ decend_cpu

6969 :: Int -> Double -> Double -> Double -> Ptr Double -> Ptr Double -> Ptr Double -> Ptr Double -> Ptr Double -> IO ()

7070

src/Grenade/Layers/Logit.hs+9−23

@@ -3,13 +3,6 @@

33{-# LANGUAGE TypeFamilies #-}

44{-# LANGUAGE FlexibleInstances #-}

55{-# LANGUAGE MultiParamTypeClasses #-}

6−{-|

7−Module : Grenade.Layers.Logit

8−Description : Sigmoid nonlinear layer

9−Copyright : (c) Huw Campbell, 2016-2017

10−License : BSD2

11−Stability : experimental

12−-}

136module Grenade.Layers.Logit (

147 Logit (..)

158 ) where

@@ -21,7 +14,6 @@ import Data.Singletons

2114import Grenade.Core

2215

2316-- | A Logit layer.

24−--

2517-- A layer which can act between any shape of the same dimension, perfoming an sigmoid function.

2618-- This layer should be used as the output layer of a network for logistic regression (classification)

2719-- problems.

@@ -34,24 +26,18 @@ instance UpdateLayer Logit where

3426 createRandom = return Logit

3527

3628instance (a ~ b, SingI a) => Layer Logit a b where

37− -- Wengert tape optimisation:

38− --

39− -- Derivative of the sigmoid function is

40− -- d σ(x) / dx = σ(x) • (1 - σ(x))

41− -- but we have already calculated σ(x) in

42− -- the forward pass, so just store that

43− -- and use it in the backwards pass.

4429 type Tape Logit a b = S a

45− runForwards _ a =

46− let l = sigmoid a

47− in (l, l)

48− runBackwards _ l g =

49− let sigmoid' = l * (1 - l)

50− in ((), sigmoid' * g)

30+ runForwards _ a = (a, logistic a)

31+ runBackwards _ a g = ((), logistic' a * g)

5132

5233instance Serialize Logit where

5334 put _ = return ()

5435 get = return Logit

5536

56−sigmoid :: Floating a => a -> a

57−sigmoid x = 1 / (1 + exp (-x))

37+logistic :: Floating a => a -> a

38+logistic x = 1 / (1 + exp (-x))

39+

40+logistic' :: Floating a => a -> a

41+logistic' x = logix * (1 - logix)

42+ where

43+ logix = logistic x

src/Grenade/Layers/Merge.hs+2−13

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

43{-# LANGUAGE TypeOperators #-}

@@ -11,7 +10,7 @@

1110{-# LANGUAGE StandaloneDeriving #-}

1211{-|

1312Module : Grenade.Core.Network

14−Description : Merging layer for parallel network composition

13+Description : Core definition a simple neural etwork

1514Copyright : (c) Huw Campbell, 2016-2017

1615License : BSD2

1716Stability : experimental

@@ -20,21 +19,15 @@ module Grenade.Layers.Merge (

2019 Merge (..)

2120 ) where

2221

23−import Data.Serialize

24−

2522import Data.Singletons

2623

27−#if MIN_VERSION_base(4,9,0)

28−import Data.Kind (Type)

29−#endif

30−

3124import Grenade.Core

3225

3326-- | A Merging layer.

3427--

3528-- Similar to Concat layer, except sums the activations instead of creating a larger

3629-- shape.

37−data Merge :: Type -> Type -> Type where

30+data Merge :: * -> * -> * where

3831 Merge :: x -> y -> Merge x y

3932

4033instance (Show x, Show y) => Show (Merge x y) where

@@ -60,7 +53,3 @@ instance (SingI i, SingI o, Layer x i o, Layer y i o) => Layer (Merge x y) i o w

6053 let (x', xB) = runBackwards x xTape o

6154 (y', yB) = runBackwards y yTape o

6255 in ((x', y'), xB + yB)

63−

64−instance (Serialize a, Serialize b) => Serialize (Merge a b) where

65− put (Merge a b) = put a *> put b

66− get = Merge <$> get <*> get

src/Grenade/Layers/Pad.hs+4−16

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE ScopedTypeVariables #-}

43{-# LANGUAGE GADTs #-}

@@ -6,14 +5,6 @@

65{-# LANGUAGE TypeFamilies #-}

76{-# LANGUAGE MultiParamTypeClasses #-}

87{-# LANGUAGE FlexibleContexts #-}

9−{-# LANGUAGE UndecidableInstances #-}

10−{-|

11−Module : Grenade.Core.Pad

12−Description : Padding layer for 2D and 3D images

13−Copyright : (c) Huw Campbell, 2016-2017

14−License : BSD2

15−Stability : experimental

16−-}

178module Grenade.Layers.Pad (

189 Pad (..)

1910 ) where

@@ -21,9 +12,8 @@ module Grenade.Layers.Pad (

2112import Data.Maybe

2213import Data.Proxy

2314import Data.Serialize

24−

15+import Data.Singletons.TypeLits

2516import GHC.TypeLits

26−import Data.Kind (Type)

2717

2818import Grenade.Core

2919import Grenade.Layers.Internal.Pad

@@ -32,12 +22,10 @@ import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

3222import Numeric.LinearAlgebra.Static (extract, create)

3323

3424-- | A padding layer for a neural network.

35−--

36−-- Pads on the X and Y dimension of an image.

3725data Pad :: Nat

3826 -> Nat

3927 -> Nat

40− -> Nat -> Type where

28+ -> Nat -> * where

4129 Pad :: Pad padLeft padTop padRight padBottom

4230

4331instance Show (Pad padLeft padTop padRight padBottom) where

@@ -52,7 +40,7 @@ instance Serialize (Pad l t r b) where

5240 put _ = return ()

5341 get = return Pad

5442

55−-- | A two dimentional image can be padded.

43+-- | A two dimentional image can be padped.

5644instance ( KnownNat padLeft

5745 , KnownNat padTop

5846 , KnownNat padRight

@@ -82,7 +70,7 @@ instance ( KnownNat padLeft

8270 vs = subMatrix (padt, padl) (nrows, ncols) m

8371 in ((), S2D . fromJust . create $ vs)

8472

85−-- | A two dimentional image can be padded.

73+-- | A two dimentional image can be padped.

8674instance ( KnownNat padLeft

8775 , KnownNat padTop

8876 , KnownNat padRight

src/Grenade/Layers/Pooling.hs+3−14

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE ScopedTypeVariables #-}

43{-# LANGUAGE StandaloneDeriving #-}

@@ -7,14 +6,6 @@

76{-# LANGUAGE TypeFamilies #-}

87{-# LANGUAGE MultiParamTypeClasses #-}

98{-# LANGUAGE FlexibleContexts #-}

10−{-# LANGUAGE UndecidableInstances #-}

11−{-|

12−Module : Grenade.Core.Pooling

13−Description : Max Pooling layer for 2D and 3D images

14−Copyright : (c) Huw Campbell, 2016-2017

15−License : BSD2

16−Stability : experimental

17−-}

189module Grenade.Layers.Pooling (

1910 Pooling (..)

2011 ) where

@@ -22,9 +13,8 @@ module Grenade.Layers.Pooling (

2213import Data.Maybe

2314import Data.Proxy

2415import Data.Serialize

25−

16+import Data.Singletons.TypeLits

2617import GHC.TypeLits

27−import Data.Kind (Type)

2818

2919import Grenade.Core

3020import Grenade.Layers.Internal.Pooling

@@ -32,21 +22,20 @@ import Grenade.Layers.Internal.Pooling

3222import Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRows)

3323

3424-- | A pooling layer for a neural network.

35−--

3625-- Does a max pooling, looking over a kernel similarly to the convolution network, but returning

3726-- maxarg only. This layer is often used to provide minor amounts of translational invariance.

3827--

3928-- The kernel size dictates which input and output sizes will "fit". Fitting the equation:

4029-- `out = (in - kernel) / stride + 1` for both dimensions.

4130--

42−data Pooling :: Nat -> Nat -> Nat -> Nat -> Type where

31+data Pooling :: Nat -> Nat -> Nat -> Nat -> * where

4332 Pooling :: Pooling kernelRows kernelColumns strideRows strideColumns

4433

4534instance Show (Pooling k k' s s') where

4635 show Pooling = "Pooling"

4736

4837instance UpdateLayer (Pooling kernelRows kernelColumns strideRows strideColumns) where

49− type Gradient (Pooling kernelRows kernelColumns strideRows strideColumns) = ()

38+ type Gradient (Pooling kr kc sr sc) = ()

5039 runUpdate _ Pooling _ = Pooling

5140 createRandom = return Pooling

5241

src/Grenade/Layers/Relu.hs−7

@@ -2,13 +2,6 @@

22{-# LANGUAGE TypeOperators #-}

33{-# LANGUAGE TypeFamilies #-}

44{-# LANGUAGE MultiParamTypeClasses #-}

5−{-|

6−Module : Grenade.Layers.Relu

7−Description : Rectifying linear unit layer

8−Copyright : (c) Huw Campbell, 2016-2017

9−License : BSD2

10−Stability : experimental

11−-}

125module Grenade.Layers.Relu (

136 Relu (..)

147 ) where

src/Grenade/Layers/Reshape.hs+4−11

@@ -3,20 +3,13 @@

33{-# LANGUAGE TypeFamilies #-}

44{-# LANGUAGE MultiParamTypeClasses #-}

55{-# LANGUAGE FlexibleContexts #-}

6−{-# LANGUAGE UndecidableInstances #-}

7−{-|

8−Module : Grenade.Layers.Reshape

9−Description : Multipurpose reshaping layer

10−Copyright : (c) Huw Campbell, 2016-2017

11−License : BSD2

12−Stability : experimental

13−-}

146module Grenade.Layers.Reshape (

157 Reshape (..)

168 ) where

179

1810import Data.Serialize

1911

12+import Data.Singletons.TypeLits

2013import GHC.TypeLits

2114

2215import Numeric.LinearAlgebra.Static

@@ -26,9 +19,9 @@ import Grenade.Core

2619

2720-- | Reshape Layer

2821--

29−-- The Reshape layer can flatten any 2D or 3D image to 1D vector with the

30−-- same number of activations, as well as cast up from 1D to a 2D or 3D

31−-- shape.

22+-- Flattens input down to D1 from either 2D or 3D data.

23+--

24+-- Casts input D1 up to either 2D or 3D data if the shapes are good.

3225--

3326-- Can also be used to turn a 3D image with only one channel into a 2D image

3427-- or vice versa.

src/Grenade/Layers/Sinusoid.hsdeleted−39

@@ -1,39 +0,0 @@

1−{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE FlexibleInstances #-}

3−{-# LANGUAGE MultiParamTypeClasses #-}

4−{-# LANGUAGE TypeFamilies #-}

5−{-# LANGUAGE TypeOperators #-}

6−{-|

7−Module : Grenade.Layers.Sinusoid

8−Description : Sinusoid nonlinear layer

9−Copyright : (c) Manuel Schneckenreither, 2018

10−License : BSD2

11−Stability : experimental

12−-}

13−module Grenade.Layers.Sinusoid (

14− Sinusoid (..)

15− ) where

16−

17−import Data.Serialize

18−import Data.Singletons

19−

20−import Grenade.Core

21−

22−-- | A Sinusoid layer.

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

24−data Sinusoid = Sinusoid

25− deriving Show

26−

27−instance UpdateLayer Sinusoid where

28− type Gradient Sinusoid = ()

29− runUpdate _ _ _ = Sinusoid

30− createRandom = return Sinusoid

31−

32−instance Serialize Sinusoid where

33− put _ = return ()

34− get = return Sinusoid

35−

36−instance (a ~ b, SingI a) => Layer Sinusoid a b where

37− type Tape Sinusoid a b = S a

38− runForwards _ a = (a, sin a)

39− runBackwards _ a g = ((), cos a * g)

src/Grenade/Layers/Softmax.hs−11

@@ -3,13 +3,6 @@

33{-# LANGUAGE TypeFamilies #-}

44{-# LANGUAGE FlexibleInstances #-}

55{-# LANGUAGE MultiParamTypeClasses #-}

6−{-|

7−Module : Grenade.Core.Softmax

8−Description : Softmax loss layer

9−Copyright : (c) Huw Campbell, 2016-2017

10−License : BSD2

11−Stability : experimental

12−-}

136module Grenade.Layers.Softmax (

147 Softmax (..)

158 , softmax

@@ -24,12 +17,8 @@ import Grenade.Core

2417import Numeric.LinearAlgebra.Static as LAS

2518

2619-- | A Softmax layer

27−--

2820-- This layer is like a logit layer, but normalises

2921-- a set of matricies to be probabilities.

30−--

31−-- One can use this layer as the last layer in a network

32−-- if they need normalised probabilities.

3322data Softmax = Softmax

3423 deriving Show

3524

src/Grenade/Layers/Tanh.hs+3−10

@@ -1,15 +1,8 @@

11{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE TypeOperators #-}

3+{-# LANGUAGE TypeFamilies #-}

24{-# LANGUAGE FlexibleInstances #-}

35{-# LANGUAGE MultiParamTypeClasses #-}

4−{-# LANGUAGE TypeFamilies #-}

5−{-# LANGUAGE TypeOperators #-}

6−{-|

7−Module : Grenade.Layers.Tanh

8−Description : Hyperbolic tangent nonlinear layer

9−Copyright : (c) Huw Campbell, 2016-2017

10−License : BSD2

11−Stability : experimental

12−-}

136module Grenade.Layers.Tanh (

147 Tanh (..)

158 ) where

@@ -20,7 +13,7 @@ import Data.Singletons

2013import Grenade.Core

2114

2215-- | A Tanh layer.

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

16+-- A layer which can act between any shape of the same dimension, perfoming a tanh function.

2417data Tanh = Tanh

2518 deriving Show

2619

src/Grenade/Layers/Trivial.hs+1−11

@@ -3,13 +3,6 @@

33{-# LANGUAGE TypeFamilies #-}

44{-# LANGUAGE MultiParamTypeClasses #-}

55{-# LANGUAGE FlexibleInstances #-}

6−{-|

7−Module : Grenade.Core.Trivial

8−Description : Trivial layer which perfoms no operations on the data

9−Copyright : (c) Huw Campbell, 2016-2017

10−License : BSD2

11−Stability : experimental

12−-}

136module Grenade.Layers.Trivial (

147 Trivial (..)

158 ) where

@@ -18,10 +11,7 @@ import Data.Serialize

1811

1912import Grenade.Core

2013

21−-- | A Trivial layer.

22−--

23−-- This can be used to pass an unchanged value up one side of a

24−-- graph, for a Residual network for example.

14+-- | A trivial layer.

2515data Trivial = Trivial

2616 deriving Show

2717

src/Grenade/Recurrent.hs−22

@@ -1,29 +1,7 @@

11module Grenade.Recurrent (

2− -- | This is an empty module which simply re-exports public definitions

3− -- for recurrent networks in Grenade.

4−

5− -- * Exported modules

6− --

7− -- | The core types and runners for Recurrent Networks.

82 module Grenade.Recurrent.Core

9−

10− -- | The recurrent neural network layer zoo

113 , module Grenade.Recurrent.Layers

12−

13− -- * Overview of recurrent Networks

14− -- $recurrent

15−

164 ) where

175

186import Grenade.Recurrent.Core

197import Grenade.Recurrent.Layers

20−

21−{- $recurrent

22−There are two ways in which deep learning libraries choose to represent

23−recurrent Neural Networks, as an unrolled graph, or at a first class

24−level. Grenade chooses the latter representation, and provides a network

25−type which is specifically suited for recurrent neural networks.

26−

27−Currently grenade supports two layers, a basic recurrent layer, and an

28−LSTM layer.

29−-}

src/Grenade/Recurrent/Core/Layer.hs+6−9

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE TypeFamilies #-}

43{-# LANGUAGE MultiParamTypeClasses #-}

@@ -9,9 +8,7 @@ module Grenade.Recurrent.Core.Layer (

98 , RecurrentUpdateLayer (..)

109 ) where

1110

12−#if MIN_VERSION_base(4,9,0)

13−import Data.Kind (Type)

14−#endif

11+import Data.Singletons ( SingI )

1512

1613import Grenade.Core

1714

@@ -20,16 +17,16 @@ import Grenade.Core

2017-- of an extra recurrent data shape.

2118class UpdateLayer x => RecurrentUpdateLayer x where

2219 -- | Shape of data that is passed between each subsequent run of the layer

23− type RecurrentShape x :: Type

20+ type RecurrentShape x :: Shape

2421

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

22+class (RecurrentUpdateLayer x, SingI (RecurrentShape x)) => RecurrentLayer x (i :: Shape) (o :: Shape) where

2623 -- | Wengert Tape

27− type RecTape x i o :: Type

24+ type RecTape x i o :: *

2825 -- | Used in training and scoring. Take the input from the previous

2926 -- layer, and give the output from this layer.

30− runRecurrentForwards :: x -> RecurrentShape x -> S i -> (RecTape x i o, RecurrentShape x, S o)

27+ runRecurrentForwards :: x -> S (RecurrentShape x) -> S i -> (RecTape x i o, S (RecurrentShape x), S o)

3128 -- | Back propagate a step. Takes the current layer, the input that the

3229 -- layer gave from the input and the back propagated derivatives from

3330 -- the layer above.

3431 -- Returns the gradient layer and the derivatives to push back further.

35− runRecurrentBackwards :: x -> RecTape x i o -> RecurrentShape x -> S o -> (Gradient x, RecurrentShape x, S i)

32+ runRecurrentBackwards :: x -> RecTape x i o -> S (RecurrentShape x) -> S o -> (Gradient x, S (RecurrentShape x), S i)

src/Grenade/Recurrent/Core/Network.hs+118−122

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

43{-# LANGUAGE TypeOperators #-}

@@ -18,34 +17,37 @@ module Grenade.Recurrent.Core.Network (

1817

1918 , RecurrentNetwork (..)

2019 , RecurrentInputs (..)

21− , RecurrentTape (..)

22− , RecurrentGradient (..)

20+ , RecurrentTapes (..)

21+ , RecurrentGradients (..)

2322

2423 , randomRecurrent

25− , runRecurrent

26− , runRecurrent'

24+ , runRecurrentNetwork

25+ , runRecurrentGradient

2726 , applyRecurrentUpdate

2827 ) where

2928

3029

3130import Control.Monad.Random ( MonadRandom )

31+import Data.Singletons ( SingI )

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

3233import Data.Serialize

33−

34−import Data.Kind (Type)

34+import qualified Data.Vector.Storable as V

3535

3636import Grenade.Core

3737import Grenade.Recurrent.Core.Layer

38−import Prelude.Singletons

38+

39+import qualified Numeric.LinearAlgebra as LA

40+import qualified Numeric.LinearAlgebra.Static as LAS

3941

4042-- | Witness type to say indicate we're building up with a normal feed

4143-- forward layer.

42−data FeedForward :: Type -> Type

44+data FeedForward :: * -> *

4345-- | Witness type to say indicate we're building up with a recurrent layer.

