more cleaning

1 files changed, 6 insertions, 14 deletions

diff --git a/mnist/Neuronet.hs b/mnist/Neuronet.hs
index 6c3ea32..8a77622 100644
--- a/mnist/Neuronet.hs
+++ b/mnist/Neuronet.hs
@@ -1,12 +1,8 @@
 module Neuronet
     ( Neuronet      -- the neuronet
-     ,neuronet      -- initalize neuronet
-     ,train         -- train with one sample
-     ,trainBatch    -- train with batch
+     ,newnet        -- initalize neuronet
+     ,train         -- train with batch
      ,asknet        -- ask the neuroal net
-
-     ,wghtact       
-     ,backprop
     )where
 
 import Data.List
@@ -23,8 +19,8 @@ type Neuronet = [Layer]
 -- | Initialize a fresh neuronal network given the number of neurons on
 -- each layer, as a list. Weights and biases are initialized randomly 
 -- using gaussian distribution with mean 0 and standard deviation 1.
-neuronet :: [Int] -> IO Neuronet
-neuronet l = mapM nl $ zip l (tail l)
+newnet :: [Int] -> IO Neuronet
+newnet l = mapM nl $ zip l (tail l)
     where nl (i,l) = (,) <$> randn l i <*> 
                             (randn 1 l >>= return.fromList.head.toLists)
 
@@ -62,13 +58,9 @@ sigmoid' x = sigmoid x * (1-sigmoid x)
 cost_derivative :: Vector Double -> Vector Double -> Vector Double
 cost_derivative a y = a-y
 
--- | Train on one single sample
-train :: Double -> Neuronet -> Vector Double -> Vector Double -> Neuronet
-train r net x y = zipWith (upd r) net (backprop net x y)
-
 -- | Train on a batch of samples
-trainBatch :: Double -> Neuronet -> [Vector Double] -> [Vector Double] -> Neuronet
-trainBatch r net xs ys = zipWith (upd r) net bp
+train :: Double -> Neuronet -> [Vector Double] -> [Vector Double] -> Neuronet
+train r net xs ys = zipWith (upd r) net bp
     where bp = foldl1' fc $ map (uncurry $ backprop net) (zip xs ys)
           fc v a = zipWith ff v a
           ff (a,b) (c,d) = (a+c,b+d)