48263cd...master · grenade

.gitignore+3−1

since splitsnapshots

unifiedsplit

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

1+cabal.project.local

1.cabal-sandbox/

2.cabal-sandbox/

2cabal.sandbox.config

3cabal.sandbox.config

3dist/

4dist/

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 @@

2All rights reserved.

3

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

README.md+97−21

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

1Grenade

2=======

3

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+

4```

9```

5First shalt thou take out the Holy Pin, then shalt thou count to three, no more, no less.

10First shalt thou take out the Holy Pin, then shalt thou count to three, no more, no less.

6Three shall be the number thou shalt count, and the number of the counting shall be three.

11Three shall be the number thou shalt count, and the number of the counting shall be three.

@@ -10,40 +15,68 @@ Five is right out.

10

15

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

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

12

17

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

14for concise and precise specifications of complex networks in Haskell.

19for concise and precise specifications of complex networks in Haskell.

15

20

16As an example, a network which can achieve ~1.5% error on MNIST can be

21As an example, a network which can achieve ~1.5% error on MNIST can be

17specified and initialised with random weights in a few lines of code with

22specified and initialised with random weights in a few lines of code with

18```haskell

23```haskell

24+type MNIST

25+ = Network

26+ '[ Convolution 1 10 5 5 1 1, Pooling 2 2 2 2, Relu

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

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]

28+ , 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]

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]

21

32

22randomMnist :: MonadRandom m => m MNIST

33randomMnist :: MonadRandom m => m MNIST

23randomMnist = randomNetwork

34randomMnist = randomNetwork

24```

35```

25

36

26−And that's it. Because the types are rich, there's no specific term level code

37+And that's it. Because the types are so rich, there's no specific term level code

27−required; although it is of course possible and easy to construct one explicitly

38+required to construct this network; although it is of course possible and

28−oneself.

39+easy to construct and deconstruct the networks and layers explicitly oneself.

40+

41+If recurrent neural networks are more your style, you can try defining something

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

43+with

44+```haskell

45+type Shakespeare

46+ = RecurrentNetwork

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

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

49+```

29

50

30−The network can be thought of as a heterogeneous list of layers, where its type

51+Design

31−includes not only the layers of the network, but also the shapes of data that

52+------

32−are passed between the layers.

33

53

54+Networks in Grenade can be thought of as a heterogeneous lists of layers, where

55+their type includes not only the layers of the network, but also the shapes of

56+data that are passed between the layers.

57+

58+The definition of a network is surprisingly simple:

34```haskell

59```haskell

35data Network :: [*] -> [Shape] -> * where

60data Network :: [*] -> [Shape] -> * where

61+ NNil :: SingI i

36− O :: Layer x i o => !x -> Network '[x] '[i, o]

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

63+

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

65+ => !x

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

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

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

38```

68```

39

69

40The `Layer x i o` constraint ensures that the layer `x` can sensibly perform a

70The `Layer x i o` constraint ensures that the layer `x` can sensibly perform a

41transformation between the input and output shapes `i` and `o`.

71transformation between the input and output shapes `i` and `o`.

42

72

73+The lifted data kind `Shape` defines our 1, 2, and 3 dimension types, used to

74+declare what shape of data is passed between the layers.

75+

43−In the above example, the input layer can be seen to be a two dimensional (`D2`),

76+In the MNIST example above, the input layer can be seen to be a two dimensional

44−image with 28 by 28 pixels. When the first *Convolution* layer runs, it outputs

77+(`D2`), image with 28 by 28 pixels. When the first *Convolution* layer runs, it

45−a three dimensional (`D3`) 24x24x10 image. The last item in the list is one

78+outputs a three dimensional (`D3`) 24x24x10 image. The last item in the list is

46−dimensional (`D1`) with 10 values, representing the categories of the MNIST

79+one dimensional (`D1`) with 10 values, representing the categories of the MNIST

47data.

80data.

48

81

49Usage

82Usage

@@ -51,12 +84,12 @@ Usage

51

84

52To perform back propagation, one can call the eponymous function

85To perform back propagation, one can call the eponymous function

53```haskell

86```haskell

54−backPropagate :: forall input target shapes layers. (Head shapes ~ input, Last shapes ~ target)

87+backPropagate :: forall shapes layers.

55− => Network layers shapes -> S input -> S target -> Gradients layers

88+ Network layers shapes -> S (Head shapes) -> S (Last shapes) -> Gradients layers

56```

89```

57which takes a network, appropriate input and target data, and returns the

90which takes a network, appropriate input and target data, and returns the

58back propagated gradients for the network. The shapes of the gradients are

91back propagated gradients for the network. The shapes of the gradients are

59−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

60have no learnable parameters.

93have no learnable parameters.

61

94

62The gradients however can always be applied, yielding a new (hopefully better)

95The gradients however can always be applied, yielding a new (hopefully better)

@@ -70,9 +103,48 @@ easy in downstream code. If the shapes of a network are not specified correctly

70and a layer can not sensibly perform the operation between two shapes, then

103and a layer can not sensibly perform the operation between two shapes, then

71it will result in a compile time error.

104it will result in a compile time error.

72

105

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+

73Build Instructions

145Build Instructions

74------------------

146------------------

75−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)

76script that is located in the repository. You will also need the `lapack` and

148script that is located in the repository. You will also need the `lapack` and

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

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

78build using:

150build using:

@@ -87,8 +159,7 @@ and the tests run using:

87./mafia test

159./mafia test

88```

160```

89

161

90−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.

91−

92

163

93Thanks

164Thanks

94------

165------

@@ -114,3 +185,8 @@ core took around 12 minutes, achieving 1.5% error rate on a 1000 sample holdout

114Contributing

185Contributing

115------------

186------------

116Contributions are welcome.

187Contributions 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 #-}

1import Criterion.Main

3import Criterion.Main

2

4

5+import Grenade

6+

3import Grenade.Layers.Internal.Convolution

7import Grenade.Layers.Internal.Convolution

4import Grenade.Layers.Internal.Pooling

8import Grenade.Layers.Internal.Pooling

5

9

6import Numeric.LinearAlgebra

10import Numeric.LinearAlgebra

7

11

8main :: IO ()

12main :: IO ()

13+main = do

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

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

16+

9−main = defaultMain [

17+ defaultMain [

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

18+ 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..])

19+ , 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..])

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

13− ]

21+ ]

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

22+ , 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..])

23+ , 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..])

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

17− ]

25+ ]

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

26+ , 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..])

27+ , 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..])

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

21− ]

29+ ]

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

30+ , 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..])

31+ , 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..])

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

25− ]

38+ ]

26− ]

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"

2

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

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

4 const double* weights,

5 const double* gradient,

6 const double* last,

7 double* outputWeights, double* outputMomentum) {

8

9− for (int i = 0; i <= len; i++) {

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

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

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

12 }

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

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

1#include <stdio.h>

2#include <stdint.h>

3

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

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

5 const double* weights,

6 const double* gradient,

7 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+41−18

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

1{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE CPP #-}

2{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE ScopedTypeVariables #-}

4{-# LANGUAGE ScopedTypeVariables #-}

4{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeOperators #-}

@@ -8,6 +9,11 @@ import Control.Monad

8import Control.Monad.Random

9import Control.Monad.Random

9import Data.List ( foldl' )

10import Data.List ( foldl' )

10

11

12+import qualified Data.ByteString as B

13+import Data.Serialize

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

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

16+#endif

11import GHC.TypeLits

17import GHC.TypeLits

12

18

13import qualified Numeric.LinearAlgebra.Static as SA

19import qualified Numeric.LinearAlgebra.Static as SA

@@ -16,13 +22,12 @@ import Options.Applicative

16

22

17import Grenade

23import Grenade

18

24

19−-- The defininition for our simple feed forward network.

20−-- The type level list represents the shapes passed through the layers. One can see that for this demonstration

21−-- we are using relu, tanh and logit non-linear units, which can be easily subsituted for each other in and out.

22−

23−-- It's important to keep the type signatures, as there's many layers which can "squeeze" into the gaps

24−-- between the shapes, so inference can't do it all for us.

25

26+-- The defininition for our simple feed forward network.

27+-- The type level lists represents the layers and the shapes passed through the layers.

28+-- One can see that for this demonstration we are using relu, tanh and logit non-linear

29+-- units, which can be easily subsituted for each other in and out.

30+--

26-- With around 100000 examples, this should show two clear circles which have been learned by the network.

31-- With around 100000 examples, this should show two clear circles which have been learned by the network.

27type FFNet = Network '[ FullyConnected 2 40, Tanh, FullyConnected 40 10, Relu, FullyConnected 10 1, Logit ]

32type FFNet = Network '[ FullyConnected 2 40, Tanh, FullyConnected 40 10, Relu, FullyConnected 10 1, Logit ]

28 '[ 'D1 2, 'D1 40, 'D1 40, 'D1 10, 'D1 10, 'D1 1, 'D1 1]

33 '[ 'D1 2, 'D1 40, 'D1 40, 'D1 10, 'D1 10, 'D1 1, 'D1 1]

@@ -30,8 +35,8 @@ type FFNet = Network '[ FullyConnected 2 40, Tanh, FullyConnected 40 10, Relu, F

30randomNet :: MonadRandom m => m FFNet

35randomNet :: MonadRandom m => m FFNet

31randomNet = randomNetwork

36randomNet = randomNetwork

32

37

33−netTest :: MonadRandom m => LearningParameters -> Int -> m String

38+netTrain :: FFNet -> LearningParameters -> Int -> IO FFNet

34−netTest rate n = do

39+netTrain net0 rate n = do

35 inps <- replicateM n $ do

40 inps <- replicateM n $ do

36 s <- getRandom

41 s <- getRandom

37 return $ S1D $ SA.randomVector s SA.Uniform * 2 - 1

42 return $ S1D $ SA.randomVector s SA.Uniform * 2 - 1

@@ -39,20 +44,28 @@ netTest rate n = do

39 if v `inCircle` (fromRational 0.33, 0.33) || v `inCircle` (fromRational (-0.33), 0.33)

44 if v `inCircle` (fromRational 0.33, 0.33) || v `inCircle` (fromRational (-0.33), 0.33)

40 then S1D $ fromRational 1

45 then S1D $ fromRational 1

41 else S1D $ fromRational 0

46 else S1D $ fromRational 0

42− net0 <- randomNet

43

47

44 let trained = foldl' trainEach net0 (zip inps outs)

48 let trained = foldl' trainEach net0 (zip inps outs)

45− let testIns = [ [ (x,y) | x <- [0..50] ]

49+ return trained

46− | y <- [0..20] ]

47−

48− let outMat = fmap (fmap (\(x,y) -> (render . normx) $ runNet trained (S1D $ SA.vector [x / 25 - 1,y / 10 - 1]))) testIns

49− return $ unlines outMat

50

51 where

52 inCircle :: KnownNat n => SA.R n -> (SA.R n, Double) -> Bool

53 v `inCircle` (o, r) = SA.norm_2 (v - o) <= r

54 trainEach !network (i,o) = train rate network i o

55

56+netLoad :: FilePath -> IO FFNet

57+netLoad modelPath = do

58+ modelData <- B.readFile modelPath

59+ either fail return $ runGet (get :: Get FFNet) modelData

60+

61+netScore :: FFNet -> IO ()

62+netScore network = do

63+ let testIns = [ [ (x,y) | x <- [0..50] ]

64+ | y <- [0..20] ]

65+ outMat = fmap (fmap (\(x,y) -> (render . normx) $ runNet network (S1D $ SA.vector [x / 25 - 1,y / 10 - 1]))) testIns

66+ putStrLn $ unlines outMat

67+

68+ where

56 render n' | n' <= 0.2 = ' '

69 render n' | n' <= 0.2 = ' '

57 | n' <= 0.4 = '.'

70 | n' <= 0.4 = '.'

58 | n' <= 0.6 = '-'

71 | n' <= 0.6 = '-'

@@ -62,7 +75,7 @@ netTest rate n = do

62 normx :: S ('D1 1) -> Double

75 normx :: S ('D1 1) -> Double

63 normx (S1D r) = SA.mean r

76 normx (S1D r) = SA.mean r

64

77

65−data FeedForwardOpts = FeedForwardOpts Int LearningParameters

78+data FeedForwardOpts = FeedForwardOpts Int LearningParameters (Maybe FilePath) (Maybe FilePath)

66

79

67feedForward' :: Parser FeedForwardOpts

80feedForward' :: Parser FeedForwardOpts

68feedForward' =

81feedForward' =

@@ -72,9 +85,19 @@ feedForward' =

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

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

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

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

74 )

87 )

88+ <*> optional (strOption (long "load"))

89+ <*> optional (strOption (long "save"))

75

90

76main :: IO ()

91main :: IO ()

77main = do

92main = do

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

93+ FeedForwardOpts examples rate load save <- execParser (info (feedForward' <**> helper) idm)

79− putStrLn "Training network..."

94+ net0 <- case load of

80− putStrLn =<< evalRandIO (netTest rate examples)

95+ Just loadFile -> netLoad loadFile

96+ Nothing -> randomNet

97+

98+ net <- netTrain net0 rate examples

99+ netScore net

100+

101+ case save of

102+ Just saveFile -> B.writeFile saveFile $ runPut (put net)

103+ Nothing -> return ()

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 @@

1{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE CPP #-}

2{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE ScopedTypeVariables #-}

4{-# LANGUAGE ScopedTypeVariables #-}

4{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TupleSections #-}

6{-# LANGUAGE TupleSections #-}

6{-# LANGUAGE TypeFamilies #-}

7{-# LANGUAGE TypeFamilies #-}

7{-# LANGUAGE FlexibleContexts #-}

8{-# LANGUAGE FlexibleContexts #-}

9+

8import Control.Applicative

10import Control.Applicative

9import Control.Monad

11import Control.Monad

10import Control.Monad.Random

12import Control.Monad.Random

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

12

14

13import qualified Data.Attoparsec.Text as A

15import qualified Data.Attoparsec.Text as A

14import Data.List ( foldl' )

16import Data.List ( foldl' )

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

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

19+#endif

20+

15import qualified Data.Text as T

21import qualified Data.Text as T

16import qualified Data.Text.IO as T

22import qualified Data.Text.IO as T

17import qualified Data.Vector.Storable as V

23import qualified Data.Vector.Storable as V

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

24import Grenade

30import Grenade

25import Grenade.Utils.OneHot

31import Grenade.Utils.OneHot

26

32

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+

27-- The definition of our convolutional neural network.

42-- The definition of our convolutional neural network.

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

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

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

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

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

33

48

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

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

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

50-- 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]

51+--

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]

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 ]

38

66

39randomMnist :: MonadRandom m => m MNIST

67randomMnist :: MonadRandom m => m MNIST

40randomMnist = randomNetwork

68randomMnist = randomNetwork

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

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

1{-# LANGUAGE BangPatterns #-}

2+{-# LANGUAGE CPP #-}

2{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE ScopedTypeVariables #-}

4{-# LANGUAGE ScopedTypeVariables #-}

4{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeOperators #-}

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

8import Control.Monad ( foldM )

9import Control.Monad ( foldM )

9import Control.Monad.Random ( MonadRandom, getRandomR )

10import Control.Monad.Random ( MonadRandom, getRandomR )

10

11

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

13+import Data.List ( unfoldr )

14+#else

11import Data.List ( cycle, unfoldr )

15import Data.List ( cycle, unfoldr )

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

17+#endif

18+

12import qualified Numeric.LinearAlgebra.Static as SA

19import qualified Numeric.LinearAlgebra.Static as SA

13

20

14import Options.Applicative

21import Options.Applicative

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

22--

29--

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

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

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

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

25−type F = FeedForward

26type R = Recurrent

32type R = Recurrent

27

33

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

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

29− '[ 'D1 1, 'D1 4, 'D1 1, 'D1 1 ]

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

30

36

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

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

32

38

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

39+randomNet :: MonadRandom m => m RecNet

34randomNet = randomRecurrent

40randomNet = randomRecurrent

35

41

36netTest :: MonadRandom m => RecNet -> RecInput -> LearningParameters -> Int -> m (RecNet, RecInput)

42netTest :: MonadRandom m => RecNet -> RecInput -> LearningParameters -> Int -> m (RecNet, RecInput)

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

51data FeedForwardOpts = FeedForwardOpts Int LearningParameters

57data FeedForwardOpts = FeedForwardOpts Int LearningParameters

52

58

53feedForward' :: Parser FeedForwardOpts

59feedForward' :: Parser FeedForwardOpts

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

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

55 <*> (LearningParameters

61 <*> (LearningParameters

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

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

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

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

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

63 unfoldr go (s, i)

69 unfoldr go (s, i)

64 where

70 where

65 go (x, y) =

71 go (x, y) =

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

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

67− o' = heat o

73+ o' = heat o

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

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

69

75

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

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

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

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

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

77 putStrLn "Training network..."

83 putStrLn "Training network..."

78

84

79− (net0, i0) <- randomNet

85+ net0 <- randomNet

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

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

81

87

82 let results = generateRecurrent trained bestInput (c 1)

88 let results = generateRecurrent trained bestInput (c 1)

83

89

main/shakespeare.hs → examples/main/shakespeare.hsrenamed+63−32

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

1{-# LANGUAGE BangPatterns #-}

2−{-# LANGUAGE RecordWildCards #-}

2+{-# LANGUAGE CPP #-}

3{-# LANGUAGE DataKinds #-}

4{-# LANGUAGE ScopedTypeVariables #-}

5{-# LANGUAGE TypeOperators #-}

@@ -11,17 +11,18 @@ import Control.Monad.Random

11import Control.Monad.Trans.Except

12

13import Data.Char ( isUpper, toUpper, toLower )

14−import Data.List ( unfoldr, foldl' )

14+import Data.List ( foldl' )

15import Data.Maybe ( fromMaybe )

16

17+

17import qualified Data.Vector as V

18import qualified Data.Vector as V

18import Data.Vector ( Vector )

19import Data.Vector ( Vector )

19

20

20import qualified Data.Map as M

21import qualified Data.Map as M

21−import Data.Proxy ( Proxy (..) )

22

23+import qualified Data.ByteString as B

24+import Data.Serialize

23

25

24−import Data.Singletons.Prelude

25import GHC.TypeLits

26import GHC.TypeLits

26

27

27import Numeric.LinearAlgebra.Static ( konst )

28import Numeric.LinearAlgebra.Static ( konst )

@@ -32,31 +33,38 @@ import Grenade

32import Grenade.Recurrent

33import Grenade.Recurrent

33import Grenade.Utils.OneHot

34import Grenade.Utils.OneHot

34

35

36+import System.IO.Unsafe ( unsafeInterleaveIO )

37+import Data.Proxy

38+import Prelude.Singletons

39+

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

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

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

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

37-- about an hour.

42-- about an hour.

38--

43--

44+-- Grab the input from

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

46+--

39−-- This is a first class recurrent net, although it's similar to

47+-- This is a first class recurrent net.

40−-- an unrolled graph.

41--

48--

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

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

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

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

44--

51--

45-- As an example, here's a short sequence generated.

52-- As an example, here's a short sequence generated.