44−data Recurrent :: Type -> Type

46+data Recurrent :: * -> *

4547

4648-- | Type of a recurrent neural network.

4749--

48−-- The [Type] type specifies the types of the layers.

50+-- The [*] type specifies the types of the layers.

4951--

5052-- The [Shape] type specifies the shapes of data passed between the layers.

5153--

@@ -54,7 +56,7 @@ data Recurrent :: Type -> Type

5456--

5557-- Often, to make the definitions more concise, one will use a type alias

5658-- for these empty data types.

57−data RecurrentNetwork :: [Type] -> [Shape] -> Type where

59+data RecurrentNetwork :: [*] -> [Shape] -> * where

5860 RNil :: SingI i

5961 => RecurrentNetwork '[] '[i]

6062

@@ -73,82 +75,96 @@ infixr 5 :~@>

7375-- | Gradient of a network.

7476--

7577-- Parameterised on the layers of the network.

76−data RecurrentGradient :: [Type] -> Type where

77− RGNil :: RecurrentGradient '[]

78+data RecurrentGradients :: [*] -> * where

79+ RGNil :: RecurrentGradients '[]

7880

7981 (://>) :: UpdateLayer x

80− => Gradient x

81− -> RecurrentGradient xs

82− -> RecurrentGradient (phantom x ': xs)

82+ => [Gradient x]

83+ -> RecurrentGradients xs

84+ -> RecurrentGradients (phantom x ': xs)

8385

8486-- | Recurrent inputs (sideways shapes on an imaginary unrolled graph)

8587-- Parameterised on the layers of a Network.

86−data RecurrentInputs :: [Type] -> Type where

88+data RecurrentInputs :: [*] -> * where

8789 RINil :: RecurrentInputs '[]

8890

89− (:~~+>) :: (UpdateLayer x, Fractional (RecurrentInputs xs))

91+ (:~~+>) :: UpdateLayer x

9092 => () -> !(RecurrentInputs xs) -> RecurrentInputs (FeedForward x ': xs)

9193

92− (:~@+>) :: (Fractional (RecurrentShape x), Fractional (RecurrentInputs xs), RecurrentUpdateLayer x)

93− => !(RecurrentShape x) -> !(RecurrentInputs xs) -> RecurrentInputs (Recurrent x ': xs)

94+ (:~@+>) :: (SingI (RecurrentShape x), RecurrentUpdateLayer x)

95+ => !(S (RecurrentShape x)) -> !(RecurrentInputs xs) -> RecurrentInputs (Recurrent x ': xs)

9496

9597-- | All the information required to backpropogate

9698-- through time safely.

9799--

98100-- We index on the time step length as well, to ensure

99101-- that that all Tape lengths are the same.

100−data RecurrentTape :: [Type] -> [Shape] -> Type where

102+data RecurrentTapes :: [*] -> [Shape] -> * where

101103 TRNil :: SingI i

102− => RecurrentTape '[] '[i]

104+ => RecurrentTapes '[] '[i]

105+

106+ (:\~>) :: [Tape x i h]

107+ -> !(RecurrentTapes xs (h ': hs))

108+ -> RecurrentTapes (FeedForward x ': xs) (i ': h ': hs)

103109

104− (:\~>) :: Tape x i h

105− -> !(RecurrentTape xs (h ': hs))

106− -> RecurrentTape (FeedForward x ': xs) (i ': h ': hs)

107110

108− (:\@>) :: RecTape x i h

109− -> !(RecurrentTape xs (h ': hs))

110− -> RecurrentTape (Recurrent x ': xs) (i ': h ': hs)

111+ (:\@>) :: [RecTape x i h]

112+ -> !(RecurrentTapes xs (h ': hs))

113+ -> RecurrentTapes (Recurrent x ': xs) (i ': h ': hs)

111114

112115

113−runRecurrent :: forall shapes layers.

114− RecurrentNetwork layers shapes

115− -> RecurrentInputs layers

116− -> S (Head shapes)

117− -> (RecurrentTape layers shapes, RecurrentInputs layers, S (Last shapes))

118−runRecurrent =

116+runRecurrentNetwork :: forall shapes layers.

117+ RecurrentNetwork layers shapes

118+ -> RecurrentInputs layers

119+ -> [S (Head shapes)]

120+ -> (RecurrentTapes layers shapes, RecurrentInputs layers, [S (Last shapes)])

121+runRecurrentNetwork =

119122 go

120123 where

121124 go :: forall js sublayers. (Last js ~ Last shapes)

122125 => RecurrentNetwork sublayers js

123126 -> RecurrentInputs sublayers

124− -> S (Head js)

125− -> (RecurrentTape sublayers js, RecurrentInputs sublayers, S (Last js))

126− go (!layer :~~> n) (() :~~+> nIn) !x

127− = let (!tape, !forwards) = runForwards layer x

128−

127+ -> [S (Head js)]

128+ -> (RecurrentTapes sublayers js, RecurrentInputs sublayers, [S (Last js)])

129+ -- This is a simple non-recurrent layer, just map it forwards

130+ go (layer :~~> n) (() :~~+> nIn) !xs

131+ = let tys = runForwards layer <$> xs

132+ feedForwardTapes = fst <$> tys

133+ forwards = snd <$> tys

129134 -- recursively run the rest of the network, and get the gradients from above.

130− (!newFN, !ig, !answer) = go n nIn forwards

131− in (tape :\~> newFN, () :~~+> ig, answer)

135+ (newFN, ig, answer) = go n nIn forwards

136+ in (feedForwardTapes :\~> newFN, () :~~+> ig, answer)

132137

133138 -- This is a recurrent layer, so we need to do a scan, first input to last, providing

134139 -- the recurrent shape output to the next layer.

135− go (layer :~@> n) (recIn :~@+> nIn) !x

136− = let (tape, shape, forwards) = runRecurrentForwards layer recIn x

140+ go (layer :~@> n) (recIn :~@+> nIn) !xs

141+ = let (recOut, tys) = goR layer recIn xs

142+ recurrentTapes = fst <$> tys

143+ forwards = snd <$> tys

144+

137145 (newFN, ig, answer) = go n nIn forwards

138− in (tape :\@> newFN, shape :~@+> ig, answer)

146+ in (recurrentTapes :\@> newFN, recOut :~@+> ig, answer)

139147

140148 -- Handle the output layer, bouncing the derivatives back down.

141149 -- We may not have a target for each example, so when we don't use 0 gradient.

142150 go RNil RINil !x

143151 = (TRNil, RINil, x)

144152

145−runRecurrent' :: forall layers shapes.

146− RecurrentNetwork layers shapes

147− -> RecurrentTape layers shapes

148− -> RecurrentInputs layers

149− -> S (Last shapes)

150− -> (RecurrentGradient layers, RecurrentInputs layers, S (Head shapes))

151−runRecurrent' net tapes r o =

153+ -- Helper function for recurrent layers

154+ -- Scans over the recurrent direction of the graph.

155+ goR !layer !recShape (x:xs) =

156+ let (tape, lerec, lepush) = runRecurrentForwards layer recShape x

157+ (rems, push) = goR layer lerec xs

158+ in (rems, (tape, lepush) : push)

159+ goR _ rin [] = (rin, [])

160+

161+runRecurrentGradient :: forall layers shapes.

162+ RecurrentNetwork layers shapes

163+ -> RecurrentTapes layers shapes

164+ -> RecurrentInputs layers

165+ -> [S (Last shapes)]

166+ -> (RecurrentGradients layers, RecurrentInputs layers, [S (Head shapes)])

167+runRecurrentGradient net tapes r o =

152168 go net tapes r

153169 where

154170 -- We have to be careful regarding the direction of the lists

@@ -156,41 +172,51 @@ runRecurrent' net tapes r o =

156172 -- through time.

157173 go :: forall js ss. (Last js ~ Last shapes)

158174 => RecurrentNetwork ss js

159− -> RecurrentTape ss js

175+ -> RecurrentTapes ss js

160176 -> RecurrentInputs ss

161− -> (RecurrentGradient ss, RecurrentInputs ss, S (Head js))

177+ -> (RecurrentGradients ss, RecurrentInputs ss, [S (Head js)])

162178 -- This is a simple non-recurrent layer

163− -- Run the rest of the network, update with the tapes and gradients

164− go (!layer :~~> n) (!tape :\~> nTapes) (() :~~+> nRecs) =

165− let (!gradients, !rins, !feed) = go n nTapes nRecs

166− (!grad, !back) = runBackwards layer tape feed

167− in (grad ://> gradients, () :~~+> rins, back)

179+ -- Run the rest of the network, then fmap the tapes and gradients

180+ go (layer :~~> n) (feedForwardTapes :\~> nTapes) (() :~~+> nRecs) =

181+ let (gradients, rins, feed) = go n nTapes nRecs

182+ backs = uncurry (runBackwards layer) <$> zip (reverse feedForwardTapes) feed

183+ in ((fst <$> backs) ://> gradients, () :~~+> rins, snd <$> backs)

168184

169185 -- This is a recurrent layer

170186 -- Run the rest of the network, scan over the tapes in reverse

171− go (!layer :~@> n) (!tape :\@> nTapes) (!recGrad :~@+> nRecs) =

172− let (!gradients, !rins, !feed) = go n nTapes nRecs

173− (!grad, !sidegrad, !back) = runRecurrentBackwards layer tape recGrad feed

174− in (grad ://> gradients, sidegrad :~@+> rins, back)

187+ go (layer :~@> n) (recurrentTapes :\@> nTapes) (recGrad :~@+> nRecs) =

188+ let (gradients, rins, feed) = go n nTapes nRecs

189+ backExamples = zip (reverse recurrentTapes) feed

190+ (rg, backs) = goX layer recGrad backExamples

191+ in ((fst <$> backs) ://> gradients, rg :~@+> rins, snd <$> backs)

175192

176193 -- End of the road, so we reflect the given gradients backwards.

177194 -- Crucially, we reverse the list, so it's backwards in time as

178195 -- well.

179− go !RNil !TRNil !RINil

180− = (RGNil, RINil, o)

196+ go RNil TRNil RINil

197+ = (RGNil, RINil, reverse o)

198+

199+ -- Helper function for recurrent layers

200+ -- Scans over the recurrent direction of the graph.

201+ goX :: RecurrentLayer x i o => x -> S (RecurrentShape x) -> [(RecTape x i o, S o)] -> (S (RecurrentShape x), [(Gradient x, S i)])

202+ goX layer !lastback ((recTape, backgrad):xs) =

203+ let (layergrad, recgrad, ingrad) = runRecurrentBackwards layer recTape lastback backgrad

204+ (pushedback, ll) = goX layer recgrad xs

205+ in (pushedback, (layergrad, ingrad) : ll)

206+ goX _ !lastback [] = (lastback, [])

181207

182208-- | Apply a batch of gradients to the network

183209-- Uses runUpdates which can be specialised for

184210-- a layer.

185211applyRecurrentUpdate :: LearningParameters

186212 -> RecurrentNetwork layers shapes

187− -> RecurrentGradient layers

213+ -> RecurrentGradients layers

188214 -> RecurrentNetwork layers shapes

189215applyRecurrentUpdate rate (layer :~~> rest) (gradient ://> grest)

190− = runUpdate rate layer gradient :~~> applyRecurrentUpdate rate rest grest

216+ = runUpdates rate layer gradient :~~> applyRecurrentUpdate rate rest grest

191217

192218applyRecurrentUpdate rate (layer :~@> rest) (gradient ://> grest)

193− = runUpdate rate layer gradient :~@> applyRecurrentUpdate rate rest grest

219+ = runUpdates rate layer gradient :~@> applyRecurrentUpdate rate rest grest

194220

195221applyRecurrentUpdate _ RNil RGNil

196222 = RNil

@@ -206,25 +232,26 @@ instance (Show x, Show (RecurrentNetwork xs rs)) => Show (RecurrentNetwork (Recu

206232

207233-- | A network can easily be created by hand with (:~~>) and (:~@>), but an easy way to initialise a random

208234-- recurrent network and a set of random inputs for it is with the randomRecurrent.

209−class CreatableRecurrent (xs :: [Type]) (ss :: [Shape]) where

235+class CreatableRecurrent (xs :: [*]) (ss :: [Shape]) where

210236 -- | Create a network of the types requested

211− randomRecurrent :: MonadRandom m => m (RecurrentNetwork xs ss)

237+ randomRecurrent :: MonadRandom m => m (RecurrentNetwork xs ss, RecurrentInputs xs)

212238

213239instance SingI i => CreatableRecurrent '[] '[i] where

214240 randomRecurrent =

215− return RNil

241+ return (RNil, RINil)

216242

217243instance (SingI i, Layer x i o, CreatableRecurrent xs (o ': rs)) => CreatableRecurrent (FeedForward x ': xs) (i ': o ': rs) where

218244 randomRecurrent = do

219245 thisLayer <- createRandom

220− rest <- randomRecurrent

221− return (thisLayer :~~> rest)

246+ (rest, resti) <- randomRecurrent

247+ return (thisLayer :~~> rest, () :~~+> resti)

222248

223249instance (SingI i, RecurrentLayer x i o, CreatableRecurrent xs (o ': rs)) => CreatableRecurrent (Recurrent x ': xs) (i ': o ': rs) where

224250 randomRecurrent = do

225251 thisLayer <- createRandom

226− rest <- randomRecurrent

227− return (thisLayer :~@> rest)

252+ thisShape <- randomOfShape

253+ (rest, resti) <- randomRecurrent

254+ return (thisLayer :~@> rest, thisShape :~@+> resti)

228255

229256-- | Add very simple serialisation to the recurrent network

230257instance SingI i => Serialize (RecurrentNetwork '[] '[i]) where

@@ -243,13 +270,23 @@ instance (Serialize (RecurrentInputs '[])) where

243270 put _ = return ()

244271 get = return RINil

245272

246−instance (UpdateLayer x, Serialize (RecurrentInputs ys), Fractional (RecurrentInputs ys)) => (Serialize (RecurrentInputs (FeedForward x ': ys))) where

273+instance (UpdateLayer x, Serialize (RecurrentInputs ys)) => (Serialize (RecurrentInputs (FeedForward x ': ys))) where

247274 put ( () :~~+> rest) = put rest

248275 get = ( () :~~+> ) <$> get

249276

250−instance (Serialize (RecurrentShape x), Fractional (RecurrentShape x), RecurrentUpdateLayer x, Serialize (RecurrentInputs ys), Fractional (RecurrentInputs ys)) => (Serialize (RecurrentInputs (Recurrent x ': ys))) where

251− put ( i :~@+> rest ) = put i >> put rest

252− get = (:~@+>) <$> get <*> get

277+instance (SingI (RecurrentShape x), RecurrentUpdateLayer x, Serialize (RecurrentInputs ys)) => (Serialize (RecurrentInputs (Recurrent x ': ys))) where

278+ put ( i :~@+> rest ) = do

279+ _ <- (case i of

280+ (S1D x) -> putListOf put . LA.toList . LAS.extract $ x

281+ (S2D x) -> putListOf put . LA.toList . LA.flatten . LAS.extract $ x

282+ (S3D x) -> putListOf put . LA.toList . LA.flatten . LAS.extract $ x

283+ ) :: PutM ()

284+ put rest

285+

286+ get = do

287+ Just i <- fromStorable . V.fromList <$> getListOf get

288+ rest <- get

289+ return ( i :~@+> rest)

253290

254291

255292-- Num instance for `RecurrentInputs layers`

@@ -270,7 +307,7 @@ instance (Num (RecurrentInputs '[])) where

270307 signum _ = RINil

271308 fromInteger _ = RINil

272309

273−instance (UpdateLayer x, Fractional (RecurrentInputs ys)) => (Num (RecurrentInputs (FeedForward x ': ys))) where

310+instance (UpdateLayer x, Num (RecurrentInputs ys)) => (Num (RecurrentInputs (FeedForward x ': ys))) where

274311 (+) (() :~~+> x) (() :~~+> y) = () :~~+> (x + y)

275312 (-) (() :~~+> x) (() :~~+> y) = () :~~+> (x - y)

276313 (*) (() :~~+> x) (() :~~+> y) = () :~~+> (x * y)

@@ -278,51 +315,10 @@ instance (UpdateLayer x, Fractional (RecurrentInputs ys)) => (Num (RecurrentInpu

278315 signum (() :~~+> x) = () :~~+> signum x

279316 fromInteger x = () :~~+> fromInteger x

280317

281−instance (Fractional (RecurrentShape x), RecurrentUpdateLayer x, Fractional (RecurrentInputs ys)) => (Num (RecurrentInputs (Recurrent x ': ys))) where

318+instance (SingI (RecurrentShape x), RecurrentUpdateLayer x, Num (RecurrentInputs ys)) => (Num (RecurrentInputs (Recurrent x ': ys))) where

282319 (+) (x :~@+> x') (y :~@+> y') = (x + y) :~@+> (x' + y')

283320 (-) (x :~@+> x') (y :~@+> y') = (x - y) :~@+> (x' - y')

284321 (*) (x :~@+> x') (y :~@+> y') = (x * y) :~@+> (x' * y')

285322 abs (x :~@+> x') = abs x :~@+> abs x'

286323 signum (x :~@+> x') = signum x :~@+> signum x'

287324 fromInteger x = fromInteger x :~@+> fromInteger x

288−

289−instance (Fractional (RecurrentInputs '[])) where

290− (/) _ _ = RINil

291− recip _ = RINil

292− fromRational _ = RINil

293−

294−instance (UpdateLayer x, Fractional (RecurrentInputs ys)) => (Fractional (RecurrentInputs (FeedForward x ': ys))) where

295− (/) (() :~~+> x) (() :~~+> y) = () :~~+> (x / y)

296− recip (() :~~+> x) = () :~~+> recip x

297− fromRational x = () :~~+> fromRational x

298−

299−instance (Fractional (RecurrentShape x), RecurrentUpdateLayer x, Fractional (RecurrentInputs ys)) => (Fractional (RecurrentInputs (Recurrent x ': ys))) where

300− (/) (x :~@+> x') (y :~@+> y') = (x / y) :~@+> (x' / y')

301− recip (x :~@+> x') = recip x :~@+> recip x'

302− fromRational x = fromRational x :~@+> fromRational x

303−

304−

305−-- | Ultimate composition.

306−--

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

308−instance CreatableRecurrent sublayers subshapes => UpdateLayer (RecurrentNetwork sublayers subshapes) where

309− type Gradient (RecurrentNetwork sublayers subshapes) = RecurrentGradient sublayers

310− runUpdate = applyRecurrentUpdate

311− createRandom = randomRecurrent

312−

313−-- | Ultimate composition.

314−--

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

316−instance CreatableRecurrent sublayers subshapes => RecurrentUpdateLayer (RecurrentNetwork sublayers subshapes) where

317− type RecurrentShape (RecurrentNetwork sublayers subshapes) = RecurrentInputs sublayers

318−

319−-- | Ultimate composition.

