master..c8cfa39 · 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.ymldeleted−31

@@ -1,31 +0,0 @@

1−# NB: don't set `language: haskell` here

2−

3−# 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.

4−env:

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

11−

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

13−before_install:

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

15− - 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

18−

19−install:

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

21− - travis_retry cabal-3.2 update

22−

23−notifications:

24− email: false

25−

26−# 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.

27−script:

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

29− - cabal-3.2 build all

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

31−

LICENSE+1−1

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

22All rights reserved.

33

44Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

README.md+10−72

@@ -1,11 +1,6 @@

11Grenade

22=======

33

4−[![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−

8−

94```

105First shalt thou take out the Holy Pin, then shalt thou count to three, no more, no less.

116Three shall be the number thou shalt count, and the number of the counting shall be three.

@@ -15,20 +10,14 @@ Five is right out.

1510

1611💣 Machine learning which might blow up in your face 💣

1712

18−Grenade is a composable, dependently typed, practical, and fast recurrent neural network library

13+Grenade is a dependently typed, practical, and fast recurrent neural network library

1914for concise and precise specifications of complex networks in Haskell.

2015

2116As an example, a network which can achieve ~1.5% error on MNIST can be

2217specified and initialised with random weights in a few lines of code with

2318```haskell

24−type MNIST

25− = Network

26− '[ 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

28− , FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]

29− '[ 'D2 28 28, 'D3 24 24 10, 'D3 12 12 10, 'D3 12 12 10

30− , 'D3 8 8 16, 'D3 4 4 16, 'D1 256, 'D1 256

31− , 'D1 80, 'D1 80, 'D1 10, 'D1 10]

19+type MNIST = Network '[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu, Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, FlattenLayer, Relu, FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]

20+ '[ 'D2 28 28, 'D3 24 24 10, 'D3 12 12 10, 'D3 12 12 10, 'D3 8 8 16, 'D3 4 4 16, 'D1 256, 'D1 256, 'D1 80, 'D1 80, 'D1 10, 'D1 10]

3221

3322randomMnist :: MonadRandom m => m MNIST

3423randomMnist = randomNetwork

@@ -42,10 +31,8 @@ If recurrent neural networks are more your style, you can try defining something

4231["unreasonably effective"](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)

4332with

4433```haskell

45−type Shakespeare

46− = RecurrentNetwork

47− '[ R (LSTM 40 80), R (LSTM 80 40), F (FullyConnected 40 40), F Logit]

48− '[ 'D1 40, 'D1 80, 'D1 40, 'D1 40, 'D1 40 ]

34+type Shakespeare = RecurrentNetwork '[ R (LSTM 40 80), R (LSTM 80 40), F (FullyConnected 40 40), F Logit]

35+ '[ 'D1 40, 'D1 80, 'D1 40, 'D1 40, 'D1 40 ]

4936```

5037

5138Design

@@ -58,13 +45,8 @@ data that are passed between the layers.

5845The definition of a network is surprisingly simple:

5946```haskell

6047data Network :: [*] -> [Shape] -> * where

61− NNil :: SingI i

62− => Network '[] '[i]

63−

64− (:~>) :: (SingI i, SingI h, Layer x i h)

65− => !x

66− -> !(Network xs (h ': hs))

67− -> Network (x ': xs) (i ': h ': hs)

48+ O :: (SingI i, SingI o, Layer x i o) => !x -> Network '[x] '[i, o]

49+ (:~>) :: (SingI i, SingI h, Layer x i h) => !x -> !(Network xs (h ': hs)) -> Network (x ': xs) (i ': h ': hs)

6850```

6951

7052The `Layer x i o` constraint ensures that the layer `x` can sensibly perform a

@@ -89,7 +71,7 @@ backPropagate :: forall shapes layers.

8971```

9072which takes a network, appropriate input and target data, and returns the

9173back 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

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

9375have no learnable parameters.

9476

9577The gradients however can always be applied, yielding a new (hopefully better)

@@ -103,48 +85,9 @@ easy in downstream code. If the shapes of a network are not specified correctly

10385and a layer can not sensibly perform the operation between two shapes, then

10486it will result in a compile time error.

10587

106−Composition

107−-----------

108−

109−Networks and Layers in Grenade are easily composed at the type level. As a `Network`

110−is an instance of `Layer`, one can use a trained Network as a small component in a

111−larger network easily. Furthermore, we provide 2 layers which are designed to run

112−layers in parallel and merge their output (either by concatenating them across one

113−dimension or summing by pointwise adding their activations). This allows one to

114−write any Network which can be expressed as a

115−[series parallel graph](https://en.wikipedia.org/wiki/Series-parallel_graph).

116−

117−A residual network layer specification for instance could be written as

118−```haskell

119−type Residual net = Merge Trivial net

120−```

121−If the type `net` is an instance of `Layer`, then `Residual net` will be too. It will

122−run the network, while retaining its input by passing it through the `Trivial` layer,

123−and merge the original image with the output.

124−

125−See the [MNIST](https://github.com/HuwCampbell/grenade/blob/master/examples/main/mnist.hs)

126−example, which has been overengineered to contain both residual style learning as well

127−as inception style convolutions.

128−

129−Generative Adversarial Networks

130−-------------------------------

131−

132−As Grenade is purely functional, one can compose its training functions in flexible

133−ways. [GAN-MNIST](https://github.com/HuwCampbell/grenade/blob/master/examples/main/gan-mnist.hs)

134−example displays an interesting, type safe way of writing a generative adversarial

135−training function in 10 lines of code.

136−

137−Layer Zoo

138−---------

139−

140−Grenade layers are normal haskell data types which are an instance of `Layer`, so

141−it's easy to build one's own downstream code. We do however provide a decent set

142−of layers, including convolution, deconvolution, pooling, pad, crop, logit, relu,

143−elu, tanh, and fully connected.

144−

14588Build Instructions

14689------------------

147−Grenade is most easily built with the [mafia](https://github.com/ambiata/mafia)

90+Grenade currently only builds with the [mafia](https://github.com/ambiata/mafia)

14891script that is located in the repository. You will also need the `lapack` and

14992`blas` libraries and development tools. Once you have all that, Grenade can be

15093build using:

@@ -159,7 +102,7 @@ and the tests run using:

159102./mafia test

160103```

161104

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

105+Grenade is currently known to build with ghc 7.10 and 8.0.

163106

164107Thanks

165108------

@@ -185,8 +128,3 @@ core took around 12 minutes, achieving 1.5% error rate on a 1000 sample holdout

185128Contributing

186129------------

187130Contributions 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

_config.ymldeleted−1

@@ -1 +0,0 @@

1−theme: jekyll-theme-minimal

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/LICENSEdeleted−10

@@ -1,10 +0,0 @@

3−

4−Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

5−

6−1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

7−

8−2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

9−

10−THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

examples/grenade-examples.cabaldeleted−109

@@ -1,109 +0,0 @@

1−name: grenade-examples

2−version: 0.0.1

3−license: BSD2

4−license-file: LICENSE

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

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

8−synopsis: grenade-examples

9−category: System

10−cabal-version: >= 1.8

11−build-type: Simple

12−description: grenade-examples

13−

14−source-repository head

15− type: git

16− location: https://github.com/HuwCampbell/grenade.git

17−

18−library

19−

20−executable feedforward

21− ghc-options: -Wall -threaded -O2

22− main-is: main/feedforward.hs

23− build-depends: base

24− , grenade

25− , attoparsec

26− , bytestring

27− , cereal

28− , either

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

30− , text == 1.2.*

31− , mtl >= 2.2.1 && < 2.3

32− , hmatrix

33− , transformers

34− , singletons

35− , semigroups

36− , MonadRandom

37−

38−executable mnist

39− ghc-options: -Wall -threaded -O2

40− main-is: main/mnist.hs

41− build-depends: base

42− , grenade

43− , attoparsec

44− , either

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

46− , text == 1.2.*

47− , mtl >= 2.2.1 && < 2.3

48− , hmatrix >= 0.18 && < 0.21

49− , transformers

50− , semigroups

51− , singletons

52− , MonadRandom

53− , vector

54−

55−executable gan-mnist

56− ghc-options: -Wall -threaded -O2

57− main-is: main/gan-mnist.hs

58− build-depends: base

59− , grenade

60− , attoparsec

61− , bytestring

62− , cereal

63− , either

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

65− , text == 1.2.*

66− , mtl >= 2.2.1 && < 2.3

67− , hmatrix >= 0.18 && < 0.21

68− , transformers

69− , semigroups

70− , singletons

71− , MonadRandom

72− , vector

73−

74−executable recurrent

75− ghc-options: -Wall -threaded -O2

76− main-is: main/recurrent.hs

77− build-depends: base

78− , grenade

79− , attoparsec

80− , either

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

82− , text == 1.2.*

83− , mtl >= 2.2.1 && < 2.3

84− , hmatrix >= 0.18 && < 0.21

85− , transformers

86− , semigroups

87− , singletons

88− , MonadRandom

89−

90−executable shakespeare

91− ghc-options: -Wall -threaded -O2

92− main-is: main/shakespeare.hs

93− build-depends: base

94− , grenade

95− , attoparsec

96− , bytestring

97− , cereal

98− , either

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

100− , text == 1.2.*

101− , mtl >= 2.2.1 && < 2.3

102− , hmatrix >= 0.18 && < 0.21

103− , transformers

104− , semigroups

105− , singletons

106− , singletons-base

107− , vector

108− , MonadRandom

109− , 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/gan-mnist.hsdeleted−185

@@ -1,185 +0,0 @@

1−{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE CPP #-}

3−{-# LANGUAGE DataKinds #-}

4−{-# LANGUAGE GADTs #-}

5−{-# LANGUAGE ScopedTypeVariables #-}

6−{-# LANGUAGE TypeOperators #-}

7−{-# LANGUAGE TupleSections #-}

8−{-# LANGUAGE TypeFamilies #-}

9−{-# LANGUAGE FlexibleContexts #-}

10−

11−-- This is a simple generative adversarial network to make pictures

12−-- of numbers similar to those in MNIST.

13−--

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

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

16−--

17−-- --.

18−-- .=-.--..#=###

19−-- -##==#########.

20−-- #############-

21−-- -###-.=..-.-==

22−-- ###-

23−-- .###-

24−-- .####...==-.

25−-- -####=--.=##=

26−-- -##=- -##

27−-- =##

28−-- -##=

29−-- -###-

30−-- .####.

31−-- .#####.

32−-- ...---=#####-

33−-- .=#########. .

34−-- .#######=. .

35−-- . =-.

36−--

37−-- It's a 5!

38−--

39−import Control.Applicative

40−import Control.Monad

41−import Control.Monad.Random

42−import Control.Monad.Trans.Except

43−

44−import qualified Data.Attoparsec.Text as A

45−import qualified Data.ByteString as B

46−import Data.List ( foldl' )

47−#if ! MIN_VERSION_base(4,13,0)

48−import Data.Semigroup ( (<>) )

49−#endif

50−import Data.Serialize

51−import qualified Data.Text as T

52−import qualified Data.Text.IO as T

53−import qualified Data.Vector.Storable as V

54−

55−import qualified Numeric.LinearAlgebra.Static as SA

56−import Numeric.LinearAlgebra.Data ( toLists )

57−

58−import Options.Applicative

59−

60−import Grenade

61−import Grenade.Utils.OneHot

62−

63−type Discriminator =

64− Network

65− '[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu

66− , Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, Relu

67− , Reshape, FullyConnected 256 80, Logit, FullyConnected 80 1, Logit]

68− '[ 'D2 28 28

69− , 'D3 24 24 10, 'D3 12 12 10, 'D3 12 12 10

70− , 'D3 8 8 16, 'D3 4 4 16, 'D3 4 4 16

71− , 'D1 256, 'D1 80, 'D1 80, 'D1 1, 'D1 1]

72−

73−type Generator =

74− Network

75− '[ FullyConnected 80 256, Relu, Reshape

76− , Deconvolution 16 10 5 5 2 2, Relu

77− , Deconvolution 10 1 8 8 2 2, Logit]

78− '[ 'D1 80

79− , 'D1 256, 'D1 256, 'D3 4 4 16

80− , 'D3 11 11 10, 'D3 11 11 10

81− , 'D2 28 28, 'D2 28 28 ]

82−

83−randomDiscriminator :: MonadRandom m => m Discriminator

84−randomDiscriminator = randomNetwork

85−

86−randomGenerator :: MonadRandom m => m Generator

87−randomGenerator = randomNetwork

88−

89−trainExample :: LearningParameters -> Discriminator -> Generator -> S ('D2 28 28) -> S ('D1 80) -> ( Discriminator, Generator )

90−trainExample rate discriminator generator realExample noiseSource

91− = let (generatorTape, fakeExample) = runNetwork generator noiseSource

92−

93− (discriminatorTapeReal, guessReal) = runNetwork discriminator realExample

94− (discriminatorTapeFake, guessFake) = runNetwork discriminator fakeExample

95−

96− (discriminator'real, _) = runGradient discriminator discriminatorTapeReal ( guessReal - 1 )

97− (discriminator'fake, _) = runGradient discriminator discriminatorTapeFake guessFake

98− (_, push) = runGradient discriminator discriminatorTapeFake ( guessFake - 1)

99−

100− (generator', _) = runGradient generator generatorTape push

101−

102− newDiscriminator = foldl' (applyUpdate rate { learningRegulariser = learningRegulariser rate * 10}) discriminator [ discriminator'real, discriminator'fake ]

103− newGenerator = applyUpdate rate generator generator'

104− in ( newDiscriminator, newGenerator )

105−

106−

107−ganTest :: (Discriminator, Generator) -> Int -> FilePath -> LearningParameters -> ExceptT String IO (Discriminator, Generator)

108−ganTest (discriminator0, generator0) iterations trainFile rate = do

109− trainData <- fmap fst <$> readMNIST trainFile

110−

111− lift $ foldM (runIteration trainData) ( discriminator0, generator0 ) [1..iterations]

112−

113− where

114−

115− showShape' :: S ('D2 a b) -> IO ()

116− showShape' (S2D mm) = putStrLn $

117− let m = SA.extract mm

118− ms = toLists m

119− render n' | n' <= 0.2 = ' '

120− | n' <= 0.4 = '.'

