diff options
Diffstat (limited to 'mnist/main.hs')
| -rw-r--r-- | mnist/main.hs | 42 |
1 files changed, 17 insertions, 25 deletions
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] |