320−--

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

322−instance ( CreatableRecurrent sublayers subshapes

323− , i ~ (Head subshapes), o ~ (Last subshapes)

324− , Num (RecurrentShape (RecurrentNetwork sublayers subshapes))

325− ) => RecurrentLayer (RecurrentNetwork sublayers subshapes) i o where

326− type RecTape (RecurrentNetwork sublayers subshapes) i o = RecurrentTape sublayers subshapes

327− runRecurrentForwards = runRecurrent

328− runRecurrentBackwards = runRecurrent'

src/Grenade/Recurrent/Core/Runner.hs+34−54

@@ -6,67 +6,33 @@

66{-# LANGUAGE TypeFamilies #-}

77{-# LANGUAGE FlexibleContexts #-}

88{-# LANGUAGE RankNTypes #-}

9+{-# LANGUAGE RecordWildCards #-}

910

1011module Grenade.Recurrent.Core.Runner (

11− runRecurrentExamples

12− , runRecurrentBackprop

12+ trainRecurrent

13+ , runRecurrent

1314 , backPropagateRecurrent

14− , trainRecurrent

15−

16− , RecurrentGradients

1715 ) where

1816

19−import Data.List ( foldl' )

17+import Data.Singletons.Prelude

2018import Grenade.Core

2119

20+import Grenade.Recurrent.Core.Layer

2221import Grenade.Recurrent.Core.Network

23−import Prelude.Singletons

24−

25−type RecurrentGradients layers = [RecurrentGradient layers]

26−

27−runRecurrentExamples :: forall shapes layers.

28− RecurrentNetwork layers shapes

29− -> RecurrentInputs layers

30− -> [S (Head shapes)]

31− -> ([(RecurrentTape layers shapes, S (Last shapes))], RecurrentInputs layers)

32−runRecurrentExamples net =

33− go

34− where

35− go !side [] = ([], side)

36− go !side (!x : xs) =

37− let (!tape, !side', !o) = runRecurrent net side x

38− (!res, !finalSide) = go side' xs

39− in (( tape, o ) : res, finalSide)

40−

41−runRecurrentBackprop :: forall layers shapes.

42− RecurrentNetwork layers shapes

43− -> RecurrentInputs layers

44− -> [(RecurrentTape layers shapes, S (Last shapes))]

45− -> ([(RecurrentGradient layers, S (Head shapes))], RecurrentInputs layers)

46−runRecurrentBackprop net =

47− go

48− where

49− go !side [] = ([], side)

50− go !side ((!tape,!x):xs) =

51− let (res, !side') = go side xs

52− (!grad, !finalSide, !o) = runRecurrent' net tape side' x

53− in (( grad, o ) : res, finalSide)

54−

5522

5623-- | Drive and network and collect its back propogated gradients.

57−backPropagateRecurrent :: forall shapes layers. (SingI (Last shapes), Fractional (RecurrentInputs layers))

24+backPropagateRecurrent :: forall shapes layers. (SingI (Last shapes), Num (RecurrentInputs layers))

5825 => RecurrentNetwork layers shapes

5926 -> RecurrentInputs layers

6027 -> [(S (Head shapes), Maybe (S (Last shapes)))]

6128 -> (RecurrentGradients layers, RecurrentInputs layers)

6229backPropagateRecurrent network recinputs examples =

63− let (outForwards, _) = runRecurrentExamples network recinputs inputs

30+ let (tapes, _, guesses) = runRecurrentNetwork network recinputs inputs

6431

65− backPropagations = zipWith makeError outForwards targets

32+ backPropagations = zipWith makeError guesses targets

6633

67− (outBackwards, input') = runRecurrentBackprop network 0 backPropagations

34+ (gradients, input', _) = runRecurrentGradient network tapes 0 backPropagations

6835

69− gradients = fmap fst outBackwards

7036 in (gradients, input')

7137

7238 where

@@ -74,12 +40,12 @@ backPropagateRecurrent network recinputs examples =

7440 inputs = fst <$> examples

7541 targets = snd <$> examples

7642

77− makeError :: (x, S (Last shapes)) -> Maybe (S (Last shapes)) -> (x, S (Last shapes))

78− makeError (x, _) Nothing = (x, 0)

79− makeError (x, y) (Just t) = (x, y - t)

43+ makeError :: S (Last shapes) -> Maybe (S (Last shapes)) -> S (Last shapes)

44+ makeError _ Nothing = 0

45+ makeError y (Just t) = y - t

8046

8147

82−trainRecurrent :: forall shapes layers. (SingI (Last shapes), Fractional (RecurrentInputs layers))

48+trainRecurrent :: forall shapes layers. (SingI (Last shapes), Num (RecurrentInputs layers))

8349 => LearningParameters

8450 -> RecurrentNetwork layers shapes

8551 -> RecurrentInputs layers

@@ -90,21 +56,35 @@ trainRecurrent rate network recinputs examples =

9056

9157 newInputs = updateRecInputs rate recinputs recinputs'

9258

93− newNetwork = foldl' (applyRecurrentUpdate rate) network gradients

59+ newNetwork = applyRecurrentUpdate rate network gradients

9460

9561 in (newNetwork, newInputs)

9662

97−updateRecInputs :: Fractional (RecurrentInputs sublayers)

98− => LearningParameters

63+updateRecInputs :: LearningParameters

9964 -> RecurrentInputs sublayers

10065 -> RecurrentInputs sublayers

10166 -> RecurrentInputs sublayers

10267

103−updateRecInputs lp (() :~~+> xs) (() :~~+> ys)

104− = () :~~+> updateRecInputs lp xs ys

68+updateRecInputs l@LearningParameters {..} (() :~~+> xs) (() :~~+> ys)

69+ = () :~~+> updateRecInputs l xs ys

10570

106−updateRecInputs lp (x :~@+> xs) (y :~@+> ys)

107− = (realToFrac (1 - learningRate lp * learningRegulariser lp) * x - realToFrac (learningRate lp) * y) :~@+> updateRecInputs lp xs ys

71+updateRecInputs l@LearningParameters {..} (x :~@+> xs) (y :~@+> ys)

72+ = (realToFrac (1 - learningRate * learningRegulariser) * x - realToFrac learningRate * y) :~@+> updateRecInputs l xs ys

10873

10974updateRecInputs _ RINil RINil

11075 = RINil

76+

77+-- | Just forwards propagation with no training.

78+runRecurrent :: RecurrentNetwork layers shapes

79+ -> RecurrentInputs layers -> S (Head shapes)

80+ -> (RecurrentInputs layers, S (Last shapes))

81+runRecurrent (layer :~~> n) (() :~~+> nr) !x

82+ = let (_, ys) = runForwards layer x

83+ (nr', o) = runRecurrent n nr ys

84+ in (() :~~+> nr', o)

85+runRecurrent (layer :~@> n) (recin :~@+> nr) !x

86+ = let (_, recin', y) = runRecurrentForwards layer recin x

87+ (nr', o) = runRecurrent n nr y

88+ in (recin' :~@+> nr', o)

89+runRecurrent RNil RINil !x

90+ = (RINil, x)

src/Grenade/Recurrent/Layers.hs−2

@@ -1,9 +1,7 @@

11module Grenade.Recurrent.Layers (

22 module Grenade.Recurrent.Layers.BasicRecurrent

3− , module Grenade.Recurrent.Layers.ConcatRecurrent

43 , module Grenade.Recurrent.Layers.LSTM

54 ) where

65

76import Grenade.Recurrent.Layers.BasicRecurrent

8−import Grenade.Recurrent.Layers.ConcatRecurrent

97import Grenade.Recurrent.Layers.LSTM

src/Grenade/Recurrent/Layers/BasicRecurrent.hs+9−10

@@ -1,6 +1,6 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

3+{-# LANGUAGE RecordWildCards #-}

44{-# LANGUAGE TypeOperators #-}

55{-# LANGUAGE TypeFamilies #-}

66{-# LANGUAGE MultiParamTypeClasses #-}

@@ -16,8 +16,7 @@ module Grenade.Recurrent.Layers.BasicRecurrent (

1616

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

1818

19−

20−import Data.Kind (Type)

19+import Data.Singletons.TypeLits

2120

2221import Numeric.LinearAlgebra.Static

2322

@@ -28,7 +27,7 @@ import Grenade.Recurrent.Core

2827

2928data BasicRecurrent :: Nat -- Input layer size

3029 -> Nat -- Output layer size

31− -> Type where

30+ -> * where

3231 BasicRecurrent :: ( KnownNat input

3332 , KnownNat output

3433 , KnownNat matrixCols

@@ -41,7 +40,7 @@ data BasicRecurrent :: Nat -- Input layer size

4140

4241data BasicRecurrent' :: Nat -- Input layer size

4342 -> Nat -- Output layer size

44− -> Type where

43+ -> * where

4544 BasicRecurrent' :: ( KnownNat input

4645 , KnownNat output

4746 , KnownNat matrixCols

@@ -56,18 +55,18 @@ instance Show (BasicRecurrent i o) where

5655instance (KnownNat i, KnownNat o, KnownNat (i + o)) => UpdateLayer (BasicRecurrent i o) where

5756 type Gradient (BasicRecurrent i o) = (BasicRecurrent' i o)

5857

59− runUpdate lp (BasicRecurrent oldBias oldBiasMomentum oldActivations oldMomentum) (BasicRecurrent' biasGradient activationGradient) =

60− let newBiasMomentum = konst (learningMomentum lp) * oldBiasMomentum - konst (learningRate lp) * biasGradient

58+ runUpdate LearningParameters {..} (BasicRecurrent oldBias oldBiasMomentum oldActivations oldMomentum) (BasicRecurrent' biasGradient activationGradient) =

59+ let newBiasMomentum = konst learningMomentum * oldBiasMomentum - konst learningRate * biasGradient

6160 newBias = oldBias + newBiasMomentum

62− newMomentum = konst (learningMomentum lp) * oldMomentum - konst (learningRate lp) * activationGradient

63− regulariser = konst (learningRegulariser lp * learningRate lp) * oldActivations

61+ newMomentum = konst learningMomentum * oldMomentum - konst learningRate * activationGradient

62+ regulariser = konst (learningRegulariser * learningRate) * oldActivations

6463 newActivations = oldActivations + newMomentum - regulariser

6564 in BasicRecurrent newBias newBiasMomentum newActivations newMomentum

6665

6766 createRandom = randomBasicRecurrent

6867

6968instance (KnownNat i, KnownNat o, KnownNat (i + o), i <= (i + o), o ~ ((i + o) - i)) => RecurrentUpdateLayer (BasicRecurrent i o) where

70− type RecurrentShape (BasicRecurrent i o) = S ('D1 o)

69+ type RecurrentShape (BasicRecurrent i o) = 'D1 o

7170

7271instance (KnownNat i, KnownNat o, KnownNat (i + o), i <= (i + o), o ~ ((i + o) - i)) => RecurrentLayer (BasicRecurrent i o) ('D1 i) ('D1 o) where

7372

src/Grenade/Recurrent/Layers/ConcatRecurrent.hsdeleted−180

@@ -1,180 +0,0 @@

1−{-# LANGUAGE CPP #-}

2−{-# LANGUAGE DataKinds #-}

3−{-# LANGUAGE GADTs #-}

4−{-# LANGUAGE TypeOperators #-}

5−{-# LANGUAGE TypeFamilies #-}

6−{-# LANGUAGE MultiParamTypeClasses #-}

7−{-# LANGUAGE FlexibleContexts #-}

8−{-# LANGUAGE RankNTypes #-}

9−{-# LANGUAGE FlexibleInstances #-}

10−{-# LANGUAGE ScopedTypeVariables #-}

11−{-# LANGUAGE StandaloneDeriving #-}

12−{-# LANGUAGE UndecidableInstances #-}

13−{-|

14−Module : Grenade.Layers.Concat

15−Description : Concatenation layer

16−Copyright : (c) Huw Campbell, 2016-2017

17−License : BSD2

18−Stability : experimental

19−

20−This module provides the concatenation layer, which runs two chilld layers in parallel and combines their outputs.

21−-}

22−module Grenade.Recurrent.Layers.ConcatRecurrent (

23− ConcatRecurrent (..)

24− ) where

25−

26−import Data.Serialize

27−

28−import Data.Singletons

29−import GHC.TypeLits

30−

31−#if MIN_VERSION_base(4,9,0)

32−import Data.Kind (Type)

33−#endif

34−

35−import Grenade.Core

36−import Grenade.Recurrent.Core

37−

38−import Numeric.LinearAlgebra.Static ( (#), split, R )

39−

40−-- | A Concatentating Layer.

41−--

42−-- This layer shares it's input state between two sublayers, and concatenates their output.

43−--

44−-- With Networks able to be Layers, this allows for very expressive composition of complex Networks.

45−--

46−-- The Concat layer has a few instances, which allow one to flexibly "bash" together the outputs.

47−--

48−-- Two 1D vectors, can go to a 2D shape with 2 rows if their lengths are identical.

49−-- Any 2 1D vectors can also become a longer 1D Vector.

50−--

51−-- 3D images become 3D images with more channels. The sizes must be the same, one can use Pad

52−-- and Crop layers to ensure this is the case.

53−data ConcatRecurrent :: Shape -> Type -> Shape -> Type -> Type where

54− ConcatRecLeft :: x -> y -> ConcatRecurrent m (Recurrent x) n (FeedForward y)

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

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

57−

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

59− show (ConcatRecLeft x y) = "ConcatRecLeft\n" ++ show x ++ "\n" ++ show y

60− show (ConcatRecRight x y) = "ConcatRecRight\n" ++ show x ++ "\n" ++ show y

61− show (ConcatRecBoth x y) = "ConcatRecBoth\n" ++ show x ++ "\n" ++ show y

62−

63−instance (RecurrentUpdateLayer x, UpdateLayer y) => UpdateLayer (ConcatRecurrent m (Recurrent x) n (FeedForward y)) where

64− type Gradient (ConcatRecurrent m (Recurrent x) n (FeedForward y)) = (Gradient x, Gradient y)

65− runUpdate lr (ConcatRecLeft x y) (x', y')

66− = ConcatRecLeft (runUpdate lr x x') (runUpdate lr y y')

67− createRandom

68− = ConcatRecLeft <$> createRandom <*> createRandom

69−

70−instance (UpdateLayer x, RecurrentUpdateLayer y) => UpdateLayer (ConcatRecurrent m (FeedForward x) n (Recurrent y)) where

71− type Gradient (ConcatRecurrent m (FeedForward x) n (Recurrent y)) = (Gradient x, Gradient y)

72− runUpdate lr (ConcatRecRight x y) (x', y')

73− = ConcatRecRight (runUpdate lr x x') (runUpdate lr y y')

74− createRandom

75− = ConcatRecRight <$> createRandom <*> createRandom

76−

77−instance (RecurrentUpdateLayer x, RecurrentUpdateLayer y) => UpdateLayer (ConcatRecurrent m (Recurrent x) n (Recurrent y)) where

78− type Gradient (ConcatRecurrent m (Recurrent x) n (Recurrent y)) = (Gradient x, Gradient y)

79− runUpdate lr (ConcatRecBoth x y) (x', y')

80− = ConcatRecBoth (runUpdate lr x x') (runUpdate lr y y')

81− createRandom

82− = ConcatRecBoth <$> createRandom <*> createRandom

83−

84−instance (RecurrentUpdateLayer x, UpdateLayer y) => RecurrentUpdateLayer (ConcatRecurrent m (Recurrent x) n (FeedForward y)) where

85− type RecurrentShape (ConcatRecurrent m (Recurrent x) n (FeedForward y)) = RecurrentShape x

86−

87−instance (UpdateLayer x, RecurrentUpdateLayer y) => RecurrentUpdateLayer (ConcatRecurrent m (FeedForward x) n (Recurrent y)) where

88− type RecurrentShape (ConcatRecurrent m (FeedForward x) n (Recurrent y)) = RecurrentShape y

89−

90−instance (RecurrentUpdateLayer x, RecurrentUpdateLayer y) => RecurrentUpdateLayer (ConcatRecurrent m (Recurrent x) n (Recurrent y)) where

91− type RecurrentShape (ConcatRecurrent m (Recurrent x) n (Recurrent y)) = RecurrentInputs '[ Recurrent x, Recurrent y ]

92−

93−instance ( SingI i

94− , Layer x i ('D1 m)

95− , RecurrentLayer y i ('D1 n)

96− , KnownNat o

97− , KnownNat m

98− , KnownNat n

99− , o ~ (m + n)

100− , n ~ (o - m)

101− , (m <=? o) ~ 'True

102− ) => RecurrentLayer (ConcatRecurrent ('D1 m) (FeedForward x) ('D1 n) (Recurrent y)) i ('D1 o) where

103− type RecTape (ConcatRecurrent ('D1 m) (FeedForward x) ('D1 n) (Recurrent y)) i ('D1 o) = (Tape x i ('D1 m), RecTape y i ('D1 n))

104−

105− runRecurrentForwards (ConcatRecRight x y) s input =

106− let (xT, xOut :: S ('D1 m)) = runForwards x input

107− (yT, side, yOut :: S ('D1 n)) = runRecurrentForwards y s input

108− in case (xOut, yOut) of

109− (S1D xOut', S1D yOut') ->

110− ((xT, yT), side, S1D (xOut' # yOut'))

111−

112− runRecurrentBackwards (ConcatRecRight x y) (xTape, yTape) s (S1D o) =

113− let (ox :: R m , oy :: R n) = split o

114− (x', xB :: S i) = runBackwards x xTape (S1D ox)

115− (y', side, yB :: S i) = runRecurrentBackwards y yTape s (S1D oy)

116− in ((x', y'), side, xB + yB)

117−

118−instance ( SingI i

119− , RecurrentLayer x i ('D1 m)

120− , Layer y i ('D1 n)

121− , KnownNat o

122− , KnownNat m

123− , KnownNat n

124− , o ~ (m + n)

125− , n ~ (o - m)

126− , (m <=? o) ~ 'True

127− ) => RecurrentLayer (ConcatRecurrent ('D1 m) (Recurrent x) ('D1 n) (FeedForward y)) i ('D1 o) where

128− type RecTape (ConcatRecurrent ('D1 m) (Recurrent x) ('D1 n) (FeedForward y)) i ('D1 o) = (RecTape x i ('D1 m), Tape y i ('D1 n))

129−

130− runRecurrentForwards (ConcatRecLeft x y) s input =

131− let (xT, side, xOut :: S ('D1 m)) = runRecurrentForwards x s input

132− (yT, yOut :: S ('D1 n)) = runForwards y input

133− in case (xOut, yOut) of

134− (S1D xOut', S1D yOut') ->

135− ((xT, yT), side, S1D (xOut' # yOut'))

136−

137− runRecurrentBackwards (ConcatRecLeft x y) (xTape, yTape) s (S1D o) =

138− let (ox :: R m , oy :: R n) = split o

139− (x', side, xB :: S i) = runRecurrentBackwards x xTape s (S1D ox)

140− (y', yB :: S i) = runBackwards y yTape (S1D oy)

141− in ((x', y'), side, xB + yB)

142−

143−instance ( SingI i

144− , RecurrentLayer x i ('D1 m)