121− | n' <= 0.6 = '-'

122− | n' <= 0.8 = '='

123− | otherwise = '#'

124−

125− px = (fmap . fmap) render ms

126− in unlines px

127−

128− runIteration :: [S ('D2 28 28)] -> (Discriminator, Generator) -> Int -> IO (Discriminator, Generator)

129− runIteration trainData ( !discriminator, !generator ) _ = do

130− trained' <- foldM ( \(!discriminatorX, !generatorX ) realExample -> do

131− fakeExample <- randomOfShape

132− return $ trainExample rate discriminatorX generatorX realExample fakeExample

133− ) ( discriminator, generator ) trainData

134−

135−

136− showShape' . snd . runNetwork (snd trained') =<< randomOfShape

137−

138− return trained'

139−

140−data GanOpts = GanOpts FilePath Int LearningParameters (Maybe FilePath) (Maybe FilePath)

141−

142−mnist' :: Parser GanOpts

143−mnist' = GanOpts <$> argument str (metavar "TRAIN")

144− <*> option auto (long "iterations" <> short 'i' <> value 15)

145− <*> (LearningParameters

146− <$> option auto (long "train_rate" <> short 'r' <> value 0.01)

147− <*> option auto (long "momentum" <> value 0.9)

148− <*> option auto (long "l2" <> value 0.0005)

149− )

150− <*> optional (strOption (long "load"))

151− <*> optional (strOption (long "save"))

152−

153−

154−main :: IO ()

155−main = do

156− GanOpts mnist iter rate load save <- execParser (info (mnist' <**> helper) idm)

157− putStrLn "Training stupidly simply GAN"

158− nets0 <- case load of

159− Just loadFile -> netLoad loadFile

160− Nothing -> (,) <$> randomDiscriminator <*> randomGenerator

161−

162− res <- runExceptT $ ganTest nets0 iter mnist rate

163− case res of

164− Right nets1 -> case save of

165− Just saveFile -> B.writeFile saveFile $ runPut (put nets1)

166− Nothing -> return ()

167−

168− Left err -> putStrLn err

169−

170−readMNIST :: FilePath -> ExceptT String IO [(S ('D2 28 28), S ('D1 10))]

171−readMNIST mnist = ExceptT $ do

172− mnistdata <- T.readFile mnist

173− return $ traverse (A.parseOnly parseMNIST) (T.lines mnistdata)

174−

175−parseMNIST :: A.Parser (S ('D2 28 28), S ('D1 10))

176−parseMNIST = do

177− Just lab <- oneHot <$> A.decimal

178− pixels <- many (A.char ',' >> A.double)

179− image <- maybe (fail "Parsed row was of an incorrect size") pure (fromStorable . V.fromList $ pixels)

180− return (image, lab)

181−

182−netLoad :: FilePath -> IO (Discriminator, Generator)

183−netLoad modelPath = do

184− modelData <- B.readFile modelPath

185− either fail return $ runGet (get :: Get (Discriminator, Generator)) modelData

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+123−113

@@ -1,69 +1,46 @@

11name: grenade

2−version: 0.1.0

2+version: 0.0.1

33license: BSD2

4−license-file: LICENSE

54author: Huw Campbell <huw.campbell@gmail.com>

65maintainer: Huw Campbell <huw.campbell@gmail.com>

8−synopsis: Practical Deep Learning in Haskell

9−category: AI, Machine Learning

10−cabal-version: >= 1.10

7+synopsis: grenade

8+category: System

9+cabal-version: >= 1.8

1110build-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>.

11+description: grenade.

2412

2513extra-source-files:

26− README.md

27− cbits/im2col.h

28− cbits/im2col.c

29− cbits/gradient_descent.h

30− cbits/gradient_descent.c

31− cbits/pad.h

32− cbits/pad.c

33−

34−source-repository head

35− type: git

36− location: https://github.com/HuwCampbell/grenade.git

14+ cbits/im2col.h

15+ cbits/im2col.c

16+ cbits/gradient_decent.h

17+ cbits/gradient_decent.c

18+ cbits/pad.h

19+ cbits/pad.c

3720

3821library

3922 build-depends:

4023 base >= 4.8 && < 5

41− , bytestring >= 0.10.0

42− , containers >= 0.5 && < 0.7

43− , cereal >= 0.5 && < 0.6

44− , deepseq >= 1.4 && < 1.5

45− , hmatrix >= 0.18 && < 0.21

46− , 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

52− , vector >= 0.11 && < 0.13

24+ , bytestring == 0.10.*

25+ , containers

26+ , deepseq

27+ , either == 4.4.*

28+ , cereal

29+ , exceptions == 0.8.*

30+ , hmatrix == 0.18.*

31+ , MonadRandom

32+ , mtl >= 2.2.1 && < 2.3

33+ , primitive

34+ , text == 1.2.*

35+ , transformers

36+ , singletons >= 2.1 && < 2.3

37+ , vector == 0.11.*

5338

5439 ghc-options:

5540 -Wall

5641 hs-source-dirs:

5742 src

5843

59− default-language: Haskell2010

60−

61− if impl(ghc < 8.0)

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

63− cpp-options: -DType=*

64−

65− if impl(ghc >= 8.6)

66− default-extensions: NoStarIsType

6744

6845 exposed-modules:

6946 Grenade

@@ -73,26 +50,19 @@ library

7350 Grenade.Core.Network

7451 Grenade.Core.Runner

7552 Grenade.Core.Shape

76−

77− Grenade.Layers

53+ Grenade.Layers.Crop

7854 Grenade.Layers.Concat

7955 Grenade.Layers.Convolution

80− Grenade.Layers.Crop

81− Grenade.Layers.Deconvolution

8256 Grenade.Layers.Dropout

83− Grenade.Layers.Elu

8457 Grenade.Layers.FullyConnected

85− Grenade.Layers.Inception

58+ Grenade.Layers.Reshape

8659 Grenade.Layers.Logit

8760 Grenade.Layers.Merge

88− Grenade.Layers.Pad

89− Grenade.Layers.Pooling

9061 Grenade.Layers.Relu

91− Grenade.Layers.Reshape

92− Grenade.Layers.Sinusoid

93− Grenade.Layers.Softmax

62+ Grenade.Layers.Elu

9463 Grenade.Layers.Tanh

95− Grenade.Layers.Trivial

64+ Grenade.Layers.Pad

65+ Grenade.Layers.Pooling

9666

9767 Grenade.Layers.Internal.Convolution

9868 Grenade.Layers.Internal.Pad

@@ -101,79 +71,123 @@ library

10171

10272 Grenade.Recurrent

10373

104− Grenade.Recurrent.Core

105− Grenade.Recurrent.Core.Layer

10674 Grenade.Recurrent.Core.Network

10775 Grenade.Recurrent.Core.Runner

10876

109− Grenade.Recurrent.Layers

11077 Grenade.Recurrent.Layers.BasicRecurrent

111− Grenade.Recurrent.Layers.ConcatRecurrent

11278 Grenade.Recurrent.Layers.LSTM

11379

11480 Grenade.Utils.OneHot

11581

116− includes: cbits/im2col.h

117− cbits/gradient_descent.h

118− cbits/pad.h

119− c-sources: cbits/im2col.c

120− cbits/gradient_descent.c

121− cbits/pad.c

82+ includes: cbits/im2col.h

83+ cbits/gradient_decent.h

84+ cbits/pad.h

85+ c-sources: cbits/im2col.c

86+ cbits/gradient_decent.c

87+ cbits/pad.c

12288

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

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

12490

125−test-suite test

126− type: exitcode-stdio-1.0

91+executable feedforward

92+ ghc-options: -Wall -threaded -O2

93+ main-is: main/feedforward.hs

94+ build-depends: base

95+ , grenade

96+ , attoparsec

97+ , bytestring

98+ , cereal

99+ , either

100+ , optparse-applicative == 0.13.*

101+ , text == 1.2.*

102+ , mtl >= 2.2.1 && < 2.3

103+ , hmatrix

104+ , transformers

105+ , singletons

106+ , semigroups

107+ , MonadRandom

127108

128− main-is: test.hs

109+executable mnist

110+ ghc-options: -Wall -threaded -O2

111+ main-is: main/mnist.hs

112+ build-depends: base

113+ , grenade

114+ , attoparsec

115+ , either

116+ , optparse-applicative == 0.13.*

117+ , text == 1.2.*

118+ , mtl >= 2.2.1 && < 2.3

119+ , hmatrix >= 0.18 && < 0.19

120+ , transformers

121+ , semigroups

122+ , singletons

123+ , MonadRandom

124+ , vector

129125

130− ghc-options: -Wall -threaded -O2

126+executable recurrent

127+ ghc-options: -Wall -threaded -O2

128+ main-is: main/recurrent.hs

129+ build-depends: base

130+ , grenade

131+ , attoparsec

132+ , either

133+ , optparse-applicative == 0.13.*

134+ , text == 1.2.*

135+ , mtl >= 2.2.1 && < 2.3

136+ , hmatrix >= 0.18 && < 0.19

137+ , transformers

138+ , semigroups

139+ , singletons

140+ , MonadRandom

131141

132− hs-source-dirs:

133− test

134142

135− default-language: Haskell2010

143+executable shakespeare

144+ ghc-options: -Wall -threaded -O2

145+ main-is: main/shakespeare.hs

146+ build-depends: base

147+ , grenade

148+ , attoparsec

149+ , bytestring

150+ , cereal

151+ , either

152+ , optparse-applicative == 0.13.*

153+ , text == 1.2.*

154+ , mtl >= 2.2.1 && < 2.3

155+ , hmatrix >= 0.18 && < 0.19

156+ , transformers

157+ , semigroups

158+ , singletons

159+ , vector

160+ , MonadRandom

161+ , containers

136162

137− other-modules: Test.Hedgehog.Compat

138− Test.Hedgehog.Hmatrix

139− Test.Hedgehog.TypeLits

140163

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

164+test-suite test

165+ type: exitcode-stdio-1.0

150166

151− Test.Grenade.Recurrent.Layers.LSTM

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

167+ main-is: test.hs

153168

154− if impl(ghc < 8.0)

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

156− cpp-options: -DType=*

169+ ghc-options: -Wall -threaded -O2

157170

158− if impl(ghc >= 8.6)

159− default-extensions: NoStarIsType

171+ hs-source-dirs:

172+ test

160173

161174 build-depends:

162− base

175+ base >= 4.8 && < 5

163176 , grenade

164− , hedgehog >= 1.0 && < 1.1

177+ , ambiata-disorder-core

178+ , ambiata-disorder-jack

165179 , hmatrix

166180 , mtl

167181 , singletons

168182 , text == 1.2.*

169− , typelits-witnesses < 0.4

170− , transformers

183+ , typelits-witnesses

171184 , constraints

185+ , QuickCheck >= 2.7 && < 2.9

186+ , quickcheck-instances == 0.3.*

172187 , MonadRandom

173188 , random

174189 , ad

175190 , reflection

176− , vector

177191

178192

179193benchmark bench

@@ -186,12 +200,10 @@ benchmark bench

186200 hs-source-dirs:

187201 bench

188202

189− default-language: Haskell2010

190−

191203 build-depends:

192− base

193− , bytestring

194− , criterion >= 1.1 && < 1.6

204+ base >= 3 && < 5

205+ , bytestring == 0.10.*

206+ , criterion == 1.1.*

195207 , grenade

196208 , hmatrix

197209

@@ -205,11 +217,9 @@ benchmark bench-lstm

205217 hs-source-dirs:

206218 bench

207219

208− default-language: Haskell2010

209−

210220 build-depends:

211− base

212− , bytestring

213− , criterion

221+ base >= 3 && < 5

222+ , bytestring == 0.10.*

223+ , criterion == 1.1.*

214224 , grenade

215225 , 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

examples/main/feedforward.hs → main/feedforward.hsrenamed+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/mnist.hs → main/mnist.hsrenamed+2−29

@@ -1,12 +1,10 @@

11{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE CPP #-}

32{-# LANGUAGE DataKinds #-}

43{-# LANGUAGE ScopedTypeVariables #-}

54{-# LANGUAGE TypeOperators #-}

65{-# LANGUAGE TupleSections #-}

76{-# LANGUAGE TypeFamilies #-}

87{-# LANGUAGE FlexibleContexts #-}

9−

108import Control.Applicative

119import Control.Monad

1210import Control.Monad.Random

@@ -14,10 +12,7 @@ import Control.Monad.Trans.Except

1412

1513import qualified Data.Attoparsec.Text as A

1614import Data.List ( foldl' )

17−#if ! MIN_VERSION_base(4,13,0)

1815import Data.Semigroup ( (<>) )

19−#endif

20−

2116import qualified Data.Text as T

2217import qualified Data.Text.IO as T

2318import qualified Data.Vector.Storable as V

@@ -30,15 +25,6 @@ import Options.Applicative

3025import Grenade

3126import Grenade.Utils.OneHot

3227

33−-- It's logistic regression!

34−--

35−-- This network is used to show how we can embed a Network as a layer in the larger MNIST

36−-- type.

37−type FL i o =

38− Network

39− '[ FullyConnected i o, Logit ]

40− '[ 'D1 i, 'D1 o, 'D1 o ]

41−

4228-- The definition of our convolutional neural network.

4329-- In the type signature, we have a type level list of shapes which are passed between the layers.

4430-- One can see that the images we are inputing are two dimensional with 28 * 28 pixels.

@@ -48,21 +34,8 @@ type FL i o =

4834

4935-- With the mnist data from Kaggle normalised to doubles between 0 and 1, learning rate of 0.01 and 15 iterations,

5036-- this network should get down to about a 1.3% error rate.

51−--

52−-- /NOTE:/ This model is actually too complex for MNIST, and one should use the type given in the readme instead.

53−-- This one is just here to demonstrate Inception layers in use.

