From 1afb966ff3f995b4ac08b9ad30a77caee85721fd Mon Sep 17 00:00:00 2001
From: Miguel <m.i@gmx.at>
Date: Sat, 23 Mar 2019 13:15:09 +0100
Subject: more cleaning

---
 mnist/Neuronet.hs | 20 ++++++--------------
 mnist/main.hs     | 42 +++++++++++++++++-------------------------
 2 files changed, 23 insertions(+), 39 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)
diff --git a/mnist/main.hs b/mnist/main.hs
index 02ba6f7..d1b5471 100644
--- a/mnist/main.hs
+++ b/mnist/main.hs
@@ -8,6 +8,7 @@ import Data.Tuple.Extra
 import System.Random.Shuffle
 import qualified Data.ByteString as BS
 import System.IO
+import Control.DeepSeq
 
 -- a single data-sample with input and expected output
 type Sample = (Vector Double,Vector Double)
@@ -29,7 +30,7 @@ epochs e n s = (:) <$> batches n s <*> epochs (e-1) n s
 training :: Bool -> (Neuronet->String) -> Double -> Neuronet -> [[Samples]] -> IO Neuronet
 training tst tstf r net s = foldlM f net (zip s [1..])
     where f nt (v,i) = do  putStr $ "Epoch "++ show i ++ "...."
-                           let n = foldl' (\n x->trainBatch r n (fst.unzip $ x) (snd.unzip $ x)) nt v
+                           let n = foldl' (\n x->train r n (fst.unzip $ x) (snd.unzip $ x)) nt v
                            putStrLn $ tstf n
                            return n
 
@@ -37,11 +38,6 @@ training tst tstf r net s = foldlM f net (zip s [1..])
 testing :: (Vector Double -> Vector Double -> Bool) -> Neuronet -> Samples -> Int
 testing f net s = length . filter id $ map (\(x,y)->f y (asknet net x)) s
 
--- finally some learning and testing with MNIST
--- MNIST files from http://yann.lecun.com/exdb/mnist/
-main :: IO ()
-main = mainMNIST
-
 -- create Samples given two MNIST files for images and labels
 read_samples :: FilePath -> FilePath -> IO Samples
 read_samples f1 f2 = do
@@ -55,29 +51,25 @@ read_samples f1 f2 = do
         where  zrs= take 9 $ repeat 0
                val x= take x zrs ++ [1] ++ drop x zrs
 
--- MNIST main function
-mainMNIST :: IO ()
-mainMNIST =do
-
-        let ep  = 10        -- number of epochs
-        let mbs = 10        -- mini-batch size
-        let lr  = 3         -- learning rate
-        let cap = 999999    -- cap number of training samples
+-- finally some learning and testing with MNIST
+-- MNIST files from http://yann.lecun.com/exdb/mnist/
+main = do s  <- read_samples "train-images-idx3-ubyte" "train-labels-idx1-ubyte"
+          print $ s `deepseq` length s
 
-        putStrLn "= Init ="
-        str "Initializing Net"
-        nt          <- neuronet [28*28,30,10]
-        done
+main2 :: IO ()
+main2 = do
 
-        str "Reading Samples"
+        let ep  = 10            -- number of epochs
+        let mbs = 10            -- mini-batch size
+        let lr  = 3             -- learning rate
+        let lay = [28*28,30,10] -- number of neurons by layer
+        let cap = 999999        -- cap number of training samples
+        
+        nt          <- newnet lay
         smpl_train  <- take cap <$> read_samples "train-images-idx3-ubyte" "train-labels-idx1-ubyte"
         smpl_test   <- read_samples "t10k-images-idx3-ubyte" "t10k-labels-idx1-ubyte"
-        done
-
-        putStrLn "= Training ="
-        tr          <- epochs ep mbs smpl_train >>= training True (tst smpl_test) (lr/fromIntegral mbs) nt
-
-        putStrLn "= THE END ="
+        tr <- epochs ep mbs smpl_train >>= training True (tst smpl_test) (lr/fromIntegral mbs) nt
+        putStrLn "end"
 
         where chk y1 y2 = val y1 == val y2
               val x=snd . last . sort $ zip (toList x) [0..9]
-- 
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