145− , RecurrentLayer y i ('D1 n)

146− , Fractional (RecurrentShape x)

147− , Fractional (RecurrentShape y)

148− , KnownNat o

149− , KnownNat m

150− , KnownNat n

151− , o ~ (m + n)

152− , n ~ (o - m)

153− , (m <=? o) ~ 'True

154− ) => RecurrentLayer (ConcatRecurrent ('D1 m) (Recurrent x) ('D1 n) (Recurrent y)) i ('D1 o) where

155− type RecTape (ConcatRecurrent ('D1 m) (Recurrent x) ('D1 n) (Recurrent y)) i ('D1 o) = (RecTape x i ('D1 m), RecTape y i ('D1 n))

156−

157− runRecurrentForwards (ConcatRecBoth x y) (sx :~@+> (sy :~@+> RINil)) input =

158− let (xT, s'x, xOut :: S ('D1 m)) = runRecurrentForwards x sx input

159− (yT, s'y, yOut :: S ('D1 n)) = runRecurrentForwards y sy input

160− in case (xOut, yOut) of

161− (S1D xOut', S1D yOut') ->

162− ((xT, yT), (s'x :~@+> (s'y :~@+> RINil)), S1D (xOut' # yOut'))

163−

164− runRecurrentBackwards (ConcatRecBoth x y) (xTape, yTape) (sx :~@+> (sy :~@+> RINil)) (S1D o) =

165− let (ox :: R m , oy :: R n) = split o

166− (x', s'x, xB :: S i) = runRecurrentBackwards x xTape sx (S1D ox)

167− (y', s'y, yB :: S i) = runRecurrentBackwards y yTape sy (S1D oy)

168− in ((x', y'), (s'x :~@+> (s'y :~@+> RINil)), xB + yB)

169−

170−instance (Serialize a, Serialize b) => Serialize (ConcatRecurrent sa (FeedForward a) sb (Recurrent b)) where

171− put (ConcatRecRight a b) = put a *> put b

172− get = ConcatRecRight <$> get <*> get

173−

174−instance (Serialize a, Serialize b) => Serialize (ConcatRecurrent sa (Recurrent a) sb (Recurrent b)) where

175− put (ConcatRecBoth a b) = put a *> put b

176− get = ConcatRecBoth <$> get <*> get

177−

178−instance (Serialize a, Serialize b) => Serialize (ConcatRecurrent sa (Recurrent a) sb (FeedForward b)) where

179− put (ConcatRecLeft a b) = put a *> put b

180− get = ConcatRecLeft <$> get <*> get

src/Grenade/Recurrent/Layers/LSTM.hs+80−57

@@ -1,8 +1,8 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE BangPatterns #-}

32{-# LANGUAGE DataKinds #-}

43{-# LANGUAGE GADTs #-}

54{-# LANGUAGE RankNTypes #-}

5+{-# LANGUAGE RecordWildCards #-}

66{-# LANGUAGE TypeOperators #-}

77{-# LANGUAGE TypeFamilies #-}

88{-# LANGUAGE MultiParamTypeClasses #-}

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

2121-- import Data.List ( foldl1' )

2222import Data.Proxy

2323import Data.Serialize

24−import Data.Kind (Type)

24+import Data.Singletons.TypeLits

2525

2626import qualified Numeric.LinearAlgebra as LA

2727import Numeric.LinearAlgebra.Static

@@ -29,7 +29,6 @@ import Numeric.LinearAlgebra.Static

2929import Grenade.Core

3030import Grenade.Recurrent.Core

3131import Grenade.Layers.Internal.Update

32−import GHC.TypeLits

3332

3433

3534-- | Long Short Term Memory Recurrent unit

@@ -37,14 +36,14 @@ import GHC.TypeLits

3736-- This is a Peephole formulation, so the recurrent shape is

3837-- just the cell state, the previous output is not held or used

3938-- at all.

40−data LSTM :: Nat -> Nat -> Type where

39+data LSTM :: Nat -> Nat -> * where

4140 LSTM :: ( KnownNat input

4241 , KnownNat output

4342 ) => !(LSTMWeights input output) -- Weights

4443 -> !(LSTMWeights input output) -- Momentums

4544 -> LSTM input output

4645

47−data LSTMWeights :: Nat -> Nat -> Type where

46+data LSTMWeights :: Nat -> Nat -> * where

4847 LSTMWeights :: ( KnownNat input

4948 , KnownNat output

5049 ) => {

@@ -71,7 +70,7 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

7170

7271 -- Run the update function for each group matrix/vector of weights, momentums and gradients.

7372 -- Hmm, maybe the function should be used instead of passing in the learning parameters.

74− runUpdate lp (LSTM w m) g =

73+ runUpdate LearningParameters {..} (LSTM w m) g =

7574 let (wf, wf') = u lstmWf w m g

7675 (uf, uf') = u lstmUf w m g

7776 (bf, bf') = v lstmBf w m g

@@ -88,11 +87,11 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

8887 -- Utility function for updating with the momentum, gradients, and weights.

8988 u :: forall x ix out. (KnownNat ix, KnownNat out) => (x -> (L out ix)) -> x -> x -> x -> ((L out ix), (L out ix))

9089 u e (e -> weights) (e -> momentum) (e -> gradient) =

91− descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) weights gradient momentum

90+ decendMatrix learningRate learningMomentum learningRegulariser weights gradient momentum

9291

9392 v :: forall x ix. (KnownNat ix) => (x -> (R ix)) -> x -> x -> x -> ((R ix), (R ix))

9493 v e (e -> weights) (e -> momentum) (e -> gradient) =

95− descendVector (learningRate lp) (learningMomentum lp) (learningRegulariser lp) weights gradient momentum

94+ decendVector learningRate learningMomentum learningRegulariser weights gradient momentum

9695

9796 -- There's a lot of updates here, so to try and minimise the number of data copies

9897 -- we'll create a mutable bucket for each.

@@ -124,33 +123,27 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

124123instance (KnownNat i, KnownNat o) => RecurrentUpdateLayer (LSTM i o) where

125124 -- The recurrent shape is the same size as the output.

126125 -- It's actually the cell state however, as this is a peephole variety LSTM.

127− type RecurrentShape (LSTM i o) = S ('D1 o)

126+ type RecurrentShape (LSTM i o) = 'D1 o

128127

129128instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) where

130129

131− -- The tape stores essentially every variable we calculate,

132− -- so we don't have to run any forwards component again.

133− type RecTape (LSTM i o) ('D1 i) ('D1 o) = (R o, R i, R o, R o, R o, R o, R o, R o, R o, R o, R o)

130+ type RecTape (LSTM i o) ('D1 i) ('D1 o) = (S ('D1 o), S ('D1 i))

134131 -- Forward propagation for the LSTM layer.

135132 -- The size of the cell state is also the size of the output.

136− runRecurrentForwards (LSTM lw _) (S1D cell) (S1D input) =

133+ runRecurrentForwards (LSTM (LSTMWeights {..}) _) (S1D cell) (S1D input) =

137134 let -- Forget state vector

138− f_s = lstmBf lw + lstmWf lw #> input + lstmUf lw #> cell

139− f_t = sigmoid f_s

135+ f_t = sigmoid $ lstmBf + lstmWf #> input + lstmUf #> cell

140136 -- Input state vector

141− i_s = lstmBi lw + lstmWi lw #> input + lstmUi lw #> cell

142− i_t = sigmoid i_s

137+ i_t = sigmoid $ lstmBi + lstmWi #> input + lstmUi #> cell

143138 -- Output state vector

144− o_s = lstmBo lw + lstmWo lw #> input + lstmUo lw #> cell

145− o_t = sigmoid o_s

139+ o_t = sigmoid $ lstmBo + lstmWo #> input + lstmUo #> cell

146140 -- Cell input state vector

147− c_s = lstmBc lw + lstmWc lw #> input

148− c_x = tanh c_s

141+ c_x = tanh $ lstmBc + lstmWc #> input

149142 -- Cell state

150143 c_t = f_t * cell + i_t * c_x

151144 -- Output (it's sometimes recommended to use tanh c_t)

152145 h_t = o_t * c_t

153− in ((cell, input, f_s, f_t, i_s, i_t, o_s, o_t, c_s, c_x, c_t), S1D c_t, S1D h_t)

146+ in ((S1D cell, S1D input), S1D c_t, S1D h_t)

154147

155148 -- Run a backpropogation step for an LSTM layer.

156149 -- We're doing all the derivatives by hand here, so one should

@@ -158,8 +151,29 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

158151 --

159152 -- There's a test version using the AD library without hmatrix in the test

160153 -- suite. These should match always.

161− runRecurrentBackwards (LSTM lw _) (cell, input, f_s, f_t, i_s, i_t, o_s, o_t, c_s, c_x, c_t) (S1D cellGrad) (S1D h_t') =

162− let -- Reverse Mode AD Derivitives

154+ runRecurrentBackwards (LSTM (LSTMWeights {..}) _) (S1D cell, S1D input) (S1D cellGrad) (S1D h_t') =

155+ -- We're not keeping the Wengert tape during the forward pass,

156+ -- so we're duplicating some work here.

157+ --

158+ -- If I was being generous, I'd call it checkpointing.

159+ --

160+ -- Maybe think about better ways to store some intermediate states.

161+ let -- Forget state vector

162+ f_s = lstmBf + lstmWf #> input + lstmUf #> cell

163+ f_t = sigmoid f_s

164+ -- Input state vector

165+ i_s = lstmBi + lstmWi #> input + lstmUi #> cell

166+ i_t = sigmoid i_s

167+ -- Output state vector

168+ o_s = lstmBo + lstmWo #> input + lstmUo #> cell

169+ o_t = sigmoid o_s

170+ -- Cell input state vector

171+ c_s = lstmBc + lstmWc #> input

172+ c_x = tanh c_s

173+ -- Cell state

174+ c_t = f_t * cell + i_t * c_x

175+

176+ -- Reverse Mode AD Derivitives

163177 c_t' = h_t' * o_t + cellGrad

164178

165179 f_t' = c_t' * cell

@@ -175,8 +189,8 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

175189 c_s' = tanh' c_s * c_x'

176190

177191 -- The derivatives to pass sideways (recurrent) and downwards

178− cell' = tr (lstmUf lw) #> f_s' + tr (lstmUo lw) #> o_s' + tr (lstmUi lw) #> i_s' + c_t' * f_t

179− input' = tr (lstmWf lw) #> f_s' + tr (lstmWo lw) #> o_s' + tr (lstmWi lw) #> i_s' + tr (lstmWc lw) #> c_s'

192+ cell' = tr lstmUf #> f_s' + tr lstmUo #> o_s' + tr lstmUi #> i_s' + c_t' * f_t

193+ input' = tr lstmWf #> f_s' + tr lstmWo #> o_s' + tr lstmWi #> i_s' + tr lstmWc #> c_s'

180194

181195 -- Calculate the gradient Matricies for the input

182196 lstmWf' = f_s' `outer` input

@@ -221,8 +235,7 @@ randomLSTM = do

221235

222236-- | Maths

223237--

224−-- TODO: Move to not here

225−-- Optimise backwards derivative

238+-- TODO: move to not here

226239sigmoid :: Floating a => a -> a

227240sigmoid x = 1 / (1 + exp (-x))

228241

@@ -235,34 +248,44 @@ tanh' :: (Floating a) => a -> a

235248tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

236249

237250instance (KnownNat i, KnownNat o) => Serialize (LSTM i o) where

238− put (LSTM lw _) = do

239− u (lstmWf lw)

240− u (lstmUf lw)

241− v (lstmBf lw)

242− u (lstmWi lw)

243− u (lstmUi lw)

244− v (lstmBi lw)

245− u (lstmWo lw)

246− u (lstmUo lw)

247− v (lstmBo lw)

248− u (lstmWc lw)

249− v (lstmBc lw)

250− where

251− u :: forall a b. (KnownNat a, KnownNat b) => Putter (L b a)

252− u = putListOf put . LA.toList . LA.flatten . extract

253− v :: forall a. (KnownNat a) => Putter (R a)

254− v = putListOf put . LA.toList . extract

251+ put (LSTM LSTMWeights {..} _) = do

252+ u lstmWf

253+ u lstmUf

254+ v lstmBf

255+ u lstmWi

256+ u lstmUi

257+ v lstmBi

258+ u lstmWo

259+ u lstmUo

260+ v lstmBo

261+ u lstmWc

262+ v lstmBc

263+ where

264+ u :: forall a b. (KnownNat a, KnownNat b) => Putter (L b a)

265+ u = putListOf put . LA.toList . LA.flatten . extract

266+ v :: forall a. (KnownNat a) => Putter (R a)

267+ v = putListOf put . LA.toList . extract

255268

256269 get = do

257− w <- LSTMWeights <$> u <*> u <*> v <*> u <*> u <*> v <*> u <*> u <*> v <*> u <*> v

258− return $ LSTM w (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

259− where

260− u :: forall a b. (KnownNat a, KnownNat b) => Get (L b a)

261− u = let f = fromIntegral $ natVal (Proxy :: Proxy a)

262− in maybe (fail "Vector of incorrect size") return . create . LA.reshape f . LA.fromList =<< getListOf get

263− v :: forall a. (KnownNat a) => Get (R a)

264− v = maybe (fail "Vector of incorrect size") return . create . LA.fromList =<< getListOf get

265−

266− w0 = konst 0

267− u0 = konst 0

268− v0 = konst 0

270+ lstmWf <- u

271+ lstmUf <- u

272+ lstmBf <- v

273+ lstmWi <- u

274+ lstmUi <- u

275+ lstmBi <- v

276+ lstmWo <- u

277+ lstmUo <- u

278+ lstmBo <- v

279+ lstmWc <- u

280+ lstmBc <- v

281+ return $ LSTM (LSTMWeights {..}) (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

282+ where

283+ u :: forall a b. (KnownNat a, KnownNat b) => Get (L b a)

284+ u = let f = fromIntegral $ natVal (Proxy :: Proxy a)

285+ in maybe (fail "Vector of incorrect size") return . create . LA.reshape f . LA.fromList =<< getListOf get

286+ v :: forall a. (KnownNat a) => Get (R a)

287+ v = maybe (fail "Vector of incorrect size") return . create . LA.fromList =<< getListOf get

288+

289+ w0 = konst 0

290+ u0 = konst 0

291+ v0 = konst 0

src/Grenade/Utils/OneHot.hs+5−6

@@ -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

@@ -52,16 +52,15 @@ oneHot hot =

5252

5353-- | Create a one hot map from any enumerable.

5454-- Returns a map, and the ordered list for the reverse transformation

55−hotMap :: (Ord a, KnownNat n) => Proxy n -> [a] -> Either String (Map a Int, Vector a)

55+hotMap :: (Ord a, KnownNat n) => Proxy n -> [a] -> Maybe (Map a Int, Vector a)

5656hotMap n as =

5757 let len = fromIntegral $ natVal n

5858 uniq = [ c | (c:_) <- group $ sort as]

5959 hotl = length uniq

60− in if hotl == len

60+ in if hotl <= len

6161 then

62− Right (M.fromList $ zip uniq [0..], V.fromList uniq)

63− else

64− Left ("Couldn't create hotMap of size " ++ show len ++ " from vector with " ++ show hotl ++ " unique characters")

62+ Just (M.fromList $ zip uniq [0..], V.fromList uniq)

63+ else Nothing

6564

6665-- | From a map and value, create a 1D Shape

6766-- with one index hot (1) with the rest 0.

stack.yamldeleted−78

@@ -1,78 +0,0 @@

1−# This file was automatically generated by 'stack init'

2−#

3−# Some commonly used options have been documented as comments in this file.

4−# For advanced use and comprehensive documentation of the format, please see:

5−# https://docs.haskellstack.org/en/stable/yaml_configuration/

6−

7−# A warning or info to be displayed to the user on config load.

8−# user-message: |

9−

10−# Resolver to choose a 'specific' stackage snapshot or a compiler version.

11−# A snapshot resolver dictates the compiler version and the set of packages

12−# to be used for project dependencies. For example:

13−#

14−# resolver: lts-3.5

15−# resolver: nightly-2015-09-21

16−# resolver: ghc-7.10.2

17−#

18−# The location of a snapshot can be provided as a file or url. Stack assumes

19−# a snapshot provided as a file might change, whereas a url resource does not.

20−#

21−# resolver: ./custom-snapshot.yaml

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

23−resolver: lts-20.18

24−

25−# User packages to be built.

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

27−#

28−# packages:

29−# - some-directory

30−# - https://example.com/foo/bar/baz-0.0.2.tar.gz

31−# subdirs:

32−# - auto-update

33−# - wai

34−packages:

35− - .

36− - examples

37−

38−# Dependency packages to be pulled from upstream that are not in the resolver.

39−# These entries can reference officially published versions as well as

40−# forks / in-progress versions pinned to a git hash. For example:

41−#

42−# extra-deps:

43−# - acme-missiles-0.3

44−# - git: https://github.com/commercialhaskell/stack.git

45−# commit: e7b331f14bcffb8367cd58fbfc8b40ec7642100a

46−#

47−extra-deps:

48− - typelits-witnesses-0.3.0.3

49−

50−# Override default flag values for local packages and extra-deps

51−# flags: {}

52−

53−# Extra package databases containing global packages

54−# extra-package-dbs: []

55−

56−# Control whether we use the GHC we find on the path

57−# system-ghc: true

58−#

59−# Require a specific version of stack, using version ranges

60−# require-stack-version: -any # Default

61−# require-stack-version: ">=2.1"

62−#

63−# Override the architecture used by stack, especially useful on Windows

64−# arch: i386

65−# arch: x86_64

66−#

67−# Extra directories used by stack for building

68−# extra-include-dirs: [/path/to/dir]

69−# extra-lib-dirs: [/path/to/dir]

70−#

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

72−# compiler-check: newer-minor

73−

74−nix:

75− enable: true

76− packages:

77− - blas

78− - lapack

stack.yaml.lockdeleted−19

@@ -1,19 +0,0 @@

1−# This file was autogenerated by Stack.

2−# You should not edit this file by hand.