54−--

55−type MNIST =

56− Network

57− '[ Reshape,

58− Concat ('D3 28 28 1) Trivial ('D3 28 28 14) (InceptionMini 28 28 1 5 9),

59− Pooling 2 2 2 2, Relu,

60− Concat ('D3 14 14 3) (Convolution 15 3 1 1 1 1) ('D3 14 14 15) (InceptionMini 14 14 15 5 10), Crop 1 1 1 1, Pooling 3 3 3 3, Relu,

61− Reshape, FL 288 80, FL 80 10 ]

62− '[ 'D2 28 28, 'D3 28 28 1,

63− 'D3 28 28 15, 'D3 14 14 15, 'D3 14 14 15, 'D3 14 14 18,

64− 'D3 12 12 18, 'D3 4 4 18, 'D3 4 4 18,

65− 'D1 288, 'D1 80, 'D1 10 ]

37+type MNIST = Network '[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu, Convolution 10 16 5 5 1 1, Pooling 2 2 2 2, Reshape, Relu, FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]

38+ '[ 'D2 28 28, 'D3 24 24 10, 'D3 12 12 10, 'D3 12 12 10, 'D3 8 8 16, 'D3 4 4 16, 'D1 256, 'D1 256, 'D1 80, 'D1 80, 'D1 10, 'D1 10]

6639

6740randomMnist :: MonadRandom m => m MNIST

6841randomMnist = randomNetwork

examples/main/recurrent.hs → main/recurrent.hsrenamed+11−10

@@ -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

@@ -23,6 +23,7 @@ import Options.Applicative

2323import Grenade

2424import Grenade.Recurrent

2525

26+{-# OPTIONS_GHC -fno-redundant-imports #-}

2627-- The defininition for our simple recurrent network.

2728-- This file just trains a network to generate a repeating sequence

2829-- of 0 0 1.

@@ -31,12 +32,12 @@ import Grenade.Recurrent

3132-- creation function know how to treat the layers.

3233type R = Recurrent

3334

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

35− '[ 'D1 1, 'D1 1]

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

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

3637

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

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

3839

39−randomNet :: MonadRandom m => m RecNet

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

4041randomNet = randomRecurrent

4142

4243netTest :: MonadRandom m => RecNet -> RecInput -> LearningParameters -> Int -> m (RecNet, RecInput)

@@ -69,8 +70,8 @@ generateRecurrent n s i =

6970 unfoldr go (s, i)

7071 where

7172 go (x, y) =

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

73− o' = heat o

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

74+ o' = heat o

7475 Just (o', (ns, fromIntegral o'))

7576

7677 heat :: S ('D1 1) -> Int

@@ -82,8 +83,8 @@ main = do

8283 FeedForwardOpts examples rate <- execParser (info (feedForward' <**> helper) idm)

8384 putStrLn "Training network..."

8485

85− net0 <- randomNet

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

86+ (net0, i0) <- randomNet

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

8788

8889 let results = generateRecurrent trained bestInput (c 1)

8990

examples/main/shakespeare.hs → main/shakespeare.hsrenamed+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 {

src/Grenade.hs+20−50

@@ -1,53 +1,23 @@

11module Grenade (

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

3− -- for machine learning with Grenade.

4−

5− -- * Exported modules

6− --

7− -- | The core types and runners for Grenade.

8− module Grenade.Core

9−

10− -- | The neural network layer zoo

11− , module Grenade.Layers

12−

13−

14− -- * Overview of the library

15− -- $library

16−

17− -- * Example usage

18− -- $example

19−

2+ module X

203 ) where

214

22−import Grenade.Core

23−import Grenade.Layers

24−

25−{- $library

26−Grenade is a purely functional deep learning library.

27−

28−It provides an expressive type level API for the construction

29−of complex neural network architectures. Backing this API is and

30−implementation written using BLAS and LAPACK, mostly provided by

31−the hmatrix library.

32−

33−-}

34−

35−{- $example

36−A few examples are provided at https://github.com/HuwCampbell/grenade

37−under the examples folder.

38−

39−The starting place is to write your neural network type and a

40−function to create a random layer of that type. The following

41−is a simple example which runs a logistic regression.

42−

43−> type MyNet = Network '[ FullyConnected 10 1, Logit ] '[ 'D1 10, 'D1 1, 'D1 1 ]

44−>

45−> randomMyNet :: MonadRandom MyNet

46−> randomMyNet = randomNetwork

47−

48−The function `randomMyNet` witnesses the `CreatableNetwork`

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

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

51−-}

52−

53−

5+import Grenade.Core.LearningParameters as X

6+import Grenade.Core.Layer as X

7+import Grenade.Core.Network as X

8+import Grenade.Core.Runner as X

9+import Grenade.Core.Shape as X

10+import Grenade.Layers.Concat as X

11+import Grenade.Layers.Crop as X

12+import Grenade.Layers.Dropout as X

13+import Grenade.Layers.Pad as X

14+import Grenade.Layers.Pooling as X

15+import Grenade.Layers.Reshape as X

16+import Grenade.Layers.FullyConnected as X

17+import Grenade.Layers.Logit as X

18+import Grenade.Layers.Merge as X

19+import Grenade.Layers.Convolution as X

20+import Grenade.Layers.Relu as X

21+import Grenade.Layers.Elu as X

22+import Grenade.Layers.Tanh as X

23+import Grenade.Layers.Softmax as X

src/Grenade/Core.hs+5−10

@@ -1,13 +1,8 @@

11module Grenade.Core (

2− module Grenade.Core.Layer

3− , module Grenade.Core.LearningParameters

4− , module Grenade.Core.Network

5− , module Grenade.Core.Runner

6− , module Grenade.Core.Shape

2+ module X

73 ) where

84

9−import Grenade.Core.Layer

10−import Grenade.Core.LearningParameters

11−import Grenade.Core.Network

12−import Grenade.Core.Runner

13−import Grenade.Core.Shape

5+import Grenade.Core.Layer as X

6+import Grenade.Core.LearningParameters as X

7+import Grenade.Core.Shape as X

8+import Grenade.Core.Network as X

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−11

@@ -1,15 +1,4 @@

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 (

9− -- | This module contains learning algorithm specific

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

11− -- unstable, due to issue #26.

12−

132 LearningParameters (..)

143 ) where

154

src/Grenade/Core/Network.hs+21−25

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

1010{-# LANGUAGE FlexibleInstances #-}

1111{-|

1212Module : Grenade.Core.Network

13−Description : Core definition of a Neural Network

13+Description : Core definition a simple neural etwork

1414Copyright : (c) Huw Campbell, 2016-2017

1515License : BSD2

1616Stability : experimental

@@ -19,6 +19,10 @@ This module defines the core data types and functions

1919for non-recurrent neural networks.

2020-}

2121

22+#if __GLASGOW_HASKELL__ < 800

23+{-# OPTIONS_GHC -fno-warn-incomplete-patterns #-}

24+#endif

25+

2226module Grenade.Core.Network (

2327 Network (..)

2428 , Gradients (..)

@@ -34,26 +38,22 @@ module Grenade.Core.Network (

3438import Control.Monad.Random ( MonadRandom )

3539

3640import Data.Singletons

41+import Data.Singletons.Prelude

3742import Data.Serialize

3843

39−#if MIN_VERSION_base(4,9,0)

40−import Data.Kind (Type)

41−#endif

42−

4344import Grenade.Core.Layer

4445import Grenade.Core.LearningParameters

4546import Grenade.Core.Shape

46−import Prelude.Singletons

4747

4848-- | Type of a network.

4949--

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

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

5151--

52−-- The @[Shape]@ type specifies the shapes of data passed between the layers.

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

5353--

5454-- Can be considered to be a heterogeneous list of layers which are able to

5555-- transform the data shapes of the network.

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

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

5757 NNil :: SingI i

5858 => Network '[] '[i]

5959

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

7171-- | Gradient of a network.

7272--

7373-- Parameterised on the layers of the network.

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

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

7575 GNil :: Gradients '[]

7676

7777 (:/>) :: UpdateLayer x

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

8282-- | Wegnert Tape of a network.

8383--

8484-- Parameterised on the layers and shapes of the network.

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

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

8686 TNil :: SingI i

8787 => Tapes '[] '[i]

8888

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

9696--

9797-- This gives the output, and the Wengert tape required for back

9898-- propagation.

99+--

99100runNetwork :: forall layers shapes.

100101 Network layers shapes

101102 -> S (Head shapes)

@@ -123,8 +124,8 @@ runNetwork =

123124--

124125-- Gives the gradients for the layer, and the gradient across the

125126-- input (which may not be required).

126−runGradient :: forall layers shapes.

127− Network layers shapes

127+--

128+runGradient :: forall layers shapes. Network layers shapes

128129 -> Tapes layers shapes

129130 -> S (Last shapes)

130131 -> (Gradients layers, S (Head shapes))

@@ -144,7 +145,7 @@ runGradient net tapes o =

144145 = (GNil, o)

145146

146147

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

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

148149applyUpdate :: LearningParameters

149150 -> Network layers shapes

150151 -> Gradients layers

@@ -155,13 +156,11 @@ applyUpdate rate (layer :~> rest) (gradient :/> grest)

155156applyUpdate _ NNil GNil

156157 = NNil

157158

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

159−-- initialise a random network is with the randomNetwork.

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

161− -- | Create a network with randomly initialised weights.

162− --

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

164− -- network is not sound.

159+

160+-- | A network can easily be created by hand with (:~>), but an easy way to initialise a random

161+-- network is with the randomNetwork.

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

163+ -- | Create a network of the types requested

165164 randomNetwork :: MonadRandom m => m (Network xs ss)

166165

167166instance SingI i => CreatableNetwork '[] '[i] where

@@ -182,15 +181,12 @@ instance (SingI i, SingI o, Layer x i o, Serialize x, Serialize (Network xs (o '

182181

183182-- | Ultimate composition.

184183--

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

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

186185instance CreatableNetwork sublayers subshapes => UpdateLayer (Network sublayers subshapes) where

187186 type Gradient (Network sublayers subshapes) = Gradients sublayers

188187 runUpdate = applyUpdate

189188 createRandom = randomNetwork

190189

191−-- | Ultimate composition.

192−--

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

194190instance (CreatableNetwork sublayers subshapes, i ~ (Head subshapes), o ~ (Last subshapes)) => Layer (Network sublayers subshapes) i o where

195191 type Tape (Network sublayers subshapes) i o = Tapes sublayers subshapes

196192 runForwards = runNetwork

src/Grenade/Core/Runner.hs+6−6

@@ -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.

@@ -40,7 +42,6 @@ backPropagate network input target =

4042 (grads, _) = runGradient network tapes (output - target)

4143 in grads

4244

43−

4445-- | Update a network with new weights after training with an instance.

4546train :: SingI (Last shapes)

4647 => LearningParameters

@@ -52,7 +53,6 @@ train rate network input output =

5253 let grads = backPropagate network input output

5354 in applyUpdate rate network grads

5455

55−

5656-- | Run the network with input and return the given output.

5757runNet :: Network layers shapes -> S (Head shapes) -> S (Last shapes)

5858runNet net = snd . runNetwork net

src/Grenade/Core/Shape.hs+32−58

@@ -8,33 +8,41 @@

88{-# LANGUAGE FlexibleContexts #-}

99{-# LANGUAGE ScopedTypeVariables #-}

1010{-# LANGUAGE RankNTypes #-}

11−{-# LANGUAGE UndecidableInstances #-}

11+

12+-- Ghc 7.10 fails to recognise n2 is complete.

13+#if __GLASGOW_HASKELL__ < 800

14+{-# OPTIONS_GHC -fno-warn-incomplete-patterns #-}

15+#endif

1216{-|

1317Module : Grenade.Core.Shape

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

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

1519Copyright : (c) Huw Campbell, 2016-2017

1620License : BSD2

1721Stability : experimental

1822

19−

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

24+are understood by Grenade.

2025-}

2126module Grenade.Core.Shape (

22− S (..)

23− , Shape (..)

24− , SShape (..)

27+ Shape (..)

28+ , S (..)

29+ , Sing (..)

30+

2531 , randomOfShape

2632 , fromStorable

2733 ) where

2834

2935import Control.DeepSeq (NFData (..))

3036import Control.Monad.Random ( MonadRandom, getRandom )

31−import Data.Kind (Type)

37+

3238import Data.Proxy

33−import Data.Serialize

3439import Data.Singletons

40+import Data.Singletons.TypeLits

3541import Data.Vector.Storable ( Vector )

3642import qualified Data.Vector.Storable as V

43+

3744import GHC.TypeLits

45+

3846import qualified Numeric.LinearAlgebra.Static as H

3947import Numeric.LinearAlgebra.Static

4048import qualified Numeric.LinearAlgebra as NLA

@@ -57,14 +65,17 @@ data Shape

5765-- All shapes are held in contiguous memory.

5866-- 3D is held in a matrix (usually row oriented) which has height depth * rows.

5967data S (n :: Shape) where

68+ -- | One dimensional data

6069 S1D :: ( KnownNat len )

6170 => R len

6271 -> S ('D1 len)

6372

73+ -- | Two dimensional data

6474 S2D :: ( KnownNat rows, KnownNat columns )

6575 => L rows columns

6676 -> S ('D2 rows columns)

6777

78+ -- | Three dimensional data

6879 S3D :: ( KnownNat rows

6980 , KnownNat columns

7081 , KnownNat depth

@@ -79,20 +90,10 @@ deriving instance Show (S n)

7990-- These could probably be derived with template haskell, but this seems

8091-- clear and makes adding the KnownNat constraints simple.

8192-- 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

9193data 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

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

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

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

9697

9798instance KnownNat a => SingI ('D1 a) where

9899 sing = D1Sing

@@ -148,28 +149,18 @@ randomOfShape :: forall x m. ( MonadRandom m, SingI x ) => m (S x)

148149randomOfShape = do

149150 seed :: Int <- getRandom

150151 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)

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

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

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

159155

160156-- | Generate a shape from a Storable Vector.

161157--

162158-- Returns Nothing if the vector is of the wrong size.

