Remove use of RecordWildCards

examples/main/shakespeare.hs

@@ -1,6 +1,5 @@
 {-# LANGUAGE BangPatterns          #-}
 {-# LANGUAGE CPP                   #-}
-{-# LANGUAGE RecordWildCards       #-}
 {-# LANGUAGE DataKinds             #-}
 {-# LANGUAGE ScopedTypeVariables   #-}
 {-# LANGUAGE TypeOperators         #-}
@@ -83,34 +82,34 @@ loadShakespeare path = do
   return (V.fromList hot, m, cs)
 
 trainSlice :: LearningParameters -> Shakespeare -> Shakespearian -> Vector Int -> Int -> Int -> (Shakespeare, Shakespearian)
-trainSlice !rate !net !recIns input offset size =
+trainSlice !lrate !net !recIns input offset size =
   let e = fmap (x . oneHot) . V.toList $ V.slice offset size input
   in case reverse e of
     (o : l : xs) ->
       let examples = reverse $ (l, Just o) : ((,Nothing) <$> xs)
-      in  trainRecurrent rate net recIns examples
+      in  trainRecurrent lrate net recIns examples
     _ -> error "Not enough input"
     where
       x = fromMaybe (error "Hot variable didn't fit.")
 
 runShakespeare :: ShakespeareOpts -> ExceptT String IO ()
-runShakespeare ShakespeareOpts {..} = do
+runShakespeare opts = do
-  (shakespeare, oneHotMap, oneHotDictionary) <- loadShakespeare trainingFile
+  (shakespeare, oneHotMap, oneHotDictionary) <- loadShakespeare $ trainingFile opts
   (net0, i0) <- lift $
-    case loadPath of
+    case loadPath opts of
       Just loadFile -> netLoad loadFile
       Nothing -> (,0) <$> randomNet
 
   (trained, bestInput) <- lift $ foldM (\(!net, !io) size -> do
-    xs <- take (iterations `div` 10) <$> getRandomRs (0, length shakespeare - size - 1)
+    xs <- take (iterations opts `div` 10) <$> getRandomRs (0, length shakespeare - size - 1)
-    let (!trained, !bestInput) = foldl' (\(!n, !i) offset -> trainSlice rate n i shakespeare offset size) (net, io) xs
+    let (!trained, !bestInput) = foldl' (\(!n, !i) offset -> trainSlice (rate opts) n i shakespeare offset size) (net, io) xs
-    results <- take 1000 <$> generateParagraph trained bestInput temperature oneHotMap oneHotDictionary ( S1D $ konst 0)
+    results <- take 1000 <$> generateParagraph trained bestInput (temperature opts) oneHotMap oneHotDictionary ( S1D $ konst 0)
     putStrLn ("TRAINING STEP WITH SIZE: " ++ show size)
     putStrLn (unAnnotateCapitals results)
     return (trained, bestInput)
-    ) (net0, i0) $ replicate 10 sequenceSize
+    ) (net0, i0) $ replicate 10 (sequenceSize opts)
 
-  case savePath of
+  case savePath opts of
     Just saveFile -> lift . B.writeFile saveFile $ runPut (put trained >> put bestInput)
     Nothing -> return ()
 
@@ -122,12 +121,12 @@ generateParagraph :: forall layers shapes n a. (Last shapes ~ 'D1 n, Head shapes
   -> Vector a
   -> S ('D1 n)
   -> IO [a]
-generateParagraph n s temperature hotmap hotdict =
+generateParagraph n s temp hotmap hotdict =
   go s
     where
   go x y =
     do let (_, ns, o) = runRecurrent n x y
-       un            <- sample temperature hotdict o
+       un            <- sample temp hotdict o
        Just re       <- return $ makeHot hotmap un
        rest          <- unsafeInterleaveIO $ go ns re
        return (un : rest)