3−# For more information, please see the documentation at:

4−# https://docs.haskellstack.org/en/stable/lock_files

5−

6−packages:

7−- completed:

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

9− pantry-tree:

10− sha256: 6a42a462f98e94933b6e9721acd912c6c6b6a4743635efd15cc5871908c816a0

11− size: 469

12− original:

13− hackage: typelits-witnesses-0.3.0.3

14−snapshots:

15−- 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

test/Test/Grenade/Layers/Convolution.hs+24−29

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE TemplateHaskell #-}

32{-# LANGUAGE DataKinds #-}

43{-# LANGUAGE GADTs #-}

@@ -16,21 +15,15 @@ import Data.Singletons ()

1615import GHC.TypeLits

1716import GHC.TypeLits.Witnesses

1817

19−#if MIN_VERSION_base(4,9,0)

20−import Data.Kind (Type)

21−#endif

22−

2318import Grenade.Core

2419import Grenade.Layers.Convolution

2520

26−import Hedgehog

27−import qualified Hedgehog.Gen as Gen

21+import Disorder.Jack

2822

29−import Test.Hedgehog.Hmatrix

30−import Test.Hedgehog.TypeLits

31−import Test.Hedgehog.Compat

23+import Test.Jack.Hmatrix

24+import Test.Jack.TypeLits

3225

33−data OpaqueConvolution :: Type where

26+data OpaqueConvolution :: * where

3427 OpaqueConvolution :: Convolution channels filters kernelRows kernelColumns strideRows strideColumns -> OpaqueConvolution

3528

3629instance Show OpaqueConvolution where

@@ -44,10 +37,10 @@ genConvolution :: ( KnownNat channels

4437 , KnownNat strideColumns

4538 , KnownNat kernelFlattened

4639 , kernelFlattened ~ (kernelRows * kernelColumns * channels)

47− ) => Gen (Convolution channels filters kernelRows kernelColumns strideRows strideColumns)

40+ ) => Jack (Convolution channels filters kernelRows kernelColumns strideRows strideColumns)

4841genConvolution = Convolution <$> uniformSample <*> uniformSample

4942

50−genOpaqueOpaqueConvolution :: Gen OpaqueConvolution

43+genOpaqueOpaqueConvolution :: Jack OpaqueConvolution

5144genOpaqueOpaqueConvolution = do

5245 channels <- genNat

5346 filters <- genNat

@@ -63,17 +56,17 @@ genOpaqueOpaqueConvolution = do

6356 p2 = natDict pkc

6457 p3 = natDict pch

6558 in case p1 %* p2 %* p3 of

66− Dict -> OpaqueConvolution <$> (genConvolution :: Gen (Convolution ch fl kr kc sr sc))

67−

68−prop_conv_net_witness = property $

69− blindForAll genOpaqueOpaqueConvolution >>= \onet ->

70− case onet of

71− (OpaqueConvolution ((Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) -> success

59+ Dict -> OpaqueConvolution <$> (genConvolution :: Jack (Convolution ch fl kr kc sr sc))

7260

61+prop_conv_net_witness =

62+ gamble genOpaqueOpaqueConvolution $ \onet ->

63+ (case onet of

64+ (OpaqueConvolution ((Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) -> True

65+ ) :: Bool

7366

74−prop_conv_net = property $

75− blindForAll genOpaqueOpaqueConvolution >>= \onet ->

76− case onet of

67+prop_conv_net =

68+ gamble genOpaqueOpaqueConvolution $ \onet ->

69+ (case onet of

7770 (OpaqueConvolution (convLayer@(Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) ->

7871 let ok stride kernel = [extent | extent <- [(kernel + 1) .. 30 ], (extent - kernel) `mod` stride == 0]

7972 kr = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

@@ -81,15 +74,15 @@ prop_conv_net = property $

8174 sr = fromIntegral $ natVal (Proxy :: Proxy strideRows)

8275 sc = fromIntegral $ natVal (Proxy :: Proxy strideCols)

8376

84− in forAll (Gen.element (ok sr kr)) >>= \er ->

85− forAll (Gen.element (ok sc kc)) >>= \ec ->

77+ in gamble (elements (ok sr kr)) $ \er ->

78+ gamble (elements (ok sc kc)) $ \ec ->

8679 let rr = ((er - kr) `div` sr) + 1

8780 rc = ((ec - kc) `div` sc) + 1

8881 Just er' = someNatVal er

8982 Just ec' = someNatVal ec

9083 Just rr' = someNatVal rr

9184 Just rc' = someNatVal rc

92− in case (er', ec', rr', rc') of

85+ in (case (er', ec', rr', rc') of

9386 ( SomeNat (pinr :: Proxy inRows), SomeNat (_ :: Proxy inCols), SomeNat (pour :: Proxy outRows), SomeNat (_ :: Proxy outCols)) ->

9487 case ( natDict pinr %* natDict (Proxy :: Proxy channels)

9588 , natDict pour %* natDict (Proxy :: Proxy filters)

@@ -97,12 +90,14 @@ prop_conv_net = property $

9790 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

9891 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

9992 (Dict, Dict, Dict, Dict) ->

100− blindForAll (S3D <$> uniformSample) >>= \(input :: S ('D3 inRows inCols channels)) ->

93+ gamble (S3D <$> uniformSample) $ \(input :: S ('D3 inRows inCols channels)) ->

10194 let (tape, output :: S ('D3 outRows outCols filters)) = runForwards convLayer input

10295 backed :: (Gradient (Convolution channels filters kernelRows kernelCols strideRows strideCols), S ('D3 inRows inCols channels))

10396 = runBackwards convLayer tape output

104− in backed `seq` success

105−

97+ in backed `seq` True

98+ ) :: Property

99+ ) :: Property

106100

101+return []

107102tests :: IO Bool

108−tests = checkParallel $$(discover)

103+tests = $quickCheckAll

test/Test/Grenade/Layers/FullyConnected.hs+17−21

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE BangPatterns #-}

32{-# LANGUAGE TemplateHaskell #-}

43{-# LANGUAGE DataKinds #-}

@@ -13,28 +12,24 @@ import Data.Singletons ()

1312

1413import GHC.TypeLits

1514

16−#if MIN_VERSION_base(4,9,0)

17−import Data.Kind (Type)

18−#endif

19−

2015import Grenade.Core

2116import Grenade.Layers.FullyConnected

2217

23−import Hedgehog

18+import Disorder.Jack

19+

20+import Test.Jack.Hmatrix

2421

25−import Test.Hedgehog.Compat

26−import Test.Hedgehog.Hmatrix

2722

28−data OpaqueFullyConnected :: Type where

23+data OpaqueFullyConnected :: * where

2924 OpaqueFullyConnected :: (KnownNat i, KnownNat o) => FullyConnected i o -> OpaqueFullyConnected

3025

3126instance Show OpaqueFullyConnected where

3227 show (OpaqueFullyConnected n) = show n

3328

34−genOpaqueFullyConnected :: Gen OpaqueFullyConnected

29+genOpaqueFullyConnected :: Jack OpaqueFullyConnected

3530genOpaqueFullyConnected = do

36− input :: Integer <- choose 2 100

37− output :: Integer <- choose 1 100

31+ input :: Integer <- choose (2, 100)

32+ output :: Integer <- choose (1, 100)

3833 let Just input' = someNatVal input

3934 let Just output' = someNatVal output

4035 case (input', output') of

@@ -46,13 +41,14 @@ genOpaqueFullyConnected = do

4641 return . OpaqueFullyConnected $ (FullyConnected (FullyConnected' wB wN) (FullyConnected' bM kM) :: FullyConnected i' o')

4742

4843prop_fully_connected_forwards :: Property

49−prop_fully_connected_forwards = property $ do

50− OpaqueFullyConnected (fclayer :: FullyConnected i o) <- blindForAll genOpaqueFullyConnected

51− input :: S ('D1 i) <- blindForAll (S1D <$> randomVector)

52− let (tape, output :: S ('D1 o)) = runForwards fclayer input

53− backed :: (Gradient (FullyConnected i o), S ('D1 i))

54− = runBackwards fclayer tape output

55− backed `seq` success

56−

44+prop_fully_connected_forwards =

45+ gamble genOpaqueFullyConnected $ \(OpaqueFullyConnected (fclayer :: FullyConnected i o)) ->

46+ gamble (S1D <$> randomVector) $ \(input :: S ('D1 i)) ->

47+ let (tape, output :: S ('D1 o)) = runForwards fclayer input

48+ backed :: (Gradient (FullyConnected i o), S ('D1 i))

49+ = runBackwards fclayer tape output

50+ in backed `seq` True

51+

52+return []

5753tests :: IO Bool

58−tests = checkParallel $$(discover)

54+tests = $quickCheckAll

test/Test/Grenade/Layers/Internal/Convolution.hs+29−34

@@ -12,46 +12,41 @@ import Grenade.Layers.Internal.Convolution

1212

1313import Numeric.LinearAlgebra hiding (uniformSample, konst, (===))

1414

15−import Hedgehog

16−import qualified Hedgehog.Gen as Gen

17−import qualified Hedgehog.Range as Range

15+import Disorder.Jack

1816

1917import qualified Test.Grenade.Layers.Internal.Reference as Reference

20−import Test.Hedgehog.Compat

2118

2219prop_im2col_col2im_symmetrical_with_kernel_stride =

2320 let factors n = [x | x <- [1..n], n `mod` x == 0]

24− in property $ do

25− height <- forAll $ choose 2 100

26− width <- forAll $ choose 2 100

27− kernel_h <- forAll $ (height `div`) <$> Gen.element (factors height)

28− kernel_w <- forAll $ (width `div`) <$> Gen.element (factors width)

29− input <- forAll $ (height >< width) <$> Gen.list (Range.singleton $ height * width) (Gen.realFloat $ Range.linearFracFrom 0 (-100) 100)

30−

31− let stride_h = kernel_h

32− let stride_w = kernel_w

33− let out = col2im kernel_h kernel_w stride_h stride_w height width . im2col kernel_h kernel_w stride_h stride_w $ input

34− input === out

21+ in gamble (choose (2, 100)) $ \height ->

22+ gamble (choose (2, 100)) $ \width ->

23+ gamble ((height `div`) <$> elements (factors height)) $ \kernel_h ->

24+ gamble ((width `div`) <$> elements (factors width)) $ \kernel_w ->

25+ gamble (listOfN (height * width) (height * width) sizedRealFrac) $ \input ->

26+ let input' = (height >< width) input

27+ stride_h = kernel_h

28+ stride_w = kernel_w

29+ out = col2im kernel_h kernel_w stride_h stride_w height width . im2col kernel_h kernel_w stride_h stride_w $ input'

30+ in input' === out

3531

3632prop_im2col_col2im_behaves_as_reference =

3733 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

38− in property $ do

39− height <- forAll (choose 2 100)

40− width <- forAll (choose 2 100)

41− kernel_h <- forAll (choose 2 (height - 1))

42− kernel_w <- forAll (choose 2 (width - 1))

43− stride_h <- forAll (Gen.element (ok height kernel_h))

44− stride_w <- forAll (Gen.element (ok width kernel_w))

45− input <- forAll ((height >< width) <$> Gen.list (Range.singleton $ height * width) (Gen.realFloat $ Range.linearFracFrom 0 (-100) 100))

46−

47− let outFast = im2col kernel_h kernel_w stride_h stride_w input

48− let retFast = col2im kernel_h kernel_w stride_h stride_w height width outFast

49−

50− let outReference = Reference.im2col kernel_h kernel_w stride_h stride_w input

51− let retReference = Reference.col2im kernel_h kernel_w stride_h stride_w height width outReference

52−

53− outFast === outReference

54− retFast === retReference

55−

34+ in gamble (choose (2, 100)) $ \height ->

35+ gamble (choose (2, 100)) $ \width ->

36+ gamble (choose (2, height - 1)) $ \kernel_h ->

37+ gamble (choose (2, width - 1)) $ \kernel_w ->

38+ gamble (elements (ok height kernel_h)) $ \stride_h ->

39+ gamble (elements (ok width kernel_w)) $ \stride_w ->

40+ gamble (listOfN (height * width) (height * width) sizedRealFrac) $ \input ->

41+ let input' = (height >< width) input

42+ outFast = im2col kernel_h kernel_w stride_h stride_w input'

43+ retFast = col2im kernel_h kernel_w stride_h stride_w height width outFast

44+

45+ outReference = Reference.im2col kernel_h kernel_w stride_h stride_w input'

46+ retReference = Reference.col2im kernel_h kernel_w stride_h stride_w height width outReference

47+ in outFast === outReference .&&. retFast === retReference

48+

49+

50+return []

5651tests :: IO Bool

57−tests = checkParallel $$(discover)

52+tests = $quickCheckAll

test/Test/Grenade/Layers/Internal/Pooling.hs+18−24

@@ -12,34 +12,28 @@ import Grenade.Layers.Internal.Pooling

1212

1313import Numeric.LinearAlgebra hiding (uniformSample, konst, (===))

1414

15−import Hedgehog

16−import qualified Hedgehog.Gen as Gen

17−import qualified Hedgehog.Range as Range

15+import Disorder.Jack

1816

1917import qualified Test.Grenade.Layers.Internal.Reference as Reference

20−import Test.Hedgehog.Compat

2118

2219prop_poolForwards_poolBackwards_behaves_as_reference =

2320 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

2421 output extent kernel stride = (extent - kernel) `div` stride + 1

25− in property $ do

26− height <- forAll $ choose 2 100

27− width <- forAll $ choose 2 100

28− kernel_h <- forAll $ choose 1 (height - 1)

29− kernel_w <- forAll $ choose 1 (width - 1)

30− stride_h <- forAll $ Gen.element (ok height kernel_h)

31− stride_w <- forAll $ Gen.element (ok width kernel_w)

32− input <- forAll $ (height >< width) <$> Gen.list (Range.singleton $ height * width) (Gen.realFloat $ Range.linearFracFrom 0 (-100) 100)

33−

34− let outFast = poolForward 1 height width kernel_h kernel_w stride_h stride_w input

35− let retFast = poolBackward 1 height width kernel_h kernel_w stride_h stride_w input outFast

36−

37− let outReference = Reference.poolForward kernel_h kernel_w stride_h stride_w (output height kernel_h stride_h) (output width kernel_w stride_w) input

38− let retReference = Reference.poolBackward kernel_h kernel_w stride_h stride_w input outReference

39−

40− outFast === outReference

41− retFast === retReference

42−

43−

22+ in gamble (choose (2, 100)) $ \height ->

23+ gamble (choose (2, 100)) $ \width ->

24+ gamble (choose (1, height - 1)) $ \kernel_h ->

25+ gamble (choose (1, width - 1)) $ \kernel_w ->

26+ gamble (elements (ok height kernel_h)) $ \stride_h ->

27+ gamble (elements (ok width kernel_w)) $ \stride_w ->

28+ gamble (listOfN (height * width) (height * width) sizedRealFrac) $ \input ->

29+ let input' = (height >< width) input

30+ outFast = poolForward 1 height width kernel_h kernel_w stride_h stride_w input'

31+ retFast = poolBackward 1 height width kernel_h kernel_w stride_h stride_w input' outFast

32+

33+ outReference = Reference.poolForward kernel_h kernel_w stride_h stride_w (output height kernel_h stride_h) (output width kernel_w stride_w) input'

34+ retReference = Reference.poolBackward kernel_h kernel_w stride_h stride_w input' outReference

35+ in outFast === outReference .&&. retFast === retReference

36+

37+return []

4438tests :: IO Bool

45−tests = checkParallel $$(discover)

39+tests = $quickCheckAll

test/Test/Grenade/Layers/Nonlinear.hs+36−13

@@ -1,4 +1,5 @@

11{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE CPP #-}

23{-# LANGUAGE TemplateHaskell #-}

34{-# LANGUAGE DataKinds #-}

45{-# LANGUAGE KindSignatures #-}

@@ -10,45 +11,67 @@ module Test.Grenade.Layers.Nonlinear where

1011

1112import Data.Singletons

1213

14+#if __GLASGOW_HASKELL__ < 800

15+import Data.Proxy

16+#endif

17+

1318import Grenade

19+import GHC.TypeLits

1420

15−import Hedgehog

21+import Disorder.Jack

1622

17−import Test.Hedgehog.Compat

18−import Test.Hedgehog.Hmatrix

19−import Test.Hedgehog.TypeLits

23+import Test.Jack.Hmatrix

24+import Test.Jack.TypeLits

2025

2126import Numeric.LinearAlgebra.Static ( norm_Inf )

2227

2328prop_sigmoid_grad :: Property

24−prop_sigmoid_grad = property $

25− forAllWith rss genShape >>= \case

29+prop_sigmoid_grad =

30+ gambleDisplay (const "Shape") genShape $ \case

2631 (SomeSing (r :: Sing s)) ->

2732 withSingI r $

28− blindForAll genOfShape >>= \(ds :: S s) ->

33+ gamble genOfShape $ \(ds :: S s) ->

2934 let (tape, f :: S s) = runForwards Logit ds

3035 ((), ret :: S s) = runBackwards Logit tape (1 :: S s)

3136 (_, numer :: S s) = runForwards Logit (ds + 0.0001)

3237 numericalGradient = (numer - f) * 10000

33− in assert ((case numericalGradient - ret of

38+ in counterexample (show numericalGradient ++ show ret)

39+ ((case numericalGradient - ret of

3440 (S1D x) -> norm_Inf x < 0.0001

3541 (S2D x) -> norm_Inf x < 0.0001

3642 (S3D x) -> norm_Inf x < 0.0001) :: Bool)

3743

3844prop_tanh_grad :: Property

39−prop_tanh_grad = property $

40− forAllWith rss genShape >>= \case

45+prop_tanh_grad =

46+ gambleDisplay (const "Shape") genShape $ \case

4147 (SomeSing (r :: Sing s)) ->

4248 withSingI r $

43− blindForAll genOfShape >>= \(ds :: S s) ->

49+ gamble genOfShape $ \(ds :: S s) ->

4450 let (tape, f :: S s) = runForwards Tanh ds

4551 ((), ret :: S s) = runBackwards Tanh tape (1 :: S s)

4652 (_, numer :: S s) = runForwards Tanh (ds + 0.0001)

4753 numericalGradient = (numer - f) * 10000

48− in assert ((case numericalGradient - ret of

54+ in counterexample (show numericalGradient ++ show ret)

55+ ((case numericalGradient - ret of

4956 (S1D x) -> norm_Inf x < 0.001

5057 (S2D x) -> norm_Inf x < 0.001

5158 (S3D x) -> norm_Inf x < 0.001) :: Bool)

5259

60+prop_softmax_grad :: Property

61+prop_softmax_grad =

62+ gamble genNat $ \case

63+ (SomeNat (_ :: Proxy s)) ->

64+ gamble genOfShape $ \(ds :: S ('D1 s)) ->

65+ let (tape, f :: S ('D1 s)) = runForwards Relu ds

66+ ((), ret :: S ('D1 s)) = runBackwards Relu tape (1 :: S ('D1 s))

67+ (_, numer :: S ('D1 s)) = runForwards Relu (ds + 0.0001)

68+ numericalGradient = (numer - f) * 10000

69+ in counterexample (show numericalGradient ++ show ret)

70+ ((case numericalGradient - ret of

71+ (S1D x) -> norm_Inf x < 0.0001) :: Bool)

72+

73+

74+return []

5375tests :: IO Bool

54−tests = checkParallel $$(discover)

76+tests = $quickCheckAll

77+

test/Test/Grenade/Layers/PadCrop.hs+12−12

@@ -7,43 +7,43 @@

77{-# LANGUAGE ScopedTypeVariables #-}

88{-# OPTIONS_GHC -fno-warn-missing-signatures #-}

99

10+#if __GLASGOW_HASKELL__ < 800

11+{-# OPTIONS_GHC -fno-warn-incomplete-patterns #-}

12+#endif

13+

1014module Test.Grenade.Layers.PadCrop where

1115

1216import Grenade

1317

14−import Hedgehog

18+import Disorder.Jack

1519

1620import Numeric.LinearAlgebra.Static ( norm_Inf )

