f9c282b · grenade · git.iko.soy

grenade.cabal+11−2

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unifiedsplit

@@ -5,11 +5,20 @@ license-file: LICENSE

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

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

8−synopsis: grenade

8+synopsis: Practical Deep Learning in Haskell

9category: System

10cabal-version: >= 1.8

11build-type: Simple

12−description: grenade.

12+description:

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

14+ recurrent neural network library for concise and precise

15+ specifications of complex 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.

19+ .

20+ Documentation and examples are available on github

21+ <https://github.com/HuwCampbell/grenade>.

13

22

14extra-source-files:

23extra-source-files:

15 cbits/im2col.h

24 cbits/im2col.h

src/Grenade.hs+2−1

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

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

30implementation written using BLAS and LAPACK, mostly provided by

31the hmatrix library.

32+

32-}

33-}

33

34

34{- $example

35{- $example

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

45> randomMyNet = randomNetwork

46> randomMyNet = randomNetwork

46

47

47The function `randomMyNet` witnesses the `CreatableNetwork`

48The function `randomMyNet` witnesses the `CreatableNetwork`

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

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

49can be built, and hence, that the architecture is sound.

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

50-}

51-}

51

52

src/Grenade/Core/Layer.hs+18−2

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

7{-# LANGUAGE RankNTypes #-}

8{-# LANGUAGE FlexibleInstances #-}

9{-|

10−Module : Grenade.Core.Network

10+Module : Grenade.Core.Layer

11−Description : Core definition a simple neural etwork

11+Description : Defines the Layer Classes

12Copyright : (c) Huw Campbell, 2016-2017

13License : BSD2

14Stability : experimental

15

16This module defines what a Layer is in a Grenade

17neural network.

18+

19+There are two classes of interest: `UpdateLayer` and `Layer`.

20+

21+`UpdateLayer` is required for all types which are used as a layer

22+in a network. Having no shape information, this class is agnotostic

23+to the input and output data of the layer.

24+

25+An instance of `Layer` on the other hand is required for usage in

26+a neural network, but also specifies the shapes of data that the

27+network can transform. Multiple instance of `Layer` are permitted

28+for a single type, to transform different shapes. The `Reshape` layer

29+for example can act as a flattening layer, and its inverse, projecting

30+a 1D shape up to 2 or 3 dimensions.

31+

32+Instances of `Layer` should be as strict as possible, and not emit

33+runtime errors.

18-}

34-}

19module Grenade.Core.Layer (

35module Grenade.Core.Layer (

20 Layer (..)

36 Layer (..)

src/Grenade/Core/LearningParameters.hs+7

@@ -1,3 +1,10 @@

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+-}

1module Grenade.Core.LearningParameters (

8module Grenade.Core.LearningParameters (

2 -- | This module contains learning algorithm specific

9 -- | This module contains learning algorithm specific

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

10 -- code. Currently, this module should be consifered

src/Grenade/Core/Network.hs+5−4

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

9{-# LANGUAGE FlexibleInstances #-}

10{-|

11Module : Grenade.Core.Network

12−Description : Core definition a simple neural etwork

12+Description : Core definition of a Neural Network

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

14License : BSD2

15Stability : experimental

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

91--

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

93-- propagation.

94−--

95runNetwork :: forall layers shapes.

94runNetwork :: forall layers shapes.

96 Network layers shapes

95 Network layers shapes

97 -> S (Head shapes)

96 -> S (Head shapes)

@@ -119,7 +118,6 @@ runNetwork =

119--

118--

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

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

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

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

122−--

123runGradient :: forall layers shapes.

121runGradient :: forall layers shapes.

124 Network layers shapes

122 Network layers shapes

125 -> Tapes layers shapes

123 -> Tapes layers shapes

@@ -179,12 +177,15 @@ instance (SingI i, SingI o, Layer x i o, Serialize x, Serialize (Network xs (o '

179

177

180-- | Ultimate composition.

178-- | Ultimate composition.

