d82a121 · grenade · git.iko.soy

mafia+1−1

compare with master

unifiedsplit

@@ -120,4 +120,4 @@ case "$MODE" in

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

121*) exec_mafia "$@"

122esac

123−# Version: 3044e63eb472fb9e16926d4ab2ca9dd9e255829c

123+# Version: 360716306a06db842ec022a7b9f161d2208483f0

main/mnist.hs+2−2

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

33

34-- 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.

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]

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, Relu, FlattenLayer, FullyConnected 256 80, Logit, FullyConnected 80 10, Logit]

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

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

38

39randomMnist :: MonadRandom m => m MNIST

40randomMnist = randomNetwork

src/Grenade.hs+4−3

@@ -13,6 +13,7 @@ import Grenade.Layers.Flatten as X

13import Grenade.Layers.Fuse as X

14import Grenade.Layers.FullyConnected as X

15import Grenade.Layers.Logit as X

16−import Grenade.Layers.Convolution as X

16+import Grenade.Layers.Convolution as X

17−import Grenade.Layers.Relu as X

17+import Grenade.Layers.Relu as X

18−import Grenade.Layers.Tanh as X

18+import Grenade.Layers.Tanh as X

19+import Grenade.Layers.Softmax as X

src/Grenade/Core/Network.hs+5−3

@@ -42,7 +42,7 @@ data LearningParameters = LearningParameters {

42

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

44-- shape independent.

45−class Show x => UpdateLayer x where

45+class UpdateLayer x where

46 {-# MINIMAL runUpdate, createRandom #-}

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

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

@@ -86,9 +86,11 @@ data Network :: [*] -> [Shape] -> * where

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

87infixr 5 :~>

88

89−instance Show (Network l h) where

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

90 show NNil = "NNil"

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

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

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

93+

92

94

93-- | Gradients of a network.

95-- | Gradients of a network.

94-- Parameterised on the layers of a Network.

96-- Parameterised on the layers of a Network.

src/Grenade/Core/Runner.hs+3−1

@@ -14,6 +14,9 @@ Stability : experimental

14This module defines simple back propagation and training functions

15for a network.

16-}

17+

18+-- GHC 7.10 doesn't think that go is complete

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

17module Grenade.Core.Runner (

20module Grenade.Core.Runner (

18 train

21 train

19 , backPropagate

22 , backPropagate

@@ -48,7 +51,6 @@ backPropagate network input target =

48 (n', dWs') = go y n

51 (n', dWs') = go y n

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

52 -- calculate the gradient for this layer to pass down,

50 (layer', dWs) = runBackwards layer tape dWs'

53 (layer', dWs) = runBackwards layer tape dWs'

51−

52 in (layer' :/> n', dWs)

54 in (layer' :/> n', dWs)

53

55

54 -- Bouncing the derivatives back down.

56 -- Bouncing the derivatives back down.

src/Grenade/Layers/Flatten.hs+1−1

@@ -31,7 +31,7 @@ instance UpdateLayer FlattenLayer where

31 runUpdate _ _ _ = FlattenLayer

32 createRandom = return FlattenLayer

33

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

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

35 type Tape FlattenLayer ('D2 x y) ('D1 a) = ()

36 runForwards _ (S2D y) = ((), fromJust' . fromStorable . flatten . extract $ y)

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

src/Grenade/Recurrent/Core/Network.hs+6−5

@@ -59,11 +59,12 @@ data RecurrentNetwork :: [*] -> [Shape] -> * where

59infixr 5 :~~>

60infixr 5 :~@>

61

62−instance Show (RecurrentNetwork l h) where

62+instance Show (RecurrentNetwork '[] '[i]) where

63− show RNil = "RNil"

63+ show RNil = "NNil"

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

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

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

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

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

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

66−

67

68

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

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

69-- Parameterised on the layers of a Network.

70-- Parameterised on the layers of a Network.

src/Grenade/Recurrent/Core/Runner.hs+3

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

7{-# LANGUAGE FlexibleContexts #-}

8{-# LANGUAGE RankNTypes #-}

9{-# LANGUAGE RecordWildCards #-}

10+

11+-- GHC 7.10 doesn't think that go is complete

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

10module Grenade.Recurrent.Core.Runner (

13module Grenade.Recurrent.Core.Runner (

11 trainRecurrent

14 trainRecurrent

12 , runRecurrent

15 , runRecurrent