Nicer types, and better regulariser so mnist actually works

main/feedforward.hs

@@ -26,7 +26,7 @@ import           Grenade
 -- between the shapes, so inference can't do it all for us.
 
 -- With around 100000 examples, this should show two clear circles which have been learned by the network.
-randomNet :: (MonadRandom m)  => m (Network '[ 'D1 2, 'D1 40, 'D1 40, 'D1 10, 'D1 10, 'D1 1, 'D1 1])
+randomNet :: MonadRandom m => m (Network '[ 'D1 2, 'D1 40, 'D1 40, 'D1 10, 'D1 10, 'D1 1, 'D1 1])
 randomNet = do
   a :: FullyConnected 2 40  <- randomFullyConnected
   b :: FullyConnected 40 10 <- randomFullyConnected
@@ -74,7 +74,7 @@ feedForward' =
                   <*> (LearningParameters
                       <$> option auto (long "train_rate" <> short 'r' <> value 0.01)
                       <*> option auto (long "momentum" <> value 0.9)
-                      <*> option auto (long "l2" <> value 0.0001)
+                      <*> option auto (long "l2" <> value 0.0005)
                       )
 
 main :: IO ()

main/mnist.hs

@@ -74,11 +74,11 @@ data MnistOpts = MnistOpts FilePath FilePath Int LearningParameters
 mnist' :: Parser MnistOpts
 mnist' = MnistOpts <$> argument str (metavar "TRAIN")
                    <*> argument str (metavar "VALIDATE")
-                   <*> option auto (long "iterations" <> short 'i' <> value 15)
+                   <*> option auto (long "iterations" <> short 'i' <> value 10)
                    <*> (LearningParameters
                        <$> option auto (long "train_rate" <> short 'r' <> value 0.01)
                        <*> option auto (long "momentum" <> value 0.9)
-                       <*> option auto (long "l2" <> value 0.0001)
+                       <*> option auto (long "l2" <> value 0.0005)
                        )
 
 main :: IO ()

src/Grenade/Core/Network.hs

@@ -31,7 +31,7 @@ class UpdateLayer x where
   --   Unit if there isn't a gradient to pass back.
   type Gradient x :: *
   -- | Update a layer with its gradient and learning parameters
-  runUpdate      :: LearningParameters -> x -> Gradient x -> x
+  runUpdate       :: LearningParameters -> x -> Gradient x -> x
 
 -- | Class for a layer. All layers implement this, however, they don't
 --   need to implement it for all shapes, only ones which are appropriate.