181--

179--

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

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

183instance CreatableNetwork sublayers subshapes => UpdateLayer (Network sublayers subshapes) where

181instance CreatableNetwork sublayers subshapes => UpdateLayer (Network sublayers subshapes) where

184 type Gradient (Network sublayers subshapes) = Gradients sublayers

182 type Gradient (Network sublayers subshapes) = Gradients sublayers

185 runUpdate = applyUpdate

183 runUpdate = applyUpdate

186 createRandom = randomNetwork

184 createRandom = randomNetwork

187

185

186+-- | Ultimate composition.

187+--

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

188instance (CreatableNetwork sublayers subshapes, i ~ (Head subshapes), o ~ (Last subshapes)) => Layer (Network sublayers subshapes) i o where

189instance (CreatableNetwork sublayers subshapes, i ~ (Head subshapes), o ~ (Last subshapes)) => Layer (Network sublayers subshapes) i o where

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

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

190 runForwards = runNetwork

191 runForwards = runNetwork

src/Grenade/Core/Runner.hs+2−5

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

1{-# LANGUAGE DataKinds #-}

2{-# LANGUAGE FlexibleContexts #-}

3{-|

4−Module : Grenade.Core.Shape

4+Module : Grenade.Core.Runner

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

5+Description : Functions to perform training and backpropagation

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

7License : BSD2

8Stability : experimental

9−

10−This module defines simple back propagation and training functions

11−for a network.

12-}

9-}

13module Grenade.Core.Runner (

10module Grenade.Core.Runner (

14 train

11 train

src/Grenade/Core/Shape.hs+2−3

@@ -9,13 +9,12 @@

9{-# LANGUAGE RankNTypes #-}

10{-|

11Module : Grenade.Core.Shape

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

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

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

14License : BSD2

15Stability : experimental

16

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

17+

18−are understood by Grenade.

19-}

18-}

20module Grenade.Core.Shape (

19module Grenade.Core.Shape (

21 Shape (..)

20 Shape (..)

src/Grenade/Layers/Concat.hs+1−1

@@ -15,7 +15,7 @@ Copyright : (c) Huw Campbell, 2016-2017

15License : BSD2

16Stability : experimental

17

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

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

19-}

20module Grenade.Layers.Concat (

21 Concat (..)

src/Grenade/Layers/Convolution.hs+10

@@ -7,6 +7,16 @@

7{-# LANGUAGE MultiParamTypeClasses #-}

8{-# LANGUAGE FlexibleInstances #-}

9{-# LANGUAGE FlexibleContexts #-}

10+{-|

11+Module : Grenade.Layers.Convolution

12+Description : Convolution layer

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

14+License : BSD2

15+Stability : experimental

16+

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

18+

19+-}

10module Grenade.Layers.Convolution (

20module Grenade.Layers.Convolution (

11 Convolution (..)

21 Convolution (..)

12 , Convolution' (..)

22 , Convolution' (..)

src/Grenade/Layers/Crop.hs+7

@@ -5,6 +5,13 @@

5{-# LANGUAGE TypeFamilies #-}

6{-# LANGUAGE FlexibleContexts #-}

7{-# LANGUAGE MultiParamTypeClasses #-}

8+{-|

9+Module : Grenade.Layers.Crop

10+Description : Cropping layer

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

12+License : BSD2

13+Stability : experimental

14+-}

8module Grenade.Layers.Crop (

15module Grenade.Layers.Crop (

9 Crop (..)

16 Crop (..)

10 ) where

17 ) where

src/Grenade/Layers/Deconvolution.hs+39−30

@@ -7,6 +7,20 @@

7{-# LANGUAGE MultiParamTypeClasses #-}

8{-# LANGUAGE FlexibleInstances #-}

9{-# LANGUAGE FlexibleContexts #-}

10+{-|

11+Module : Grenade.Layers.Deconvolution

12+Description : Deconvolution layer

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

14+License : BSD2

15+Stability : experimental

16+

17+A deconvolution layer is in many ways a convolution layer in reverse.

18+It learns a kernel to apply to each pixel location, spreading it out

19+into a larger layer.

20+

21+This layer is important for image generation tasks, such as GANs on

22+images.

23+-}

10module Grenade.Layers.Deconvolution (

24module Grenade.Layers.Deconvolution (

11 Deconvolution (..)

25 Deconvolution (..)

12 , Deconvolution' (..)

