1657315...master · grenade

.gitignore+3−1

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

1+cabal.project.local

12.cabal-sandbox/

23cabal.sandbox.config

34dist/

4−

5+dist-newstyle/

6+.stack-work/

.gitmodulesdeleted−15

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

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.ymlnew file+31

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

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+72−10

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

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+

49```

510First shalt thou take out the Holy Pin, then shalt thou count to three, no more, no less.

611Three shall be the number thou shalt count, and the number of the counting shall be three.

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

1015

1116💣 Machine learning which might blow up in your face 💣

1217

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

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

1419for concise and precise specifications of complex networks in Haskell.

1520

1621As an example, a network which can achieve ~1.5% error on MNIST can be

1722specified and initialised with random weights in a few lines of code with

1823```haskell

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]

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]

2132

2233randomMnist :: MonadRandom m => m MNIST

2334randomMnist = randomNetwork

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

3142["unreasonably effective"](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)

3243with

3344```haskell

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 ]

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 ]

3649```

3750

3851Design

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

4558The definition of a network is surprisingly simple:

4659```haskell

4760data Network :: [*] -> [Shape] -> * where

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)

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)

5068```

5169

5270The `Layer x i o` constraint ensures that the layer `x` can sensibly perform a

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

7189```

7290which takes a network, appropriate input and target data, and returns the

7391back propagated gradients for the network. The shapes of the gradients are

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

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

7593have no learnable parameters.

7694

7795The gradients however can always be applied, yielding a new (hopefully better)

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

85103and a layer can not sensibly perform the operation between two shapes, then

86104it will result in a compile time error.

87105

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+

88145Build Instructions

89146------------------

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

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

91148script that is located in the repository. You will also need the `lapack` and

92149`blas` libraries and development tools. Once you have all that, Grenade can be

93150build using:

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

102159./mafia test

103160```

104161

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

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

106163

107164Thanks

108165------

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

128185Contributing

129186------------

130187Contributions 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.ymlnew file+1

@@ -0,0 +1 @@

1+theme: jekyll-theme-minimal

bench/bench-lstm.hsnew file+30

@@ -0,0 +1,30 @@

1+{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE BangPatterns #-}

3+{-# LANGUAGE ScopedTypeVariables #-}

4+import Criterion.Main

5+

6+import Grenade

7+import Grenade.Recurrent

8+

9+main :: IO ()

10+main = do

11+ input40 :: S ('D1 40) <- randomOfShape

12+ lstm :: RecNet <- randomRecurrent

13+

14+ 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)

18+ ]

19+ ]

20+

21+nfT2 :: (a, b) -> (a, b)

22+nfT2 (!a, !b) = (a, b)

23+

24+

25+type R = Recurrent

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

27+ '[ 'D1 40, 'D1 512, 'D1 40 ]

28+

29+lp :: LearningParameters

30+lp = LearningParameters 0.1 0 0

bench/bench.hs+47−18

@@ -1,26 +1,55 @@

1+{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE ScopedTypeVariables #-}

13import Criterion.Main

24

5+import Grenade

6+

37import Grenade.Layers.Internal.Convolution

48import Grenade.Layers.Internal.Pooling

59

610import Numeric.LinearAlgebra

711

812main :: IO ()

9−main = defaultMain [

10− bgroup "im2col" [ bench "im2col 3x4" $ whnf (im2col 2 2 1 1) ((3><4) [1..])

11− , bench "im2col 28x28" $ whnf (im2col 5 5 1 1) ((28><28) [1..])

12− , bench "im2col 100x100" $ whnf (im2col 10 10 1 1) ((100><100) [1..])

13− ]

14− , bgroup "col2im" [ bench "col2im 3x4" $ whnf (col2im 2 2 1 1 3 4) ((6><4) [1..])

15− , bench "col2im 28x28" $ whnf (col2im 5 5 1 1 28 28) ((576><25) [1..])

16− , bench "col2im 100x100" $ whnf (col2im 10 10 1 1 100 100) ((8281><100) [1..])

17− ]

18− , bgroup "poolfw" [ bench "poolforwards 3x4" $ whnf (poolForward 1 3 4 2 2 1 1) ((3><4) [1..])

19− , bench "poolforwards 28x28" $ whnf (poolForward 1 28 28 5 5 1 1) ((28><28) [1..])

20− , bench "poolforwards 100x100" $ whnf (poolForward 1 100 100 10 10 1 1) ((100><100) [1..])

21− ]

22− , bgroup "poolbw" [ bench "poolbackwards 3x4" $ whnf (poolBackward 1 3 4 2 2 1 1 ((3><4) [1..])) ((2><3) [1..])

23− , bench "poolbackwards 28x28" $ whnf (poolBackward 1 28 28 5 5 1 1 ((28><28) [1..])) ((24><24) [1..])

24− , bench "poolbackwards 100x100" $ whnf (poolBackward 1 100 100 10 10 1 1 ((100><100) [1..])) ((91><91) [1..])

25− ]

26− ]

13+main = do

14+ x :: S ('D2 60 60 ) <- randomOfShape

15+ y :: S ('D3 60 60 1) <- randomOfShape

16+

17+ defaultMain [

18+ bgroup "im2col" [ bench "im2col 3x4" $ whnf (im2col 2 2 1 1) ((3><4) [1..])

19+ , bench "im2col 28x28" $ whnf (im2col 5 5 1 1) ((28><28) [1..])

20+ , bench "im2col 100x100" $ whnf (im2col 10 10 1 1) ((100><100) [1..])

21+ ]

22+ , bgroup "col2im" [ bench "col2im 3x4" $ whnf (col2im 2 2 1 1 3 4) ((6><4) [1..])

23+ , bench "col2im 28x28" $ whnf (col2im 5 5 1 1 28 28) ((576><25) [1..])

24+ , bench "col2im 100x100" $ whnf (col2im 10 10 1 1 100 100) ((8281><100) [1..])

25+ ]

26+ , bgroup "poolfw" [ bench "poolforwards 3x4" $ whnf (poolForward 1 3 4 2 2 1 1) ((3><4) [1..])

27+ , bench "poolforwards 28x28" $ whnf (poolForward 1 28 28 5 5 1 1) ((28><28) [1..])

28+ , bench "poolforwards 100x100" $ whnf (poolForward 1 100 100 10 10 1 1) ((100><100) [1..])

29+ ]

30+ , bgroup "poolbw" [ bench "poolbackwards 3x4" $ whnf (poolBackward 1 3 4 2 2 1 1 ((3><4) [1..])) ((2><3) [1..])

31+ , bench "poolbackwards 28x28" $ whnf (poolBackward 1 28 28 5 5 1 1 ((28><28) [1..])) ((24><24) [1..])

32+ , bench "poolbackwards 100x100" $ whnf (poolBackward 1 100 100 10 10 1 1 ((100><100) [1..])) ((91><91) [1..])

33+ ]

34+ , bgroup "padcrop" [ bench "pad 2D 60x60" $ whnf (testRun2D Pad) x

35+ , bench "pad 3D 60x60" $ whnf (testRun3D Pad) y

36+ , bench "crop 2D 60x60" $ whnf (testRun2D' Crop) x

37+ , bench "crop 3D 60x60" $ whnf (testRun3D' Crop) y

38+ ]

39+ ]

40+

41+

42+testRun2D :: Pad 1 1 1 1 -> S ('D2 60 60) -> S ('D2 62 62)

43+testRun2D = snd ... runForwards

44+

45+testRun3D :: Pad 1 1 1 1 -> S ('D3 60 60 1) -> S ('D3 62 62 1)

46+testRun3D = snd ... runForwards

47+

48+testRun2D' :: Crop 1 1 1 1 -> S ('D2 60 60) -> S ('D2 58 58)

49+testRun2D' = snd ... runForwards

50+

51+testRun3D' :: Crop 1 1 1 1 -> S ('D3 60 60 1) -> S ('D3 58 58 1)

52+testRun3D' = snd ... runForwards

53+

54+(...) :: (a -> b) -> (c -> d -> a) -> c -> d -> b

55+(...) = (.) . (.)

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

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

1−#include "gradient_decent.h"

1+#include "gradient_descent.h"

22

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

3+void descend_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_decent.h → cbits/gradient_descent.hrenamed+1−1

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

11#include <stdio.h>

22#include <stdint.h>

33

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

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

55 const double* weights,

66 const double* gradient,

77 const double* last,

cbits/pad.cnew file+39

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

1+#include "pad.h"

2+

3+void pad_cpu(double* data, const int channels,

4+ const int height, const int width, const int pad_left, const int pad_top,

5+ const int pad_right, const int pad_bottom,

6+ double* data_padded) {

7+

8+ const int pad_width = width + pad_left + pad_right;

9+ const int pad_height = height + pad_top + pad_bottom;

10+

11+ memset(data_padded, 0, pad_height * pad_width * channels * sizeof(double));

12+

13+ for (int channel = 0; channel < channels; channel++) {

14+ double* px = data_padded + (pad_width * pad_top + pad_left) + channel * (pad_width * pad_height);

15+ for (int y = 0; y < height; y++) {

16+ memcpy(px, data, sizeof(double) * width);

17+ px += pad_width;

18+ data += width;

19+ }

20+ }

21+}

22+

23+void crop_cpu(double* data, const int channels,

24+ const int height, const int width, const int crop_left, const int crop_top,

25+ const int crop_right, const int crop_bottom,

26+ double* data_cropped) {

27+

28+ const int crop_width = width + crop_left + crop_right;

29+ const int crop_height = height + crop_top + crop_bottom;

30+

31+ for (int channel = 0; channel < channels; channel++) {

32+ double* px = data + (crop_width * crop_top + crop_left) + channel * (crop_width * crop_height);

33+ for (int y = 0; y < height; y++) {

34+ memcpy(data_cropped, px, sizeof(double) * width);

35+ px += crop_width;

36+ data_cropped += width;

37+ }

38+ }

39+}

cbits/pad.hnew file+13

@@ -0,0 +1,13 @@

1+#include <stdio.h>

2+#include <stdint.h>

3+#include <string.h>

4+

5+void pad_cpu(double* data_im, const int channels,

6+ const int height, const int width, const int pad_left, const int pad_top,

7+ const int pad_right, const int pad_bottom,

8+ double* data_col);

9+

10+void crop_cpu(double* data_im, const int channels,

11+ const int height, const int width, const int crop_left, const int crop_top,

12+ const int crop_right, const int crop_bottom,

13+ double* data_col);

examples/LICENSEnew file+10

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

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.cabalnew file+109

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

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/mafianew file+62

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

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

main/feedforward.hs → examples/main/feedforward.hsrenamed+4−1

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

11{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE CPP #-}

23{-# LANGUAGE DataKinds #-}

34{-# LANGUAGE ScopedTypeVariables #-}

45{-# LANGUAGE TypeOperators #-}

@@ -10,7 +11,9 @@ import Data.List ( foldl' )

1011

1112import qualified Data.ByteString as B

1213import Data.Serialize

13−

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

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

16+#endif

1417import GHC.TypeLits

1518

1619import qualified Numeric.LinearAlgebra.Static as SA

examples/main/gan-mnist.hsnew file+185

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

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

main/mnist.hs → examples/main/mnist.hsrenamed+30−2

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

11{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE CPP #-}

23{-# LANGUAGE DataKinds #-}

34{-# LANGUAGE ScopedTypeVariables #-}

45{-# LANGUAGE TypeOperators #-}

56{-# LANGUAGE TupleSections #-}

67{-# LANGUAGE TypeFamilies #-}

78{-# LANGUAGE FlexibleContexts #-}

9+

810import Control.Applicative

911import Control.Monad

1012import Control.Monad.Random

@@ -12,6 +14,10 @@ import Control.Monad.Trans.Except

1214

1315import qualified Data.Attoparsec.Text as A

1416import Data.List ( foldl' )

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

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

19+#endif

20+

1521import qualified Data.Text as T

1622import qualified Data.Text.IO as T

1723import qualified Data.Vector.Storable as V

@@ -24,6 +30,15 @@ import Options.Applicative

2430import Grenade

2531import Grenade.Utils.OneHot

2632

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+

2742-- The definition of our convolutional neural network.

2843-- In the type signature, we have a type level list of shapes which are passed between the layers.

2944-- One can see that the images we are inputing are two dimensional with 28 * 28 pixels.

@@ -33,8 +48,21 @@ import Grenade.Utils.OneHot

3348

3449-- With the mnist data from Kaggle normalised to doubles between 0 and 1, learning rate of 0.01 and 15 iterations,

3550-- this network should get down to about a 1.3% error rate.

36−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]

37− '[ '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]

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 ]

3866

3967randomMnist :: MonadRandom m => m MNIST

4068randomMnist = randomNetwork

main/recurrent.hs → examples/main/recurrent.hsrenamed+16−10

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

11{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE CPP #-}

23{-# LANGUAGE DataKinds #-}

34{-# LANGUAGE ScopedTypeVariables #-}

45{-# LANGUAGE TypeOperators #-}

@@ -8,7 +9,13 @@

89import Control.Monad ( foldM )

910import Control.Monad.Random ( MonadRandom, getRandomR )

1011

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

13+import Data.List ( unfoldr )

14+#else

1115import Data.List ( cycle, unfoldr )

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

17+#endif

18+

1219import qualified Numeric.LinearAlgebra.Static as SA

1320

1421import Options.Applicative

@@ -22,15 +29,14 @@ import Grenade.Recurrent

2229--

2330-- The F and R types are Tagging types to ensure that the runner and

2431-- creation function know how to treat the layers.

25−type F = FeedForward

2632type R = Recurrent

2733

28−type RecNet = RecurrentNetwork '[ R (LSTM 1 4), R (LSTM 4 1), F Trivial]

29− '[ 'D1 1, 'D1 4, 'D1 1, 'D1 1 ]

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

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

3036

31−type RecInput = RecurrentInputs '[ R (LSTM 1 4), R (LSTM 4 1), F Trivial]

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

3238

33−randomNet :: MonadRandom m => m (RecNet, RecInput)

39+randomNet :: MonadRandom m => m RecNet

3440randomNet = randomRecurrent

3541

3642netTest :: MonadRandom m => RecNet -> RecInput -> LearningParameters -> Int -> m (RecNet, RecInput)

@@ -51,7 +57,7 @@ netTest net0 i0 rate iterations =

5157data FeedForwardOpts = FeedForwardOpts Int LearningParameters

5258

5359feedForward' :: Parser FeedForwardOpts

54−feedForward' = FeedForwardOpts <$> option auto (long "examples" <> short 'e' <> value 20000)

60+feedForward' = FeedForwardOpts <$> option auto (long "examples" <> short 'e' <> value 40000)

5561 <*> (LearningParameters

5662 <$> option auto (long "train_rate" <> short 'r' <> value 0.01)

5763 <*> option auto (long "momentum" <> value 0.9)

@@ -63,8 +69,8 @@ generateRecurrent n s i =

6369 unfoldr go (s, i)

6470 where

6571 go (x, y) =

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

67− o' = heat o

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

73+ o' = heat o

6874 Just (o', (ns, fromIntegral o'))

6975

7076 heat :: S ('D1 1) -> Int

@@ -76,8 +82,8 @@ main = do

7682 FeedForwardOpts examples rate <- execParser (info (feedForward' <**> helper) idm)

7783 putStrLn "Training network..."

7884

79− (net0, i0) <- randomNet

80− (trained, bestInput) <- netTest net0 i0 rate examples

85+ net0 <- randomNet

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

8187

8288 let results = generateRecurrent trained bestInput (c 1)

8389

main/shakespeare.hs → examples/main/shakespeare.hsrenamed+27−23

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

11{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE RecordWildCards #-}

2+{-# LANGUAGE CPP #-}

33{-# LANGUAGE DataKinds #-}

44{-# LANGUAGE ScopedTypeVariables #-}

55{-# LANGUAGE TypeOperators #-}

@@ -14,16 +14,15 @@ import Data.Char ( isUpper, toUpper, toLower )

1414import Data.List ( foldl' )

1515import Data.Maybe ( fromMaybe )

1616

17+

1718import qualified Data.Vector as V

1819import Data.Vector ( Vector )

1920

2021import qualified Data.Map as M

21−import Data.Proxy ( Proxy (..) )

2222

2323import qualified Data.ByteString as B

2424import Data.Serialize

2525

26−import Data.Singletons.Prelude

2726import GHC.TypeLits

2827

2928import Numeric.LinearAlgebra.Static ( konst )

@@ -35,11 +34,16 @@ import Grenade.Recurrent

3534import Grenade.Utils.OneHot

3635

3736import System.IO.Unsafe ( unsafeInterleaveIO )

37+import Data.Proxy

38+import Prelude.Singletons

3839

3940-- The defininition for our natural language recurrent network.

4041-- This network is able to learn and generate simple words in

4142-- about an hour.

4243--

44+-- Grab the input from

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

46+--

4347-- This is a first class recurrent net.

4448--

4549-- The F and R types are tagging types to ensure that the runner and

@@ -57,10 +61,10 @@ type R = Recurrent

5761type Shakespeare = RecurrentNetwork '[ R (LSTM 40 80), R (LSTM 80 40), F (FullyConnected 40 40), F Logit]

5862 '[ 'D1 40, 'D1 80, 'D1 40, 'D1 40, 'D1 40 ]

5963

60−-- The definition of the "sideways" input, which the network if fed recurrently.

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

6165type Shakespearian = RecurrentInputs '[ R (LSTM 40 80), R (LSTM 80 40), F (FullyConnected 40 40), F Logit]

6266

63−randomNet :: MonadRandom m => m (Shakespeare, Shakespearian)

67+randomNet :: MonadRandom m => m Shakespeare

6468randomNet = randomRecurrent

6569

6670-- | Load the data files and prepare a map of characters to a compressed int representation.

@@ -68,40 +72,40 @@ loadShakespeare :: FilePath -> ExceptT String IO (Vector Int, M.Map Char Int, Ve

6872loadShakespeare path = do

6973 contents <- lift $ readFile path

7074 let annotated = annotateCapitals contents

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

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

7276 hot <- ExceptT . return . note "Couldn't generate hot values" $ traverse (`M.lookup` m) annotated

7377 return (V.fromList hot, m, cs)

7478

7579trainSlice :: LearningParameters -> Shakespeare -> Shakespearian -> Vector Int -> Int -> Int -> (Shakespeare, Shakespearian)

76−trainSlice !rate !net !recIns input offset size =

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

7781 let e = fmap (x . oneHot) . V.toList $ V.slice offset size input

7882 in case reverse e of

7983 (o : l : xs) ->

8084 let examples = reverse $ (l, Just o) : ((,Nothing) <$> xs)

81− in trainRecurrent rate net recIns examples

85+ in trainRecurrent lrate net recIns examples

8286 _ -> error "Not enough input"

8387 where

8488 x = fromMaybe (error "Hot variable didn't fit.")