src/Grenade/Layers/Convolution.hs

@@ -1,7 +1,6 @@
 {-# LANGUAGE CPP                   #-}
 {-# LANGUAGE DataKinds             #-}
 {-# LANGUAGE ScopedTypeVariables   #-}
-{-# LANGUAGE RecordWildCards       #-}
 {-# LANGUAGE GADTs                 #-}
 {-# LANGUAGE TypeOperators         #-}
 {-# LANGUAGE TypeFamilies          #-}
@@ -139,8 +138,8 @@ instance ( KnownNat channels
          , KnownNat (kernelRows * kernelColumns * channels)
          ) => UpdateLayer (Convolution channels filters kernelRows kernelColumns strideRows strideColumns) where
   type Gradient (Convolution channels filters kernelRows kernelColumns strideRows strideColumns) = (Convolution' channels filters kernelRows kernelColumns strideRows strideColumns)
-  runUpdate LearningParameters {..} (Convolution oldKernel oldMomentum) (Convolution' kernelGradient) =
+  runUpdate lp (Convolution oldKernel oldMomentum) (Convolution' kernelGradient) =
-    let (newKernel, newMomentum) = descendMatrix learningRate learningMomentum learningRegulariser oldKernel kernelGradient oldMomentum
+    let (newKernel, newMomentum) = descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldKernel kernelGradient oldMomentum
     in Convolution newKernel newMomentum
 
   createRandom = randomConvolution

src/Grenade/Layers/Deconvolution.hs

@@ -1,7 +1,6 @@
 {-# LANGUAGE CPP                   #-}
 {-# LANGUAGE DataKinds             #-}
 {-# LANGUAGE ScopedTypeVariables   #-}
-{-# LANGUAGE RecordWildCards       #-}
 {-# LANGUAGE GADTs                 #-}
 {-# LANGUAGE TypeOperators         #-}
 {-# LANGUAGE TypeFamilies          #-}
@@ -138,8 +137,8 @@ instance ( KnownNat channels
          , KnownNat (kernelRows * kernelColumns * filters)
          ) => UpdateLayer (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) where
   type Gradient (Deconvolution channels filters kernelRows kernelColumns strideRows strideColumns) = (Deconvolution' channels filters kernelRows kernelColumns strideRows strideColumns)
-  runUpdate LearningParameters {..} (Deconvolution oldKernel oldMomentum) (Deconvolution' kernelGradient) =
+  runUpdate lp (Deconvolution oldKernel oldMomentum) (Deconvolution' kernelGradient) =
-    let (newKernel, newMomentum) = descendMatrix learningRate learningMomentum learningRegulariser oldKernel kernelGradient oldMomentum
+    let (newKernel, newMomentum) = descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldKernel kernelGradient oldMomentum
     in Deconvolution newKernel newMomentum
 
   createRandom = randomDeconvolution

src/Grenade/Layers/FullyConnected.hs

@@ -1,5 +1,4 @@
 {-# LANGUAGE DataKinds             #-}
-{-# LANGUAGE RecordWildCards       #-}
 {-# LANGUAGE TypeOperators         #-}
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
@@ -38,9 +37,9 @@ instance Show (FullyConnected i o) where
 instance (KnownNat i, KnownNat o) => UpdateLayer (FullyConnected i o) where
   type Gradient (FullyConnected i o) = (FullyConnected' i o)
 
-  runUpdate LearningParameters {..} (FullyConnected (FullyConnected' oldBias oldActivations) (FullyConnected' oldBiasMomentum oldMomentum)) (FullyConnected' biasGradient activationGradient) =
+  runUpdate lp (FullyConnected (FullyConnected' oldBias oldActivations) (FullyConnected' oldBiasMomentum oldMomentum)) (FullyConnected' biasGradient activationGradient) =
-    let (newBias, newBiasMomentum)    = descendVector learningRate learningMomentum learningRegulariser oldBias biasGradient oldBiasMomentum
+    let (newBias, newBiasMomentum)    = descendVector (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldBias biasGradient oldBiasMomentum
-        (newActivations, newMomentum) = descendMatrix learningRate learningMomentum learningRegulariser oldActivations activationGradient oldMomentum
+        (newActivations, newMomentum) = descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) oldActivations activationGradient oldMomentum
     in FullyConnected (FullyConnected' newBias newActivations) (FullyConnected' newBiasMomentum newMomentum)
 