46--

53--

47−-- > the see and and the sir, and and the make and the make and go the make and go the make and the

54+-- > KING RICHARD III:

48−--

55+-- > And as the heaven her his words, we the son, I show sand stape but the lament to shall were the sons with a strend

56+

49type F = FeedForward

57type F = FeedForward

50type R = Recurrent

58type R = Recurrent

51

59

52-- The definition of our network

60-- The definition of our network

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

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

54− '[ 'D1 40, 'D1 40, 'D1 40, 'D1 40 ]

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

55

63

56−-- 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.

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

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

58

66

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

67+randomNet :: MonadRandom m => m Shakespeare

60randomNet = randomRecurrent

68randomNet = randomRecurrent

61

69

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

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

@@ -64,54 +72,68 @@ loadShakespeare :: FilePath -> ExceptT String IO (Vector Int, M.Map Char Int, Ve

64loadShakespeare path = do

72loadShakespeare path = do

65 contents <- lift $ readFile path

73 contents <- lift $ readFile path

66 let annotated = annotateCapitals contents

74 let annotated = annotateCapitals contents

67− (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

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

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

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

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

70

78

71trainSlice :: LearningParameters -> Shakespeare -> Shakespearian -> Vector Int -> Int -> Int -> (Shakespeare, Shakespearian)

79trainSlice :: LearningParameters -> Shakespeare -> Shakespearian -> Vector Int -> Int -> Int -> (Shakespeare, Shakespearian)

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

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

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

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

74 in case reverse e of

82 in case reverse e of

75 (o : l : xs) ->

83 (o : l : xs) ->

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

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

77− in trainRecurrent rate net recIns examples

85+ in trainRecurrent lrate net recIns examples

78 _ -> error "Not enough input"

86 _ -> error "Not enough input"

79 where

87 where

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

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

81

89

82runShakespeare :: ShakespeareOpts -> ExceptT String IO ()

90runShakespeare :: ShakespeareOpts -> ExceptT String IO ()

83−runShakespeare ShakespeareOpts {..} = do

91+runShakespeare opts = do

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

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

85− (net0, i0) <- lift randomNet

93+ (net0, i0) <- lift $

94+ case loadPath opts of

95+ Just loadFile -> netLoad loadFile

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

97+

86− lift $ foldM_ (\(!net, !io) size -> do

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

87− xs <- take (iterations `div` 15) <$> getRandomRs (0, length shakespeare - size - 1)

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

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

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

89− let results = take 100 $ generateParagraph trained bestInput oneHotMap oneHotDictionary ( S1D $ konst 0)

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

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

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

91 putStrLn (unAnnotateCapitals results)

103 putStrLn (unAnnotateCapitals results)

92 return (trained, bestInput)

104 return (trained, bestInput)

93− ) (net0, i0) [10,10,15,15,20,20,25,25,30,30,35,35,40,40,50 :: Int]

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

106+

107+ case savePath opts of

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

109+ Nothing -> return ()

94

110

95generateParagraph :: forall layers shapes n a. (Last shapes ~ 'D1 n, Head shapes ~ 'D1 n, KnownNat n, Ord a)

111generateParagraph :: forall layers shapes n a. (Last shapes ~ 'D1 n, Head shapes ~ 'D1 n, KnownNat n, Ord a)

96 => RecurrentNetwork layers shapes

112 => RecurrentNetwork layers shapes

97 -> RecurrentInputs layers

113 -> RecurrentInputs layers

114+ -> Double

98 -> M.Map a Int

115 -> M.Map a Int

99 -> Vector a

116 -> Vector a

100 -> S ('D1 n)

117 -> S ('D1 n)

101− -> [a]

118+ -> IO [a]

102−generateParagraph n s hotmap hotdict i =

119+generateParagraph n s temp hotmap hotdict =

103− unfoldr go (s, i)

120+ go s

104 where

121 where

105− go (x, y) =

122+ go x y =

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

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

107− un <- unHot hotdict o

124+ un <- sample temp hotdict o

108− re <- makeHot hotmap un

125+ Just re <- return $ makeHot hotmap un

109− Just (un, (ns, re))

126+ rest <- unsafeInterleaveIO $ go ns re

127+ return (un : rest)

110

128

111data ShakespeareOpts = ShakespeareOpts {

129data ShakespeareOpts = ShakespeareOpts {

112 trainingFile :: FilePath

130 trainingFile :: FilePath

113 , iterations :: Int

131 , iterations :: Int

114 , rate :: LearningParameters

132 , rate :: LearningParameters

133+ , sequenceSize :: Int

134+ , temperature :: Double

135+ , loadPath :: Maybe FilePath

136+ , savePath :: Maybe FilePath

115 }

137 }

116

138

117shakespeare' :: Parser ShakespeareOpts

139shakespeare' :: Parser ShakespeareOpts

@@ -122,6 +144,10 @@ shakespeare' = ShakespeareOpts <$> argument str (metavar "TRAIN")

122 <*> option auto (long "momentum" <> value 0.95)

144 <*> option auto (long "momentum" <> value 0.95)

123 <*> option auto (long "l2" <> value 0.000001)

145 <*> option auto (long "l2" <> value 0.000001)

124 )

146 )

147+ <*> option auto (long "sequence-length" <> short 's' <> value 50)

148+ <*> option auto (long "temperature" <> short 't' <> value 0.4)

149+ <*> optional (strOption (long "load"))

150+ <*> optional (strOption (long "save"))

125

151

126main :: IO ()

152main :: IO ()

127main = do

153main = do

@@ -132,6 +158,11 @@ main = do

132 Left err -> putStrLn err

158 Left err -> putStrLn err

133

159

134

160

161+netLoad :: FilePath -> IO (Shakespeare, Shakespearian)

162+netLoad modelPath = do

163+ modelData <- B.readFile modelPath

164+ either fail return $ runGet get modelData

165+

135-- Replace capitals with an annotation and the lower case letter

166-- Replace capitals with an annotation and the lower case letter

136-- http://fastml.com/one-weird-trick-for-training-char-rnns/

167-- http://fastml.com/one-weird-trick-for-training-char-rnns/

137annotateCapitals :: String -> String

168annotateCapitals :: String -> String

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+118−111

@@ -1,148 +1,126 @@

1name: grenade

2−version: 0.0.1

2+version: 0.1.0

3license: BSD2

4+license-file: LICENSE

4author: Huw Campbell <huw.campbell@gmail.com>

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

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

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

7−synopsis: grenade

8+synopsis: Practical Deep Learning in Haskell

8−category: System

9+category: AI, Machine Learning

9−cabal-version: >= 1.8

10+cabal-version: >= 1.10

10build-type: Simple

11build-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>.

12

24

13extra-source-files:

25extra-source-files:

26+ README.md

14− cbits/im2col.h

27+ cbits/im2col.h

15− cbits/im2col.c

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

16

37

17library

38library

18 build-depends:

39 build-depends:

19 base >= 4.8 && < 5

40 base >= 4.8 && < 5

20− , bytestring == 0.10.*

41+ , bytestring >= 0.10.0

21− , async

22− , containers

42+ , containers >= 0.5 && < 0.7

43+ , cereal >= 0.5 && < 0.6

23− , deepseq

44+ , deepseq >= 1.4 && < 1.5

24− , either == 4.4.*

25− , exceptions == 0.8.*

26− , hmatrix

45+ , hmatrix >= 0.18 && < 0.21

27− , MonadRandom

46+ , MonadRandom >= 0.4 && < 0.6

28− , mtl >= 2.2.1 && < 2.3

29− , parallel == 3.2.*

30− , primitive

47+ , primitive >= 0.6 && < 0.8

31− , reflection

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

32− , text == 1.2.*

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

33− , transformers

34 , singletons

50 , singletons

51+ , singletons-base

35− , vector

52+ , vector >= 0.11 && < 0.13

36

53

37 ghc-options:

54 ghc-options:

38 -Wall

55 -Wall

39 hs-source-dirs:

56 hs-source-dirs:

40 src

57 src

41

58

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

42

67

43 exposed-modules:

68 exposed-modules:

44 Grenade

69 Grenade

70+ Grenade.Core

71+ Grenade.Core.Layer

72+ Grenade.Core.LearningParameters

45 Grenade.Core.Network

73 Grenade.Core.Network

46 Grenade.Core.Runner

74 Grenade.Core.Runner

47 Grenade.Core.Shape

75 Grenade.Core.Shape

76+

77+ Grenade.Layers

48− Grenade.Layers.Crop

78+ Grenade.Layers.Concat

49 Grenade.Layers.Convolution

79 Grenade.Layers.Convolution

80+ Grenade.Layers.Crop

81+ Grenade.Layers.Deconvolution

50 Grenade.Layers.Dropout

82 Grenade.Layers.Dropout

83+ Grenade.Layers.Elu

51 Grenade.Layers.FullyConnected

84 Grenade.Layers.FullyConnected

52− Grenade.Layers.Flatten

85+ Grenade.Layers.Inception

53− Grenade.Layers.Fuse

54 Grenade.Layers.Logit

86 Grenade.Layers.Logit

55− Grenade.Layers.Relu

87+ Grenade.Layers.Merge

56− Grenade.Layers.Tanh

57 Grenade.Layers.Pad

88 Grenade.Layers.Pad

58 Grenade.Layers.Pooling

89 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

59

96

60 Grenade.Layers.Internal.Convolution

97 Grenade.Layers.Internal.Convolution

98+ Grenade.Layers.Internal.Pad

61 Grenade.Layers.Internal.Pooling

99 Grenade.Layers.Internal.Pooling

62 Grenade.Layers.Internal.Update

100 Grenade.Layers.Internal.Update

63

101

64 Grenade.Recurrent

102 Grenade.Recurrent

65

103

104+ Grenade.Recurrent.Core

105+ Grenade.Recurrent.Core.Layer

66 Grenade.Recurrent.Core.Network

106 Grenade.Recurrent.Core.Network

67 Grenade.Recurrent.Core.Runner

107 Grenade.Recurrent.Core.Runner

68

108

109+ Grenade.Recurrent.Layers

69 Grenade.Recurrent.Layers.BasicRecurrent

110 Grenade.Recurrent.Layers.BasicRecurrent

111+ Grenade.Recurrent.Layers.ConcatRecurrent

70 Grenade.Recurrent.Layers.LSTM

112 Grenade.Recurrent.Layers.LSTM

71− Grenade.Recurrent.Layers.Trivial

72

113

73 Grenade.Utils.OneHot

114 Grenade.Utils.OneHot

74

115

75− includes: cbits/im2col.h

116+ includes: cbits/im2col.h

76− cbits/gradient_decent.h

117+ cbits/gradient_descent.h

118+ cbits/pad.h

77− c-sources: cbits/im2col.c

119+ c-sources: cbits/im2col.c

78− cbits/gradient_decent.c

120+ cbits/gradient_descent.c

79−

121+ cbits/pad.c

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

81−

82−executable feedforward

83− ghc-options: -Wall -threaded -O2

84− main-is: main/feedforward.hs

85− build-depends: base

86− , grenade

87− , attoparsec

88− , either

89− , optparse-applicative == 0.12.*

90− , text == 1.2.*

91− , mtl >= 2.2.1 && < 2.3

92− , hmatrix

93− , transformers

94− , singletons

95− , MonadRandom

96−

97−executable mnist

98− ghc-options: -Wall -threaded -O2

99− main-is: main/mnist.hs

100− build-depends: base

101− , grenade

102− , attoparsec

103− , either

104− , optparse-applicative == 0.12.*

105− , text == 1.2.*

106− , mtl >= 2.2.1 && < 2.3

107− , hmatrix >= 0.18 && < 0.19

108− , transformers

109− , singletons

110− , MonadRandom

111− , vector

112−

113−executable recurrent

114− ghc-options: -Wall -threaded -O2

115− main-is: main/recurrent.hs

116− build-depends: base

117− , grenade

118− , attoparsec

119− , either

120− , optparse-applicative == 0.12.*

121− , text == 1.2.*

122− , mtl >= 2.2.1 && < 2.3

123− , hmatrix >= 0.18 && < 0.19

124− , transformers

125− , singletons

126− , MonadRandom

127−

128−

129−executable shakespeare

130− ghc-options: -Wall -threaded -O2

131− main-is: main/shakespeare.hs

132− build-depends: base

133− , grenade

134− , attoparsec

135− , either

136− , optparse-applicative == 0.12.*

137− , text == 1.2.*

138− , mtl >= 2.2.1 && < 2.3

139− , hmatrix >= 0.18 && < 0.19

140− , transformers

141− , singletons

142− , vector

143− , MonadRandom

144− , containers

145

122

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

146

124

147test-suite test

125test-suite test

148 type: exitcode-stdio-1.0

126 type: exitcode-stdio-1.0

@@ -154,23 +132,48 @@ test-suite test

154 hs-source-dirs:

132 hs-source-dirs:

155 test

133 test

156

134

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+

157 build-depends:

161 build-depends:

158− base >= 4.8 && < 5

162+ base

159 , grenade

163 , grenade

160− , ambiata-disorder-core

164+ , hedgehog >= 1.0 && < 1.1

161− , ambiata-disorder-jack

162 , hmatrix

165 , hmatrix

163 , mtl

166 , mtl

164 , singletons

167 , singletons

165 , text == 1.2.*

168 , text == 1.2.*

166− , typelits-witnesses

169+ , typelits-witnesses < 0.4

170+ , transformers

167 , constraints

171 , constraints

168− , QuickCheck >= 2.7 && < 2.9

169− , quickcheck-instances == 0.3.*

170 , MonadRandom

172 , MonadRandom

171 , random

173 , random

172 , ad

174 , ad

173 , reflection

175 , reflection

176+ , vector

174

177

175

178

176benchmark bench

179benchmark bench

@@ -183,10 +186,12 @@ benchmark bench

183 hs-source-dirs:

186 hs-source-dirs:

184 bench

187 bench

185

188

189+ default-language: Haskell2010

190+

186 build-depends:

191 build-depends:

187− base >= 3 && < 5

192+ base

188− , bytestring == 0.10.*

193+ , bytestring

189− , criterion == 1.1.*

194+ , criterion >= 1.1 && < 1.6

190 , grenade

195 , grenade

191 , hmatrix

196 , hmatrix

192

197

@@ -200,9 +205,11 @@ benchmark bench-lstm

200 hs-source-dirs:

205 hs-source-dirs:

201 bench

206 bench

202

207

208+ default-language: Haskell2010

209+

203 build-depends:

210 build-depends:

204− base >= 3 && < 5

211+ base

205− , bytestring == 0.10.*

212+ , bytestring

206− , criterion == 1.1.*

213+ , criterion

207 , grenade

214 , grenade

208 , hmatrix

215 , 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 @@

1module Grenade (

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

3+ -- for machine learning with Grenade.

4+

5+ -- * Exported modules

6+ --

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

2− module X

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+

3 ) where

20 ) where

4

21

5−import Grenade.Core.Network as X

22+import Grenade.Core

6−import Grenade.Core.Runner as X

23+import Grenade.Layers

7−import Grenade.Core.Shape as X

24+

8−import Grenade.Layers.Crop as X

25+{- $library

9−import Grenade.Layers.Dropout as X

26+Grenade is a purely functional deep learning library.

10−import Grenade.Layers.Pad as X

27+

11−import Grenade.Layers.Pooling as X

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

12−import Grenade.Layers.Flatten as X

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

13−import Grenade.Layers.Fuse as X

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

14−import Grenade.Layers.FullyConnected as X

31+the hmatrix library.

15−import Grenade.Layers.Logit as X

32+

16−import Grenade.Layers.Convolution as X

33+-}

17−import Grenade.Layers.Relu as X

34+

18−import Grenade.Layers.Tanh as X

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+161−67

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

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

3+{-# LANGUAGE BangPatterns #-}

2{-# LANGUAGE GADTs #-}

4{-# LANGUAGE GADTs #-}

5+{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE TypeOperators #-}

6{-# LANGUAGE TypeOperators #-}

4{-# LANGUAGE TypeFamilies #-}

7{-# LANGUAGE TypeFamilies #-}

5{-# LANGUAGE MultiParamTypeClasses #-}

8{-# LANGUAGE MultiParamTypeClasses #-}

@@ -7,97 +10,188 @@

7{-# LANGUAGE FlexibleInstances #-}

10{-# LANGUAGE FlexibleInstances #-}

8{-|

11{-|

9Module : Grenade.Core.Network

12Module : Grenade.Core.Network

10−Description : Core definition a simple neural etwork

13+Description : Core definition of a Neural Network

11Copyright : (c) Huw Campbell, 2016-2017

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

12License : BSD2

15License : BSD2

13Stability : experimental

16Stability : experimental

14

17

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

18+This module defines the core data types and functions

16−Neural network we support.

19+for non-recurrent neural networks.

17−

18-}

20-}

21+

19module Grenade.Core.Network (

22module Grenade.Core.Network (

20− Layer (..)

21− , Network (..)

23+ Network (..)

22− , UpdateLayer (..)

23− , LearningParameters (..)

24 , Gradients (..)

25− , CreatableNetwork (..)

25+ , Tapes (..)

26+

27+ , runNetwork

28+ , runGradient

29+ , applyUpdate

30+

31+ , randomNetwork

26 ) where

32 ) where

27

33

28−import Control.Monad.Random (MonadRandom)

34+import Control.Monad.Random ( MonadRandom )

29−import Data.List ( foldl' )

35+

30import Data.Singletons

36import Data.Singletons

37+import Data.Serialize

31

38

32−import Grenade.Core.Shape

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

40+import Data.Kind (Type)

41+#endif

33

42

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

43+import Grenade.Core.Layer

35−data LearningParameters = LearningParameters {

44+import Grenade.Core.LearningParameters

36− learningRate :: Double

45+import Grenade.Core.Shape

37− , learningMomentum :: Double

46+import Prelude.Singletons

38− , learningRegulariser :: Double

39− } deriving (Eq, Show)

40−

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

42−-- shape independent.

43−class Show x => UpdateLayer x where

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

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

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

47− type Gradient x :: *

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

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

50−

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

52− createRandom :: MonadRandom m => m x

53−

54− -- | Update a layer with many Gradients

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

56− runUpdates rate = foldl' (runUpdate rate)

57−

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

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

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

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

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

63− runForwards :: x -> S i -> S o

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

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

66− -- the layer above.

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

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

69

47

70-- | Type of a network.

48-- | Type of a network.

71--

49--

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

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

73--

51--

74−-- 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.

75--

53--

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

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

77-- transform the data shapes of the network.

55-- transform the data shapes of the network.

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

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

57+ NNil :: SingI i

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

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

59+

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

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

61+ => !x

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

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

81infixr 5 :~>

64infixr 5 :~>

82

65

83−instance Show (Network l h) where

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

84− show (O a) = "O " ++ show a

67+ show NNil = "NNil"

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

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

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)

81+

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]

88+

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+

86

146

87−-- | Gradients of a network.

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

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

148+applyUpdate :: LearningParameters

89−data Gradients :: [*] -> * where

149+ -> Network layers shapes

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

150+ -> Gradients layers

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

151+ -> Network layers shapes

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

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

92

154

155+applyUpdate _ NNil GNil

156+ = NNil

157+

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

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

94−-- network is with the randomNetwork.