1721

18−import Test.Hedgehog.Hmatrix

22+import Test.Jack.Hmatrix

1923

2024prop_pad_crop :: Property

2125prop_pad_crop =

2226 let net :: Network '[Pad 2 3 4 6, Crop 2 3 4 6] '[ 'D3 7 9 5, 'D3 16 15 5, 'D3 7 9 5 ]

2327 net = Pad :~> Crop :~> NNil

24− in property $

25− forAll genOfShape >>= \(d :: S ('D3 7 9 5)) ->

28+ in gamble genOfShape $ \(d :: S ('D3 7 9 5)) ->

2629 let (tapes, res) = runForwards net d

2730 (_ , grad) = runBackwards net tapes d

28− in do assert $ d ~~~ res

29− assert $ grad ~~~ d

31+ in d ~~~ res .&&. grad ~~~ d

3032

3133prop_pad_crop_2d :: Property

3234prop_pad_crop_2d =

3335 let net :: Network '[Pad 2 3 4 6, Crop 2 3 4 6] '[ 'D2 7 9, 'D2 16 15, 'D2 7 9 ]

3436 net = Pad :~> Crop :~> NNil

35− in property $

36− forAll genOfShape >>= \(d :: S ('D2 7 9)) ->

37+ in gamble genOfShape $ \(d :: S ('D2 7 9)) ->

3738 let (tapes, res) = runForwards net d

3839 (_ , grad) = runBackwards net tapes d

39− in do assert $ d ~~~ res

40− assert $ grad ~~~ d

40+ in d ~~~ res .&&. grad ~~~ d

4141

4242(~~~) :: S x -> S x -> Bool

4343(S1D x) ~~~ (S1D y) = norm_Inf (x - y) < 0.00001

4444(S2D x) ~~~ (S2D y) = norm_Inf (x - y) < 0.00001

4545(S3D x) ~~~ (S3D y) = norm_Inf (x - y) < 0.00001

4646

47−

47+return []

4848tests :: IO Bool

49−tests = checkParallel $$(discover)

49+tests = $quickCheckAll

test/Test/Grenade/Layers/Pooling.hs+14−19

@@ -1,4 +1,3 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE TemplateHaskell #-}

32{-# LANGUAGE DataKinds #-}

43{-# LANGUAGE KindSignatures #-}

@@ -10,40 +9,36 @@ module Test.Grenade.Layers.Pooling where

109import Data.Proxy

1110import Data.Singletons ()

1211

13−#if MIN_VERSION_base(4,9,0)

14−import Data.Kind (Type)

15−#endif

16−

1712import GHC.TypeLits

1813import Grenade.Layers.Pooling

1914

20−import Hedgehog

15+import Disorder.Jack

2116

22−import Test.Hedgehog.Compat

2317

24−data OpaquePooling :: Type where

18+data OpaquePooling :: * where

2519 OpaquePooling :: (KnownNat kh, KnownNat kw, KnownNat sh, KnownNat sw) => Pooling kh kw sh sw -> OpaquePooling

2620

2721instance Show OpaquePooling where

2822 show (OpaquePooling n) = show n

2923

30−genOpaquePooling :: Gen OpaquePooling

24+genOpaquePooling :: Jack OpaquePooling

3125genOpaquePooling = do

32− ~(Just kernelHeight) <- someNatVal <$> choose 2 15

33− ~(Just kernelWidth ) <- someNatVal <$> choose 2 15

34− ~(Just strideHeight) <- someNatVal <$> choose 2 15

35− ~(Just strideWidth ) <- someNatVal <$> choose 2 15

26+ Just kernelHeight <- someNatVal <$> choose (2, 15)

27+ Just kernelWidth <- someNatVal <$> choose (2, 15)

28+ Just strideHeight <- someNatVal <$> choose (2, 15)

29+ Just strideWidth <- someNatVal <$> choose (2, 15)

3630

3731 case (kernelHeight, kernelWidth, strideHeight, strideWidth) of

3832 (SomeNat (_ :: Proxy kh), SomeNat (_ :: Proxy kw), SomeNat (_ :: Proxy sh), SomeNat (_ :: Proxy sw)) ->

3933 return $ OpaquePooling (Pooling :: Pooling kh kw sh sw)

4034

4135prop_pool_layer_witness =

42− property $ do

43− onet <- forAll genOpaquePooling

44− case onet of

45− (OpaquePooling (Pooling :: Pooling kernelRows kernelCols strideRows strideCols)) ->

46− assert True

36+ gamble genOpaquePooling $ \onet ->

37+ (case onet of

38+ (OpaquePooling (Pooling :: Pooling kernelRows kernelCols strideRows strideCols)) -> True

39+ ) :: Bool

40+

4741

42+return []

4843tests :: IO Bool

49−tests = checkParallel $$(discover)

44+tests = $quickCheckAll

test/Test/Grenade/Network.hsdeleted−504

@@ -1,504 +0,0 @@

1−{-# LANGUAGE CPP #-}

2−{-# LANGUAGE BangPatterns #-}

3−{-# LANGUAGE RankNTypes #-}

4−{-# LANGUAGE DataKinds #-}

5−{-# LANGUAGE ScopedTypeVariables #-}

6−{-# LANGUAGE PolyKinds #-}

7−{-# LANGUAGE GADTs #-}

8−{-# LANGUAGE TypeOperators #-}

9−{-# LANGUAGE TemplateHaskell #-}

10−{-# LANGUAGE FlexibleContexts #-}

11−module Test.Grenade.Network where

12−

13−import Control.Monad ( guard )

14−import Control.Monad.ST ( runST )

15−

16−import Data.Constraint

17−import qualified Data.Vector.Storable as VS

18−import qualified Data.Vector.Storable.Mutable as VS ( write )

19−import Data.Singletons

20−import Data.Singletons.Prelude.List

21−import Data.Singletons.TypeLits

22−

23−import Hedgehog

24−import qualified Hedgehog.Gen as Gen

25−import Hedgehog.Internal.Source

26−import Hedgehog.Internal.Property ( failWith )

27−

28−import Grenade

29−

30−#if MIN_VERSION_base(4,11,0)

31−import GHC.TypeLits hiding (natVal)

32−#else

33−import GHC.TypeLits

34−#endif

35−#if MIN_VERSION_base(4,9,0)

36−import Data.Kind (Type)

37−#endif

38−

39−import GHC.TypeLits.Witnesses

40−import Test.Hedgehog.Compat

41−import Test.Hedgehog.TypeLits

42−import Test.Hedgehog.Hmatrix

43−import Test.Grenade.Layers.Convolution

44−

45−import Numeric.LinearAlgebra ( flatten )

46−import Numeric.LinearAlgebra.Static ( extract, norm_Inf )

47−import Unsafe.Coerce

48−

49−data SomeNetwork :: Type where

50− SomeNetwork :: ( SingI shapes, SingI (Head shapes), SingI (Last shapes), Show (Network layers shapes) ) => Network layers shapes -> SomeNetwork

51−

52−instance Show SomeNetwork where

53− show (SomeNetwork net) = show net

54−

55−-- | Generate a random network of a random type

56−--

57−-- This is slightly insane for a few reasons. Everything must be wrapped up

58−-- in a SomeNetwork.

59−genNetwork :: Gen SomeNetwork

60−genNetwork =

61− Gen.recursive Gen.choice [

62− do SomeSing ( r :: Sing final ) <- genShape

63− withSingI r $

64− pure (SomeNetwork (NNil :: Network '[] '[ final ] ))

65− ] [

66− do SomeNetwork ( rest :: Network layers shapes ) <- genNetwork

67− case ( sing :: Sing shapes ) of

68− SNil -> Gen.discard -- Can't occur

69− SCons ( h :: Sing h ) ( _ :: Sing hs ) ->

70− withSingI h $

71− case h of

72− D1Sing l@SNat ->

73− Gen.choice [

74− pure (SomeNetwork (Tanh :~> rest :: Network ( Tanh ': layers ) ( h ': h ': hs )))

75− , pure (SomeNetwork (Logit :~> rest :: Network ( Logit ': layers ) ( h ': h ': hs )))

76− , pure (SomeNetwork (Relu :~> rest :: Network ( Relu ': layers ) ( h ': h ': hs )))

77− , pure (SomeNetwork (Elu :~> rest :: Network ( Elu ': layers ) ( h ': h ': hs )))

78− , pure (SomeNetwork (Softmax :~> rest :: Network ( Softmax ': layers ) ( h ': h ': hs )))

79− , do -- Reshape to two dimensions

80− let divisors :: Integer -> [Integer]

81− divisors n = 1 : [x | x <- [2..(n-1)], n `rem` x == 0]

82− let len = fromIntegral $ natVal l

83− rs <- Gen.element $ divisors len

84− let cs = len `quot` rs

85− case ( someNatVal rs, someNatVal cs, someNatVal len ) of

86− ( Just (SomeNat (rs' :: Proxy inRows)), Just (SomeNat (cs' :: Proxy inCols)), Just (SomeNat (_ :: Proxy outLen ) )) ->

87− let p1 = natDict rs'

88− p2 = natDict cs'

89− in case ( p1 %* p2, unsafeCoerce (Dict :: Dict ()) :: Dict ((inRows * inCols) ~ outLen), unsafeCoerce (Dict :: Dict ()) :: Dict (( 'D1 outLen ) ~ h )) of

90− ( Dict, Dict, Dict ) ->

91− pure (SomeNetwork (Reshape :~> rest :: Network ( Reshape ': layers ) ( ('D2 inRows inCols) ': h ': hs )))

92− _ -> Gen.discard -- Doesn't occur

93− ]

94− D2Sing r@SNat c@SNat ->

95− Gen.choice [

96− pure (SomeNetwork (Tanh :~> rest :: Network ( Tanh ': layers ) ( h ': h ': hs )))

97− , pure (SomeNetwork (Logit :~> rest :: Network ( Logit ': layers ) ( h ': h ': hs )))

98− , pure (SomeNetwork (Relu :~> rest :: Network ( Relu ': layers ) ( h ': h ': hs )))

99− , pure (SomeNetwork (Elu :~> rest :: Network ( Elu ': layers ) ( h ': h ': hs )))

100− , do -- Build a convolution layer with one filter output

101− -- Figure out some kernel sizes which work for this layer

102− -- There must be a better way than this...

103− let output_r = fromIntegral $ natVal r

104− let output_c = fromIntegral $ natVal c

105−

106− let ok extent kernel = [stride | stride <- [ 1 .. extent ], (extent - kernel) `mod` stride == 0]

107−

108− -- Get some kernels which will fit

109− kernel_r <- choose 1 output_r

110− kernel_c <- choose 1 output_c

111−

112− -- Build up some strides which also fit

113− stride_r <- Gen.element $ ok output_r kernel_r

114− stride_c <- Gen.element $ ok output_c kernel_c

115−

116− -- Determine the input size

117− let input_r = (output_r - 1) * stride_r + kernel_r

118− let input_c = (output_c - 1) * stride_c + kernel_c

119−

120− guard (input_r < 100)

121− guard (input_c < 100)

122−

123− -- Remake types for input

124− case ( someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal kernel_r, someNatVal kernel_c, someNatVal stride_r, someNatVal stride_c ) of

125− ( Just (SomeNat (_ :: Proxy inRows)), Just (SomeNat (_ :: Proxy inCols)),

126− Just (SomeNat (_ :: Proxy outRows)), Just (SomeNat (_ :: Proxy outCols)),

127− Just (SomeNat (pkr :: Proxy kernelRows)), Just (SomeNat (pkc :: Proxy kernelCols)),

128− Just (SomeNat (_ :: Proxy strideRows)), Just (SomeNat (_ :: Proxy strideCols))) ->

129− let p1 = natDict pkr

130− p2 = natDict pkc

131− in case ( p1 %* p2

132− -- Fake it till you make it.

133− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D2 outRows outCols ) ~ h ))

134− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

135− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

136− (Dict, Dict, Dict, Dict) -> do

137− conv <- genConvolution

138− pure (SomeNetwork (conv :~> rest :: Network ( Convolution 1 1 kernelRows kernelCols strideRows strideCols ': layers ) ( ('D2 inRows inCols) ': h ': hs )))

139− _ -> Gen.discard -- Can't occur

140− , do -- Build a convolution layer with one filter output

141− -- Figure out some kernel sizes which work for this layer

142− -- There must be a better way than this...

143− let output_r = fromIntegral $ natVal r

144− let output_c = fromIntegral $ natVal c

145−

146− let ok extent kernel = [stride | stride <- [ 1 .. extent ], (extent - kernel) `mod` stride == 0]

147−

148− -- Get some kernels which will fit

149− kernel_r <- choose 1 output_r

150− kernel_c <- choose 1 output_c

151− channels <- choose 1 10

152−

153− -- Build up some strides which also fit

154− stride_r <- Gen.element $ ok output_r kernel_r

155− stride_c <- Gen.element $ ok output_c kernel_c

156−

157− -- Determine the input size

158− let input_r = (output_r - 1) * stride_r + kernel_r

159− let input_c = (output_c - 1) * stride_c + kernel_c

160−

161− guard (input_r < 100)

162− guard (input_c < 100)

163−

164− -- Remake types for input

165− case ( someNatVal channels, someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal kernel_r, someNatVal kernel_c, someNatVal stride_r, someNatVal stride_c ) of

166− ( Just (SomeNat (chan :: Proxy channels)),

167− Just (SomeNat (pinr :: Proxy inRows)), Just (SomeNat (_ :: Proxy inCols)),

168− Just (SomeNat (_ :: Proxy outRows)), Just (SomeNat (_ :: Proxy outCols)),

169− Just (SomeNat (pkr :: Proxy kernelRows)), Just (SomeNat (pkc :: Proxy kernelCols)),

170− Just (SomeNat (_ :: Proxy strideRows)), Just (SomeNat (_ :: Proxy strideCols))) ->

171− let p1 = natDict pkr

172− p2 = natDict pkc

173− p3 = natDict chan

174− in case ( p1 %* p2 %* p3

175− , natDict pinr %* natDict chan

176− -- Fake it till you make it.

177− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D2 outRows outCols ) ~ h ))

178− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

179− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

180− (Dict, Dict, Dict, Dict, Dict) -> do

181− conv <- genConvolution

182− pure (SomeNetwork (conv :~> rest :: Network ( Convolution channels 1 kernelRows kernelCols strideRows strideCols ': layers ) ( ('D3 inRows inCols channels) ': h ': hs )))

183− _ -> Gen.discard -- Can't occur

184− , do -- Build a Pooling layer

185− let output_r = fromIntegral $ natVal r

186− let output_c = fromIntegral $ natVal c

187−

188− let ok extent kernel = [stride | stride <- [ 1 .. extent ], (extent - kernel) `mod` stride == 0]

189−

190− -- Get some kernels which will fit

191− kernel_r <- choose 1 output_r

192− kernel_c <- choose 1 output_c

193−

194− -- Build up some strides which also fit

195− stride_r <- Gen.element $ ok output_r kernel_r

196− stride_c <- Gen.element $ ok output_c kernel_c

197−

198− -- Determine the input size

199− let input_r = (output_r - 1) * stride_r + kernel_r

200− let input_c = (output_c - 1) * stride_c + kernel_c

201−

202− guard (input_r < 100)

203− guard (input_c < 100)

204−

205− -- Remake types for input

206− case ( someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal kernel_r, someNatVal kernel_c, someNatVal stride_r, someNatVal stride_c ) of

207− ( Just (SomeNat (_ :: Proxy inRows)), Just (SomeNat (_ :: Proxy inCols)),

208− Just (SomeNat (_ :: Proxy outRows)), Just (SomeNat (_ :: Proxy outCols)),

209− Just (SomeNat (pkr :: Proxy kernelRows)), Just (SomeNat (pkc :: Proxy kernelCols)),

210− Just (SomeNat (_ :: Proxy strideRows)), Just (SomeNat (_ :: Proxy strideCols))) ->

211− let p1 = natDict pkr

212− p2 = natDict pkc

213− in case ( p1 %* p2

214− -- Fake it till you make it.

215− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D2 outRows outCols ) ~ h ))

216− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

217− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

218− (Dict, Dict, Dict, Dict) ->

219− pure (SomeNetwork (Pooling :~> rest :: Network ( Pooling kernelRows kernelCols strideRows strideCols ': layers ) ( ('D2 inRows inCols) ': h ': hs )))

220− _ -> Gen.discard -- Can't occur

221− , do -- Build a Pad layer

222− let output_r = fromIntegral $ natVal r

223− let output_c = fromIntegral $ natVal c

224−

225− pad_left <- choose 0 (output_r - 1)

226− pad_right <- choose 0 (output_r - 1 - pad_left)

227− pad_top <- choose 0 (output_c - 1)

228− pad_bottom <- choose 0 (output_c - 1 - pad_top)

229−

230− let input_r = output_r - pad_top - pad_bottom

231− let input_c = output_c - pad_left - pad_right

232−

233− guard (input_r > 1)

234− guard (input_c > 1)

235−

236− -- Remake types for input

237− case ( someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal pad_left, someNatVal pad_right, someNatVal pad_top, someNatVal pad_bottom ) of

238− ( Just (SomeNat (_ :: Proxy inputRows)), Just (SomeNat (_ :: Proxy inputColumns)),

239− Just (SomeNat (_ :: Proxy outputRows)), Just (SomeNat (_ :: Proxy outputColumns)),

240− Just (SomeNat (_ :: Proxy padLeft)), Just (SomeNat (_ :: Proxy padRight)),

241− Just (SomeNat (_ :: Proxy padTop)), Just (SomeNat (_ :: Proxy padBottom))) ->

242− case ( (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D2 outputRows outputColumns) ~ h ))

243− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (inputRows + padTop + padBottom) ~ outputRows) )

244− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (inputColumns + padLeft + padRight) ~ outputColumns ))) of

245− ( Dict, Dict, Dict ) ->

246− pure (SomeNetwork (Pad :~> rest :: Network ( Pad padLeft padTop padRight padBottom ': layers ) ( ('D2 inputRows inputColumns) ': h ': hs )))

247− _ -> Gen.discard -- Can't occur

248− , do -- Build a Crop layer

249− let output_r = fromIntegral $ natVal r

250− let output_c = fromIntegral $ natVal c

251−

252− crop_left <- choose 0 10

253− crop_right <- choose 0 10

254− crop_top <- choose 0 10

255− crop_bottom <- choose 0 10

256−

257− let input_r = output_r + crop_top + crop_bottom

258− let input_c = output_c + crop_left + crop_right

259−

260− -- Remake types for input

261− case ( someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal crop_left, someNatVal crop_right, someNatVal crop_top, someNatVal crop_bottom ) of

262− ( Just (SomeNat (_ :: Proxy inputRows)), Just (SomeNat (_ :: Proxy inputColumns)),

263− Just (SomeNat (_ :: Proxy outputRows)), Just (SomeNat (_ :: Proxy outputColumns)),

264− Just (SomeNat (_ :: Proxy cropLeft)), Just (SomeNat (_ :: Proxy cropRight)),

265− Just (SomeNat (_ :: Proxy cropTop)), Just (SomeNat (_ :: Proxy cropBottom))) ->

266− case ( (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D2 outputRows outputColumns) ~ h ))

267− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (outputRows + cropTop + cropBottom) ~ inputRows ) )

268− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (outputColumns + cropLeft + cropRight) ~ inputColumns ))) of

269− (Dict, Dict, Dict) ->

270− pure (SomeNetwork (Crop :~> rest :: Network ( Crop cropLeft cropTop cropRight cropBottom ': layers ) ( ('D2 inputRows inputColumns) ': h ': hs )))

271− _ -> Gen.discard -- Can't occur

272− ]

273− D3Sing r@SNat c@SNat f@SNat ->

274− Gen.choice [

275− pure (SomeNetwork (Tanh :~> rest :: Network ( Tanh ': layers ) ( h ': h ': hs )))

276− , pure (SomeNetwork (Logit :~> rest :: Network ( Logit ': layers ) ( h ': h ': hs )))

277− , pure (SomeNetwork (Relu :~> rest :: Network ( Relu ': layers ) ( h ': h ': hs )))

278− , pure (SomeNetwork (Elu :~> rest :: Network ( Elu ': layers ) ( h ': h ': hs )))

279− , do -- Build a convolution layer with one filter output

280− -- Figure out some kernel sizes which work for this layer

281− -- There must be a better way than this...