   createRandom = randomFullyConnected

src/Grenade/Recurrent/Core/Runner.hs

@@ -6,7 +6,6 @@
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE FlexibleContexts      #-}
 {-# LANGUAGE RankNTypes            #-}
-{-# LANGUAGE RecordWildCards       #-}
 
 module Grenade.Recurrent.Core.Runner (
     runRecurrentExamples
@@ -101,11 +100,11 @@ updateRecInputs :: Fractional (RecurrentInputs sublayers)
                 -> RecurrentInputs sublayers
                 -> RecurrentInputs sublayers
 
-updateRecInputs l@LearningParameters {..} (() :~~+> xs) (() :~~+> ys)
+updateRecInputs lp (() :~~+> xs) (() :~~+> ys)
-  = () :~~+> updateRecInputs l xs ys
+  = () :~~+> updateRecInputs lp xs ys
 
-updateRecInputs l@LearningParameters {..} (x :~@+> xs) (y :~@+> ys)
+updateRecInputs lp (x :~@+> xs) (y :~@+> ys)
-  = (realToFrac (1 - learningRate * learningRegulariser) * x - realToFrac learningRate * y) :~@+> updateRecInputs l xs ys
+  = (realToFrac (1 - learningRate lp * learningRegulariser lp) * x - realToFrac (learningRate lp) * y) :~@+> updateRecInputs lp xs ys
 
 updateRecInputs _ RINil RINil
   = RINil

src/Grenade/Recurrent/Layers/BasicRecurrent.hs

@@ -1,7 +1,6 @@
 {-# LANGUAGE CPP                   #-}
 {-# LANGUAGE DataKinds             #-}
 {-# LANGUAGE GADTs                 #-}
-{-# LANGUAGE RecordWildCards       #-}
 {-# LANGUAGE TypeOperators         #-}
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
@@ -60,11 +59,11 @@ instance Show (BasicRecurrent i o) where
 instance (KnownNat i, KnownNat o, KnownNat (i + o)) => UpdateLayer (BasicRecurrent i o) where
   type Gradient (BasicRecurrent i o) = (BasicRecurrent' i o)
 
-  runUpdate LearningParameters {..} (BasicRecurrent oldBias oldBiasMomentum oldActivations oldMomentum) (BasicRecurrent' biasGradient activationGradient) =
+  runUpdate lp (BasicRecurrent oldBias oldBiasMomentum oldActivations oldMomentum) (BasicRecurrent' biasGradient activationGradient) =
-    let newBiasMomentum = konst learningMomentum * oldBiasMomentum - konst learningRate * biasGradient
+    let newBiasMomentum = konst (learningMomentum lp) * oldBiasMomentum - konst (learningRate lp) * biasGradient
         newBias         = oldBias + newBiasMomentum
-        newMomentum     = konst learningMomentum * oldMomentum - konst learningRate * activationGradient
+        newMomentum     = konst (learningMomentum lp) * oldMomentum - konst (learningRate lp) * activationGradient
-        regulariser     = konst (learningRegulariser * learningRate) * oldActivations
+        regulariser     = konst (learningRegulariser lp * learningRate lp) * oldActivations
         newActivations  = oldActivations + newMomentum - regulariser
     in BasicRecurrent newBias newBiasMomentum newActivations newMomentum
 

src/Grenade/Recurrent/Layers/LSTM.hs

@@ -3,7 +3,6 @@
 {-# LANGUAGE DataKinds             #-}
 {-# LANGUAGE GADTs                 #-}
 {-# LANGUAGE RankNTypes            #-}
-{-# LANGUAGE RecordWildCards       #-}
 {-# LANGUAGE TypeOperators         #-}
 {-# LANGUAGE TypeFamilies          #-}
 {-# LANGUAGE MultiParamTypeClasses #-}
@@ -75,7 +74,7 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where
 