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

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

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

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

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.

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

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

98

166

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

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

100− randomNetwork = O <$> createRandom

168+ randomNetwork = return NNil

101

169

102−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

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

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

172+

173+-- | Add very simple serialisation to the network

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

175+ put NNil = pure ()

176+ get = return NNil

177+

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

179+ put (x :~> r) = put x >> put r

180+ 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 #-}

3{-# LANGUAGE DataKinds #-}

1{-# LANGUAGE DataKinds #-}

4−{-# LANGUAGE ScopedTypeVariables #-}

2+{-# LANGUAGE FlexibleContexts #-}

5−{-# LANGUAGE TypeOperators #-}

6−{-# LANGUAGE TypeFamilies #-}

7{-|

3{-|

8−Module : Grenade.Core.Shape

4+Module : Grenade.Core.Runner

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

5+Description : Functions to perform training and backpropagation

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

6Copyright : (c) Huw Campbell, 2016-2017

11License : BSD2

7License : BSD2

12Stability : experimental

8Stability : experimental

13−

14−This module defines simple back propagation and training functions

15−for a network.

16-}

9-}

17module Grenade.Core.Runner (

10module Grenade.Core.Runner (

18 train

11 train

19 , backPropagate

12 , backPropagate

20 , runNet

13 , runNet

21− , applyUpdate

22 ) where

14 ) where

23

15

24−import Data.Singletons.Prelude

16+

17+import Grenade.Core.LearningParameters

25import Grenade.Core.Network

18import Grenade.Core.Network

26import Grenade.Core.Shape

19import Grenade.Core.Shape

20+import Data.Singletons

21+import Prelude.Singletons

27

22

28-- | Perform reverse automatic differentiation on the network

23-- | Perform reverse automatic differentiation on the network

29-- for the current input and expected output.

24-- for the current input and expected output.

30--

25--

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

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

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

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

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

28+-- or log-loss respectively.

34−-- implemented.

29+--

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

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

32+--

35−backPropagate :: forall shapes layers.

33+backPropagate :: SingI (Last shapes)

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

34+ => Network layers shapes

35+ -> S (Head shapes)

36+ -> S (Last shapes)

37+ -> Gradients layers

37backPropagate network input target =

38backPropagate network input target =

38− fst $ go input network

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

39− where

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

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

41+ in grads

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'

51

42

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"

70

43

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

44-- | 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

73train rate network input output =

51train rate network input output =

74 let grads = backPropagate network input output

52 let grads = backPropagate network input output

75 in applyUpdate rate network grads

53 in applyUpdate rate network grads

76

54

55+

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

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

78runNet :: Network layers shapes -> S (Head shapes) -> S (Last shapes)

57runNet :: Network layers shapes -> S (Head shapes) -> S (Last shapes)

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

58+runNet net = snd . runNetwork net

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

src/Grenade/Core/Shape.hs+97−52

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

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE GADTs #-}

3{-# LANGUAGE GADTs #-}

3{-# LANGUAGE KindSignatures #-}

4{-# LANGUAGE KindSignatures #-}

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

7{-# LANGUAGE FlexibleContexts #-}

8{-# LANGUAGE FlexibleContexts #-}

8{-# LANGUAGE ScopedTypeVariables #-}

9{-# LANGUAGE ScopedTypeVariables #-}

9{-# LANGUAGE RankNTypes #-}

10{-# LANGUAGE RankNTypes #-}

10−

11+{-# LANGUAGE UndecidableInstances #-}

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−

15{-|

12{-|

16Module : Grenade.Core.Shape

13Module : 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

18Copyright : (c) Huw Campbell, 2016-2017

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

19License : BSD2

16License : BSD2

20Stability : experimental

17Stability : experimental

21

18

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

19+

23−are understood by Grenade.

24-}

20-}

25module Grenade.Core.Shape (

21module Grenade.Core.Shape (

22+ S (..)

26− Shape (..)

23+ , Shape (..)

27− , S (..)

24+ , SShape (..)

28 , randomOfShape

25 , randomOfShape

29 , fromStorable

26 , fromStorable

30 ) where

27 ) where

31

28

32import Control.DeepSeq (NFData (..))

29import Control.DeepSeq (NFData (..))

33import Control.Monad.Random ( MonadRandom, getRandom )

30import Control.Monad.Random ( MonadRandom, getRandom )

34−

31+import Data.Kind (Type)

35import Data.Proxy

32import Data.Proxy

33+import Data.Serialize

36import Data.Singletons

34import Data.Singletons

37−import Data.Singletons.TypeLits

38import Data.Vector.Storable ( Vector )

35import Data.Vector.Storable ( Vector )

39import qualified Data.Vector.Storable as V

36import qualified Data.Vector.Storable as V

40−

41import GHC.TypeLits

37import GHC.TypeLits

42−

43import qualified Numeric.LinearAlgebra.Static as H

38import qualified Numeric.LinearAlgebra.Static as H

44import Numeric.LinearAlgebra.Static

39import Numeric.LinearAlgebra.Static

45import qualified Numeric.LinearAlgebra as NLA

40import qualified Numeric.LinearAlgebra as NLA

@@ -47,48 +42,80 @@ import qualified Numeric.LinearAlgebra as NLA

47-- | The current shapes we accept.

42-- | The current shapes we accept.

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

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

49-- Vectors/Matricies.

44-- Vectors/Matricies.

45+--

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

50data Shape

47data Shape

51 = D1 Nat

48 = D1 Nat

49+ -- ^ One dimensional vector

52 | D2 Nat Nat

50 | D2 Nat Nat

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

53 | D3 Nat Nat Nat

52 | D3 Nat Nat Nat

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

54

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

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

56+--

56-- All shapes are held in contiguous memory.

57-- All shapes are held in contiguous memory.

57-- 3D is held in a matrix (usually row oriented) which has height depth * rows.

58-- 3D is held in a matrix (usually row oriented) which has height depth * rows.

58data S (n :: Shape) where

59data S (n :: Shape) where

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

60+ S1D :: ( KnownNat len )

61+ => R len

62+ -> S ('D1 len)

63+

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

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

65+ => L rows columns

66+ -> S ('D2 rows columns)

67+

61 S3D :: ( KnownNat rows

68 S3D :: ( KnownNat rows

62 , KnownNat columns

69 , KnownNat columns

63 , KnownNat depth

70 , KnownNat depth

71+ , KnownNat (rows * depth))

72+ => L (rows * depth) columns

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

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

65

74

66deriving instance Show (S n)

75deriving instance Show (S n)

67

76

77+-- Singleton instances.

78+--

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

80+-- 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

91+data instance Sing (n :: Shape) where

92+ D1Sing :: Sing a -> Sing ('D1 a)

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

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

95+#endif

96+

97+instance KnownNat a => SingI ('D1 a) where

98+ sing = D1Sing

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

100+ sing = D2Sing

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

102+ sing = D3Sing

103+

68instance SingI x => Num (S x) where

104instance SingI x => Num (S x) where

69 (+) = n2 (+)

105 (+) = n2 (+)

70 (-) = n2 (-)

106 (-) = n2 (-)

71 (*) = n2 (*)

107 (*) = n2 (*)

72 abs = n1 abs

108 abs = n1 abs

73 signum = n1 signum

109 signum = n1 signum

74− fromInteger x = case (sing :: Sing x) of

75− D1Sing -> S1D (konst $ fromInteger x)

76− D2Sing -> S2D (konst $ fromInteger x)

77− D3Sing -> S3D (konst $ fromInteger x)

110+ fromInteger x = nk (fromInteger x)

78

111

79instance SingI x => Fractional (S x) where

112instance SingI x => Fractional (S x) where

80 (/) = n2 (/)

113 (/) = n2 (/)

81 recip = n1 recip

114 recip = n1 recip

82− fromRational x = case (sing :: Sing x) of

83− D1Sing -> S1D (konst $ fromRational x)

84− D2Sing -> S2D (konst $ fromRational x)

85− D3Sing -> S3D (konst $ fromRational x)

115+ fromRational x = nk (fromRational x)

86

116

87instance SingI x => Floating (S x) where

117instance SingI x => Floating (S x) where

88− pi = case (sing :: Sing x) of

118+ pi = nk pi

89− D1Sing -> S1D (konst pi)

90− D2Sing -> S2D (konst pi)

91− D3Sing -> S3D (konst pi)

92 exp = n1 exp

119 exp = n1 exp

93 log = n1 log

120 log = n1 log

94 sqrt = n1 sqrt

121 sqrt = n1 sqrt

@@ -107,21 +134,6 @@ instance SingI x => Floating (S x) where

107 acosh = n1 acosh

134 acosh = n1 acosh

108 atanh = n1 atanh

135 atanh = n1 atanh

109

136

110−-- Singletons

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

112−-- clear and makes adding the KnownNat constraints simple.

113−data instance Sing (n :: Shape) where

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

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

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

117−

118−instance KnownNat a => SingI ('D1 a) where

119− sing = D1Sing

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

121− sing = D2Sing

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

123− sing = D3Sing

124−

125--

137--

126-- I haven't made shapes strict, as sometimes they're not needed

138-- I haven't made shapes strict, as sometimes they're not needed

127-- (the last input gradient back for instance)

139-- (the last input gradient back for instance)

@@ -136,18 +148,28 @@ randomOfShape :: forall x m. ( MonadRandom m, SingI x ) => m (S x)

136randomOfShape = do

148randomOfShape = do

137 seed :: Int <- getRandom

149 seed :: Int <- getRandom

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

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

151+ D1Sing ->

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

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

153+

154+ D2Sing ->

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

155+ S2D (uniformSample seed (-1) 1)

156+

157+ D3Sing ->

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

158+ S3D (uniformSample seed (-1) 1)

142

159

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

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

144--

161--

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

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

146fromStorable :: forall x. SingI x => Vector Double -> Maybe (S x)

163fromStorable :: forall x. SingI x => Vector Double -> Maybe (S x)

147fromStorable xs = case sing :: Sing x of

164fromStorable xs = case sing :: Sing x of

165+ D1Sing ->

148− D1Sing -> S1D <$> H.create xs

166+ S1D <$> H.create xs

167+

168+ D2Sing ->

149− D2Sing -> S2D <$> mkL xs

169+ S2D <$> mkL xs

170+

171+ D3Sing ->

150− D3Sing -> S3D <$> mkL xs

172+ S3D <$> mkL xs

151 where

173 where

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

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

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

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

@@ -158,6 +180,18 @@ fromStorable xs = case sing :: Sing x of

158 then H.create $ NLA.reshape columns v

180 then H.create $ NLA.reshape columns v

159 else Nothing

181 else Nothing

160

182

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+

161-- Helper function for creating the number instances

195-- Helper function for creating the number instances

162n1 :: ( forall a. Floating a => a -> a ) -> S x -> S x

196n1 :: ( forall a. Floating a => a -> a ) -> S x -> S x

163n1 f (S1D x) = S1D (f x)

197n1 f (S1D x) = S1D (f x)

@@ -169,4 +203,15 @@ n2 :: ( forall a. Floating a => a -> a -> a ) -> S x -> S x -> S x

169n2 f (S1D x) (S1D y) = S1D (f x y)

203n2 f (S1D x) (S1D y) = S1D (f x y)

170n2 f (S2D x) (S2D y) = S2D (f x y)

204n2 f (S2D x) (S2D y) = S2D (f x y)

171n2 f (S3D x) (S3D y) = S3D (f x y)

205n2 f (S3D x) (S3D y) = S3D (f x y)

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

206+

207+-- Helper function for creating the number instances

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

209+nk x = case (sing :: Sing x) of

210+ D1Sing ->

211+ S1D (konst x)

212+

213+ D2Sing ->

214+ S2D (konst x)

215+

216+ D3Sing ->

217+ S3D (konst x)

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+107−60

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

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE ScopedTypeVariables #-}

3−{-# LANGUAGE RecordWildCards #-}

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.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+-}

10module Grenade.Layers.Convolution (

21module Grenade.Layers.Convolution (

11 Convolution (..)

22 Convolution (..)

12 , Convolution' (..)

23 , Convolution' (..)

@@ -16,15 +27,16 @@ module Grenade.Layers.Convolution (

16import Control.Monad.Random hiding ( fromList )

27import Control.Monad.Random hiding ( fromList )

17import Data.Maybe

28import Data.Maybe

18import Data.Proxy

29import Data.Proxy

19−import Data.Singletons.TypeLits

30+import Data.Serialize

31+

20import GHC.TypeLits

32import GHC.TypeLits

33+import Data.Kind (Type)

21

34

22import Numeric.LinearAlgebra hiding ( uniformSample, konst )

35import Numeric.LinearAlgebra hiding ( uniformSample, konst )

23import qualified Numeric.LinearAlgebra as LA

36import qualified Numeric.LinearAlgebra as LA

24import Numeric.LinearAlgebra.Static hiding ((|||), build, toRows)

37import Numeric.LinearAlgebra.Static hiding ((|||), build, toRows)

25

38

26−import Grenade.Core.Network

39+import Grenade.Core

27−import Grenade.Core.Shape

28import Grenade.Layers.Internal.Convolution

40import Grenade.Layers.Internal.Convolution

29import Grenade.Layers.Internal.Update

41import Grenade.Layers.Internal.Update

30

42

@@ -45,7 +57,7 @@ data Convolution :: Nat -- Number of channels, for the first layer this could be

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

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

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

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

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

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

48− -> * where

60+ -> Type where

49 Convolution :: ( KnownNat channels

61 Convolution :: ( KnownNat channels

50 , KnownNat filters

62 , KnownNat filters

51 , KnownNat kernelRows

63 , KnownNat kernelRows

@@ -64,7 +76,7 @@ data Convolution' :: Nat -- Number of channels, for the first layer this could b

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

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

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

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

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

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

67− -> * where

79+ -> Type where

68 Convolution' :: ( KnownNat channels

80 Convolution' :: ( KnownNat channels

69 , KnownNat filters

81 , KnownNat filters

70 , KnownNat kernelRows

82 , KnownNat kernelRows

@@ -118,14 +130,30 @@ instance ( KnownNat channels

118 , KnownNat strideColumns

130 , KnownNat strideColumns

119 , KnownNat (kernelRows * kernelColumns * channels)

131 , KnownNat (kernelRows * kernelColumns * channels)

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

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

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

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

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

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

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

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

124 in Convolution newKernel newMomentum

136 in Convolution newKernel newMomentum

125

137

126 createRandom = randomConvolution

138 createRandom = randomConvolution

127

139

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

140+instance ( KnownNat channels

141+ , KnownNat filters

142+ , KnownNat kernelRows

143+ , KnownNat kernelColumns

144+ , KnownNat strideRows

145+ , KnownNat strideColumns

146+ , KnownNat (kernelRows * kernelColumns * channels)

147+ ) => Serialize (Convolution channels filters kernelRows kernelColumns strideRows strideColumns) where

148+ put (Convolution w _) = putListOf put . toList . flatten . extract $ w

149+ get = do

150+ let f = fromIntegral $ natVal (Proxy :: Proxy filters)

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

152+ let mm = konst 0

153+ return $ Convolution wN mm

154+

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

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

129instance ( KnownNat kernelRows

157instance ( KnownNat kernelRows

130 , KnownNat kernelCols

158 , KnownNat kernelCols

131 , KnownNat filters

159 , KnownNat filters

@@ -135,27 +163,33 @@ instance ( KnownNat kernelRows

135 , KnownNat inputCols

163 , KnownNat inputCols

136 , KnownNat outputRows

164 , KnownNat outputRows

137 , KnownNat outputCols

165 , KnownNat outputCols

166+ , KnownNat channels

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

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

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

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

140− , KnownNat (kernelRows * kernelCols * 1)

169+ , KnownNat (kernelRows * kernelCols * channels)

141 , KnownNat (outputRows * filters)

170 , KnownNat (outputRows * filters)

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

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+

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

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

144 let ex = extract input

176 let ex = extract input

145 ek = extract kernel

177 ek = extract kernel

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

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

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

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

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

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

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

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

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

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

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

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

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

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

186+

152− c = im2col kx ky sx sy ex

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

153 mt = c LA.<> ek

188 mt = c LA.<> ek

154 r = col2vid 1 1 1 1 ox oy mt

189 r = col2vid 1 1 1 1 ox oy mt

155 rs = fromJust . create $ r

190 rs = fromJust . create $ r

156− in S3D rs

191+ in (S3D input, S3D rs)

157−

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

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

159 let ex = extract input

193 let ex = extract input

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

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

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

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

@@ -166,7 +200,7 @@ instance ( KnownNat kernelRows

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

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

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

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

168

202

169− c = im2col kx ky sx sy ex

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

170

204

171 eo = extract dEdy

205 eo = extract dEdy

172 ek = extract kernel

206 ek = extract kernel

@@ -174,14 +208,14 @@ instance ( KnownNat kernelRows

174 vs = vid2col 1 1 1 1 ox oy eo

208 vs = vid2col 1 1 1 1 ox oy eo

175

209

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

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

211+

177 dW = vs LA.<> tr ek

212 dW = vs LA.<> tr ek

178

213

179− xW = col2im kx ky sx sy ix iy dW

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

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

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

181

216

182

217

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

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

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

185instance ( KnownNat kernelRows

219instance ( KnownNat kernelRows

186 , KnownNat kernelCols

220 , KnownNat kernelCols

187 , KnownNat filters

221 , KnownNat filters

@@ -191,50 +225,63 @@ instance ( KnownNat kernelRows

191 , KnownNat inputCols

225 , KnownNat inputCols

192 , KnownNat outputRows

226 , KnownNat outputRows

193 , KnownNat outputCols

227 , KnownNat outputCols

194− , KnownNat channels

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

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

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

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

197− , KnownNat (kernelRows * kernelCols * channels)

230+ , KnownNat (kernelRows * kernelCols * 1)

198 , KnownNat (outputRows * filters)

231 , KnownNat (outputRows * filters)

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

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

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

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

201− let ex = extract input

234+ runForwards c (S2D input) =

202− ek = extract kernel

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

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

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

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

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

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

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

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

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

211−

212− c = vid2col kx ky sx sy ix iy ex

213− mt = c LA.<> ek

214− r = col2vid 1 1 1 1 ox oy mt

215− rs = fromJust . create $ r

216− in S3D rs

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

218− let ex = extract input

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

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

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

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

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

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

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

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

227

236

228− 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)

229

240

230− eo = extract dEdy

231− ek = extract kernel

232

241

233− 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)

234

261

235− 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)

236

265

237− 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)

238

285

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

286+ runBackwards c tape (S2D grads) =

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

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 #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE GADTs #-}

4{-# LANGUAGE GADTs #-}

4{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeFamilies #-}

6{-# LANGUAGE TypeFamilies #-}

7+{-# LANGUAGE FlexibleContexts #-}

6{-# LANGUAGE MultiParamTypeClasses #-}

8{-# 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+-}

7module Grenade.Layers.Crop (

17module Grenade.Layers.Crop (

8 Crop (..)

18 Crop (..)

9 ) where

19 ) where

10

20

11import Data.Maybe

21import Data.Maybe

12import Data.Proxy

22import Data.Proxy

13−import Data.Singletons.TypeLits

23+import Data.Serialize

24+

14import GHC.TypeLits

25import GHC.TypeLits

26+import Data.Kind (Type)

15

27

16−import Grenade.Core.Network

28+import Grenade.Core

17−import Grenade.Core.Shape

29+import Grenade.Layers.Internal.Pad

18

30

19import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

31import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

20import Numeric.LinearAlgebra.Static (extract, create)

32import Numeric.LinearAlgebra.Static (extract, create)

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

23data Crop :: Nat

35data Crop :: Nat

24 -> Nat

36 -> Nat

25 -> Nat

37 -> Nat

26− -> Nat -> * where

38+ -> Nat -> Type where

27 Crop :: Crop cropLeft cropTop cropRight cropBottom

39 Crop :: Crop cropLeft cropTop cropRight cropBottom

28

40

29instance Show (Crop cropLeft cropTop cropRight cropBottom) where

41instance Show (Crop cropLeft cropTop cropRight cropBottom) where

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

34 runUpdate _ x _ = x

46 runUpdate _ x _ = x

35 createRandom = return Crop

47 createRandom = return Crop

36

48

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

50+ put _ = return ()

51+ get = return Crop

52+

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

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

38instance ( KnownNat cropLeft

54instance ( KnownNat cropLeft

39 , KnownNat cropTop

55 , KnownNat cropTop

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

43 , KnownNat inputColumns

59 , KnownNat inputColumns

44 , KnownNat outputRows

60 , KnownNat outputRows

45 , KnownNat outputColumns

61 , KnownNat outputColumns

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

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

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

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

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

64 ) => 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) = ()

49 runForwards Crop (S2D input) =

66 runForwards Crop (S2D input) =

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

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

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

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

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

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

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

54 m = extract input

71 m = extract input

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

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

56− in S2D . fromJust . create $ r

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

57 runBackwards _ _ (S2D dEdy) =

74 runBackwards _ _ (S2D dEdy) =

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

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

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

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

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

62 eo = extract dEdy

79 eo = extract dEdy

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

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

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

81 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 (

10import Control.Monad.Random hiding (fromList)

11

12import GHC.TypeLits

13−import Grenade.Core.Shape

13+import Grenade.Core

14−import Grenade.Core.Network

15−

16−import Numeric.LinearAlgebra.Static

17−

18

14

19-- Dropout layer help to reduce overfitting.