8589

8690runShakespeare :: ShakespeareOpts -> ExceptT String IO ()

87−runShakespeare ShakespeareOpts {..} = do

88− (shakespeare, oneHotMap, oneHotDictionary) <- loadShakespeare trainingFile

91+runShakespeare opts = do

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

8993 (net0, i0) <- lift $

90− case loadPath of

94+ case loadPath opts of

9195 Just loadFile -> netLoad loadFile

92− Nothing -> randomNet

96+ Nothing -> (,0) <$> randomNet

9397

9498 (trained, bestInput) <- lift $ foldM (\(!net, !io) size -> do

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

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

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

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)

98102 putStrLn ("TRAINING STEP WITH SIZE: " ++ show size)

99103 putStrLn (unAnnotateCapitals results)

100104 return (trained, bestInput)

101− ) (net0, i0) $ replicate 10 sequenceSize

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

102106

103− case savePath of

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

107+ case savePath opts of

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

105109 Nothing -> return ()

106110

107111generateParagraph :: forall layers shapes n a. (Last shapes ~ 'D1 n, Head shapes ~ 'D1 n, KnownNat n, Ord a)

@@ -112,14 +116,14 @@ generateParagraph :: forall layers shapes n a. (Last shapes ~ 'D1 n, Head shapes

112116 -> Vector a

113117 -> S ('D1 n)

114118 -> IO [a]

115−generateParagraph n s temperature hotmap hotdict =

119+generateParagraph n s temp hotmap hotdict =

116120 go s

117121 where

118122 go x y =

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

120− un <- sample temperature hotdict o

121− Just re <- return $ makeHot hotmap un

122− rest <- unsafeInterleaveIO $ go ns re

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

123127 return (un : rest)

124128

125129data ShakespeareOpts = ShakespeareOpts {

framework/mafianew file+62

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

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+118−116

@@ -1,153 +1,126 @@

11name: grenade

2−version: 0.0.1

2+version: 0.1.0

33license: BSD2

4+license-file: LICENSE

45author: Huw Campbell <huw.campbell@gmail.com>

56maintainer: Huw Campbell <huw.campbell@gmail.com>

7−synopsis: grenade

8−category: System

9−cabal-version: >= 1.8

8+synopsis: Practical Deep Learning in Haskell

9+category: AI, Machine Learning

10+cabal-version: >= 1.10

1011build-type: Simple

11−description: grenade.

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>.

1224

1325extra-source-files:

14− cbits/im2col.h

15− cbits/im2col.c

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

1637

1738library

1839 build-depends:

1940 base >= 4.8 && < 5

20− , bytestring == 0.10.*

21− , async

22− , containers

23− , deepseq

24− , either == 4.4.*

25− , cereal

26− , exceptions == 0.8.*

27− , hmatrix

28− , MonadRandom

29− , mtl >= 2.2.1 && < 2.3

30− , parallel == 3.2.*

31− , primitive

32− , reflection

33− , text == 1.2.*

34− , transformers

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.

3550 , singletons

36− , vector

51+ , singletons-base

52+ , vector >= 0.11 && < 0.13

3753

3854 ghc-options:

3955 -Wall

4056 hs-source-dirs:

4157 src

4258

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

4367

4468 exposed-modules:

4569 Grenade

70+ Grenade.Core

71+ Grenade.Core.Layer

72+ Grenade.Core.LearningParameters

4673 Grenade.Core.Network

4774 Grenade.Core.Runner

4875 Grenade.Core.Shape

49− Grenade.Layers.Crop

76+

77+ Grenade.Layers

78+ Grenade.Layers.Concat

5079 Grenade.Layers.Convolution

80+ Grenade.Layers.Crop

81+ Grenade.Layers.Deconvolution

5182 Grenade.Layers.Dropout

83+ Grenade.Layers.Elu

5284 Grenade.Layers.FullyConnected

53− Grenade.Layers.Flatten

54− Grenade.Layers.Fuse

85+ Grenade.Layers.Inception

5586 Grenade.Layers.Logit

56− Grenade.Layers.Relu

57− Grenade.Layers.Tanh

87+ Grenade.Layers.Merge

5888 Grenade.Layers.Pad

5989 Grenade.Layers.Pooling

90+ Grenade.Layers.Relu

91+ Grenade.Layers.Reshape

92+ Grenade.Layers.Sinusoid

93+ Grenade.Layers.Softmax

94+ Grenade.Layers.Tanh

95+ Grenade.Layers.Trivial

6096

6197 Grenade.Layers.Internal.Convolution

98+ Grenade.Layers.Internal.Pad

6299 Grenade.Layers.Internal.Pooling

63100 Grenade.Layers.Internal.Update

64101

65102 Grenade.Recurrent

66103

104+ Grenade.Recurrent.Core

105+ Grenade.Recurrent.Core.Layer

67106 Grenade.Recurrent.Core.Network

68107 Grenade.Recurrent.Core.Runner

69108

109+ Grenade.Recurrent.Layers

70110 Grenade.Recurrent.Layers.BasicRecurrent

111+ Grenade.Recurrent.Layers.ConcatRecurrent

71112 Grenade.Recurrent.Layers.LSTM

72− Grenade.Recurrent.Layers.Trivial

73113

74114 Grenade.Utils.OneHot

75115

76− includes: cbits/im2col.h

77− cbits/gradient_decent.h

78− c-sources: cbits/im2col.c

79− cbits/gradient_decent.c

80−

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

82−

83−executable feedforward

84− ghc-options: -Wall -threaded -O2

85− main-is: main/feedforward.hs

86− build-depends: base

87− , grenade

88− , attoparsec

89− , bytestring

90− , cereal

91− , either

92− , optparse-applicative == 0.12.*

93− , text == 1.2.*

94− , mtl >= 2.2.1 && < 2.3

95− , hmatrix

96− , transformers

97− , singletons

98− , MonadRandom

99−

100−executable mnist

101− ghc-options: -Wall -threaded -O2

102− main-is: main/mnist.hs

103− build-depends: base

104− , grenade

105− , attoparsec

106− , either

107− , optparse-applicative == 0.12.*

108− , text == 1.2.*

109− , mtl >= 2.2.1 && < 2.3

110− , hmatrix >= 0.18 && < 0.19

111− , transformers

112− , singletons

113− , MonadRandom

114− , vector

115−

116−executable recurrent

117− ghc-options: -Wall -threaded -O2

118− main-is: main/recurrent.hs

119− build-depends: base

120− , grenade

121− , attoparsec

122− , either

123− , optparse-applicative == 0.12.*

124− , text == 1.2.*

125− , mtl >= 2.2.1 && < 2.3

126− , hmatrix >= 0.18 && < 0.19

127− , transformers

128− , singletons

129− , MonadRandom

130−

131−

132−executable shakespeare

133− ghc-options: -Wall -threaded -O2

134− main-is: main/shakespeare.hs

135− build-depends: base

136− , grenade

137− , attoparsec

138− , bytestring

139− , cereal

140− , either

141− , optparse-applicative == 0.12.*

142− , text == 1.2.*

143− , mtl >= 2.2.1 && < 2.3

144− , hmatrix >= 0.18 && < 0.19

145− , transformers

146− , singletons

147− , vector

148− , MonadRandom

149− , containers

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

150122

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

151124

152125test-suite test

153126 type: exitcode-stdio-1.0

@@ -159,23 +132,48 @@ test-suite test

159132 hs-source-dirs:

160133 test

161134

135+ default-language: Haskell2010

136+

137+ other-modules: Test.Hedgehog.Compat

138+ Test.Hedgehog.Hmatrix

139+ Test.Hedgehog.TypeLits

140+

141+ Test.Grenade.Network

142+ Test.Grenade.Layers.Convolution

143+ Test.Grenade.Layers.FullyConnected

144+ Test.Grenade.Layers.Nonlinear

145+ Test.Grenade.Layers.PadCrop

146+ Test.Grenade.Layers.Pooling

147+ Test.Grenade.Layers.Internal.Convolution

148+ Test.Grenade.Layers.Internal.Pooling

149+ Test.Grenade.Layers.Internal.Reference

150+

151+ Test.Grenade.Recurrent.Layers.LSTM

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

153+

154+ if impl(ghc < 8.0)

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

156+ cpp-options: -DType=*

157+

158+ if impl(ghc >= 8.6)

159+ default-extensions: NoStarIsType

160+

162161 build-depends:

163− base >= 4.8 && < 5

162+ base

164163 , grenade

165− , ambiata-disorder-core

166− , ambiata-disorder-jack

164+ , hedgehog >= 1.0 && < 1.1

167165 , hmatrix

168166 , mtl

169167 , singletons

170168 , text == 1.2.*

171− , typelits-witnesses

169+ , typelits-witnesses < 0.4

170+ , transformers

172171 , constraints

173− , QuickCheck >= 2.7 && < 2.9

174− , quickcheck-instances == 0.3.*

175172 , MonadRandom

176173 , random

177174 , ad

178175 , reflection

176+ , vector

179177

180178

181179benchmark bench

@@ -188,10 +186,12 @@ benchmark bench

188186 hs-source-dirs:

189187 bench

190188

189+ default-language: Haskell2010

190+

191191 build-depends:

192− base >= 3 && < 5

193− , bytestring == 0.10.*

194− , criterion == 1.1.*

192+ base

193+ , bytestring

194+ , criterion >= 1.1 && < 1.6

195195 , grenade

196196 , hmatrix

197197

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

205205 hs-source-dirs:

206206 bench

207207

208+ default-language: Haskell2010

209+

208210 build-depends:

209− base >= 3 && < 5

210− , bytestring == 0.10.*

211− , criterion == 1.1.*

211+ base

212+ , bytestring

213+ , criterion

212214 , grenade

213215 , hmatrix

lib/disorder.hsdeleted−1

Submodule changed.

lib/pdeleted−1

Submodule changed.

mafiamode 100755 → 120000+1−123

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

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: 3044e63eb472fb9e16926d4ab2ca9dd9e255829c

@@ -0,0 +1 @@

1+framework/mafia

src/Grenade.hs+50−15

@@ -1,18 +1,53 @@

11module Grenade (

2− module X

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+

320 ) where

421

5−import Grenade.Core.Network as X

6−import Grenade.Core.Runner as X

7−import Grenade.Core.Shape as X

8−import Grenade.Layers.Crop as X

9−import Grenade.Layers.Dropout as X

10−import Grenade.Layers.Pad as X

11−import Grenade.Layers.Pooling as X

12−import Grenade.Layers.Flatten as X

13−import Grenade.Layers.Fuse as X

14−import Grenade.Layers.FullyConnected as X

15−import Grenade.Layers.Logit as X

16−import Grenade.Layers.Convolution as X

17−import Grenade.Layers.Relu as X

18−import Grenade.Layers.Tanh as X

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+

src/Grenade/Core.hsnew file+13

@@ -0,0 +1,13 @@

1+module 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

7+ ) where

8+

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

src/Grenade/Core/Layer.hsnew file+92

@@ -0,0 +1,92 @@

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+{-|

11+Module : Grenade.Core.Layer

12+Description : Defines the Layer Classes

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

14+License : BSD2

15+Stability : experimental

16+

17+This module defines what a Layer is in a Grenade

18+neural 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.

35+-}

36+module Grenade.Core.Layer (

37+ Layer (..)

38+ , UpdateLayer (..)

39+ ) where

40+

41+import Control.Monad.Random ( MonadRandom )

42+

43+import Data.List ( foldl' )

44+

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

46+import Data.Kind (Type)

47+#endif

48+

49+import Grenade.Core.Shape

50+import Grenade.Core.LearningParameters

51+

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

53+-- describes how to create and update the layer.

54+--

55+class UpdateLayer x where

56+ -- | The type for the gradient for this layer.

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

58+ type Gradient x :: Type

59+

60+ -- | Update a layer with its gradient and learning parameters

61+ runUpdate :: LearningParameters -> x -> Gradient x -> x

62+

63+ -- | Create a random layer, many layers will use pure

64+ createRandom :: MonadRandom m => m x

65+

66+ -- | Update a layer with many Gradients

67+ runUpdates :: LearningParameters -> x -> [Gradient x] -> x

68+ runUpdates rate = foldl' (runUpdate rate)

69+

70+ {-# MINIMAL runUpdate, createRandom #-}

71+

72+-- | Class for a layer. All layers implement this, however, they don't

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

74+-- appropriate.

75+--

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

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

78+ -- to generate the back propagated gradients efficiently. As a

79+ -- default, `S i` is fine.

80+ type Tape x i o :: Type

81+

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

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

84+ runForwards :: x -> S i -> (Tape x i o, S o)

85+

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

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

88+ -- from the layer above.

89+ --

90+ -- Returns the gradient layer and the derivatives to push back

91+ -- further.

92+ runBackwards :: x -> Tape x i o -> S o -> (Gradient x, S i)

src/Grenade/Core/LearningParameters.hsnew file+21

@@ -0,0 +1,21 @@

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+-}

8+module 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+

13+ LearningParameters (..)

14+ ) where

15+

16+-- | Learning parameters for stochastic gradient descent.

17+data LearningParameters = LearningParameters {

18+ learningRate :: Double

19+ , learningMomentum :: Double

20+ , learningRegulariser :: Double

21+ } deriving (Eq, Show)

src/Grenade/Core/Network.hs+155−74

@@ -1,5 +1,8 @@

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

3+{-# LANGUAGE BangPatterns #-}

24{-# LANGUAGE GADTs #-}

5+{-# LANGUAGE ScopedTypeVariables #-}

36{-# LANGUAGE TypeOperators #-}

47{-# LANGUAGE TypeFamilies #-}

58{-# LANGUAGE MultiParamTypeClasses #-}

@@ -7,110 +10,188 @@

710{-# LANGUAGE FlexibleInstances #-}

811{-|

912Module : Grenade.Core.Network

10−Description : Core definition a simple neural etwork

13+Description : Core definition of a Neural Network

1114Copyright : (c) Huw Campbell, 2016-2017

1215License : BSD2

1316Stability : experimental

1417

15−This module defines the core data type for the simplest

16−Neural network we support.

17−

18+This module defines the core data types and functions

19+for non-recurrent neural networks.

1820-}

21+

1922module Grenade.Core.Network (

20− Layer (..)

21− , Network (..)

22− , UpdateLayer (..)

23− , LearningParameters (..)

23+ Network (..)

2424 , Gradients (..)

25− , CreatableNetwork (..)

25+ , Tapes (..)

26+

27+ , runNetwork

28+ , runGradient

29+ , applyUpdate

30+

31+ , randomNetwork

2632 ) where

2733

28−import Control.Monad.Random (MonadRandom)

29−import Data.List ( foldl' )

30−import Data.Singletons

34+import Control.Monad.Random ( MonadRandom )

3135

36+import Data.Singletons

3237import Data.Serialize

3338

34−import Grenade.Core.Shape

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

40+import Data.Kind (Type)

41+#endif

3542

36−-- | Learning parameters for stochastic gradient descent.

37−data LearningParameters = LearningParameters {

38− learningRate :: Double

39− , learningMomentum :: Double

40− , learningRegulariser :: Double

41− } deriving (Eq, Show)

42−

43−-- | Class for updating a layer. All layers implement this, and it is

44−-- shape independent.

45−class Show x => UpdateLayer x where

46− {-# MINIMAL runUpdate, createRandom #-}

47− -- | The type for the gradient for this layer.

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

49− type Gradient x :: *

50− -- | Update a layer with its gradient and learning parameters

51− runUpdate :: LearningParameters -> x -> Gradient x -> x

52−

53− -- | Create a random layer, many layers will use pure

54− createRandom :: MonadRandom m => m x

55−

56− -- | Update a layer with many Gradients

57− runUpdates :: LearningParameters -> x -> [Gradient x] -> x

58− runUpdates rate = foldl' (runUpdate rate)

59−

60−-- | Class for a layer. All layers implement this, however, they don't

61−-- need to implement it for all shapes, only ones which are appropriate.

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

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

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

65− runForwards :: x -> S i -> S o

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

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

68− -- the layer above.

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

70− runBackwards :: x -> S i -> S o -> (Gradient x, S i)

43+import Grenade.Core.Layer

44+import Grenade.Core.LearningParameters

45+import Grenade.Core.Shape

46+import Prelude.Singletons

7147

7248-- | Type of a network.

7349--

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

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

7551--

76−-- 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.

7753--

7854-- Can be considered to be a heterogeneous list of layers which are able to

7955-- transform the data shapes of the network.

80−data Network :: [*] -> [Shape] -> * where

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

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

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

57+ NNil :: SingI i

58+ => Network '[] '[i]

59+

60+ (:~>) :: (SingI i, SingI h, Layer x i h)

61+ => !x

62+ -> !(Network xs (h ': hs))

63+ -> Network (x ': xs) (i ': h ': hs)

8364infixr 5 :~>

8465

85−instance Show (Network l h) where

86− show (O a) = "O " ++ show a

87− show (i :~> o) = show i ++ "\n:~>\n" ++ show o

66+instance Show (Network '[] '[i]) where

67+ show NNil = "NNil"

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

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

70+

71+-- | Gradient of a network.

72+--

73+-- Parameterised on the layers of the network.

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

75+ GNil :: Gradients '[]

76+

77+ (:/>) :: UpdateLayer x

78+ => Gradient x

79+ -> Gradients xs

80+ -> Gradients (x ': xs)

8881

89−-- | Gradients of a network.

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

91−data Gradients :: [*] -> * where

92− OG :: UpdateLayer x => Gradient x -> Gradients '[x]

93− (:/>) :: UpdateLayer x => Gradient x -> Gradients xs -> Gradients (x ': xs)

82+-- | Wegnert Tape of a network.

83+--

84+-- Parameterised on the layers and shapes of the network.

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

86+ TNil :: SingI i

87+ => Tapes '[] '[i]

9488

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

96−-- network is with the randomNetwork.

97−class CreatableNetwork (xs :: [*]) (ss :: [Shape]) where

98− -- | Create a network of the types requested

89+ (:\>) :: (SingI i, SingI h, Layer x i h)

90+ => !(Tape x i h)

91+ -> !(Tapes xs (h ': hs))

92+ -> Tapes (x ': xs) (i ': h ': hs)

93+

94+

95+-- | Running a network forwards with some input data.

96+--

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

98+-- propagation.

99+runNetwork :: forall layers shapes.

100+ Network layers shapes

101+ -> S (Head shapes)

102+ -> (Tapes layers shapes, S (Last shapes))

103+runNetwork =

104+ go

105+ where

106+ go :: forall js ss. (Last js ~ Last shapes)

107+ => Network ss js

108+ -> S (Head js)

109+ -> (Tapes ss js, S (Last js))

110+ go (layer :~> n) !x =

111+ let (tape, forward) = runForwards layer x

112+ (tapes, answer) = go n forward

113+ in (tape :\> tapes, answer)

114+

115+ go NNil !x

116+ = (TNil, x)

117+

118+

119+-- | Running a loss gradient back through the network.

120+--

121+-- This requires a Wengert tape, generated with the appropriate input

122+-- for the loss.

123+--

124+-- Gives the gradients for the layer, and the gradient across the

125+-- input (which may not be required).

126+runGradient :: forall layers shapes.

127+ Network layers shapes

128+ -> Tapes layers shapes

129+ -> S (Last shapes)

130+ -> (Gradients layers, S (Head shapes))

131+runGradient net tapes o =

132+ go net tapes

133+ where

134+ go :: forall js ss. (Last js ~ Last shapes)

135+ => Network ss js

136+ -> Tapes ss js

137+ -> (Gradients ss, S (Head js))

138+ go (layer :~> n) (tape :\> nt) =

139+ let (gradients, feed) = go n nt

140+ (layer', backGrad) = runBackwards layer tape feed

141+ in (layer' :/> gradients, backGrad)

142+

143+ go NNil TNil

144+ = (GNil, o)

145+

146+

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

148+applyUpdate :: LearningParameters

149+ -> Network layers shapes

150+ -> Gradients layers

151+ -> Network layers shapes

152+applyUpdate rate (layer :~> rest) (gradient :/> grest)

153+ = runUpdate rate layer gradient :~> applyUpdate rate rest grest

154+

155+applyUpdate _ NNil GNil

156+ = NNil

157+

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.