   -- Run the update function for each group matrix/vector of weights, momentums and gradients.
   -- Hmm, maybe the function should be used instead of passing in the learning parameters.
-  runUpdate LearningParameters {..} (LSTM w m) g =
+  runUpdate lp (LSTM w m) g =
     let (wf, wf') = u lstmWf w m g
         (uf, uf') = u lstmUf w m g
         (bf, bf') = v lstmBf w m g
@@ -92,11 +91,11 @@ instance (KnownNat i, KnownNat o) => UpdateLayer (LSTM i o) where
     -- Utility function for updating with the momentum, gradients, and weights.
     u :: forall x ix out. (KnownNat ix, KnownNat out) => (x -> (L out ix)) -> x -> x -> x -> ((L out ix), (L out ix))
     u e (e -> weights) (e -> momentum) (e -> gradient) =
-      descendMatrix learningRate learningMomentum learningRegulariser weights gradient momentum
+      descendMatrix (learningRate lp) (learningMomentum lp) (learningRegulariser lp) weights gradient momentum
 
     v :: forall x ix. (KnownNat ix) => (x -> (R ix)) -> x -> x -> x -> ((R ix), (R ix))
     v e (e -> weights) (e -> momentum) (e -> gradient) =
-      descendVector learningRate learningMomentum learningRegulariser weights gradient momentum
+      descendVector (learningRate lp) (learningMomentum lp) (learningRegulariser lp) weights gradient momentum
 
   -- There's a lot of updates here, so to try and minimise the number of data copies
   -- we'll create a mutable bucket for each.
@@ -137,18 +136,18 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w
   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)
   -- Forward propagation for the LSTM layer.
   -- The size of the cell state is also the size of the output.
-  runRecurrentForwards (LSTM (LSTMWeights {..}) _) (S1D cell) (S1D input) =
+  runRecurrentForwards (LSTM lw _) (S1D cell) (S1D input) =
     let -- Forget state vector
-        f_s = lstmBf + lstmWf #> input + lstmUf #> cell
+        f_s = lstmBf lw + lstmWf lw #> input + lstmUf lw #> cell
         f_t = sigmoid f_s
         -- Input state vector
-        i_s = lstmBi + lstmWi #> input + lstmUi #> cell
+        i_s = lstmBi lw + lstmWi lw #> input + lstmUi lw #> cell
         i_t = sigmoid i_s
         -- Output state vector
-        o_s = lstmBo + lstmWo #> input + lstmUo #> cell
+        o_s = lstmBo lw + lstmWo lw #> input + lstmUo lw #> cell
         o_t = sigmoid o_s
         -- Cell input state vector
-        c_s = lstmBc + lstmWc #> input
+        c_s = lstmBc lw + lstmWc lw #> input
         c_x = tanh c_s
         -- Cell state
         c_t = f_t * cell + i_t * c_x
@@ -162,7 +161,7 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w
   --
   -- There's a test version using the AD library without hmatrix in the test
   -- suite. These should match always.
-  runRecurrentBackwards (LSTM (LSTMWeights {..}) _) (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') =
+  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') =
     let -- Reverse Mode AD Derivitives
         c_t' = h_t' * o_t + cellGrad
 
@@ -179,8 +178,8 @@ instance (KnownNat i, KnownNat o) => RecurrentLayer (LSTM i o) ('D1 i) ('D1 o) w
         c_s' = tanh' c_s * c_x'
 
         -- The derivatives to pass sideways (recurrent) and downwards
-        cell'  = tr lstmUf #> f_s' + tr lstmUo #> o_s' + tr lstmUi #> i_s' + c_t' * f_t
+        cell'  = tr (lstmUf lw) #> f_s' + tr (lstmUo lw) #> o_s' + tr (lstmUi lw) #> i_s' + c_t' * f_t
-        input' = tr lstmWf #> f_s' + tr lstmWo #> o_s' + tr lstmWi #> i_s' + tr lstmWc #> c_s'
+        input' = tr (lstmWf lw) #> f_s' + tr (lstmWo lw) #> o_s' + tr (lstmWi lw) #> i_s' + tr (lstmWc lw) #> c_s'
 