15-- Dropout layer help to reduce overfitting.

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

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

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

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

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

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

23-- reasonable).

19-- reasonable).

24−data Dropout o =

20+data Dropout = Dropout {

25− Dropout (R o)

26− | Pass Double

21+ dropoutRate :: Double

22+ , dropoutSeed :: Int

27− deriving Show

23+ } deriving Show

28

24

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

25+instance UpdateLayer Dropout where

30− type Gradient (Dropout i) = ()

26+ type Gradient Dropout = ()

31 runUpdate _ x _ = x

27 runUpdate _ x _ = x

32 createRandom = randomDropout 0.95

28 createRandom = randomDropout 0.95

33

29

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

30+randomDropout :: MonadRandom m

35− => Double -> m (Dropout i)

31+ => Double -> m Dropout

36−randomDropout rate = do

32+randomDropout rate = Dropout rate <$> getRandom

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

41

33

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

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

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

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

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

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

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

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

@@ -1,46 +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.Singletons.TypeLits

11−import GHC.TypeLits

12−

13−import Numeric.LinearAlgebra.Static

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

15−

16−import Grenade.Core.Shape

17−import Grenade.Core.Network

18−

19−-- | Flatten Layer

20−--

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

22−--

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

24−data FlattenLayer = FlattenLayer

25− deriving Show

26−

27−instance UpdateLayer FlattenLayer where

28− type Gradient FlattenLayer = ()

29− runUpdate _ _ _ = FlattenLayer

30− createRandom = return FlattenLayer

31−

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

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

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

35−

36−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

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

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

39−

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

41− runForwards _ (S3D y) = S2D y

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

43−

44−fromJust' :: Maybe x -> x

45−fromJust' (Just x) = x

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

src/Grenade/Layers/FullyConnected.hs+32−17

@@ -1,34 +1,35 @@

1{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE RecordWildCards #-}

3{-# LANGUAGE TypeOperators #-}

2{-# LANGUAGE TypeOperators #-}

4{-# LANGUAGE TypeFamilies #-}

3{-# LANGUAGE TypeFamilies #-}

5{-# LANGUAGE MultiParamTypeClasses #-}

4{-# LANGUAGE MultiParamTypeClasses #-}

5+{-# LANGUAGE ScopedTypeVariables #-}

6module Grenade.Layers.FullyConnected (

7 FullyConnected (..)

8+ , FullyConnected' (..)

8 , randomFullyConnected

9 , randomFullyConnected

9 ) where

10 ) where

10

11

11import Control.Monad.Random hiding (fromList)

12import Control.Monad.Random hiding (fromList)

12

13

13−import Data.Singletons.TypeLits

14+import Data.Proxy

15+import Data.Serialize

14

16

17+import qualified Numeric.LinearAlgebra as LA

15import Numeric.LinearAlgebra.Static

18import Numeric.LinearAlgebra.Static

16

19

17−import Grenade.Core.Network

20+import Grenade.Core

18−import Grenade.Core.Shape

19

21

20import Grenade.Layers.Internal.Update

22import Grenade.Layers.Internal.Update

23+import GHC.TypeLits

21

24

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

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

23data FullyConnected i o = FullyConnected

26data FullyConnected i o = FullyConnected

24− !(R o) -- Bias neuron weights

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

25− !(R o) -- Bias neuron momentum

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

26− !(L o i) -- Activation weights

27− !(L o i) -- Momentum

28

29

29data FullyConnected' i o = FullyConnected'

30data FullyConnected' i o = FullyConnected'

30− !(R o) -- Bias neuron gradient

31+ !(R o) -- Bias

31− !(L o i) -- Activation gradient

32+ !(L o i) -- Activations

32

33

33instance Show (FullyConnected i o) where

34instance Show (FullyConnected i o) where

34 show FullyConnected {} = "FullyConnected"

35 show FullyConnected {} = "FullyConnected"

@@ -36,25 +37,39 @@ instance Show (FullyConnected i o) where

36instance (KnownNat i, KnownNat o) => UpdateLayer (FullyConnected i o) where

37instance (KnownNat i, KnownNat o) => UpdateLayer (FullyConnected i o) where

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

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

38

39

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

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

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

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

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

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

42− in FullyConnected newBias newBiasMomentum newActivations newMomentum

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

43

44

44 createRandom = randomFullyConnected

45 createRandom = randomFullyConnected

45

46

46instance (KnownNat i, KnownNat o) => Layer (FullyConnected i o) ('D1 i) ('D1 o) where

47instance (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

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

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

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

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

49

51

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

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

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

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

52 let wB' = dEdy

54 let wB' = dEdy

53 mm' = dEdy `outer` x

55 mm' = dEdy `outer` x

54 -- calcluate derivatives for next step

56 -- calcluate derivatives for next step

55 dWs = tr wN #> dEdy

57 dWs = tr wN #> dEdy

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

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

57

59

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

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

62+ putListOf put . LA.toList . extract $ b

63+ putListOf put . LA.toList . LA.flatten . extract $ w

64+

65+ get = do

66+ let f = fromIntegral $ natVal (Proxy :: Proxy i)

67+ b <- maybe (fail "Vector of incorrect size") return . create . LA.fromList =<< getListOf get

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

69+ let bm = konst 0

70+ let mm = konst 0

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

72+

58randomFullyConnected :: (MonadRandom m, KnownNat i, KnownNat o)

73randomFullyConnected :: (MonadRandom m, KnownNat i, KnownNat o)

59 => m (FullyConnected i o)

74 => m (FullyConnected i o)

60randomFullyConnected = do

75randomFullyConnected = do

@@ -64,4 +79,4 @@ randomFullyConnected = do

64 wN = uniformSample s2 (-1) 1

79 wN = uniformSample s2 (-1) 1

65 bm = konst 0

80 bm = konst 0

66 mm = konst 0

81 mm = konst 0

67− 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 @@

1{-# LANGUAGE ForeignFunctionInterface #-}

2module Grenade.Layers.Internal.Update (

3− decendMatrix

3+ descendMatrix

4− , decendVector

4+ , descendVector

5 ) where

6

7import Data.Maybe ( fromJust )

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

17

18import System.IO.Unsafe ( unsafePerformIO )

19

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

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−decendMatrix rate momentum regulariser weights gradient lastUpdate =

21+descendMatrix rate momentum regulariser weights gradient lastUpdate =

22 let (rows, cols) = size weights

23 len = rows * cols

24 -- Most gradients come in in ColumnMajor,

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

29 weights' = flatten . tr . extract $ weights

30 gradient' = flatten . tr . extract $ gradient

31 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'

33

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

35 -- using the internal vectors.

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

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

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

39

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

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

41−decendVector rate momentum regulariser weights gradient lastUpdate =

41+descendVector rate momentum regulariser weights gradient lastUpdate =

42 let len = size weights

43 weights' = extract weights

44 gradient' = extract gradient

45 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'

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

48

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

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

50−decendUnsafe len rate momentum regulariser weights gradient lastUpdate =

50+descendUnsafe len rate momentum regulariser weights gradient lastUpdate =

51 unsafePerformIO $ do

52 outWPtr <- mallocForeignPtrArray len

53 outMPtr <- mallocForeignPtrArray len

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

60 withForeignPtr lPtr $ \lPtr' ->

61 withForeignPtr outWPtr $ \outWPtr' ->

62 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'

64

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

66

67foreign import ccall unsafe

68− decend_cpu

68+ descend_cpu

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

70

src/Grenade/Layers/Logit.hs+35−22

@@ -1,17 +1,27 @@

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE TypeOperators #-}

3{-# LANGUAGE TypeFamilies #-}

4+{-# LANGUAGE FlexibleInstances #-}

4{-# LANGUAGE MultiParamTypeClasses #-}

5{-# 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+-}

5module Grenade.Layers.Logit (

13module Grenade.Layers.Logit (

6 Logit (..)

14 Logit (..)

7 ) where

15 ) where

8

16

9

17

10−import Data.Singletons.TypeLits

18+import Data.Serialize

11−import Grenade.Core.Network

19+import Data.Singletons

20+

12−import Grenade.Core.Shape

21+import Grenade.Core

13

22

14-- | A Logit layer.

23-- | A Logit layer.

24+--

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

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

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

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

17-- problems.

27-- problems.

@@ -23,22 +33,25 @@ instance UpdateLayer Logit where

23 runUpdate _ _ _ = Logit

33 runUpdate _ _ _ = Logit

24 createRandom = return Logit

34 createRandom = return Logit

25

35

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

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

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

37+ -- Wengert tape optimisation:

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

38+ --

29−

39+ -- Derivative of the sigmoid function is

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

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

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

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

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

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

33−

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

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

44+ type Tape Logit a b = S a

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

45+ runForwards _ a =

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

46+ let l = sigmoid a

37−

47+ in (l, l)

48+ runBackwards _ l g =

49+ let sigmoid' = l * (1 - l)

50+ in ((), sigmoid' * g)

51+

52+instance Serialize Logit where

53+ put _ = return ()

54+ get = return Logit

55+

38−logistic :: Floating a => a -> a

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

39−logistic x = 1 / (1 + exp (-x))

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

40−

41−logistic' :: Floating a => a -> a

42−logistic' x = logix * (1 - logix)

43− where

44− logix = logistic 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+69−6

@@ -1,29 +1,43 @@

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE GADTs #-}

4{-# LANGUAGE GADTs #-}

4{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeFamilies #-}

6{-# LANGUAGE TypeFamilies #-}

6{-# LANGUAGE MultiParamTypeClasses #-}

7{-# 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+-}

7module Grenade.Layers.Pad (

17module Grenade.Layers.Pad (

8 Pad (..)

18 Pad (..)

9 ) where

19 ) where

10

20

11import Data.Maybe

21import Data.Maybe

12import Data.Proxy

22import Data.Proxy

13−import Data.Singletons.TypeLits

23+import Data.Serialize

24+

14import GHC.TypeLits

25import GHC.TypeLits

26+import Data.Kind (Type)

15

27

16−import Grenade.Core.Network

28+import Grenade.Core

17−import Grenade.Core.Shape

29+import Grenade.Layers.Internal.Pad

18

30

19import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

31import Numeric.LinearAlgebra (konst, subMatrix, diagBlock)

20import Numeric.LinearAlgebra.Static (extract, create)

32import Numeric.LinearAlgebra.Static (extract, create)

21

33

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

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

35+--

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

23data Pad :: Nat

37data Pad :: Nat

24 -> Nat

38 -> Nat

25 -> Nat

39 -> Nat

26− -> Nat -> * where

40+ -> Nat -> Type where

27 Pad :: Pad padLeft padTop padRight padBottom

41 Pad :: Pad padLeft padTop padRight padBottom

28

42

29instance Show (Pad padLeft padTop padRight padBottom) where

43instance Show (Pad padLeft padTop padRight padBottom) where

@@ -34,7 +48,11 @@ instance UpdateLayer (Pad l t r b) where

34 runUpdate _ x _ = x

48 runUpdate _ x _ = x

35 createRandom = return Pad

49 createRandom = return Pad

36

50

51+instance Serialize (Pad l t r b) where

52+ put _ = return ()

53+ get = return Pad

54+

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

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

38instance ( KnownNat padLeft

56instance ( KnownNat padLeft

39 , KnownNat padTop

57 , KnownNat padTop

40 , KnownNat padRight

58 , KnownNat padRight

@@ -46,6 +64,7 @@ instance ( KnownNat padLeft

46 , (inputRows + padTop + padBottom) ~ outputRows

64 , (inputRows + padTop + padBottom) ~ outputRows

47 , (inputColumns + padLeft + padRight) ~ outputColumns

65 , (inputColumns + padLeft + padRight) ~ outputColumns

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

66 ) => 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) = ()

49 runForwards Pad (S2D input) =

68 runForwards Pad (S2D input) =

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

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

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

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

@@ -53,7 +72,7 @@ instance ( KnownNat padLeft

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

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

54 m = extract input

73 m = extract input

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

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

56− in S2D . fromJust . create $ r

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

57 runBackwards Pad _ (S2D dEdy) =

76 runBackwards Pad _ (S2D dEdy) =

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

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

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

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

@@ -62,3 +81,47 @@ instance ( KnownNat padLeft

62 m = extract dEdy

81 m = extract dEdy

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

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

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

83 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+24−7

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

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE ScopedTypeVariables #-}

3{-# LANGUAGE StandaloneDeriving #-}

4{-# LANGUAGE StandaloneDeriving #-}

@@ -6,39 +7,53 @@

6{-# LANGUAGE TypeFamilies #-}

7{-# LANGUAGE TypeFamilies #-}

7{-# LANGUAGE MultiParamTypeClasses #-}

8{-# LANGUAGE MultiParamTypeClasses #-}

8{-# LANGUAGE FlexibleContexts #-}

9{-# 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+-}

9module Grenade.Layers.Pooling (

18module Grenade.Layers.Pooling (

10 Pooling (..)

19 Pooling (..)

11 ) where

20 ) where

12

21

13import Data.Maybe

22import Data.Maybe

14import Data.Proxy

23import Data.Proxy

15−import Data.Singletons.TypeLits

24+import Data.Serialize

25+

16import GHC.TypeLits

26import GHC.TypeLits

27+import Data.Kind (Type)

17

28

18−import Grenade.Core.Network

29+import Grenade.Core

19−import Grenade.Core.Shape

20import Grenade.Layers.Internal.Pooling

30import Grenade.Layers.Internal.Pooling

21

31

22import Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRows)

32import Numeric.LinearAlgebra.Static as LAS hiding ((|||), build, toRows)

23

33

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

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

35+--

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

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

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

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

27--

38--

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

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

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

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

30--

41--

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

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

32 Pooling :: Pooling kernelRows kernelColumns strideRows strideColumns

43 Pooling :: Pooling kernelRows kernelColumns strideRows strideColumns

33

44

34instance Show (Pooling k k' s s') where

45instance Show (Pooling k k' s s') where

35 show Pooling = "Pooling"

46 show Pooling = "Pooling"

36

47

37instance UpdateLayer (Pooling kernelRows kernelColumns strideRows strideColumns) where

48instance UpdateLayer (Pooling kernelRows kernelColumns strideRows strideColumns) where

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

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

39 runUpdate _ Pooling _ = Pooling

50 runUpdate _ Pooling _ = Pooling

40 createRandom = return Pooling

51 createRandom = return Pooling

41

52

53+instance Serialize (Pooling kernelRows kernelColumns strideRows strideColumns) where

54+ put _ = return ()

55+ get = return Pooling

56+

42-- | A two dimentional image can be pooled.

57-- | A two dimentional image can be pooled.