99165 randomNetwork :: MonadRandom m => m (Network xs ss)

100166

101−instance (SingI i, SingI o, Layer x i o) => CreatableNetwork (x ': '[]) (i ': o ': '[]) where

102− randomNetwork = O <$> createRandom

167+instance SingI i => CreatableNetwork '[] '[i] where

168+ randomNetwork = return NNil

103169

104−instance (SingI i, SingI o, Layer x i o, CreatableNetwork xs (o ': r ': rs)) => CreatableNetwork (x ': xs) (i ': o ': r ': rs) where

170+instance (SingI i, SingI o, Layer x i o, CreatableNetwork xs (o ': rs)) => CreatableNetwork (x ': xs) (i ': o ': rs) where

105171 randomNetwork = (:~>) <$> createRandom <*> randomNetwork

106172

107−

108173-- | Add very simple serialisation to the network

109−instance (SingI i, SingI o, Layer x i o, Serialize x) => Serialize (Network '[x] '[i, o]) where

110− put (O x) = put x

111− put _ = error "impossible"

112− get = O <$> get

174+instance SingI i => Serialize (Network '[] '[i]) where

175+ put NNil = pure ()

176+ get = return NNil

113177

114−instance (SingI i, SingI o, Layer x i o, Serialize x, Serialize (Network xs (o ': r ': rs))) => Serialize (Network (x ': xs) (i ': o ': r ': rs)) where

178+instance (SingI i, SingI o, Layer x i o, Serialize x, Serialize (Network xs (o ': rs))) => Serialize (Network (x ': xs) (i ': o ': rs)) where

115179 put (x :~> r) = put x >> put r

116180 get = (:~>) <$> get <*> get

181+

182+

183+-- | Ultimate composition.

184+--

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

186+instance CreatableNetwork sublayers subshapes => UpdateLayer (Network sublayers subshapes) where

187+ type Gradient (Network sublayers subshapes) = Gradients sublayers

188+ runUpdate = applyUpdate

189+ createRandom = randomNetwork

190+

191+-- | Ultimate composition.

192+--

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

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

195+ type Tape (Network sublayers subshapes) i o = Tapes sublayers subshapes

196+ runForwards = runNetwork

197+ runBackwards = runGradient

src/Grenade/Core/Runner.hs+28−50

@@ -1,80 +1,58 @@

1−{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE GADTs #-}

31{-# LANGUAGE DataKinds #-}

4−{-# LANGUAGE ScopedTypeVariables #-}

5−{-# LANGUAGE TypeOperators #-}

6−{-# LANGUAGE TypeFamilies #-}

2+{-# LANGUAGE FlexibleContexts #-}

73{-|

8−Module : Grenade.Core.Shape

9−Description : Core definition of the Shapes of data we understand

4+Module : Grenade.Core.Runner

5+Description : Functions to perform training and backpropagation

106Copyright : (c) Huw Campbell, 2016-2017

117License : BSD2

128Stability : experimental

13−

14−This module defines simple back propagation and training functions

15−for a network.

169-}

1710module Grenade.Core.Runner (

1811 train

1912 , backPropagate

2013 , runNet

21− , applyUpdate

2214 ) where

2315

24−import Data.Singletons.Prelude

16+

17+import Grenade.Core.LearningParameters

2518import Grenade.Core.Network

2619import Grenade.Core.Shape

20+import Data.Singletons

21+import Prelude.Singletons

2722

2823-- | Perform reverse automatic differentiation on the network

2924-- for the current input and expected output.

3025--

3126-- /Note:/ The loss function pushed backwards is appropriate

3227-- for both regression and classification as a squared loss

33−-- or log-loss respectively. Other loss functions are not yet

34−-- implemented.

35−backPropagate :: forall shapes layers.

36− Network layers shapes -> S (Head shapes) -> S (Last shapes) -> Gradients layers

28+-- or log-loss respectively.

29+--

30+-- For other loss functions, use runNetwork and runGradient

31+-- with the back propagated gradient of your loss.

32+--

33+backPropagate :: SingI (Last shapes)

34+ => Network layers shapes

35+ -> S (Head shapes)

36+ -> S (Last shapes)

37+ -> Gradients layers

3738backPropagate network input target =

38− fst $ go input network

39− where

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

41− => S (Head js) -- ^ input vector

42− -> Network sublayers js -- ^ network to train

43− -> (Gradients sublayers, S (Head js))

44− -- handle input from the beginning, feeding upwards.

45− go !x (layer :~> n)

46− = let y = runForwards layer x

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

48− (n', dWs') = go y n

49− -- calculate the gradient for this layer to pass down,

50− (layer', dWs) = runBackwards layer x dWs'

39+ let (tapes, output) = runNetwork network input

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

41+ in grads

5142

52− in (layer' :/> n', dWs)

53−

54− -- handle the output layer, bouncing the derivatives back down.

55− go !x (O layer)

56− = let y = runForwards layer x

57− -- the gradient (how much y affects the error)

58− (layer', dWs) = runBackwards layer x (y - target)

59−

60− in (OG layer', dWs)

61−

62−-- | Apply one step of stochastic gradient decent across the network.

63−applyUpdate :: LearningParameters -> Network ls ss -> Gradients ls -> Network ls ss

64−applyUpdate rate (O layer) (OG gradient)

65− = O (runUpdate rate layer gradient)

66−applyUpdate rate (layer :~> rest) (gradient :/> grest)

67− = runUpdate rate layer gradient :~> applyUpdate rate rest grest

68−applyUpdate _ _ _

69− = error "Impossible for the gradients of a network to have a different length to the network"

7043

7144-- | Update a network with new weights after training with an instance.

72−train :: LearningParameters -> Network layers shapes -> S (Head shapes) -> S (Last shapes) -> Network layers shapes

45+train :: SingI (Last shapes)

46+ => LearningParameters

47+ -> Network layers shapes

48+ -> S (Head shapes)

49+ -> S (Last shapes)

50+ -> Network layers shapes

7351train rate network input output =

7452 let grads = backPropagate network input output

7553 in applyUpdate rate network grads

7654

55+

7756-- | Run the network with input and return the given output.

7857runNet :: Network layers shapes -> S (Head shapes) -> S (Last shapes)

79−runNet (layer :~> n) !x = let y = runForwards layer x in runNet n y

80−runNet (O layer) !x = runForwards layer x

58+runNet net = snd . runNetwork net

src/Grenade/Core/Shape.hs+80−33

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

23{-# LANGUAGE GADTs #-}

34{-# LANGUAGE KindSignatures #-}

@@ -7,39 +8,33 @@

78{-# LANGUAGE FlexibleContexts #-}

89{-# LANGUAGE ScopedTypeVariables #-}

910{-# LANGUAGE RankNTypes #-}

10−

11−-- Ghc 8.0 gives a warning on `n2 _ _ = error ...` but ghc 7.10 fails to

12−-- compile without this default pattern.

13−{-# OPTIONS_GHC -fno-warn-overlapping-patterns #-}

14−

11+{-# LANGUAGE UndecidableInstances #-}

1512{-|

1613Module : Grenade.Core.Shape

17−Description : Core definition of the Shapes of data we understand

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

1815Copyright : (c) Huw Campbell, 2016-2017

1916License : BSD2

2017Stability : experimental

2118

22−This module defines the core data types for the shapes of data that

23−are understood by Grenade.

19+

2420-}

2521module Grenade.Core.Shape (

26− Shape (..)

27− , S (..)

22+ S (..)

23+ , Shape (..)

24+ , SShape (..)

2825 , randomOfShape

2926 , fromStorable

3027 ) where

3128

3229import Control.DeepSeq (NFData (..))

3330import Control.Monad.Random ( MonadRandom, getRandom )

34−

31+import Data.Kind (Type)

3532import Data.Proxy

33+import Data.Serialize

3634import Data.Singletons

37−import Data.Singletons.TypeLits

3835import Data.Vector.Storable ( Vector )

3936import qualified Data.Vector.Storable as V

40−

4137import GHC.TypeLits

42−

4338import qualified Numeric.LinearAlgebra.Static as H

4439import Numeric.LinearAlgebra.Static

4540import qualified Numeric.LinearAlgebra as NLA

@@ -47,31 +42,57 @@ import qualified Numeric.LinearAlgebra as NLA

4742-- | The current shapes we accept.

4843-- at the moment this is just one, two, and three dimensional

4944-- Vectors/Matricies.

45+--

46+-- These are only used with DataKinds, as Kind `Shape`, with Types 'D1, 'D2, 'D3.

5047data Shape

5148 = D1 Nat

49+ -- ^ One dimensional vector

5250 | D2 Nat Nat

51+ -- ^ Two dimensional matrix. Row, Column.

5352 | D3 Nat Nat Nat

53+ -- ^ Three dimensional matrix. Row, Column, Channels.

5454

55−-- | Given a Shape n, these are the possible data structures with that shape.

55+-- | Concrete data structures for a Shape.

56+--

5657-- All shapes are held in contiguous memory.

5758-- 3D is held in a matrix (usually row oriented) which has height depth * rows.

5859data S (n :: Shape) where

59− S1D :: ( KnownNat o ) => R o -> S ('D1 o)

60− S2D :: ( KnownNat rows, KnownNat columns ) => L rows columns -> S ('D2 rows columns)

60+ S1D :: ( KnownNat len )

61+ => R len

62+ -> S ('D1 len)

63+

64+ S2D :: ( KnownNat rows, KnownNat columns )

65+ => L rows columns

66+ -> S ('D2 rows columns)

67+

6168 S3D :: ( KnownNat rows

6269 , KnownNat columns

6370 , KnownNat depth

64− , KnownNat (rows * depth)) => L (rows * depth) columns -> S ('D3 rows columns depth)

71+ , KnownNat (rows * depth))

72+ => L (rows * depth) columns

73+ -> S ('D3 rows columns depth)

6574

6675deriving instance Show (S n)

6776

68−-- Singletons

77+-- Singleton instances.

78+--

6979-- These could probably be derived with template haskell, but this seems

7080-- clear and makes adding the KnownNat constraints simple.

81+-- 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

7191data instance Sing (n :: Shape) where

72− D1Sing :: KnownNat a => Sing ('D1 a)

73− D2Sing :: (KnownNat a, KnownNat b) => Sing ('D2 a b)

74− D3Sing :: (KnownNat a, KnownNat b, KnownNat c, KnownNat (a * c)) => Sing ('D3 a b c)

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

7596

7697instance KnownNat a => SingI ('D1 a) where

7798 sing = D1Sing

@@ -127,18 +148,28 @@ randomOfShape :: forall x m. ( MonadRandom m, SingI x ) => m (S x)

127148randomOfShape = do

128149 seed :: Int <- getRandom

129150 return $ case (sing :: Sing x) of

130− D1Sing -> S1D (randomVector seed Uniform * 2 - 1)

131− D2Sing -> S2D (uniformSample seed (-1) 1)

132− D3Sing -> S3D (uniformSample seed (-1) 1)

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)

133159

134160-- | Generate a shape from a Storable Vector.

135161--

136162-- Returns Nothing if the vector is of the wrong size.

137163fromStorable :: forall x. SingI x => Vector Double -> Maybe (S x)

138164fromStorable xs = case sing :: Sing x of

139− D1Sing -> S1D <$> H.create xs

140− D2Sing -> S2D <$> mkL xs

141− D3Sing -> S3D <$> mkL xs

165+ D1Sing ->

166+ S1D <$> H.create xs

167+

168+ D2Sing ->

169+ S2D <$> mkL xs

170+

171+ D3Sing ->

172+ S3D <$> mkL xs

142173 where

143174 mkL :: forall rows columns. (KnownNat rows, KnownNat columns)

144175 => Vector Double -> Maybe (L rows columns)

@@ -149,6 +180,18 @@ fromStorable xs = case sing :: Sing x of

149180 then H.create $ NLA.reshape columns v

150181 else Nothing

151182

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+

152195-- Helper function for creating the number instances

153196n1 :: ( forall a. Floating a => a -> a ) -> S x -> S x

154197n1 f (S1D x) = S1D (f x)

@@ -160,11 +203,15 @@ n2 :: ( forall a. Floating a => a -> a -> a ) -> S x -> S x -> S x

160203n2 f (S1D x) (S1D y) = S1D (f x y)

161204n2 f (S2D x) (S2D y) = S2D (f x y)

162205n2 f (S3D x) (S3D y) = S3D (f x y)

163−n2 _ _ _ = error "Impossible to have different constructors for the same shaped network"

164206

165207-- Helper function for creating the number instances

166−nk :: forall x. SingI x => Double -> S x

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

167209nk x = case (sing :: Sing x) of

168− D1Sing -> S1D (konst x)

169− D2Sing -> S2D (konst x)

170− D3Sing -> S3D (konst x)

210+ D1Sing ->

211+ S1D (konst x)

212+

213+ D2Sing ->

214+ S2D (konst x)

215+

216+ D3Sing ->

217+ S3D (konst x)

src/Grenade/Layers.hsnew file+35

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

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.hsnew file+139

@@ -0,0 +1,139 @@

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.Layers.Concat (

23+ Concat (..)

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+

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

38+

39+-- | A Concatentating Layer.

40+--

41+-- This layer shares it's input state between two sublayers, and concatenates their output.

42+--

43+-- With Networks able to be Layers, this allows for very expressive composition of complex Networks.

44+--

45+-- The Concat layer has a few instances, which allow one to flexibly "bash" together the outputs.

46+--

47+-- Two 1D vectors, can go to a 2D shape with 2 rows if their lengths are identical.

48+-- Any 2 1D vectors can also become a longer 1D Vector.

49+--

50+-- 3D images become 3D images with more channels. The sizes must be the same, one can use Pad

51+-- and Crop layers to ensure this is the case.

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

53+ Concat :: x -> y -> Concat m x n y

54+

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

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

57+

58+-- | Run two layers in parallel, combining their outputs.

59+instance (UpdateLayer x, UpdateLayer y) => UpdateLayer (Concat m x n y) where

60+ type Gradient (Concat m x n y) = (Gradient x, Gradient y)

61+ runUpdate lr (Concat x y) (x', y') = Concat (runUpdate lr x x') (runUpdate lr y y')

62+ createRandom = Concat <$> createRandom <*> createRandom

63+

64+instance ( SingI i

65+ , Layer x i ('D1 o)

66+ , Layer y i ('D1 o)

67+ ) => Layer (Concat ('D1 o) x ('D1 o) y) i ('D2 2 o) where

68+ type Tape (Concat ('D1 o) x ('D1 o) y) i ('D2 2 o) = (Tape x i ('D1 o), Tape y i ('D1 o))

69+

70+ runForwards (Concat x y) input =

71+ let (xT, xOut :: S ('D1 o)) = runForwards x input

72+ (yT, yOut :: S ('D1 o)) = runForwards y input

73+ in case (xOut, yOut) of

74+ (S1D xOut', S1D yOut') ->

75+ ((xT, yT), S2D (row xOut' === row yOut'))

76+

77+ runBackwards (Concat x y) (xTape, yTape) (S2D o) =

78+ let (ox, oy) = splitRows o

79+ (x', xB :: S i) = runBackwards x xTape (S1D $ unrow ox)

80+ (y', yB :: S i) = runBackwards y yTape (S1D $ unrow oy)

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

82+

83+instance ( SingI i

84+ , Layer x i ('D1 m)

85+ , Layer y i ('D1 n)

86+ , KnownNat o

87+ , KnownNat m

88+ , KnownNat n

89+ , o ~ (m + n)

90+ , n ~ (o - m)

91+ , (m <=? o) ~ 'True

92+ ) => Layer (Concat ('D1 m) x ('D1 n) y) i ('D1 o) where

93+ type Tape (Concat ('D1 m) x ('D1 n) y) i ('D1 o) = (Tape x i ('D1 m), Tape y i ('D1 n))

94+

95+ runForwards (Concat x y) input =

96+ let (xT, xOut :: S ('D1 m)) = runForwards x input

97+ (yT, yOut :: S ('D1 n)) = runForwards y input

98+ in case (xOut, yOut) of

99+ (S1D xOut', S1D yOut') ->

100+ ((xT, yT), S1D (xOut' # yOut'))

101+

102+ runBackwards (Concat x y) (xTape, yTape) (S1D o) =

103+ let (ox :: R m , oy :: R n) = split o

104+ (x', xB :: S i) = runBackwards x xTape (S1D ox)

105+ (y', yB :: S i) = runBackwards y yTape (S1D oy)

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

107+

108+-- | Concat 3D shapes, increasing the number of channels.

109+instance ( SingI i

110+ , Layer x i ('D3 rows cols m)

111+ , Layer y i ('D3 rows cols n)

112+ , KnownNat (rows * n)

113+ , KnownNat (rows * m)

114+ , KnownNat (rows * o)

115+ , KnownNat o

116+ , KnownNat m

117+ , KnownNat n

118+ , ((rows * m) + (rows * n)) ~ (rows * o)

119+ , ((rows * o) - (rows * m)) ~ (rows * n)

120+ , ((rows * m) <=? (rows * o)) ~ 'True

121+ ) => Layer (Concat ('D3 rows cols m) x ('D3 rows cols n) y) i ('D3 rows cols o) where

122+ type Tape (Concat ('D3 rows cols m) x ('D3 rows cols n) y) i ('D3 rows cols o) = (Tape x i ('D3 rows cols m), Tape y i ('D3 rows cols n))

123+

124+ runForwards (Concat x y) input =

125+ let (xT, xOut :: S ('D3 rows cols m)) = runForwards x input

126+ (yT, yOut :: S ('D3 rows cols n)) = runForwards y input

127+ in case (xOut, yOut) of

128+ (S3D xOut', S3D yOut') ->

129+ ((xT, yT), S3D (xOut' === yOut'))

130+

131+ runBackwards (Concat x y) (xTape, yTape) (S3D o) =

132+ let (ox, oy) = splitRows o

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

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

135+ 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+90−60

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

23{-# 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+-}

1021module Grenade.Layers.Convolution (

1122 Convolution (..)

1223 , Convolution' (..)