         -- Calculate the gradient Matricies for the input
         lstmWf' = f_s' `outer` input
@@ -239,18 +238,18 @@ tanh' :: (Floating a) => a -> a
 tanh' t = 1 - s ^ (2 :: Int)  where s = tanh t
 
 instance (KnownNat i, KnownNat o) => Serialize (LSTM i o) where
-  put (LSTM LSTMWeights {..} _) = do
+  put (LSTM lw _) = do
-    u lstmWf
+      u (lstmWf lw)
-    u lstmUf
+      u (lstmUf lw)
-    v lstmBf
+      v (lstmBf lw)
-    u lstmWi
+      u (lstmWi lw)
-    u lstmUi
+      u (lstmUi lw)
-    v lstmBi
+      v (lstmBi lw)
-    u lstmWo
+      u (lstmWo lw)
-    u lstmUo
+      u (lstmUo lw)
-    v lstmBo
+      v (lstmBo lw)
-    u lstmWc
+      u (lstmWc lw)
-    v lstmBc
+      v (lstmBc lw)
     where
       u :: forall a b. (KnownNat a, KnownNat b) => Putter  (L b a)
       u = putListOf put . LA.toList . LA.flatten . extract
@@ -258,18 +257,8 @@ instance (KnownNat i, KnownNat o) => Serialize (LSTM i o) where
       v = putListOf put . LA.toList . extract
 
   get = do
-    lstmWf <- u
+      w <- LSTMWeights <$> u <*> u <*> v <*> u <*> u <*> v <*> u <*> u <*> v <*> u <*> v
-    lstmUf <- u
+      return $ LSTM w (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)
-    lstmBf <- v
-    lstmWi <- u
-    lstmUi <- u
-    lstmBi <- v
-    lstmWo <- u
-    lstmUo <- u
-    lstmBo <- v
-    lstmWc <- u
-    lstmBc <- v
-    return $ LSTM (LSTMWeights {..}) (LSTMWeights w0 u0 v0 w0 u0 v0 w0 u0 v0 w0 v0)
     where
       u :: forall a b. (KnownNat a, KnownNat b) => Get  (L b a)
       u = let f = fromIntegral $ natVal (Proxy :: Proxy a)

test/Test/Grenade/Recurrent/Layers/LSTM/Reference.hs

@@ -6,7 +6,6 @@
 {-# LANGUAGE DeriveFunctor         #-}
 {-# LANGUAGE DeriveFoldable        #-}
 {-# LANGUAGE DeriveTraversable     #-}
-{-# LANGUAGE RecordWildCards       #-}
 {-# LANGUAGE FlexibleContexts      #-}
 {-# LANGUAGE RankNTypes            #-}
 
@@ -17,7 +16,11 @@ import           Data.Reflection
 import           Numeric.AD.Mode.Reverse
 import           Numeric.AD.Internal.Reverse (Tape)
 
+import           GHC.TypeLits (KnownNat)
+
+import           Grenade.Recurrent.Layers.LSTM (LSTMWeights (..))
 import qualified Grenade.Recurrent.Layers.LSTM as LSTM
+
 import qualified Numeric.LinearAlgebra.Static as S
 import qualified Numeric.LinearAlgebra as H
 
@@ -54,31 +57,32 @@ data RefLSTM a = RefLSTM
     , refLstmBc :: Vector a -- Bias Cell         (b_c)
     } deriving (Functor, Foldable, Traversable, Eq, Show)
 