43instance ( KnownNat kernelRows

58instance ( KnownNat kernelRows

44 , KnownNat kernelColumns

59 , KnownNat kernelColumns

@@ -51,6 +66,7 @@ instance ( KnownNat kernelRows

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

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

52 , ((outputColumns - 1) * strideColumns) ~ (inputColumns - kernelColumns)

67 , ((outputColumns - 1) * strideColumns) ~ (inputColumns - kernelColumns)

53 ) => Layer (Pooling kernelRows kernelColumns strideRows strideColumns) ('D2 inputRows inputColumns) ('D2 outputRows outputColumns) where

68 ) => 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)

54 runForwards Pooling (S2D input) =

70 runForwards Pooling (S2D input) =

55 let height = fromIntegral $ natVal (Proxy :: Proxy inputRows)

71 let height = fromIntegral $ natVal (Proxy :: Proxy inputRows)

56 width = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

72 width = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

@@ -61,7 +77,7 @@ instance ( KnownNat kernelRows

61 ex = extract input

77 ex = extract input

62 r = poolForward 1 height width kx ky sx sy ex

78 r = poolForward 1 height width kx ky sx sy ex

63 rs = fromJust . create $ r

79 rs = fromJust . create $ r

64− in S2D $ rs

80+ in (S2D input, S2D rs)

65 runBackwards Pooling (S2D input) (S2D dEdy) =

81 runBackwards Pooling (S2D input) (S2D dEdy) =

66 let height = fromIntegral $ natVal (Proxy :: Proxy inputRows)

82 let height = fromIntegral $ natVal (Proxy :: Proxy inputRows)

67 width = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

83 width = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

@@ -89,6 +105,7 @@ instance ( KnownNat kernelRows

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

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

90 , ((outputColumns - 1) * strideColumns) ~ (inputColumns - kernelColumns)

106 , ((outputColumns - 1) * strideColumns) ~ (inputColumns - kernelColumns)

91 ) => Layer (Pooling kernelRows kernelColumns strideRows strideColumns) ('D3 inputRows inputColumns channels) ('D3 outputRows outputColumns channels) where

107 ) => 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)

92 runForwards Pooling (S3D input) =

109 runForwards Pooling (S3D input) =

93 let ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

110 let ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

94 iy = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

111 iy = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

@@ -100,7 +117,7 @@ instance ( KnownNat kernelRows

100 ex = extract input

117 ex = extract input

101 r = poolForward ch ix iy kx ky sx sy ex

118 r = poolForward ch ix iy kx ky sx sy ex

102 rs = fromJust . create $ r

119 rs = fromJust . create $ r

103− in S3D rs

120+ in (S3D input, S3D rs)

104 runBackwards Pooling (S3D input) (S3D dEdy) =

121 runBackwards Pooling (S3D input) (S3D dEdy) =

105 let ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

122 let ix = fromIntegral $ natVal (Proxy :: Proxy inputRows)

106 iy = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

123 iy = fromIntegral $ natVal (Proxy :: Proxy inputColumns)

src/Grenade/Layers/Relu.hs+24−5

@@ -2,13 +2,21 @@

2{-# LANGUAGE TypeOperators #-}

3{-# LANGUAGE TypeFamilies #-}

4{-# 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+-}

5module Grenade.Layers.Relu (

12module Grenade.Layers.Relu (

6 Relu (..)

13 Relu (..)

7 ) where

14 ) where

8

15

16+import Data.Serialize

17+

9import GHC.TypeLits

18import GHC.TypeLits

10−import Grenade.Core.Network

19+import Grenade.Core

11−import Grenade.Core.Shape

12

20

13import qualified Numeric.LinearAlgebra.Static as LAS

21import qualified Numeric.LinearAlgebra.Static as LAS

14

22

@@ -23,8 +31,14 @@ instance UpdateLayer Relu where

23 runUpdate _ _ _ = Relu

31 runUpdate _ _ _ = Relu

24 createRandom = return Relu

32 createRandom = return Relu

25

33

34+instance Serialize Relu where

35+ put _ = return ()

36+ get = return Relu

37+

26instance ( KnownNat i) => Layer Relu ('D1 i) ('D1 i) where

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

39+ type Tape Relu ('D1 i) ('D1 i) = S ('D1 i)

40+

27− runForwards _ (S1D y) = S1D (relu y)

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

28 where

42 where

29 relu = LAS.dvmap (\a -> if a <= 0 then 0 else a)

43 relu = LAS.dvmap (\a -> if a <= 0 then 0 else a)

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

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

@@ -32,7 +46,9 @@ instance ( KnownNat i) => Layer Relu ('D1 i) ('D1 i) where

32 relu' = LAS.dvmap (\a -> if a <= 0 then 0 else 1)

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

33

47

34instance (KnownNat i, KnownNat j) => Layer Relu ('D2 i j) ('D2 i j) where

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

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

50+

35− runForwards _ (S2D y) = S2D (relu y)

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

36 where

52 where

37 relu = LAS.dmmap (\a -> if a <= 0 then 0 else a)

53 relu = LAS.dmmap (\a -> if a <= 0 then 0 else a)

38 runBackwards _ (S2D y) (S2D dEdy) = ((), S2D (relu' y * dEdy))

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

@@ -40,7 +56,10 @@ instance (KnownNat i, KnownNat j) => Layer Relu ('D2 i j) ('D2 i j) where

40 relu' = LAS.dmmap (\a -> if a <= 0 then 0 else 1)

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

41

57

42instance (KnownNat i, KnownNat j, KnownNat k) => Layer Relu ('D3 i j k) ('D3 i j k) where

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

59+

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

61+

43− runForwards _ (S3D y) = S3D (relu y)

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

44 where

63 where

45 relu = LAS.dmmap (\a -> if a <= 0 then 0 else a)

64 relu = LAS.dmmap (\a -> if a <= 0 then 0 else a)

46 runBackwards _ (S3D y) (S3D dEdy) = ((), S3D (relu' y * dEdy))

65 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+22−16

@@ -1,17 +1,26 @@

1{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE TypeOperators #-}

2+{-# LANGUAGE FlexibleInstances #-}

3−{-# LANGUAGE TypeFamilies #-}

4{-# LANGUAGE MultiParamTypeClasses #-}

3{-# 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+-}

5module Grenade.Layers.Tanh (

13module Grenade.Layers.Tanh (

6 Tanh (..)

14 Tanh (..)

7 ) where

15 ) where

8

16

9−import GHC.TypeLits

17+import Data.Serialize

10−import Grenade.Core.Network

18+import Data.Singletons

19+

11−import Grenade.Core.Shape

20+import Grenade.Core

12

21

13-- | A Tanh layer.

22-- | A Tanh layer.

14−-- 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.

15data Tanh = Tanh

24data Tanh = Tanh

16 deriving Show

25 deriving Show

17

26

@@ -20,17 +29,14 @@ instance UpdateLayer Tanh where

20 runUpdate _ _ _ = Tanh

29 runUpdate _ _ _ = Tanh

21 createRandom = return Tanh

30 createRandom = return Tanh

22

31

23−instance KnownNat i => Layer Tanh ('D1 i) ('D1 i) where

32+instance Serialize Tanh where

24− runForwards _ (S1D y) = S1D (tanh y)

33+ put _ = return ()

25− runBackwards _ (S1D y) (S1D dEdy) = ((), S1D (tanh' y * dEdy))

34+ get = return Tanh

26−

27−instance (KnownNat i, KnownNat j) => Layer Tanh ('D2 i j) ('D2 i j) where

28− runForwards _ (S2D y) = S2D (tanh y)

29− runBackwards _ (S2D y) (S2D dEdy) = ((), S2D (tanh' y * dEdy))

30

35

31−instance (KnownNat i, KnownNat j, KnownNat k) => Layer Tanh ('D3 i j k) ('D3 i j k) where

36+instance (a ~ b, SingI a) => Layer Tanh a b where

37+ type Tape Tanh a b = S a

32− runForwards _ (S3D y) = S3D (tanh y)

38+ runForwards _ a = (a, tanh a)

33− runBackwards _ (S3D y) (S3D dEdy) = ((), S3D (tanh' y * dEdy))

39+ runBackwards _ a g = ((), tanh' a * g)

34

40

35tanh' :: (Floating a) => a -> a

41tanh' :: (Floating a) => a -> a

36tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

42tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

src/Grenade/Layers/Trivial.hsnew file+40

@@ -0,0 +1,40 @@

1+{-# LANGUAGE DataKinds #-}

2+{-# LANGUAGE TypeOperators #-}

3+{-# LANGUAGE TypeFamilies #-}

4+{-# LANGUAGE MultiParamTypeClasses #-}

5+{-# LANGUAGE FlexibleInstances #-}

6+{-|

7+Module : Grenade.Core.Trivial

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

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

10+License : BSD2

11+Stability : experimental

12+-}

13+module Grenade.Layers.Trivial (

14+ Trivial (..)

15+ ) where

16+

17+import Data.Serialize

18+

19+import Grenade.Core

20+

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.

25+data Trivial = Trivial

26+ deriving Show

27+

28+instance Serialize Trivial where

29+ put _ = return ()

30+ get = return Trivial

31+

32+instance UpdateLayer Trivial where

33+ type Gradient Trivial = ()

34+ runUpdate _ _ _ = Trivial

35+ createRandom = return Trivial

36+

37+instance (a ~ b) => Layer Trivial a b where

38+ type Tape Trivial a b = ()

39+ runForwards _ a = ((), a)

40+ runBackwards _ _ y = ((), y)

src/Grenade/Recurrent.hs+26−6

@@ -1,9 +1,29 @@

1module 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+

3 ) where

16 ) where

4

17

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

18+import Grenade.Recurrent.Core

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

19+import Grenade.Recurrent.Layers

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

20+

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

21+{- $recurrent

9−import Grenade.Recurrent.Layers.Trivial as X

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+286−56

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

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE GADTs #-}

3{-# LANGUAGE GADTs #-}

3{-# LANGUAGE TypeOperators #-}

4{-# LANGUAGE TypeOperators #-}

@@ -6,93 +7,322 @@

6{-# LANGUAGE FlexibleContexts #-}

7{-# LANGUAGE FlexibleContexts #-}

7{-# LANGUAGE FlexibleInstances #-}

8{-# LANGUAGE FlexibleInstances #-}

8{-# LANGUAGE EmptyDataDecls #-}

9{-# LANGUAGE EmptyDataDecls #-}

10+{-# LANGUAGE RankNTypes #-}

11+{-# LANGUAGE BangPatterns #-}

12+{-# LANGUAGE ScopedTypeVariables #-}

13+{-# LANGUAGE UndecidableInstances #-}

14+

9module Grenade.Recurrent.Core.Network (

15module Grenade.Recurrent.Core.Network (

10 Recurrent

16 Recurrent

11 , FeedForward

17 , FeedForward

12− , RecurrentLayer (..)

18+

13− , RecurrentUpdateLayer (..)

14 , RecurrentNetwork (..)

19 , RecurrentNetwork (..)

15 , RecurrentInputs (..)

20 , RecurrentInputs (..)

16− , CreatableRecurrent (..)

21+ , RecurrentTape (..)

22+ , RecurrentGradient (..)

23+

24+ , randomRecurrent

25+ , runRecurrent

26+ , runRecurrent'

27+ , applyRecurrentUpdate

17 ) where

28 ) where

18

29

19

30

20import Control.Monad.Random ( MonadRandom )

31import Control.Monad.Random ( MonadRandom )

21−import Data.Singletons ( SingI )

32+import Data.Serialize

22

33

23−import Grenade.Core.Shape

34+import Data.Kind (Type)

24−import Grenade.Core.Network

25

35

36+import Grenade.Core

37+import Grenade.Recurrent.Core.Layer

38+import Prelude.Singletons

26

39

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

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

28-- forward layer.

41-- forward layer.

29−data FeedForward :: * -> *

42+data FeedForward :: Type -> Type

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

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

31−data Recurrent :: * -> *

44+data Recurrent :: Type -> Type

32−

45+

33−-- | Class for a recurrent layer.

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

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

47+--

35−-- of an extra recurrent data shape.

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

36−class UpdateLayer x => RecurrentUpdateLayer x where

49+--

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

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

38− type RecurrentShape x :: Shape

51+--

39−

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

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

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

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

54+--

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

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

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

56+-- for these empty data types.

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

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

46− -- the layer above.

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

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

49−

50−data RecurrentNetwork :: [*] -> [Shape] -> * where

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

58+ RNil :: SingI i

51− OR :: (SingI i, SingI o, Layer x i o) => !x -> RecurrentNetwork '[FeedForward x] '[i, o]

59+ => RecurrentNetwork '[] '[i]

60+

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

62+ => !x

63+ -> !(RecurrentNetwork xs (h ': hs))

52− (:~~>) :: (SingI i, Layer x i h) => !x -> !(RecurrentNetwork xs (h ': hs)) -> RecurrentNetwork (FeedForward x ': xs) (i ': 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))

53− (:~@>) :: (SingI i, RecurrentLayer x i h) => !x -> !(RecurrentNetwork xs (h ': hs)) -> RecurrentNetwork (Recurrent x ': xs) (i ': h ': hs)

69+ -> RecurrentNetwork (Recurrent x ': xs) (i ': h ': hs)

54infixr 5 :~~>

70infixr 5 :~~>

55infixr 5 :~@>

71infixr 5 :~@>

56

72

57−instance Show (RecurrentNetwork l h) where

73+-- | Gradient of a network.

58− show (OR a) = "OR " ++ show a

74+--

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

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

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

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

77+ RGNil :: RecurrentGradient '[]

61

78

79+ (://>) :: UpdateLayer x

80+ => Gradient x

81+ -> RecurrentGradient xs

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

62

83

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

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

64-- Parameterised on the layers of a Network.

85-- Parameterised on the layers of a Network.

65−data RecurrentInputs :: [*] -> * where

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

66− ORS :: UpdateLayer x

87+ RINil :: RecurrentInputs '[]

67− => () -> RecurrentInputs '[FeedForward x]

88+

68− (:~~+>) :: UpdateLayer x

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

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

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

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

91+

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

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

72−infixr 5 :~~+>

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

73−infixr 5 :~@+>

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+

74

206

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

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

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

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

77−class CreatableRecurrent (xs :: [*]) (ss :: [Shape]) where

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

78 -- | Create a network of the types requested

210 -- | Create a network of the types requested

79− randomRecurrent :: MonadRandom m => m (RecurrentNetwork xs ss, RecurrentInputs xs)

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

80

212

81−instance (SingI i, SingI o, Layer x i o) => CreatableRecurrent (FeedForward x ': '[]) (i ': o ': '[]) where

213+instance SingI i => CreatableRecurrent '[] '[i] where

82− randomRecurrent = do

214+ randomRecurrent =

83− thisLayer <- createRandom

84− return (OR thisLayer, ORS ())

215+ return RNil

85

216

86−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

87 randomRecurrent = do

218 randomRecurrent = do

88 thisLayer <- createRandom

219 thisLayer <- createRandom

89− (rest, resti) <- randomRecurrent

220+ rest <- randomRecurrent

90− return (thisLayer :~~> rest, () :~~+> resti)

221+ return (thisLayer :~~> rest)

91

222

92−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

93 randomRecurrent = do

224 randomRecurrent = do

94 thisLayer <- createRandom

225 thisLayer <- createRandom

95− thisShape <- randomOfShape

96− (rest, resti) <- randomRecurrent

226+ rest <- randomRecurrent

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

227+ return (thisLayer :~@> rest)

228+

229+-- | Add very simple serialisation to the recurrent network

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

231+ put RNil = pure ()

232+ get = pure RNil

233+

234+instance (SingI i, Layer x i o, Serialize x, Serialize (RecurrentNetwork xs (o ': rs))) => Serialize (RecurrentNetwork (FeedForward x ': xs) (i ': o ': rs)) where

235+ put (x :~~> r) = put x >> put r

236+ get = (:~~>) <$> get <*> get

237+

238+instance (SingI i, RecurrentLayer x i o, Serialize x, Serialize (RecurrentNetwork xs (o ': rs))) => Serialize (RecurrentNetwork (Recurrent x ': xs) (i ': o ': rs)) where

239+ put (x :~@> r) = put x >> put r

240+ get = (:~@>) <$> get <*> get

241+

242+instance (Serialize (RecurrentInputs '[])) where

243+ put _ = return ()

244+ get = return RINil

245+

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

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

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

249+

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

98

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+89−123

@@ -6,139 +6,105 @@

6{-# LANGUAGE TypeFamilies #-}

7{-# LANGUAGE FlexibleContexts #-}

8{-# LANGUAGE RankNTypes #-}

9−{-# LANGUAGE RecordWildCards #-}

9+

10module Grenade.Recurrent.Core.Runner (

11+ runRecurrentExamples

12+ , runRecurrentBackprop

13+ , backPropagateRecurrent

11− trainRecurrent

14+ , trainRecurrent

12− , runRecurrent

15+

16+ , RecurrentGradients

13 ) where

17 ) where

14

18

15−import Data.Singletons.Prelude

19+import Data.List ( foldl' )

16−import Grenade.Core.Network

17−import Grenade.Core.Shape

20+import Grenade.Core

18

21

19import Grenade.Recurrent.Core.Network

22import 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+

20

55

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

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

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

58+ => RecurrentNetwork layers shapes

59+ -> RecurrentInputs layers

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

61+ -> (RecurrentGradients layers, RecurrentInputs layers)

62+backPropagateRecurrent network recinputs examples =

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

64+

65+ backPropagations = zipWith makeError outForwards targets

66+

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

68+

69+ gradients = fmap fst outBackwards

70+ in (gradients, input')

71+

72+ where

73+

74+ inputs = fst <$> examples

75+ targets = snd <$> examples

76+

77+ makeError :: (x, S (Last shapes)) -> Maybe (S (Last shapes)) -> (x, S (Last shapes))

78+ makeError (x, _) Nothing = (x, 0)

79+ makeError (x, y) (Just t) = (x, y - t)

80+

81+

22−trainRecurrent :: forall shapes layers. SingI (Last shapes)

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

23 => LearningParameters

83 => LearningParameters

24 -> RecurrentNetwork layers shapes

84 -> RecurrentNetwork layers shapes

25 -> RecurrentInputs layers

85 -> RecurrentInputs layers

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

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

27 -> (RecurrentNetwork layers shapes, RecurrentInputs layers)

87 -> (RecurrentNetwork layers shapes, RecurrentInputs layers)

28trainRecurrent rate network recinputs examples =

88trainRecurrent rate network recinputs examples =

29− updateBack $ go inputs network recinputs

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

30− where

90+

31− inputs = fst <$> examples

32− targets = snd <$> examples

33− updateBack (a,recgrad,_) = (a,updateRecInputs rate recinputs recgrad)

91+ newInputs = updateRecInputs rate recinputs recinputs'

34−

92+

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

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

36− => [S (Head js)] -- ^ input vector

94+

37− -> RecurrentNetwork sublayers js -- ^ network to train

95+ in (newNetwork, newInputs)

38− -> RecurrentInputs sublayers

96+

39− -> (RecurrentNetwork sublayers js, RecurrentInputs sublayers, [S (Head js)])

97+updateRecInputs :: Fractional (RecurrentInputs sublayers)

40−

98+ => LearningParameters

41− -- This is a simple non-recurrent layer, just map it forwards

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

43− -- (and probably will in future).

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

45− = let ys = runForwards layer <$> xs

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

47− (newFN, ig, grads) = go ys n nIn

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

49− back = uncurry (runBackwards layer) <$> zip (reverse xs) grads

50− -- the new trained layer.

51− newlayer = runUpdates rate layer (fst <$> back)

52−

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

54−

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

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

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

58− = let ys = scanlFrom layer g xs

59−

60− (newFN, ig, grads) = go (snd <$> ys) n nIn

61−

62− backExamples = zip3 (fst <$> reverse ys) (reverse xs) grads

63−

64− (rg, back) = myscanbackward layer backExamples

65− -- the new trained layer.

66− newlayer = runUpdates rate layer (fst <$> back)

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

68−

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

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

71− go !xs (OR layer) (ORS ())

72− = let ys = runForwards layer <$> xs

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

74− back = uncurry (runBackwards layer) <$> zip xs (zipWith makeError ys targets)

75− -- the new trained layer.

76− newlayer = runUpdates rate layer (reverse $ fst <$> back)

77− in (OR newlayer, ORS (), reverse (snd <$> back))

78−

79− go _ _ _ =

80− error "Impossible for network and recurrent inputs to have different shapes"

81−

82−

83− makeError :: S (Last shapes) -> Maybe (S (Last shapes)) -> S (Last shapes)

84− makeError _ Nothing = 0

85− makeError y (Just t) = y - t

86−

87− updateRecInputs :: forall sublayers.