@@ -17,16 +28,15 @@ import Control.Monad.Random hiding ( fromList )

1728import Data.Maybe

1829import Data.Proxy

1930import Data.Serialize

20−import Data.Singletons.TypeLits

2131

2232import GHC.TypeLits

33+import Data.Kind (Type)

2334

2435import Numeric.LinearAlgebra hiding ( uniformSample, konst )

2536import qualified Numeric.LinearAlgebra as LA

2637import Numeric.LinearAlgebra.Static hiding ((|||), build, toRows)

2738

28−import Grenade.Core.Network

29−import Grenade.Core.Shape

39+import Grenade.Core

3040import Grenade.Layers.Internal.Convolution

3141import Grenade.Layers.Internal.Update

3242

@@ -47,7 +57,7 @@ data Convolution :: Nat -- Number of channels, for the first layer this could be

4757 -> Nat -- The number of column in the kernel filter

4858 -> Nat -- The row stride of the convolution filter

4959 -> Nat -- The columns stride of the convolution filter

50− -> * where

60+ -> Type where

5161 Convolution :: ( KnownNat channels

5262 , KnownNat filters

5363 , KnownNat kernelRows

@@ -66,7 +76,7 @@ data Convolution' :: Nat -- Number of channels, for the first layer this could b

6676 -> Nat -- The number of column in the kernel filter

6777 -> Nat -- The row stride of the convolution filter

6878 -> Nat -- The columns stride of the convolution filter

69− -> * where

79+ -> Type where

7080 Convolution' :: ( KnownNat channels

7181 , KnownNat filters

7282 , KnownNat kernelRows

@@ -120,9 +130,9 @@ instance ( KnownNat channels

120130 , KnownNat strideColumns

121131 , KnownNat (kernelRows * kernelColumns * channels)

122132 ) => UpdateLayer (Convolution channels filters kernelRows kernelColumns strideRows strideColumns) where

123− type Gradient (Convolution channels filters kernelRows kernelCols strideRows strideCols) = (Convolution' channels filters kernelRows kernelCols strideRows strideCols)

124− runUpdate LearningParameters {..} (Convolution oldKernel oldMomentum) (Convolution' kernelGradient) =

125− let (newKernel, newMomentum) = decendMatrix learningRate learningMomentum learningRegulariser oldKernel kernelGradient oldMomentum

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

126136 in Convolution newKernel newMomentum

127137

128138 createRandom = randomConvolution

@@ -142,7 +152,8 @@ instance ( KnownNat channels

142152 let mm = konst 0

143153 return $ Convolution wN mm

144154

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

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

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

146157instance ( KnownNat kernelRows

147158 , KnownNat kernelCols

148159 , KnownNat filters

@@ -152,27 +163,33 @@ instance ( KnownNat kernelRows

152163 , KnownNat inputCols

153164 , KnownNat outputRows

154165 , KnownNat outputCols

166+ , KnownNat channels

155167 , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

156168 , ((outputCols - 1) * strideCols) ~ (inputCols - kernelCols)

157− , KnownNat (kernelRows * kernelCols * 1)

169+ , KnownNat (kernelRows * kernelCols * channels)

158170 , KnownNat (outputRows * filters)

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

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

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) =

161176 let ex = extract input

162177 ek = extract kernel

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

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

163180 kx = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

164181 ky = fromIntegral $ natVal (Proxy :: Proxy kernelCols)

165182 sx = fromIntegral $ natVal (Proxy :: Proxy strideRows)

166183 sy = fromIntegral $ natVal (Proxy :: Proxy strideCols)

167184 ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

168185 oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

169− c = im2col kx ky sx sy ex

186+

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

170188 mt = c LA.<> ek

171189 r = col2vid 1 1 1 1 ox oy mt

172190 rs = fromJust . create $ r

173− in S3D rs

174−

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

191+ in (S3D input, S3D rs)

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

176193 let ex = extract input

177194 ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

178195 iy = fromIntegral $ natVal (Proxy :: Proxy inputCols)

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

183200 ox = fromIntegral $ natVal (Proxy :: Proxy outputRows)

184201 oy = fromIntegral $ natVal (Proxy :: Proxy outputCols)

185202

186− c = im2col kx ky sx sy ex

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

187204

188205 eo = extract dEdy

189206 ek = extract kernel

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

191208 vs = vid2col 1 1 1 1 ox oy eo

192209

193210 kN = fromJust . create $ tr c LA.<> vs

211+

194212 dW = vs LA.<> tr ek

195213

196− xW = col2im kx ky sx sy ix iy dW

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

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

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

198216

199217

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

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

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

202219instance ( KnownNat kernelRows

203220 , KnownNat kernelCols

204221 , KnownNat filters

@@ -208,50 +225,63 @@ instance ( KnownNat kernelRows

208225 , KnownNat inputCols

209226 , KnownNat outputRows

210227 , KnownNat outputCols

211− , KnownNat channels

212228 , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

213229 , ((outputCols - 1) * strideCols) ~ (inputCols - kernelCols)

214− , KnownNat (kernelRows * kernelCols * channels)

230+ , KnownNat (kernelRows * kernelCols * 1)

215231 , KnownNat (outputRows * filters)

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

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

218− let ex = extract input

219− ek = extract kernel

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

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

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

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

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

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

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

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

228−

229− c = vid2col kx ky sx sy ix iy ex

230− mt = c LA.<> ek

231− r = col2vid 1 1 1 1 ox oy mt

232− rs = fromJust . create $ r

233− in S3D rs

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

235− let ex = extract input

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

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

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

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

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

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

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

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

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))

244236

245− c = vid2col kx ky sx sy ix iy ex

237+ runBackwards c tape grads =

238+ case runBackwards c tape grads of

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

246240

247− eo = extract dEdy

248− ek = extract kernel

249241

250− vs = vid2col 1 1 1 1 ox oy eo

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)

251261

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

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)

253265

254− dW = vs LA.<> tr ek

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)

255285

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

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

286+ runBackwards c tape (S2D grads) =

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

src/Grenade/Layers/Crop.hs+69−7

@@ -1,20 +1,32 @@

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

23{-# LANGUAGE ScopedTypeVariables #-}

34{-# LANGUAGE GADTs #-}

45{-# LANGUAGE TypeOperators #-}

56{-# LANGUAGE TypeFamilies #-}

7+{-# LANGUAGE FlexibleContexts #-}

68{-# 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+-}

717module Grenade.Layers.Crop (

818 Crop (..)

919 ) where

1020

1121import Data.Maybe

1222import Data.Proxy

13−import Data.Singletons.TypeLits

23+import Data.Serialize

24+

1425import GHC.TypeLits

26+import Data.Kind (Type)

1527

16−import Grenade.Core.Network

17−import Grenade.Core.Shape

28+import Grenade.Core

29+import Grenade.Layers.Internal.Pad

1830

1931import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

2032import Numeric.LinearAlgebra.Static (extract, create)

@@ -23,7 +35,7 @@ import Numeric.LinearAlgebra.Static (extract, create)

2335data Crop :: Nat

2436 -> Nat

2537 -> Nat

26− -> Nat -> * where

38+ -> Nat -> Type where

2739 Crop :: Crop cropLeft cropTop cropRight cropBottom

2840

2941instance Show (Crop cropLeft cropTop cropRight cropBottom) where

@@ -34,6 +46,10 @@ instance UpdateLayer (Crop l t r b) where

3446 runUpdate _ x _ = x

3547 createRandom = return Crop

3648

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

50+ put _ = return ()

51+ get = return Crop

52+

3753-- | A two dimentional image can be cropped.

3854instance ( KnownNat cropLeft

3955 , KnownNat cropTop

@@ -43,9 +59,10 @@ instance ( KnownNat cropLeft

4359 , KnownNat inputColumns

4460 , KnownNat outputRows

4561 , KnownNat outputColumns

46− , (inputRows - cropTop - cropBottom) ~ outputRows

47− , (inputColumns - cropLeft - cropRight) ~ outputColumns

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

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

4864 ) => Layer (Crop cropLeft cropTop cropRight cropBottom) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) where

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

4966 runForwards Crop (S2D input) =

5067 let cropl = fromIntegral $ natVal (Proxy :: Proxy cropLeft)

5168 cropt = fromIntegral $ natVal (Proxy :: Proxy cropTop)

@@ -53,7 +70,7 @@ instance ( KnownNat cropLeft

5370 ncols = fromIntegral $ natVal (Proxy :: Proxy outputColumns)

5471 m = extract input

5572 r = subMatrix (cropt, cropl) (nrows, ncols) m

56− in S2D . fromJust . create $ r

73+ in ((), S2D . fromJust . create $ r)

5774 runBackwards _ _ (S2D dEdy) =

5875 let cropl = fromIntegral $ natVal (Proxy :: Proxy cropLeft)

5976 cropt = fromIntegral $ natVal (Proxy :: Proxy cropTop)

@@ -62,3 +79,48 @@ instance ( KnownNat cropLeft

6279 eo = extract dEdy

6380 vs = diagBlock [konst 0 (cropt,cropl), eo, konst 0 (cropb,cropr)]

6481 in ((), S2D . fromJust . create $ vs)

82+

83+

84+-- | A two dimentional image can be cropped.

85+instance ( KnownNat cropLeft

86+ , KnownNat cropTop

87+ , KnownNat cropRight

88+ , KnownNat cropBottom

89+ , KnownNat inputRows

90+ , KnownNat inputColumns

91+ , KnownNat outputRows

92+ , KnownNat outputColumns

93+ , KnownNat channels

94+ , KnownNat (inputRows * channels)

95+ , KnownNat (outputRows * channels)

96+ , (outputRows + cropTop + cropBottom) ~ inputRows

97+ , (outputColumns + cropLeft + cropRight) ~ inputColumns

98+ ) => Layer (Crop cropLeft cropTop cropRight cropBottom) ('D3 inputRows inputColumns channels) ('D3 outputRows outputColumns channels) where

99+ type Tape (Crop cropLeft cropTop cropRight cropBottom) ('D3 inputRows inputColumns channels) ('D3 outputRows outputColumns channels) = ()

100+ runForwards Crop (S3D input) =

101+ let padl = fromIntegral $ natVal (Proxy :: Proxy cropLeft)

102+ padt = fromIntegral $ natVal (Proxy :: Proxy cropTop)

103+ padr = fromIntegral $ natVal (Proxy :: Proxy cropRight)

104+ padb = fromIntegral $ natVal (Proxy :: Proxy cropBottom)

105+ inr = fromIntegral $ natVal (Proxy :: Proxy inputRows)

106+ inc = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

107+ outr = fromIntegral $ natVal (Proxy :: Proxy outputRows)

108+ outc = fromIntegral $ natVal (Proxy :: Proxy outputColumns)

109+ ch = fromIntegral $ natVal (Proxy :: Proxy channels)

110+ m = extract input

111+ cropped = crop ch padl padt padr padb outr outc inr inc m

112+ in ((), S3D . fromJust . create $ cropped)

113+

114+ runBackwards Crop () (S3D gradient) =

115+ let padl = fromIntegral $ natVal (Proxy :: Proxy cropLeft)

116+ padt = fromIntegral $ natVal (Proxy :: Proxy cropTop)

117+ padr = fromIntegral $ natVal (Proxy :: Proxy cropRight)

118+ padb = fromIntegral $ natVal (Proxy :: Proxy cropBottom)

119+ inr = fromIntegral $ natVal (Proxy :: Proxy inputRows)

120+ inc = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

121+ outr = fromIntegral $ natVal (Proxy :: Proxy outputRows)

122+ outc = fromIntegral $ natVal (Proxy :: Proxy outputColumns)

123+ ch = fromIntegral $ natVal (Proxy :: Proxy channels)

124+ m = extract gradient

125+ padded = pad ch padl padt padr padb outr outc inr inc m

126+ in ((), S3D . fromJust . create $ padded)

src/Grenade/Layers/Deconvolution.hsnew file+285

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

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/Dropout.hs+14−23

@@ -10,37 +10,28 @@ module Grenade.Layers.Dropout (

1010import Control.Monad.Random hiding (fromList)

1111

1212import GHC.TypeLits

13−import Grenade.Core.Shape

14−import Grenade.Core.Network

15−

16−import Numeric.LinearAlgebra.Static

17−

13+import Grenade.Core

1814

1915-- Dropout layer help to reduce overfitting.

2016-- Idea here is that the vector is a shape of 1s and 0s, which we multiply the input by.

2117-- After backpropogation, we return a new matrix/vector, with different bits dropped out.

2218-- Double is the proportion to drop in each training iteration (like 1% or 5% would be

2319-- reasonable).

24−data Dropout o =

25− Dropout (R o)

26− | Pass Double

27− deriving Show

20+data Dropout = Dropout {

21+ dropoutRate :: Double

22+ , dropoutSeed :: Int

23+ } deriving Show

2824

29−instance (KnownNat i) => UpdateLayer (Dropout i) where

30− type Gradient (Dropout i) = ()

25+instance UpdateLayer Dropout where

26+ type Gradient Dropout = ()

3127 runUpdate _ x _ = x

3228 createRandom = randomDropout 0.95

3329

34−randomDropout :: (MonadRandom m, KnownNat i)

35− => Double -> m (Dropout i)

36−randomDropout rate = do

37− seed <- getRandom

38− let wN = randomVector seed Uniform

39− xs = dvmap (\a -> if a <= rate then 0 else 1) wN

40− return $ Dropout xs

30+randomDropout :: MonadRandom m

31+ => Double -> m Dropout

32+randomDropout rate = Dropout rate <$> getRandom

4133

42−instance (KnownNat i) => Layer (Dropout i) ('D1 i) ('D1 i) where

43− runForwards (Dropout drops) (S1D x) = S1D $ x * drops

44− runForwards (Pass rate) (S1D x)= S1D $ dvmap (* (1 - rate)) x

45− runBackwards (Dropout drops) _ (S1D x) = ((), S1D $ x * drops)

46− runBackwards (Pass rate) _ (S1D x) = ((), S1D $ dvmap (* (1 - rate)) x)

34+instance (KnownNat i) => Layer Dropout ('D1 i) ('D1 i) where

35+ type Tape Dropout ('D1 i) ('D1 i) = ()

36+ runForwards (Dropout _ _) (S1D x) = ((), S1D x)

37+ runBackwards (Dropout _ _) _ (S1D x) = ((), S1D x)

src/Grenade/Layers/Elu.hsnew file+67

@@ -0,0 +1,67 @@

1+{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE TypeOperators #-}

3+{-# LANGUAGE TypeFamilies #-}

4+{-# 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+-}

12+module Grenade.Layers.Elu (

13+ Elu (..)

14+ ) where

15+

16+import Data.Serialize

17+

18+import GHC.TypeLits

19+import Grenade.Core

20+

21+import qualified Numeric.LinearAlgebra.Static as LAS

22+

23+-- | An exponential linear unit.

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

25+-- diode on every neuron individually.

26+data Elu = Elu

27+ deriving Show

28+

29+instance UpdateLayer Elu where

30+ type Gradient Elu = ()

31+ runUpdate _ _ _ = Elu

32+ createRandom = return Elu

33+

34+instance Serialize Elu where

35+ put _ = return ()

36+ get = return Elu

37+

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

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

40+

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

42+ where

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

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

45+ where

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

47+

48+instance (KnownNat i, KnownNat j) => Layer Elu ('D2 i j) ('D2 i j) where

49+ type Tape Elu ('D2 i j) ('D2 i j) = S ('D2 i j)

50+

51+ runForwards _ (S2D y) = (S2D y, S2D (elu y))

52+ where

53+ elu = LAS.dmmap (\a -> if a <= 0 then exp a - 1 else a)

54+ runBackwards _ (S2D y) (S2D dEdy) = ((), S2D (elu' y * dEdy))

55+ where

56+ elu' = LAS.dmmap (\a -> if a <= 0 then exp a else 1)

57+

58+instance (KnownNat i, KnownNat j, KnownNat k) => Layer Elu ('D3 i j k) ('D3 i j k) where

59+

60+ type Tape Elu ('D3 i j k) ('D3 i j k) = S ('D3 i j k)

61+

62+ runForwards _ (S3D y) = (S3D y, S3D (elu y))

63+ where

64+ elu = LAS.dmmap (\a -> if a <= 0 then exp a - 1 else a)

65+ runBackwards _ (S3D y) (S3D dEdy) = ((), S3D (elu' y * dEdy))

66+ where

67+ elu' = LAS.dmmap (\a -> if a <= 0 then exp a else 1)

src/Grenade/Layers/Flatten.hsdeleted−53

@@ -1,53 +0,0 @@

1−{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE TypeOperators #-}

3−{-# LANGUAGE TypeFamilies #-}

4−{-# LANGUAGE MultiParamTypeClasses #-}

5−{-# LANGUAGE FlexibleContexts #-}

6−module Grenade.Layers.Flatten (

7− FlattenLayer (..)

8− ) where

9−

10−import Data.Serialize

11−

12−import Data.Singletons.TypeLits

13−import GHC.TypeLits

14−

15−import Numeric.LinearAlgebra.Static

16−import Numeric.LinearAlgebra.Data as LA ( flatten )

17−

18−import Grenade.Core.Shape

19−import Grenade.Core.Network

20−

21−-- | Flatten Layer

22−--

23−-- Flattens input down to D1 from either 2D or 3D data.

24−--

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

26−data FlattenLayer = FlattenLayer

27− deriving Show

28−

29−instance UpdateLayer FlattenLayer where

30− type Gradient FlattenLayer = ()

31− runUpdate _ _ _ = FlattenLayer

32− createRandom = return FlattenLayer

33−

34−instance (KnownNat a, KnownNat x, KnownNat y, a ~ (x * z)) => Layer FlattenLayer ('D2 x y) ('D1 a) where

35− runForwards _ (S2D y) = fromJust' . fromStorable . flatten . extract $ y

36− runBackwards _ _ (S1D y) = ((), fromJust' . fromStorable . extract $ y)

37−

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

39− runForwards _ (S3D y) = fromJust' . fromStorable . flatten . extract $ y

40− runBackwards _ _ (S1D y) = ((), fromJust' . fromStorable . extract $ y)

41−

42−instance (KnownNat y, KnownNat x, KnownNat z, z ~ 1) => Layer FlattenLayer ('D3 x y z) ('D2 x y) where

43− runForwards _ (S3D y) = S2D y

44− runBackwards _ _ (S2D y) = ((), S3D y)

45−

46−instance Serialize FlattenLayer where

47− put _ = return ()

48− get = return FlattenLayer

49−

50−

51−fromJust' :: Maybe x -> x

52−fromJust' (Just x) = x

53−fromJust' Nothing = error $ "FlattenLayer error: data shape couldn't be converted."

src/Grenade/Layers/FullyConnected.hs+17−19

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

11{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE RecordWildCards #-}

32{-# LANGUAGE TypeOperators #-}

43{-# LANGUAGE TypeFamilies #-}

54{-# LANGUAGE MultiParamTypeClasses #-}

65{-# LANGUAGE ScopedTypeVariables #-}

76module Grenade.Layers.FullyConnected (

87 FullyConnected (..)

8+ , FullyConnected' (..)

99 , randomFullyConnected

1010 ) where

1111

@@ -13,26 +13,23 @@ import Control.Monad.Random hiding (fromList)

1313

1414import Data.Proxy

1515import Data.Serialize

16−import Data.Singletons.TypeLits

1716

1817import qualified Numeric.LinearAlgebra as LA

1918import Numeric.LinearAlgebra.Static

2019

21−import Grenade.Core.Network

22−import Grenade.Core.Shape

20+import Grenade.Core

2321

2422import Grenade.Layers.Internal.Update

23+import GHC.TypeLits

2524

2625-- | A basic fully connected (or inner product) neural network layer.

2726data FullyConnected i o = FullyConnected

28− !(R o) -- Bias neuron weights

29− !(R o) -- Bias neuron momentum

30− !(L o i) -- Activation weights

31− !(L o i) -- Momentum

27+ !(FullyConnected' i o) -- Neuron weights

28+ !(FullyConnected' i o) -- Neuron momentum

3229

3330data FullyConnected' i o = FullyConnected'

34− !(R o) -- Bias neuron gradient

35− !(L o i) -- Activation gradient

31+ !(R o) -- Bias

32+ !(L o i) -- Activations

3633

3734instance Show (FullyConnected i o) where

3835 show FullyConnected {} = "FullyConnected"

@@ -40,19 +37,20 @@ instance Show (FullyConnected i o) where

4037instance (KnownNat i, KnownNat o) => UpdateLayer (FullyConnected i o) where

4138 type Gradient (FullyConnected i o) = (FullyConnected' i o)

4239

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

44− let (newBias, newBiasMomentum) = decendVector learningRate learningMomentum learningRegulariser oldBias biasGradient oldBiasMomentum

45− (newActivations, newMomentum) = decendMatrix learningRate learningMomentum learningRegulariser oldActivations activationGradient oldMomentum

46− in FullyConnected newBias newBiasMomentum newActivations newMomentum

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

43+ in FullyConnected (FullyConnected' newBias newActivations) (FullyConnected' newBiasMomentum newMomentum)

4744

4845 createRandom = randomFullyConnected

4946

5047instance (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

5149 -- Do a matrix vector multiplication and return the result.

52− runForwards (FullyConnected wB _ wN _) (S1D v) = S1D (wB + wN #> v)

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

5351

5452 -- Run a backpropogation step for a full connected layer.