-lstmToReference :: LSTM.LSTMWeights a b -> RefLSTM Double
+lstmToReference :: (KnownNat a, KnownNat b) => LSTM.LSTMWeights a b -> RefLSTM Double
-lstmToReference LSTM.LSTMWeights {..} =
+lstmToReference lw =
-    let refLstmWf = Matrix . H.toLists . S.extract $ lstmWf -- Weight Forget     (W_f)
+    RefLSTM
-        refLstmUf = Matrix . H.toLists . S.extract $ lstmUf -- Cell State Forget (U_f)
+      { refLstmWf = Matrix . H.toLists . S.extract $ lstmWf lw -- Weight Forget     (W_f)
-        refLstmBf = Vector . H.toList  . S.extract $ lstmBf -- Bias Forget       (b_f)
+      , refLstmUf = Matrix . H.toLists . S.extract $ lstmUf lw -- Cell State Forget (U_f)
-        refLstmWi = Matrix . H.toLists . S.extract $ lstmWi -- Weight Input      (W_i)
+      , refLstmBf = Vector . H.toList  . S.extract $ lstmBf lw -- Bias Forget       (b_f)
-        refLstmUi = Matrix . H.toLists . S.extract $ lstmUi -- Cell State Input  (U_i)
+      , refLstmWi = Matrix . H.toLists . S.extract $ lstmWi lw -- Weight Input      (W_i)
-        refLstmBi = Vector . H.toList  . S.extract $ lstmBi -- Bias Input        (b_i)
+      , refLstmUi = Matrix . H.toLists . S.extract $ lstmUi lw -- Cell State Input  (U_i)
-        refLstmWo = Matrix . H.toLists . S.extract $ lstmWo -- Weight Output     (W_o)
+      , refLstmBi = Vector . H.toList  . S.extract $ lstmBi lw -- Bias Input        (b_i)
-        refLstmUo = Matrix . H.toLists . S.extract $ lstmUo -- Cell State Output (U_o)
+      , refLstmWo = Matrix . H.toLists . S.extract $ lstmWo lw -- Weight Output     (W_o)
-        refLstmBo = Vector . H.toList  . S.extract $ lstmBo -- Bias Output       (b_o)
+      , refLstmUo = Matrix . H.toLists . S.extract $ lstmUo lw -- Cell State Output (U_o)
-        refLstmWc = Matrix . H.toLists . S.extract $ lstmWc -- Weight Cell       (W_c)
+      , refLstmBo = Vector . H.toList  . S.extract $ lstmBo lw -- Bias Output       (b_o)
-        refLstmBc = Vector . H.toList  . S.extract $ lstmBc -- Bias Cell         (b_c)
+      , refLstmWc = Matrix . H.toLists . S.extract $ lstmWc lw -- Weight Cell       (W_c)
-    in RefLSTM {..}
+      , refLstmBc = Vector . H.toList  . S.extract $ lstmBc lw -- Bias Cell         (b_c)
+      }
 
 runLSTM :: Floating a => RefLSTM a -> Vector a -> Vector a -> (Vector a, Vector a)
-runLSTM RefLSTM {..} cell input =
+runLSTM rl cell input =
     let -- Forget state vector
-        f_t = sigmoid   $ refLstmBf #+ refLstmWf #> input #+ refLstmUf #> cell
+        f_t = sigmoid   $ refLstmBf rl #+ refLstmWf rl #> input #+ refLstmUf rl #> cell
         -- Input state vector
-        i_t = sigmoid   $ refLstmBi #+ refLstmWi #> input #+ refLstmUi #> cell
+        i_t = sigmoid   $ refLstmBi rl #+ refLstmWi rl #> input #+ refLstmUi rl #> cell
         -- Output state vector
-        o_t = sigmoid   $ refLstmBo #+ refLstmWo #> input #+ refLstmUo #> cell
+        o_t = sigmoid   $ refLstmBo rl #+ refLstmWo rl #> input #+ refLstmUo rl #> cell
         -- Cell input state vector
-        c_x = fmap tanh $ refLstmBc #+ refLstmWc #> input
+        c_x = fmap tanh $ refLstmBc rl #+ refLstmWc rl #> input
         -- Cell state
         c_t = f_t #* cell #+ i_t #* c_x
         -- Output (it's sometimes recommended to use tanh c_t)