163159fromStorable :: forall x. SingI x => Vector Double -> Maybe (S x)

164160fromStorable 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

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

162+ D2Sing -> S2D <$> mkL xs

163+ D3Sing -> S3D <$> mkL xs

173164 where

174165 mkL :: forall rows columns. (KnownNat rows, KnownNat columns)

175166 => Vector Double -> Maybe (L rows columns)

@@ -180,18 +171,6 @@ fromStorable xs = case sing :: Sing x of

180171 then H.create $ NLA.reshape columns v

181172 else Nothing

182173

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−

195174-- Helper function for creating the number instances

196175n1 :: ( forall a. Floating a => a -> a ) -> S x -> S x

197176n1 f (S1D x) = S1D (f x)

@@ -205,13 +184,8 @@ n2 f (S2D x) (S2D y) = S2D (f x y)

205184n2 f (S3D x) (S3D y) = S3D (f x y)

206185

207186-- Helper function for creating the number instances

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

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

209188nk 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)

189+ D1Sing -> S1D (konst x)

190+ D2Sing -> S2D (konst x)

191+ D3Sing -> S3D (konst x)

src/Grenade/Layers.hsdeleted−35

@@ -1,35 +0,0 @@

1−module Grenade.Layers (

2− module Grenade.Layers.Concat

3− , module Grenade.Layers.Convolution

4− , module Grenade.Layers.Crop

5− , module Grenade.Layers.Deconvolution

6− , module Grenade.Layers.Elu

7− , module Grenade.Layers.FullyConnected

8− , module Grenade.Layers.Inception

9− , module Grenade.Layers.Logit

10− , module Grenade.Layers.Merge

11− , module Grenade.Layers.Pad

12− , module Grenade.Layers.Pooling

13− , module Grenade.Layers.Reshape

14− , module Grenade.Layers.Relu

15− , module Grenade.Layers.Softmax

16− , module Grenade.Layers.Tanh

17− , module Grenade.Layers.Trivial

18− ) where

19−

20−import Grenade.Layers.Concat

21−import Grenade.Layers.Convolution

22−import Grenade.Layers.Crop

23−import Grenade.Layers.Deconvolution

24−import Grenade.Layers.Elu

25−import Grenade.Layers.Pad

26−import Grenade.Layers.FullyConnected

27−import Grenade.Layers.Inception

28−import Grenade.Layers.Logit

29−import Grenade.Layers.Merge

30−import Grenade.Layers.Pooling

31−import Grenade.Layers.Reshape

32−import Grenade.Layers.Relu

33−import Grenade.Layers.Softmax

34−import Grenade.Layers.Tanh

35−import Grenade.Layers.Trivial

src/Grenade/Layers/Concat.hs+3−17

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

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

43{-# LANGUAGE TypeOperators #-}

@@ -9,29 +8,20 @@

98{-# LANGUAGE FlexibleInstances #-}

109{-# LANGUAGE ScopedTypeVariables #-}

1110{-# LANGUAGE StandaloneDeriving #-}

12−{-# LANGUAGE UndecidableInstances #-}

1311{-|

14−Module : Grenade.Layers.Concat

15−Description : Concatenation layer

12+Module : Grenade.Core.Network

13+Description : Core definition a simple neural etwork

1614Copyright : (c) Huw Campbell, 2016-2017

1715License : BSD2

1816Stability : experimental

19−

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

2117-}

2218module Grenade.Layers.Concat (

2319 Concat (..)

2420 ) where

2521

26−import Data.Serialize

27−

2822import Data.Singletons

2923import GHC.TypeLits

3024

31−#if MIN_VERSION_base(4,9,0)

32−import Data.Kind (Type)

33−#endif

34−

3525import Grenade.Core

3626

3727import Numeric.LinearAlgebra.Static ( row, (===), splitRows, unrow, (#), split, R )

@@ -49,7 +39,7 @@ import Numeric.LinearAlgebra.Static ( row, (===), splitRows, unrow, (#

4939--

5040-- 3D images become 3D images with more channels. The sizes must be the same, one can use Pad

5141-- and Crop layers to ensure this is the case.

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

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

5343 Concat :: x -> y -> Concat m x n y

5444

5545instance (Show x, Show y) => Show (Concat m x n y) where

@@ -133,7 +123,3 @@ instance ( SingI i

133123 (x', xB :: S i) = runBackwards x xTape (S3D ox :: S ('D3 rows cols m))

134124 (y', yB :: S i) = runBackwards y yTape (S3D oy :: S ('D3 rows cols n))

135125 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+62−89

@@ -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

@@ -152,8 +141,7 @@ instance ( KnownNat channels

152141 let mm = konst 0

153142 return $ Convolution wN mm

154143

155−-- | A three dimensional image (or 2d with many channels) can have

156−-- an appropriately sized convolution filter run across it.

144+-- | A two dimentional image may have a convolution filter applied to it

157145instance ( KnownNat kernelRows

158146 , KnownNat kernelCols

159147 , KnownNat filters

@@ -163,33 +151,28 @@ instance ( KnownNat kernelRows

163151 , KnownNat inputCols

164152 , KnownNat outputRows

165153 , KnownNat outputCols

166− , KnownNat channels

167154 , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

168155 , ((outputCols - 1) * strideCols) ~ (inputCols - kernelCols)

169− , KnownNat (kernelRows * kernelCols * channels)

156+ , KnownNat (kernelRows * kernelCols * 1)

170157 , KnownNat (outputRows * filters)

171− ) => Layer (Convolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) where

172−

173− type Tape (Convolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) = S ('D3 inputRows inputCols channels)

174−

175− runForwards (Convolution kernel _) (S3D input) =

158+ ) => Layer (Convolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) where

159+ type Tape (Convolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) = S ('D2 inputRows inputCols)

160+ runForwards (Convolution kernel _) (S2D input) =

176161 let ex = extract input

177162 ek = extract kernel

178− ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

179− iy = fromIntegral $ natVal (Proxy :: Proxy inputCols)

180163 kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

181164 ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols)

182165 sx = fromIntegral $ natVal (Proxy :: Proxy strideRows)

183166 sy = fromIntegral $ natVal (Proxy :: Proxy strideCols)

184167 ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

185168 oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

186−

187− c = vid2col kx ky sx sy ix iy ex

169+ c = im2col kx ky sx sy ex

188170 mt = c LA.<> ek

189171 r = col2vid 1 1 1 1 ox oy mt

190172 rs = fromJust . create $ r

191− in (S3D input, S3D rs)

192− runBackwards (Convolution kernel _) (S3D input) (S3D dEdy) =

173+ in (S2D input, S3D rs)

174+

175+ runBackwards (Convolution kernel _) (S2D input) (S3D dEdy) =

193176 let ex = extract input

194177 ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

195178 iy = fromIntegral $ natVal (Proxy :: Proxy inputCols)

@@ -200,7 +183,7 @@ instance ( KnownNat kernelRows

200183 ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

201184 oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

202185

203− c = vid2col kx ky sx sy ix iy ex

186+ c = im2col kx ky sx sy ex

204187

205188 eo = extract dEdy

206189 ek = extract kernel

@@ -208,14 +191,14 @@ instance ( KnownNat kernelRows

208191 vs = vid2col 1 1 1 1 ox oy eo

209192

210193 kN = fromJust . create $ tr c LA.<> vs

211−

212194 dW = vs LA.<> tr ek

213195

214− xW = col2vid kx ky sx sy ix iy dW

215− in (Convolution' kN, S3D . fromJust . create $ xW)

196+ xW = col2im kx ky sx sy ix iy dW

197+ in (Convolution' kN, S2D . fromJust . create $ xW)

216198

217199

218−-- | A two dimentional image may have a convolution filter applied to it

200+-- | A three dimensional image (or 2d with many channels) can have

201+-- an appropriately sized convolution filter run across it.

219202instance ( KnownNat kernelRows

220203 , KnownNat kernelCols

221204 , KnownNat filters

@@ -225,63 +208,53 @@ instance ( KnownNat kernelRows

225208 , KnownNat inputCols

226209 , KnownNat outputRows

227210 , KnownNat outputCols

211+ , KnownNat channels

228212 , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

229213 , ((outputCols - 1) * strideCols) ~ (inputCols - kernelCols)

230− , KnownNat (kernelRows * kernelCols * 1)

214+ , KnownNat (kernelRows * kernelCols * channels)

231215 , KnownNat (outputRows * filters)

232− ) => Layer (Convolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) where

233− type Tape (Convolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) = S ('D3 inputRows inputCols 1)

234− runForwards c (S2D input) =

235− runForwards c (S3D input :: S ('D3 inputRows inputCols 1))

216+ ) => Layer (Convolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) where

236217

237− runBackwards c tape grads =

238− case runBackwards c tape grads of

239− (c', S3D back :: S ('D3 inputRows inputCols 1)) -> (c', S2D back)

218+ type Tape (Convolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) = S ('D3 inputRows inputCols channels)

240219

220+ runForwards (Convolution kernel _) (S3D input) =

221+ let ex = extract input

222+ ek = extract kernel

223+ ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

224+ iy = fromIntegral $ natVal (Proxy :: Proxy inputCols)

225+ kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

226+ ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols)

227+ sx = fromIntegral $ natVal (Proxy :: Proxy strideRows)

228+ sy = fromIntegral $ natVal (Proxy :: Proxy strideCols)

229+ ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

230+ oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

241231

242−-- | A two dimensional image may have a convolution filter applied to it producing

243−-- a two dimensional image if both channels and filters is 1.

244−instance ( KnownNat kernelRows

245− , KnownNat kernelCols

246− , KnownNat strideRows

247− , KnownNat strideCols

248− , KnownNat inputRows

249− , KnownNat inputCols

250− , KnownNat outputRows

251− , KnownNat outputCols

252− , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

253− , ((outputCols - 1) * strideCols) ~ (inputCols - kernelCols)

254− , KnownNat (kernelRows * kernelCols * 1)

255− , KnownNat (outputRows * 1)

256− ) => Layer (Convolution 1 1 kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D2 outputRows outputCols) where

257− type Tape (Convolution 1 1 kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D2 outputRows outputCols) = S ('D3 inputRows inputCols 1)

258− runForwards c (S2D input) =

259− case runForwards c (S3D input :: S ('D3 inputRows inputCols 1)) of

260− (tps, S3D back :: S ('D3 outputRows outputCols 1)) -> (tps, S2D back)

232+ c = vid2col kx ky sx sy ix iy ex

233+ mt = c LA.<> ek

234+ r = col2vid 1 1 1 1 ox oy mt

235+ rs = fromJust . create $ r

236+ in (S3D input, S3D rs)

237+ runBackwards (Convolution kernel _) (S3D input) (S3D dEdy) =

238+ let ex = extract input

239+ ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

240+ iy = fromIntegral $ natVal (Proxy :: Proxy inputCols)

241+ kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

242+ ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols)

243+ sx = fromIntegral $ natVal (Proxy :: Proxy strideRows)

244+ sy = fromIntegral $ natVal (Proxy :: Proxy strideCols)

245+ ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

246+ oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

261247

262− runBackwards c tape (S2D grads) =

263− case runBackwards c tape (S3D grads :: S ('D3 outputRows outputCols 1)) of

264− (c', S3D back :: S ('D3 inputRows inputCols 1)) -> (c', S2D back)

248+ c = vid2col kx ky sx sy ix iy ex

265249

266−-- | A three dimensional image can produce a 2D image from a convolution with 1 filter

267−instance ( KnownNat kernelRows

268− , KnownNat kernelCols

269− , KnownNat strideRows

270− , KnownNat strideCols

271− , KnownNat inputRows

272− , KnownNat inputCols

273− , KnownNat outputRows

274− , KnownNat outputCols

275− , KnownNat channels

276− , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

277− , ((outputCols - 1) * strideCols) ~ (inputCols - kernelCols)

278− , KnownNat (kernelRows * kernelCols * channels)

279− , KnownNat (outputRows * 1)

280− ) => Layer (Convolution channels 1 kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D2 outputRows outputCols) where

281− type Tape (Convolution channels 1 kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D2 outputRows outputCols) = S ('D3 inputRows inputCols channels)

282− runForwards c input =

283− case runForwards c input of

284− (tps, S3D back :: S ('D3 outputRows outputCols 1)) -> (tps, S2D back)

250+ eo = extract dEdy

251+ ek = extract kernel

285252

286− runBackwards c tape (S2D grads) =

287− runBackwards c tape (S3D grads :: S ('D3 outputRows outputCols 1))

253+ vs = vid2col 1 1 1 1 ox oy eo

254+

255+ kN = fromJust . create $ tr c LA.<> vs

256+

257+ dW = vs LA.<> tr ek

258+

259+ xW = col2vid kx ky sx sy ix iy dW

260+ in (Convolution' kN, S3D . fromJust . create $ xW)

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.hsdeleted−285

@@ -1,285 +0,0 @@

1−{-# LANGUAGE CPP #-}

2−{-# LANGUAGE DataKinds #-}

3−{-# LANGUAGE ScopedTypeVariables #-}

4−{-# LANGUAGE GADTs #-}

5−{-# LANGUAGE TypeOperators #-}

6−{-# LANGUAGE TypeFamilies #-}

7−{-# LANGUAGE MultiParamTypeClasses #-}

8−{-# LANGUAGE FlexibleInstances #-}

9−{-# 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−-}

25−module Grenade.Layers.Deconvolution (

26− Deconvolution (..)

27− , Deconvolution' (..)

28− , randomDeconvolution

29− ) where

30−

31−import Control.Monad.Random hiding ( fromList )

32−import Data.Maybe

33−import Data.Proxy

34−import Data.Serialize

35−

36−import GHC.TypeLits

37−import Data.Kind (Type)

38−

39−import Numeric.LinearAlgebra hiding ( uniformSample, konst )

40−import qualified Numeric.LinearAlgebra as LA

41−import Numeric.LinearAlgebra.Static hiding ((|||), build, toRows)

42−

43−import Grenade.Core

44−import Grenade.Layers.Internal.Convolution

45−import Grenade.Layers.Internal.Update

46−

47−-- | A Deconvolution layer for a neural network.

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

49−--

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

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

52−--

53−data 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

60− 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

71−

72−data 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

79− Deconvolution' :: ( KnownNat channels

80− , KnownNat filters

81− , KnownNat kernelRows

82− , KnownNat kernelColumns

83− , KnownNat strideRows

84− , KnownNat strideColumns

85− , KnownNat kernelFlattened

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

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

88− -> Deconvolution' channels filters kernelRows kernelColumns strideRows strideColumns

89−

90−instance Show (Deconvolution c f k k' s s') where

91− show (Deconvolution a _) = renderConv a

92− where

93− renderConv mm =

94− let m = extract mm

95− ky = fromIntegral $ natVal (Proxy :: Proxy k)

96− rs = LA.toColumns m

97− ms = map (take ky) $ toLists . reshape ky <$> rs

98−

99− render n' | n' <= 0.2 = ' '

100− | n' <= 0.4 = '.'

