diff options
Diffstat (limited to 'mnist/Neuronet.hs')
| -rw-r--r-- | mnist/Neuronet.hs | 20 |
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) |