55− runBackwards (FullyConnected _ _ wN _) (S1D x) (S1D dEdy) =

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

5654 let wB' = dEdy

5755 mm' = dEdy `outer` x

5856 -- calcluate derivatives for next step

@@ -60,7 +58,7 @@ instance (KnownNat i, KnownNat o) => Layer (FullyConnected i o) ('D1 i) ('D1 o)

6058 in (FullyConnected' wB' mm', S1D dWs)

6159

6260instance (KnownNat i, KnownNat o) => Serialize (FullyConnected i o) where

63− put (FullyConnected b _ w _) = do

61+ put (FullyConnected (FullyConnected' b w) _) = do

6462 putListOf put . LA.toList . extract $ b

6563 putListOf put . LA.toList . LA.flatten . extract $ w

6664

@@ -70,7 +68,7 @@ instance (KnownNat i, KnownNat o) => Serialize (FullyConnected i o) where

7068 k <- maybe (fail "Vector of incorrect size") return . create . LA.reshape f . LA.fromList =<< getListOf get

7169 let bm = konst 0

7270 let mm = konst 0

73− return $ FullyConnected b bm k mm

71+ return $ FullyConnected (FullyConnected' b k) (FullyConnected' bm mm)

7472

7573randomFullyConnected :: (MonadRandom m, KnownNat i, KnownNat o)

7674 => m (FullyConnected i o)

@@ -81,4 +79,4 @@ randomFullyConnected = do

8179 wN = uniformSample s2 (-1) 1

8280 bm = konst 0

8381 mm = konst 0

84− return $ FullyConnected wB bm wN mm

82+ return $ FullyConnected (FullyConnected' wB wN) (FullyConnected' bm mm)

src/Grenade/Layers/Fuse.hsdeleted−47

@@ -1,47 +0,0 @@

1−{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE GADTs #-}

3−{-# LANGUAGE ScopedTypeVariables #-}

4−{-# LANGUAGE TypeOperators #-}

5−{-# LANGUAGE TypeFamilies #-}

6−{-# LANGUAGE MultiParamTypeClasses #-}

7−{-# LANGUAGE FlexibleContexts #-}

8−{-# LANGUAGE FlexibleInstances #-}

9−module Grenade.Layers.Fuse (

10− Fuse (..)

11− ) where

12−

13−import Grenade.Core.Network

14−import Grenade.Core.Shape

15−

16−-- | Fuse two layers into one layer.

17−-- This can be used to simplify a network if a complicated repeated structure is used.

18−-- This does however have a trade off, internal incremental states in the Wengert tape are

19−-- not retained during reverse accumulation. So less RAM is used, but more compute is required.

20−data Fuse :: * -> * -> Shape -> Shape -> Shape -> * where

21− (:$$) :: (Layer x i h, Layer y h o)

22− => !x

23− -> !y

24− -> Fuse x y i h o

25−infixr 5 :$$

26−

27−instance (Show x, Show y) => Show (Fuse x y i h o) where

28− show (x :$$ y) = "(" ++ show x ++ " :$$ " ++ show y ++ ")"

29−

30−instance (Layer x i h, Layer y h o) => UpdateLayer (Fuse x y i h o) where

31− type Gradient (Fuse x y i h o) = (Gradient x, Gradient y)

32− runUpdate lr (x :$$ y) (x', y') =

33− let newX = runUpdate lr x x'

34− newY = runUpdate lr y y'

35− in (newX :$$ newY)

36− createRandom = (:$$) <$> createRandom <*> createRandom

37−

38−instance (Layer x i h, Layer y h o) => Layer (Fuse x y i h o) i o where

39− runForwards (x :$$ y) input =

40− let yInput :: S h = runForwards x input

41− in runForwards y yInput

42−

43− runBackwards (x :$$ y) input backGradient =

44− let yInput :: S h = runForwards x input

45− (y', yGrad) = runBackwards y yInput backGradient

46− (x', xGrad) = runBackwards x input yGrad

47− in ((x', y'), xGrad)

src/Grenade/Layers/Inception.hsnew file+67

@@ -0,0 +1,67 @@

1+{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE GADTs #-}

3+{-# LANGUAGE TypeOperators #-}

4+{-# LANGUAGE TypeFamilies #-}

5+{-# LANGUAGE MultiParamTypeClasses #-}

6+{-# LANGUAGE FlexibleContexts #-}

7+{-# LANGUAGE RankNTypes #-}

8+{-# LANGUAGE FlexibleInstances #-}

9+{-# LANGUAGE ScopedTypeVariables #-}

10+{-|

11+Module : Grenade.Core.Network

12+Description : Inception style parallel convolutional network composition.

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

14+License : BSD2

15+Stability : experimental

16+

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

18+complex multiconvolution size networks.

19+-}

20+module Grenade.Layers.Inception (

21+ Inception

22+ , InceptionMini

23+ , Resnet

24+ ) where

25+

26+import GHC.TypeLits

27+

28+import Grenade.Core

29+import Grenade.Layers.Convolution

30+import Grenade.Layers.Pad

31+import Grenade.Layers.Concat

32+import Grenade.Layers.Merge

33+import Grenade.Layers.Trivial

34+

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.

47+type 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) ]

50+

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) ]

54+

55+type 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 ]

58+

59+type 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 ]

62+

63+type 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 ]

66+

67+type Resnet branch = Merge Trivial branch

src/Grenade/Layers/Internal/Pad.hsnew file+54

@@ -0,0 +1,54 @@

1+{-# LANGUAGE ForeignFunctionInterface #-}

2+module Grenade.Layers.Internal.Pad (

3+ pad

4+ , crop

5+ ) where

6+

7+import qualified Data.Vector.Storable as U ( unsafeToForeignPtr0, unsafeFromForeignPtr0 )

8+

9+import Foreign ( mallocForeignPtrArray, withForeignPtr )

10+import Foreign.Ptr ( Ptr )

11+

12+import Numeric.LinearAlgebra ( flatten, Matrix )

13+import qualified Numeric.LinearAlgebra.Devel as U

14+

15+import System.IO.Unsafe ( unsafePerformIO )

16+

17+pad :: Int -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> Matrix Double -> Matrix Double

18+pad channels padLeft padTop padRight padBottom rows cols rows' cols' m

19+ = let outMatSize = rows' * cols' * channels

20+ vec = flatten m

21+ in unsafePerformIO $ do

22+ outPtr <- mallocForeignPtrArray outMatSize

23+ let (inPtr, _) = U.unsafeToForeignPtr0 vec

24+

25+ withForeignPtr inPtr $ \inPtr' ->

26+ withForeignPtr outPtr $ \outPtr' ->

27+ pad_cpu inPtr' channels rows cols padLeft padTop padRight padBottom outPtr'

28+

29+ let matVec = U.unsafeFromForeignPtr0 outPtr outMatSize

30+ return (U.matrixFromVector U.RowMajor (rows' * channels) cols' matVec)

31+{-# INLINE pad #-}

32+

33+foreign import ccall unsafe

34+ pad_cpu

35+ :: Ptr Double -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> Ptr Double -> IO ()

36+

37+crop :: Int -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> Matrix Double -> Matrix Double

38+crop channels padLeft padTop padRight padBottom rows cols _ _ m

39+ = let outMatSize = rows * cols * channels

40+ vec = flatten m

41+ in unsafePerformIO $ do

42+ outPtr <- mallocForeignPtrArray outMatSize

43+ let (inPtr, _) = U.unsafeToForeignPtr0 vec

44+

45+ withForeignPtr inPtr $ \inPtr' ->

46+ withForeignPtr outPtr $ \outPtr' ->

47+ crop_cpu inPtr' channels rows cols padLeft padTop padRight padBottom outPtr'

48+

49+ let matVec = U.unsafeFromForeignPtr0 outPtr outMatSize

50+ return (U.matrixFromVector U.RowMajor (rows * channels) cols matVec)

51+

52+foreign import ccall unsafe

53+ crop_cpu

54+ :: Ptr Double -> Int -> Int -> Int -> Int -> Int -> Int -> Int -> Ptr Double -> IO ()

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

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

11{-# LANGUAGE ForeignFunctionInterface #-}

22module Grenade.Layers.Internal.Update (

3− decendMatrix

4− , decendVector

3+ descendMatrix

4+ , descendVector

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−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 =

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 =

2222 let (rows, cols) = size weights

2323 len = rows * cols

2424 -- Most gradients come in in ColumnMajor,

@@ -29,7 +29,7 @@ decendMatrix 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) = decendUnsafe len rate momentum regulariser weights' gradient' lastUpdate'

32+ (vw, vm) = descendUnsafe 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 @@ decendMatrix 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−decendVector :: (KnownNat r) => Double -> Double -> Double -> R r -> R r -> R r -> (R r, R r)

41−decendVector rate momentum regulariser weights gradient lastUpdate =

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 =

4242 let len = size weights

4343 weights' = extract weights

4444 gradient' = extract gradient

4545 lastUpdate' = extract lastUpdate

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

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

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

4848

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 =

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 =

5151 unsafePerformIO $ do

5252 outWPtr <- mallocForeignPtrArray len

5353 outMPtr <- mallocForeignPtrArray len

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

6060 withForeignPtr lPtr $ \lPtr' ->

6161 withForeignPtr outWPtr $ \outWPtr' ->

6262 withForeignPtr outMPtr $ \outMPtr' ->

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

63+ descend_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− decend_cpu

68+ descend_cpu

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

7070

src/Grenade/Layers/Logit.hs+28−21

@@ -1,19 +1,27 @@

11{-# LANGUAGE DataKinds #-}

22{-# LANGUAGE TypeOperators #-}

33{-# LANGUAGE TypeFamilies #-}

4+{-# LANGUAGE FlexibleInstances #-}

45{-# 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+-}

513module Grenade.Layers.Logit (

614 Logit (..)

715 ) where

816

917

1018import Data.Serialize

19+import Data.Singletons

1120

12−import Data.Singletons.TypeLits

13−import Grenade.Core.Network

14−import Grenade.Core.Shape

21+import Grenade.Core

1522

1623-- | A Logit layer.

24+--

1725-- A layer which can act between any shape of the same dimension, perfoming an sigmoid function.

1826-- This layer should be used as the output layer of a network for logistic regression (classification)

1927-- problems.

@@ -25,26 +33,25 @@ instance UpdateLayer Logit where

2533 runUpdate _ _ _ = Logit

2634 createRandom = return Logit

2735

28−instance (KnownNat i) => Layer Logit ('D1 i) ('D1 i) where

29− runForwards _ (S1D y) = S1D (logistic y)

30− runBackwards _ (S1D y) (S1D dEdy) = ((), S1D (logistic' y * dEdy))

31−

32−instance (KnownNat i, KnownNat j) => Layer Logit ('D2 i j) ('D2 i j) where

33− runForwards _ (S2D y) = S2D (logistic y)

34− runBackwards _ (S2D y) (S2D dEdy) = ((), S2D (logistic' y * dEdy))

35−

36−instance (KnownNat i, KnownNat j, KnownNat k) => Layer Logit ('D3 i j k) ('D3 i j k) where

37− runForwards _ (S3D y) = S3D (logistic y)

38− runBackwards _ (S3D y) (S3D dEdy) = ((), S3D (logistic' y * dEdy))

36+instance (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.

44+ 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)

3951

4052instance Serialize Logit where

4153 put _ = return ()

4254 get = return Logit

4355

44−logistic :: Floating a => a -> a

45−logistic x = 1 / (1 + exp (-x))

46−

47−logistic' :: Floating a => a -> a

48−logistic' x = logix * (1 - logix)

49− where

50− logix = logistic x

56+sigmoid :: Floating a => a -> a

57+sigmoid x = 1 / (1 + exp (-x))

src/Grenade/Layers/Merge.hsnew file+66

@@ -0,0 +1,66 @@

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+{-|

13+Module : Grenade.Core.Network

14+Description : Merging layer for parallel network composition

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

16+License : BSD2

17+Stability : experimental

18+-}

19+module Grenade.Layers.Merge (

20+ Merge (..)

21+ ) where

22+

23+import Data.Serialize

24+

25+import Data.Singletons

26+

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

28+import Data.Kind (Type)

29+#endif

30+

31+import Grenade.Core

32+

33+-- | A Merging layer.

34+--

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

36+-- shape.

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

38+ Merge :: x -> y -> Merge x y

39+

40+instance (Show x, Show y) => Show (Merge x y) where

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

42+

43+-- | Run two layers in parallel, combining their outputs.

44+-- This just kind of "smooshes" the weights together.

45+instance (UpdateLayer x, UpdateLayer y) => UpdateLayer (Merge x y) where

46+ type Gradient (Merge x y) = (Gradient x, Gradient y)

47+ runUpdate lr (Merge x y) (x', y') = Merge (runUpdate lr x x') (runUpdate lr y y')

48+ createRandom = Merge <$> createRandom <*> createRandom

49+

50+-- | Combine the outputs and the inputs, summing the output shape

51+instance (SingI i, SingI o, Layer x i o, Layer y i o) => Layer (Merge x y) i o where

52+ type Tape (Merge x y) i o = (Tape x i o, Tape y i o)

53+

54+ runForwards (Merge x y) input =

55+ let (xT, xOut) = runForwards x input

56+ (yT, yOut) = runForwards y input

57+ in ((xT, yT), xOut + yOut)

58+

59+ runBackwards (Merge x y) (xTape, yTape) o =

60+ let (x', xB) = runBackwards x xTape o

61+ (y', yB) = runBackwards y yTape o

62+ 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+64−6

@@ -1,9 +1,19 @@

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

23{-# LANGUAGE ScopedTypeVariables #-}

34{-# LANGUAGE GADTs #-}

45{-# LANGUAGE TypeOperators #-}

56{-# LANGUAGE TypeFamilies #-}

67{-# LANGUAGE MultiParamTypeClasses #-}

8+{-# 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+-}

717module Grenade.Layers.Pad (

818 Pad (..)

919 ) where

@@ -11,20 +21,23 @@ module Grenade.Layers.Pad (

1121import Data.Maybe

1222import Data.Proxy

1323import Data.Serialize

14−import Data.Singletons.TypeLits

24+

1525import GHC.TypeLits

26+import Data.Kind (Type)

1627

17−import Grenade.Core.Network

18−import Grenade.Core.Shape

28+import Grenade.Core

29+import Grenade.Layers.Internal.Pad

1930

2031import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

2132import Numeric.LinearAlgebra.Static (extract, create)

2233

2334-- | A padding layer for a neural network.

35+--

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

2437data Pad :: Nat

2538 -> Nat

2639 -> Nat

27− -> Nat -> * where

40+ -> Nat -> Type where

2841 Pad :: Pad padLeft padTop padRight padBottom

2942

3043instance Show (Pad padLeft padTop padRight padBottom) where

@@ -39,7 +52,7 @@ instance Serialize (Pad l t r b) where

3952 put _ = return ()

4053 get = return Pad

4154

42−-- | A two dimentional image can be padped.

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

4356instance ( KnownNat padLeft

4457 , KnownNat padTop

4558 , KnownNat padRight

@@ -51,6 +64,7 @@ instance ( KnownNat padLeft

5164 , (inputRows + padTop + padBottom) ~ outputRows

5265 , (inputColumns + padLeft + padRight) ~ outputColumns

5366 ) => Layer (Pad padLeft padTop padRight padBottom) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) where

67+ type Tape (Pad padLeft padTop padRight padBottom) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) = ()

5468 runForwards Pad (S2D input) =

5569 let padl = fromIntegral $ natVal (Proxy :: Proxy padLeft)

5670 padt = fromIntegral $ natVal (Proxy :: Proxy padTop)

@@ -58,7 +72,7 @@ instance ( KnownNat padLeft

5872 padb = fromIntegral $ natVal (Proxy :: Proxy padBottom)

5973 m = extract input

6074 r = diagBlock [konst 0 (padt,padl), m, konst 0 (padb,padr)]

61− in S2D . fromJust . create $ r

75+ in ((), S2D . fromJust . create $ r)

6276 runBackwards Pad _ (S2D dEdy) =

6377 let padl = fromIntegral $ natVal (Proxy :: Proxy padLeft)

6478 padt = fromIntegral $ natVal (Proxy :: Proxy padTop)

@@ -67,3 +81,47 @@ instance ( KnownNat padLeft

6781 m = extract dEdy

6882 vs = subMatrix (padt, padl) (nrows, ncols) m

6983 in ((), S2D . fromJust . create $ vs)

84+

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

86+instance ( KnownNat padLeft

87+ , KnownNat padTop

88+ , KnownNat padRight

89+ , KnownNat padBottom

90+ , KnownNat inputRows

91+ , KnownNat inputColumns

92+ , KnownNat outputRows

93+ , KnownNat outputColumns

94+ , KnownNat channels

95+ , KnownNat (inputRows * channels)

96+ , KnownNat (outputRows * channels)

97+ , (inputRows + padTop + padBottom) ~ outputRows

98+ , (inputColumns + padLeft + padRight) ~ outputColumns

99+ ) => Layer (Pad padLeft padTop padRight padBottom) ('D3 inputRows inputColumns channels) ('D3 outputRows outputColumns channels) where

100+ type Tape (Pad padLeft padTop padRight padBottom) ('D3 inputRows inputColumns channels) ('D3 outputRows outputColumns channels) = ()

101+ runForwards Pad (S3D input) =

102+ let padl = fromIntegral $ natVal (Proxy :: Proxy padLeft)

103+ padt = fromIntegral $ natVal (Proxy :: Proxy padTop)

104+ padr = fromIntegral $ natVal (Proxy :: Proxy padRight)

105+ padb = fromIntegral $ natVal (Proxy :: Proxy padBottom)

106+ outr = fromIntegral $ natVal (Proxy :: Proxy outputRows)

107+ outc = fromIntegral $ natVal (Proxy :: Proxy outputColumns)

108+ inr = fromIntegral $ natVal (Proxy :: Proxy inputRows)

109+ inc = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

110+ ch = fromIntegral $ natVal (Proxy :: Proxy channels)

111+ m = extract input

112+ padded = pad ch padl padt padr padb inr inc outr outc m

113+ in ((), S3D . fromJust . create $ padded)

114+

115+ runBackwards Pad () (S3D gradient) =

116+ let padl = fromIntegral $ natVal (Proxy :: Proxy padLeft)

117+ padt = fromIntegral $ natVal (Proxy :: Proxy padTop)

118+ padr = fromIntegral $ natVal (Proxy :: Proxy padRight)

119+ padb = fromIntegral $ natVal (Proxy :: Proxy padBottom)

120+ outr = fromIntegral $ natVal (Proxy :: Proxy outputRows)

121+ outc = fromIntegral $ natVal (Proxy :: Proxy outputColumns)

122+ inr = fromIntegral $ natVal (Proxy :: Proxy inputRows)

123+ inc = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

124+ ch = fromIntegral $ natVal (Proxy :: Proxy channels)

125+ m = extract gradient

126+ cropped = crop ch padl padt padr padb inr inc outr outc m

127+ in ((), S3D . fromJust . create $ cropped)

src/Grenade/Layers/Pooling.hs+19−7

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

23{-# LANGUAGE ScopedTypeVariables #-}

34{-# LANGUAGE StandaloneDeriving #-}

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

67{-# LANGUAGE TypeFamilies #-}

78{-# LANGUAGE MultiParamTypeClasses #-}

89{-# 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+-}

918module Grenade.Layers.Pooling (

1019 Pooling (..)

1120 ) where

@@ -13,30 +22,31 @@ module Grenade.Layers.Pooling (

1322import Data.Maybe

1423import Data.Proxy

1524import Data.Serialize

16−import Data.Singletons.TypeLits

25+

1726import GHC.TypeLits

27+import Data.Kind (Type)

1828

19−import Grenade.Core.Network

20−import Grenade.Core.Shape

29+import Grenade.Core

2130import Grenade.Layers.Internal.Pooling

2231

2332import Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRows)

2433

2534-- | A pooling layer for a neural network.

35+--

2636-- Does a max pooling, looking over a kernel similarly to the convolution network, but returning

2737-- maxarg only. This layer is often used to provide minor amounts of translational invariance.