101− | n' <= 0.6 = '-'

102− | n' <= 0.8 = '='

103− | otherwise = '#'

104−

105− px = (fmap . fmap . fmap) render ms

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

107−

108−randomDeconvolution :: ( MonadRandom m

109− , KnownNat channels

110− , KnownNat filters

111− , KnownNat kernelRows

112− , KnownNat kernelColumns

113− , KnownNat strideRows

114− , KnownNat strideColumns

115− , KnownNat kernelFlattened

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

117− => m (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns)

118−randomDeconvolution = do

119− s <- getRandom

120− let wN = uniformSample s (-1) 1

121− mm = konst 0

122− return $ Deconvolution wN mm

123−

124−instance ( KnownNat channels

125− , KnownNat filters

126− , KnownNat kernelRows

127− , KnownNat kernelColumns

128− , KnownNat strideRows

129− , KnownNat strideColumns

130− , KnownNat (kernelRows * kernelColumns * filters)

131− ) => 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

135− in Deconvolution newKernel newMomentum

136−

137− createRandom = randomDeconvolution

138−

139−instance ( KnownNat channels

140− , KnownNat filters

141− , KnownNat kernelRows

142− , KnownNat kernelColumns

143− , KnownNat strideRows

144− , KnownNat strideColumns

145− , KnownNat (kernelRows * kernelColumns * filters)

146− ) => Serialize (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) where

147− put (Deconvolution w _) = putListOf put . toList . flatten . extract $ w

148− get = do

149− let f = fromIntegral $ natVal (Proxy :: Proxy channels)

150− wN <- maybe (fail "Vector of incorrect size") return . create . reshape f . LA.fromList =<< getListOf get

151− let mm = konst 0

152− return $ Deconvolution wN mm

153−

154−-- | A two dimentional image may have a Deconvolution filter applied to it

155−instance ( KnownNat kernelRows

156− , KnownNat kernelCols

157− , KnownNat filters

158− , KnownNat strideRows

159− , KnownNat strideCols

160− , KnownNat inputRows

161− , KnownNat inputCols

162− , KnownNat outputRows

163− , KnownNat outputCols

164− , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows)

165− , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols)

166− , KnownNat (kernelRows * kernelCols * filters)

167− , KnownNat (outputRows * filters)

168− ) => Layer (Deconvolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) where

169− type Tape (Deconvolution 1 filters kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D3 outputRows outputCols filters) = S ('D3 inputRows inputCols 1)

170− runForwards c (S2D input) =

171− runForwards c (S3D input :: S ('D3 inputRows inputCols 1))

172−

173− runBackwards c tape grads =

174− case runBackwards c tape grads of

175− (c', S3D back :: S ('D3 inputRows inputCols 1)) -> (c', S2D back)

176−

177−-- | A two dimentional image may have a Deconvolution filter applied to it

178−instance ( KnownNat kernelRows

179− , KnownNat kernelCols

180− , KnownNat strideRows

181− , KnownNat strideCols

182− , KnownNat inputRows

183− , KnownNat inputCols

184− , KnownNat outputRows

185− , KnownNat outputCols

186− , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows)

187− , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols)

188− , KnownNat (kernelRows * kernelCols * 1)

189− , KnownNat (outputRows * 1)

190− ) => Layer (Deconvolution 1 1 kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D2 outputRows outputCols) where

191− type Tape (Deconvolution 1 1 kernelRows kernelCols strideRows strideCols) ('D2 inputRows inputCols) ('D2 outputRows outputCols) = S ('D3 inputRows inputCols 1)

192− runForwards c (S2D input) =

193− case runForwards c (S3D input :: S ('D3 inputRows inputCols 1)) of

194− (tps, S3D fore :: S ('D3 outputRows outputCols 1)) -> (tps, S2D fore)

195−

196− runBackwards c tape (S2D grads) =

197− case runBackwards c tape (S3D grads :: S ('D3 outputRows outputCols 1)) of

198− (c', S3D back :: S ('D3 inputRows inputCols 1)) -> (c', S2D back)

199−

200−-- | A two dimentional image may have a Deconvolution filter applied to it

201−instance ( KnownNat kernelRows

202− , KnownNat kernelCols

203− , KnownNat strideRows

204− , KnownNat strideCols

205− , KnownNat inputRows

206− , KnownNat inputCols

207− , KnownNat outputRows

208− , KnownNat outputCols

209− , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows)

210− , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols)

211− , KnownNat (kernelRows * kernelCols * 1)

212− , KnownNat (outputRows * 1)

213− , KnownNat channels

214− ) => Layer (Deconvolution channels 1 kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D2 outputRows outputCols) where

215− type Tape (Deconvolution channels 1 kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D2 outputRows outputCols) = S ('D3 inputRows inputCols channels)

216− runForwards c input =

217− case runForwards c input of

218− (tps, S3D fore :: S ('D3 outputRows outputCols 1)) -> (tps, S2D fore)

219−

220− runBackwards c tape (S2D grads) =

221− runBackwards c tape (S3D grads :: S ('D3 outputRows outputCols 1))

222−

223−-- | A three dimensional image (or 2d with many channels) can have

224−-- an appropriately sized Deconvolution filter run across it.

225−instance ( KnownNat kernelRows

226− , KnownNat kernelCols

227− , KnownNat filters

228− , KnownNat strideRows

229− , KnownNat strideCols

230− , KnownNat inputRows

231− , KnownNat inputCols

232− , KnownNat outputRows

233− , KnownNat outputCols

234− , KnownNat channels

235− , ((inputRows - 1) * strideRows) ~ (outputRows - kernelRows)

236− , ((inputCols - 1) * strideCols) ~ (outputCols - kernelCols)

237− , KnownNat (kernelRows * kernelCols * filters)

238− , KnownNat (outputRows * filters)

239− ) => Layer (Deconvolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) where

240−

241− type Tape (Deconvolution channels filters kernelRows kernelCols strideRows strideCols) ('D3 inputRows inputCols channels) ('D3 outputRows outputCols filters) = S ('D3 inputRows inputCols channels)

242−

243− runForwards (Deconvolution kernel _) (S3D input) =

244− let ex = extract input

245− ek = extract kernel

246− ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

247− iy = fromIntegral $ natVal (Proxy :: Proxy inputCols)

248− kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

249− ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols)

250− sx = fromIntegral $ natVal (Proxy :: Proxy strideRows)

251− sy = fromIntegral $ natVal (Proxy :: Proxy strideCols)

252− ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

253− oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

254−

255− c = vid2col 1 1 1 1 ix iy ex

256−

257− mt = c LA.<> tr ek

258−

259− r = col2vid kx ky sx sy ox oy mt

260− rs = fromJust . create $ r

261− in (S3D input, S3D rs)

262− runBackwards (Deconvolution kernel _) (S3D input) (S3D dEdy) =

263− let ex = extract input

264− ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

265− iy = fromIntegral $ natVal (Proxy :: Proxy inputCols)

266− kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

267− ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols)

268− sx = fromIntegral $ natVal (Proxy :: Proxy strideRows)

269− sy = fromIntegral $ natVal (Proxy :: Proxy strideCols)

270− ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

271− oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

272−

273− c = vid2col 1 1 1 1 ix iy ex

274−

275− eo = extract dEdy

276− ek = extract kernel

277−

278− vs = vid2col kx ky sx sy ox oy eo

279−

280− kN = fromJust . create . tr $ tr c LA.<> vs

281−

282− dW = vs LA.<> ek

283−

284− xW = col2vid 1 1 1 1 ix iy dW

285− in (Deconvolution' kN, S3D . fromJust . create $ xW)

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/Inception.hs+13−32

@@ -9,18 +9,13 @@

99{-# LANGUAGE ScopedTypeVariables #-}

1010{-|

1111Module : Grenade.Core.Network

12−Description : Inception style parallel convolutional network composition.

12+Description : Core definition a simple neural etwork

1313Copyright : (c) Huw Campbell, 2016-2017

1414License : BSD2

1515Stability : experimental

16−

17−Export an Inception style type, which can be used to build up

18−complex multiconvolution size networks.

1916-}

2017module Grenade.Layers.Inception (

2118 Inception

22− , InceptionMini

23− , Resnet

2419 ) where

2520

2621import GHC.TypeLits

@@ -29,39 +24,25 @@ import Grenade.Core

2924import Grenade.Layers.Convolution

3025import Grenade.Layers.Pad

3126import Grenade.Layers.Concat

32−import Grenade.Layers.Merge

33−import Grenade.Layers.Trivial

3427

35−-- | Type of an inception layer.

36−--

37−-- It looks like a bit of a handful, but is actually pretty easy to use.

38−--

39−-- The first three type parameters are the size of the (3D) data the

40−-- inception layer will take. It will emit 3D data with the number of

41−-- channels being the sum of @chx@, @chy@, @chz@, which are the number

42−-- of convolution filters in the 3x3, 5x5, and 7x7 convolutions Layers

43−-- respectively.

44−--

45−-- The network get padded effectively before each convolution filters

46−-- such that the output dimension is the same x and y as the input.

28+

4729type Inception rows cols channels chx chy chz

48− = Network '[ Concat ('D3 rows cols (chx + chy)) (InceptionMini rows cols channels chx chy) ('D3 rows cols chz) (Inception7x7 rows cols channels chz) ]

49− '[ 'D3 rows cols channels, 'D3 rows cols (chx + chy + chz) ]

30+ = Network '[ Concat ('D3 (rows - 2) (cols - 2) (chx + chy)) (InceptionS rows cols channels chx chy) ('D3 (rows - 2) (cols - 2) chz) (Inception7x7 rows cols channels chz) ]

31+ '[ 'D3 rows cols channels, 'D3 (rows -2) (cols -2) (chx + chy + chz) ]

5032

51−type InceptionMini rows cols channels chx chy

52− = Network '[ Concat ('D3 rows cols chx) (Inception3x3 rows cols channels chx) ('D3 rows cols chy) (Inception5x5 rows cols channels chy) ]

53− '[ 'D3 rows cols channels, 'D3 rows cols (chx + chy) ]

33+type InceptionS rows cols channels chx chy

34+ = Network '[ Concat ('D3 (rows - 2) (cols - 2) chx) (Inception3x3 rows cols channels chx) ('D3 (rows - 2) (cols - 2) chy) (Inception5x5 rows cols channels chy) ]

35+ '[ 'D3 rows cols channels, 'D3 (rows -2) (cols -2) (chx + chy) ]

5436

5537type Inception3x3 rows cols channels chx

56− = Network '[ Pad 1 1 1 1, Convolution channels chx 3 3 1 1 ]

57− '[ 'D3 rows cols channels, 'D3 (rows + 2) (cols + 2) channels, 'D3 rows cols chx ]

38+ = Network '[ Convolution channels chx 3 3 1 1 ]

39+ '[ 'D3 rows cols channels, 'D3 (rows -2) (cols -2) chx ]

5840

5941type Inception5x5 rows cols channels chx

60− = Network '[ Pad 2 2 2 2, Convolution channels chx 5 5 1 1 ]

61− '[ 'D3 rows cols channels, 'D3 (rows + 4) (cols + 4) channels, 'D3 rows cols chx ]

42+ = Network '[ Pad 1 1 1 1, Convolution channels chx 5 5 1 1 ]

43+ '[ 'D3 rows cols channels, 'D3 (rows + 2) (cols + 2) channels, 'D3 (rows - 2) (cols - 2) chx ]

6244

6345type Inception7x7 rows cols channels chx

64− = Network '[ Pad 3 3 3 3, Convolution channels chx 7 7 1 1 ]

65− '[ 'D3 rows cols channels, 'D3 (rows + 6) (cols + 6) channels, 'D3 rows cols chx ]

46+ = Network '[ Pad 2 2 2 2, Convolution channels chx 7 7 1 1 ]

47+ '[ 'D3 rows cols channels, 'D3 (rows + 4) (cols + 4) channels, 'D3 (rows - 2) (cols - 2) chx ]

6648

67−type Resnet branch = Merge Trivial branch

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+3−23

@@ -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

@@ -26,12 +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.

3223--

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

34−-- or vice versa.

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

3525data Reshape = Reshape

3626 deriving Show

3727

@@ -60,16 +50,6 @@ instance (KnownNat y, KnownNat x, KnownNat z, z ~ 1) => Layer Reshape ('D2 x y)

6050 runForwards _ (S2D y) = ((), S3D y)

6151 runBackwards _ _ (S3D y) = ((), S2D y)

6252

63−instance (KnownNat a, KnownNat x, KnownNat y, a ~ (x * y)) => Layer Reshape ('D1 a) ('D2 x y) where

64− type Tape Reshape ('D1 a) ('D2 x y) = ()

65− runForwards _ (S1D y) = ((), fromJust' . fromStorable . extract $ y)

66− runBackwards _ _ (S2D y) = ((), fromJust' . fromStorable . flatten . extract $ y)

67−

68−instance (KnownNat a, KnownNat x, KnownNat y, KnownNat (x * z), KnownNat z, a ~ (x * y * z)) => Layer Reshape ('D1 a) ('D3 x y z) where

69− type Tape Reshape ('D1 a) ('D3 x y z) = ()

70− runForwards _ (S1D y) = ((), fromJust' . fromStorable . extract $ y)

71− runBackwards _ _ (S3D y) = ((), fromJust' . fromStorable . flatten . extract $ y)

72−

7353instance Serialize Reshape where

7454 put _ = return ()

7555 get = return Reshape

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+3−14

@@ -3,25 +3,15 @@

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

169

1710import Data.Serialize

1811

19−import Grenade.Core

12+import Grenade.Core.Network

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

@@ -35,6 +25,5 @@ instance UpdateLayer Trivial where

3525 createRandom = return Trivial

3626

3727instance (a ~ b) => Layer Trivial a b where

38− type Tape Trivial a b = ()

39− runForwards _ a = ((), a)

28+ runForwards _ = id

4029 runBackwards _ _ y = ((), y)

src/Grenade/Recurrent.hs+5−26

@@ -1,29 +1,8 @@

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.