26 , Deconvolution' (..)

@@ -30,42 +44,37 @@ import Grenade.Layers.Internal.Convolution

30import Grenade.Layers.Internal.Update

44import Grenade.Layers.Internal.Update

31

45

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

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

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

47+-- This uses the im2col Convolution trick popularised by Caffe.

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

35−--

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

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

38--

48--

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

49+-- The Deconvolution layer is a way of spreading out a single response

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

50+-- into a larger image, and is useful in generating images.

41--

51--

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

43data Deconvolution :: Nat -- Number of channels, for the first layer this could be RGB for instance.

52data Deconvolution :: Nat -- Number of channels, for the first layer this could be RGB for instance.

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

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

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

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

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

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

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

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

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

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

49− -> * where

58+ -> * where

50 Deconvolution :: ( KnownNat channels

59 Deconvolution :: ( KnownNat channels

51− , KnownNat filters

60+ , KnownNat filters

52− , KnownNat kernelRows

61+ , KnownNat kernelRows

53− , KnownNat kernelColumns

62+ , KnownNat kernelColumns

54− , KnownNat strideRows

63+ , KnownNat strideRows

55− , KnownNat strideColumns

64+ , KnownNat strideColumns

56− , KnownNat kernelFlattened

65+ , KnownNat kernelFlattened

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

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

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

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

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

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

60− -> Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns

69+ -> Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns

61

70

62data Deconvolution' :: Nat -- Number of channels, for the first layer this could be RGB for instance.

71data Deconvolution' :: Nat -- Number of channels, for the first layer this could be RGB for instance.

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

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

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

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

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

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

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

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

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

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

68− -> * where

77+ -> * where

69 Deconvolution' :: ( KnownNat channels

78 Deconvolution' :: ( KnownNat channels

70 , KnownNat filters

79 , KnownNat filters

71 , KnownNat kernelRows

80 , KnownNat kernelRows

src/Grenade/Layers/Merge.hs+1−1

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

10{-# LANGUAGE StandaloneDeriving #-}

11{-|

12Module : Grenade.Core.Network

13−Description : Core definition a simple neural etwork

13+Description : Merging layer for parallel network composition

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

15License : BSD2

16Stability : experimental

src/Grenade/Recurrent.hs+22

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

1module Grenade.Recurrent (

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

3+ -- for recurrent networks in Grenade.

4+

5+ -- * Exported modules

6+ --

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

2 module Grenade.Recurrent.Core

8 module Grenade.Recurrent.Core

9+

10+ -- | The recurrent neural network layer zoo

3 , module Grenade.Recurrent.Layers

11 , module Grenade.Recurrent.Layers

12+

13+ -- * Overview of recurrent Networks

14+ -- $recurrent

15+

4 ) where

16 ) where

5

17

6import Grenade.Recurrent.Core

18import Grenade.Recurrent.Core

7import Grenade.Recurrent.Layers

19import Grenade.Recurrent.Layers

20+

21+{- $recurrent

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

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

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

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

26+

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

28+LSTM layer.

29+-}

test/test.hs+16−1

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

1−import Disorder.Core.Main

1+import Control.Monad

2

3import qualified Test.Grenade.Layers.Pooling

4import qualified Test.Grenade.Layers.Convolution

@@ -11,6 +11,9 @@ import qualified Test.Grenade.Layers.Internal.Pooling

11

12import qualified Test.Grenade.Recurrent.Layers.LSTM

13

14+import System.Exit

15+import System.IO

16+

14main :: IO ()

17main :: IO ()

15main =

18main =

16 disorderMain [

19 disorderMain [

@@ -25,3 +28,15 @@ main =

25

28

26 , Test.Grenade.Recurrent.Layers.LSTM.tests

29 , Test.Grenade.Recurrent.Layers.LSTM.tests

27 ]

30 ]

31+

32+disorderMain :: [IO Bool] -> IO ()

33+disorderMain tests = do

34+ lineBuffer

35+ rs <- sequence tests

36+ unless (and rs) exitFailure

37+

38+

39+lineBuffer :: IO ()

40+lineBuffer = do

41+ hSetBuffering stdout LineBuffering

42+ hSetBuffering stderr LineBuffering