2838--

2939-- The kernel size dictates which input and output sizes will "fit". Fitting the equation:

3040-- `out = (in - kernel) / stride + 1` for both dimensions.

3141--

32−data Pooling :: Nat -> Nat -> Nat -> Nat -> * where

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

3343 Pooling :: Pooling kernelRows kernelColumns strideRows strideColumns

3444

3545instance Show (Pooling k k' s s') where

3646 show Pooling = "Pooling"

3747

3848instance UpdateLayer (Pooling kernelRows kernelColumns strideRows strideColumns) where

39− type Gradient (Pooling kr kc sr sc) = ()

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

4050 runUpdate _ Pooling _ = Pooling

4151 createRandom = return Pooling

4252

@@ -56,6 +66,7 @@ instance ( KnownNat kernelRows

5666 , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

5767 , ((outputColumns - 1) * strideColumns) ~ (inputColumns - kernelColumns)

5868 ) => Layer (Pooling kernelRows kernelColumns strideRows strideColumns) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) where

69+ type Tape (Pooling kernelRows kernelColumns strideRows strideColumns) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) = S ('D2 inputRows inputColumns)

5970 runForwards Pooling (S2D input) =

6071 let height = fromIntegral $ natVal (Proxy :: Proxy inputRows)

6172 width = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

@@ -66,7 +77,7 @@ instance ( KnownNat kernelRows

6677 ex = extract input

6778 r = poolForward 1 height width kx ky sx sy ex

6879 rs = fromJust . create $ r

69− in S2D $ rs

80+ in (S2D input, S2D rs)

7081 runBackwards Pooling (S2D input) (S2D dEdy) =

7182 let height = fromIntegral $ natVal (Proxy :: Proxy inputRows)

7283 width = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

@@ -94,6 +105,7 @@ instance ( KnownNat kernelRows

94105 , ((outputRows - 1) * strideRows) ~ (inputRows - kernelRows)

95106 , ((outputColumns - 1) * strideColumns) ~ (inputColumns - kernelColumns)

96107 ) => Layer (Pooling kernelRows kernelColumns strideRows strideColumns) ('D3 inputRows inputColumns channels) ('D3 outputRows outputColumns channels) where

108+ type Tape (Pooling kernelRows kernelColumns strideRows strideColumns) ('D3 inputRows inputColumns channels) ('D3 outputRows outputColumns channels) = S ('D3 inputRows inputColumns channels)

97109 runForwards Pooling (S3D input) =

98110 let ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

99111 iy = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

@@ -105,7 +117,7 @@ instance ( KnownNat kernelRows

105117 ex = extract input

106118 r = poolForward ch ix iy kx ky sx sy ex

107119 rs = fromJust . create $ r

108− in S3D rs

120+ in (S3D input, S3D rs)

109121 runBackwards Pooling (S3D input) (S3D dEdy) =

110122 let ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

111123 iy = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

src/Grenade/Layers/Relu.hs+18−5

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

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+-}

512module Grenade.Layers.Relu (

613 Relu (..)

714 ) where

@@ -9,8 +16,7 @@ module Grenade.Layers.Relu (

916import Data.Serialize

1017

1118import GHC.TypeLits

12−import Grenade.Core.Network

13−import Grenade.Core.Shape

19+import Grenade.Core

1420

1521import qualified Numeric.LinearAlgebra.Static as LAS

1622

@@ -30,7 +36,9 @@ instance Serialize Relu where

3036 get = return Relu

3137

3238instance ( KnownNat i) => Layer Relu ('D1 i) ('D1 i) where

33− runForwards _ (S1D y) = S1D (relu y)

39+ type Tape Relu ('D1 i) ('D1 i) = S ('D1 i)

40+

41+ runForwards _ (S1D y) = (S1D y, S1D (relu y))

3442 where

3543 relu = LAS.dvmap (\a -> if a <= 0 then 0 else a)

3644 runBackwards _ (S1D y) (S1D dEdy) = ((), S1D (relu' y * dEdy))

@@ -38,7 +46,9 @@ instance ( KnownNat i) => Layer Relu ('D1 i) ('D1 i) where

3846 relu' = LAS.dvmap (\a -> if a <= 0 then 0 else 1)

3947

4048instance (KnownNat i, KnownNat j) => Layer Relu ('D2 i j) ('D2 i j) where

41− runForwards _ (S2D y) = S2D (relu y)

49+ type Tape Relu ('D2 i j) ('D2 i j) = S ('D2 i j)

50+

51+ runForwards _ (S2D y) = (S2D y, S2D (relu y))

4252 where

4353 relu = LAS.dmmap (\a -> if a <= 0 then 0 else a)

4454 runBackwards _ (S2D y) (S2D dEdy) = ((), S2D (relu' y * dEdy))

@@ -46,7 +56,10 @@ instance (KnownNat i, KnownNat j) => Layer Relu ('D2 i j) ('D2 i j) where

4656 relu' = LAS.dmmap (\a -> if a <= 0 then 0 else 1)

4757

4858instance (KnownNat i, KnownNat j, KnownNat k) => Layer Relu ('D3 i j k) ('D3 i j k) where

49− runForwards _ (S3D y) = S3D (relu y)

59+

60+ type Tape Relu ('D3 i j k) ('D3 i j k) = S ('D3 i j k)

61+

62+ runForwards _ (S3D y) = (S3D y, S3D (relu y))

5063 where

5164 relu = LAS.dmmap (\a -> if a <= 0 then 0 else a)

5265 runBackwards _ (S3D y) (S3D dEdy) = ((), S3D (relu' y * dEdy))

src/Grenade/Layers/Reshape.hsnew file+80

@@ -0,0 +1,80 @@

1+{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE TypeOperators #-}

3+{-# LANGUAGE TypeFamilies #-}

4+{-# LANGUAGE MultiParamTypeClasses #-}

5+{-# 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+-}

14+module Grenade.Layers.Reshape (

15+ Reshape (..)

16+ ) where

17+

18+import Data.Serialize

19+

20+import GHC.TypeLits

21+

22+import Numeric.LinearAlgebra.Static

23+import Numeric.LinearAlgebra.Data as LA ( flatten )

24+

25+import Grenade.Core

26+

27+-- | Reshape Layer

28+--

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.

32+--

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

34+-- or vice versa.

35+data Reshape = Reshape

36+ deriving Show

37+

38+instance UpdateLayer Reshape where

39+ type Gradient Reshape = ()

40+ runUpdate _ _ _ = Reshape

41+ createRandom = return Reshape

42+

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

44+ type Tape Reshape ('D2 x y) ('D1 a) = ()

45+ runForwards _ (S2D y) = ((), fromJust' . fromStorable . flatten . extract $ y)

46+ runBackwards _ _ (S1D y) = ((), fromJust' . fromStorable . extract $ y)

47+

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

49+ type Tape Reshape ('D3 x y z) ('D1 a) = ()

50+ runForwards _ (S3D y) = ((), fromJust' . fromStorable . flatten . extract $ y)

51+ runBackwards _ _ (S1D y) = ((), fromJust' . fromStorable . extract $ y)

52+

53+instance (KnownNat y, KnownNat x, KnownNat z, z ~ 1) => Layer Reshape ('D3 x y z) ('D2 x y) where

54+ type Tape Reshape ('D3 x y z) ('D2 x y) = ()

55+ runForwards _ (S3D y) = ((), S2D y)

56+ runBackwards _ _ (S2D y) = ((), S3D y)

57+

58+instance (KnownNat y, KnownNat x, KnownNat z, z ~ 1) => Layer Reshape ('D2 x y) ('D3 x y z) where

59+ type Tape Reshape ('D2 x y) ('D3 x y z) = ()

60+ runForwards _ (S2D y) = ((), S3D y)

61+ runBackwards _ _ (S3D y) = ((), S2D y)

62+

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+

73+instance Serialize Reshape where

74+ put _ = return ()

75+ get = return Reshape

76+

77+

78+fromJust' :: Maybe x -> x

79+fromJust' (Just x) = x

80+fromJust' Nothing = error $ "Reshape error: data shape couldn't be converted."

src/Grenade/Layers/Sinusoid.hsnew file+39

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

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.hsnew file+64

@@ -0,0 +1,64 @@

1+{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE TypeOperators #-}

3+{-# LANGUAGE TypeFamilies #-}

4+{-# LANGUAGE FlexibleInstances #-}

5+{-# 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+-}

13+module Grenade.Layers.Softmax (

14+ Softmax (..)

15+ , softmax

16+ , softmax'

17+ ) where

18+

19+import Data.Serialize

20+

21+import GHC.TypeLits

22+import Grenade.Core

23+

24+import Numeric.LinearAlgebra.Static as LAS

25+

26+-- | A Softmax layer

27+--

28+-- This layer is like a logit layer, but normalises

29+-- 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.

33+data Softmax = Softmax

34+ deriving Show

35+

36+instance UpdateLayer Softmax where

37+ type Gradient Softmax = ()

38+ runUpdate _ _ _ = Softmax

39+ createRandom = return Softmax

40+

41+instance ( KnownNat i ) => Layer Softmax ('D1 i) ('D1 i) where

42+ type Tape Softmax ('D1 i) ('D1 i) = S ('D1 i)

43+

44+ runForwards _ (S1D y) = (S1D y, S1D (softmax y))

45+ runBackwards _ (S1D y) (S1D dEdy) = ((), S1D (softmax' y dEdy))

46+

47+instance Serialize Softmax where

48+ put _ = return ()

49+ get = return Softmax

50+

51+softmax :: KnownNat i => LAS.R i -> LAS.R i

52+softmax xs =

53+ let xs' = LAS.dvmap exp xs

54+ s = LAS.dot xs' 1

55+ in LAS.dvmap (/ s) xs'

56+

57+softmax' :: KnownNat i => LAS.R i -> LAS.R i -> LAS.R i

58+softmax' x grad =

59+ let yTy = outer sm sm

60+ d = diag sm

61+ g = d - yTy

62+ in g #> grad

63+ where

64+ sm = softmax x

src/Grenade/Layers/Tanh.hs+17−17

@@ -1,19 +1,26 @@

11{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE TypeOperators #-}

3−{-# LANGUAGE TypeFamilies #-}

2+{-# LANGUAGE FlexibleInstances #-}

43{-# 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+-}

513module Grenade.Layers.Tanh (

614 Tanh (..)

715 ) where

816

917import Data.Serialize

18+import Data.Singletons

1019

11−import GHC.TypeLits

12−import Grenade.Core.Network

13−import Grenade.Core.Shape

20+import Grenade.Core

1421

1522-- | A Tanh layer.

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

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

1724data Tanh = Tanh

1825 deriving Show

1926

@@ -26,17 +33,10 @@ instance Serialize Tanh where

2633 put _ = return ()

2734 get = return Tanh

2835

29−instance KnownNat i => Layer Tanh ('D1 i) ('D1 i) where

30− runForwards _ (S1D y) = S1D (tanh y)

31− runBackwards _ (S1D y) (S1D dEdy) = ((), S1D (tanh' y * dEdy))

32−

33−instance (KnownNat i, KnownNat j) => Layer Tanh ('D2 i j) ('D2 i j) where

34− runForwards _ (S2D y) = S2D (tanh y)

35− runBackwards _ (S2D y) (S2D dEdy) = ((), S2D (tanh' y * dEdy))

36−

37−instance (KnownNat i, KnownNat j, KnownNat k) => Layer Tanh ('D3 i j k) ('D3 i j k) where

38− runForwards _ (S3D y) = S3D (tanh y)

39− runBackwards _ (S3D y) (S3D dEdy) = ((), S3D (tanh' y * dEdy))

36+instance (a ~ b, SingI a) => Layer Tanh a b where

37+ type Tape Tanh a b = S a

38+ runForwards _ a = (a, tanh a)

39+ runBackwards _ a g = ((), tanh' a * g)

4040

4141tanh' :: (Floating a) => a -> a

4242tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

src/Grenade/Recurrent/Layers/Trivial.hs → src/Grenade/Layers/Trivial.hsrenamed+15−4

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

33{-# LANGUAGE TypeFamilies #-}

44{-# LANGUAGE MultiParamTypeClasses #-}

55{-# LANGUAGE FlexibleInstances #-}

6−module Grenade.Recurrent.Layers.Trivial (

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+-}

13+module Grenade.Layers.Trivial (

714 Trivial (..)

815 ) where

916

1017import Data.Serialize

1118

12−import Grenade.Core.Network

19+import Grenade.Core

1320

14−-- | A trivial layer.

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.

1525data Trivial = Trivial

1626 deriving Show

1727

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

2535 createRandom = return Trivial

2636

2737instance (a ~ b) => Layer Trivial a b where

28− runForwards _ = id

38+ type Tape Trivial a b = ()

39+ runForwards _ a = ((), a)

2940 runBackwards _ _ y = ((), y)

src/Grenade/Recurrent.hs+26−6

@@ -1,9 +1,29 @@

11module Grenade.Recurrent (

2− module X

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+

316 ) where

417

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

9−import Grenade.Recurrent.Layers.Trivial as X

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+-}

src/Grenade/Recurrent/Core.hsnew file+9

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

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.hsnew file+35

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

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+272−82

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

23{-# LANGUAGE GADTs #-}

34{-# LANGUAGE TypeOperators #-}

@@ -6,133 +7,322 @@

67{-# LANGUAGE FlexibleContexts #-}

78{-# LANGUAGE FlexibleInstances #-}

89{-# LANGUAGE EmptyDataDecls #-}

10+{-# LANGUAGE RankNTypes #-}

11+{-# LANGUAGE BangPatterns #-}

12+{-# LANGUAGE ScopedTypeVariables #-}

13+{-# LANGUAGE UndecidableInstances #-}

14+

915module Grenade.Recurrent.Core.Network (

1016 Recurrent

1117 , FeedForward

12− , RecurrentLayer (..)

13− , RecurrentUpdateLayer (..)

18+

1419 , RecurrentNetwork (..)

1520 , RecurrentInputs (..)

16− , CreatableRecurrent (..)

21+ , RecurrentTape (..)

22+ , RecurrentGradient (..)

23+

24+ , randomRecurrent

25+ , runRecurrent

26+ , runRecurrent'

27+ , applyRecurrentUpdate

1728 ) where

1829

1930

2031import Control.Monad.Random ( MonadRandom )

21−import Data.Singletons ( SingI )

2232import Data.Serialize

23−import qualified Data.Vector.Storable as V

2433

25−import Grenade.Core.Shape

26−import Grenade.Core.Network

27−

28−import qualified Numeric.LinearAlgebra as LA

29−import qualified Numeric.LinearAlgebra.Static as LAS

34+import Data.Kind (Type)

3035

36+import Grenade.Core

37+import Grenade.Recurrent.Core.Layer

38+import Prelude.Singletons

3139

3240-- | Witness type to say indicate we're building up with a normal feed

3341-- forward layer.

34−data FeedForward :: * -> *

42+data FeedForward :: Type -> Type

3543-- | Witness type to say indicate we're building up with a recurrent layer.

36−data Recurrent :: * -> *

37−

38−-- | Class for a recurrent layer.

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

40−-- of an extra recurrent data shape.

41−class UpdateLayer x => RecurrentUpdateLayer x where

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

43− type RecurrentShape x :: Shape

44−

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

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

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

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

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

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

51− -- the layer above.

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

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

54−

55−data RecurrentNetwork :: [*] -> [Shape] -> * where

56− OR :: (SingI i, SingI o, Layer x i o) => !x -> RecurrentNetwork '[FeedForward x] '[i, o]

57− (:~~>) :: (SingI i, Layer x i h) => !x -> !(RecurrentNetwork xs (h ': hs)) -> RecurrentNetwork (FeedForward x ': xs) (i ': h ': hs)

58− (:~@>) :: (SingI i, RecurrentLayer x i h) => !x -> !(RecurrentNetwork xs (h ': hs)) -> RecurrentNetwork (Recurrent x ': xs) (i ': h ': hs)

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

58+ RNil :: SingI i

59+ => RecurrentNetwork '[] '[i]

60+

61+ (:~~>) :: (SingI i, Layer x i h)

62+ => !x

63+ -> !(RecurrentNetwork xs (h ': hs))

64+ -> RecurrentNetwork (FeedForward x ': xs) (i ': h ': hs)

65+

66+ (:~@>) :: (SingI i, RecurrentLayer x i h)

67+ => !x

68+ -> !(RecurrentNetwork xs (h ': hs))

69+ -> RecurrentNetwork (Recurrent x ': xs) (i ': h ': hs)

5970infixr 5 :~~>

6071infixr 5 :~@>

6172

62−instance Show (RecurrentNetwork l h) where

63− show (OR a) = "OR " ++ show a

64− show (i :~~> o) = show i ++ "\n:~~>\n" ++ show o

65− show (i :~@> o) = show i ++ "\n:~@>\n" ++ show o

73+-- | Gradient of a network.

74+--

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

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

77+ RGNil :: RecurrentGradient '[]

6678

79+ (://>) :: UpdateLayer x

80+ => Gradient x

81+ -> RecurrentGradient xs

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

6783

6884-- | Recurrent inputs (sideways shapes on an imaginary unrolled graph)

6985-- Parameterised on the layers of a Network.

70−data RecurrentInputs :: [*] -> * where

71− ORS :: UpdateLayer x

72− => () -> RecurrentInputs '[FeedForward x]

73− (:~~+>) :: UpdateLayer x

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

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

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

77−infixr 5 :~~+>

78−infixr 5 :~@+>

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+

199+instance Show (RecurrentNetwork '[] '[i]) where

200+ show RNil = "NNil"

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

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

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

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

205+

79206

80207-- | A network can easily be created by hand with (:~~>) and (:~@>), but an easy way to initialise a random

81208-- recurrent network and a set of random inputs for it is with the randomRecurrent.