8− module Grenade.Recurrent.Core

9−

10− -- | The recurrent neural network layer zoo

11− , module Grenade.Recurrent.Layers

12−

13− -- * Overview of recurrent Networks

14− -- $recurrent

15−

2+ module X

163 ) where

174

18−import Grenade.Recurrent.Core

19−import 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−-}

5+import Grenade.Recurrent.Core.Network as X

6+import Grenade.Recurrent.Core.Runner as X

7+import Grenade.Recurrent.Layers.BasicRecurrent as X

8+import Grenade.Recurrent.Layers.LSTM as X

src/Grenade/Recurrent/Core.hsdeleted−9

@@ -1,9 +0,0 @@

1−module Grenade.Recurrent.Core (

2− module Grenade.Recurrent.Core.Layer

3− , module Grenade.Recurrent.Core.Network

4− , module Grenade.Recurrent.Core.Runner

5− ) where

6−

7−import Grenade.Recurrent.Core.Layer

8−import Grenade.Recurrent.Core.Network

9−import Grenade.Recurrent.Core.Runner

src/Grenade/Recurrent/Core/Layer.hsdeleted−35

@@ -1,35 +0,0 @@

1−{-# LANGUAGE CPP #-}

2−{-# LANGUAGE DataKinds #-}

3−{-# LANGUAGE TypeFamilies #-}

4−{-# LANGUAGE MultiParamTypeClasses #-}

5−{-# LANGUAGE FlexibleContexts #-}

6−{-# LANGUAGE FlexibleInstances #-}

7−module Grenade.Recurrent.Core.Layer (

8− RecurrentLayer (..)

9− , RecurrentUpdateLayer (..)

10− ) where

11−

12−#if MIN_VERSION_base(4,9,0)

13−import Data.Kind (Type)

14−#endif

15−

16−import Grenade.Core

17−

18−-- | Class for a recurrent layer.

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

20−-- of an extra recurrent data shape.

21−class UpdateLayer x => RecurrentUpdateLayer x where

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

23− type RecurrentShape x :: Type

24−

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

26− -- | Wengert Tape

27− type RecTape x i o :: Type

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

29− -- layer, and give the output from this layer.

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

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

32− -- layer gave from the input and the back propagated derivatives from

33− -- the layer above.

34− -- 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)

src/Grenade/Recurrent/Core/Network.hs+64−246

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

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

43{-# LANGUAGE TypeOperators #-}

@@ -7,54 +6,54 @@

76{-# LANGUAGE FlexibleContexts #-}

87{-# LANGUAGE FlexibleInstances #-}

98{-# LANGUAGE EmptyDataDecls #-}

10−{-# LANGUAGE RankNTypes #-}

11−{-# LANGUAGE BangPatterns #-}

12−{-# LANGUAGE ScopedTypeVariables #-}

13−{-# LANGUAGE UndecidableInstances #-}

14−

159module Grenade.Recurrent.Core.Network (

1610 Recurrent

1711 , FeedForward

18−

12+ , RecurrentLayer (..)

13+ , RecurrentUpdateLayer (..)

1914 , RecurrentNetwork (..)

2015 , RecurrentInputs (..)

21− , RecurrentTape (..)

22− , RecurrentGradient (..)

23−

24− , randomRecurrent

25− , runRecurrent

26− , runRecurrent'

27− , applyRecurrentUpdate

16+ , CreatableRecurrent (..)

2817 ) where

2918

3019

3120import Control.Monad.Random ( MonadRandom )

21+import Data.Singletons ( SingI )

3222import Data.Serialize

33−

34−import Data.Kind (Type)

23+import qualified Data.Vector.Storable as V

3524

3625import Grenade.Core

37−import Grenade.Recurrent.Core.Layer

38−import Prelude.Singletons

26+

27+import qualified Numeric.LinearAlgebra as LA

28+import qualified Numeric.LinearAlgebra.Static as LAS

29+

3930

4031-- | Witness type to say indicate we're building up with a normal feed

4132-- forward layer.

42−data FeedForward :: Type -> Type

33+data FeedForward :: * -> *

4334-- | Witness type to say indicate we're building up with a recurrent layer.

44−data Recurrent :: Type -> Type

45−

46−-- | Type of a recurrent neural network.

47−--

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

49−--

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

51−--

52−-- The definition is similar to a Network, but every layer in the

53−-- type is tagged by whether it's a FeedForward Layer of a Recurrent layer.

54−--

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

56−-- for these empty data types.

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

35+data Recurrent :: * -> *

36+

37+-- | Class for a recurrent layer.

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

39+-- of an extra recurrent data shape.

40+class UpdateLayer x => RecurrentUpdateLayer x where

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

42+ type RecurrentShape x :: Shape

43+

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

45+ -- | Wengert Tape

46+ type RecTape x i o :: *

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

48+ -- layer, and give the output from this layer.

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

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

51+ -- layer gave from the input and the back propagated derivatives from

52+ -- the layer above.

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

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

55+

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

5857 RNil :: SingI i

5958 => RecurrentNetwork '[] '[i]

6059

@@ -70,132 +69,6 @@ data RecurrentNetwork :: [Type] -> [Shape] -> Type where

7069infixr 5 :~~>

7170infixr 5 :~@>

7271

73−-- | Gradient of a network.

74−--

75−-- Parameterised on the layers of the network.

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

77− RGNil :: RecurrentGradient '[]

78−

79− (://>) :: UpdateLayer x

80− => Gradient x

81− -> RecurrentGradient xs

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

83−

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

85−-- Parameterised on the layers of a Network.

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

87− RINil :: RecurrentInputs '[]

88−

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

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

91−

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

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

94−

95−-- | All the information required to backpropogate

96−-- through time safely.

97−--

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

99−-- that that all Tape lengths are the same.

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

101− TRNil :: SingI i

102− => RecurrentTape '[] '[i]

103−

104− (:\~>) :: Tape x i h

105− -> !(RecurrentTape xs (h ': hs))

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

107−

108− (:\@>) :: RecTape x i h

109− -> !(RecurrentTape xs (h ': hs))

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

111−

112−

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 =

119− go

120− where

121− go :: forall js sublayers. (Last js ~ Last shapes)

122− => RecurrentNetwork sublayers js

123− -> 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−

129− -- 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)

132−

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

134− -- the recurrent shape output to the next layer.

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

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

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

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

139−

140− -- Handle the output layer, bouncing the derivatives back down.

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

142− go RNil RINil !x

143− = (TRNil, RINil, x)

144−

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 =

152− go net tapes r

153− where

154− -- We have to be careful regarding the direction of the lists

155− -- Inputs come in forwards, but our return value is backwards

156− -- through time.

157− go :: forall js ss. (Last js ~ Last shapes)

158− => RecurrentNetwork ss js

159− -> RecurrentTape ss js

160− -> RecurrentInputs ss

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

162− -- 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)

168−

169− -- This is a recurrent layer

170− -- 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)

175−

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

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

178− -- well.

179− go !RNil !TRNil !RINil

180− = (RGNil, RINil, o)

181−

182−-- | Apply a batch of gradients to the network

183−-- Uses runUpdates which can be specialised for

184−-- a layer.

185−applyRecurrentUpdate :: LearningParameters

186− -> RecurrentNetwork layers shapes

187− -> RecurrentGradient layers

188− -> RecurrentNetwork layers shapes

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

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

191−

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

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

194−

195−applyRecurrentUpdate _ RNil RGNil

196− = RNil

197−

198−

19972instance Show (RecurrentNetwork '[] '[i]) where

20073 show RNil = "NNil"

20174instance (Show x, Show (RecurrentNetwork xs rs)) => Show (RecurrentNetwork (FeedForward x ': xs) (i ': rs)) where

@@ -203,28 +76,39 @@ instance (Show x, Show (RecurrentNetwork xs rs)) => Show (RecurrentNetwork (Feed

20376instance (Show x, Show (RecurrentNetwork xs rs)) => Show (RecurrentNetwork (Recurrent x ': xs) (i ': rs)) where

20477 show (x :~@> xs) = show x ++ "\n~~>\n" ++ show xs

20578

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

80+-- Parameterised on the layers of a Network.

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

82+ RINil :: RecurrentInputs '[]

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

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

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

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

87+infixr 5 :~~+>

88+infixr 5 :~@+>

20689

20790-- | A network can easily be created by hand with (:~~>) and (:~@>), but an easy way to initialise a random

20891-- recurrent network and a set of random inputs for it is with the randomRecurrent.

209−class CreatableRecurrent (xs :: [Type]) (ss :: [Shape]) where

92+class CreatableRecurrent (xs :: [*]) (ss :: [Shape]) where

21093 -- | Create a network of the types requested

211− randomRecurrent :: MonadRandom m => m (RecurrentNetwork xs ss)

94+ randomRecurrent :: MonadRandom m => m (RecurrentNetwork xs ss, RecurrentInputs xs)

21295

21396instance SingI i => CreatableRecurrent '[] '[i] where

21497 randomRecurrent =

215− return RNil

98+ return (RNil, RINil)

21699

217100instance (SingI i, Layer x i o, CreatableRecurrent xs (o ': rs)) => CreatableRecurrent (FeedForward x ': xs) (i ': o ': rs) where

218101 randomRecurrent = do

219102 thisLayer <- createRandom

220− rest <- randomRecurrent

221− return (thisLayer :~~> rest)

103+ (rest, resti) <- randomRecurrent

104+ return (thisLayer :~~> rest, () :~~+> resti)

222105

223106instance (SingI i, RecurrentLayer x i o, CreatableRecurrent xs (o ': rs)) => CreatableRecurrent (Recurrent x ': xs) (i ': o ': rs) where

224107 randomRecurrent = do

225108 thisLayer <- createRandom

226− rest <- randomRecurrent

227− return (thisLayer :~@> rest)

109+ thisShape <- randomOfShape

110+ (rest, resti) <- randomRecurrent

111+ return (thisLayer :~@> rest, thisShape :~@+> resti)

228112

229113-- | Add very simple serialisation to the recurrent network

230114instance SingI i => Serialize (RecurrentNetwork '[] '[i]) where

@@ -243,86 +127,20 @@ instance (Serialize (RecurrentInputs '[])) where

243127 put _ = return ()

244128 get = return RINil

245129

246−instance (UpdateLayer x, Serialize (RecurrentInputs ys), Fractional (RecurrentInputs ys)) => (Serialize (RecurrentInputs (FeedForward x ': ys))) where

130+instance (UpdateLayer x, Serialize (RecurrentInputs ys)) => (Serialize (RecurrentInputs (FeedForward x ': ys))) where

247131 put ( () :~~+> rest) = put rest

248132 get = ( () :~~+> ) <$> get

249133

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

253−

254−

255−-- Num instance for `RecurrentInputs layers`

256−-- Not sure if this is really needed, as I only need a `fromInteger 0` at

257−-- the moment for training, to create a null gradient on the recurrent

258−-- edge.

259−--

260−-- It does raise an interesting question though? Is a 0 gradient actually

261−-- the best?

262−--

263−-- I could imaging that weakly push back towards the optimum input could

264−-- help make a more stable generator.

265−instance (Num (RecurrentInputs '[])) where

266− (+) _ _ = RINil

267− (-) _ _ = RINil

268− (*) _ _ = RINil

269− abs _ = RINil

270− signum _ = RINil

271− fromInteger _ = RINil

272−

273−instance (UpdateLayer x, Fractional (RecurrentInputs ys)) => (Num (RecurrentInputs (FeedForward x ': ys))) where

274− (+) (() :~~+> x) (() :~~+> y) = () :~~+> (x + y)

275− (-) (() :~~+> x) (() :~~+> y) = () :~~+> (x - y)

276− (*) (() :~~+> x) (() :~~+> y) = () :~~+> (x * y)

277− abs (() :~~+> x) = () :~~+> abs x

278− signum (() :~~+> x) = () :~~+> signum x

279− fromInteger x = () :~~+> fromInteger x

280−

281−instance (Fractional (RecurrentShape x), RecurrentUpdateLayer x, Fractional (RecurrentInputs ys)) => (Num (RecurrentInputs (Recurrent x ': ys))) where

282− (+) (x :~@+> x') (y :~@+> y') = (x + y) :~@+> (x' + y')

283− (-) (x :~@+> x') (y :~@+> y') = (x - y) :~@+> (x' - y')

284− (*) (x :~@+> x') (y :~@+> y') = (x * y) :~@+> (x' * y')

285− abs (x :~@+> x') = abs x :~@+> abs x'

286− signum (x :~@+> x') = signum x :~@+> signum x'

287− 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'

134+instance (SingI (RecurrentShape x), RecurrentUpdateLayer x, Serialize (RecurrentInputs ys)) => (Serialize (RecurrentInputs (Recurrent x ': ys))) where

135+ put ( i :~@+> rest ) = do

136+ _ <- (case i of

137+ (S1D x) -> putListOf put . LA.toList . LAS.extract $ x

138+ (S2D x) -> putListOf put . LA.toList . LA.flatten . LAS.extract $ x

139+ (S3D x) -> putListOf put . LA.toList . LA.flatten . LAS.extract $ x

140+ ) :: PutM ()

141+ put rest

142+

143+ get = do

144+ Just i <- fromStorable . V.fromList <$> getListOf get

145+ rest <- get

146+ return ( i :~@+> rest)

src/Grenade/Recurrent/Core/Runner.hs+122−87

@@ -3,108 +3,143 @@

33{-# LANGUAGE DataKinds #-}

44{-# LANGUAGE ScopedTypeVariables #-}

55{-# LANGUAGE TypeOperators #-}

6+{-# LANGUAGE CPP #-}

67{-# LANGUAGE TypeFamilies #-}

78{-# LANGUAGE FlexibleContexts #-}

89{-# LANGUAGE RankNTypes #-}

10+{-# LANGUAGE RecordWildCards #-}

911

10−module Grenade.Recurrent.Core.Runner (

11− runRecurrentExamples

12− , runRecurrentBackprop

13− , backPropagateRecurrent

14− , trainRecurrent

12+#if __GLASGOW_HASKELL__ < 800

13+{-# OPTIONS_GHC -fno-warn-incomplete-patterns #-}

14+#endif

1515

16− , RecurrentGradients

16+module Grenade.Recurrent.Core.Runner (

17+ trainRecurrent

18+ , runRecurrent

1719 ) where

1820

19−import Data.List ( foldl' )

21+import Data.Singletons.Prelude

2022import Grenade.Core

2123

2224import 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−

5525

5626-- | Drive and network and collect its back propogated gradients.