282− let output_r = fromIntegral $ natVal r

283− let output_c = fromIntegral $ natVal c

284− let output_f = fromIntegral $ natVal f

285−

286− let ok extent kernel = [stride | stride <- [ 1 .. extent ], (extent - kernel) `mod` stride == 0]

287−

288− -- Get some kernels which will fit

289− kernel_r <- choose 1 output_r

290− kernel_c <- choose 1 output_c

291− channels <- choose 1 10

292−

293− -- Build up some strides which also fit

294− stride_r <- Gen.element $ ok output_r kernel_r

295− stride_c <- Gen.element $ ok output_c kernel_c

296−

297− -- Determine the input size

298− let input_r = (output_r - 1) * stride_r + kernel_r

299− let input_c = (output_c - 1) * stride_c + kernel_c

300−

301− guard (input_r < 100)

302− guard (input_c < 100)

303−

304− -- Remake types for input

305− case ( someNatVal channels, someNatVal output_f, someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal kernel_r, someNatVal kernel_c, someNatVal stride_r, someNatVal stride_c ) of

306− ( Just (SomeNat (chan :: Proxy channels)), Just (SomeNat (_ :: Proxy filters)),

307− Just (SomeNat (pinr :: Proxy inRows)), Just (SomeNat (_ :: Proxy inCols)),

308− Just (SomeNat (_ :: Proxy outRows)), Just (SomeNat (_ :: Proxy outCols)),

309− Just (SomeNat (pkr :: Proxy kernelRows)), Just (SomeNat (pkc :: Proxy kernelCols)),

310− Just (SomeNat (_ :: Proxy strideRows)), Just (SomeNat (_ :: Proxy strideCols))) ->

311− let p1 = natDict pkr

312− p2 = natDict pkc

313− p3 = natDict chan

314− in case ( p1 %* p2 %* p3

315− , natDict pinr %* natDict chan

316− -- Fake it till you make it.

317− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D3 outRows outCols filters) ~ h ))

318− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

319− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

320− (Dict, Dict, Dict, Dict, Dict) -> do

321− conv <- genConvolution

322− pure (SomeNetwork (conv :~> rest :: Network ( Convolution channels filters kernelRows kernelCols strideRows strideCols ': layers ) ( ('D3 inRows inCols channels) ': h ': hs )))

323− _ -> Gen.discard -- Can't occur

324− , do -- Build a Pooling layer

325− let output_r = fromIntegral $ natVal r

326− let output_c = fromIntegral $ natVal c

327− let output_f = fromIntegral $ natVal f

328−

329− let ok extent kernel = [stride | stride <- [ 1 .. extent ], (extent - kernel) `mod` stride == 0]

330−

331− -- Get some kernels which will fit

332− kernel_r <- choose 1 output_r

333− kernel_c <- choose 1 output_c

334−

335− -- Build up some strides which also fit

336− stride_r <- Gen.element $ ok output_r kernel_r

337− stride_c <- Gen.element $ ok output_c kernel_c

338−

339− -- Determine the input size

340− let input_r = (output_r - 1) * stride_r + kernel_r

341− let input_c = (output_c - 1) * stride_c + kernel_c

342−

343− guard (input_r < 100)

344− guard (input_c < 100)

345−

346− -- Remake types for input

347− case ( someNatVal output_f, someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal kernel_r, someNatVal kernel_c, someNatVal stride_r, someNatVal stride_c ) of

348− ( Just (SomeNat (chan :: Proxy filters)),

349− Just (SomeNat (pinr :: Proxy inRows)), Just (SomeNat (_ :: Proxy inCols)),

350− Just (SomeNat (_ :: Proxy outRows)), Just (SomeNat (_ :: Proxy outCols)),

351− Just (SomeNat (pkr :: Proxy kernelRows)), Just (SomeNat (pkc :: Proxy kernelCols)),

352− Just (SomeNat (_ :: Proxy strideRows)), Just (SomeNat (_ :: Proxy strideCols))) ->

353− let p1 = natDict pkr

354− p2 = natDict pkc

355− p3 = natDict chan

356− in case ( p1 %* p2 %* p3

357− , natDict pinr %* natDict chan

358− -- Fake it till you make it.

359− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D3 outRows outCols filters) ~ h ))

360− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

361− , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

362− (Dict, Dict, Dict, Dict, Dict) ->

363− pure (SomeNetwork (Pooling :~> rest :: Network ( Pooling kernelRows kernelCols strideRows strideCols ': layers ) ( ('D3 inRows inCols filters) ': h ': hs )))

364− _ -> Gen.discard -- Can't occur

365− , do -- Build a Pad layer

366− let output_r = fromIntegral $ natVal r

367− let output_c = fromIntegral $ natVal c

368− let output_f = fromIntegral $ natVal f

369−

370− pad_left <- choose 0 (output_r - 1)

371− pad_right <- choose 0 (output_r - 1 - pad_left)

372− pad_top <- choose 0 (output_c - 1)

373− pad_bottom <- choose 0 (output_c - 1 - pad_top)

374−

375− let input_r = output_r - pad_top - pad_bottom

376− let input_c = output_c - pad_left - pad_right

377−

378− guard (input_r > 1)

379− guard (input_c > 1)

380−

381− -- Remake types for input

382− case ( someNatVal output_f, someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal pad_left, someNatVal pad_right, someNatVal pad_top, someNatVal pad_bottom ) of

383− ( Just (SomeNat (chan :: Proxy filters)),

384− Just (SomeNat (pinr :: Proxy inputRows)), Just (SomeNat (_ :: Proxy inputColumns)),

385− Just (SomeNat (_ :: Proxy outputRows)), Just (SomeNat (_ :: Proxy outputColumns)),

386− Just (SomeNat (_ :: Proxy padLeft)), Just (SomeNat (_ :: Proxy padRight)),

387− Just (SomeNat (_ :: Proxy padTop)), Just (SomeNat (_ :: Proxy padBottom))) ->

388− case ( natDict pinr %* natDict chan

389− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D3 outputRows outputColumns filters) ~ h ))

390− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (inputRows + padTop + padBottom) ~ outputRows) )

391− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (inputColumns + padLeft + padRight) ~ outputColumns ))) of

392− (Dict, Dict, Dict, Dict) ->

393− pure (SomeNetwork (Pad :~> rest :: Network ( Pad padLeft padTop padRight padBottom ': layers ) ( ('D3 inputRows inputColumns filters) ': h ': hs )))

394− _ -> Gen.discard -- Can't occur

395− , do -- Build a Crop layer

396− let output_r = fromIntegral $ natVal r

397− let output_c = fromIntegral $ natVal c

398− let output_f = fromIntegral $ natVal f

399−

400− crop_left <- choose 0 10

401− crop_right <- choose 0 10

402− crop_top <- choose 0 10

403− crop_bottom <- choose 0 10

404−

405− let input_r = output_r + crop_top + crop_bottom

406− let input_c = output_c + crop_left + crop_right

407−

408− -- Remake types for input

409− case ( someNatVal output_f, someNatVal input_r, someNatVal input_c, someNatVal output_r, someNatVal output_c, someNatVal crop_left, someNatVal crop_right, someNatVal crop_top, someNatVal crop_bottom ) of

410− ( Just (SomeNat (chan :: Proxy filters)),

411− Just (SomeNat (pinr :: Proxy inputRows)), Just (SomeNat (_ :: Proxy inputColumns)),

412− Just (SomeNat (_ :: Proxy outputRows)), Just (SomeNat (_ :: Proxy outputColumns)),

413− Just (SomeNat (_ :: Proxy cropLeft)), Just (SomeNat (_ :: Proxy cropRight)),

414− Just (SomeNat (_ :: Proxy cropTop)), Just (SomeNat (_ :: Proxy cropBottom))) ->

415− case ( natDict pinr %* natDict chan

416− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( ( 'D3 outputRows outputColumns filters) ~ h ))

417− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (outputRows + cropTop + cropBottom) ~ inputRows ) )

418− , (unsafeCoerce (Dict :: Dict ()) :: Dict ( (outputColumns + cropLeft + cropRight) ~ inputColumns ))) of

419− (Dict, Dict, Dict, Dict) ->

420− pure (SomeNetwork (Crop :~> rest :: Network ( Crop cropLeft cropTop cropRight cropBottom ': layers ) ( ('D3 inputRows inputColumns filters) ': h ': hs )))

421− _ -> Gen.discard -- Can't occur

422− ]

423− ]

424−

425−-- | Test a partial derivative numerically for a random network and input

426−--

427−-- This is the most important test.

428−prop_auto_diff :: Property

429−prop_auto_diff = withDiscards 1000 . withTests 10000 . property $ do

430− SomeNetwork (network :: Network layers shapes) <- forAll genNetwork

431− (input :: S (Head shapes)) <- forAllWith nice genOfShape

432− (target :: S (Last shapes)) <- forAllWith nice oneUp

433− (tested :: S (Head shapes)) <- forAllWith nice oneUp

434−

435− let (!tapes, !output) = runNetwork network input

436− let (_, !backgrad) = runGradient network tapes target

437− let inputDiff = input + tested * 0.00001

438− let expected = maxVal ( backgrad * tested )

439− let (_, !outputDiff) = runNetwork network inputDiff

440− let result = maxVal ( outputDiff * target - output * target ) / 0.00001

441−

442− result ~~~ expected

443−

444−-- Make a shape where all are 0 except for 1 value, which is 1.

445−oneUp :: forall shape. ( SingI shape ) => Gen (S shape)

446−oneUp =

447− case ( sing :: Sing shape ) of

448− D1Sing SNat ->

449− let x = 0 :: S ( shape )

450− in case x of

451− ( S1D x' ) -> do

452− let ex = extract x'

453− let len = VS.length ex

454− ix <- choose 0 (len - 1)

455− let nx = runST $ do ex' <- VS.thaw ex

456− VS.write ex' ix 1.0

457− VS.freeze ex'

458− maybe Gen.discard pure . fromStorable $ nx

459−

460− D2Sing SNat SNat ->

461− let x = 0 :: S ( shape )

462− in case x of

463− ( S2D x' ) -> do

464− let ex = flatten ( extract x' )

465− let len = VS.length ex

466− ix <- choose 0 (len - 1)

467− let nx = runST $ do ex' <- VS.thaw ex

468− VS.write ex' ix 1

469− VS.freeze ex'

470− maybe Gen.discard pure . fromStorable $ nx

471−

472− D3Sing SNat SNat SNat ->

473− let x = 0 :: S ( shape )

474− in case x of

475− ( S3D x' ) -> do

476− let ex = flatten ( extract x' )

477− let len = VS.length ex

478− ix <- choose 0 (len - 1)

479− let nx = runST $ do ex' <- VS.thaw ex

480− VS.write ex' ix 1

481− VS.freeze ex'

482− maybe Gen.discard pure . fromStorable $ nx

483−

484−maxVal :: S shape -> Double

485−maxVal ( S1D x ) = norm_Inf x

486−maxVal ( S2D x ) = norm_Inf x

487−maxVal ( S3D x ) = norm_Inf x

488−

489−(~~~) :: (Monad m, HasCallStack) => Double -> Double -> PropertyT m ()

490−(~~~) x y =

491− if abs (x - y) < 2e-5 then

492− success

493− else

494− withFrozenCallStack $

495− failWith Nothing $ unlines [

496− "━━━ Not Simliar ━━━"

497− , show x

498− , show y

499− ]

500−infix 4 ~~~

501−

502−

503−tests :: IO Bool

504−tests = checkParallel $$(discover)

test/Test/Grenade/Recurrent/Layers/LSTM.hs+54−83

@@ -10,10 +10,7 @@

1010{-# OPTIONS_GHC -fno-warn-missing-signatures #-}

1111module Test.Grenade.Recurrent.Layers.LSTM where

1212

13−import Hedgehog

14−import Hedgehog.Internal.Source

15−import Hedgehog.Internal.Show

16−import Hedgehog.Internal.Property ( failWith, Diff (..) )

13+import Disorder.Jack

1714

1815import Data.Foldable ( toList )

1916import Data.Singletons.TypeLits

@@ -26,9 +23,9 @@ import qualified Numeric.LinearAlgebra.Static as S

2623

2724

2825import qualified Test.Grenade.Recurrent.Layers.LSTM.Reference as Reference

29−import Test.Hedgehog.Hmatrix

26+import Test.Jack.Hmatrix

3027

31−genLSTM :: forall i o. (KnownNat i, KnownNat o) => Gen (LSTM i o)

28+genLSTM :: forall i o. (KnownNat i, KnownNat o) => Jack (LSTM i o)

3229genLSTM = do

3330 let w = uniformSample

3431 u = uniformSample

@@ -42,89 +39,63 @@ genLSTM = do

4239 <*> pure (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

4340

4441prop_lstm_reference_forwards =

45− property $ do

46− input :: S.R 3 <- forAll randomVector

47− cell :: S.R 2 <- forAll randomVector

48− net@(LSTM lstmWeights _) :: LSTM 3 2 <- forAll genLSTM

49−

50− let actual = runRecurrentForwards net (S1D cell) (S1D input)

51− case actual of

52− (_, (S1D cellOut) :: S ('D1 2), (S1D output) :: S ('D1 2)) ->

53− let cellOut' = Reference.Vector . H.toList . S.extract $ cellOut

54− output' = Reference.Vector . H.toList . S.extract $ output

55− refNet = Reference.lstmToReference lstmWeights

56− refCell = Reference.Vector . H.toList . S.extract $ cell

57− refInput = Reference.Vector . H.toList . S.extract $ input

58− (refCO, refO) = Reference.runLSTM refNet refCell refInput

59− in do toList refCO ~~~ toList cellOut'

60− toList refO ~~~ toList output'

61−

42+ gamble randomVector $ \(input :: S.R 3) ->

43+ gamble randomVector $ \(cell :: S.R 2) ->

44+ gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

45+ let actual = runRecurrentForwards net (S1D cell) (S1D input)

46+ in case actual of

47+ (_, (S1D cellOut) :: S ('D1 2), (S1D output) :: S ('D1 2)) ->

48+ let cellOut' = Reference.Vector . H.toList . S.extract $ cellOut

49+ output' = Reference.Vector . H.toList . S.extract $ output

50+ refNet = Reference.lstmToReference lstmWeights

51+ refCell = Reference.Vector . H.toList . S.extract $ cell

52+ refInput = Reference.Vector . H.toList . S.extract $ input

53+ (refCO, refO) = Reference.runLSTM refNet refCell refInput

54+ in toList refCO ~~~ toList cellOut' .&&. toList refO ~~~ toList output'

6255

6356prop_lstm_reference_backwards =

64− property $ do

65− input :: S.R 3 <- forAll randomVector

66− cell :: S.R 2 <- forAll randomVector

67− net@(LSTM lstmWeights _) :: LSTM 3 2 <- forAll genLSTM

68− let (tape, _ :: S ('D1 2), _ :: S ('D1 2))

69− = runRecurrentForwards net (S1D cell) (S1D input)

70− actualBacks = runRecurrentBackwards net tape (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

71− case actualBacks of

72− (actualGradients, _, _ :: S ('D1 3)) ->

73− let refNet = Reference.lstmToReference lstmWeights

74− refCell = Reference.Vector . H.toList . S.extract $ cell

75− refInput = Reference.Vector . H.toList . S.extract $ input

76− refGradients = Reference.runLSTMback refCell refInput refNet

77− in toList refGradients ~~~ toList (Reference.lstmToReference actualGradients)

57+ gamble randomVector $ \(input :: S.R 3) ->

58+ gamble randomVector $ \(cell :: S.R 2) ->

59+ gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

60+ let actualBacks = runRecurrentBackwards net (S1D cell, S1D input) (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

61+ in case actualBacks of

62+ (actualGradients, _, _ :: S ('D1 3)) ->

63+ let refNet = Reference.lstmToReference lstmWeights

64+ refCell = Reference.Vector . H.toList . S.extract $ cell

65+ refInput = Reference.Vector . H.toList . S.extract $ input

66+ refGradients = Reference.runLSTMback refCell refInput refNet

67+ in toList refGradients ~~~ toList (Reference.lstmToReference actualGradients)

7868

7969prop_lstm_reference_backwards_input =

80− property $ do

81− input :: S.R 3 <- forAll randomVector

82− cell :: S.R 2 <- forAll randomVector

83− net@(LSTM lstmWeights _) :: LSTM 3 2 <- forAll genLSTM

84− let (tape, _ :: S ('D1 2), _ :: S ('D1 2))

85− = runRecurrentForwards net (S1D cell) (S1D input)

86− actualBacks = runRecurrentBackwards net tape (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

87− case actualBacks of

88− (_, _, S1D actualGradients :: S ('D1 3)) ->

89− let refNet = Reference.lstmToReference lstmWeights

90− refCell = Reference.Vector . H.toList . S.extract $ cell

91− refInput = Reference.Vector . H.toList . S.extract $ input

92− refGradients = Reference.runLSTMbackOnInput refCell refNet refInput

93− in toList refGradients ~~~ H.toList (S.extract actualGradients)

70+ gamble randomVector $ \(input :: S.R 3) ->

71+ gamble randomVector $ \(cell :: S.R 2) ->

72+ gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

73+ let actualBacks = runRecurrentBackwards net (S1D cell, S1D input) (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