82−class CreatableRecurrent (xs :: [*]) (ss :: [Shape]) where

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

83210 -- | Create a network of the types requested

84− randomRecurrent :: MonadRandom m => m (RecurrentNetwork xs ss, RecurrentInputs xs)

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

85212

86−instance (SingI i, SingI o, Layer x i o) => CreatableRecurrent (FeedForward x ': '[]) (i ': o ': '[]) where

87− randomRecurrent = do

88− thisLayer <- createRandom

89− return (OR thisLayer, ORS ())

213+instance SingI i => CreatableRecurrent '[] '[i] where

214+ randomRecurrent =

215+ return RNil

90216

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

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

92218 randomRecurrent = do

93219 thisLayer <- createRandom

94− (rest, resti) <- randomRecurrent

95− return (thisLayer :~~> rest, () :~~+> resti)

220+ rest <- randomRecurrent

221+ return (thisLayer :~~> rest)

96222

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

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

98224 randomRecurrent = do

99225 thisLayer <- createRandom

100− thisShape <- randomOfShape

101− (rest, resti) <- randomRecurrent

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

226+ rest <- randomRecurrent

227+ return (thisLayer :~@> rest)

103228

104229-- | Add very simple serialisation to the recurrent network

105−instance (SingI i, SingI o, Layer x i o, Serialize x) => Serialize (RecurrentNetwork '[FeedForward x] '[i, o]) where

106− put (OR x) = put x

107− put _ = error "impossible"

108− get = OR <$> get

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

231+ put RNil = pure ()

232+ get = pure RNil

109233

110−instance (SingI i, Layer x i o, Serialize x, Serialize (RecurrentNetwork xs (o ': r ': rs))) => Serialize (RecurrentNetwork (FeedForward x ': xs) (i ': o ': r ': rs)) where

234+instance (SingI i, Layer x i o, Serialize x, Serialize (RecurrentNetwork xs (o ': rs))) => Serialize (RecurrentNetwork (FeedForward x ': xs) (i ': o ': rs)) where

111235 put (x :~~> r) = put x >> put r

112236 get = (:~~>) <$> get <*> get

113237

114−instance (SingI i, RecurrentLayer x i o, Serialize x, Serialize (RecurrentNetwork xs (o ': r ': rs))) => Serialize (RecurrentNetwork (Recurrent x ': xs) (i ': o ': r ': rs)) where

238+instance (SingI i, RecurrentLayer x i o, Serialize x, Serialize (RecurrentNetwork xs (o ': rs))) => Serialize (RecurrentNetwork (Recurrent x ': xs) (i ': o ': rs)) where

115239 put (x :~@> r) = put x >> put r

116240 get = (:~@>) <$> get <*> get

117241

118−instance (UpdateLayer x) => (Serialize (RecurrentInputs '[FeedForward x])) where

242+instance (Serialize (RecurrentInputs '[])) where

119243 put _ = return ()

120− get = return (ORS ())

244+ get = return RINil

121245

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

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

123247 put ( () :~~+> rest) = put rest

124248 get = ( () :~~+> ) <$> get

125249

126−instance (SingI (RecurrentShape x), RecurrentUpdateLayer x, Serialize (RecurrentInputs (y ': ys))) => (Serialize (RecurrentInputs (Recurrent x ': y ': ys))) where

127− put ( i :~@+> rest ) = do

128− _ <- (case i of

129− (S1D x) -> putListOf put . LA.toList . LAS.extract $ x

130− (S2D x) -> putListOf put . LA.toList . LA.flatten . LAS.extract $ x

131− (S3D x) -> putListOf put . LA.toList . LA.flatten . LAS.extract $ x

132− ) :: PutM ()

133− put rest

134−

135− get = do

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

137− rest <- get

138− return ( i :~@+> rest)

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'

src/Grenade/Recurrent/Core/Runner.hs+89−129

@@ -6,145 +6,105 @@

66{-# LANGUAGE TypeFamilies #-}

77{-# LANGUAGE FlexibleContexts #-}

88{-# LANGUAGE RankNTypes #-}

9−{-# LANGUAGE RecordWildCards #-}

9+

1010module Grenade.Recurrent.Core.Runner (

11− trainRecurrent

12− , runRecurrent

11+ runRecurrentExamples

12+ , runRecurrentBackprop

13+ , backPropagateRecurrent

14+ , trainRecurrent

15+

16+ , RecurrentGradients

1317 ) where

1418

15−import Data.Singletons.Prelude

16−import Grenade.Core.Network

17−import Grenade.Core.Shape

19+import Data.List ( foldl' )

20+import Grenade.Core

1821

1922import 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+

2055

2156-- | Drive and network and collect its back propogated gradients.

22−--

23−-- TODO: split this nicely into backpropagate and update.

24−--

25−-- QUESTION: Should we return a list of gradients or the sum of

26−-- the gradients? It's different taking into account

27−-- momentum and L2.

28−trainRecurrent :: forall shapes layers. SingI (Last shapes)

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))

2983 => LearningParameters

3084 -> RecurrentNetwork layers shapes

3185 -> RecurrentInputs layers

3286 -> [(S (Head shapes), Maybe (S (Last shapes)))]

3387 -> (RecurrentNetwork layers shapes, RecurrentInputs layers)

3488trainRecurrent rate network recinputs examples =

35− updateBack $ go inputs network recinputs

36− where

37− inputs = fst <$> examples

38− targets = snd <$> examples

39− updateBack (a,recgrad,_) = (a,updateRecInputs rate recinputs recgrad)

40−

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

42− => [S (Head js)] -- ^ input vector

43− -> RecurrentNetwork sublayers js -- ^ network to train

44− -> RecurrentInputs sublayers

45− -> (RecurrentNetwork sublayers js, RecurrentInputs sublayers, [S (Head js)])

46−

47− -- This is a simple non-recurrent layer, just map it forwards

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

49− -- (and probably will in future).

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

51− = let ys = runForwards layer <$> xs

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

53− (newFN, ig, grads) = go ys n nIn

54− -- calculate the gradient for this layer to pass down,

55− back = uncurry (runBackwards layer) <$> zip (reverse xs) grads

56− -- the new trained layer.

57− newlayer = runUpdates rate layer (fst <$> back)

58−

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

60−

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

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

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

64− = let ys = scanlFrom layer g xs

65−

66− (newFN, ig, grads) = go (snd <$> ys) n nIn

67−

68− backExamples = zip3 (fst <$> reverse ys) (reverse xs) grads

69−

70− (rg, back) = myscanbackward layer backExamples

71− -- the new trained layer.

72− newlayer = runUpdates rate layer (fst <$> back)

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

74−

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

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

77− go !xs (OR layer) (ORS ())

78− = let ys = runForwards layer <$> xs

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

80− back = uncurry (runBackwards layer) <$> zip xs (zipWith makeError ys targets)

81− -- the new trained layer.

82− newlayer = runUpdates rate layer (reverse $ fst <$> back)

83− in (OR newlayer, ORS (), reverse (snd <$> back))

84−

85− go _ _ _ =

86− error "Impossible for network and recurrent inputs to have different shapes"

87−

88−

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 _ (ORS ()) (ORS ())

106− = ORS ()

107− updateRecInputs _ _ _

108− = error "Impossible for updateRecInputs to have different shapes"

109−

110−scanlFrom :: forall x i o. RecurrentLayer x i o

111− => x -- ^ the layer

112− -> S (RecurrentShape x) -- ^ place to start

113− -> [S i] -- ^ list of inputs to scan through

114− -> [(S (RecurrentShape x), S o)] -- ^ list of scan inputs and outputs

115−scanlFrom !layer !recShape (x:xs) =

116− let (lerec, lepush) = runRecurrentForwards layer recShape x

117− in (recShape, lepush) : scanlFrom layer lerec xs

118−scanlFrom _ _ [] = []

119−

120−myscanbackward :: forall x i o. RecurrentLayer x i o

121− => x -- ^ the layer

122− -> [(S (RecurrentShape x), S i, S o)] -- ^ the list of inputs and output to scan over

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

124−myscanbackward layer =

125− goX 0

126− where

127− goX :: S (RecurrentShape x) -> [(S (RecurrentShape x), S i, S o)] -> (S (RecurrentShape x), [(Gradient x, S i)])

128− goX !lastback ((recShape, lastin, backgrad):xs) =

129− let (layergrad, recgrad, ingrad) = runRecurrentBackwards layer recShape lastin lastback backgrad

130− (pushedback, ll) = goX recgrad xs

131− in (pushedback, (layergrad, ingrad) : ll)

132− goX !lastback [] = (lastback, [])

133−

134−-- | Just forwards propagation with no training.

135−runRecurrent :: RecurrentNetwork layers shapes

136− -> RecurrentInputs layers -> S (Head shapes)

137− -> (RecurrentInputs layers, S (Last shapes))

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

139− = let ys = runForwards layer x

140− (nr', o) = runRecurrent n nr ys

141− in (() :~~+> nr', o)

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

143− = let (recin', y) = runRecurrentForwards layer recin x

144− (nr', o) = runRecurrent n nr y

145− in (recin' :~@+> nr', o)

146−runRecurrent (OR layer) (ORS ()) !x

147− = (ORS (), runForwards layer x)

148−

149−runRecurrent _ _ _

150− = error "Impossible for the gradients of a network to have a different length or shape to the network"

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

src/Grenade/Recurrent/Layers.hsnew file+9

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

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+16−16

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE DataKinds #-}

23{-# 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 #-}

1210module Grenade.Recurrent.Layers.BasicRecurrent (

1311 BasicRecurrent (..)

1412 , randomBasicRecurrent

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

1816

1917import Control.Monad.Random ( MonadRandom, getRandom )

2018

21−import Data.Singletons.TypeLits

19+

20+import Data.Kind (Type)

2221

2322import Numeric.LinearAlgebra.Static

2423

2524import GHC.TypeLits

2625

27−import Grenade.Core.Network

28−import Grenade.Core.Shape

29−import Grenade.Recurrent.Core.Network

26+import Grenade.Core

27+import Grenade.Recurrent.Core

3028

3129data BasicRecurrent :: Nat -- Input layer size

3230 -> Nat -- Output layer size

33− -> * where

31+ -> Type where

3432 BasicRecurrent :: ( KnownNat input

3533 , KnownNat output

3634 , KnownNat matrixCols

@@ -43,7 +41,7 @@ data BasicRecurrent :: Nat -- Input layer size

4341

4442data BasicRecurrent' :: Nat -- Input layer size

4543 -> Nat -- Output layer size

46− -> * where

44+ -> Type where

4745 BasicRecurrent' :: ( KnownNat input

4846 , KnownNat output

4947 , KnownNat matrixCols

@@ -58,27 +56,29 @@ instance Show (BasicRecurrent i o) where

5856instance (KnownNat i, KnownNat o, KnownNat (i + o)) => UpdateLayer (BasicRecurrent i o) where

5957 type Gradient (BasicRecurrent i o) = (BasicRecurrent' i o)

6058

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

62− let newBiasMomentum = konst learningMomentum * oldBiasMomentum - konst learningRate * biasGradient

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

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

6361 newBias = oldBias + newBiasMomentum

64− newMomentum = konst learningMomentum * oldMomentum - konst learningRate * activationGradient

65− regulariser = konst (learningRegulariser * learningRate) * oldActivations

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

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

6664 newActivations = oldActivations + newMomentum - regulariser

6765 in BasicRecurrent newBias newBiasMomentum newActivations newMomentum

6866

6967 createRandom = randomBasicRecurrent

7068

7169instance (KnownNat i, KnownNat o, KnownNat (i + o), i <= (i + o), o ~ ((i + o) - i)) => RecurrentUpdateLayer (BasicRecurrent i o) where

72− type RecurrentShape (BasicRecurrent i o) = 'D1 o

70+ type RecurrentShape (BasicRecurrent i o) = S ('D1 o)

7371

7472instance (KnownNat i, KnownNat o, KnownNat (i + o), i <= (i + o), o ~ ((i + o) - i)) => RecurrentLayer (BasicRecurrent i o) ('D1 i) ('D1 o) where

73+

74+ type RecTape (BasicRecurrent i o) ('D1 i) ('D1 o) = (S ('D1 o), S ('D1 i))

7575 -- Do a matrix vector multiplication and return the result.

7676 runRecurrentForwards (BasicRecurrent wB _ wN _) (S1D lastOutput) (S1D thisInput) =

7777 let thisOutput = S1D $ wB + wN #> (thisInput # lastOutput)

78− in (thisOutput, thisOutput)

78+ in ((S1D lastOutput, S1D thisInput), thisOutput, thisOutput)

7979

8080 -- Run a backpropogation step for a full connected layer.

81− runRecurrentBackwards (BasicRecurrent _ _ wN _) (S1D lastOutput) (S1D thisInput) (S1D dRec) (S1D dEdy) =

81+ runRecurrentBackwards (BasicRecurrent _ _ wN _) (S1D lastOutput, S1D thisInput) (S1D dRec) (S1D dEdy) =

8282 let biasGradient = (dRec + dEdy)

8383 layerGrad = (dRec + dEdy) `outer` (thisInput # lastOutput)

8484 -- calcluate derivatives for next step

src/Grenade/Recurrent/Layers/ConcatRecurrent.hsnew file+180

@@ -0,0 +1,180 @@

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+60−86

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE BangPatterns #-}

23{-# LANGUAGE DataKinds #-}

34{-# LANGUAGE GADTs #-}

45{-# LANGUAGE RankNTypes #-}

5−{-# LANGUAGE RecordWildCards #-}

66{-# LANGUAGE TypeOperators #-}

77{-# LANGUAGE TypeFamilies #-}

88{-# LANGUAGE MultiParamTypeClasses #-}

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

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 #-}

1513module Grenade.Recurrent.Layers.LSTM (

1614 LSTM (..)

1715 , LSTMWeights (..)

@@ -20,35 +18,33 @@ module Grenade.Recurrent.Layers.LSTM (

2018

2119import Control.Monad.Random ( MonadRandom, getRandom )

2220

23−

2421-- import Data.List ( foldl1' )

2522import Data.Proxy

2623import Data.Serialize

27−import Data.Singletons.TypeLits

24+import Data.Kind (Type)

2825

2926import qualified Numeric.LinearAlgebra as LA

3027import Numeric.LinearAlgebra.Static

3128

32−import Grenade.Core.Network

33−import Grenade.Core.Shape

34−

29+import Grenade.Core

30+import Grenade.Recurrent.Core

3531import Grenade.Layers.Internal.Update

32+import GHC.TypeLits

3633

37−import Grenade.Recurrent.Core.Network

3834

3935-- | Long Short Term Memory Recurrent unit

4036--

4137-- This is a Peephole formulation, so the recurrent shape is

4238-- just the cell state, the previous output is not held or used

4339-- at all.

44−data LSTM :: Nat -> Nat -> * where

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

4541 LSTM :: ( KnownNat input

4642 , KnownNat output

4743 ) => !(LSTMWeights input output) -- Weights

4844 -> !(LSTMWeights input output) -- Momentums

4945 -> LSTM input output

5046

51−data LSTMWeights :: Nat -> Nat -> * where

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

5248 LSTMWeights :: ( KnownNat input

5349 , KnownNat output

5450 ) => {

@@ -75,7 +71,7 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

7571

7672 -- Run the update function for each group matrix/vector of weights, momentums and gradients.

7773 -- Hmm, maybe the function should be used instead of passing in the learning parameters.

78− runUpdate LearningParameters {..} (LSTM w m) g =

74+ runUpdate lp (LSTM w m) g =

7975 let (wf, wf') = u lstmWf w m g

8076 (uf, uf') = u lstmUf w m g

8177 (bf, bf') = v lstmBf w m g

@@ -92,11 +88,11 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

9288 -- Utility function for updating with the momentum, gradients, and weights.

9389 u :: forall x ix out. (KnownNat ix, KnownNat out) => (x -> (L out ix)) -> x -> x -> x -> ((L out ix), (L out ix))

9490 u e (e -> weights) (e -> momentum) (e -> gradient) =

95− decendMatrix learningRate learningMomentum learningRegulariser weights gradient momentum

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

9692

9793 v :: forall x ix. (KnownNat ix) => (x -> (R ix)) -> x -> x -> x -> ((R ix), (R ix))

9894 v e (e -> weights) (e -> momentum) (e -> gradient) =

99− decendVector learningRate learningMomentum learningRegulariser weights gradient momentum

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

10096

10197 -- There's a lot of updates here, so to try and minimise the number of data copies

10298 -- we'll create a mutable bucket for each.

@@ -128,25 +124,33 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

128124instance (KnownNat i, KnownNat o) => RecurrentUpdateLayer (LSTM i o) where

129125 -- The recurrent shape is the same size as the output.

130126 -- It's actually the cell state however, as this is a peephole variety LSTM.

131− type RecurrentShape (LSTM i o) = 'D1 o

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

132128

133129instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) where

130+

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)

134134 -- Forward propagation for the LSTM layer.

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

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

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

137137 let -- Forget state vector

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

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

139+ f_t = sigmoid f_s

139140 -- Input state vector

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

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

142+ i_t = sigmoid i_s

141143 -- Output state vector

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

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

145+ o_t = sigmoid o_s

143146 -- Cell input state vector

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

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

148+ c_x = tanh c_s

145149 -- Cell state

146150 c_t = f_t * cell + i_t * c_x

147151 -- Output (it's sometimes recommended to use tanh c_t)

148152 h_t = o_t * c_t

149− in (S1D c_t, S1D h_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)

150154

151155 -- Run a backpropogation step for an LSTM layer.

152156 -- We're doing all the derivatives by hand here, so one should

@@ -154,29 +158,8 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

154158 --

155159 -- There's a test version using the AD library without hmatrix in the test

156160 -- suite. These should match always.

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

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

180163 c_t' = h_t' * o_t + cellGrad

181164

182165 f_t' = c_t' * cell

@@ -192,8 +175,8 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

192175 c_s' = tanh' c_s * c_x'

193176

194177 -- The derivatives to pass sideways (recurrent) and downwards

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'

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'

197180

198181 -- Calculate the gradient Matricies for the input

199182 lstmWf' = f_s' `outer` input

@@ -238,7 +221,8 @@ randomLSTM = do

238221

239222-- | Maths

240223--

241−-- TODO: move to not here

224+-- TODO: Move to not here

225+-- Optimise backwards derivative

242226sigmoid :: Floating a => a -> a

243227sigmoid x = 1 / (1 + exp (-x))

244228

@@ -251,44 +235,34 @@ tanh' :: (Floating a) => a -> a

251235tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

252236

253237instance (KnownNat i, KnownNat o) => Serialize (LSTM i o) where

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

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

271255

272256 get = do

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

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

src/Grenade/Utils/OneHot.hs+6−5

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

2222import qualified Data.Map as M

2323

2424import Data.Proxy

25−import Data.Singletons.TypeLits

2625

2726import Data.Vector ( Vector )

2827import qualified Data.Vector as V

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

3130import Numeric.LinearAlgebra ( maxIndex )

3231import Numeric.LinearAlgebra.Devel

3332import Numeric.LinearAlgebra.Static

33+import GHC.TypeLits

3434

3535import Grenade.Core.Shape

3636

@@ -52,15 +52,16 @@ 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] -> Maybe (Map a Int, Vector a)

55+hotMap :: (Ord a, KnownNat n) => Proxy n -> [a] -> Either String (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− Just (M.fromList $ zip uniq [0..], V.fromList uniq)

63− else Nothing

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")

6465

6566-- | From a map and value, create a 1D Shape

6667-- with one index hot (1) with the rest 0.

stack.yamlnew file+78

@@ -0,0 +1,78 @@

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.locknew file+19

@@ -0,0 +1,19 @@

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+32−28

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE TemplateHaskell #-}

23{-# LANGUAGE DataKinds #-}

34{-# LANGUAGE GADTs #-}

@@ -15,16 +16,21 @@ import Data.Singletons ()

1516import GHC.TypeLits

1617import GHC.TypeLits.Witnesses

1718

18−import Grenade.Core.Shape

19−import Grenade.Core.Network

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

20+import Data.Kind (Type)

21+#endif

22+

23+import Grenade.Core

2024import Grenade.Layers.Convolution

2125

22−import Disorder.Jack

26+import Hedgehog

27+import qualified Hedgehog.Gen as Gen

2328

24−import Test.Jack.Hmatrix

25−import Test.Jack.TypeLits

29+import Test.Hedgehog.Hmatrix

30+import Test.Hedgehog.TypeLits

31+import Test.Hedgehog.Compat

2632

27−data OpaqueConvolution :: * where

33+data OpaqueConvolution :: Type where

2834 OpaqueConvolution :: Convolution channels filters kernelRows kernelColumns strideRows strideColumns -> OpaqueConvolution

2935

3036instance Show OpaqueConvolution where

@@ -38,10 +44,10 @@ genConvolution :: ( KnownNat channels

3844 , KnownNat strideColumns

3945 , KnownNat kernelFlattened

4046 , kernelFlattened ~ (kernelRows * kernelColumns * channels)

41− ) => Jack (Convolution channels filters kernelRows kernelColumns strideRows strideColumns)

47+ ) => Gen (Convolution channels filters kernelRows kernelColumns strideRows strideColumns)

4248genConvolution = Convolution <$> uniformSample <*> uniformSample

4349

44−genOpaqueOpaqueConvolution :: Jack OpaqueConvolution

50+genOpaqueOpaqueConvolution :: Gen OpaqueConvolution

4551genOpaqueOpaqueConvolution = do

4652 channels <- genNat

4753 filters <- genNat

@@ -57,17 +63,17 @@ genOpaqueOpaqueConvolution = do

5763 p2 = natDict pkc

5864 p3 = natDict pch

5965 in case p1 %* p2 %* p3 of

60− Dict -> OpaqueConvolution <$> (genConvolution :: Jack (Convolution ch fl kr kc sr sc))