57−backPropagateRecurrent :: forall shapes layers. (SingI (Last shapes), Fractional (RecurrentInputs layers))

58− => RecurrentNetwork layers shapes

59− -> RecurrentInputs layers

60− -> [(S (Head shapes), Maybe (S (Last shapes)))]

61− -> (RecurrentGradients layers, RecurrentInputs layers)

62−backPropagateRecurrent network recinputs examples =

63− let (outForwards, _) = runRecurrentExamples network recinputs inputs

64−

65− backPropagations = zipWith makeError outForwards targets

66−

67− (outBackwards, input') = runRecurrentBackprop network 0 backPropagations

68−

69− gradients = fmap fst outBackwards

70− in (gradients, input')

71−

72− where

73−

74− inputs = fst <$> examples

75− targets = snd <$> examples

76−

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)

80−

81−

82−trainRecurrent :: forall shapes layers. (SingI (Last shapes), Fractional (RecurrentInputs layers))

27+--

28+-- TODO: split this nicely into backpropagate and update.

29+--

30+-- QUESTION: Should we return a list of gradients or the sum of

31+-- the gradients? It's different taking into account

32+-- momentum and L2.

33+trainRecurrent :: forall shapes layers. SingI (Last shapes)

8334 => LearningParameters

8435 -> RecurrentNetwork layers shapes

8536 -> RecurrentInputs layers

8637 -> [(S (Head shapes), Maybe (S (Last shapes)))]

8738 -> (RecurrentNetwork layers shapes, RecurrentInputs layers)

8839trainRecurrent rate network recinputs examples =

89− let (gradients, recinputs') = backPropagateRecurrent network recinputs examples

90−

91− newInputs = updateRecInputs rate recinputs recinputs'

92−

93− newNetwork = foldl' (applyRecurrentUpdate rate) network gradients

94−

95− in (newNetwork, newInputs)

96−

97−updateRecInputs :: Fractional (RecurrentInputs sublayers)

98− => LearningParameters

99− -> RecurrentInputs sublayers

100− -> RecurrentInputs sublayers

101− -> RecurrentInputs sublayers

102−

103−updateRecInputs lp (() :~~+> xs) (() :~~+> ys)

104− = () :~~+> updateRecInputs lp xs ys

105−

106−updateRecInputs lp (x :~@+> xs) (y :~@+> ys)

107− = (realToFrac (1 - learningRate lp * learningRegulariser lp) * x - realToFrac (learningRate lp) * y) :~@+> updateRecInputs lp xs ys

108−

109−updateRecInputs _ RINil RINil

110− = RINil

40+ updateBack $ go inputs network recinputs

41+ where

42+ inputs = fst <$> examples

43+ targets = snd <$> examples

44+ updateBack (a,recgrad,_) = (a,updateRecInputs rate recinputs recgrad)

45+

46+ go :: forall js sublayers. (Last js ~ Last shapes)

47+ => [S (Head js)] -- ^ input vector

48+ -> RecurrentNetwork sublayers js -- ^ network to train

49+ -> RecurrentInputs sublayers

50+ -> (RecurrentNetwork sublayers js, RecurrentInputs sublayers, [S (Head js)])

51+

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

53+ -- Note we're doing training here, we could just return a list of gradients

54+ -- (and probably will in future).

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

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

57+ tapes = fst <$> tys

58+ ys = snd <$> tys

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

60+ (newFN, ig, grads) = go ys n nIn

61+ -- calculate the gradient for this layer to pass down,

62+ back = uncurry (runBackwards layer) <$> zip (reverse tapes) grads

63+ -- the new trained layer.

64+ newlayer = runUpdates rate layer (fst <$> back)

65+

66+ in (newlayer :~~> newFN, () :~~+> ig, snd <$> back)

67+

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

69+ -- the recurrent shape output to the next layer.

70+ go !xs (layer :~@> n) (g :~@+> nIn)

71+ = let tys = scanlFrom layer g xs

72+ tapes = fst <$> tys

73+ ys = snd <$> tys

74+

75+ (newFN, ig, grads) = go ys n nIn

76+

77+ backExamples = zip (reverse tapes) grads

78+

79+ (rg, back) = myscanbackward layer backExamples

80+ -- the new trained layer.

81+ newlayer = runUpdates rate layer (fst <$> back)

82+ in (newlayer :~@> newFN, rg :~@+> ig, snd <$> back)

83+

84+ -- Handle the output layer, bouncing the derivatives back down.

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

86+ go !xs RNil RINil

87+ = (RNil, RINil, reverse (zipWith makeError xs targets))

88+ where

89+ makeError :: S (Last shapes) -> Maybe (S (Last shapes)) -> S (Last shapes)

90+ makeError _ Nothing = 0

91+ makeError y (Just t) = y - t

92+

93+ updateRecInputs :: forall sublayers.

94+ LearningParameters

95+ -> RecurrentInputs sublayers

96+ -> RecurrentInputs sublayers

97+ -> RecurrentInputs sublayers

98+

99+ updateRecInputs l@LearningParameters {..} (() :~~+> xs) (() :~~+> ys)

100+ = () :~~+> updateRecInputs l xs ys

101+

102+ updateRecInputs l@LearningParameters {..} (x :~@+> xs) (y :~@+> ys)

103+ = (realToFrac (learningRate * learningRegulariser) * x - realToFrac learningRate * y) :~@+> updateRecInputs l xs ys

104+

105+ updateRecInputs _ RINil RINil

106+ = RINil

107+

108+scanlFrom :: forall x i o. RecurrentLayer x i o

109+ => x -- ^ the layer

110+ -> S (RecurrentShape x) -- ^ place to start

111+ -> [S i] -- ^ list of inputs to scan through

112+ -> [(RecTape x i o, S o)] -- ^ list of scan inputs and outputs

113+scanlFrom !layer !recShape (x:xs) =

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

115+ in (tape, lepush) : scanlFrom layer lerec xs

116+scanlFrom _ _ [] = []

117+

118+myscanbackward :: forall x i o. RecurrentLayer x i o

119+ => x -- ^ the layer

120+ -> [(RecTape x i o, S o)] -- ^ the list of inputs and output to scan over

121+ -> (S (RecurrentShape x), [(Gradient x, S i)]) -- ^ list of gradients to fold and inputs to backprop

122+myscanbackward layer =

123+ goX 0

124+ where

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

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

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

128+ (pushedback, ll) = goX recgrad xs

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

130+ goX !lastback [] = (lastback, [])

131+

132+-- | Just forwards propagation with no training.

133+runRecurrent :: RecurrentNetwork layers shapes

134+ -> RecurrentInputs layers -> S (Head shapes)

135+ -> (RecurrentInputs layers, S (Last shapes))

136+runRecurrent (layer :~~> n) (() :~~+> nr) !x

137+ = let (_, ys) = runForwards layer x

138+ (nr', o) = runRecurrent n nr ys

139+ in (() :~~+> nr', o)

140+runRecurrent (layer :~@> n) (recin :~@+> nr) !x

141+ = let (_, recin', y) = runRecurrentForwards layer recin x

142+ (nr', o) = runRecurrent n nr y

143+ in (recin' :~@+> nr', o)

144+runRecurrent RNil RINil !x

145+ = (RINil, x)

src/Grenade/Recurrent/Layers.hsdeleted−9

@@ -1,9 +0,0 @@

1−module Grenade.Recurrent.Layers (

2− module Grenade.Recurrent.Layers.BasicRecurrent

3− , module Grenade.Recurrent.Layers.ConcatRecurrent

4− , module Grenade.Recurrent.Layers.LSTM

5− ) where

6−

7−import Grenade.Recurrent.Layers.BasicRecurrent

8−import Grenade.Recurrent.Layers.ConcatRecurrent

9−import Grenade.Recurrent.Layers.LSTM

src/Grenade/Recurrent/Layers/BasicRecurrent.hs+12−11

@@ -1,12 +1,14 @@

1−{-# LANGUAGE CPP #-}

21{-# LANGUAGE DataKinds #-}

32{-# LANGUAGE GADTs #-}

3+{-# LANGUAGE RecordWildCards #-}

44{-# LANGUAGE TypeOperators #-}

55{-# LANGUAGE TypeFamilies #-}

66{-# LANGUAGE MultiParamTypeClasses #-}

77{-# LANGUAGE FlexibleContexts #-}

88{-# LANGUAGE UndecidableInstances #-}

99

10+-- GHC 7.10 doesn't see recurrent run functions as total.

11+{-# OPTIONS_GHC -fno-warn-incomplete-patterns #-}

1012module Grenade.Recurrent.Layers.BasicRecurrent (

1113 BasicRecurrent (..)

1214 , randomBasicRecurrent

@@ -16,19 +18,18 @@ module Grenade.Recurrent.Layers.BasicRecurrent (

1618

1719import Control.Monad.Random ( MonadRandom, getRandom )

1820

19−

20−import Data.Kind (Type)

21+import Data.Singletons.TypeLits

2122

2223import Numeric.LinearAlgebra.Static

2324

2425import GHC.TypeLits

2526

2627import Grenade.Core

27−import Grenade.Recurrent.Core

28+import Grenade.Recurrent.Core.Network

2829

2930data BasicRecurrent :: Nat -- Input layer size

3031 -> Nat -- Output layer size

31− -> Type where

32+ -> * where

3233 BasicRecurrent :: ( KnownNat input

3334 , KnownNat output

3435 , KnownNat matrixCols

@@ -41,7 +42,7 @@ data BasicRecurrent :: Nat -- Input layer size

4142

4243data BasicRecurrent' :: Nat -- Input layer size

4344 -> Nat -- Output layer size

44− -> Type where

45+ -> * where

4546 BasicRecurrent' :: ( KnownNat input

4647 , KnownNat output

4748 , KnownNat matrixCols

@@ -56,18 +57,18 @@ instance Show (BasicRecurrent i o) where

5657instance (KnownNat i, KnownNat o, KnownNat (i + o)) => UpdateLayer (BasicRecurrent i o) where

5758 type Gradient (BasicRecurrent i o) = (BasicRecurrent' i o)

5859

59− runUpdate lp (BasicRecurrent oldBias oldBiasMomentum oldActivations oldMomentum) (BasicRecurrent' biasGradient activationGradient) =

60− let newBiasMomentum = konst (learningMomentum lp) * oldBiasMomentum - konst (learningRate lp) * biasGradient

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

61+ let newBiasMomentum = konst learningMomentum * oldBiasMomentum - konst learningRate * biasGradient

6162 newBias = oldBias + newBiasMomentum

62− newMomentum = konst (learningMomentum lp) * oldMomentum - konst (learningRate lp) * activationGradient

63− regulariser = konst (learningRegulariser lp * learningRate lp) * oldActivations

63+ newMomentum = konst learningMomentum * oldMomentum - konst learningRate * activationGradient

64+ regulariser = konst (learningRegulariser * learningRate) * oldActivations

6465 newActivations = oldActivations + newMomentum - regulariser

6566 in BasicRecurrent newBias newBiasMomentum newActivations newMomentum

6667

6768 createRandom = randomBasicRecurrent

6869

6970instance (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)

71+ type RecurrentShape (BasicRecurrent i o) = 'D1 o

7172

7273instance (KnownNat i, KnownNat o, KnownNat (i + o), i <= (i + o), o ~ ((i + o) - i)) => RecurrentLayer (BasicRecurrent i o) ('D1 i) ('D1 o) where

7374

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+84−58

@@ -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 #-}

@@ -10,6 +10,8 @@

1010{-# LANGUAGE ViewPatterns #-}

1111{-# LANGUAGE ScopedTypeVariables #-}

1212

13+-- GHC 7.10 doesn't see recurrent run functions as total.

14+{-# OPTIONS_GHC -fno-warn-incomplete-patterns #-}

1315module Grenade.Recurrent.Layers.LSTM (

1416 LSTM (..)

1517 , LSTMWeights (..)

@@ -21,30 +23,30 @@ import Control.Monad.Random ( MonadRandom, getRandom )

2123-- import Data.List ( foldl1' )

2224import Data.Proxy

2325import Data.Serialize

24−import Data.Kind (Type)

26+import Data.Singletons.TypeLits

2527

2628import qualified Numeric.LinearAlgebra as LA

2729import Numeric.LinearAlgebra.Static

2830

2931import Grenade.Core

30−import Grenade.Recurrent.Core

32+

3133import Grenade.Layers.Internal.Update

32−import GHC.TypeLits

3334

35+import Grenade.Recurrent.Core.Network

3436

3537-- | Long Short Term Memory Recurrent unit

3638--

3739-- This is a Peephole formulation, so the recurrent shape is

3840-- just the cell state, the previous output is not held or used

3941-- at all.

40−data LSTM :: Nat -> Nat -> Type where

42+data LSTM :: Nat -> Nat -> * where

4143 LSTM :: ( KnownNat input

4244 , KnownNat output

4345 ) => !(LSTMWeights input output) -- Weights

4446 -> !(LSTMWeights input output) -- Momentums

4547 -> LSTM input output

4648

47−data LSTMWeights :: Nat -> Nat -> Type where

49+data LSTMWeights :: Nat -> Nat -> * where

4850 LSTMWeights :: ( KnownNat input

4951 , KnownNat output

5052 ) => {

@@ -71,7 +73,7 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

7173

7274 -- Run the update function for each group matrix/vector of weights, momentums and gradients.

7375 -- Hmm, maybe the function should be used instead of passing in the learning parameters.

74− runUpdate lp (LSTM w m) g =

76+ runUpdate LearningParameters {..} (LSTM w m) g =

7577 let (wf, wf') = u lstmWf w m g

7678 (uf, uf') = u lstmUf w m g

7779 (bf, bf') = v lstmBf w m g

@@ -88,11 +90,11 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

8890 -- Utility function for updating with the momentum, gradients, and weights.

8991 u :: forall x ix out. (KnownNat ix, KnownNat out) => (x -> (L out ix)) -> x -> x -> x -> ((L out ix), (L out ix))

9092 u e (e -> weights) (e -> momentum) (e -> gradient) =

91− descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) weights gradient momentum

93+ decendMatrix learningRate learningMomentum learningRegulariser weights gradient momentum

9294

9395 v :: forall x ix. (KnownNat ix) => (x -> (R ix)) -> x -> x -> x -> ((R ix), (R ix))

9496 v e (e -> weights) (e -> momentum) (e -> gradient) =

95− descendVector (learningRate lp) (learningMomentum lp) (learningRegulariser lp) weights gradient momentum

97+ decendVector learningRate learningMomentum learningRegulariser weights gradient momentum

9698

9799 -- There's a lot of updates here, so to try and minimise the number of data copies

98100 -- we'll create a mutable bucket for each.