88− LearningParameters

89− -> RecurrentInputs sublayers

90− -> RecurrentInputs sublayers

91− -> RecurrentInputs sublayers

92−

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

94− = () :~~+> updateRecInputs l xs ys

95−

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

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

98−

99− updateRecInputs _ (ORS ()) (ORS ())

100− = ORS ()

101− updateRecInputs _ _ _

102− = error "Impossible for updateRecInputs to have different shapes"

103−

104−scanlFrom :: forall x i o. RecurrentLayer x i o

105− => x -- ^ the layer

106− -> S (RecurrentShape x) -- ^ place to start

107− -> [S i] -- ^ list of inputs to scan through

108− -> [(S (RecurrentShape x), S o)] -- ^ list of scan inputs and outputs

109−scanlFrom !layer !recShape (x:xs) =

110− let (lerec, lepush) = runRecurrentForwards layer recShape x

111− in (recShape, lepush) : scanlFrom layer lerec xs

112−scanlFrom _ _ [] = []

113−

114−myscanbackward :: forall x i o. RecurrentLayer x i o

115− => x -- ^ the layer

116− -> [(S (RecurrentShape x), S i, S o)] -- ^ the list of inputs and output to scan over

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

118−myscanbackward layer =

119− goX 0

120− where

121− goX :: S (RecurrentShape x) -> [(S (RecurrentShape x), S i, S o)] -> (S (RecurrentShape x), [(Gradient x, S i)])

122− goX !lastback ((recShape, lastin, backgrad):xs) =

123− let (layergrad, recgrad, ingrad) = runRecurrentBackwards layer recShape lastin lastback backgrad

124− (pushedback, ll) = goX recgrad xs

125− in (pushedback, (layergrad, ingrad) : ll)

126− goX !lastback [] = (lastback, [])

127−

128−-- | Just forwards propagation with no training.

129−runRecurrent :: RecurrentNetwork layers shapes

130− -> RecurrentInputs layers -> S (Head shapes)

99+ -> RecurrentInputs sublayers

100+ -> RecurrentInputs sublayers

131− -> (RecurrentInputs layers, S (Last shapes))

101+ -> RecurrentInputs sublayers

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

102+

133− = let ys = runForwards layer x

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

134− (nr', o) = runRecurrent n nr ys

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

135− in (() :~~+> nr', o)

105+

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

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

137− = let (recin', y) = runRecurrentForwards layer recin x

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

138− (nr', o) = runRecurrent n nr y

108+

139− in (recin' :~@+> nr', o)

109+updateRecInputs _ RINil RINil

140−runRecurrent (OR layer) (ORS ()) !x

110+ = RINil

141− = (ORS (), runForwards layer x)

142−

143−runRecurrent _ _ _

144− = error "Impossible for the gradients of a network to have a different length or shape to the network"

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 #-}

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE GADTs #-}

3{-# LANGUAGE GADTs #-}

3−{-# LANGUAGE RecordWildCards #-}

4{-# LANGUAGE TypeOperators #-}

5{-# LANGUAGE TypeFamilies #-}

6{-# LANGUAGE MultiParamTypeClasses #-}

7{-# LANGUAGE FlexibleContexts #-}

8{-# LANGUAGE UndecidableInstances #-}

9

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

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

12module Grenade.Recurrent.Layers.BasicRecurrent (

10module Grenade.Recurrent.Layers.BasicRecurrent (

13 BasicRecurrent (..)

11 BasicRecurrent (..)

14 , randomBasicRecurrent

12 , randomBasicRecurrent

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

18

16

19import Control.Monad.Random ( MonadRandom, getRandom )

17import Control.Monad.Random ( MonadRandom, getRandom )

20

18

19+

21−import Data.Singletons.TypeLits

20+import Data.Kind (Type)

22

21

23import Numeric.LinearAlgebra.Static

22import Numeric.LinearAlgebra.Static

24

23

25import GHC.TypeLits

24import GHC.TypeLits

26

25

27−import Grenade.Core.Network

26+import Grenade.Core

28−import Grenade.Core.Shape

29−import Grenade.Recurrent.Core.Network

27+import Grenade.Recurrent.Core

30

28

31data BasicRecurrent :: Nat -- Input layer size

29data BasicRecurrent :: Nat -- Input layer size

32 -> Nat -- Output layer size

30 -> Nat -- Output layer size

33− -> * where

31+ -> Type where

34 BasicRecurrent :: ( KnownNat input

32 BasicRecurrent :: ( KnownNat input

35 , KnownNat output

33 , KnownNat output

36 , KnownNat matrixCols

34 , KnownNat matrixCols

@@ -43,7 +41,7 @@ data BasicRecurrent :: Nat -- Input layer size

43

41

44data BasicRecurrent' :: Nat -- Input layer size

42data BasicRecurrent' :: Nat -- Input layer size

45 -> Nat -- Output layer size

43 -> Nat -- Output layer size

46− -> * where

44+ -> Type where

47 BasicRecurrent' :: ( KnownNat input

45 BasicRecurrent' :: ( KnownNat input

48 , KnownNat output

46 , KnownNat output

49 , KnownNat matrixCols

47 , KnownNat matrixCols

@@ -58,27 +56,29 @@ instance Show (BasicRecurrent i o) where

58instance (KnownNat i, KnownNat o, KnownNat (i + o)) => UpdateLayer (BasicRecurrent i o) where

56instance (KnownNat i, KnownNat o, KnownNat (i + o)) => UpdateLayer (BasicRecurrent i o) where

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

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

60

58

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

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

62− let newBiasMomentum = konst learningMomentum * oldBiasMomentum - konst learningRate * biasGradient

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

63 newBias = oldBias + newBiasMomentum

61 newBias = oldBias + newBiasMomentum

64− newMomentum = konst learningMomentum * oldMomentum - konst learningRate * activationGradient

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

65− regulariser = konst (learningRegulariser * learningRate) * oldActivations

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

66 newActivations = oldActivations + newMomentum - regulariser

64 newActivations = oldActivations + newMomentum - regulariser

67 in BasicRecurrent newBias newBiasMomentum newActivations newMomentum

65 in BasicRecurrent newBias newBiasMomentum newActivations newMomentum

68

66

69 createRandom = randomBasicRecurrent

67 createRandom = randomBasicRecurrent

70

68

71instance (KnownNat i, KnownNat o, KnownNat (i + o), i <= (i + o), o ~ ((i + o) - i)) => RecurrentUpdateLayer (BasicRecurrent i o) where

69instance (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)

73

71

74instance (KnownNat i, KnownNat o, KnownNat (i + o), i <= (i + o), o ~ ((i + o) - i)) => RecurrentLayer (BasicRecurrent i o) ('D1 i) ('D1 o) where

72instance (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))

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

76 runRecurrentForwards (BasicRecurrent wB _ wN _) (S1D lastOutput) (S1D thisInput) =

77 let thisOutput = S1D $ wB + wN #> (thisInput # lastOutput)

78− in (thisOutput, thisOutput)

78+ in ((S1D lastOutput, S1D thisInput), thisOutput, thisOutput)

79

80 -- 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) =

82 let biasGradient = (dRec + dEdy)

83 layerGrad = (dRec + dEdy) `outer` (thisInput # lastOutput)

84 -- 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+68−47

@@ -1,16 +1,15 @@

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE BangPatterns #-}

2{-# LANGUAGE BangPatterns #-}

2{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE GADTs #-}

4{-# LANGUAGE GADTs #-}

4{-# LANGUAGE RankNTypes #-}

5{-# LANGUAGE RankNTypes #-}

5−{-# LANGUAGE RecordWildCards #-}

6{-# LANGUAGE TypeOperators #-}

7{-# LANGUAGE TypeFamilies #-}

8{-# LANGUAGE MultiParamTypeClasses #-}

9{-# LANGUAGE FlexibleContexts #-}

10{-# LANGUAGE ViewPatterns #-}

11+{-# LANGUAGE ScopedTypeVariables #-}

11

12

12−-- GHC 7.10 doesn't see recurrent run functions as total.

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

14module Grenade.Recurrent.Layers.LSTM (

13module Grenade.Recurrent.Layers.LSTM (

15 LSTM (..)

14 LSTM (..)

16 , LSTMWeights (..)

15 , LSTMWeights (..)

@@ -20,30 +19,32 @@ module Grenade.Recurrent.Layers.LSTM (

20import Control.Monad.Random ( MonadRandom, getRandom )

19import Control.Monad.Random ( MonadRandom, getRandom )

21

20

22-- import Data.List ( foldl1' )

21-- import Data.List ( foldl1' )

23−import Data.Singletons.TypeLits

22+import Data.Proxy

23+import Data.Serialize

24+import Data.Kind (Type)

24

25

26+import qualified Numeric.LinearAlgebra as LA

25import Numeric.LinearAlgebra.Static

27import Numeric.LinearAlgebra.Static

26

28

27−import Grenade.Core.Network

29+import Grenade.Core

28−import Grenade.Core.Shape

30+import Grenade.Recurrent.Core

29−

30import Grenade.Layers.Internal.Update

31import Grenade.Layers.Internal.Update

32+import GHC.TypeLits

31

33

32−import Grenade.Recurrent.Core.Network

33

34

34-- | Long Short Term Memory Recurrent unit

35-- | Long Short Term Memory Recurrent unit

35--

36--

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

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

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

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

38-- at all.

39-- at all.

39−data LSTM :: Nat -> Nat -> * where

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

40 LSTM :: ( KnownNat input

41 LSTM :: ( KnownNat input

41 , KnownNat output

42 , KnownNat output

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

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

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

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

44 -> LSTM input output

45 -> LSTM input output

45

46

46−data LSTMWeights :: Nat -> Nat -> * where

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

47 LSTMWeights :: ( KnownNat input

48 LSTMWeights :: ( KnownNat input

48 , KnownNat output

49 , KnownNat output

49 ) => {

50 ) => {

@@ -70,7 +71,7 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

70

71

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

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

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

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

73− runUpdate LearningParameters {..} (LSTM w m) g =

74+ runUpdate lp (LSTM w m) g =

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

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

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

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

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

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

@@ -87,11 +88,11 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

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

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

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

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

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

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

90− decendMatrix learningRate learningMomentum learningRegulariser weights gradient momentum

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

91

92

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

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

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

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

94− decendVector learningRate learningMomentum learningRegulariser weights gradient momentum

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

95

96

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

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

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

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

@@ -118,31 +119,38 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where

118

119

119 -- v :: forall x ix. (x -> (R ix)) -> x -> x -> R ix

120 -- v :: forall x ix. (x -> (R ix)) -> x -> x -> R ix

120 -- v e (e -> a) (e -> b) = a + b

121 -- v e (e -> a) (e -> b) = a + b

121−

122 createRandom = randomLSTM

123

124instance (KnownNat i, KnownNat o) => RecurrentUpdateLayer (LSTM i o) where

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

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

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

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

128

129instance (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)

130 -- Forward propagation for the LSTM layer.

134 -- Forward propagation for the LSTM layer.

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

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

132− runRecurrentForwards (LSTM (LSTMWeights {..}) _) (S1D cell) (S1D input) =

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

133 let -- Forget state vector

137 let -- Forget state vector

134− 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

135 -- Input state vector

140 -- Input state vector

136− 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

137 -- Output state vector

143 -- Output state vector

138− 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

139 -- Cell input state vector

146 -- Cell input state vector

140− c_x = tanh $ lstmBc + lstmWc #> input

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

148+ c_x = tanh c_s

141 -- Cell state

149 -- Cell state

142 c_t = f_t * cell + i_t * c_x

150 c_t = f_t * cell + i_t * c_x

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

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

144 h_t = o_t * c_t

152 h_t = o_t * c_t

145− 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)

146

154

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

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

148 -- We're doing all the derivatives by hand here, so one should

156 -- We're doing all the derivatives by hand here, so one should

@@ -150,29 +158,8 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

150 --

158 --

151 -- There's a test version using the AD library without hmatrix in the test

159 -- There's a test version using the AD library without hmatrix in the test

152 -- suite. These should match always.

160 -- suite. These should match always.

153− runRecurrentBackwards (LSTM (LSTMWeights {..}) _) (S1D cell) (S1D input) (S1D cellGrad) (S1D h_t') =

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') =

154− -- We're not keeping the Wengert tape during the forward pass,

155− -- so we're duplicating some work here.

156− --

157− -- If I was being generous, I'd call it checkpointing.

158− --

159− -- Maybe think about better ways to store some intermediate states.

160− let -- Forget state vector

161− f_s = lstmBf + lstmWf #> input + lstmUf #> cell

162− f_t = sigmoid f_s

163− -- Input state vector

164− i_s = lstmBi + lstmWi #> input + lstmUi #> cell

165− i_t = sigmoid i_s

166− -- Output state vector

167− o_s = lstmBo + lstmWo #> input + lstmUo #> cell

168− o_t = sigmoid o_s

169− -- Cell input state vector

170− c_s = lstmBc + lstmWc #> input

171− c_x = tanh c_s

172− -- Cell state

173− c_t = f_t * cell + i_t * c_x

174−

175− -- Reverse Mode AD Derivitives

162+ let -- Reverse Mode AD Derivitives

176 c_t' = h_t' * o_t + cellGrad

163 c_t' = h_t' * o_t + cellGrad

177

164

178 f_t' = c_t' * cell

165 f_t' = c_t' * cell

@@ -188,8 +175,8 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w

188 c_s' = tanh' c_s * c_x'

175 c_s' = tanh' c_s * c_x'

189

176

190 -- The derivatives to pass sideways (recurrent) and downwards

177 -- The derivatives to pass sideways (recurrent) and downwards

191− cell' = tr lstmUf #> f_s' + tr lstmUo #> o_s' + tr lstmUi #> i_s' + c_t' * f_t

178+ cell' = tr (lstmUf lw) #> f_s' + tr (lstmUo lw) #> o_s' + tr (lstmUi lw) #> i_s' + c_t' * f_t

192− input' = tr lstmWf #> f_s' + tr lstmWo #> o_s' + tr lstmWi #> i_s' + tr lstmWc #> c_s'

179+ input' = tr (lstmWf lw) #> f_s' + tr (lstmWo lw) #> o_s' + tr (lstmWi lw) #> i_s' + tr (lstmWc lw) #> c_s'

193

180

194 -- Calculate the gradient Matricies for the input

181 -- Calculate the gradient Matricies for the input

195 lstmWf' = f_s' `outer` input

182 lstmWf' = f_s' `outer` input

@@ -234,7 +221,8 @@ randomLSTM = do

234

221

235-- | Maths

222-- | Maths

236--

223--

237−-- TODO: move to not here

224+-- TODO: Move to not here

225+-- Optimise backwards derivative

238sigmoid :: Floating a => a -> a

226sigmoid :: Floating a => a -> a

239sigmoid x = 1 / (1 + exp (-x))

227sigmoid x = 1 / (1 + exp (-x))

240

228

@@ -245,3 +233,36 @@ sigmoid' x = logix * (1 - logix)

245

233

246tanh' :: (Floating a) => a -> a

234tanh' :: (Floating a) => a -> a

247tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

235tanh' t = 1 - s ^ (2 :: Int) where s = tanh t

236+

237+instance (KnownNat i, KnownNat o) => Serialize (LSTM i o) where

238+ put (LSTM lw _) = do

239+ u (lstmWf lw)

240+ u (lstmUf lw)

241+ v (lstmBf lw)

242+ u (lstmWi lw)

243+ u (lstmUi lw)

244+ v (lstmBi lw)

245+ u (lstmWo lw)

246+ u (lstmUo lw)

247+ v (lstmBo lw)

248+ u (lstmWc lw)

249+ v (lstmBc lw)

250+ where

251+ u :: forall a b. (KnownNat a, KnownNat b) => Putter (L b a)

252+ u = putListOf put . LA.toList . LA.flatten . extract

253+ v :: forall a. (KnownNat a) => Putter (R a)

254+ v = putListOf put . LA.toList . extract

255+

256+ get = do

257+ w <- LSTMWeights <$> u <*> u <*> v <*> u <*> u <*> v <*> u <*> u <*> v <*> u <*> v

258+ return $ LSTM w (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

259+ where

260+ u :: forall a b. (KnownNat a, KnownNat b) => Get (L b a)

261+ u = let f = fromIntegral $ natVal (Proxy :: Proxy a)

262+ in maybe (fail "Vector of incorrect size") return . create . LA.reshape f . LA.fromList =<< getListOf get

263+ v :: forall a. (KnownNat a) => Get (R a)

264+ v = maybe (fail "Vector of incorrect size") return . create . LA.fromList =<< getListOf get

265+

266+ w0 = konst 0

267+ u0 = konst 0

268+ v0 = konst 0

src/Grenade/Recurrent/Layers/Trivial.hsdeleted−23

@@ -1,23 +0,0 @@

1−{-# LANGUAGE DataKinds #-}

2−{-# LANGUAGE TypeOperators #-}

3−{-# LANGUAGE TypeFamilies #-}

4−{-# LANGUAGE MultiParamTypeClasses #-}

5−{-# LANGUAGE FlexibleInstances #-}

6−module Grenade.Recurrent.Layers.Trivial (

7− Trivial (..)

8− ) where

9−

10−import Grenade.Core.Network

11−

12−-- | A trivial layer.

13−data Trivial = Trivial

14− deriving Show

15−

16−instance UpdateLayer Trivial where

17− type Gradient Trivial = ()

18− runUpdate _ _ _ = Trivial

19− createRandom = return Trivial

20−

21−instance (a ~ b) => Layer Trivial a b where

22− runForwards _ = id

23− runBackwards _ _ y = ((), y)

src/Grenade/Utils/OneHot.hs+17−7

@@ -11,22 +11,26 @@ module Grenade.Utils.OneHot (

11 , hotMap

12 , makeHot

13 , unHot

14+ , sample

14 ) where

15 ) where

15

16

17+import qualified Control.Monad.Random as MR

18+

16import Data.List ( group, sort )

19import Data.List ( group, sort )

17

20

18import Data.Map ( Map )

21import Data.Map ( Map )

19import qualified Data.Map as M

22import qualified Data.Map as M

20

23

21import Data.Proxy

24import Data.Proxy

22−import Data.Singletons.TypeLits

23

25

24import Data.Vector ( Vector )

26import Data.Vector ( Vector )

25import qualified Data.Vector as V

27import qualified Data.Vector as V

28+import qualified Data.Vector.Storable as VS

26

29

27import Numeric.LinearAlgebra ( maxIndex )

30import Numeric.LinearAlgebra ( maxIndex )

28import Numeric.LinearAlgebra.Devel

31import Numeric.LinearAlgebra.Devel

29import Numeric.LinearAlgebra.Static

32import Numeric.LinearAlgebra.Static

33+import GHC.TypeLits

30

34

31import Grenade.Core.Shape

35import Grenade.Core.Shape

32

36

@@ -48,15 +52,16 @@ oneHot hot =

48

52

49-- | Create a one hot map from any enumerable.

53-- | Create a one hot map from any enumerable.

50-- Returns a map, and the ordered list for the reverse transformation

54-- Returns a map, and the ordered list for the reverse transformation

51−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)

52hotMap n as =

56hotMap n as =

53 let len = fromIntegral $ natVal n

57 let len = fromIntegral $ natVal n

54 uniq = [ c | (c:_) <- group $ sort as]

58 uniq = [ c | (c:_) <- group $ sort as]

55 hotl = length uniq

59 hotl = length uniq

56− in if hotl <= len

60+ in if hotl == len

57 then

61 then

58− Just (M.fromList $ zip uniq [0..], V.fromList uniq)

62+ Right (M.fromList $ zip uniq [0..], V.fromList uniq)

59− else Nothing

63+ else

64+ Left ("Couldn't create hotMap of size " ++ show len ++ " from vector with " ++ show hotl ++ " unique characters")

60

65

61-- | From a map and value, create a 1D Shape

66-- | From a map and value, create a 1D Shape

62-- with one index hot (1) with the rest 0.