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

6172

62−prop_conv_net_witness =

63− gamble genOpaqueOpaqueConvolution $ \onet ->

64− (case onet of

65− (OpaqueConvolution ((Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) -> True

66− ) :: Bool

6773

68−prop_conv_net =

69− gamble genOpaqueOpaqueConvolution $ \onet ->

70− (case onet of

74+prop_conv_net = property $

75+ blindForAll genOpaqueOpaqueConvolution >>= \onet ->

76+ case onet of

7177 (OpaqueConvolution (convLayer@(Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) ->

7278 let ok stride kernel = [extent | extent <- [(kernel + 1) .. 30 ], (extent - kernel) `mod` stride == 0]

7379 kr = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

@@ -75,15 +81,15 @@ prop_conv_net =

7581 sr = fromIntegral $ natVal (Proxy :: Proxy strideRows)

7682 sc = fromIntegral $ natVal (Proxy :: Proxy strideCols)

7783

78− in gamble (elements (ok sr kr)) $ \er ->

79− gamble (elements (ok sc kc)) $ \ec ->

84+ in forAll (Gen.element (ok sr kr)) >>= \er ->

85+ forAll (Gen.element (ok sc kc)) >>= \ec ->

8086 let rr = ((er - kr) `div` sr) + 1

8187 rc = ((ec - kc) `div` sc) + 1

8288 Just er' = someNatVal er

8389 Just ec' = someNatVal ec

8490 Just rr' = someNatVal rr

8591 Just rc' = someNatVal rc

86− in (case (er', ec', rr', rc') of

92+ in case (er', ec', rr', rc') of

8793 ( SomeNat (pinr :: Proxy inRows), SomeNat (_ :: Proxy inCols), SomeNat (pour :: Proxy outRows), SomeNat (_ :: Proxy outCols)) ->

8894 case ( natDict pinr %* natDict (Proxy :: Proxy channels)

8995 , natDict pour %* natDict (Proxy :: Proxy filters)

@@ -91,14 +97,12 @@ prop_conv_net =

9197 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

9298 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

9399 (Dict, Dict, Dict, Dict) ->

94− gamble (S3D <$> uniformSample) $ \(input :: S ('D3 inRows inCols channels)) ->

95− let output :: S ('D3 outRows outCols filters) = runForwards convLayer input

100+ blindForAll (S3D <$> uniformSample) >>= \(input :: S ('D3 inRows inCols channels)) ->

101+ let (tape, output :: S ('D3 outRows outCols filters)) = runForwards convLayer input

96102 backed :: (Gradient (Convolution channels filters kernelRows kernelCols strideRows strideCols), S ('D3 inRows inCols channels))

97− = runBackwards convLayer input output

98− in backed `seq` True

99− ) :: Property

100− ) :: Property

103+ = runBackwards convLayer tape output

104+ in backed `seq` success

105+

101106

102−return []

103107tests :: IO Bool

104−tests = $quickCheckAll

108+tests = checkParallel $$(discover)

test/Test/Grenade/Layers/FullyConnected.hs+23−20

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE BangPatterns #-}

23{-# LANGUAGE TemplateHaskell #-}

34{-# LANGUAGE DataKinds #-}

@@ -12,25 +13,28 @@ import Data.Singletons ()

1213

1314import GHC.TypeLits

1415

15−import Grenade.Core.Shape

16−import Grenade.Core.Network

17−import Grenade.Layers.FullyConnected

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

17+import Data.Kind (Type)

18+#endif

1819

19−import Disorder.Jack

20+import Grenade.Core

21+import Grenade.Layers.FullyConnected

2022

21−import Test.Jack.Hmatrix

23+import Hedgehog

2224

25+import Test.Hedgehog.Compat

26+import Test.Hedgehog.Hmatrix

2327

24−data OpaqueFullyConnected :: * where

28+data OpaqueFullyConnected :: Type where

2529 OpaqueFullyConnected :: (KnownNat i, KnownNat o) => FullyConnected i o -> OpaqueFullyConnected

2630

2731instance Show OpaqueFullyConnected where

2832 show (OpaqueFullyConnected n) = show n

2933

30−genOpaqueFullyConnected :: Jack OpaqueFullyConnected

34+genOpaqueFullyConnected :: Gen OpaqueFullyConnected

3135genOpaqueFullyConnected = do

32− input :: Integer <- choose (2, 100)

33− output :: Integer <- choose (1, 100)

36+ input :: Integer <- choose 2 100

37+ output :: Integer <- choose 1 100

3438 let Just input' = someNatVal input

3539 let Just output' = someNatVal output

3640 case (input', output') of

@@ -39,17 +43,16 @@ genOpaqueFullyConnected = do

3943 bM <- randomVector

4044 wN <- uniformSample

4145 kM <- uniformSample

42− return . OpaqueFullyConnected $ (FullyConnected wB bM wN kM :: FullyConnected i' o')

46+ return . OpaqueFullyConnected $ (FullyConnected (FullyConnected' wB wN) (FullyConnected' bM kM) :: FullyConnected i' o')

4347

4448prop_fully_connected_forwards :: Property

45−prop_fully_connected_forwards =

46− gamble genOpaqueFullyConnected $ \(OpaqueFullyConnected (fclayer :: FullyConnected i o)) ->

47− gamble (S1D <$> randomVector) $ \(input :: S ('D1 i)) ->

48− let output :: S ('D1 o) = runForwards fclayer input

49− backed :: (Gradient (FullyConnected i o), S ('D1 i))

50− = runBackwards fclayer input output

51− in backed `seq` True

52−

53−return []

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+

5457tests :: IO Bool

55−tests = $quickCheckAll

58+tests = checkParallel $$(discover)

test/Test/Grenade/Layers/Internal/Convolution.hs+34−29

@@ -12,41 +12,46 @@ import Grenade.Layers.Internal.Convolution

1212

1313import Numeric.LinearAlgebra hiding (uniformSample, konst, (===))

1414

15−import Disorder.Jack

15+import Hedgehog

16+import qualified Hedgehog.Gen as Gen

17+import qualified Hedgehog.Range as Range

1618

1719import qualified Test.Grenade.Layers.Internal.Reference as Reference

20+import Test.Hedgehog.Compat

1821

1922prop_im2col_col2im_symmetrical_with_kernel_stride =

2023 let factors n = [x | x <- [1..n], n `mod` x == 0]

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

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

3135

3236prop_im2col_col2im_behaves_as_reference =

3337 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

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 []

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+

5156tests :: IO Bool

52−tests = $quickCheckAll

57+tests = checkParallel $$(discover)

test/Test/Grenade/Layers/Internal/Pooling.hs+24−18

@@ -12,28 +12,34 @@ import Grenade.Layers.Internal.Pooling

1212

1313import Numeric.LinearAlgebra hiding (uniformSample, konst, (===))

1414

15−import Disorder.Jack

15+import Hedgehog

16+import qualified Hedgehog.Gen as Gen

17+import qualified Hedgehog.Range as Range

1618

1719import qualified Test.Grenade.Layers.Internal.Reference as Reference

20+import Test.Hedgehog.Compat

1821

1922prop_poolForwards_poolBackwards_behaves_as_reference =

2023 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

2124 output extent kernel stride = (extent - kernel) `div` stride + 1

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 []

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+

3844tests :: IO Bool

39−tests = $quickCheckAll

45+tests = checkParallel $$(discover)

test/Test/Grenade/Layers/Nonlinear.hsnew file+54

@@ -0,0 +1,54 @@

1+{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE TemplateHaskell #-}

3+{-# LANGUAGE DataKinds #-}

4+{-# LANGUAGE KindSignatures #-}

5+{-# LANGUAGE GADTs #-}

6+{-# LANGUAGE ScopedTypeVariables #-}

7+{-# LANGUAGE LambdaCase #-}

8+{-# OPTIONS_GHC -fno-warn-missing-signatures #-}

9+module Test.Grenade.Layers.Nonlinear where

10+

11+import Data.Singletons

12+

13+import Grenade

14+

15+import Hedgehog

16+

17+import Test.Hedgehog.Compat

18+import Test.Hedgehog.Hmatrix

19+import Test.Hedgehog.TypeLits

20+

21+import Numeric.LinearAlgebra.Static ( norm_Inf )

22+

23+prop_sigmoid_grad :: Property

24+prop_sigmoid_grad = property $

25+ forAllWith rss genShape >>= \case

26+ (SomeSing (r :: Sing s)) ->

27+ withSingI r $

28+ blindForAll genOfShape >>= \(ds :: S s) ->

29+ let (tape, f :: S s) = runForwards Logit ds

30+ ((), ret :: S s) = runBackwards Logit tape (1 :: S s)

31+ (_, numer :: S s) = runForwards Logit (ds + 0.0001)

32+ numericalGradient = (numer - f) * 10000

33+ in assert ((case numericalGradient - ret of

34+ (S1D x) -> norm_Inf x < 0.0001

35+ (S2D x) -> norm_Inf x < 0.0001

36+ (S3D x) -> norm_Inf x < 0.0001) :: Bool)

37+

38+prop_tanh_grad :: Property

39+prop_tanh_grad = property $

40+ forAllWith rss genShape >>= \case

41+ (SomeSing (r :: Sing s)) ->

42+ withSingI r $

43+ blindForAll genOfShape >>= \(ds :: S s) ->

44+ let (tape, f :: S s) = runForwards Tanh ds

45+ ((), ret :: S s) = runBackwards Tanh tape (1 :: S s)

46+ (_, numer :: S s) = runForwards Tanh (ds + 0.0001)

47+ numericalGradient = (numer - f) * 10000

48+ in assert ((case numericalGradient - ret of

49+ (S1D x) -> norm_Inf x < 0.001

50+ (S2D x) -> norm_Inf x < 0.001

51+ (S3D x) -> norm_Inf x < 0.001) :: Bool)

52+

53+tests :: IO Bool

54+tests = checkParallel $$(discover)

test/Test/Grenade/Layers/PadCrop.hsnew file+49

@@ -0,0 +1,49 @@

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+19−14

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

1+{-# LANGUAGE CPP #-}

12{-# LANGUAGE TemplateHaskell #-}

23{-# LANGUAGE DataKinds #-}

34{-# LANGUAGE KindSignatures #-}

@@ -9,36 +10,40 @@ module Test.Grenade.Layers.Pooling where

910import Data.Proxy

1011import Data.Singletons ()

1112

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

14+import Data.Kind (Type)

15+#endif

16+

1217import GHC.TypeLits

1318import Grenade.Layers.Pooling

1419

15−import Disorder.Jack

20+import Hedgehog

1621

22+import Test.Hedgehog.Compat

1723

18−data OpaquePooling :: * where

24+data OpaquePooling :: Type where

1925 OpaquePooling :: (KnownNat kh, KnownNat kw, KnownNat sh, KnownNat sw) => Pooling kh kw sh sw -> OpaquePooling

2026

2127instance Show OpaquePooling where

2228 show (OpaquePooling n) = show n

2329

24−genOpaquePooling :: Jack OpaquePooling

30+genOpaquePooling :: Gen OpaquePooling

2531genOpaquePooling = do

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)

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

3036

3137 case (kernelHeight, kernelWidth, strideHeight, strideWidth) of

3238 (SomeNat (_ :: Proxy kh), SomeNat (_ :: Proxy kw), SomeNat (_ :: Proxy sh), SomeNat (_ :: Proxy sw)) ->

3339 return $ OpaquePooling (Pooling :: Pooling kh kw sh sw)

3440

3541prop_pool_layer_witness =

36− gamble genOpaquePooling $ \onet ->

37− (case onet of

38− (OpaquePooling (Pooling :: Pooling kernelRows kernelCols strideRows strideCols)) -> True

39− ) :: Bool

40−

42+ property $ do

43+ onet <- forAll genOpaquePooling

44+ case onet of

45+ (OpaquePooling (Pooling :: Pooling kernelRows kernelCols strideRows strideCols)) ->

46+ assert True

4147

42−return []

4348tests :: IO Bool

44−tests = $quickCheckAll

49+tests = checkParallel $$(discover)

test/Test/Grenade/Network.hsnew file+504

@@ -0,0 +1,504 @@

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+83−54

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

1010{-# OPTIONS_GHC -fno-warn-missing-signatures #-}

1111module Test.Grenade.Recurrent.Layers.LSTM where

1212

13−import Disorder.Jack

13+import Hedgehog

14+import Hedgehog.Internal.Source

15+import Hedgehog.Internal.Show

16+import Hedgehog.Internal.Property ( failWith, Diff (..) )

1417

1518import Data.Foldable ( toList )

1619import Data.Singletons.TypeLits

@@ -23,9 +26,9 @@ import qualified Numeric.LinearAlgebra.Static as S

2326

2427

2528import qualified Test.Grenade.Recurrent.Layers.LSTM.Reference as Reference

26−import Test.Jack.Hmatrix

29+import Test.Hedgehog.Hmatrix

2730

28−genLSTM :: forall i o. (KnownNat i, KnownNat o) => Jack (LSTM i o)

31+genLSTM :: forall i o. (KnownNat i, KnownNat o) => Gen (LSTM i o)

2932genLSTM = do

3033 let w = uniformSample

3134 u = uniformSample

@@ -39,63 +42,89 @@ genLSTM = do

3942 <*> pure (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

4043

4144prop_lstm_reference_forwards =

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'

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+

5562

5663prop_lstm_reference_backwards =

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, _, _) ->

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)

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)

6878

6979prop_lstm_reference_backwards_input =

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) ->

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)

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)

8194

8295prop_lstm_reference_backwards_cell =

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, _) ->

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)

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) ""

97127infix 4 ~~~

98128

99−return []

100129tests :: IO Bool

101−tests = $quickCheckAll

130+tests = checkParallel $$(discover)

test/Test/Grenade/Recurrent/Layers/LSTM/Reference.hs+25−21

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

66{-# LANGUAGE DeriveFunctor #-}

77{-# LANGUAGE DeriveFoldable #-}

88{-# LANGUAGE DeriveTraversable #-}

9−{-# LANGUAGE RecordWildCards #-}

109{-# LANGUAGE FlexibleContexts #-}

1110{-# LANGUAGE RankNTypes #-}

1211

@@ -15,9 +14,13 @@ module Test.Grenade.Recurrent.Layers.LSTM.Reference where

1514

1615import Data.Reflection

1716import Numeric.AD.Mode.Reverse

18−import Numeric.AD.Internal.Reverse ( Tape )

17+import Numeric.AD.Internal.Reverse (Tape)

1918

19+import GHC.TypeLits (KnownNat)

20+

21+import Grenade.Recurrent.Layers.LSTM (LSTMWeights (..))

2022import qualified Grenade.Recurrent.Layers.LSTM as LSTM

23+

2124import qualified Numeric.LinearAlgebra.Static as S

2225import qualified Numeric.LinearAlgebra as H

2326

@@ -54,31 +57,32 @@ data RefLSTM a = RefLSTM

5457 , refLstmBc :: Vector a -- Bias Cell (b_c)

5558 } deriving (Functor, Foldable, Traversable, Eq, Show)

5659

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 {..}

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+ }

7175

7276runLSTM :: Floating a => RefLSTM a -> Vector a -> Vector a -> (Vector a, Vector a)

73−runLSTM RefLSTM {..} cell input =

77+runLSTM rl cell input =

7478 let -- Forget state vector

75− f_t = sigmoid $ refLstmBf #+ refLstmWf #> input #+ refLstmUf #> cell

79+ f_t = sigmoid $ refLstmBf rl #+ refLstmWf rl #> input #+ refLstmUf rl #> cell

7680 -- Input state vector

77− i_t = sigmoid $ refLstmBi #+ refLstmWi #> input #+ refLstmUi #> cell

81+ i_t = sigmoid $ refLstmBi rl #+ refLstmWi rl #> input #+ refLstmUi rl #> cell

7882 -- Output state vector

79− o_t = sigmoid $ refLstmBo #+ refLstmWo #> input #+ refLstmUo #> cell

83+ o_t = sigmoid $ refLstmBo rl #+ refLstmWo rl #> input #+ refLstmUo rl #> cell

8084 -- Cell input state vector

81− c_x = fmap tanh $ refLstmBc #+ refLstmWc #> input

85+ c_x = fmap tanh $ refLstmBc rl #+ refLstmWc rl #> input

8286 -- Cell state

8387 c_t = f_t #* cell #+ i_t #* c_x

8488 -- Output (it's sometimes recommended to use tanh c_t)

test/Test/Hedgehog/Compat.hsnew file+21

@@ -0,0 +1,21 @@

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.hsnew file+42

@@ -0,0 +1,42 @@

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.hsnew file+65

@@ -0,0 +1,65 @@

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.hsdeleted−17

@@ -1,17 +0,0 @@

1−{-# LANGUAGE RankNTypes #-}

2−{-# LANGUAGE DataKinds #-}

3−{-# LANGUAGE ScopedTypeVariables #-}

4−

5−module Test.Jack.Hmatrix where

6−

7−import Disorder.Jack

8−

9−import GHC.TypeLits

10−

11−import qualified Numeric.LinearAlgebra.Static as HStatic

12−

13−randomVector :: forall n. KnownNat n => Jack (HStatic.R n)

14−randomVector = (\s -> HStatic.randomVector s HStatic.Uniform * 2 - 1) <$> sizedNat

15−

16−uniformSample :: forall m n. (KnownNat m, KnownNat n) => Jack (HStatic.L m n)

17−uniformSample = (\s -> HStatic.uniformSample s (-1) 1 ) <$> sizedNat

test/Test/Jack/TypeLits.hsdeleted−14

@@ -1,14 +0,0 @@

1−{-# LANGUAGE RankNTypes #-}

2−{-# LANGUAGE DataKinds #-}

3−{-# LANGUAGE ScopedTypeVariables #-}

4−

5−module Test.Jack.TypeLits where

6−

7−import Disorder.Jack

8−

9−import GHC.TypeLits

10−

11−genNat :: Jack SomeNat

12−genNat = do

13− Just n <- someNatVal <$> choose (1, 10)

14− return n

test/test.hs+26−2

@@ -1,23 +1,47 @@

1−import Disorder.Core.Main

1+import Control.Monad

2+

3+import qualified Test.Grenade.Network

24

35import qualified Test.Grenade.Layers.Pooling

46import qualified Test.Grenade.Layers.Convolution

57import qualified Test.Grenade.Layers.FullyConnected

8+import qualified Test.Grenade.Layers.Nonlinear

9+import qualified Test.Grenade.Layers.PadCrop

610

711import qualified Test.Grenade.Layers.Internal.Convolution

812import qualified Test.Grenade.Layers.Internal.Pooling

913

1014import qualified Test.Grenade.Recurrent.Layers.LSTM

1115

16+import System.Exit

17+import System.IO

18+

1219main :: IO ()

1320main =

1421 disorderMain [

15− Test.Grenade.Layers.Pooling.tests

22+ Test.Grenade.Network.tests

23+

24+ , Test.Grenade.Layers.Pooling.tests

1625 , Test.Grenade.Layers.Convolution.tests

1726 , Test.Grenade.Layers.FullyConnected.tests

27+ , Test.Grenade.Layers.Nonlinear.tests

28+ , Test.Grenade.Layers.PadCrop.tests

1829

1930 , Test.Grenade.Layers.Internal.Convolution.tests

2031 , Test.Grenade.Layers.Internal.Pooling.tests

2132

2233 , Test.Grenade.Recurrent.Layers.LSTM.tests

34+

2335 ]

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