74+ in case actualBacks of

75+ (_, _, S1D actualGradients :: S ('D1 3)) ->

76+ let refNet = Reference.lstmToReference lstmWeights

77+ refCell = Reference.Vector . H.toList . S.extract $ cell

78+ refInput = Reference.Vector . H.toList . S.extract $ input

79+ refGradients = Reference.runLSTMbackOnInput refCell refNet refInput

80+ in toList refGradients ~~~ H.toList (S.extract actualGradients)

9481

9582prop_lstm_reference_backwards_cell =

96− property $ do

97− input :: S.R 3 <- forAll randomVector

98− cell :: S.R 2 <- forAll randomVector

99− net@(LSTM lstmWeights _) :: LSTM 3 2 <- forAll genLSTM

100− let (tape, _ :: S ('D1 2), _ :: S ('D1 2))

101− = runRecurrentForwards net (S1D cell) (S1D input)

102− actualBacks = runRecurrentBackwards net tape (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

103− case actualBacks of

104− (_, S1D actualGradients, _ :: S ('D1 3)) ->

105− let refNet = Reference.lstmToReference lstmWeights

106− refCell = Reference.Vector . H.toList . S.extract $ cell

107− refInput = Reference.Vector . H.toList . S.extract $ input

108− refGradients = Reference.runLSTMbackOnCell refInput refNet refCell

109− in toList refGradients ~~~ H.toList (S.extract actualGradients)

110−

111−(~~~) :: (Monad m, Eq a, Ord a, Num a, Fractional a, Show a, HasCallStack) => [a] -> [a] -> PropertyT m ()

112−(~~~) x y =

113− if all (< 1e-8) (zipWith (-) x y) then

114− success

115− else

116− case valueDiff <$> mkValue x <*> mkValue y of

117− Nothing ->

118− withFrozenCallStack $

119− failWith Nothing $ unlines [

120− "━━━ Not Simliar ━━━"

121− , showPretty x

122− , showPretty y

123− ]

124− Just differ ->

125− withFrozenCallStack $

126− failWith (Just $ Diff "Failed (" "- lhs" "~/~" "+ rhs" ")" differ) ""

83+ gamble randomVector $ \(input :: S.R 3) ->

84+ gamble randomVector $ \(cell :: S.R 2) ->

85+ gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

86+ let actualBacks = runRecurrentBackwards net (S1D cell, S1D input) (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

87+ in case actualBacks of

88+ (_, S1D actualGradients, _ :: S ('D1 3)) ->

89+ let refNet = Reference.lstmToReference lstmWeights

90+ refCell = Reference.Vector . H.toList . S.extract $ cell

91+ refInput = Reference.Vector . H.toList . S.extract $ input

92+ refGradients = Reference.runLSTMbackOnCell refInput refNet refCell

93+ in toList refGradients ~~~ H.toList (S.extract actualGradients)

94+

95+

96+(~~~) as bs = all (< 1e-8) (zipWith (-) as bs)

12797infix 4 ~~~

12898

99+return []

129100tests :: IO Bool

130−tests = checkParallel $$(discover)

101+tests = $quickCheckAll

test/Test/Grenade/Recurrent/Layers/LSTM/Reference.hs+21−25

@@ -6,6 +6,7 @@

66{-# LANGUAGE DeriveFunctor #-}

77{-# LANGUAGE DeriveFoldable #-}

88{-# LANGUAGE DeriveTraversable #-}

9+{-# LANGUAGE RecordWildCards #-}

910{-# LANGUAGE FlexibleContexts #-}

1011{-# LANGUAGE RankNTypes #-}

1112

@@ -14,13 +15,9 @@ module Test.Grenade.Recurrent.Layers.LSTM.Reference where

1415

1516import Data.Reflection

1617import Numeric.AD.Mode.Reverse

17−import Numeric.AD.Internal.Reverse (Tape)

18+import Numeric.AD.Internal.Reverse ( Tape )

1819

19−import GHC.TypeLits (KnownNat)

20−

21−import Grenade.Recurrent.Layers.LSTM (LSTMWeights (..))

2220import qualified Grenade.Recurrent.Layers.LSTM as LSTM

23−

2421import qualified Numeric.LinearAlgebra.Static as S

2522import qualified Numeric.LinearAlgebra as H

2623

@@ -57,32 +54,31 @@ data RefLSTM a = RefLSTM

5754 , refLstmBc :: Vector a -- Bias Cell (b_c)

5855 } deriving (Functor, Foldable, Traversable, Eq, Show)

5956

60−lstmToReference :: (KnownNat a, KnownNat b) => LSTM.LSTMWeights a b -> RefLSTM Double

61−lstmToReference lw =

62− RefLSTM

63− { refLstmWf = Matrix . H.toLists . S.extract $ lstmWf lw -- Weight Forget (W_f)

64− , refLstmUf = Matrix . H.toLists . S.extract $ lstmUf lw -- Cell State Forget (U_f)

65− , refLstmBf = Vector . H.toList . S.extract $ lstmBf lw -- Bias Forget (b_f)

66− , refLstmWi = Matrix . H.toLists . S.extract $ lstmWi lw -- Weight Input (W_i)

67− , refLstmUi = Matrix . H.toLists . S.extract $ lstmUi lw -- Cell State Input (U_i)

68− , refLstmBi = Vector . H.toList . S.extract $ lstmBi lw -- Bias Input (b_i)

69− , refLstmWo = Matrix . H.toLists . S.extract $ lstmWo lw -- Weight Output (W_o)

70− , refLstmUo = Matrix . H.toLists . S.extract $ lstmUo lw -- Cell State Output (U_o)

71− , refLstmBo = Vector . H.toList . S.extract $ lstmBo lw -- Bias Output (b_o)

72− , refLstmWc = Matrix . H.toLists . S.extract $ lstmWc lw -- Weight Cell (W_c)

73− , refLstmBc = Vector . H.toList . S.extract $ lstmBc lw -- Bias Cell (b_c)

74− }

57+lstmToReference :: LSTM.LSTMWeights a b -> RefLSTM Double

58+lstmToReference LSTM.LSTMWeights {..} =

59+ let refLstmWf = Matrix . H.toLists . S.extract $ lstmWf -- Weight Forget (W_f)

60+ refLstmUf = Matrix . H.toLists . S.extract $ lstmUf -- Cell State Forget (U_f)

61+ refLstmBf = Vector . H.toList . S.extract $ lstmBf -- Bias Forget (b_f)

62+ refLstmWi = Matrix . H.toLists . S.extract $ lstmWi -- Weight Input (W_i)

63+ refLstmUi = Matrix . H.toLists . S.extract $ lstmUi -- Cell State Input (U_i)

64+ refLstmBi = Vector . H.toList . S.extract $ lstmBi -- Bias Input (b_i)

65+ refLstmWo = Matrix . H.toLists . S.extract $ lstmWo -- Weight Output (W_o)

66+ refLstmUo = Matrix . H.toLists . S.extract $ lstmUo -- Cell State Output (U_o)

67+ refLstmBo = Vector . H.toList . S.extract $ lstmBo -- Bias Output (b_o)

68+ refLstmWc = Matrix . H.toLists . S.extract $ lstmWc -- Weight Cell (W_c)

69+ refLstmBc = Vector . H.toList . S.extract $ lstmBc -- Bias Cell (b_c)

70+ in RefLSTM {..}

7571

7672runLSTM :: Floating a => RefLSTM a -> Vector a -> Vector a -> (Vector a, Vector a)

77−runLSTM rl cell input =

73+runLSTM RefLSTM {..} cell input =

7874 let -- Forget state vector

79− f_t = sigmoid $ refLstmBf rl #+ refLstmWf rl #> input #+ refLstmUf rl #> cell

75+ f_t = sigmoid $ refLstmBf #+ refLstmWf #> input #+ refLstmUf #> cell

8076 -- Input state vector

81− i_t = sigmoid $ refLstmBi rl #+ refLstmWi rl #> input #+ refLstmUi rl #> cell

77+ i_t = sigmoid $ refLstmBi #+ refLstmWi #> input #+ refLstmUi #> cell

8278 -- Output state vector

83− o_t = sigmoid $ refLstmBo rl #+ refLstmWo rl #> input #+ refLstmUo rl #> cell

79+ o_t = sigmoid $ refLstmBo #+ refLstmWo #> input #+ refLstmUo #> cell

8480 -- Cell input state vector

85− c_x = fmap tanh $ refLstmBc rl #+ refLstmWc rl #> input

81+ c_x = fmap tanh $ refLstmBc #+ refLstmWc #> input

8682 -- Cell state

8783 c_t = f_t #* cell #+ i_t #* c_x

8884 -- Output (it's sometimes recommended to use tanh c_t)

test/Test/Hedgehog/Compat.hsdeleted−21

@@ -1,21 +0,0 @@

1−{-# LANGUAGE RankNTypes #-}

2−module Test.Hedgehog.Compat (

3− (...)

4− , choose

5− , blindForAll

6− )where

7−

8−import Hedgehog (Gen)

9−import qualified Hedgehog.Gen as Gen

10−import qualified Hedgehog.Range as Range

11−import Hedgehog.Internal.Property

12−

13−(...) :: (c -> d) -> (a -> b -> c) -> a -> b -> d

14−(...) = (.) . (.)

15−{-# INLINE (...) #-}

16−

17−choose :: Integral a => a -> a -> Gen a

18−choose = Gen.integral ... Range.constant

19−

20−blindForAll :: Monad m => Gen a -> PropertyT m a

21−blindForAll = forAllWith (const "blind")

test/Test/Hedgehog/Hmatrix.hsdeleted−42

@@ -1,42 +0,0 @@

1−{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE KindSignatures #-}

3−{-# LANGUAGE GADTs #-}

4−{-# LANGUAGE RankNTypes #-}

5−{-# LANGUAGE ScopedTypeVariables #-}

6−

7−module Test.Hedgehog.Hmatrix where

8−

9−import Grenade

10−import Data.Singletons

11−import Data.Singletons.TypeLits

12−

13−import Hedgehog (Gen)

14−import qualified Hedgehog.Gen as Gen

15−import qualified Hedgehog.Range as Range

16−

17−import qualified Numeric.LinearAlgebra.Static as HStatic

18−

19−randomVector :: forall n. ( KnownNat n ) => Gen (HStatic.R n)

20−randomVector = (\s -> HStatic.randomVector s HStatic.Uniform * 2 - 1) <$> Gen.int Range.linearBounded

21−

22−uniformSample :: forall m n. ( KnownNat m, KnownNat n ) => Gen (HStatic.L m n)

23−uniformSample = (\s -> HStatic.uniformSample s (-1) 1 ) <$> Gen.int Range.linearBounded

24−

25−-- | Generate random data of the desired shape

26−genOfShape :: forall x. ( SingI x ) => Gen (S x)

27−genOfShape =

28− case (sing :: Sing x) of

29− D1Sing l ->

30− withKnownNat l $

31− S1D <$> randomVector

32− D2Sing r c ->

33− withKnownNat r $ withKnownNat c $

34− S2D <$> uniformSample

35− D3Sing r c d ->

36− withKnownNat r $ withKnownNat c $ withKnownNat d $

37− S3D <$> uniformSample

38−

39−nice :: S shape -> String

40−nice (S1D x) = show . HStatic.extract $ x

41−nice (S2D x) = show . HStatic.extract $ x

42−nice (S3D x) = show . HStatic.extract $ x

test/Test/Hedgehog/TypeLits.hsdeleted−65

@@ -1,65 +0,0 @@

1−{-# LANGUAGE CPP #-}

2−{-# LANGUAGE RankNTypes #-}

3−{-# LANGUAGE DataKinds #-}

4−{-# LANGUAGE ScopedTypeVariables #-}

5−{-# LANGUAGE PolyKinds #-}

6−{-# LANGUAGE TypeOperators #-}

7−{-# LANGUAGE GADTs #-}

8−module Test.Hedgehog.TypeLits where

9−

10−import Data.Constraint (Dict (..))

11−import Data.Singletons (Proxy (..), Sing (..), SomeSing (..), sing)

12−#if MIN_VERSION_singletons(2,6,0)

13−import Data.Singletons.TypeLits (SNat (..))

14−#endif

15−

16−import Hedgehog (Gen)

17−import qualified Hedgehog.Gen as Gen

18−

19−import Grenade

20−

21−import GHC.TypeLits (SomeNat (..), natVal, someNatVal)

22−import GHC.TypeLits.Witnesses ((%*), natDict)

23−import Test.Hedgehog.Compat (choose)

24−

25−genNat :: Gen SomeNat

26−genNat = do

27− ~(Just n) <- someNatVal <$> choose 1 10

28− return n

29−

30−genShape :: Gen (SomeSing Shape)

31−genShape

32− = Gen.choice [

33− genD1

34− , genD2

35− , genD3

36− ]

37−

38−genD1 :: Gen (SomeSing Shape)

39−genD1 = do

40− n <- genNat

41− return $ case n of

42− SomeNat (_ :: Proxy x) -> SomeSing (sing :: Sing ('D1 x))

43−

44−genD2 :: Gen (SomeSing Shape)

45−genD2 = do

46− n <- genNat

47− m <- genNat

48− return $ case (n, m) of

49− (SomeNat (_ :: Proxy x), SomeNat (_ :: Proxy y)) -> SomeSing (sing :: Sing ('D2 x y))

50−

51−genD3 :: Gen (SomeSing Shape)

52−genD3 = do

53− n <- genNat

54− m <- genNat

55− o <- genNat

56− return $ case (n, m, o) of

57− (SomeNat (px :: Proxy x), SomeNat (_ :: Proxy y), SomeNat (pz :: Proxy z)) ->

58− case natDict px %* natDict pz of

59− Dict -> SomeSing (sing :: Sing ('D3 x y z))

60−

61−rss :: SomeSing Shape -> String

62−rss (SomeSing (r :: Sing s)) = case r of

63− (D1Sing a@SNat) -> "D1 " ++ show (natVal a)

64− (D2Sing a@SNat b@SNat) -> "D2 " ++ show (natVal a) ++ " " ++ show (natVal b)

65− (D3Sing a@SNat b@SNat c@SNat) -> "D3 " ++ show (natVal a) ++ " " ++ show (natVal b) ++ " " ++ show (natVal c)

test/Test/Jack/Hmatrix.hsnew file+29

@@ -0,0 +1,29 @@

1+{-# LANGUAGE RankNTypes #-}

2+{-# LANGUAGE DataKinds #-}

3+{-# LANGUAGE ScopedTypeVariables #-}

4+{-# LANGUAGE GADTs #-}

5+

6+module Test.Jack.Hmatrix where

7+

8+import Grenade

9+import Data.Singletons

10+

11+import Disorder.Jack

12+

13+import GHC.TypeLits

14+

15+import qualified Numeric.LinearAlgebra.Static as HStatic

16+

17+randomVector :: forall n. KnownNat n => Jack (HStatic.R n)

18+randomVector = (\s -> HStatic.randomVector s HStatic.Uniform * 2 - 1) <$> sizedNat

19+

20+uniformSample :: forall m n. (KnownNat m, KnownNat n) => Jack (HStatic.L m n)

21+uniformSample = (\s -> HStatic.uniformSample s (-1) 1 ) <$> sizedNat

22+

23+-- | Generate random data of the desired shape

24+genOfShape :: forall x. ( SingI x ) => Jack (S x)

25+genOfShape =

26+ case (sing :: Sing x) of

27+ D1Sing -> S1D <$> randomVector

28+ D2Sing -> S2D <$> uniformSample

29+ D3Sing -> S3D <$> uniformSample

test/Test/Jack/TypeLits.hsnew file+60

@@ -0,0 +1,60 @@

1+{-# LANGUAGE CPP #-}

2+{-# LANGUAGE RankNTypes #-}

3+{-# LANGUAGE DataKinds #-}

4+{-# LANGUAGE ScopedTypeVariables #-}

5+{-# LANGUAGE PolyKinds #-}

6+module Test.Jack.TypeLits where

7+

8+import Data.Constraint

9+#if __GLASGOW_HASKELL__ < 800

10+import Data.Proxy

11+#endif

12+import Data.Singletons

13+import Disorder.Jack

14+

15+import Grenade

16+

17+import GHC.TypeLits

18+import GHC.TypeLits.Witnesses

19+

20+genNat :: Jack SomeNat

21+genNat = do

22+ Just n <- someNatVal <$> choose (1, 10)

23+ return n

24+

25+#if __GLASGOW_HASKELL__ < 800

26+type Shape' = ('KProxy :: KProxy Shape)

27+#else

28+type Shape' = Shape

29+#endif

30+

31+genShape :: Jack (SomeSing Shape')

32+genShape

33+ = oneOf [

34+ genD1

35+ , genD2

36+ , genD3

37+ ]

38+

39+genD1 :: Jack (SomeSing Shape')

40+genD1 = do

41+ n <- genNat

42+ return $ case n of

43+ SomeNat (_ :: Proxy x) -> SomeSing (sing :: Sing ('D1 x))

44+

45+genD2 :: Jack (SomeSing Shape')

46+genD2 = do

47+ n <- genNat

48+ m <- genNat

49+ return $ case (n, m) of

50+ (SomeNat (_ :: Proxy x), SomeNat (_ :: Proxy y)) -> SomeSing (sing :: Sing ('D2 x y))

51+

52+genD3 :: Jack (SomeSing Shape')

53+genD3 = do

54+ n <- genNat

55+ m <- genNat

56+ o <- genNat

57+ return $ case (n, m, o) of

58+ (SomeNat (px :: Proxy x), SomeNat (_ :: Proxy y), SomeNat (pz :: Proxy z)) ->

59+ case natDict px %* natDict pz of

60+ Dict -> SomeSing (sing :: Sing ('D3 x y z))

test/test.hs+2−22

@@ -1,6 +1,4 @@

1−import Control.Monad

2−

3−import qualified Test.Grenade.Network

1+import Disorder.Core.Main

42

53import qualified Test.Grenade.Layers.Pooling

64import qualified Test.Grenade.Layers.Convolution

@@ -13,15 +11,10 @@ import qualified Test.Grenade.Layers.Internal.Pooling

1311

1412import qualified Test.Grenade.Recurrent.Layers.LSTM

1513

16−import System.Exit

17−import System.IO

18−

1914main :: IO ()

2015main =

2116 disorderMain [

22− Test.Grenade.Network.tests

23−

24− , Test.Grenade.Layers.Pooling.tests

17+ Test.Grenade.Layers.Pooling.tests

2518 , Test.Grenade.Layers.Convolution.tests

2619 , Test.Grenade.Layers.FullyConnected.tests

2720 , Test.Grenade.Layers.Nonlinear.tests

@@ -31,17 +24,4 @@ main =

3124 , Test.Grenade.Layers.Internal.Pooling.tests

3225

3326 , Test.Grenade.Recurrent.Layers.LSTM.tests

34−

3527 ]

36−

37−disorderMain :: [IO Bool] -> IO ()

38−disorderMain tests = do

39− lineBuffer

40− rs <- sequence tests

41− unless (and rs) exitFailure

42−

43−

44−lineBuffer :: IO ()

45−lineBuffer = do

46− hSetBuffering stdout LineBuffering

47− hSetBuffering stderr LineBuffering