67-- with one index hot (1) with the rest 0.

@@ -76,9 +81,14 @@ makeHot m x = do

76 return vec

81 return vec

77 else Nothing

82 else Nothing

78

83

79−unHot :: forall a n. (KnownNat n)

84+unHot :: forall a n. KnownNat n

80− => Vector a -> (S ('D1 n)) -> Maybe a

85+ => Vector a -> S ('D1 n) -> Maybe a

81unHot v (S1D xs)

86unHot v (S1D xs)

82 = (V.!?) v

87 = (V.!?) v

83 $ maxIndex (extract xs)

88 $ maxIndex (extract xs)

84

89

90+sample :: forall a n m. (KnownNat n, MR.MonadRandom m)

91+ => Double -> Vector a -> S ('D1 n) -> m a

92+sample temperature v (S1D xs) = do

93+ ix <- MR.fromList . zip [0..] . fmap (toRational . exp . (/ temperature) . log) . VS.toList . extract $ xs

94+ return $ v V.! ix

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+38−33

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

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE TemplateHaskell #-}

2{-# LANGUAGE TemplateHaskell #-}

2{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE GADTs #-}

4{-# LANGUAGE GADTs #-}

@@ -15,15 +16,21 @@ import Data.Singletons ()

15import GHC.TypeLits

16import GHC.TypeLits

16import GHC.TypeLits.Witnesses

17import GHC.TypeLits.Witnesses

17

18

18−import Grenade.Core.Shape

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

20+import Data.Kind (Type)

21+#endif

22+

19−import Grenade.Core.Network

23+import Grenade.Core

20import Grenade.Layers.Convolution

24import Grenade.Layers.Convolution

21

25

22−import Disorder.Jack

26+import Hedgehog

27+import qualified Hedgehog.Gen as Gen

23

28

24−import Test.Jack.Hmatrix

29+import Test.Hedgehog.Hmatrix

30+import Test.Hedgehog.TypeLits

31+import Test.Hedgehog.Compat

25

32

26−data OpaqueConvolution :: * where

33+data OpaqueConvolution :: Type where

27 OpaqueConvolution :: Convolution channels filters kernelRows kernelColumns strideRows strideColumns -> OpaqueConvolution

34 OpaqueConvolution :: Convolution channels filters kernelRows kernelColumns strideRows strideColumns -> OpaqueConvolution

28

35

29instance Show OpaqueConvolution where

36instance Show OpaqueConvolution where

@@ -37,17 +44,17 @@ genConvolution :: ( KnownNat channels

37 , KnownNat strideColumns

44 , KnownNat strideColumns

38 , KnownNat kernelFlattened

45 , KnownNat kernelFlattened

39 , kernelFlattened ~ (kernelRows * kernelColumns * channels)

46 , kernelFlattened ~ (kernelRows * kernelColumns * channels)

40− ) => Jack (Convolution channels filters kernelRows kernelColumns strideRows strideColumns)

47+ ) => Gen (Convolution channels filters kernelRows kernelColumns strideRows strideColumns)

41genConvolution = Convolution <$> uniformSample <*> uniformSample

48genConvolution = Convolution <$> uniformSample <*> uniformSample

42

49

43−genOpaqueOpaqueConvolution :: Jack OpaqueConvolution

50+genOpaqueOpaqueConvolution :: Gen OpaqueConvolution

44genOpaqueOpaqueConvolution = do

51genOpaqueOpaqueConvolution = do

45− Just channels <- someNatVal <$> choose (1, 10)

52+ channels <- genNat

46− Just filters <- someNatVal <$> choose (1, 10)

53+ filters <- genNat

47− Just kernel_h <- someNatVal <$> choose (2, 20)

54+ kernel_h <- genNat

48− Just kernel_w <- someNatVal <$> choose (2, 20)

55+ kernel_w <- genNat

49− Just stride_h <- someNatVal <$> choose (1, 10)

56+ stride_h <- genNat

50− Just stride_w <- someNatVal <$> choose (1, 10)

57+ stride_w <- genNat

51 case (channels, filters, kernel_h, kernel_w, stride_h, stride_w) of

58 case (channels, filters, kernel_h, kernel_w, stride_h, stride_w) of

52 ( SomeNat (pch :: Proxy ch), SomeNat (_ :: Proxy fl),

59 ( SomeNat (pch :: Proxy ch), SomeNat (_ :: Proxy fl),

53 SomeNat (pkr :: Proxy kr), SomeNat (pkc :: Proxy kc),

60 SomeNat (pkr :: Proxy kr), SomeNat (pkc :: Proxy kc),

@@ -56,17 +63,17 @@ genOpaqueOpaqueConvolution = do

56 p2 = natDict pkc

63 p2 = natDict pkc

57 p3 = natDict pch

64 p3 = natDict pch

58 in case p1 %* p2 %* p3 of

65 in case p1 %* p2 %* p3 of

59− 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

60

72

61−prop_conv_net_witness =

62− gamble genOpaqueOpaqueConvolution $ \onet ->

63− (case onet of

64− (OpaqueConvolution ((Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) -> True

65− ) :: Bool

66

73

67−prop_conv_net =

74+prop_conv_net = property $

68− gamble genOpaqueOpaqueConvolution $ \onet ->

75+ blindForAll genOpaqueOpaqueConvolution >>= \onet ->

69− (case onet of

76+ case onet of

70 (OpaqueConvolution (convLayer@(Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) ->

77 (OpaqueConvolution (convLayer@(Convolution _ _) :: Convolution channels filters kernelRows kernelCols strideRows strideCols)) ->

71 let ok stride kernel = [extent | extent <- [(kernel + 1) .. 30 ], (extent - kernel) `mod` stride == 0]

78 let ok stride kernel = [extent | extent <- [(kernel + 1) .. 30 ], (extent - kernel) `mod` stride == 0]

72 kr = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

79 kr = fromIntegral $ natVal (Proxy :: Proxy kernelRows)

@@ -74,15 +81,15 @@ prop_conv_net =

74 sr = fromIntegral $ natVal (Proxy :: Proxy strideRows)

81 sr = fromIntegral $ natVal (Proxy :: Proxy strideRows)

75 sc = fromIntegral $ natVal (Proxy :: Proxy strideCols)

82 sc = fromIntegral $ natVal (Proxy :: Proxy strideCols)

76

83

77− in gamble (elements (ok sr kr)) $ \er ->

84+ in forAll (Gen.element (ok sr kr)) >>= \er ->

78− gamble (elements (ok sc kc)) $ \ec ->

85+ forAll (Gen.element (ok sc kc)) >>= \ec ->

79 let rr = ((er - kr) `div` sr) + 1

86 let rr = ((er - kr) `div` sr) + 1

80 rc = ((ec - kc) `div` sc) + 1

87 rc = ((ec - kc) `div` sc) + 1

81 Just er' = someNatVal er

88 Just er' = someNatVal er

82 Just ec' = someNatVal ec

89 Just ec' = someNatVal ec

83 Just rr' = someNatVal rr

90 Just rr' = someNatVal rr

84 Just rc' = someNatVal rc

91 Just rc' = someNatVal rc

85− in (case (er', ec', rr', rc') of

92+ in case (er', ec', rr', rc') of

86 ( SomeNat (pinr :: Proxy inRows), SomeNat (_ :: Proxy inCols), SomeNat (pour :: Proxy outRows), SomeNat (_ :: Proxy outCols)) ->

93 ( SomeNat (pinr :: Proxy inRows), SomeNat (_ :: Proxy inCols), SomeNat (pour :: Proxy outRows), SomeNat (_ :: Proxy outCols)) ->

87 case ( natDict pinr %* natDict (Proxy :: Proxy channels)

94 case ( natDict pinr %* natDict (Proxy :: Proxy channels)

88 , natDict pour %* natDict (Proxy :: Proxy filters)

95 , natDict pour %* natDict (Proxy :: Proxy filters)

@@ -90,14 +97,12 @@ prop_conv_net =

90 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

97 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outRows - 1) * strideRows) ~ (inRows - kernelRows)))

91 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

98 , (unsafeCoerce (Dict :: Dict ()) :: Dict (((outCols - 1) * strideCols) ~ (inCols - kernelCols)))) of

92 (Dict, Dict, Dict, Dict) ->

99 (Dict, Dict, Dict, Dict) ->

93− gamble (S3D <$> uniformSample) $ \(input :: S ('D3 inRows inCols channels)) ->

100+ blindForAll (S3D <$> uniformSample) >>= \(input :: S ('D3 inRows inCols channels)) ->

94− let output :: S ('D3 outRows outCols filters) = runForwards convLayer input

101+ let (tape, output :: S ('D3 outRows outCols filters)) = runForwards convLayer input

95 backed :: (Gradient (Convolution channels filters kernelRows kernelCols strideRows strideCols), S ('D3 inRows inCols channels))

102 backed :: (Gradient (Convolution channels filters kernelRows kernelCols strideRows strideCols), S ('D3 inRows inCols channels))

96− = runBackwards convLayer input output

103+ = runBackwards convLayer tape output

97− in backed `seq` True

104+ in backed `seq` success

98− ) :: Property

105+

99− ) :: Property

100

106

101−return []

102tests :: IO Bool

107tests :: IO Bool

103−tests = $quickCheckAll

108+tests = checkParallel $$(discover)

test/Test/Grenade/Layers/FullyConnected.hs+23−20

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

1+{-# LANGUAGE CPP #-}

1{-# LANGUAGE BangPatterns #-}

2{-# LANGUAGE BangPatterns #-}

2{-# LANGUAGE TemplateHaskell #-}

3{-# LANGUAGE TemplateHaskell #-}

3{-# LANGUAGE DataKinds #-}

4{-# LANGUAGE DataKinds #-}

@@ -12,25 +13,28 @@ import Data.Singletons ()

12

13

13import GHC.TypeLits

14import GHC.TypeLits

14

15

15−import Grenade.Core.Shape

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

16−import Grenade.Core.Network

17+import Data.Kind (Type)

17−import Grenade.Layers.FullyConnected

18+#endif

18

19

19−import Disorder.Jack

20+import Grenade.Core

21+import Grenade.Layers.FullyConnected

20

22

21−import Test.Jack.Hmatrix

23+import Hedgehog

22

24

25+import Test.Hedgehog.Compat

26+import Test.Hedgehog.Hmatrix

23

27

24−data OpaqueFullyConnected :: * where

28+data OpaqueFullyConnected :: Type where

25 OpaqueFullyConnected :: (KnownNat i, KnownNat o) => FullyConnected i o -> OpaqueFullyConnected

29 OpaqueFullyConnected :: (KnownNat i, KnownNat o) => FullyConnected i o -> OpaqueFullyConnected

26

30

27instance Show OpaqueFullyConnected where

31instance Show OpaqueFullyConnected where

28 show (OpaqueFullyConnected n) = show n

32 show (OpaqueFullyConnected n) = show n

29

33

30−genOpaqueFullyConnected :: Jack OpaqueFullyConnected

34+genOpaqueFullyConnected :: Gen OpaqueFullyConnected

31genOpaqueFullyConnected = do

35genOpaqueFullyConnected = do

32− input :: Integer <- choose (2, 100)

36+ input :: Integer <- choose 2 100

33− output :: Integer <- choose (1, 100)

37+ output :: Integer <- choose 1 100

34 let Just input' = someNatVal input

38 let Just input' = someNatVal input

35 let Just output' = someNatVal output

39 let Just output' = someNatVal output

36 case (input', output') of

40 case (input', output') of

@@ -39,17 +43,16 @@ genOpaqueFullyConnected = do

39 bM <- randomVector

43 bM <- randomVector

40 wN <- uniformSample

44 wN <- uniformSample

41 kM <- uniformSample

45 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')

43

47

44prop_fully_connected_forwards :: Property

48prop_fully_connected_forwards :: Property

45−prop_fully_connected_forwards =

49+prop_fully_connected_forwards = property $ do

46− gamble genOpaqueFullyConnected $ \(OpaqueFullyConnected (fclayer :: FullyConnected i o)) ->

50+ OpaqueFullyConnected (fclayer :: FullyConnected i o) <- blindForAll genOpaqueFullyConnected

47− gamble (S1D <$> randomVector) $ \(input :: S ('D1 i)) ->

51+ input :: S ('D1 i) <- blindForAll (S1D <$> randomVector)

48− let output :: S ('D1 o) = runForwards fclayer input

52+ let (tape, output :: S ('D1 o)) = runForwards fclayer input

49− backed :: (Gradient (FullyConnected i o), S ('D1 i))

53+ backed :: (Gradient (FullyConnected i o), S ('D1 i))

50− = runBackwards fclayer input output

54+ = runBackwards fclayer tape output

51− in backed `seq` True

55+ backed `seq` success

52−

56+

53−return []

54tests :: IO Bool

57tests :: 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

12

13import Numeric.LinearAlgebra hiding (uniformSample, konst, (===))

14

15−import Disorder.Jack

15+import Hedgehog

16+import qualified Hedgehog.Gen as Gen

17+import qualified Hedgehog.Range as Range

16

18

17import qualified Test.Grenade.Layers.Internal.Reference as Reference

19import qualified Test.Grenade.Layers.Internal.Reference as Reference

20+import Test.Hedgehog.Compat

18

21

19prop_im2col_col2im_symmetrical_with_kernel_stride =

22prop_im2col_col2im_symmetrical_with_kernel_stride =

20 let factors n = [x | x <- [1..n], n `mod` x == 0]

23 let factors n = [x | x <- [1..n], n `mod` x == 0]

21− in gamble (choose (2, 100)) $ \height ->

24+ in property $ do

22− gamble (choose (2, 100)) $ \width ->

25+ height <- forAll $ choose 2 100

23− gamble ((height `div`) <$> elements (factors height)) $ \kernel_h ->

26+ width <- forAll $ choose 2 100

24− gamble ((width `div`) <$> elements (factors width)) $ \kernel_w ->

27+ kernel_h <- forAll $ (height `div`) <$> Gen.element (factors height)

25− gamble (listOfN (height * width) (height * width) sizedRealFrac) $ \input ->

28+ kernel_w <- forAll $ (width `div`) <$> Gen.element (factors width)

26− let input' = (height >< width) input

29+ input <- forAll $ (height >< width) <$> Gen.list (Range.singleton $ height * width) (Gen.realFloat $ Range.linearFracFrom 0 (-100) 100)

30+

27− stride_h = kernel_h

31+ let stride_h = kernel_h

28− stride_w = kernel_w

32+ let 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'

33+ let 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

34+ input === out

31

35

32prop_im2col_col2im_behaves_as_reference =

36prop_im2col_col2im_behaves_as_reference =

33 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

37 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

38+ in property $ do

34− in gamble (choose (2, 100)) $ \height ->

39+ height <- forAll (choose 2 100)

35− gamble (choose (2, 100)) $ \width ->

40+ width <- forAll (choose 2 100)

36− gamble (choose (2, height - 1)) $ \kernel_h ->

41+ kernel_h <- forAll (choose 2 (height - 1))

37− gamble (choose (2, width - 1)) $ \kernel_w ->

42+ kernel_w <- forAll (choose 2 (width - 1))

38− gamble (elements (ok height kernel_h)) $ \stride_h ->

43+ stride_h <- forAll (Gen.element (ok height kernel_h))

39− gamble (elements (ok width kernel_w)) $ \stride_w ->

44+ stride_w <- forAll (Gen.element (ok width kernel_w))

40− gamble (listOfN (height * width) (height * width) sizedRealFrac) $ \input ->

45+ input <- forAll ((height >< width) <$> Gen.list (Range.singleton $ height * width) (Gen.realFloat $ Range.linearFracFrom 0 (-100) 100))

41− let input' = (height >< width) input

46+

42− outFast = im2col kernel_h kernel_w stride_h stride_w input'

47+ let 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

48+ let retFast = col2im kernel_h kernel_w stride_h stride_w height width outFast

44−

49+

45− outReference = Reference.im2col kernel_h kernel_w stride_h stride_w input'

50+ let 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

51+ let retReference = Reference.col2im kernel_h kernel_w stride_h stride_w height width outReference

52+

47− in outFast === outReference .&&. retFast === retReference

53+ outFast === outReference

48−

54+ retFast === retReference

49−

55+

50−return []

51tests :: IO Bool

56tests :: 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

12

13import Numeric.LinearAlgebra hiding (uniformSample, konst, (===))

14

15−import Disorder.Jack

15+import Hedgehog

16+import qualified Hedgehog.Gen as Gen

17+import qualified Hedgehog.Range as Range

16

18

17import qualified Test.Grenade.Layers.Internal.Reference as Reference

19import qualified Test.Grenade.Layers.Internal.Reference as Reference

20+import Test.Hedgehog.Compat

18

21

19prop_poolForwards_poolBackwards_behaves_as_reference =

22prop_poolForwards_poolBackwards_behaves_as_reference =

20 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

23 let ok extent kernel = [stride | stride <- [1..extent], (extent - kernel) `mod` stride == 0]

21 output extent kernel stride = (extent - kernel) `div` stride + 1

24 output extent kernel stride = (extent - kernel) `div` stride + 1

22− in gamble (choose (2, 100)) $ \height ->

25+ in property $ do

26+ height <- forAll $ choose 2 100

23− gamble (choose (2, 100)) $ \width ->

27+ width <- forAll $ choose 2 100

24− gamble (choose (1, height - 1)) $ \kernel_h ->

28+ kernel_h <- forAll $ choose 1 (height - 1)

25− gamble (choose (1, width - 1)) $ \kernel_w ->

29+ kernel_w <- forAll $ choose 1 (width - 1)

26− gamble (elements (ok height kernel_h)) $ \stride_h ->

30+ stride_h <- forAll $ Gen.element (ok height kernel_h)

27− gamble (elements (ok width kernel_w)) $ \stride_w ->

31+ stride_w <- forAll $ Gen.element (ok width kernel_w)

28− gamble (listOfN (height * width) (height * width) sizedRealFrac) $ \input ->

32+ input <- forAll $ (height >< width) <$> Gen.list (Range.singleton $ height * width) (Gen.realFloat $ Range.linearFracFrom 0 (-100) 100)

29− let input' = (height >< width) input

33+

30− outFast = poolForward 1 height width kernel_h kernel_w stride_h stride_w input'

34+ let 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

35+ let retFast = poolBackward 1 height width kernel_h kernel_w stride_h stride_w input outFast

32−

36+

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'

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

34− retReference = Reference.poolBackward kernel_h kernel_w stride_h stride_w input' outReference

38+ let retReference = Reference.poolBackward kernel_h kernel_w stride_h stride_w input outReference

39+

35− in outFast === outReference .&&. retFast === retReference

40+ outFast === outReference

36−

41+ retFast === retReference

37−return []

42+

43+

38tests :: IO Bool

44tests :: 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 #-}

1{-# LANGUAGE TemplateHaskell #-}

2{-# LANGUAGE TemplateHaskell #-}

2{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE DataKinds #-}

3{-# LANGUAGE KindSignatures #-}

4{-# LANGUAGE KindSignatures #-}

@@ -9,36 +10,40 @@ module Test.Grenade.Layers.Pooling where

9import Data.Proxy

10import Data.Proxy

10import Data.Singletons ()