@@ -124,33 +126,27 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

124126instance (KnownNat i, KnownNat o) => RecurrentUpdateLayer (LSTM i o) where

125127 -- The recurrent shape is the same size as the output.

126128 -- It's actually the cell state however, as this is a peephole variety LSTM.

127− type RecurrentShape (LSTM i o) = S ('D1 o)

129+ type RecurrentShape (LSTM i o) = 'D1 o

128130

129131instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) where

130132

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)

133+ type RecTape (LSTM i o) ('D1 i) ('D1 o) = (S ('D1 o), S ('D1 i))

134134 -- Forward propagation for the LSTM layer.

135135 -- The size of the cell state is also the size of the output.

136− runRecurrentForwards (LSTM lw _) (S1D cell) (S1D input) =

136+ runRecurrentForwards (LSTM (LSTMWeights {..}) _) (S1D cell) (S1D input) =

137137 let -- Forget state vector

138− f_s = lstmBf lw + lstmWf lw #> input + lstmUf lw #> cell

139− f_t = sigmoid f_s

138+ f_t = sigmoid $ lstmBf + lstmWf #> input + lstmUf #> cell

140139 -- Input state vector

141− i_s = lstmBi lw + lstmWi lw #> input + lstmUi lw #> cell

142− i_t = sigmoid i_s

140+ i_t = sigmoid $ lstmBi + lstmWi #> input + lstmUi #> cell

143141 -- Output state vector

144− o_s = lstmBo lw + lstmWo lw #> input + lstmUo lw #> cell

145− o_t = sigmoid o_s

142+ o_t = sigmoid $ lstmBo + lstmWo #> input + lstmUo #> cell

146143 -- Cell input state vector

147− c_s = lstmBc lw + lstmWc lw #> input

148− c_x = tanh c_s

144+ c_x = tanh $ lstmBc + lstmWc #> input

149145 -- Cell state

150146 c_t = f_t * cell + i_t * c_x

151147 -- Output (it's sometimes recommended to use tanh c_t)

152148 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)

149+ in ((S1D cell, S1D input), S1D c_t, S1D h_t)

154150

155151 -- Run a backpropogation step for an LSTM layer.

156152 -- We're doing all the derivatives by hand here, so one should

@@ -158,8 +154,29 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

158154 --

159155 -- There's a test version using the AD library without hmatrix in the test

160156 -- 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

157+ runRecurrentBackwards (LSTM (LSTMWeights {..}) _) (S1D cell, S1D input) (S1D cellGrad) (S1D h_t') =

158+ -- We're not keeping the Wengert tape during the forward pass,

159+ -- so we're duplicating some work here.

160+ --

161+ -- If I was being generous, I'd call it checkpointing.

162+ --

163+ -- Maybe think about better ways to store some intermediate states.

164+ let -- Forget state vector

165+ f_s = lstmBf + lstmWf #> input + lstmUf #> cell

166+ f_t = sigmoid f_s

167+ -- Input state vector

168+ i_s = lstmBi + lstmWi #> input + lstmUi #> cell

169+ i_t = sigmoid i_s

170+ -- Output state vector

171+ o_s = lstmBo + lstmWo #> input + lstmUo #> cell

172+ o_t = sigmoid o_s

173+ -- Cell input state vector

174+ c_s = lstmBc + lstmWc #> input

175+ c_x = tanh c_s

176+ -- Cell state

177+ c_t = f_t * cell + i_t * c_x

178+

179+ -- Reverse Mode AD Derivitives

163180 c_t' = h_t' * o_t + cellGrad

164181

165182 f_t' = c_t' * cell

@@ -175,8 +192,8 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

175192 c_s' = tanh' c_s * c_x'

176193

177194 -- 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'

195+ cell' = tr lstmUf #> f_s' + tr lstmUo #> o_s' + tr lstmUi #> i_s' + c_t' * f_t

196+ input' = tr lstmWf #> f_s' + tr lstmWo #> o_s' + tr lstmWi #> i_s' + tr lstmWc #> c_s'

180197

181198 -- Calculate the gradient Matricies for the input

182199 lstmWf' = f_s' `outer` input

@@ -221,8 +238,7 @@ randomLSTM = do

221238

222239-- | Maths

223240--

224−-- TODO: Move to not here

225−-- Optimise backwards derivative

241+-- TODO: move to not here

226242sigmoid :: Floating a => a -> a

227243sigmoid x = 1 / (1 + exp (-x))

228244

@@ -235,34 +251,44 @@ tanh' :: (Floating a) => a -> a

235251tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

236252

237253instance (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

254+ put (LSTM LSTMWeights {..} _) = do

255+ u lstmWf

256+ u lstmUf

257+ v lstmBf

258+ u lstmWi

259+ u lstmUi

260+ v lstmBi

261+ u lstmWo

262+ u lstmUo

263+ v lstmBo

264+ u lstmWc

265+ v lstmBc

266+ where

267+ u :: forall a b. (KnownNat a, KnownNat b) => Putter (L b a)

268+ u = putListOf put . LA.toList . LA.flatten . extract

269+ v :: forall a. (KnownNat a) => Putter (R a)

270+ v = putListOf put . LA.toList . extract

255271

256272 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

273+ lstmWf <- u

274+ lstmUf <- u

275+ lstmBf <- v

276+ lstmWi <- u

277+ lstmUi <- u

278+ lstmBi <- v

279+ lstmWo <- u

280+ lstmUo <- u

281+ lstmBo <- v

282+ lstmWc <- u

283+ lstmBc <- v

284+ return $ LSTM (LSTMWeights {..}) (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

285+ where

286+ u :: forall a b. (KnownNat a, KnownNat b) => Get (L b a)

287+ u = let f = fromIntegral $ natVal (Proxy :: Proxy a)

288+ in maybe (fail "Vector of incorrect size") return . create . LA.reshape f . LA.fromList =<< getListOf get

289+ v :: forall a. (KnownNat a) => Get (R a)

290+ v = maybe (fail "Vector of incorrect size") return . create . LA.fromList =<< getListOf get

291+

292+ w0 = konst 0

293+ u0 = konst 0

294+ 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+31−13

@@ -11,44 +11,62 @@ module Test.Grenade.Layers.Nonlinear where

1111import Data.Singletons

1212

1313import Grenade

14+import GHC.TypeLits

1415

15−import Hedgehog

16+import Disorder.Jack

1617

17−import Test.Hedgehog.Compat

18−import Test.Hedgehog.Hmatrix

19−import Test.Hedgehog.TypeLits

18+import Test.Jack.Hmatrix

19+import Test.Jack.TypeLits

2020

2121import Numeric.LinearAlgebra.Static ( norm_Inf )

2222

2323prop_sigmoid_grad :: Property

24−prop_sigmoid_grad = property $

25− forAllWith rss genShape >>= \case

24+prop_sigmoid_grad =

25+ gambleDisplay (const "Shape") genShape $ \case

2626 (SomeSing (r :: Sing s)) ->

2727 withSingI r $

28− blindForAll genOfShape >>= \(ds :: S s) ->

28+ gamble genOfShape $ \(ds :: S s) ->

2929 let (tape, f :: S s) = runForwards Logit ds

3030 ((), ret :: S s) = runBackwards Logit tape (1 :: S s)

3131 (_, numer :: S s) = runForwards Logit (ds + 0.0001)

3232 numericalGradient = (numer - f) * 10000

33− in assert ((case numericalGradient - ret of

33+ in counterexample (show numericalGradient ++ show ret)

34+ ((case numericalGradient - ret of

3435 (S1D x) -> norm_Inf x < 0.0001

3536 (S2D x) -> norm_Inf x < 0.0001

3637 (S3D x) -> norm_Inf x < 0.0001) :: Bool)

3738

3839prop_tanh_grad :: Property

39−prop_tanh_grad = property $

40− forAllWith rss genShape >>= \case

40+prop_tanh_grad =

41+ gambleDisplay (const "Shape") genShape $ \case

4142 (SomeSing (r :: Sing s)) ->

4243 withSingI r $

43− blindForAll genOfShape >>= \(ds :: S s) ->

44+ gamble genOfShape $ \(ds :: S s) ->

4445 let (tape, f :: S s) = runForwards Tanh ds

4546 ((), ret :: S s) = runBackwards Tanh tape (1 :: S s)

4647 (_, numer :: S s) = runForwards Tanh (ds + 0.0001)

4748 numericalGradient = (numer - f) * 10000

48− in assert ((case numericalGradient - ret of

49+ in counterexample (show numericalGradient ++ show ret)

50+ ((case numericalGradient - ret of

4951 (S1D x) -> norm_Inf x < 0.001

5052 (S2D x) -> norm_Inf x < 0.001

5153 (S3D x) -> norm_Inf x < 0.001) :: Bool)

5254

55+prop_softmax_grad :: Property

56+prop_softmax_grad =

57+ gamble genNat $ \case

58+ (SomeNat (_ :: Proxy s)) ->

59+ gamble genOfShape $ \(ds :: S ('D1 s)) ->

60+ let (tape, f :: S ('D1 s)) = runForwards Relu ds

61+ ((), ret :: S ('D1 s)) = runBackwards Relu tape (1 :: S ('D1 s))

62+ (_, numer :: S ('D1 s)) = runForwards Relu (ds + 0.0001)

63+ numericalGradient = (numer - f) * 10000

64+ in counterexample (show numericalGradient ++ show ret)

65+ ((case numericalGradient - ret of

66+ (S1D x) -> norm_Inf x < 0.0001) :: Bool)

67+

68+

69+return []

5370tests :: IO Bool

54−tests = checkParallel $$(discover)

71+tests = $quickCheckAll

72+

test/Test/Grenade/Layers/PadCrop.hsdeleted−49

@@ -1,49 +0,0 @@

1−{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE CPP #-}

3−{-# LANGUAGE TemplateHaskell #-}

4−{-# LANGUAGE DataKinds #-}

5−{-# LANGUAGE KindSignatures #-}

6−{-# LANGUAGE GADTs #-}

7−{-# LANGUAGE ScopedTypeVariables #-}

8−{-# OPTIONS_GHC -fno-warn-missing-signatures #-}

9−

10−module Test.Grenade.Layers.PadCrop where

11−

12−import Grenade

13−

14−import Hedgehog

15−

16−import Numeric.LinearAlgebra.Static ( norm_Inf )

17−

18−import Test.Hedgehog.Hmatrix

19−

20−prop_pad_crop :: Property

21−prop_pad_crop =

22− 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 ]

23− net = Pad :~> Crop :~> NNil

24− in property $

25− forAll genOfShape >>= \(d :: S ('D3 7 9 5)) ->

26− let (tapes, res) = runForwards net d

27− (_ , grad) = runBackwards net tapes d

28− in do assert $ d ~~~ res

29− assert $ grad ~~~ d

30−

31−prop_pad_crop_2d :: Property

32−prop_pad_crop_2d =

33− let net :: Network '[Pad 2 3 4 6, Crop 2 3 4 6] '[ 'D2 7 9, 'D2 16 15, 'D2 7 9 ]

34− net = Pad :~> Crop :~> NNil

35− in property $

36− forAll genOfShape >>= \(d :: S ('D2 7 9)) ->

37− let (tapes, res) = runForwards net d

38− (_ , grad) = runBackwards net tapes d

39− in do assert $ d ~~~ res

40− assert $ grad ~~~ d

41−

42−(~~~) :: S x -> S x -> Bool

43−(S1D x) ~~~ (S1D y) = norm_Inf (x - y) < 0.00001

44−(S2D x) ~~~ (S2D y) = norm_Inf (x - y) < 0.00001

45−(S3D x) ~~~ (S3D y) = norm_Inf (x - y) < 0.00001

46−

47−

48−tests :: IO Bool

49−tests = checkParallel $$(discover)

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+54

@@ -0,0 +1,54 @@

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+genShape :: Jack (SomeSing Shape)

26+genShape

27+ = oneOf [

28+ genD1

29+ , genD2

30+ , genD3

31+ ]

32+

33+genD1 :: Jack (SomeSing Shape)

34+genD1 = do

35+ n <- genNat

36+ return $ case n of

37+ SomeNat (_ :: Proxy x) -> SomeSing (sing :: Sing ('D1 x))

38+

39+genD2 :: Jack (SomeSing Shape)

40+genD2 = do

41+ n <- genNat

42+ m <- genNat

43+ return $ case (n, m) of

44+ (SomeNat (_ :: Proxy x), SomeNat (_ :: Proxy y)) -> SomeSing (sing :: Sing ('D2 x y))

45+

46+genD3 :: Jack (SomeSing Shape)

47+genD3 = do

48+ n <- genNat

49+ m <- genNat

50+ o <- genNat

51+ return $ case (n, m, o) of

52+ (SomeNat (px :: Proxy x), SomeNat (_ :: Proxy y), SomeNat (pz :: Proxy z)) ->

53+ case natDict px %* natDict pz of

54+ Dict -> SomeSing (sing :: Sing ('D3 x y z))

test/test.hs+2−24

@@ -1,47 +1,25 @@

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

75import qualified Test.Grenade.Layers.FullyConnected

86import qualified Test.Grenade.Layers.Nonlinear

9−import qualified Test.Grenade.Layers.PadCrop

107

118import qualified Test.Grenade.Layers.Internal.Convolution

129import qualified Test.Grenade.Layers.Internal.Pooling

1310

1411import qualified Test.Grenade.Recurrent.Layers.LSTM

1512

16−import System.Exit

17−import System.IO

18−

1913main :: IO ()

2014main =

2115 disorderMain [

22− Test.Grenade.Network.tests

23−

24− , Test.Grenade.Layers.Pooling.tests

16+ Test.Grenade.Layers.Pooling.tests

2517 , Test.Grenade.Layers.Convolution.tests

2618 , Test.Grenade.Layers.FullyConnected.tests

2719 , Test.Grenade.Layers.Nonlinear.tests

28− , Test.Grenade.Layers.PadCrop.tests

2920

3021 , Test.Grenade.Layers.Internal.Convolution.tests

3122 , Test.Grenade.Layers.Internal.Pooling.tests

3223

3324 , Test.Grenade.Recurrent.Layers.LSTM.tests

34−

3525 ]

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