11import Data.Singletons ()

11

12

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

14+import Data.Kind (Type)

15+#endif

16+

12import GHC.TypeLits

17import GHC.TypeLits

13import Grenade.Layers.Pooling

18import Grenade.Layers.Pooling

14

19

15−import Disorder.Jack

20+import Hedgehog

16

21

22+import Test.Hedgehog.Compat

17

23

18−data OpaquePooling :: * where

24+data OpaquePooling :: Type where

19 OpaquePooling :: (KnownNat kh, KnownNat kw, KnownNat sh, KnownNat sw) => Pooling kh kw sh sw -> OpaquePooling

25 OpaquePooling :: (KnownNat kh, KnownNat kw, KnownNat sh, KnownNat sw) => Pooling kh kw sh sw -> OpaquePooling

20

26

21instance Show OpaquePooling where

27instance Show OpaquePooling where

22 show (OpaquePooling n) = show n

28 show (OpaquePooling n) = show n

23

29

24−genOpaquePooling :: Jack OpaquePooling

30+genOpaquePooling :: Gen OpaquePooling

25genOpaquePooling = do

31genOpaquePooling = do

26− Just kernelHeight <- someNatVal <$> choose (2, 15)

32+ ~(Just kernelHeight) <- someNatVal <$> choose 2 15

27− Just kernelWidth <- someNatVal <$> choose (2, 15)

33+ ~(Just kernelWidth ) <- someNatVal <$> choose 2 15

28− Just strideHeight <- someNatVal <$> choose (2, 15)

34+ ~(Just strideHeight) <- someNatVal <$> choose 2 15

29− Just strideWidth <- someNatVal <$> choose (2, 15)

35+ ~(Just strideWidth ) <- someNatVal <$> choose 2 15

30

36

31 case (kernelHeight, kernelWidth, strideHeight, strideWidth) of

37 case (kernelHeight, kernelWidth, strideHeight, strideWidth) of

32 (SomeNat (_ :: Proxy kh), SomeNat (_ :: Proxy kw), SomeNat (_ :: Proxy sh), SomeNat (_ :: Proxy sw)) ->

38 (SomeNat (_ :: Proxy kh), SomeNat (_ :: Proxy kw), SomeNat (_ :: Proxy sh), SomeNat (_ :: Proxy sw)) ->

33 return $ OpaquePooling (Pooling :: Pooling kh kw sh sw)

39 return $ OpaquePooling (Pooling :: Pooling kh kw sh sw)

34

40

35prop_pool_layer_witness =

41prop_pool_layer_witness =

42+ property $ do

36− gamble genOpaquePooling $ \onet ->

43+ onet <- forAll genOpaquePooling

37− (case onet of

44+ case onet of

38− (OpaquePooling (Pooling :: Pooling kernelRows kernelCols strideRows strideCols)) -> True

45+ (OpaquePooling (Pooling :: Pooling kernelRows kernelCols strideRows strideCols)) ->

39− ) :: Bool

46+ assert True

40−

41

47

42−return []

43tests :: IO Bool

48tests :: 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 @@

10{-# OPTIONS_GHC -fno-warn-missing-signatures #-}

11module Test.Grenade.Recurrent.Layers.LSTM where

12

13−import Disorder.Jack

13+import Hedgehog

14+import Hedgehog.Internal.Source

15+import Hedgehog.Internal.Show

16+import Hedgehog.Internal.Property ( failWith, Diff (..) )

14

17

15import Data.Foldable ( toList )

18import Data.Foldable ( toList )

16import Data.Singletons.TypeLits

19import Data.Singletons.TypeLits

@@ -23,9 +26,9 @@ import qualified Numeric.LinearAlgebra.Static as S

23

26

24

27

25import qualified Test.Grenade.Recurrent.Layers.LSTM.Reference as Reference

28import qualified Test.Grenade.Recurrent.Layers.LSTM.Reference as Reference

26−import Test.Jack.Hmatrix

29+import Test.Hedgehog.Hmatrix

27

30

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)

29genLSTM = do

32genLSTM = do

30 let w = uniformSample

33 let w = uniformSample

31 u = uniformSample

34 u = uniformSample

@@ -39,63 +42,89 @@ genLSTM = do

39 <*> pure (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

42 <*> pure (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)

40

43

41prop_lstm_reference_forwards =

44prop_lstm_reference_forwards =

42− gamble randomVector $ \(input :: S.R 3) ->

45+ property $ do

43− gamble randomVector $ \(cell :: S.R 2) ->

46+ input :: S.R 3 <- forAll randomVector

47+ cell :: S.R 2 <- forAll randomVector

44− gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

48+ net@(LSTM lstmWeights _) :: LSTM 3 2 <- forAll genLSTM

49+

45− let actual = runRecurrentForwards net (S1D cell) (S1D input)

50+ let actual = runRecurrentForwards net (S1D cell) (S1D input)

46− in case actual of

51+ case actual of

47− ((S1D cellOut) :: S ('D1 2), (S1D output) :: S ('D1 2)) ->

52+ (_, (S1D cellOut) :: S ('D1 2), (S1D output) :: S ('D1 2)) ->

48− let cellOut' = Reference.Vector . H.toList . S.extract $ cellOut

53+ let cellOut' = Reference.Vector . H.toList . S.extract $ cellOut

49− output' = Reference.Vector . H.toList . S.extract $ output

54+ output' = Reference.Vector . H.toList . S.extract $ output

50− refNet = Reference.lstmToReference lstmWeights

55+ refNet = Reference.lstmToReference lstmWeights

51− refCell = Reference.Vector . H.toList . S.extract $ cell

56+ refCell = Reference.Vector . H.toList . S.extract $ cell

52− refInput = Reference.Vector . H.toList . S.extract $ input

57+ refInput = Reference.Vector . H.toList . S.extract $ input

53− (refCO, refO) = Reference.runLSTM refNet refCell refInput

58+ (refCO, refO) = Reference.runLSTM refNet refCell refInput

54− in toList refCO ~~~ toList cellOut' .&&. toList refO ~~~ toList output'

59+ in do toList refCO ~~~ toList cellOut'

60+ toList refO ~~~ toList output'

61+

55

62

56prop_lstm_reference_backwards =

63prop_lstm_reference_backwards =

57− gamble randomVector $ \(input :: S.R 3) ->

64+ property $ do

58− gamble randomVector $ \(cell :: S.R 2) ->

65+ input :: S.R 3 <- forAll randomVector

59− gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

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)

60− let actualBacks = runRecurrentBackwards net (S1D cell) (S1D input) (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

70+ actualBacks = runRecurrentBackwards net tape (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

61− in case actualBacks of

71+ case actualBacks of

62− (actualGradients, _, _) ->

72+ (actualGradients, _, _ :: S ('D1 3)) ->

63− let refNet = Reference.lstmToReference lstmWeights

73+ let refNet = Reference.lstmToReference lstmWeights

64− refCell = Reference.Vector . H.toList . S.extract $ cell

74+ refCell = Reference.Vector . H.toList . S.extract $ cell

65− refInput = Reference.Vector . H.toList . S.extract $ input

75+ refInput = Reference.Vector . H.toList . S.extract $ input

66− refGradients = Reference.runLSTMback refCell refInput refNet

76+ refGradients = Reference.runLSTMback refCell refInput refNet

67− in toList refGradients ~~~ toList (Reference.lstmToReference actualGradients)

77+ in toList refGradients ~~~ toList (Reference.lstmToReference actualGradients)

68

78

69prop_lstm_reference_backwards_input =

79prop_lstm_reference_backwards_input =

70− gamble randomVector $ \(input :: S.R 3) ->

80+ property $ do

71− gamble randomVector $ \(cell :: S.R 2) ->

81+ input :: S.R 3 <- forAll randomVector

72− gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

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)

73− let actualBacks = runRecurrentBackwards net (S1D cell) (S1D input) (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

86+ actualBacks = runRecurrentBackwards net tape (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

74− in case actualBacks of

87+ case actualBacks of

75− (_, _, S1D actualGradients) ->

88+ (_, _, S1D actualGradients :: S ('D1 3)) ->

76− let refNet = Reference.lstmToReference lstmWeights

89+ let refNet = Reference.lstmToReference lstmWeights

77− refCell = Reference.Vector . H.toList . S.extract $ cell

90+ refCell = Reference.Vector . H.toList . S.extract $ cell

78− refInput = Reference.Vector . H.toList . S.extract $ input

91+ refInput = Reference.Vector . H.toList . S.extract $ input

79− refGradients = Reference.runLSTMbackOnInput refCell refNet refInput

92+ refGradients = Reference.runLSTMbackOnInput refCell refNet refInput

80− in toList refGradients ~~~ H.toList (S.extract actualGradients)

93+ in toList refGradients ~~~ H.toList (S.extract actualGradients)

81

94

82prop_lstm_reference_backwards_cell =

95prop_lstm_reference_backwards_cell =

83− gamble randomVector $ \(input :: S.R 3) ->

96+ property $ do

84− gamble randomVector $ \(cell :: S.R 2) ->

97+ input :: S.R 3 <- forAll randomVector

85− gamble genLSTM $ \(net@(LSTM lstmWeights _) :: LSTM 3 2) ->

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)

86− let actualBacks = runRecurrentBackwards net (S1D cell) (S1D input) (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

102+ actualBacks = runRecurrentBackwards net tape (S1D (S.konst 1) :: S ('D1 2)) (S1D (S.konst 1) :: S ('D1 2))

87− in case actualBacks of

103+ case actualBacks of

88− (_, S1D actualGradients, _) ->

104+ (_, S1D actualGradients, _ :: S ('D1 3)) ->

89− let refNet = Reference.lstmToReference lstmWeights

105+ let refNet = Reference.lstmToReference lstmWeights

90− refCell = Reference.Vector . H.toList . S.extract $ cell

106+ refCell = Reference.Vector . H.toList . S.extract $ cell

91− refInput = Reference.Vector . H.toList . S.extract $ input

107+ refInput = Reference.Vector . H.toList . S.extract $ input

92− refGradients = Reference.runLSTMbackOnCell refInput refNet refCell

108+ refGradients = Reference.runLSTMbackOnCell refInput refNet refCell

93− in toList refGradients ~~~ (H.toList . S.extract $ actualGradients)

109+ in toList refGradients ~~~ H.toList (S.extract actualGradients)

94−

110+

95−

111+(~~~) :: (Monad m, Eq a, Ord a, Num a, Fractional a, Show a, HasCallStack) => [a] -> [a] -> PropertyT m ()

112+(~~~) x y =

96−(~~~) as bs = all (< 1e-8) (zipWith (-) as bs)

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) ""

97infix 4 ~~~

127infix 4 ~~~

98

128

99−return []

100tests :: IO Bool

129tests :: IO Bool

101−tests = $quickCheckAll

130+tests = checkParallel $$(discover)

test/Test/Grenade/Recurrent/Layers/LSTM/Reference.hs+25−21

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

6{-# LANGUAGE DeriveFunctor #-}

7{-# LANGUAGE DeriveFoldable #-}

8{-# LANGUAGE DeriveTraversable #-}

9−{-# LANGUAGE RecordWildCards #-}

10{-# LANGUAGE FlexibleContexts #-}

9{-# LANGUAGE FlexibleContexts #-}

11{-# LANGUAGE RankNTypes #-}

10{-# LANGUAGE RankNTypes #-}

12

11

@@ -15,9 +14,13 @@ module Test.Grenade.Recurrent.Layers.LSTM.Reference where

15

14

16import Data.Reflection

15import Data.Reflection

17import Numeric.AD.Mode.Reverse

16import Numeric.AD.Mode.Reverse

18−import Numeric.AD.Internal.Reverse ( Tape )

17+import Numeric.AD.Internal.Reverse (Tape)

19

18

19+import GHC.TypeLits (KnownNat)

20+

21+import Grenade.Recurrent.Layers.LSTM (LSTMWeights (..))

20import qualified Grenade.Recurrent.Layers.LSTM as LSTM

22import qualified Grenade.Recurrent.Layers.LSTM as LSTM

23+

21import qualified Numeric.LinearAlgebra.Static as S

24import qualified Numeric.LinearAlgebra.Static as S

22import qualified Numeric.LinearAlgebra as H

25import qualified Numeric.LinearAlgebra as H

23

26

@@ -54,31 +57,32 @@ data RefLSTM a = RefLSTM

54 , refLstmBc :: Vector a -- Bias Cell (b_c)

57 , refLstmBc :: Vector a -- Bias Cell (b_c)

55 } deriving (Functor, Foldable, Traversable, Eq, Show)

58 } deriving (Functor, Foldable, Traversable, Eq, Show)

56

59

57−lstmToReference :: LSTM.LSTMWeights a b -> RefLSTM Double

60+lstmToReference :: (KnownNat a, KnownNat b) => LSTM.LSTMWeights a b -> RefLSTM Double

58−lstmToReference LSTM.LSTMWeights {..} =

61+lstmToReference lw =

62+ RefLSTM

59− let refLstmWf = Matrix . H.toLists . S.extract $ lstmWf -- Weight Forget (W_f)

63+ { refLstmWf = Matrix . H.toLists . S.extract $ lstmWf lw -- Weight Forget (W_f)

60− refLstmUf = Matrix . H.toLists . S.extract $ lstmUf -- Cell State Forget (U_f)

64+ , refLstmUf = Matrix . H.toLists . S.extract $ lstmUf lw -- Cell State Forget (U_f)

61− refLstmBf = Vector . H.toList . S.extract $ lstmBf -- Bias Forget (b_f)

65+ , refLstmBf = Vector . H.toList . S.extract $ lstmBf lw -- Bias Forget (b_f)

62− refLstmWi = Matrix . H.toLists . S.extract $ lstmWi -- Weight Input (W_i)

66+ , refLstmWi = Matrix . H.toLists . S.extract $ lstmWi lw -- Weight Input (W_i)

63− refLstmUi = Matrix . H.toLists . S.extract $ lstmUi -- Cell State Input (U_i)

67+ , refLstmUi = Matrix . H.toLists . S.extract $ lstmUi lw -- Cell State Input (U_i)

64− refLstmBi = Vector . H.toList . S.extract $ lstmBi -- Bias Input (b_i)

68+ , refLstmBi = Vector . H.toList . S.extract $ lstmBi lw -- Bias Input (b_i)

65− refLstmWo = Matrix . H.toLists . S.extract $ lstmWo -- Weight Output (W_o)

69+ , refLstmWo = Matrix . H.toLists . S.extract $ lstmWo lw -- Weight Output (W_o)

66− refLstmUo = Matrix . H.toLists . S.extract $ lstmUo -- Cell State Output (U_o)

70+ , refLstmUo = Matrix . H.toLists . S.extract $ lstmUo lw -- Cell State Output (U_o)

67− refLstmBo = Vector . H.toList . S.extract $ lstmBo -- Bias Output (b_o)

71+ , refLstmBo = Vector . H.toList . S.extract $ lstmBo lw -- Bias Output (b_o)

68− refLstmWc = Matrix . H.toLists . S.extract $ lstmWc -- Weight Cell (W_c)

72+ , refLstmWc = Matrix . H.toLists . S.extract $ lstmWc lw -- Weight Cell (W_c)

69− refLstmBc = Vector . H.toList . S.extract $ lstmBc -- Bias Cell (b_c)

73+ , refLstmBc = Vector . H.toList . S.extract $ lstmBc lw -- Bias Cell (b_c)

70− in RefLSTM {..}

74+ }

71

75

72runLSTM :: Floating a => RefLSTM a -> Vector a -> Vector a -> (Vector a, Vector a)

76runLSTM :: Floating a => RefLSTM a -> Vector a -> Vector a -> (Vector a, Vector a)

73−runLSTM RefLSTM {..} cell input =

77+runLSTM rl cell input =

74 let -- Forget state vector

78 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

76 -- Input state vector

80 -- Input state vector

77− i_t = sigmoid $ refLstmBi #+ refLstmWi #> input #+ refLstmUi #> cell

81+ i_t = sigmoid $ refLstmBi rl #+ refLstmWi rl #> input #+ refLstmUi rl #> cell

78 -- Output state vector

82 -- Output state vector

79− o_t = sigmoid $ refLstmBo #+ refLstmWo #> input #+ refLstmUo #> cell

83+ o_t = sigmoid $ refLstmBo rl #+ refLstmWo rl #> input #+ refLstmUo rl #> cell

80 -- Cell input state vector

84 -- Cell input state vector

81− c_x = fmap tanh $ refLstmBc #+ refLstmWc #> input

85+ c_x = fmap tanh $ refLstmBc rl #+ refLstmWc rl #> input

82 -- Cell state

86 -- Cell state

83 c_t = f_t #* cell #+ i_t #* c_x

87 c_t = f_t #* cell #+ i_t #* c_x

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

88 -- 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.hs+26−2

@@ -1,23 +1,47 @@

1−import Disorder.Core.Main

1+import Control.Monad

2+

3+import qualified Test.Grenade.Network

2

4

3import qualified Test.Grenade.Layers.Pooling

5import qualified Test.Grenade.Layers.Pooling

4import qualified Test.Grenade.Layers.Convolution

6import qualified Test.Grenade.Layers.Convolution

5import qualified Test.Grenade.Layers.FullyConnected

7import qualified Test.Grenade.Layers.FullyConnected

8+import qualified Test.Grenade.Layers.Nonlinear

9+import qualified Test.Grenade.Layers.PadCrop

6

10

7import qualified Test.Grenade.Layers.Internal.Convolution

11import qualified Test.Grenade.Layers.Internal.Convolution

8import qualified Test.Grenade.Layers.Internal.Pooling

12import qualified Test.Grenade.Layers.Internal.Pooling

9

13

10import qualified Test.Grenade.Recurrent.Layers.LSTM

14import qualified Test.Grenade.Recurrent.Layers.LSTM

11

15

16+import System.Exit

17+import System.IO

18+

12main :: IO ()

19main :: IO ()

13main =

20main =

14 disorderMain [

21 disorderMain [

22+ Test.Grenade.Network.tests

23+

15− Test.Grenade.Layers.Pooling.tests

24+ , Test.Grenade.Layers.Pooling.tests

16 , Test.Grenade.Layers.Convolution.tests

25 , Test.Grenade.Layers.Convolution.tests

17 , Test.Grenade.Layers.FullyConnected.tests

26 , Test.Grenade.Layers.FullyConnected.tests

27+ , Test.Grenade.Layers.Nonlinear.tests

28+ , Test.Grenade.Layers.PadCrop.tests

18

29

19 , Test.Grenade.Layers.Internal.Convolution.tests

30 , Test.Grenade.Layers.Internal.Convolution.tests

20 , Test.Grenade.Layers.Internal.Pooling.tests

31 , Test.Grenade.Layers.Internal.Pooling.tests

21

32

22 , Test.Grenade.Recurrent.Layers.LSTM.tests

33 , Test.Grenade.Recurrent.Layers.LSTM.tests

34+

23 ]

35 ]

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